Infoman

Member since 2017-07-15T03:50:57Z. Last seen 2025-01-02T20:05:01Z.

2729 blog posts. 128 comments.

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Posts by Infoman

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  Infoman

  Apple likely to buy Netflix, say analysts

  5 Calvin02 2 hrs 6

  https://economictimes.indiatimes.com/news/international/business/apple-more-likely-to-buy-netflix-say-analysts/articleshow/62326501.cms news.ycombinator.com/item?id=16048846

  Apple Last year, Apple shared its plans of pouring in $1 billion into TV and movie productions. Contrary to the reports of Apple launching a video subscription product in 2018 to counter Netflix, analysts from Citi have said that the iPhone maker could possibly buy the entertainment company by taking advantage of US President Donald Trump's corporate tax cut. According to Citi analysts, Jim Suva and Asiya Merchant, there is a 40 per cent likelihood that Apple will acquire Netflix. Under the new taxing rules, the tech giant will be able to repatriate about $220 billion in cash to the US. "The firm has too much cash -- nearly $250 billion -- growing at $50 billion a year. This is a good problem to have," Suva and Merchant were quoted as saying. "Historically, Apple has avoided repatriating cash to the US to avoid high taxation. As such, tax reform may allow Apple to put this cash to use. With over 90 per cent of its cash sitting overseas, a one-time 10 per cent repatriation tax would give Apple $220 billion for mergers and acquisitions (M&A) or buybacks," they added. A report in business insider said that iTunes has been a huge hit for the company, but viewers have migrated increasingly to services like Netflix, Amazon or Hulu to watch their favourite shows leaving Apple struggling to offer a compelling TV or movie offering. In September 2017, reports poured in that Apple might lease the Culver Studios in California as it plans to pour $1 billion into TV and movie productions. The battle for best movie scripts and television projects intensified between tech giants in the Silicon Valley. Google-owned YouTube is already producing original television series and Amazon has won Oscars for "Manchester by the Sea" show. The iPhone maker has already hired top Hollywood talent Sony duo Jamie Erlicht and Zack Van Amburg to lead its Hollywood push and is reportedly developing a new TV show that will star Reese Witherspoon and Jennifer Aniston. Meet prime challenger to Netflix's reign

  Netflix adds more subscribers than expected, shares hit record

  Vodafone offers Netflix subscription on select postpaid plans

  Netflix taps Oscar-winner Damien Chazelle for musical series 'The Eddy'

  Sony. Netflix. And now, HBO! Hackers are threatening the way that Hollywood does business

  2018/01/02
• 
  

  Infoman

  交友程式的宗旨是協助用戶找到心中所愛，不過現實卻是很多人在被拒絕後心靈受創，最受歡迎的交友程式Tinder看出了這個弊處，做了一個精明的舉動，只向用戶顯示真正對他們有興趣的人，當然是有代價的，就是收錢。

  Tinder推出收費服務後生意沒有變差，反而愈來愈旺，成功打破了免費程式收費就是末日的理論，也證明了這個程式是多麼的受歡迎。

  Tinder的母公司Match Group（下稱Match）在2015年招股上市，當時交友平台有衰落的趨勢，因此很多人不看好，Match也以招股價指引範圍的最低水平（每股12美元）招股上市。

  現在看來，市場那時候是太過悲觀。最近一個調查顯示，美國的大學生85%都曾經使用過Tinder。新面世的交友平台很多針對特定個人愛好，例如Bristlr就轉為鬍子愛好者而設，又或者可以透過Loveflutter Blue結識名人，如果你認為Twitter的藍色剔號認證代表知名度高的話。

  理論上，Match的收入應該會因為競爭增加而減少，不過Tinder明顯與其他交友平台不同，其用戶有增無減，而且願意付錢使用特別服務。Tinder程式是免費的，而提供特別服務的Tinder Gold要14.99美元（約116.9港元）一個月。現在Tinder有250萬名付費用戶，比前一年增加了86%，而且佔總用戶人數三分之一以上。這當然是好事，因為網上用戶收費是比網上廣告更穩定的收入模式。

  Match股價也受惠於Tinder Gold的成功，從上市至今已經漲了147%，而且上升勢頭沒有轉弱的跡象。Match也可以嘗試把Tinder的成功套用在其他40多個品牌商，現在已經證明人們願意付錢在網上找對象，其股價應該會節節上揚。

  譯自：THE LEX COLUMN

  版權所有：FINANCIAL TIMES

  2018/01/02
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  Infoman

  新年大家與親友相聚時間可能比較多，離不開在家或出外用膳，近年在家用膳多了一個選擇，就是使用網上餐飲外賣服務。有調查機構估計，本港餐飲外賣市場總額約5.5億元，年增長率低過1%，而港人使用網上外賣平台日漸普遍，內地線上外賣平台發展更成熟，當中有值得本港業界借鑑地方。

  內地千億市場增長放緩：在2016年，內地線上外賣平台市場規模為1662億元（人民幣．下同），按年增長33%。該行業近年高速發展，2016年較2011年市場規模大約6.7倍，市場預期2017年及2018年內地市場規模分別約2045億及2413億元，增長率放緩至18%至23%，因此內地業界已進入另一個發展階段。

  用戶質素提升推動生意：去年內地線上外賣平台用戶人數為2.56億，按年增長22.5%，2016年較2011年用戶人數增長3倍，反映用戶數目增長放緩情況較市場規模更嚴重。幸好，這幾年間，相信是每名用戶平均使用次數或金額有所提升，令生意增長更快，這亦是未來進一步加大市場規模的動力之一。

  進入行業整合階段：增長放緩造就行業整合良機。2017年第三季，餓了麼收購百度外賣後，市佔率提升至54.5%，由本來第二位晉升為第一，美團外賣下跌一級，市佔率為41.9%。除了這兩大巨頭，餘下的市場份額只有3.6%，可見內地市場已進入最後兩強階段，未來也是兩家企業在服務質素及附加價值上競爭的新開始。

  從兩方面提升競爭力：今年第三季，只有約29%用戶沒有遇到送餐員不良服務。換句話說，七成用戶曾有不愉快經驗，包括嚴重超時（35%）、送餐員態度差（31%）、餐品遭刻意破壞（19%）、超時取餐遭不良態度對待（16%），更甚者有客人試過投訴後遭威脅（13%），因此，業界有需要大幅改善服務水平。

  加強環保責任刻不容緩：由於外賣涉及大量即棄用品及包裝，產生的廢物遠多於堂食，對環境負擔必然增加。因此，業界須主動提供解決方法，例如鼓勵客人自備餐具、提供可降解餐具及包裝，以及其他有利環保的計劃及行動等，令客戶、業界、投資者創造共贏局面。這方面也值得本港同業反思。

  retailfun66@gmail.com

  2018/01/02
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  Infoman

  Copy-on-write friendly Python garbage collection

  37 happy-go-lucky 9 hrs 0

  https://engineering.instagram.com/copy-on-write-friendly-python-garbage-collection-ad6ed5233ddf news.ycombinator.com/item?id=16045165

  At Instagram, we have the world’s largest deployment of the Django web framework, which is written entirely in Python. We began using Python early on because of its simplicity, but we’ve had to do many hacks over the years to keep it simple as we’ve scaled. Last year we tried dismissing the Python garbage collection (GC) mechanism (which reclaims memory by collecting and freeing unused data), and gained 10% more capacity. However, as our engineering team and number of features have continued to grow, so has memory usage. Eventually, we started losing the gains we had achieved by disabling GC.

  Here’s a graph that shows how our memory grew with the number of requests. After 3,000 requests, the process used ~600MB more memory. More importantly, the trend was linear.

  From our load test, we could see that memory usage was becoming our bottleneck. Enabling GC could alleviate this problem and slow down the memory growth, but undesired Copy-on-write (COW) would still increase the overall memory footprint. So we decided to see if we could make Python GC work without COW, and hence, the memory overhead.

  Red: without GC; Blue: calling GC collect explicitly; Green: default Python GC enabled If you read our last GC post carefully, you’ll notice the culprit of the COW was ahead of each python object:

  / GC information is stored BEFORE the object structure. / typedef union _gc_head { struct { union _gc_head gc_next; union _gc_head gc_prev; Py_ssize_t gc_refs; } gc; long double dummy; / force worst-case alignment / } PyGC_Head; The theory was that every time we did a collection, it would update the gc_refs with ob_refcnt for all tracked objects — but unfortunately this write operation caused memory pages to be COW-ed. A next obvious solution was to move all the head to another chunk of memory and store densely.

  We implemented a version where the pointer in the gc_head struct didn’t change during collection:

  typedef union _gc_head_ptr { struct { union _gc_head head; } gc_ptr; double dummy; / force worst-case alignment */ } PyGC_Head_Ptr; Did it work? We used the following script to allocate the memory and fork a child process to test it:

  lists = [] strs = [] for i in range(16000): lists.append([]) for j in range(40): strs.append(' ' * 8) With the old gc_head struct, the child process’s RSS memory usage increased by ~60MB. Under the new data structure with the additional pointer, it only increased by ~0.9 MB. So it worked!

  However, you may have noticed the additional pointer in the proposed data structure introduced memory overhead (16 bytes — two pointers). It seems like a small number, but if you consider it applied to every collectable Python object (and we usually have millions of objects in one process, with ~70 processes per host), it could be a fairly big memory overhead on each server.

  16 bytes* 1,000,000 * 70 = ~1 GB Even though the new gc_head data structure showed promising gains on memory size, its overhead was not ideal. We wanted to find a solution that could enable the GC without noticeable performance impacts. Since our problem is really only on the shared objects that are created in the master process before the child processes are forked, we tried letting Python GC treat those shared objects differently. In other words, if we could hide the shared objects from the GC mechanism so they wouldn’t be examined in the GC collection cycle, our problem would be solved.

  For that purpose, we added a simple API as gc.freeze() into the Python GC module to remove the objects from the Python GC generation list that‘s maintained by Python internal for tracking objects for collection. We have upstreamed this change to Python and the new API will be available in the Python3.7 release (https://github.com/python/cpython/pull/3705).

  static PyObject gc_freeze_impl(PyObject module) { for (int i = 0; i < NUM_GENERATIONS; ++i) { gc_list_merge(GEN_HEAD(i), &_PyRuntime.gc.permanent_generation.head); _PyRuntime.gc.generations[i].count = 0; } Py_RETURN_NONE; } We deployed this change into our production and this time it worked as expected: COW no longer happened and shared memory stayed the same, while average memory growth per request dropped ~50%. The plot below shows how enabling GC helped the memory growth by stopping the linear growth and making each process live longer.

  Blue: is no-GC; Red: auto-GC Thanks to Jiahao Li, Matt Page, David Callahan, Carl S. Shapiro, and Chenyang Wu for their discussions and contributions to the COW-friendly Python garbage collection.

  Zekun Li is an infrastructure engineer at Instagram.

  2018/01/01
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  Infoman

  Why is writing mathematical proofs more fault-proof than writing code? (2011)

  108 dgellow 6 hrs 72

  https://cs.stackexchange.com/questions/85327/why-is-writing-down-mathematical-proofs-more-fault-proof-than-writing-computer-c

  news.ycombinator.com/item?id=16045581

  Basic Levels:

  Let's look at things at the simplest, and most basic level. For math, we have: 2+3=5

  I learned about that when I was very, very young. I can look at the most basic elements: two objects, and three objects. Great.

  For computer programming, most people tend to use a high-level language. Some high-level languages can even "compile" into one of the lower high-level languages, like C. C can then be translated into Assembly language. Assembly language then gets converted into machine code. A lot of people think the complexity ends there, but it doesn't: Modern CPUs take the machine code as instructions, but then run "micro code" to actually execute those instructions.

  This means that, at the most basic level (dealing with the simplest of structures), we are now dealing with micro-code, which is embedded in the hardware and which most programmers don't even use directly, nor update. In fact, not only do most programmers not touch micro code (0 levels higher than micro code), most programmers don't touch machine code (1 level higher than micro code), nor even Assembly (2 levels higher than micro code) (except, perhaps, for a bit of formal training during college). Most programmers will spend time only 3 or more levels higher.

  Furthermore, if we look at Assembly (which is as low level as people typically get), each individual step is typically understandable by people who have been trained and have the resources to interpret that step. In this sense, Assembly is much simpler than a higher level language. However, Assembly is so simple that performing complex tasks, or even mediocre tasks, is very tedious. Upper-level languages free us from that.

  In a law about "reverse engineering", a judge declared that even if code can theoretically be handled one byte at a time, modern programs involve millions of bytes, so some sorts of records (like copies of code) must be made just for such an effort to be feasible. (Therefore internal development wasn't considered a violation of the generalized "no making copies" rule of copyright law.) (I'm probably thinking of making unauthorized Sega Genesis cartridges, but may be thinking of something said during the Game Genie case.)

  Modernization:

  Do you run code meant for 286s? Or do you run 64-bit code?

  Mathematics uses fundamentals that extend back for millennia. With computers, people typically consider investment in something two decades old to be uselessly wasteful of resources. That means Mathematics can be a lot more thoroughly tested.

  Standards of Used Tools:

  I was taught (by a friend who had more formal computer programming training than myself) that there is no such thing as a bug-free C compiler that meets the C specifications. This is because the C language basically assumes the possibility of using infinite memory for the purpose of a stack. Obviously, such an impossible requirement had to be deviated from when people tried to make usable compilers that worked with actual machines that are a bit more finite in nature.

  In practice, I have found that with JScript in Windows Script Host, I've been able to accomplish a lot of good using objects. (I like the environment because the toolset needed to try new code is built into modern versions of Microsoft Windows.) When using this environment, I've found that sometimes there is no easily-findable documentation on how the object works. However, using the object is so beneficial, that I do so anyway. So what I'd do is write code, which may be buggy as a hornet's nest, and do so in a nicely sandboxed environment where I can see the effects, and learn about the object's behaviors while interacting with it.

  In other cases, sometimes only after I've figured out how an object behaves, I've found that the object (bundled with the operating system) is buggy, and that it is a known issue that Microsoft has intentionally decided will not be fixed.

  In such scenarios, do I rely on OpenBSD, created by masterful programmers that create new releases on-schedule, on a regular basis (twice a year), with a famous security record of "only two remote holes" in 10+ years? (Even they have errata patches for less severe issues.) No, by no means. I don't rely on such a product with such higher quality, because I'm working for a business that support businesses that supply people with machines that use Microsoft Windows, so that is what my code needs to work on.

  Practicality/usability require that I work on the platforms that people find useful, and that is a platform which is famously bad for security (even though tremendous improvements have been made from the early days of the millennium which the same company's products were much worse).

  Summary

  There are numerous reasons why computer programming is more error prone, and that is accepted by the community of computer users. In fact, most code is written in environments which will not tolerate error-free efforts. (Some exceptions, such as developing security protocols, may receive a bit more effort in this regard.) Besides the commonly thought of reasons of businesses not wanting to invest more money, and miss artificial deadlines to make customers happy, there is the impact of the march of technology which simply states that if you spend too much time, you will be working on an obsolete platform because things do change significantly within a decade.

  Offhand, I can recall being surprised at just how short some very useful and popular functions were, when I saw some source code for strlen and strcpy. For instance, strlen may have been something like "int strlen(char *x){char y=x;while ((y++));return (y-x)-1;}"

  However, typical computer programs are much lengthier than that. Also, a lot of modern programming will use other code which may be less thoroughly tested, or even known to be buggy. Today's systems are much more elaborate than what can easily be thought through, except by hand-waving away a lot of the minutia as "details handled by lower levels".

  This mandatory complexity, and the certainty of working with complex and even wrong systems, makes computer programming a lot hardware to verify than a lot of mathematics where things tend to boil down to a lot simpler levels.

  When you break things down in mathematics, you get to individual pieces that children can understand. Most people trust math; at least basic arithmetic (or, at least, counting).

  When you really break down computer programming to see what's happening under the hood, you end up with broken implementations of broken standards and code that is ultimately executed electronically, and that physical implementation is just one step below microcode which most university-trained computer scientists don't dare touch (if they are even aware of it).

  I've spoken with some programmers who are in college or recent graduates who outright object to the notion that bug-free code can be written. They've written off the possibility, and though they acknowledge that some impressive examples (which I have been able to show) are some convincing arguments, they consider such samples to be unrepresentative rare flukes, and still dismiss the possibility of being able to count on having such higher standards. (A much, much different attitude than the much more trustable foundation we see in math.)

  2018/01/01
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  Infoman

  Mathematical picture language program

  31 lainon 21 hrs 7

  http://www.pnas.org/content/early/2017/12/18/1710707114.full

  news.ycombinator.com/item?id=16035863

  Arthur M. Jaffea,1,2 and Zhengwei Liua,1,2 aHarvard University, Cambridge, MA 02138 Contributed by Arthur M. Jaffe, August 9, 2017 (sent for review June 18, 2017; reviewed by Jacob D. Biamonte, John Ewing, and Alina Vdovina)

  We give an overview of our philosophy of pictures in mathematics. We emphasize a bidirectional process between picture language and mathematical concepts: abstraction and simulation. This motivates a program to understand different subjects, using virtual and real mathematical concepts simulated by pictures.

  Pictures appear throughout mathematical history, and we recount some of this story. We explain insights we gained through using mathematical pictures. Our picture language program has the goal to unify ideas from different subjects. We focus here on the 3D quon language (1). This language is a topological quantum field theory (TQFT) in 3D space, with lower-dimensional defects. A quon is a 2D defect on the boundary of a 3D manifold.

  We believe quon and other languages will provide a framework for increased mathematical understanding, and we expect that looking further into the role of the mathematics of pictures will be productive. Hence, in 4: Questions, we pose a number of problems as the basis for a picture language research program.

  Pictures have been central for visualizing insights and for motivating proofs in many mathematical areas, especially in geometry, topology, algebra, and combinatorics. They extend from ancient work in the schools of Euclid and Pythagoras to modern ideas in particle physics, category theory, and TQFT. See the interesting recent account in Silver (2). Nevertheless, we mention two aspects of mathematical pictures that we feel merit special study.

  First is the importance we ascribe to mathematical analysis of pictures. We explain how we have begun to formulate a theory of mathematical analysis on pictures, in addition to the study of their topology and geometry. For example, the analytic aspect of pictures in TQFT is a less-developed area than its topological and algebraic aspects. Yet it has great potential for future advances.

  Second is the notion of proof through pictures. In focusing on general mathematical properties of pictures, we wish to distinguish this quality from using pictures in a particular concrete mathematical theory. In other words, we aim to distinguish the notion of the properties of a picture language L, on the one hand, from its use through a simulation S to model a particular mathematical reality R. We thank one referee for pointing out that the distinction between L and R parallels the distinction in linguistics between syntax and semantics.

  We propose that it is interesting to prove a result about the language L, and thereby, through simulation, ensure results in R. One can use a single picture language L to simulate several different mathematical areas. In fact, a theorem in L can ensure different theorems in different mathematical subjects R1, R2, etc., as a consequence of different simulations S1, S2, etc. This leads to the discussion of the simulation clock in What Next? Different configurations of the hands of the clock reveal the interrelation between picture proofs for seemingly unrelated mathematical results.

  We also discuss the important distinction between two types of concepts in R that we simulate by a given S. These may be ‘‘real’’ concepts, or they may be ‘‘virtual.’’ This distinction is not absolute but depends on what language and simulation one considers. We give some examples, both in mathematics and in physics, in Real and Virtual.

  We hope these remarks about pictures can enable progress in understanding both mathematics and physics. Perhaps this can even help other subjects, such as the neurosciences, where “One picture is worth one thousand symbols.”

  We propose two basic components to understanding that we call L and R. Here L denotes abstract concepts or language, while R stands for the concrete subjects or reality, which we desire to understand. We could also think of them as left and right. Simulation S represents a map from L to R.

  Our universe provides a great reservoir for ideas about the real world. We can consider this as R. We understand these ideas through abstraction, including theories of mathematics, physics, chemistry, and biology. To deal with these real concepts, one often requires virtual concepts that have no meaning in the real world. These virtual concepts may not have an immediate real meaning, yet they may provide key insight to understanding the real structures.

  Abstraction is a method to study complicated ideas and goes from R to L. For example, chemistry as L may provide a logical language to abstract certain laws in biology as R, with a simulation S: L →→ R. One can continue this chain where we regard chemistry as a new R and physics as its abstraction as a new L with a new simulation S. Abstraction can be repeated yet again with mathematics as L and physics as R. At each step, one learns the axioms in L from the real concepts in R. In order to do computation, we often need to introduce virtual concepts in L to understand the concepts in R.

  We are especially interested in the case that L is a picture language. Then one can represent the concepts in L by pictures that play the role of logical words, with axioms as their grammar. The axioms should be compatible with pictorial intuition. One can develop a picture language as an independent theory like Euclidean geometry.

  A good simulation for a particular mathematical subject R should satisfy two conditions. Mathematical concepts in R should be simulated by simple pictures in L, and mathematical identities in R should arise from performing elementary operations on the pictures in L. Abstraction and simulation could be considered as inverse processes of each other.

  The idea of whether a concept is real or virtual plays an important role. This is not an absolute concept but depends on the simulation. For example, over the real-number field, the square root xx of a positive number xx is real, while the square root of a negative number xx is virtual. By enlarging the field to complex numbers, one understands this virtual concept in a fruitful way. One gets used to the power of complex numbers, and, in a new simulation, one can regard them as “real.”

  In a picture language, it may be that a picture can represent a real concept in one theory and a virtual one in another. As an example, consider Feynman diagrams, the pictures that one uses to describe particle interactions. A contribution to the scattering of two physical electrons is described by the first electron emitting a photon (quantum) that is absorbed by the second electron. However, conservation of energy and momentum preclude the exchanged photon from being real: It must be virtual. Fig. 1 shows the original diagram in ref. 3.

  Analytic continuation (Wick rotation) to imaginary time puts the real and virtual particles on equal footing. In that situation, the above example only involves virtual particles. In this way, one will obtain many virtual concepts, which could become real based on a proper simulation.

  When Atiyah gave a mathematical definition of TQFT, he wrote “it may well be that such topological understanding is a necessary prerequisite to building the analytical apparatus of the quantum theory” (7). Today, we want to move forward to reach a full quantum field theory. Our long-term goal is to construct quantum field theory using pictures. However, that may be too hard to achieve as the initial step.

  We emphasize two properties of QFT that shed light on pictures. The first theme is “symmetry.” To understand continuum symmetry, it is useful to understand discrete symmetry that can approximate the continuum.

  The second theme, “positivity,” is especially interesting, as positivity provides the basis for analysis. The striking fact is that the analysis of pictures is a mathematical theory with great potential, but is still in its infancy. On one hand, we expect to abstract concepts from analysis to pictures. This will enrich the theory of picture language in one additional dimension. On the other hand, we want to simulate analysis using picture language and provide pictorial tools.

  How do we connect pictures with analysis? Usually, pictures without boundaries are scalars. How can we formulate a measurement pictorially? A main lesson from quantum field theory is the importance of reflection positivity. An elementary pictorial interpretation of reflection positivity is that gluing a picture to its mirror image is positive.

  In constructive quantum field theory, extensive analysis is performed through estimating Feynman diagram pictures in terms of subdiagrams; this may use a pictorial Schwarz inequality, or some more sophisticated operator norm. These estimates are central to the proof of most results in the subject. In the framework of our present discussion, such analysis takes place on pictures in R. We call this using ‘‘pictures in analysis.’’

  However, we are really interested in whether, and to what extent, one can do analysis on pictures. This means that we need to do computations in L, without reference to R. The discussion of rotation, Fourier analysis, multiplication, and convolution indicate that some progress can be made.

  Do we obtain interesting analysis based on this minimal requirement? In fact, the surprise is that we already have interesting results inspired by Fourier theory. As mentioned, the 2D pictorial operation on square-like pictures is compatible with Fourier analysis.

  Compactifying the plane to a sphere defines a measurement based on reflection positivity. Pictorial consistency on the sphere implies that the Fourier transform is a unitary. Many other results in Fourier analysis carry over to pictures.

  Freeman Dyson described two types of mathematicians: birds and frogs (17). They do mathematics in different ways. Birds soar between different fields with unifying ideas, like Yuri Manin in his book Mathematics as Metaphor (18). Frogs sort out details to achieve great depth of understanding. In fact, it is good to attempt to encompass both metaphors! One does find both of these ingredients in our story of picture language. While we began in quantum information, we ended up traveling through much of the colorful landscape of mathematics.

  In our context of language, the bird flies back and forth to discover new Rs, Ls, and Ss. The frog uses an S to understand some important problem. The shape of pictures provides key hints and insights for finding these connections. A popular article by Peter Reuell described picture language as Lego-like mathematics (19).

  The authors began our collaboration 2 years ago in late July 2015 with much discussion at Harvard. Our first goal was to understand reflection positivity for parafermions (20, 21) in a pictorial way (15). If we call this mathematical problem R1, then it led us to define the picture language L1, which we call planar para algebra. This is a generalization of planar algebra, but with strings replaced by charged strings, and topological isotopy replaced by para isotopy. The map from L1 to R1 we call the simulation S1.

  In the simulation S1, we found an elementary explanation that a 90° rotation of pictures represented Fourier transformation, and takes multiplication to convolution. We used this fact to give a geometric, picture proof of reflection positivity (15).GraphicWe also found elementary picture representations for d×dd×d unitary Pauli matrices X,Y,ZX,Y,Z with eigenvalues qjqj, where q=e2πi/dq=e2πi/d, and j=0,1,…,d−1∈ℤdj=0,1,…,d−1∈ℤd.

  Alex Wozniakowski pointed out that our work seemed related to quantum information. This led to fruitful collaboration among the three of us, in which we used the language L1 to simulate quantum communication in the mathematical framework that we name R2. Using this simulation S2, one picture deformed by isotopy into different shapes simulates different concepts in quantum information. In this way, we reproduced the standard teleportation protocol of Bennett et al. (22) by a topological design in L1 (23). With these concepts in place, we could also design other protocols, including multipartite, teleportation protocols (24).

  The important point for our understanding of R2 was to follow pictorial intuition. This led to finding a natural candidate for a resource state that we called |Max⟩|Max⟩. Our picture Max in L1 for the entangled state |Max⟩|Max⟩ is simple and natural, as well as suggesting entanglement in a pictorial fashion. Our two-string picture for |Max⟩n|Max⟩n (with nn qudits) isGraphic

  The algebraic formula for the simulation S2Maxn in R2 is|Max⟩n=1d(n−1)/2∑|k→|=0|k→⟩n,|Max⟩n=1d(n−1)/2∑|k→|=0|k→⟩n,[3]where we call |k→|=k1+⋯+kn|k→|=k1+⋯+kn the total charge in ℤdℤd. It involves dn−1dn−1 terms for a resource state with nn qudits, so, algebraically, the simulation of Maxn is complicated (23).

  We then left Massachusetts to spend 4 months visiting the Research Institute for Mathematics (FIM) at the Eidgenössische Technische Hochschule (ETH) in Zurich, Switzerland.

  We finally had time to begin writing up these results (15) and later results (23, 24). We then learned that Greenberger et al. (25) had long before introduced another multiqubit resource state in quantum information. This |GHZ⟩|GHZ⟩ state appears to be algebraically simpler, as it is a sum of only dd terms, independent of the number nn of qubits,|GHZ⟩n=1d1/2∑k∈Zd|k,k,…,k⟩n.|GHZ⟩n=1d1/2∑k∈Zd|k,k,…,k⟩n.[4]Both |Max⟩|Max⟩ and |GHZ⟩|GHZ⟩ generalize the Bell states, and they have some very similar properties. This led us eventually to observe that |GHZ⟩|GHZ⟩ and |Max⟩|Max⟩ are related by a change of basis—in fact by Fourier transformation on ℤdℤd,|Max⟩n=F⊗n|GHZ⟩n.|Max⟩n=F⊗n|GHZ⟩n.[5]To understand this further, we were intrigued by our generalization of “Kitaev’s map” from Majoranas to the Pauli spin matrices X,Y,ZX,Y,Z. We had discovered a natural generalization to represent a single qudit as a neutral parafermion/antiparafermion pair (15). Neutrality provides an elegant way to reduce the d2d2-dimensional space of states for two virtual parafermions to the correct dd-dimensional space for one real qudit. It involved introducing a “four-string” planar language L2 to describe a single qudit, and a corresponding simulation S3L2 = R3. The model R3 contains dd real and d2−dd2−d virtual one-qudit states. The representations of the qudit Pauli X,Y,ZX,Y,Z matrices are neutral, so they act on the neutral (real) subspace of dd dimensions. Another hint that neutrality holds the key is that the SFT equals the discrete Fourier transform in the neutral subspace of L2.

  However, the language L2 and simulation S3 pose a problem for describing more than one qudit. Braiding charged strings from different qudits destroys neutrality of the individual excitations. This would allow transitions from the real nn-qudit space of dimension dndn into the virtual nn-qudit space of dimension d2nd2n. Thus the obstruction to describing multiqudit states boiled down to the question: How can one ensure that real multiqudit states evolve into real multiqudit states? When we met Daniel Loss in Basel, we found that he was also considering this question. We did not find the answer immediately.

  After Zurich, we had the opportunity to spend 6 weeks visiting two institutes in Bonn. We discovered the answer to the puzzle described in ETH Zurich during June 2016, perhaps receiving inspiration from working in Fritz Hirzebruch’s former office at the Max Planck Institute for Mathematics.

  During that time, we encountered two more hints about L2. The first is that the Frobenius algebra for the mm-interval Jones–Wassermann subfactor in CFT (26) isγ=⊕X→dim(X→)X→.γ=⊕X→dim(X→)X→.[6]Here X→=X1⊗⋯⊗XmX→=X1⊗⋯⊗Xm, a tensor of simple objects in a modular tensor category (MTC) (8), and dim(X→)dim(X→) is the multiplicity of 1 in X→X→. This formula coincides with |Max⟩|Max⟩ for the group ZdZd, but they have completely different meanings. Secondly, we learned the relations for bi-Frobenius algebras in the manuscript for ref. 27, which one of the authors had shared with Alex Wozniakowski.

  The construction of Jones–Wassermann subfactors for an MTC requires an extension of pictures from 2D to 3D space. Here the coincidence between |Max⟩|Max⟩ and γγ is explained through an m−nm−n duality (28). Going from 2D to 3D also gives a natural explanation of the bi-Frobenius algebras relation.

  The last piece in solving the puzzle came by adding three manifolds to these 3D pictures; this was inspired by TQFT. Finally, we unified all these ideas by formulating the 3D quon language L3, and a simulation S4 to quantum information. Moreover, we extended our approach from ZdZd symmetry to MTCs based on work about the Jones–Wassermann subfactor.

  We designed L3 using ideas from different Rs: quantum information, subfactor theory, TQFT, and CFT. Therefore, we expected to simulate those Rs using L3 as well as others.

  In the quon language, the bi-Frobenius algebra relation has a topological interpretation. Both |Max⟩|Max⟩ and |GHZ⟩|GHZ⟩ are represented by single pictures Max and GHZ, where one picture is a 90° rotation of the other. Algebraically, one resource state is the Fourier transform of the other. Moreover, the complicated resource state |Max⟩|Max⟩ can be computed by using its relation to the elementary resource state |GHZ⟩|GHZ⟩. For three qudits, the quon state pictures are just rotations of one another, as illustrated inGraphic[7]

  Furthermore, the quon language expresses elegantly in pictures the duality between orthonormal bases for the Pauli matrices XX and ZZ, so one can begin to imagine pictures describing quantum coordinates.

  Our first discovery by S4 was the topological nature of the Feynman, or quantum controlled NOT (CNOT), gate. Fig. 2, drawn by Lusa Zheglova, captures our representation of the quantum teleportation protocol. It illustrates classical communication in the foreground. In the background, it shows the quon representation of a Bell state, a CNOT gate, the Fourier transform, a measurement, and Pauli matrices (corresponding to Kitaev’s map) used in the recovery map. Together, they give the widely used quantum teleportation protocol of Bennett et al. (22), and illustrate its elegant 3D topological interpretation. The details can be found in ref. 1.

  This quon language L3 provided an opportunity to collect ideas and to think about their meaning. It became apparent that the quon language L3 was important on its own, as it could have applications in other areas of mathematics and physics besides quantum information. Furthermore, the algebraic identity between |GHZ⟩|GHZ⟩ and |Max⟩|Max⟩ given by the rotation of the diagrams could provide insight in other subjects through use of other simulations.

  What is the meaning of this interesting algebraic identity? It actually is the Verlinde formula. Mathematically, one can generalize the pictorial construction to define GHZ and Max on higher-genus surfaces. Then the pictorial Fourier duality between GHZ and Max leads to the generalized Verlinde formula (29) for any MTC on the genus gg surface,Maxn,g=𝔉S⊗nGHZn,g,⟹dim(k→,g)=∑k(∏i=1nSki,k)Sk,02−n−2g.Maxn,g=𝔉S⊗nGHZn,g,⟹dim(k→,g)=∑k(∏i=1nSki,k)Sk,02−n−2g.Here, on the first line, nn is the number of quons, 𝔉S𝔉S is the SFT, and, on the second line, k→=(k1,k2,…,kn)k→=(k1,k2,…,kn) are punctures (or marked points) on the genus gg surface, SS is the modular transformation, and dim is the dimension of the associated (moduli) space.

  This pictorial Fourier duality also coincides with the duality of graphs on the sphere. The dual graph of the tetrahedron is also a tetrahedron. Applying the quon language to this graphic duality, we obtain a general algebraic identity Eq. 8 for 6j6j symbol self-duality. With X¯X¯ denoting the dual object to XX in an MTC,|(X6X3X5X2X4X1)|2=∑Y→(∏k=16SXkYk)|(Y1Y2Y3Y4Y5Y6)|2.|(X6X3X5X2X4X1)|2=∑Y→(∏k=16SXkYk)|(Y1Y2Y3Y4Y5Y6)|2.[8]In the special case of quantum SU(2)SU(2), this was discovered by Barrett (30), based on an interesting identity of Roberts (31). The general formulation and proof of Eq. 8 is in section 6 of ref. 32.

  For each graph on a surface, the graphic duality gives a new algebraic identity for MTCs in quon language. Most of these identities have virtual meanings. Each graph can also be considered as a linear functional generalizing integration. The pictures generalize the symbol ∫∫ and capture additional pictorial relations, such as graph duality mentioned above. It would be interesting to understand these new identities and integrations in some new R.

  We are grateful to Lusa Zheglova for the use of her drawing. We thank the Operator Algebras: Subfactors and their Applications (OAS) program at the Isaac Newton Mathematical Institute for hospitality. This research was supported in part by Grants TRT0080 and TRT0159 from the Templeton Religion Trust.

  Footnotes

  Author contributions: A.M.J. and Z.L. designed research, performed research, and wrote the paper.

  Reviewers: J.D.B., Skolkovo Institute of Science and Technology; J.E., Mathematics for America; and A.V., University of Newcastle.

  The authors declare no conflict of interest.

  ↵∗The argument depends on the inscribed angle theorem: Three points B, D, C on a circle determine the angle BDC = θθ, and the angle BOC = ψψ, where O is the center of the circle. Then, always, 2θ=ψ2θ=ψ. The proof is as follows: In the special case that BD passes through O, symmetry shows that the triangle COD is isosceles. As the sum of angles in a triangle is ππ, both 2θ2θ and ψψ complement the same angle, so 2θ=ψ2θ=ψ. The general case then follows by drawing the diameter through BO, and considering the sum or difference of two special cases.

  This is an open access article distributed under the PNAS license.

  References

  2017/12/31
• 
  

  Infoman

  25 days of Clojure

  36 brucehauman 1 day 1

  https://orestis.gr/25-days-of-clojure/

  news.ycombinator.com/item?id=16031062

  Close Subscribe Menu 29 December 2017 For the past couple of years, I have tried to learn and evaluate one new programming language each year. One nice way I've found is by solving the Advent of Code puzzles.

  Learning a new language by solving those puzzles has a few notable benefits:

  A nice 25 day streak, so that you can build a habit. Small and self contained, usually solved in 30-60 minutes without needing to bring in any dependencies. You get to exercise the standard library and built-in data structures a lot. You get to implement some well-known algorithms (e.g. A*, depth- or breadth-first tree search, graph walking etc.) You get a feel of the performance characteristics, since some puzzles require some intense calculations. You can read other solutions in your language of choice in order to learn new tricks. It's fun! In 2016, I tried Elixir. For 2017, it was Clojure. In both cases, I've started a couple of weeks in advance by actually learning the language and libraries, solving older puzzles, so I can be ready on December 1st for the new puzzles.

  Here is a rough list of my takeaways, after two solid months of daily Clojure puzzle-solving:

  It took me roughly two weeks to get used to the (parentheses). I don't notice them any more. The Clojure community is very friendly and very experienced. It helps to have some functional programming experience before trying Clojure. In my case, Elixir had many many similarities so at times I felt like just learning a new syntax, rather than a new way of programming. Clojure is fast, even when using the functional and immutable data structures. If you need to go faster, you can drop down to the transient versions, to the Java collections, to the unboxed math operations. Clojure is flexible. You get immutability by default, but if an algorithm is better expressed by having a mutable data structure you can also have that. Having a REPL that is integrated with your editor (in my case, Spacemacs + CIDER) is eye-opening. I will try and make a screencast or stream of what this looks like when solving those puzzles — it's really hard to put into writing, but it makes other REPLs seem downright primitive. The Clojure documentation can be hard to decipher, and the clojure.core namespace is huge. ClojureDocs helps, but I believe there's a need for an apropos-like functionality built into the system. In the first couple of weeks, the error messages where a bit cryptic. They still are, but now I can decipher the common ones. Doing incremental work with a REPL-connected editor mitigates this somewhat. Also, CIDER has a built-in debugger that's very easy to use and has saved me from littering my code with println statements. Java is huge, and the JVM is powerful. I haven't touched the ecosystem since 2007, and I remember now why most enterprise software is written in Java: There's a robust library for pretty much everything you want to do. Being able to explore and use those libraries from a higher-level language is very very interesting and unlocks a huge amount of potential. Even the JDK itself has a great standard library and can do pretty much everything. Reading other people's code can be either an enlightening experience or an exercise in frustration. This doesn't seem to be dependant on the author of the code, but rather on whether they are using the same 80% of the language you are using. To be fair, most people that are solving those puzzles are not aiming for readability or maintainability. In the end, my verdict on Clojure is whole-heartedly positive. However, the next avenue I want to explore is actually ClojureScript, both on client side (probably via re-frame), but also on server side (on Node.js).

  In case anyone is interested, you can find my 2017 solutions on GitHub. You can also find a community-curated list of Advent of Code solutions in Clojure. Finally, a special mention to Mike Fikes's solutions that are in cross-platform Clojure and ClojureScript.

  Orestis Markou's Picture Read more posts by this author.

  Twitter Facebook Google+ I have recently had a minor enlightenment about what programming languages I want to use in my daily work,…

  Software Deviser © 2017 Proudly published with Ghost

  2017/12/31
• 
  

  Infoman

  Making a 3D Delta Printer for Ceramic

  28 mhb 2 days 2

  http://www.keep-art.co.uk/Self_build.html

  news.ycombinator.com/item?id=16029248

  MAKE YOUR OWN 3D DELTA PRINTER FOR CERAMIC

  Annoyed and frustrated by 3D Systems buying out and then stopping the production of the RapMan 3D printer. This was the first kit printer Belgian design duo Unfold adapted to print with clay and has subsequently been copied and widely used by others, I decided to develop my own replacement 3D printer. Based on the delta type of 3D printer my aim has been to use parts that can either be made with basic DIY tools and skills, or ordered off the internet. The design is specifically for printing in clay but could be adapted to work with other materials. Many other self build 3D printers use parts printed in plastic but with this project I did not want to be reliant on already having access to a 3D printer.

  This document is not a blue print for building a 3D printer but more my documentation of what I have done. My experience is as an artist and not as an engineer or computer specialist so I am sure there are many improvements that could be made but this is what works for me. I welcome others to take up the design and develop it further but only in a private or research and education context. Please respect my ethos of sharing and the humanity to be found in generosity. This project was borne out of the disgust of the practices of big business so please do not blatantly use the information I am offering for commercial gain.

  Being more interested in what might be creatively produced with 3D printers I am happy to contribute openly to the growing knowledge based around clay and computerisation but it come with a warning. As difficult as it is to pull all the technology and computerisation together I suggest it is even more difficult to do something creative, fresh and meaningful with this technology. I look forward to seeing what others can do with it. By 3D printer standards this is a rather primitive tool set, but it does offer an accessible way into a very exciting new way of working with clay.

  2017/12/31
• 
  

  Infoman

  A 19th-Century Illustrator Found Beauty in the Slimiest of Sea Creatures 35 prismatic 2 days 4

  https://www.smithsonianmag.com/science-nature/this-19th-century-illustrator-found-beauty-slimiest-sea-creatures-180967625/?no-ist

  news.ycombinator.com/item?id=16028114

  February 16, 1864, should have been a happy day for Ernst Haeckel.

  

  On this day, the German Academy of Sciences awarded the famed naturalist, scientist, philosopher, physician and illustrator the Cothenius Medal, a high honor acknowledging his many scientific achievements. It was also Haeckel’s 30th birthday. But this date also marks the day his wife of a mere 18 months, Anna Sethe, died from what is now thought to be a burst appendix.

  Haeckel was enraptured by the woman, who—as he described to a friend—was a "completely unspoiled, pure, natural person." Never fully recovering from the loss, he captured her memory a few years later in an illustration of an unlikely creature: a jellyfish.

  anna Sethe The jellyfish Desmonema annasethe, named after Haeckel's late wife from the Cnidarians volume of Art Forms in Nature, 1899–1904, plate 8. (© TASCHEN Köln/Niedersächsische Staats- und Universitätsbibliothek Göttingen) Dubbed Desmonema annasethe, after his late wife, the jellyfish's tentacles delicately intertwine in a sweeping trail behind its bell in Haeckel's image. "You can really see how it's swinging through the ocean," says Julia Voss, an author of the new book The Art and Science of Ernst Haeckel, which chronicles Haeckel's life and work. "It looks like a dress; it looks like hair; it has this elegance."

  Such elegance and beauty graces nearly every page of the new Haeckel compendium—from the tiny spires of the microscopic radiolarian to the twist of the pitcher plant vine and the flow of the annasethe jellyfish that glides across the back cover of the book. Written along with Rainer Willmann, the director of the Zoological Museum at Göttingen University who specializes is phylogenetics and evolution, the book includes complete reprints of seven of Haeckel’s volumes of illustrations—corals, sponges, medusae and more—and adds commentary and context to each.

  Born in Potsdam, Germany (then Prussia), Haeckel was the second—and last—child in his family. Both his grandfather and his father worked in government, and his older brother eventually followed suit. Haeckel, however, was fascinated by adventure and nature. Though he reluctantly enrolled in a medical program at Berlin University, he wasn't fully sated.

  radiolarians Radiolarians from Art Forms in Nature, 1899-1904, plate 71. These tiny protozoa are single-celled organisms and either live colonially or individually, mostly floating in the water column. They form impressively intricate skeletons of silica, which Haeckel illustrated in detail. (© TASCHEN Köln/Niedersächsische Staats- und Universitätsbibliothek Göttingen) It was a research trip in 1854 to the North Sea islands of Heligoland that captured his imagination. He had no formal training in art, but dove head first into the study and detailed documentation of marine organisms. He published the first volume of his study of radiolarian in 1862—a work that played a large part in him later receiving the prized Cothenius Medal. In 1865, he was appointed a full professor of zoology at University of Jena, where he produced an "avalanche" of illustrations, as Voss puts it.

  Despite this success, his life was also punctuated by much sadness—from his fraught second marriage to Agnes Huschke to the mental illness that plagued his youngest daughter. "Haeckel himself is like this one day [February 16, 1864], where the worst things happen and the most beautiful things happen," says Voss. A German journalist and scientific historian, Voss wrote her doctoral dissertation on visual representations of Darwin’s theory of evolution.

  While undertones of sadness flow through Desmonema annasethe, there is a certain joy and unforgettable whimsy to all of his creatures, like the waggle in his calcareous sponges or the squiggle of the siphonophores.

  Siphonophorae From the Cnidarians and Siphonophores volume of Haeckel's landmark work Art Forms in Nature, released between 1899-1904. (© TASCHEN Köln/Niedersächsische Staats- und Universitätsbibliothek Göttingen) "His scientific paintings and drawings shaped a signature style of the epoch," says Voss. As she writes in the book: "[T]he deep-sea creatures he drew clambered out from the pages of his Kunstformen der Natur (Art Forms in Nature, 1899-1904) and onto the cities of Europe, their squares and facades onto stage curtains, and into paintings." For example, medusae much like those he illustrated, hung from the ceiling of the Institut Oceanographique in Monaco, France, which opened its doors in 1910. And his meticulously crafted creatures were an important inspiration in the development of the Art Nouveau movement, known for its organic designs.

  Haeckel's "heavy legacy," as Voss calls it, also looms large in modern science. Along with his meticulous documentation of every scale, spike and tentacle, Haeckel named thousands of new species. He coined terms still in use today, including phylum, phylogeny and ecology. He was also a champion of evolution, entranced by Darwin's On the Origin of Species, and was among the first to sketch out an evolutionary tree—a fundamental part of the modern study of life.

  As Voss explains, Haeckel was instrumental in popularizing evolution in Germany. The theory was met with stiff opposition in part, says Voss, because of distaste at the idea that humans came from some "slimy creature at the bottom of the sea," she says.

  Haeckel's work, however, offered a splendid counterpoint to the idea that life evolved from simple creatures. "We come from a creature from the bottom of the sea, but look at them—look how beautiful they are; look how fascinating they are," Voss says of his illustrations. Haeckel's tiny radiolarians have an entrancing three-dimensional quality, which was unusual for other art of the day, and his choice to put many of the creatures on black backgrounds makes them appear to burst from the pages.

  Medusae Monograph on the Medusae, vol. 1, 1879, plate 1. (© TASCHEN Köln/Niedersächsische Staats- und Universitätsbibliothek Göttingen) Yet not all Haeckel's ideas were scientifically—or morally—sound, Voss notes. While Darwin didn't include humans in his popular On the Origin of Species (only referring to "race" to describe groups of non-human creatures), Haeckel embraced racist ideas of the time, applying Darwin's principles to people in what Voss calls the "ugly side of evolutionary theory."

  The Art and Science of Ernst Haeckel includes one such racist depiction. The controversial 1868 image displays a dozen profiles of heads—six human and six ape, with the "highest" primates listed first. Haeckel's description of the plate states: "It is clear that the lowliest forms of humanity resemble superior apes far more closely than they resemble higher forms of humanity." Unsurprisingly, Indo-Germanic, a group to which Haeckel belonged, tops this hierarchy.

  Though this and other related depictions are undoubtedly incorrect, "I find it important to show these," Voss says. She wants to acknowledge Haeckel’s scientifically and artistically important advances as well as his racist viewpoints, so as not to erase these from history. The new book shows Haeckel in his entirety.

  Tabbing through its 704 oversized pages, it's easy to see his mastery of the medium and his dynamic capabilities of illustration. It's oft repeated that Haeckel could draw with one eye looking through a microscope and another at his page. "He would really baffle his colleagues with his capacity," Voss says.

  sponges Atlas of Calcareous Sponges, 1872, plate 6 (© TASCHEN Köln/Niedersächsische Staats- und Universitätsbibliothek Göttingen) And while some of his viewpoints are bigoted, controversial or disproven, many scientifically important images and ideas have endured through the years—even showing up in biology textbooks today. The wonder he saw in the world around him can still powerfully be felt through his imagery. When Voss' first copies of the book arrived, she called her two sons, ages six and seven, to come see. "We sat together on the sofa and we spent an hour in this book together," she says. "They would ask me, 'What is this? What is this?'"

  "I think it's so fascinating to see that none of the wonder has gone," she says. "Now, more than 100 years later, we still sit on the sofa [asking], 'What is this?'"

  Some of the creatures filling the pages aren’t exactly cute, and many are microscopic, but through Haeckel's steady hand everyone can grasp the beauty, the elegance—and sometimes the sadness—in all walks of life.

  2017/12/31
• 
  

  Infoman

  Social media,India,faked news

  India’s millions of new Internet users are falling for fake news

  74 JumpCrisscross 8 hrs 71

  https://www.washingtonpost.com/world/asia_pacific/indias-millions-of-new-internet-users-are-falling-for-fake-news--sometimes-with-deadly-consequences/2017/10/01/f078eaee-9f7f-11e7-8ed4-a750b67c552b_story.html

  news.ycombinator.com/item?id=16036066

  People use their smartphones at the Nehru Place IT Market in New Delhi. Jio, an ultracheap mobile network service, increased mobile data usage in India more than sixfold between June 2016 and March 2017. (Sanjit Das/Bloomberg News) NEW DELHI — One recent Wednesday afternoon, monsoon rains were lashing office windows in Mumbai. Inside, screens were lighting up with messages announcing the arrival of Cyclone Phyan.

  Employees of a start-up called Little Black Book, an online city guide, started panicking. Some went home early after receiving messages on their phones that roads were being closed. Others passed the message on to loved ones in Phyan’s path. Jayati Bhola, a 24-year-old writer at Little Black Book, was organizing a charity music show that evening and feared that the warning may put off her guests. She quickly checked the weather online and then sent around a message: “We’re still on guys! Rain or Shine.”

  As it turns out, Cyclone Phyan never came to Mumbai that evening, Sept. 20. In fact, it had already happened — eight years earlier, 1,400 miles away, in Sri Lanka. “That rumor about the cyclone has been going around for years,” said Pankaj Jain, founder of SMHoaxSlayer.com, a website that fact-checks circulating rumors on social media in India.

  While fake news in the United States is said to have contributed to President Trump’s election victory, in India, a nation with 355 million Internet users, false news stories have become a part of everyday life, exacerbating weather crises, increasing violence between castes and religions, and even affecting matters of public health.

  “Common sense is extinct,” Jain said. “People are ready to believe anything.”

  Last week, newspapers here carried full-page advertisements by Facebook that explained how to spot false news. Minister Raj­nath Singh, who oversees home affairs, addressed members of the armed border forces in New Delhi, advising them not to believe everything on social media.

  Much of India’s false news is spread through WhatsApp, a popular messaging app. One message that made the rounds in November, just after the government announced an overhaul of the country’s cash, claimed that a newly released 2,000 rupee bank note would contain a GPS tracking nano-chip that could locate bank notes hidden as far as 390 feet underground. Another rumor, about salt shortages last November, prompted a rush on salt in four Indian states. In southern India, a rumor about a measles and rubella vaccine thwarted a government immunization drive.

  Many false stories have led to violence. In May, rumors about child abductors in a village triggered several lynchings and the deaths of seven people. In August, rumors about an occult gang chopping off women's braids in northern India spread panic, and a low-caste woman was killed.

  Some stories exacerbate India’s rising religious and caste tensions. This week, for instance, images purportedly showing attacks against Hindus by “Rohingya Islamic terrorists” in Burma circulated on social media in India, stoking hatred in Hindu-majority India against Muslim Rohingya.

  “There was one video with two people being beheaded, and the text was saying these were Indian soldiers being killed in Pakistan. When I found the original video, it was actually taken from footage of a gang war in Brazil,” Jain said. “They’ll tell you this is fresh, these are images the media is not showing you, if you’re a true Indian patriot, you will forward this message.”

  The rumors have resulted in a small industry of fact-checkers who are setting up websites to debunk myths circulating online. Pratik Sinha is a former software engineer who started Altnews.in, a fact-checking website. “The number of fake news stories is so high that we can’t compete on the quantity of fact checks we do,” he said. “We focus on quality.”

  The fact-checkers come from various backgrounds — some are former journalists, others are software geeks, and some are just concerned citizens. Many fake news stories appear to support India’s ruling Bharatiya Janata Party and its right-wing Hindu nationalist agenda, said Jency Jacob, managing editor for boomlive.in, a fact-checking website. “If we don’t do something, it will be too late,” Jacob said. “Political parties would love to use this for their own benefit and we need to intervene.”

  The scrutiny has led to some triumphs. Ministers have deleted misleading tweets and posts after being fact-checked online; in one instance, a government ministry launched an inquiry after Altnews pointed out that an image it had used in a report to show floodlights on India’s border was actually from the Spanish-Moroccan border.

  In September 2016, India’s richest man, Mukesh Ambani, launched an ultra-cheap mobile network service, Jio. The new network brought millions of Indians online for the first time. Jio’s cheap plans increased mobile data use more than sixfold between June 2016 and March 2017, according to Mary Meeker’s Internet Trends 2017 report.

  India’s first-time users are particularly susceptible to rumors doing the rounds on social-media websites, Jacob said. “The U.S.A. is a more mature market. In India, these forwards take on a life of their own,” he said, referring to chain messages on social media.

  Asavari Sharma, a Mumbai resident, was one of many who posted images of rainy streets on Facebook. She added a caption that read “CYCLONE PHYAN REACHING US: Hope all the good souls on my list are safe.”

  “Honestly, I never believe in weather news because every time they show, something never really occurs,” Sharma said. This time they had shown a “few horrible images projected straight from the satellite. So I had to upload to Facebook.”

  By noon, rumors about Phyan had reached authorities. Mumbai’s Disaster Management Unit tweeted, “As informed by IMD [India Meteorological Department] there is no cyclone warning for Mumbai. Citizens r requested not to spread & trust rumour.”

  Jain says the rise of false rumors worries him. “Basically, somebody’s making money out of all this,” he said, saying that clicks on fake news websites are supported by advertisements. “Ultimately, people are being conned.”

  2017/12/31
• 
  

  Infoman

  Advances in Pre-Training Distributed Word Representations

  48 mpweiher 12 hrs 1

  https://arxiv.org/abs/1712.09405

  news.ycombinator.com/item?id=16034977

  (Submitted on 26 Dec 2017)

  Abstract: Many Natural Language Processing applications nowadays rely on pre-trained word representations estimated from large text corpora such as news collections, Wikipedia and Web Crawl. In this paper, we show how to train high-quality word vector representations by using a combination of known tricks that are however rarely used together. The main result of our work is the new set of publicly available pre-trained models that outperform the current state of the art by a large margin on a number of tasks. From: Tomas Mikolov [view email] [v1] Tue, 26 Dec 2017 21:00:04 GMT (20kb)

  2017/12/31
• 
  

  Infoman

  Ask HN: What was the best CS paper you read in 2017?

  372 avinassh 10 hrs 98

  news.ycombinator.com/item?id=16035402

  sulam 8 hrs I highly recommend people give this paper a read. I think it points the way to a radical redesign of fundamental parts of the system stack over the next 5-10 years. If you work in systems and you aren’t thinking about this stuff, you’re about to be lapped.

  The Case for Learned Index Structures:

  https://arxiv.org/pdf/1712.01208v1.pdf

  chubot 7 hrs I'm gonna throw some cold water on this and say this is not a new paradigm by any means.

  https://www.sigarch.org/the-unreasonable-ineffectiveness-of-...

  However, it's certainly possible that the time for this idea has come. Google is probably in the best position to apply it.

  I will say that after having worked at Google for over a decade, some of it on data center performance, there are plenty of inefficiencies that have nothing to do with algorithms or code, but have more to do with very coarse-grained resource allocation. In other words, they are "boring management problems" rather than sexy CS problems.

  I should also add that you're expanding the claims made in the paper, which appears to be a common mistake. If you look at the related work and citations, you'll see that it's mostly about B-Trees, bloom filters, and indices. But I have seen people generalizing this to "a revolution in systems research", which I don't believe is justified.

  Indexed data structures are important but a revolution in them is not going to cause a revolution in all of systems. Off the top of my head, the general area of "scheduling" (in time and space) is probably more important and more potentially impactful.

  sulam 6 hrs Have you seen the results when they let a trained model manage Borg? The power reductions were immediate, non-trivial, and performance stayed the same. There's your scheduling result for you.

  Look at it this way. As the paper points out, a Hashtable is just a heuristic that works fairly well in the worst case and reasonably well in the average case. No one would argue that you couldn't hand-roll an algorithm that is better for a specific, narrowly defined data set. This paper is demonstrating that you don't have to do it by hand, you can train a model automatically without the expected caveats like: size (doesn't have to be huge), latency (the model trains very quickly) or specialized hardware (they used bog-standard CPUs, not GPUs or TPUs).

  This is obviously just my opinion, but I think it's pretty big. It's not big because of the paper itself, although that's amazing in isolation (they improved on hashtables!). As I said above, it points the way. They have a throw-away sentence about co-optimizing data layout along with lookup and not showing those result. My guess is that beats a hashtable in every way. More importantly, if a model can beat bloom filters, b-trees, and hashtables, you'd better hold onto your socks because most algorithms are just waiting to fall. To me this paper is Deep Blue vs Kasparov. We all know what comes next.

  cinjon 2 hrs Link to paper that cedes control of Borg?

  sulam 1 hr Blog post: https://deepmind.com/blog/deepmind-ai-reduces-google-data-ce...

  Whitepaper: https://docs.google.com/a/google.com/viewer?url=www.google.c...

  Eridrus 6 hrs The article you link ends on a pretty weak claim, math didn't obsolete biologists and ML won't obsolete systems folks, but every time you write a heuristic you would probably get better results from a model.

  robotresearcher 4 hrs A heuristic is a model. A successful heuristic encodes knowledge of the distributions at hand. Heuristics are designed by people who had good insight into the data rather than learned directly from data, but they are models in the same sense.

  yazr 6 hrs Can you please give some examples of resource-mis-allocations ?!

  AFAIK storage is not the system bottle it used to be. We always want more, but network and cores are relatively plentiful.

  If we could magically (and safely) modify the software stack, which areas could give x2 or x3 improvements ?

  sulam 3 hrs Network latency right now is the biggest issue we have. If we could magically (using your term here!) get computational resources and data dramatically closer to end users, it would easily give 2 or 3x improvement. Doing this safely of course means consistently in this case, and being able to solve things like safe replication of large data sets. I dunno how to do it, but you asked and that's the biggest thing I can think of.

  rgrieselhuber 6 hrs Any resources you can recommend on the scheduling topic?

  sulam 6 hrs I can't find the exact paper, but you can find a lot after reading this article popularizing the topic:

  https://www.theverge.com/2016/7/21/12246258/google-deepmind-...

  The future is here. :)

  eggie5 5 hrs Interesting article I read from Zalando, which in the same vein as the Learned Indexes paper, takes a previously computationally expensive routine (Optimal Cart Pick) and learns it as an approximation to a complex function that, in the end, has faster run-time.

  https://jobs.zalando.com/tech/blog/accelerating-warehouse-op...

  sulam 5 hrs Cool!

  sova 5 hrs Yo! This is what I'm talkin' about! Replace directories with self-consistent neural nwots [neural blackbox thingie of weights and activation vals]. It's cool, but for every convenience we take in the direction HD space we backpeddle in processing [meaning, although the dir structure could be super condensed this way with a learned index, this approach is less flexible to cold or not-cpu-related analysis]. It's very cool actually to consider the three branches of processing, hdd, and memcache ... You want to have a balance but in the ideal computer we do no calculation, and that is something worth mentioning.

  Reading again of the abstract, allow me to add that it's an optimization beyond structures such as Btrees which are already not really decipherable datasets to the naked eyes. So any optimization in a space that's not designed to be human-legible can take whatever shape, and this is certainly a welcome achievement!

  godelmachine 5 hrs There was a time when people used to get PhD's for researching on index. This paper actually turns the table.

  eggie5 5 hrs nice pun

  AlexTelon 7 hrs A very interesting read indeed!

  Besides HN how can one find great papers like this? How did you find out about it?

  godelmachine 5 hrs 1) The Morning Paper - blog.acolyer.org - A new super interesting paper review everyday

  2) This is my personal ritual. Every weekday I check out recent entries in "Hardware architectures" and "Emerging Technologies" section of arXiv.org Many other sections are gaining traction with me, like Database.

  Hope this helps.

  juanmirocks 5 hrs Thank you for The Morning Paper

  travmatt 4 hrs You'd also like Fermat's Library

  dekayed 6 hrs I'd recommend taking a look at Papers We Love

  https://github.com/papers-we-love/papers-we-love

  rmk 14 mins Loved the two-minute papers channel that I came across on this list. Thanks!

  sulam 6 hrs I found this paper after reading through Jeff Dean's talk at NIPS. I recommend that, too, obviously!

  http://learningsys.org/nips17/assets/slides/dean-nips17.pdf

  You can generally find a paper for each section.

  CaHoop 7 hrs I saw this paper on reddit.com/r/machinelearning - usually the more interesting ML papers are posted there.

  Additionally, there is http://www.arxiv-sanity.com/ which sorts new machine learning papers by popularity.

  allcentury 7 hrs That was a good read, thanks for sharing this.

  catnaroek 6 hrs I've read that paper, and I wasn't particularly impressed. Is there any concrete evidence that learned indices scale to databases with complex schemata and a large transactional volume? Could learned indices be profitably used, say, in the database backend of an ERP system?

  sulam 6 hrs There are immediate applications to data warehousing. I hope that's obvious to you, but if not I'm happy to unpack that.

  Whether or not it applies to online databases probably depends on several factors. The ones I can think of off the top of my head are the read/write ratio, the write rate in general, and obviously size of your data set (although the approach works at GB, not TB, so most can take advantage).

  Here's a way, I, a systems engineer who doesn't design databases, could imagine using it immediately:

  1) Do I have any indexed queries? If not, nothing to do here. 2) Does the data change rapidly? If so, nothing to do here. 3) Okay, now I'm in the zone of profitability:

  1) investigate just letting the model do all the work. This is unlikely to be successful in many cases, but it's pretty easy to try.

  2) investigate "model-assist", in this mode I let the model give me an answer and, if I don't like it, I use the B-tree. The model is fast enough that this is not a huge hit to my overall performance.

  3) investigate "model-replace", in this mode I build my b-tree and do everything like normal. In the meantime I'm training my model. Once my model seems well-trained (I could hand it a randomized subset of queries and, once the accuracy and performance beats the b-tree I consider it well-trained), I switch to using it. As data changes I could potentially go back to the b-tree, update the model (literally throw it away and re-build) and do it again.

  4) Investigate "co-opetition" -- make the b-tree a component of the model. If you read the paper, you see how they layer different models. A b-tree is just a particular kind of model that happens to be computationally expensive but has high accuracy. You could include it in the model graph and let it compete for queries like everything else.

  Anyway, I'm speculating here, but it seems obvious this has immediate impact in one large space relating to databases (data warehousing) and potential impact in many of the rest.

  catnaroek 4 hrs

  There are immediate applications to data warehousing.

  I am not too worried about data warehouses. Data warehouses can be fed by batch processes, which you can run, say, once every day or once every week. This leads to the design of algorithms that optimize aggregate measures of performance (e.g. amortized complexity, average complexity, throughput) rather than microscopic measures of performance (e.g. worst-case complexity, latency). Machine learning techniques have a track record of delivering good aggregate results.

  However, online databases require optimal worst-case performance. The occasional massively slow operation is not okay - we'd rather every operation be a tiny bit slower instead.

  Do I have any indexed queries? If not, nothing to do here.

  Of course I do. Needless to say, complex schemata come with lots of indices that are necessary to speed up queries that pull data from 10-15 tables each.

  Does the data change rapidly? If so, nothing to do here.

  Of course the data changes rapidly. Crucially, an online transactional database's input comes from external parties (e.g., public-facing web servers) that cannot be considered trustworthy. Many machine learning techniques are vulnerable to so-called “adversarial examples”. Feeding adversarial examples to an ML-powered index could cause the database's performance to drop drastically in ways that are provably not possible with B-trees.

  sulam 4 hrs So, would you take a bet that this won't have been used in any self-evident way within the next 5 years?

  Because I would take the other side of that bet. :)

  catnaroek 4 hrs I'm not in the business of predicting the future, since that is beyond my control. What other people do with learned indices is up to them.

  I'm just stating my concerns.

  oh-kumudo 5 hrs Data warehousing is one obvious application: data is immutable; have a sort key. So NN as a builtin once index structure is perfect for such case. It is only a matter of time for commercial analytic DB to pick up this idea, I would presume.

  catnaroek 3 hrs You're right, data warehousing seems like a compelling use case for learned indices. I'm primarily worried about OLTP systems, though.

  ky3 6 hrs Leslie Lamport's 1978 "State the Problem Before Describing the Solution" [0]. On his web page the author adds that "The title says it all. This one-page note is as relevant today as when I wrote it. Replace "describing the solution" by "writing the program" and it becomes a practical recipe for improving software."

  Herewith, the paper in full:

  "After several years of writing papers in computer science, I discovered the basic expository rule embodied in the title of this note. As obvious as this rule may seem, there are fields in which it is seldom observed. (Computer networking is one example.) A typical paper in such a field is organized as follows:

  (1) a brief informal statement of the problem;

  (2) the solution;

  (3) a statement and proof of the precise correctness properties satisfied by the solution.

  In order to abide by the rule, the following organization should instead be used:

  (1) a brief informal statement of the problem;

  (2) the precise correctness conditions required of a solution;

  (3) the solution;

  (4) a proof that the solution satisfies the requisite conditions.

  Although it may not be obvious at first glance, there is a profound difference between these two approaches. In the first, the precise correctness conditions can be (and usually are) stated in terms of the solution itself. Some results are proved about the solution, but it is often not clear exactly what problem is being solved. This makes the comparison of two different solutions rather difficult. With the second approach, one is forced to specify the precise problem to be solved independently of the method used in the solution. This can be a surprisingly difficult and enlightening task. It has on several occasions led me to discover that a "correct" algorithm did not really accomplish what I wanted it to. I strongly urge everyone to observe the rule.

  (I am ignoring as unworthy of consideration the disturbingly large number of papers that never even attempt a precise statement of what problem they are solving.)"

  [0] https://lamport.azurewebsites.net/pubs/state-the-problem.pdf

  tybit 1 hr Thanks for this, this one is very relevant to industry(or at least to mine). The amount of software designs and programs that don't actually fit the technical/business problems from the get go is astounding.

  sktrdie 4 hrs Statecharts: a visual formalism for complex systems: http://www.inf.ed.ac.uk/teaching/courses/seoc/2005_2006/reso...

  Even though it's from 1986 it's enlightening how useful it is to think about reactive systems using statecharts - especially as a UI developer this seems to make total sense and was a ah-ha moment when I first read it a couple months ago.

  wallflower 3 hrs Elegant and simple. Thank you for sharing it even though it is over 30 years old.

  Next time you happen to be at a store that has the rare, old fashioned-type automatic doors that swing out (not slide in/out), hurry out before your friends and stand on the pressure pad that is in the direction of the door swing out. No one will be able to exit.

  This is a simple example of a state machine in the real world that I like to use to explain more complex state machines.

  alecco 41 mins A Seven-Dimensional Analysis of Hashing Methods and its Implications on Query Processing

  http://www.vldb.org/pvldb/vol9/p96-richter.pdf

  CalChris 7 hrs seL4: Formal Verification of an OS Kernel (from 2009).

  https://www.sigops.org/sosp/sosp09/papers/klein-sosp09.pdf

  seL4 is about 9000 LOC. So this gives a good indication of what formal verification (Isabelle/HOL) is currently capable of. seL4 is also quite fast as a result of removing unnecessary checks.

  https://sel4.systems/

  seL4 is smaller than L4Ka::Pistachio and it's also capability based which L4 isn't. They could have called it L5 or seL5.

  godelmachine 5 hrs I want to learn formal verification on my own. Do you know of any resources? I got Rolf Dreshler's book on circuit verification, but I would like to master both hardware and software. Would you kindly provide with some pointers?

  CalChris 5 hrs It's a broad subject and I'm only interested in software verification. It's specialized enough that you should be reading papers and reading about systems. My favorite systems+papers are:

  Coq: The world’s best macro assembler? https://www.microsoft.com/en-us/research/publication/coq-wor... Vale: Verifying High-Performance Cryptographic Assembly Code https://project-everest.github.io/assets/vale2017.pdf x86proved https://x86proved.codeplex.com/ mcguire 4 hrs Two schools:

  • Coq/dependent types. Check out Software Foundations by Benjamin Pierce, et. al. Everything's online. Also see Idris, which has a good book from Manning.

  • SMT-solver-based verification of existing languages. See SPARK/Ada (there's a good book but I can't remember the name presently) and the GNAT website. Also, Frama-C, although the documentation is more spotty. Then there's Rustan Leino's work on Dafny and Boogie. Oh, and Why3. And some work on verifying Java code that I haven't played with yet.

  auggierose 2 hrs Isabelle/HOL (the one sel4 was done in) appears in none of your two schools ...

  This might be because it is the best combination of interactive theorem proving with automated methods that currently exists out there.

  mcguire 3 hrs Three, three schools:

  • Model checking/high level formal specs. Lamport's TLA+, Alloy, possibly Z. There are some good TLA+ things online, I just couldn't get into it. (I feel bad.)

  nickpsecurity 5 hrs Note that this is formal verification of an imperative, C-based program manipulating low-level structures done years ago. I wouldnt say that represents what we're currently capable of in general case. For starters, a functional program or imperative without pointers (eg in SPARK) is much easier to verify than a C program. Plus, the seL4 organization recently reduced effort for filesystems down by a few multiples with a functional language.

  Truth is the seL4 team set themselves up for a harder-than-usual job. For good reason but most folks (esp managed languages) won't see as much difficulty.

  anilshanbhag 7 hrs Chord: http://nms.lcs.mit.edu/papers/chord.pdf

  I think this paper amazing because it solves a complex problem with a simple solution. How do you create a hash function that adjust to the varying number of underlying buckets ? Solution: hash to a circle.

  pedrosorio 6 hrs https://en.wikipedia.org/wiki/Consistent_hashing was actually introduced in 1997 by one of the authors of that paper.

  nightcracker 7 hrs If you like Chord then you'll also like Kademlia.

  babangida 9 hrs It's an old one, but this year I read The Emperor's Old Clothes by C.A.R. Hoare for the first time and it got me started in a Hoare rabbit hole, just a brilliant guy.

  I read the annotated version on Fermat's Library - great source of interesting papers (http://fermatslibrary.com/s/the-emperors-old-clothes).

  gnusouth 1 hr I really liked Vlad Zamfir's papers on "correct-by-construction" consensus protocols. What I love about this work is that it unifies traditional BFT consensus with blockchain consensus to make something better than both! Really exciting!

  Abstract CBC: https://github.com/ethereum/research/blob/master/papers/cbc-...

  Casper the friendly GHOST: https://github.com/ethereum/research/blob/master/papers/Casp...

  I found the Abstract CBC paper a little easier to understand, as it goes into more detail about the correct-by-construction process.

  johnsonjo 6 hrs I found Knuth’s “Dancing Links” paper [1] very well written and a somewhat easy read (I had to reread certain parts a couple times). I had to write a sudoku solver for one of my classes and I read that dancing links and algorithm x was one way to do it [2]. I then read some things around the internet to apply dancing links to sudoku solving [3] [4]. If you read the Wikipedia entry on exact cover problems there is a section on sudoku as an exact cover problem [5] this is one thing necessary to understand in order to implement a sudoku solver with algorithm x and dancing links.

  Knuth even talks about dancing links in his new 2017 Christmas Tree Lecture [6] specifically here at 4:28 [7]. Basically he uses dancing links to solve for certain n in the problem he sets up in that lecture.

  [1] https://arxiv.org/abs/cs/0011047

  [2] https://en.wikipedia.org/wiki/Sudoku_solving_algorithms#Exac...

  [3] http://garethrees.org/2007/06/10/zendoku-generation/

  [4] https://www.ocf.berkeley.edu/~jchu/publicportal/sudoku/sudok...

  [5] https://en.wikipedia.org/wiki/Exact_cover#Sudoku

  [6] https://www.youtube.com/watch?v=BxQw4CdxLr8

  [7] https://youtu.be/BxQw4CdxLr8?t=4m28s

  svat 5 hrs You may like to know that Knuth's fascicle 5C of The Art of Computer Programming Volume 4 is going to be entirely about Dancing Links. He's still working on it, but the “incomplete draft” is available at the (hidden) link https://cs.stanford.edu/~knuth/fasc5c.ps.gz — check it out if you're interested; it's already 130 pages of fun.

  johnsonjo 3 hrs Wow, this really is good I started it and skimmed the rest of it the first 34 pages are explanations and algorithms of dancing links and the rest of the 130 pages is all exercises and answers to those exercises. Truly awesome, thanks again for the link.

  johnsonjo 5 hrs I think he mentioned that it would be in his new book, but I had no idea there would be over 100 pages of dancing links. That is sweet! I’d love to take a look at it, so I could get a better grasp of how it applies to other problems.

  mcguire 3 hrs Knuth is, almost universally, a joy to read.

  JelteF 9 hrs The raft consensus algorithm article [1] and the follow up that modifies it slightly with some changes that are useful when implementing it [2]. Both are written very clearly and very focused on actually using the algorithm in practice. Problems that arise when implementing it are discussed and solutions are proposed as well.

  [1] https://raft.github.io/raft.pdf

  [2] http://openlife.cc/system/files/4-modifications-for-Raft-con...

  indescions_2017 8 hrs Bringing The Web Up To Speed With WebAssembly

  https://github.com/WebAssembly/spec/blob/master/papers/pldi2...

  trojanh 6 hrs That link seems to broken. Here the original link https://github.com/WebAssembly/spec/raw/master/papers/pldi20...

  Definitely a good read.

  godelmachine 5 hrs Adrian Colyer's review on WebAssembly was very helpful.

  vletrmx 7 hrs Some Were Meant for C: The Endurance of an Unmanageable Language

  https://www.cl.cam.ac.uk/~srk31/research/papers/kell17some-p...

  icodemuch 8 hrs The bitcoin white paper (https://bitcoin.org/bitcoin.pdf)

  toomim 7 hrs Yeehaw! This was the best paper I read in 2012, and then again in 2013, 2015, 2016, and 2017.

  DiThi 1 hr What paper was the best in 2014?

  grajaganDev 9 hrs * What Developers Want and Need from Program Analysis - An Empirical Study

  • Weird machines, exploitability, and provable unexploitability

  • What You Corrupt Is Not What You Crash: Challenges in Fuzzing Embedded Devices

  mafribe 8 hrs * What Developers ...: http://ieeexplore.ieee.org/document/7582770 (Sorry, no open version seems to be available)

  • Weird ...: http://ieeexplore.ieee.org/document/8226852

  • What You ...: http://s3.eurecom.fr/docs/ndss18_muench.pdf

  spenrose 7 hrs Openly accessible "What Developers Want ..."

  https://www.microsoft.com/en-us/research/wp-content/uploads/...

  obtained by pasting the title into scholar.google.com

  cynicaldevil 5 hrs "Reflections on Trusting Trust": https://www.ece.cmu.edu/~ganger/712.fall02/papers/p761-thomp...

  rozim 4 hrs I probably enjoyed 'Mastering the game of Go with deep neural networks and tree search' the most and especially appreciated the 'Methods' section where they go into a good amount of detail about their implementation.

  https://storage.googleapis.com/deepmind-media/alphago/AlphaG...

  posnet 4 hrs A General-Purpose Counting Filter: Making Every Bit Count

  A better alternative to bloom filters and they provide a well written implementation as well https://github.com/splatlab/cqf

  https://www3.cs.stonybrook.edu/~ppandey/files/p775-pandey.pd...

  akditer 6 hrs What Makes A Great Software Engineer?

  https://faculty.washington.edu/ajko/papers/Li2015GreatEngine...

  Metaphors We Compute By Code is a story that explains how to solve a particular problem

  http://delivery.acm.org/10.1145/3130000/3127495/p40-videla.p...

  leecarraher 2 hrs Dynamic Routing Between Capsules the long awaited paper from Geoffrey Hinton (although Sara Sabour is principal author, Hinton has been hinting at this for a while now) on an alternative to back-propagation https://arxiv.org/abs/1710.09829

  beefman 4 hrs Programming languages

  Slepak et al - An Array-Oriented Language with Static Rank Polymorphism [1]

  Distributed systems

  Kiayias et al - A Provably Secure Proof-of-Stake Blockchain Protocol [2]

  Cognitive computing

  Boahen - A Neuromorph's Prospectus [3]

  Machine learning

  Silver et al - Mastering Chess and Shogi [4]

  1 http://www.ccs.neu.edu/home/shivers/papers/rank-polymorphism...

  2 https://eprint.iacr.org/2016/889.pdf

  3 https://pdfs.semanticscholar.org/3767/1e53c9949fa08d9dc150ad...

  4 https://arxiv.org/pdf/1712.01815.pdf

  kzrdude 7 hrs The most useful paper I read this year was Tidy Data (Wickham) http://vita.had.co.nz/papers/tidy-data.html

  AlexCoventry 5 hrs Emergence of Invariance and Disentangling in Deep Representations

  https://arxiv.org/abs/1706.01350

  "we show that in a deep neural network invariance to nuisance factors is equivalent to information minimality of the learned representation, and that stacking layers and injecting noise during training naturally bias the network towards learning invariant representations. We then show that, in order to avoid memorization, we need to limit the quantity of information stored in the weights, which leads to a novel usage of the Information Bottleneck Lagrangian on the weights as a learning criterion"

  superzamp 4 hrs Not originally a CS paper, but this one describes a distributed algorithm medieval merchants used to simplify their debts graph: http://eh.net/eha/wp-content/uploads/2013/11/boerner.pdf

  I highly recommend it to anyone interested in CS, history & finance.

  wk2jkhkjdfglsl 3 hrs Dullien, Thomas F. "Weird machines, exploitability, and provable unexploitability." IEEE Transactions on Emerging Topics in Computing (2017). http://ieeexplore.ieee.org/abstract/document/8226852/

  vjdhama 9 hrs Lifeguard : SWIM-ing with Situational Awareness

  https://arxiv.org/pdf/1707.00788.pdf

  cygned 3 hrs “The Evolution of LISP”, Steele/Gabriel (1993).

  PDF: https://www.csee.umbc.edu//courses/331/resources/papers/Evol...

  80 pages, very well written and interesting.

  bra-ket 9 hrs "Memcomputing NP-complete problems in polynomial time using polynomial resources and collective states" http://advances.sciencemag.org/content/1/6/e1500031.full

  pelario 8 hrs Could you tell us a bit about the reñevance of this work?

  DannyBee 7 hrs They claim they can actually build things that compute np-complete problems in polynomial time for real.

  "We show an experimental demonstration of an actual memcomputing architecture that solves the NP-complete version of the subset sum problem in only one step and is composed of a number of memprocessors that scales linearly with the size of the problem. We have fabricated this architecture using standard microelectronic technology so that it can be easily realized in any laboratory setting"

  This is traditionally what people look to quantum computing to solve, but that seems much farther off in practice that the technology described here, at least, as described.

  TL;DR Memcomputing has been shown to have the same power as non-deterministic turing machines. They claim to have made some real ones with promising results. It looks like it's getting commercialized over at http://memcpu.com/.

  I'm just summarizing the papers/data, not commenting on it for real, it could all just be snake oil

  godelmachine 5 hrs They have pretty much exaggerated their capability. I implore any curious HN reader to check out Scott Aaronson's review of their work & how he debunks it.

  chadcmulligan 3 hrs thanks, link for the impatient https://www.scottaaronson.com/blog/?p=2212

  saycheese 8 hrs Does anyone have any suggestions on finding CS papers worth reading beyond those papers listed here?

  chubot 6 hrs Follow the citations of papers you liked. Note the authors and read their other publications.

  Look at other papers in the same conference. Watch conference videos on YouTube.

  Skim at first, and then reread papers that pop back into your mind.

  tnecniv 7 hrs In my experience, you read a lot of papers that sound interesting and then some of them turn out to be good.

  azhenley 7 hrs What topics are you interested in? Following the top ACM and IEEE conferences in that area is a good start!

  For example, I'm interested in human-computer interaction so I read CHI and UIST papers each year, and for software engineering I read ICSE and FSE.

  toomim 7 hrs Ah, if only CHI and UIST had good papers these days. :/

  azhenley 7 hrs Hey, I had a CHI paper this year! I think it publishes many great papers each year.

  toomim 4 hrs Welcome to the echo chamber.

  In my experience, the only people who think CHI publishes great papers are the people publishing at CHI.

  (Disclosure: I've published at CHI a few times myself. I'm done with it.)

  azhenley 2 hrs A company recruited me at CHI for an internship/collaboration and implemented portions of my research into their product, so it isn't a complete echo chamber.

  mcguire 3 hrs I remember LISA and USENIX.

  whistlerbrk 4 hrs See PapersWeLove

  saycheese 52 mins http://paperswelove.org

  azhenley 7 hrs "Foraging Goes Mobile: Foraging While Debugging on Mobile Devices" (ftp://ftp.cs.orst.edu/pub/burnett/vlhcc17-foraging-mobile.pdf)

  "Toward Principles for the Design of Navigation Affordances in Code Editors: An Empirical Investigation" (http://dl.acm.org/authorize?N37917)

  "The Patchworks Code Editor: Toward Faster Navigation with Less Code Arranging and Fewer Navigation Mistakes" (http://dl.acm.org/authorize?N84177)

  moretai 29 mins May I ask why people read cs papers to gauge if I should be reading them?

  lwh 7 hrs This miserable blog post http://www.sicpers.info/2017/12/computings-fundamental-princ...

  mcguire 3 hrs T-t-t-taligent?

  My second real job out of college was with IBM's Taligent Project Office (where I independently invented mobile IP routing); I bailed as the wheels were coming if the IBM/Apple partnership.

  nickpsecurity 9 hrs I read a lot of good ones but I'll answer what was most important rather than best. We're seeing a revival of formal methods ranging from lightweight (TLA+) to heavy. Two problems are in my sight: lack of framework plus consistent, empirical data for evaluating how suitable a given method is for a specific project or company; many who would use lightweight or practical methods will quit because someone convinced them to start with heavy ones. The paper below is a start on the former:

  https://www.scch.at/de/rse-news/passende-formale-methon?file...

  I think that was a great start on an evalution criteria because its comparisons match what I read in the various experience reports. ASM's and TLA+ coming in the lead on usability but B method on code generation. It will also help to note what projects have been completed or reusable libraries available in each. A person building verified compilers for functional languages might want to learn Isabelle/HOL due to CakeML, doing a new TLS extension learn F* due to miTLS, type-safe assembly learn Coq due to CoqASM, and so on.

  For the other issue, I made a proposal on HN first and then on Lobste.rs about avoiding people quitting early from finding formal methods too overwhelming or useless. I proposed having a default intro or lure that matched the goals of different people to tools with a warning of some's difficulty. It got some interesting responses (below) I'll turn into a mini-essay with links eventually.

  https://lobste.rs/s/n7v658/your_thoughts_on_this_advice_thos...

  2017/12/31
• 
  

  Infoman

  Coconut Milk Nutrition — 9 Benefits + Recipes

  With its creamy texture and slight natural sweetness, coconut milk might taste like something that should be bad for you, yet it’s anything but. Coconut milk is often considered a “miracle liquid” since coconut milk nutrition offers great ability to build up the body’s immune defenses and prevent disease. Coconut milk, along with it’s relatives coconut oil and coconut water are among the world’s healthiest foods.

  What exactly is coconut milk, and how is it made? Coconut milk isn’t actually “milk” at all (in the sense that you normally think of it) — it’s a liquid naturally found inside of mature coconuts, stored within coconut “meat.” When you crack open a fresh coconut, the milky white substance that leaks out is natural coconut water, but when you blend coconut meat and then strain it, the result in a thicker coconut “milk.” FREE Guide: Best ways to use coconut milk for your health

  How do the two differ? As a coconut matures, more of the water inside is replaced with coconut meat, so mature coconuts tend to be better producers of coconut milk, while younger coconuts (around five–seven months) are the best producers of coconut water. Coconut water is higher in sugar and certain electrolytes, while coconut milk is higher in healthy saturated fatty acids (from coconut oil) and calories.

  Coconut Milk Nutrition Facts

  coconut milk In addition to providing nutrients and its awesome taste, coconut milk contains beneficial fat called lauric acid, a medium-chain fatty acid that’s easily absorbed and used by the body for energy. Coconuts’ fatty acids are primarily saturated fats, but don’t think these will raise your cholesterol levels and cause heart damage. Instead, they’re known to actually do the opposite — coconut milk can help you lower cholesterol levels, improve blood pressure, and prevent heart attacks or a stroke.

  Since real, full-fat coconut milk is high in calories, it’s better to have a smaller serving than you would of regular milk or coconut water. About 1/4–1/2 cup at once is best, either as part of recipes (for example as “coconut whipped cream”) or on its own combined with other flavors (such as in a smoothie).

  Full-fat coconut milk contains all of its natural fatty acids, while “light” coconut milks are strained to remove some of the fat, which creates a thinner, lower-calorie milk. Because coconut milk is completely free from dairy, lactose, soy, nuts or grains, it’s a good option for anyone allergic to dairy and nut- or grain-based milks, plus it’s vegan and good for plant-based eaters.

  Coconut Milk Nutrition

  A quarter cup of coconut milk (full-fat/not skimmed or light milk) has about: (1)

  138 calories 1.5 grams protein 2 grams sugar 14 grams fat .55 milligrams manganese (27 percent DV) .15 milligrams copper (8 percent DV) 60 milligrams phosphorus (6 percent DV) 22 milligrams magnesium (5.5 percent DV) 3.9 milligrams iron (5.5 percent DV) 157 milligrams potassium (4.5 percent DV) Health Benefits of Coconut Milk

  1. Improves Heart Health by Lowering Blood Pressure and Cholesterol Coconuts are one of the best sources of lauric acid — 50 percent of the fat in coconuts is lauric acid, which has antibacterial and antiviral activities. According to many studies, lauric acid is a protective type of fatty acid linked with improved cholesterol levels and heart health.

  For example, when 60 healthy volunteers were given coconut milk porridge (CMP) for five days a week for eight weeks, researchers found that their low density lipoprotein (LDL) levels decreased while their “good” high density lipoprotein (HDL) levels rose significantly. They concluded that “coconut fat in the form of coconut milk does not cause a detrimental effect on the lipid profile in the general population, and in fact is beneficial due to the decrease in LDL and rise in HDL cholesterol.” (2)

  Because coconuts contain minerals important for circulation and controlling blood flow, coconut milk is also useful for lowering blood pressure and keeping blood vessels flexible, elastic and free from plaque buildup. For example, magnesium may help combat stress and muscle tension while aiding in circulation and keeping muscles relaxed, important for preventing heart attacks.

  1. Builds Muscle and Helps Lose Fat Studies find that medium-chain triglycerides (MCT) fatty acids found in coconut milk increase energy expenditure and help enhance physical performance. Following exercise, muscles also need plenty of nutrients — including electrolytes like magnesium and potassium that are found in coconut milk — to repair broken down tissue and grow back even stronger.

  Because coconut milk is high in healthy fats, it also helps fill you up and prevent overeating or snacking throughout the day, which derail your efforts to improve your body composition.

  1. Provides Electrolytes and Prevents Fatigue Although coconut water is a higher source of electrolytes, coconut milk also provides important minerals needed to maintain blood volume, regulate heart health, and prevent dehydration or diarrhea. Especially in very hot weather, following exercise or after being sick, electrolytes help prevent exhaustion, heat strokes, heart problems, muscle aches or cramps, and low immunity.

  Coconut milk also contains the types of MCTs that are easily used by your brain for energy, without even needing to be processed through your digestive tract with bile acids like some other fats. Coconut milk’s calories provide a quick and efficient source of healthy calories for the brain, which is actually primarily made up of fat and relies on a steady stream of it to function.

  1. Helps Lose Weight According to a study done by the School of Dietetics and Human Nutrition at McGill University,

  consumption of a diet rich in MCTs results in greater loss of fat (adipose tissue) compared with long-chain fatty acids, perhaps due to increased energy expenditure and fat oxidation observed with MCT intake. MCTs may be considered as agents that aid in the prevention of obesity or potentially stimulate weight loss. (3)

  As a food high in MCTs, coconut milk is a very filling, fat-burning food. Fats provide the feeling of being full and satisfied and can help prevent overeating, snacking, food cravings and potentially weight gain.

  Of course, portion control is important considering the calorie count of coconut milk, but as a part of a healthy diet it provides necessary fatty acids in addition to other minerals that support weight loss and detoxification. Coconut milk is also hydrating and helps the digestive organs, like the liver and kidneys, function properly, which helps metabolize fat and remove waste from the body.

  1. Improves Digestion and Relieves Constipation A well-hydrated digestive tract is important for preventing or treating constipation. (4) Coconut milk nourishes the digestive lining due to its electrolytes and healthy fats, improving gut health and preventing conditions like IBS.

  Coconut milk benefits 6. Manages Blood Sugar and Controls Diabetes The fat content of coconut milk can help slow the rate at which sugar is released into the bloodstream, better controlling insulin levels and preventing a “sugar high” or worse, conditions like diabetes. This is one reason why coconut milk is especially good to add to sweetened recipes, like desserts. Coconut milk’s MCTs are also a preferred source of energy for the body rather than sugar.

  1. Helps Prevent Anemia Although the iron content of coconut milk isn’t very high, it still provides a good source of plant-based iron that can contribute to a diet sufficient at preventing anemia.

  2. Prevents Joint Inflammation and Arthritis Coconut milk’s MCTs can help lower inflammation, which is associated with painful conditions like arthritis and general joint or muscle aches and pains. Coconut milk in place of refined sugar especially is helpful for people with arthritis (or other autoimmune conditions) because sugar is a pro-inflammatory and linked to low immunity, worsened pain and swelling.

  3. Prevents Ulcers Another benefit of coconut milk nutrition that may surprise you? Researchers found that coconut milk can help reduce the occurrence of ulcers even better than coconut water. When rats with ulcers were given coconut milk, they experienced a reduction in the size of ulcers of about 56 percent. The study found that coconut milk had protective effects on the ulcerated gastric mucus that can lead to painful ulcers. (5)

  Which Kind of Coconut Milk is Best to Buy?

  Merchandising. Sales assistant in supermarket lay out goods on supermarket shelves at store Merchandising. Sales assistant in supermarket lay out goods on supermarket shelves at storeIt’s simple enough to make coconut milk yourself at home, but if you’d rather buy a premade kind, look for the purest coconut milk you can. The best kinds of coconut milk are organic and contain no added sugar or sweeteners, preservatives, artificial sweeteners, and aren’t pasteurized (which can potentially destroy some of the nutrients).

  Look for coconut milk (ideally organic) that’s been “cold pressured,” which indicates it’s only been lightly heated and processed to remove certain bacteria but hasn’t been exposed to high heat that can deplete vitamins and minerals. Skip any coconut milk (or water) that’s flavored with juices, sweeteners, colors or other ingredients. You’re better off adding your own if you want to improve the flavor.

  The primary ingredient should be 100 percent coconut milk and maybe coconut water. Some companies also add guar gum, which is a natural product used to stabilize the texture. Make sure the label indicates the milk is unsweetened to avoid a total sugar bomb.

  One final note: If you buy canned coconut milk, avoid cans made with the chemical called BPA. BPA is found in some aluminum cans and has the potential to cause certain health problems when it leashes into foods (especially foods high in acid or fat, like coconut milk). Although the FDA still considers it safe, many nutrition experts disagree due to certain studies linking it to behavioral problems and other health concerns. (6) Look for an indication that the can is made without BPA and is “BPA free.”

  How to Make Homemade Coconut Milk

  dreamstime_s_29915154 Many people feel that canned or boxed coconut milk can’t compare to the homemade kind. Luckily, you can easily make your own coconut milk at home by purchasing fresh, young coconuts. Look for fresh, mature coconuts in the refrigerated section of health food stores, or try using coconut meat that’s been removed from the shell already. Just make sure to find coconuts or coconut meat that’s still fresh and is either vacuum-sealed or opened within the past three to five days. The fresher the coconut is, the longer the coconut milk lasts.

  Here’s how to make your own fresh coconut milk that you can be sure is free from any artificial ingredients or preservatives:

  1. First look for fresh coconuts and give them a good shake, making sure you can hear and feel some liquid moving around inside. That tells you they’re fresh.

  2. You need a sturdy cleaver to crack open a coconut, but you can also use any heavy knife or a hammer you may have at home.

  3. Bang the cleaver on the top of the coconut until you hear a crack. Then strain the coconut water out, and keep it for smoothies and other refreshing drinks. You’re left with two–three coconut pieces that have the white flesh/meat inside attached to the inedible shell. Remove the flesh either by cutting it out with a paring knife or continue to hit the back of the coconut until the meat falls off from the shell.

  4. Rinse the coconut meat well, and chop it into small pieces. Then add your coconut meat to a blender or food processor along with about two cups of water.

  5. Blend it into a thick liquid, and then strain it using a metal strainer or cheesecloth so you can separate the coconut pulp/meat from the coconut milk. Squeeze the coconut pulp well with your hands to get the most coconut milk to strain out.

  That’s it! Now use the coconut milk in some of the ways below, while keeping the meat for homemade coconut flour, coconut scrubs, to make dried coconut flakes or to add to smoothies.

  Coconut Milk Recipes

  How can you use coconut milk at home to take advantage of all the coconut milk nutrition benefits you just learned about? If there’s a healthy item you might want to toss in your grocery cart or make yourself experiment with in the kitchen, make it coconut milk.

  Coconut milk is loved in Thailand, India, Hawaii and other parts of Asia, plus it’s well-known in the culinary world because of the creamy texture and rich taste it gives to curries. But its uses go way beyond soups and stews. Coconut milk is really versatile and works great in both sweet and savory recipes.

  Some surprising things you can do with coconut milk? It can be added to omelettes to add creaminess without needing dairy milk or cheese, combined with nuts to make spicy “satay sauce,” whipped into coconut whipped cream, used to make coconut ice cream and many other ways too, such as adding it to homemade facial masks and hair conditioners.

  Coconut Milk Coffee Creamer Recipe Total Time: 2 minutes Serves: 4–8 INGREDIENTS: 1 can coconut milk 1–2 teaspoons vanilla extract 2 tablespoons coconut oil, melted Raw honey or vanilla stevia to taste Sea salt to taste, optional DIRECTIONS:

  Combine all ingredients in a blender and mix until well combined. Use in coffee as desired. Coconut-Milk-Coffee-Creamer Frozen Berries with Coconut and Lime Recipe — This recipe is refreshing, healthy and delicious. Next time you get a craving for something sweet, try this! Coconut Milk Baby Formula Recipe — If there’s a situation where a baby is not getting breast fed or there needs to be an additional supplementation, this is a great alternative. Although I always recommend breast milk, this substitution is free of the chemicals and additives present in most formulas and full of nutrients every child needs to develop and grow. Are There Any Concerns with Drinking Coconut Milk You Should Know About?

  Coconuts are low-allergen foods, especially compared to dairy products, soy and nuts. This makes coconut milk a good choice for many people who can’t tolerate other types of milks or creamers. One thing to be mindful of with coconut milk is how much you consume, considering it has a high calorie and fat content. While the fat is definitely a healthy type, portion control is important, especially if you’re working toward reducing your weight.

  Some of the minerals found in coconut milk could potentially interact with certain health conditions. For example, people with kidney disease need to be careful about how much potassium they obtain from foods. However, because coconut milk isn’t a very high source of potassium, it’s not much of a risk drinking it.

  Read Next: 20 Secret Ways to Use Coconut Oil for Skin From the sound of it, you might think leaky gut only affects the digestive system, but in reality it can affect more. Because Leaky Gut is so common, and such an enigma, I’m offering a free webinar on all things leaky gut. Click here to learn more about the webinar.

  2017/12/30
• 
  

  Infoman

  Things I have learnt as the software engineering lead of a multinational

  292 kuahyeow 19 hrs 159

  https://minnenratta.wordpress.com/2017/01/25/things-i-have-learnt-as-the-software-engineering-lead-of-a-multinational/

  news.ycombinator.com/item?id=16028429 A surprisingly long cycle has just closed for me and I think it’s a good time to share some lessons learned.

  I have been collecting these points in the last six months, but none of them popped up recently, they have taken shape over a few years and they include both things I did and that I failed to do.

  Most of these points act as a personal reminder as well as a set of suggestions to others; don’t be surprised if some of them read cryptic.

  So, here it is. A summary of what I learnt in the last six years :

  The productive organization

  1. When the plan is foggy, that’s the moment to communicate as broadly as possible. In fact you should not frame it as a plan, you frame it as the situation and the final state you want to achieve. When you have a detailed plan, that’s when you DON’T need to communicate broadly. So, clearly state the situation and clearly state the foggy goal to everyone who will listen.

  2. Don’t be prudish. If you fear that people will lose faith in you because of the foggy goals and dire situation statement you are painting yourself in an heroic corner. People just need to hear and share the situation they are into. Having a common understanding will act as bonding among them and with you, which is actually all you need to make them work out the right answers.

  3. Don’t assume that a specific communication medium can’t be effective. Mass emails and top-down communication are not taboo: just because most such communications are irrelevant it doesn’t mean yours will be perceived as such.

  4. Teams don’t self-organize unless you organize them to do so.

  5. Fostering personal initiative in every developer requires showing the vital need for personal initiative. These are birds that mostly don’t fly out of the cage just because you open the door. You must find a way to show them that the cage is burning.

  6. People sitting side-by-side can communicate less than people sitting a continent away. Communication is a chemical reaction that requires catalysers, the thing you get by co-locating people is lowering the cost of the catalysers, but no setup creates automatic communication.

  7. Within a development organization both good and bad communication exist, but they are not a function of politeness or rudeness, it’s much more a matter of clarity and goals. You need to learn what the good kind of communication looks like, find some examples and use them as a reference for everyone.

  8. Fire people whenever you can. There’s often someone to fire, but not many opportunities to do so. When you are given a lot of opportunities to fire people, it is often due to a crisis situation and you’ll likely fire or otherwise loose the wrong people. People appreciate when you fire the right people, so don’t worry about morale. Also, the average quality of people tends to grow more when dismissing than when recruiting.

  9. Hire only for good reasons. Being overworked is not a good reason to hire. Instead, hire to be ready to catch opportunities, not to survive the current battles.

  10. It’s often better to loose battles than to staff desperately and win desperate battles at all costs (World War I anyone?).

  11. Don’t export recruitment, recruitment must be the direct responsibility of everyone in the organization.

  12. People must select their future colleagues, there are infinite benefits in this, but it must not become a conclave. Keep the process in the hands of the people who do the work, but make it as transparent as possible.

  13. Always favor actual skill testing in recruitment. When you don’t feel that you are directly testing the candidate skills you are either not competent enough in that skill or you have switched to just playing a set piece (I call this The Interview Theatre) and you will ultimately decide on a whim. Not good.

  14. Build some of your teams as training and testing grounds for freshmen. Put some of your best people there.

  15. Lack of vision is not agile, it is not data-driven, it is not about ‘taking decisions as late as possible’, it is not something that you should paint out in a positive light at all. It’s just lack of vision, and it’s not good.

  16. Construction work is not a good metaphor for software/product development. Factory work neither. Allied junior-officer initiatives during the first week after the d-day in WWII is probably a good guideline, but it is still not a good metaphor overall and, anyway, not known enough to base your communication on.

  Yourself

  1. Train people to do all of the previous points. Including this one.

  2. Don’t shy away from leading without doing, it is unavoidable, so just do it. Then do some work to stay pertinent.

  3. If you are not able to hire and fire people, leave. Or stay for the retirement fund if you can stomach it.

  4. The Sith are right, rage propels. But the Jedi are right, you must not let it control yourself. What nobody tells you is that the rage game is intrinsically tiring and rage will take control as soon as you get too tired, so stop well before.

  5. Write down the situation, for your own understanding just as much as for the others’.

  6. If you feel like you don’t know what you are doing it’s probably because you don’t know what you are doing and that’s bad. Anyway, until you learn you don’t really have much of an alternative. Just don’t let that feeling of desperation numb your ability to learn. It does.

  7. There’s more and more good content to read and absorb on effective organizations. Don’t despair and don’t stop reading.

  8. Don’t let entropy get at your daily routine. Avoid entropy-driven work.

  9. Ask questions to people in order to make sure they understand. Trust people who do the same to you. “Do you understand?” is NOT a valid question.

  10. Avoid having people waiting on you. Don’t create direct dependencies on your work or decisions, make sure people feel that they can take decisions and still stay true to the vision without referring to you (hence the importance of point 1).

  11. Take the time to coach people in depth. Really, spend time with the people who are or have the potential to be great professionals in the organisation.

  12. The time you spend with the people you see most potential in is endorsement enough. Avoid any other kind of endorsement of individuals. Unless you are leaving.

  The Entropic Organization

  1. An organization populated by a majority of incompetents has less than zero net-worth : it is able to destroy other adjacent organizations that are not similarly populated.

  2. Incompetence is fiercely gregarious while knowledge is often fractious; the reason for this is that raw ideas transfer more easily through untrained minds than refined ideas transfer through trained minds. There’s a reason why large organisations focus so much on simple messages, pity that difficult problems often have simple solutions that don’t work.

  3. Entropy self-selects. Hierarchical and other kinds of entropic organizations always favor solutions that survive within entropic organizations. Thus they will favor easy over simple, complex over difficult, responsibility-dilution over empowerment, accountability over learning, shock-therapy over culture-nurturing. This is the reinforcing loop that brings ever-increasing entropy in the system : entropy generates easy decisions with complex and broken implementations, which in turn generate more entropy. An example of easy decision with a complexity-inducing implementation: this scenario “our company does not have a coherent strategy, as such many projects tend to deliver results that are not coherent, hampering the organic growth of our capabilities.” will be answered by the most classic knee-jerk decision-making pattern “we don’t know how to do X, so let’s overlay a new Y to enforce X”, in this case :”Group together strategic projects into a big strategic program that will ensure coherence”. The difficult but simple option will not even be entertained : “let’s discover our real strategy and shape the organization around it.”

  4. Delivery dates have often irrelevant but very simple to understand impacts. Good and bad solutions have dramatic but very difficult to understand impacts. The Entropic Organization will thus tend to make date-based decisions. The Entropic Organization will always worry about the development organization ability to deliver by a given date, never about the ability to find the right solution. There are some very rare cases where delivery date is more important than what you are delivering, but modern management seems to delight in generalizing this unusual occurrence to every situation. People do get promoted for having been able to deliver completely broken, useless and damaging solutions on time. If that’s the measure of project success you can expect dates to rule (even when they continuously slide). After all, if you are not a trained surgeon, and the only thing you are told is that a given surgery should last no more than X hours, guess what will be the one criterium for all your actions during the operation. This showcases the direct link between the constituents incompetence and the establishment of classic Entropic Organization decision-making.

  5. Having a strategy will only go so far when you face the Entropic Organization, since it will be only able to appropriate that strategy at the level of energy (understanding) they can attain, which, being entropic, is very low. This results in something that does not look like a strategy at all : ever seen a two-years old play air-traffic control? He got the basic understanding of “talking to planes”, but that’s it.

  6. Partially isolating the Development Organization to stay effective does not work. Adapting your organization to be accepted by an incompetent background does not work either. What is left in the scope of alternatives is radical isolation supported by the attempt to radical results and crossing fingers for top-management recognition (also known as `Deux-ex-machina for the worthy’) and the top-down sales-pitch (or POC) to the CEO (also known as “He who has the ear of the King…”). But don’t forget : Nemo propheta in patria, so, act and look like an outsider as long as you can.

  7. Growth-shrink symmetry. When an organization grows unhealthily (too fast, for bad reasons or through bad recruitment) it will also shrink unhealthily. When it grows it’s bold and confused, when it shrinks it’s scared and nasty.

  8. Most of the ideas that will pop up naturally from the Entropic Organization are bad in the context of modern knowledge-based work, but possess a superficial layer of common-sense to slide through. Exercise extreme prejudice.

  2017/12/30
• 
  

  Infoman

  A Message to Our Customers about iPhone Batteries and Performance

  846 jayachdee 8 hrs 614

  https://www.apple.com/iphone-battery-and-performance/

  news.ycombinator.com/item?id=16025112

  We’ve been hearing feedback from our customers about the way we handle performance for iPhones with older batteries and how we have communicated that process. We know that some of you feel Apple has let you down. We apologize. There’s been a lot of misunderstanding about this issue, so we would like to clarify and let you know about some changes we’re making.

  First and foremost, we have never — and would never — do anything to intentionally shorten the life of any Apple product, or degrade the user experience to drive customer upgrades. Our goal has always been to create products that our customers love, and making iPhones last as long as possible is an important part of that.

  How batteries age

  All rechargeable batteries are consumable components that become less effective as they chemically age and their ability to hold a charge diminishes. Time and the number of times a battery has been charged are not the only factors in this chemical aging process.

  Device use also affects the performance of a battery over its lifespan. For example, leaving or charging a battery in a hot environment can cause a battery to age faster. These are characteristics of battery chemistry, common to lithium-ion batteries across the industry.

  A chemically aged battery also becomes less capable of delivering peak energy loads, especially in a low state of charge, which may result in a device unexpectedly shutting itself down in some situations.

  To help customers learn more about iPhone’s rechargeable battery and the factors affecting its performance, we’ve posted a new support article, iPhone Battery and Performance.

  It should go without saying that we think sudden, unexpected shutdowns are unacceptable. We don’t want any of our users to lose a call, miss taking a picture or have any other part of their iPhone experience interrupted if we can avoid it.

  Preventing unexpected shutdowns

  About a year ago in iOS 10.2.1, we delivered a software update that improves power management during peak workloads to avoid unexpected shutdowns on iPhone 6, iPhone 6 Plus, iPhone 6s, iPhone 6s Plus, and iPhone SE. With the update, iOS dynamically manages the maximum performance of some system components when needed to prevent a shutdown. While these changes may go unnoticed, in some cases users may experience longer launch times for apps and other reductions in performance.

  Customer response to iOS 10.2.1 was positive, as it successfully reduced the occurrence of unexpected shutdowns. We recently extended the same support for iPhone 7 and iPhone 7 Plus in iOS 11.2.

  Of course, when a chemically aged battery is replaced with a new one, iPhone performance returns to normal when operated in standard conditions.

  Recent user feedback

  Over the course of this fall, we began to receive feedback from some users who were seeing slower performance in certain situations. Based on our experience, we initially thought this was due to a combination of two factors: a normal, temporary performance impact when upgrading the operating system as iPhone installs new software and updates apps, and minor bugs in the initial release which have since been fixed.

  We now believe that another contributor to these user experiences is the continued chemical aging of the batteries in older iPhone 6 and iPhone 6s devices, many of which are still running on their original batteries.

  Addressing customer concerns

  We’ve always wanted our customers to be able to use their iPhones as long as possible. We’re proud that Apple products are known for their durability, and for holding their value longer than our competitors’ devices.

  To address our customers’ concerns, to recognize their loyalty and to regain the trust of anyone who may have doubted Apple’s intentions, we’ve decided to take the following steps:

  Apple is reducing the price of an out-of-warranty iPhone battery replacement by $50 — from $79 to $29 — for anyone with an iPhone 6 or later whose battery needs to be replaced, starting in late January and available worldwide through December 2018. Details will be provided soon on apple.com. Early in 2018, we will issue an iOS software update with new features that give users more visibility into the health of their iPhone’s battery, so they can see for themselves if its condition is affecting performance. As always, our team is working on ways to make the user experience even better, including improving how we manage performance and avoid unexpected shutdowns as batteries age. At Apple, our customers’ trust means everything to us. We will never stop working to earn and maintain it. We are able to do the work we love only because of your faith and support — and we will never forget that or take it for granted.

  2017/12/29