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Member since 2017-07-15T03:50:57Z. Last seen 2025-04-09T16:00:01Z.
2754 blog posts. 128 comments.
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Baggage
https://www.singaporeair.com/zh_TW/tw/travel-info/baggage/checked-baggage/
https://www.singaporeair.com/en_UK/tw/travel-info/baggage/checked-baggage/
Apply for an Air Travel Pass (ATP) before entering Singapore
https://safetravel.ica.gov.sg/transit/overview
Short-term visitors arriving from Taiwan, China must apply for an Air Travel Pass between 7-30 days before entering Singapore. More details here. You should also check the latest travel advisories for all destinations in your itinerary before you book.
https://www.singaporeair.com/zh_TW/tw/travel-info/transit-through-singapore/
Test Location :
https://www.cdc.gov.tw/En/File/Get/5mmQiY1KDEQzTdxtbpHKHw
https://www.cdc.gov.tw/En/Category/MPage/G8mN-MHF7A1t5xfRMduTQQ
二、英國政府自2021年11月26日凌晨4時起恢復國際旅行燈號系統之紅色國家清單，並要求所有入境旅客進行自我隔離。另外，自12月7日凌晨4時所有12歲以上擬入境英格蘭之旅客，不論是否曾完整施打疫苗，皆須出示出發前2天之PCR核酸檢測或快篩檢測(LFD COVID-19 test)陰性之檢驗報告。我國目前未列於紅色清單，自11月30日凌晨4時起，由我國入境英國之旅客，將視疫苗接種情形適用不同檢疫規定：
(一) 倘完整接種2劑疫苗(含：AZ、BNT、Moderna或Janssen疫苗，或混打AZ、BNT及Moderna疫苗)滿14天，且持有數位或紙本接種疫苗證明之旅客，須於出發前上網預約採檢及線上填寫入境旅客追蹤表，嗣於入境英格蘭後暫時自我隔離，並須在抵英後第2天接受COVID-19 PCR核酸檢驗，檢測結果為陰性者可結束隔離。倘檢測結果為陽性或不明，則須隔離滿10天。
數位證書申請
英國每個地方都有當地的WHATSAPP GROUP, 找到幾個找工作的渠道. 基本原則: 自己能做好一份工,生活就應不是問題.
my experience is to avoid waterproof shoes because they don't keep feet dry and the waterproofing means the you feet and the shoes dry more slowly. Focus on fast drying. If keeping you feet dry is priority combine a pair of Rocky Goretex socks and some sandals. My experience is pretty much all shoes are acceptable in a casual pub (different story if we are talking fine dining or stuffy business meeting) so go with something comfortable. I tend toward light weight, zero drop which would be Xero or Merrill Trail Glove.
Why waterproof though? Water always gets in no matter what, and once it's in, it's stuck.
Maybe consider quick drying, breathable instead? As that seems to be the trend with hikers nowadays.
If your shoes aren't waterproof you can get sprays to put on your shoes to make them waterproof. They work on most kinds of shoes I think (although it would probably be a waste to try them on your canvas shoes), and they'll need respraying every so often, but I've found it works really well.
2021年對很多人來說是艱難的一年，疫情仍然未結束，前景未明朗。儘管如此，我們都順利度過了這一年，即將迎來新一年。衷心感謝你們一直以來的支持，2022年將會是一個新的開始。接下來，我們將會推出一系列新項目！
This year has been a tough year for everyone, especially with the pandemic and uncertainties of the future. Despite that, we have managed to sail through the year, all thanks to your kind support. Here's to an equally great 2022! Check out below for our new upcoming projects!
Flask is a web framework, any application written with it needs a WSGI server to host it. Although you could use the Flask builtin developer server, you shouldn't as that isn't suitable for production systems. You therefore need to use a WSGI server such as uWSGI, gunicorn or mod_wsgi (mod_wsgi-express). Since the web application is hosted by the WSGI server, it can only be in the same container, but there isn't a separate process for Flask, it runs in the web server process.
Whether you need a separate web server such as nginx then depends. In the case of mod_wsgi you don't as it uses the Apache web server and so draws direct benefits from that. When using mod_wsgi-express it also is already setup to run in an optimal base configuration and how it does that avoids the need to have a separate front facing web server like people often do with nginx when using uWSGI or gunicorn.
For containerised systems, where the platform already provides a routing layer for load balancing, as is the case for ingress in Kubernetes, using nginx in the mix could just add extra complexity you don't need and could reduce performance. This is because you either have to run nginx in the same container, or create a separate container in the same pod and use shared emptyDir volume type to allow them to communicate via a UNIX socket still. If you don't use a UNIX socket, and use INET socket, or run nginx in a completely different pod, then it is sort of pointless as you are introducing an additional hop for traffic which is going to be more expensive than having it closely bound using a UNIX socket. The uWSGI server doesn't perform as well when accepting requests over INET when coupled with nginx, and having nginx in a separate pod, potentially on different host, can make that worse.
Part of the reason for using nginx in front is that it can protect you from slow clients due to request buffering, as well as other potential issues. When using ingress though, you already have a haproxy or nginx front end load balancer that can to a degree protect you from that. So it is really going to depend on what you are doing as to whether there is a point in introducing an additional nginx proxy in the mix. It can be simpler to just put gunicorn or uWSGI directly behind the load balancer.
Suggestions are as follows.
Also look at mod_wsgi-express. It was specifically developed with containerised systems in mind to make it easier, and can be a better choice than uWSGI and gunicorn. Test different WSGI servers and configurations with your actual application with real world traffic profiles, not benchmarks which just overload it. This is important as the dynamics of a Kubernetes based system, along with how its routing may be implemented, means it all could behave a lot differently to more traditional systems you may be used to.
有人話過，係英國，如果你間屋附近有大樹會好麻煩，秋天落葉時，掃樹葉都掃到你頭暈。
其實，房屋附近有大樹，最麻煩唔係啲樹葉，而係棵樹可能會影響間屋。不過大家唔使擔心，大多數係房屋附近嘅樹木都不會對間屋造成任何損害，但係某啲情況下，房屋附近嘅樹木可以對間屋造成以下問題：
❌地基沉降造成的結構損壞
🧨英國好多地方嘅泥土會含有粘土層，粘土嘅特性就係於水份流失時收縮，收縮後當水份再回流時又會再度膨脹。當樹根於該種泥土層成長時，係乾燥天氣季節，可以大量吸收粘土中嘅水份而導致土壤收縮。如果房屋嘅地基坐落於該類粘土層，當附近出現樹根時，泥層可以因樹根吸收土壤中嘅水份而收縮，最終可能會出現地基下沉，而地基嘅不平均下沉就會造成牆身結構出現捐壞。在20世紀50年代前建造嘅四層以下嘅房屋，就最有機會出現該種問題，因為該類房屋嘅地基相對較淺，並多建於粘土上。
🧨除左樹木外，大型灌木植物都可能會吸收粘土中嘅水份而影響地基。
❌排水管損壞
🧨樹根嘅延伸可能會深入並阻塞排水管。如果排水管因損壞而出現滲漏，污水會流入周圍嘅土壤，這又可以令原本已收縮嘅土壤膨脹，引致地面突起。舊的排水管最容易受到影響。
❌路面及較小型結構物的影響
🧨不斷成長嘅根部，因不斷變大變粗可以令附近嘅行人路，甚至馬路突起，影響行人和車輛，亦會令輕小型嘅結構物升起，如車庫和棚子歪斜。
❌屋頂損壞
🧨跌落嘅樹枝會對屋頂和屋頂排水溝造成損壞。
因此，如果你有為你嘅房屋買保險，其中一個保險公司會問嘅問題就係你房屋某距離內有冇超過某高度嘅樹木，因為保險公司要考慮維修風險。
唔同樹種，因為根部需要嘅生長環境唔同，所以對房屋嘅影響都會有唔同。以下列表列出唔同樹種與房屋之間嘅安全距離，供大家參考。如果大家真係遇上大樹問題，就要咨詢相關專家意見啦。
Species Mature Height (M) Safe Distance (M)
Apple / Pear 12 10
Ash 23 21
Beech 20 15
Birch 14 10
Cypress 25 20
Cherry 17 11
Damson 12 11
Elm 25 30
Hawthorn 10 12
Holly 14 6
Horse Chestnut 20 23
Laburnum 12 9
Laurel 8 6
Magnolia 9 5
Maple 21 20
Oak 24 30
Pine 29 8
Plane 30 22
Plum 12 11
Poplar 28 35
Sycamore 24 17
Spruce 18 7
Walnut 18 14
White Beam / Rowan 12 11
Willow 24 40
Yew 12 5
An AI pioneer reflects on how companies can use machine learning to transform their operations and solve critical problems. By Karen Haoarchive page March 26, 2021
JEREMY PORTJE Andrew Ng has worn many hats in his life. You may know him as the founder of the Google Brain team or the former chief scientist at Baidu. You may also know him as your own instructor. He has taught countless students, curious listeners, and business leaders about the principles of machine learning through his wildly popular online courses.
Now in his latest venture, Landing AI, which he started in 2017, he is exploring how businesses without giant data sets to draw on can still join in the AI revolution.
On March 23, Ng joined MIT Technology Review’s virtual EmTech Digital, our annual AI event, to share the lessons he’s learned.
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Sign up Stay updated on MIT Technology Review initiatives and events? Yes No This interview has been condensed and lightly edited for clarity.
MIT Technology Review: I’m sure people frequently ask you, “How do I build an AI-first business?” What do you usually say to that? Andrew Ng: I usually say, “Don’t do that.” If I go to a team and say, “Hey, everyone, please be AI-first,” that tends to focus the team on technology, which might be great for a research lab. But in terms of how I execute the business, I tend to be customer-led or mission-led, almost never technology-led.
You now have this new venture called Landing AI. Can you tell us a bit about what it is, and why you chose to work on it? After heading the AI teams at Google and Baidu, I realized that AI has transformed software consumer internet, like web search and online advertising. But I wanted to take AI to all of the other industries, which is an even bigger part of the economy. So after looking at a lot of different industries, I decided to focus on manufacturing. I think that multiple industries are AI-ready, but one of the patterns for an industry being more AI-ready is if it’s undergone some digital transformation so there’s some data. That creates an opportunity for AI teams to come in to use the data to create value.
So one of the projects that I’ve been excited about recently is manufacturing visual inspection. Can you look at a picture of a smartphone coming off the manufacturing line and see if there’s a defect in it? Or look at an auto component and see if there’s a dent in it? One huge difference is in consumer software internet, maybe you have a billion users and a huge amount of data. But in manufacturing, no factory has manufactured a billion or even a million scratched smartphones. Thank goodness for that. So the challenge is, can you get an AI to work with a hundred images? It turns out often you can. I’ve actually been surprised quite a lot of times with how much you can do with even modest amounts of data. And so even though all the hype and excitement and PR around AI is on the giant data sets, I feel like there’s a lot of room we need to grow as well to break open these other applications where the challenges are quite different.
How do you do that? A very frequent mistake I see CEOs and CIOs make: they say to me something like “Hey, Andrew, we don’t have that much data—my data’s a mess. So give me two years to build a great IT infrastructure. Then we’ll have all this great data on which to build AI.” I always say, “That’s a mistake. Don’t do that.” First, I don’t think any company on the planet today—maybe not even the tech giants—thinks their data is completely clean and perfect. It’s a journey. Spending two or three years to build a beautiful data infrastructure means that you’re lacking feedback from the AI team to help prioritize what IT infrastructure to build.
For example, if you have a lot of users, should you prioritize asking them questions in a survey to get a little bit more data? Or in a factory, should you prioritize upgrading the sensor from something that records the vibrations 10 times a second to maybe 100 times a second? It is often starting to do an AI project with the data you already have that enables an AI team to give you the feedback to help prioritize what additional data to collect.
In industries where we just don’t have the scale of consumer software internet, I feel like we need to shift in mindset from big data to good data. If you have a million images, go ahead, use it—that’s great. But there are lots of problems that can use much smaller data sets that are cleanly labeled and carefully curated.
Could you give an example? What do you mean by good data? Let me first give an example from speech recognition. When I was working with voice search, you would get audio clips where you would hear someone say, “Um today’s weather.” The question is, what is the right transcription for that audio clip? Is it “Um (comma) today’s weather,” or is it “Um (dot, dot, dot) today’s weather,” or is the “Um” something we just don’t transcribe? It turns out any one of these is fine, but what is not fine is if different transcribers use each of the three labeling conventions. Then your data is noisy, and it hurts the speech recognition system. Now, when you have millions or a billion users, you can have that noisy data and just average it—the learning algorithm will do fine. But if you are in a setting where you have a smaller data set—say, a hundred examples—then this type of noisy data has a huge impact on performance.
Another example from manufacturing: we did a lot of work on steel inspection. If you drive a car, the side of your car was once made of a sheet of steel. Sometimes there are little wrinkles in the steel, or little dents or specks on it. So you can use a camera and computer vision to see if there are defects or not. But different labelers will label the data differently. Some will put a giant bounding box around the whole region. Some will put little bounding boxes around the little particles. When you have a modest data set, making sure that the different quality inspectors label the data consistently—that turns out to be one of the most important things.
For a lot of AI projects, the open-source model you download off GitHub—the neural network that you can get from literature—is good enough. Not for all problems, but the main problems. So I’ve gone to many of my teams and said, “Hey, everyone, the neural network is good enough. Let’s not mess with the code anymore. The only thing you’re going to do now is build processes to improve the quality of the data.” And it turns out that often results in faster improvements to performance of the algorithm.
What is the data size you are thinking about when you say smaller data sets? Are you talking about a hundred examples? Ten examples? Machine learning is so diverse that it’s become really hard to give one-size-fits-all answers. I’ve worked on problems where I had about 200 to 300 million images. I’ve also worked on problems where I had 10 images, and everything in between. When I look at manufacturing applications, I think something like tens or maybe a hundred images for a defect class is not unusual, but there’s very wide variance even within the factory.
I do find that the AI practices switch over when the training set sizes go under, let’s say, 10,000 examples, because that’s sort of the threshold where the engineer can basically look at every example and design it themselves and then make a decision.
Recently I was chatting with a very good engineer in one of the large tech companies. And I asked, “Hey, what do you do if the labels are inconsistent?” And he said, “Well, we have this team of several hundred people overseas that does the labeling. So I’ll write the labeling instructions, get three people to label every image, and then I’ll take an average.” And I said, “Yep, that’s the right thing to do when you have a giant data set.” But when I work with a smaller team and the labels are inconsistent, I just track down the two people that disagree with each other, get both of them on a Zoom call, and have them talk to each other to try to reach a resolution.
I want to turn our attention now to talk about your thoughts on the general AI industry. The Algorithm is our AI newsletter, and I gave our readers an opportunity to submit some questions to you in advance. One reader asks: AI development seems to have mostly bifurcated toward either academic research or large-scale, resource-intensive, big company programs like OpenAI and DeepMind. That doesn’t really leave a lot of space for small startups to contribute. What do you think are some practical problems that smaller companies can really focus on to help drive real commercial adoption of AI? I think a lot of the media attention tends to be on the large corporations, and sometimes on the large academic institutions. But if you go to academic conferences, there’s plenty of work done by smaller research groups and research labs. And when I speak with different people in different companies and industries, I feel like there are so many business applications they could use AI to tackle. I usually go to business leaders and ask, “What are your biggest business problems? What are the things that worry you the most?” so I can better understand the goals of the business and then brainstorm whether or not there is an AI solution. And sometimes there isn’t, and that’s fine.
Maybe I’ll just mention a couple of gaps that I find exciting. I think that today building AI systems is still very manual. You have a few brilliant machine-learning engineers and data scientists do things in a computer and then push things to production. There’s a lot of manual steps in the process. So I’m excited about ML ops [machine learning operations] as an emerging discipline to help make the process of building and deploying AI systems more systematic.
Also, if you look at a lot of the typical business problems—all the functions from marketing to talent—there’s a lot of room for automation and efficiency improvement.
I also hope that the AI community can look at the biggest social problems—see what we can do for climate change or homelessness or poverty. In addition to the sometimes very valuable business problems, we should work on the biggest social problems too.
How do you actually go about the process of identifying whether there is an opportunity to pursue something with machine learning for your business? I will try to learn a little bit about the business myself and try to help the business leaders learn a little bit about AI. Then we usually brainstorm a set of projects, and for each of the ideas, I will do both technical diligence and business diligence. We’ll look at: Do you have enough data? What’s the accuracy? Is there a long tail when you deploy into production? How do you fill the data back and close the loop for continuous learning? So—making sure the problem is technically feasible. And then business diligence: we make sure that this will achieve the ROI that we’re hoping for. After that process, you have the usual, like estimating the resources, milestones, and then hopefully going into execution.
One other suggestion: it’s more important to start quickly, and it’s okay to start small. My first meaningful business application at Google was speech recognition, not web search or advertising. But by helping the Google speech team make speech recognition more accurate, that gave the Brain team the credibility and the wherewithal to go after bigger and bigger partnerships. So Google Maps was the second big partnership where we used computer vision—to read house numbers to geolocate houses on Google maps. And only after those first two successful projects did I have a more serious conversation with the advertising team. So I think I see more companies fail by starting too big than fail by starting too small. It’s fine to do a smaller project to get started as an organization to learn what it feels like to use AI, and then go on to build bigger successes.
What is one thing that our audience should start doing tomorrow to implement AI in their companies? Jump in. AI is causing a shift in the dynamics of many industries. So if your company isn’t already making pretty aggressive and smart investments, this is a good time.
hide by Karen Hao
https://www.switchbacktravel.com/best-synthetic-insulated-jackets
1) Arc’teryx Atom LT Hoody ($259)
https://www.rei.com/product/175331/arcteryx-atom-lt-insulated-hoodie-mens?cm_mmc=aff_AL--38931--52463-_-NA&avad=52463_d259ae495
Down insulation consists of either duck or goose feathers. This is a natural insulation that is formed into clusters. Every down jacket has a fill-power rating that typically ranges from 450-900. The fill power rating is determined by measuring how many cubic inches an ounce of down creates. A simple way to put it, the higher the fill power, the warmer the jacket for the weight. Typically, common down jackets will range between 400-500. These jackets are typically lower quality. If you find a jacket that is 550 or higher, then you will be in good hands. The other factor that comes into play is the fill-weight. A lot of people tend to focus on the fill power but neglect the fill weight. The fill weight will tell you how soft or firm the jacket is and how well it compresses. Down jackets have the ability to easily be stuffed into backpacks, so a lower fill weight will make it super easy to pack versus a higher fill weight. Now that you know about fill power and fill weight, it's time to figure out how warm your jacket will actually be. Below are perfect examples by Triple F.A.T. Goose: A jacket with 500 fill power and 10 oz. of down will be warmer than a 800 fill power jacket with 5 oz. of down. With the down weight and down to feather ratio being equal, an 800 fill power jacket will be warmer than a 500 fill power jacket. An 800 fill power jacket will require less down than a 500 fill power jacket to provide the same warmth. Making sense of the fill-power to fill-weight can get confusing but you can see that a jacket that provides 800-fill doesn't necessarily mean it will be warmer unless the fill-weight is the same. This is why a majority of our top performance jackets, for example Summit Series collection, will be at a fill-power of 800 because they can keep the weight low while still offering maximum warmth. The downside to down insulation is that it loses it's warmth when it gets wet. When down gets wet it also cause the fibers to clump together creating cold spots. There is a better solution to this... Synthetic insulation
Synthetic insulation, on the other hand, is typically made of a polyester and is designed to mimic down, but retains warmth even when wet. It is also known to be quick-drying. The price of synthetic insulation also tends to be less expensive. When determining synthetics, you should know whether it is short-staple or continuous filament. Short-staple insulation uses short strands of denier filaments which makes your jacket softer, more flexible, and allows you to compress easily when you decide to pack away your jacket. The downside is that the fibers easily move around and bunch up to create cold spots within the jacket. Then you have continuous filament which is thicker, with more loft and durability. These jackets tend to be a bit stiffer and less compressible, however you don't have to worry about the fibers moving around to create cold spots.
Warmth in synthetic jackets are generally expressed in grams per square meter. They generally range from 40-200g of insulation. 50-100g of insulation are generally great for Spring/Fall weather or when you know you will be layering, while100-200g of insulation are designed for those cold temperatures that reach below freezing. Reminder: There are newer technologies of synthetic insulation where the rules don't always apply (High-Tec insulated jackets) that are built for aerobic activities to stay warm while letting your jacket breath more.
These are the basics when considering your next down or synthetic jacket and its warmth. It is important to stay safe with the proper gear when you are under extreme weather conditions. With the proper, high-quality jacket you will be warm, comfortable and enjoying every moment of your next adventure.
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