燈仔 股海心經（驚）
止蝕死硬 你要估佢會繼續跌 唯一可以做就係跌完就入貨 升完就沽貨
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Member since 2017-07-15T03:50:57Z. Last seen 2025-04-09T16:00:01Z.
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止蝕死硬 你要估佢會繼續跌 唯一可以做就係跌完就入貨 升完就沽貨
中立的我從不輕信，有直接證據，自然以此作準；沒直接證據，就要留心蛛絲馬跡，憑常理判斷。以X小姐強姦案為例，既然鄧炳強早於本年1月已宣稱她是報假案抹黑警隊的疑犯，肯定已掌握「鐵證」，那麼警方有什麼理由啞忍4個月，也不公開疑犯身分？我4個月前已有此問，但不作聲，姑且看你下一步是什麼。
好了，現在等到鄧炳強宣布X小姐是潛逃海外的「通緝犯」，左報也開喇叭帶風向說她「畏罪潛逃」了，我以為警方一定張貼疑犯照片，呼籲全球藍絲協助緝捕──奇怪，警方仍然沒這樣做。朋友替我請教律政司高官，為什麼警方要保護一個「報假案通緝犯」的私隱呢？對方解釋不到。或者你說：「是因為法庭有禁令不能披露X小姐身分。」那麼我再問：既然原告變被告，受害者是警方，為什麼過了差不多半年，警方也不申請取消禁令？
其實答案昭然若揭：警方高層完全知道X小姐是受害人，口裏說不，但潛意識很誠實，所以不自覺按照慣例，沒披露受害者的身分。當然，他們更不會因為X小姐的「抹黑」而流露任何憤怒神情。完美的演技和心計屬於高層次專業，未開化的共產黨並沒有這種訓練。黑警可以丟掉良心，但他們無法跟潛意識割席。
說謊必然露餡，去年9月本欄有篇〈無可疑才最可疑〉，講了明人祝允明《野記》一個故事：船夫因財殺害商人，跑到商人的家假裝尋找死者，敲門時第一句就說：「娘子，如何官人久不來下船？」明察秋毫的縣令審案時一聽，馬上對船夫說：「兇手是你，你已招認了。你明知官人不在家，所以敲門時才叫『娘子』，否則一定先找商人。」假如PK鄧活於古代中國，恐怕已人頭落地。然而共國不是中國。
話分兩頭，大家再看看X小姐的反應是否可疑。X報案後19天已離開香港，左報指她「畏罪」。你不妨代入X的角色想像：如果你報假案老屈警察，你夠膽留在香港19天才潛逃嗎？X小姐是知道警方沒跟進調查後，擔心自身安全才離開香港的，這是正常人的合理反應。假設你是X小姐的朋友，又假設你智力正常，相信也會勸她盡快離開。真的，我希望她不要回港。
X小姐由原告變被告，這種事在大陸司空見慣，現在香港既得「兩辦」直接管轄（參考支本法第12條），學倪匡先生話齋，「有共產黨便有這樣的事發生」。
1 噹撈侵 • 21h
小股東一到店，所有喝酒的人便都看着他笑，有的叫道，「小股東，005又跌破新低了!」他不回答，對自己說，「趁低吸納，要賺回30萬。」便排出30大錢。
他們又故意的高聲嚷道，「你一定會輸光棺材本！」小股東睜大眼睛說，「你怎麼這樣憑空惡意詛咒……」「什麼詛咒？我前天親耳聽你說輸了30萬，棺材本。」
小股東便漲紅了臉，額上的青筋條條綻出，爭辯道，「匯控不能算輸……正股！……正股驚閪，能算輸麼？」
接連便是難懂的話，什麼「正股驚閪」，什麼「留給孫兒」之類，引得衆人都哄笑起來：店內外充滿了快活的空氣。
有幾回，新聞在播匯豐小股東權益大聯盟。小股東着了慌，伸開五指將屏幕擋住，彎腰下去說道，「輸30萬，我已經不多了。」
直起身又看一看屏，自己搖頭說，「不多不多！多乎哉？不多也。」於是這一羣孩子都在笑聲裏走散了。
中秋之後，忽然間聽得一個聲音，「正股驚閪。」這聲音雖然極低，卻很耳熟。看時又全沒有人。
站起來向外一望，那小股東便在櫃臺下對了門檻坐着。他臉上黑而且瘦，已經不成樣子；穿一件破夾襖，盤着兩腿，下面墊一副棺材，用草繩在肩上掛住；見了我，又說道，「正股驚閪。」
掌櫃也伸出頭去，一面說，「小股東麼？匯控又新低呢！」小股東很頹唐的仰面答道，「正……正股驚閪。正股不怕跌，留給孫兒。」
掌櫃仍然同平常一樣，笑着對他說，「小股東，匯控39.95 了!」但他這回卻不十分分辯，單說了一句「不要取笑！」
「取笑？要是派息，怎麼會大跌價？」小股東低聲說道，「股息，跌，跌……」他的眼色，很像懇求掌櫃，不要再提。
自此以後，又長久沒有看見小股東。到了年關，掌櫃取下粉板說，「小股東還在輸30萬呢！」到第二年的端午，又說「小股東還在輸30萬呢！」到中秋可是沒有說，再到年關也沒有看見他。
我到現在終於沒有見 - 大約小股東的確輸死了。
https://drive.google.com/drive/folders/1-DR44BkrVGWeZmBD8WbK6ik5HAjewo1u?usp=sharing
Day 1 - 12/5/2020
K1 : https://youtu.be/btYChV6HPoY
香港人生活節奏快，做事講求效率。行得快、食得快、賺錢也要快。秒秒鐘幾百萬上落，確實刺激，但太過着重短期結果的代價往往比想像大。俗語有云「財不入急門」。一代投機者李佛摩（Jesse Livermore）曾說「錢是坐着賺來的，不是靠炒出炒入的。」著名投資者芒格（Charlie Munger）亦指出賺大錢的關鍵在於等待，等待買入機會、等待公司成長。
只可惜，現今世代歌頌即食文化，人人都中了「冧巴降」。長篇大論至無謂，畀個冧巴最實際。最好是講明買入及賣出價，即買即賣即賺更佳。什麼資產配置、價值投資，都比不上短炒day trade過癮快見效。
強迫思考每一次投資
為了捕捉短線波動，普遍股民的另一特徵是日夜緊貼市況、留意每項數據及新聞。自問天南地北無所不知，並熟讀不同股評人的推介，為何炒來炒去也賺不了錢呢？奮戰股海，精神壓力較返工大，卻從來沒有準時出糧這回事。出咗半斤力，不但攞不足八両，更可能會倒蝕！
想跳出困局，筆者有一個小建議：寫投資筆記。這方法看似老套，卻極為有效。正如〈建立投資系統 戒除盲目投機〉一文強調，投資最難之處，在於建立一套系統，以客觀準則而非單憑感覺行事。投資者應記錄每項買賣的原因、交易價格和時間、不同情景的部署，以及最終盈虧。寫投資筆記的第一個好處，在於強迫自己認真思考每一次投資，例如是短炒或是長線看好。當交易成本愈低，操作愈方便，真正的投資往往就愈難，投資筆記就愈是重要。試想像你一生的投資決定，如畢非德所設想的只有20次，那你定必會非常謹慎。
規劃不同情景應對策略
第二，投資筆記能夠幫助你規劃不同情景的應對策略，例如何時加注及何時止蝕。這有助戒除「買咗先算」，避免到最後被市況牽着走的情況出現。第三，投資筆記可助你檢視最新市況變化，思考當初買賣的原因，是否依然成立。投資大師林治（Peter Lynch）曾比喻投資筆記為令人回味無窮的情書：它能夠提醒我們當初為何愛上這間公司，之後又是為了什麼原因移情別戀。最後，投資筆記方便回顧往績，分析每項交易的成敗原因，以確立長線優勢，並提醒自己有幾多交易是亂炒一通。須知道記憶並不可信，人們往往選擇忘記失敗，只記低成功的快感。投資筆記可令你面對真實的自己，做到真正、全面問責。開始寫投資筆記後，讀者可以嘗試一個小挑戰：每半年或一年，回顧自己的投資表現能否跑贏大市，甚至是跑贏「全天候策略」。假若跑輸大市，便應減少主動投資的注碼，轉為被動地投資大市或「全天候策略」。對賭性甚強、自信過人的投資者，這小挑戰既可鞭策自己，亦可強迫自己轉換至表現較佳的策略，以提升投資表現。
LinkedIn@Jackie Si Tou
作者任職聯合國亞洲及太平洋經濟社會委員會。個人意見並不代表機構立場。
最近陸續發布有關黃金供應與需求的數據，似乎傳達出一些不協調的訊息，就是實體黃金需求下降，而「紙黃金」ETFs的需求上升，這令傳統的黃金價值觀受到挑戰。
上期本欄引述世界黃金協會的資料，指今年首季與去年同季相比，全球金飾需求下降37%。現再補充指該季金條與金幣需求乃下跌6%，而中國內地在此項的跌幅則為48%。此外，各國央行和官方機構需求亦下跌8%。換言之，整個季度的實體黃金需求出現頗明顯的下降。
上周又傳來印度在4月份進口黃金僅60公斤，而一個月前是13噸，跌幅可謂驚人。其後則聽到與上述有別的消息，中國內地黃金消費快速升溫，據說在廣州一些商場，100克投資金條出現斷貨。此現象未知可為時多久，但以目前情況看來，完全不足以彌補前幾個月的需求下降數量。
ETFs總持金量創新高
現又看到世界黃金協會的最新資料，指全球黃金ETFs的總持金量繼續上升，到今年4月底增至3355.2噸，創歷來新高水平。
此數據與全球實體黃金需求下降並不協調，或意味着疫情令全球的人流和物流發生斷裂，以致金市投資者被迫投資於ETFs去代替實體黃金。但此情況是向傳統黃金價值觀作出的挑戰。
黃金的傳統價值乃建立在世人對貨幣體系以及其他貨品不信任的基礎之上，認為手持實體黃金才具有真正保值和避險之用，這不是它的代替品可以做到的。例如，萬一發行黃金ETFs的機構出了問題怎麼辦？因此相信最近ETFs持金量增加，與實體黃金需求下降互不協調，只是一個短期現象，前者不能完全代替後者去推升金價，金價長期走勢仍需要傳統價值觀的支持。
最後應該一提，雖然全球ETFs的總持金量增至新高，但其中最大的一隻SPDR，現時持金量只是回升至1081.65噸，與它本身在2012年底的高峰1353.35噸，仍有一定距離。
可能因為上述原因，上周金市只是繼續在高位整固，現貨金價在1681.2元（美元．下同）至1723.7元之間上落，全周僅微升2.1元。銀市因油價喘穩而隨着商品價格略回升，現貨銀價在14.7元至15.63元之間上落，收報15.4元，全周回升0.49元。美國就業情況為戰後最差，但未激起新避險情緒，因已在估計中，而且市場正評估經濟收縮壓力。
大投機者未再入市
投資者繼續看好金市，金礦股看好百分比指數仍企穩於92.31的極高水平，但這未能轉化成為大投機者再入市的興趣，上周他們在期金市所持的淨多頭倉量再度微降。換言之，金市並無明顯的新買盤進場，只是無人敢做空頭。造成此現象的部分原因，相信是金市在目前階段欠缺實體黃金需求的支持，金價不能只靠避險需求而持續大幅攀升。
上周美國2年期孳息率曾低至0.13厘的低水平，引起市場認為聯儲局可能實施負利率猜想。但是看來該局並無此意，上周該局的資產負債表擴張的速度進一步放緩，而投資市場認為疫情最惡劣階段或已出現。
因此，估計金市將維持在高位整固，現貨金價1747元至1753元仍是重要的阻力地帶，1659元至1669元則是支持地帶，短期在1680元至1725元之間上落。銀市暫停前階段的相對弱勢，或可挑戰上月高位現貨價15.84元，但16.2元是一個阻力水平，16.5元是重大阻力；而14.5元至14.7元已成為短期支持地帶。稍後金市應看實金需求是否恢復，以及大投機者是否重燃入市興趣，若如此，金價才會再明顯攀升。
美國股市上周高收，投資專家相信，本周五大市場焦點，首要的是進一步揭示新冠肺炎疫情對經濟衝擊的美國經濟數據，其次是中美兩國貿易爭拗。
首先，美國周五將公布4月零售貨值與工業產值。經濟師預期，零售貨值將下跌11.6%，超越前一月的紀錄高位8.4%跌幅；工業產值將急挫11.5%，同樣超過3月的5.4%跌幅。
此外，美國周四也會公布每周首次申領失業救濟人數與5月消費者情緒指數。前者繼續反映持續八周的封城措施對就業市道打擊。同時，聯儲局主席鮑威爾將出席會議及發表演說，談論他對經濟前景的看法，同樣是市場關注焦點。
第二個焦點是特朗普對中國實施關稅的威脅性言論。特朗普上周五表示，如果中國未履行協承諾，考慮撕毀中美兩國首階段貿易協定。特朗普過去數度發言，表示要懲罰中國隱瞞疫情。一旦中美兩國在疫情期間再爆出貿易爭端，勢必對經濟進一步打擊。
第三個焦點是英國與德國於本周公布的首季國內生產總值(GDP)數據。市場預期，英國經濟首季萎縮2.5%，德國首季GDP則預期萎縮2.1%。不過，疫情對歐洲真正的打擊是在第二季，因此首季GDP只是一個預告。
第四個焦點是美國股市與經濟的走勢嚴重分歧。美國經濟數據惡劣，例如上周五公布的4月非農業職位跌幅為2050萬個，但美國股市不跌反升，主要是因為市場原先預期更壞。不過，儘管如此，美國股票價值是否升至不合理的高位，依然是市場關注焦點，一旦美國多州重啟經濟後，實際經濟表現未如理想，將成為投資者借勢沽售的理由。
第五個焦點是比特幣分拆的事宜。比特幣將於周二一拆為二，是該款電子貨幣11年歷史內第二度一拆為二。前兩次的分拆，讓比特幣市值飊升。不過，分析員相信，今次在新冠肺炎疫情期間進行的分拆，可能難以讓歷史重演。
一位洋人朋友傳來一則網上短評，很有意思。短評的內容大概是：「想像你出生於1900年，14歲時遇上第一次世界大戰，18歲時戰爭結束，二千二百萬人死亡了，然後有西班牙流感，你20歲時疫症消失，兩年內死了五千萬人；到了29歲，世界經濟大蕭條，失業率高達25%，世界經濟大低迷至你的33歲生辰。休養生息了六年，39歲了，第二次世界大戰開始……」
1900年出生的一輩已不在世了，那一代的英國人、美國人、中國人的一生，都是沒幾年有平靜寧靜生活的。中國人生於1900，更是悲慘，11歲遇上武昌起義，隨之而來的是軍閥割據，不到37歲便要抗戰日本入侵，46歲時以為外敵已走，怎知道內戰即來，49歲碰上毛澤東當權，隨後有天翻地覆的三反五反，大鳴大放，大躍進，大饑荒、66歲遭遇文革，要忍得過去，76歲後才有平穩生活，生命已走近黃昏。不少人是活不到76歲的，饑荒餓不死，卻在文革期間給殘酷鬥爭死掉，像鍾南山的媽媽廖月琴，像周作人。
一位在紐約的朋友看短訊後，來回應說：「我們生於戰後，應該是幸運的一代。」那要看生於何處，住在那裏，若生於和平後的1946年的廣州，今年74歲，活於中國大陸的話，到30歲還是艱苦的，可能做了紅衛兵，死於非命，進大學時代，交白卷是英雄，於是蹉跎一生。1976年在大陸出生，應是中國百多年來最幸運的一代，甫出世便沒有了四人幫，與文革拜拜，鄧小平當權後展開經濟大改革，18歲進入大學，22歲可以到美國留學，今天是中國經濟的中流砥柱。另一方面，生於1946年的香港，一生可以一帆風順，過了幾十年太平日子，今年還可以在各界領風騷，但卻要開始為自己的子孫未來擔憂！
現在有了千禧代，二次大戰後的baby boomer陸續退隱，生於1980，1990，2000的人士應該也是幸運的一代，未曾目睹祖父輩的苦況，料不到新冠肺炎一來，把全世界弄得人仰馬翻，本來生活無憂的年輕人第一次遇上死亡、失業、無收入的威脅，以後的人生態度將不免有所改變。中國逐漸重開經濟之後，還有大量年青人沒工開，沒收入。一位北京女士，29歲，是繙譯員，過去七八年全不擔心每月收入，每月花70美元飲咖啡，170美元買面霜，每月買新手袋，她對紐約時報記者說：「我打開衣櫃，把所有手袋放在床上，它們能幫助我過活嗎？以後還是要把錢放在銀行戶口，自己才有安全感！」C19大概是整個中國年青一代的鬧鐘，把慣於「血拼」的年青一代都喚醒了，儲備戶口不能沒有餘糧。股市期望中國消費報復性反彈？不要想得太美麗！
政府在第二輪防疫抗疫基金下推出5億元的「遙距營商計劃」（D-Biz），支援企業在疫情期間繼續營運和提供服務，並資助企業採用資訊科技方案開拓遙距業務，令IT界和市場企業期待已久，期望能帶動市場上對IT服務的需求，成為供應商和客戶的及時雨。
相對於以往科技券計劃的配對資助形式，今次D-Biz計劃以快速批核方式加上百分百比例資助申請項目，每個項目最多資助10萬元，每間企業最多可獲30萬元資助。龐大商機在前，網上湧現部分供應商和中介公司的廣告招徠。這幾個星期筆者收到大量業界人士關注計劃的推行是否公平和透明，所有供應商是否同一起跑線競爭？政府和推行計劃的生產力促進局宜格外留意。
「遙距營商計劃」今天起開始正式接受資訊科技服務供應商申請列入「資訊科技服務供應商參考名單」，第一批參考名單會於5月15日公布，企業最快5月18日可以開始申請資助。雖然申請企業可以選擇參考名單以外的供應商，但若能「入閘」列入參考名單對接觸客戶和申請有一定幫助，因此業界高度關注申請列入的資格和機制。
根據生產力促進局最新公布的申請表格，列入供應商參考名單除了須提供公司基本資料和提供的服務外，亦須提供完整工作參考，包括清楚顯示工作範圍和可交付成果的客戶合同或採購訂單，以及客戶最終驗收文件、付款證明等。
系統整合商或非訂閱的科技方案/平台提供商須提交過去18個月完成的兩份工作參考和項目完成證明，而屬於訂閱的科技方案/平台提供商則須提交最少兩個訂閱服務6個月的活躍客戶合同或採購訂單。供應商須提交的資料亦包括商業登記、員工人數證明文件，而所有文件的名稱也必須一致。
有意申請資助的企業則應留意申請資格。申請者須持有有效商業登記證，並在今年1月1日前已經開業，並於提交申請時仍在與申請項目相關的行業有實質業務運作。上市公司、法定機構和接受公帑資助的非政府機構都不符申請資格。
筆者早前向當局指出在疫情下小型企業面對艱難的現金流狀況，如果沒有任何預付資助安排，部分小型企業可能因為缺乏資金而未知結果而不敢申請。今次D-Biz資助金額的首30%將於申請獲批後存入指定銀行賬戶，做法有別於過往政府資助計劃要求申請者預先支付採購服務，通過審核報告才可獲發還款項。這種安排對面對資金困難的中小企較為理想。
政府方面仍未公布詳細的申請及審批詳情，例如是否須在提交申請時同時提供3份報價單，同一家企業申請者的分公司或分店如何處理等。
企業考慮供應商時應留意政府表示採用參考名單的供應商不等於自動通過審批，也不等於政府認可或推薦。但值得留意一點，申請項目必須於資助申請獲批後才可展開，企業不應預先開始計劃才申請。
計劃涵蓋12個與遙距營商有關的資訊科技類別，如網上營商、網上客戶服務和推廣、數碼支付/流動裝置零售管理系統、線上/雲端財務管理系統、網上會議工具等。對於其他科技解決方案例如現成及度身訂造的系統，若符合幫助企業進行和繼續開展遙距業務的目標，也有機會獲審批。
企業應仔細參閱服務詳情尤其是不會獲資助的開支。例如資助範圍包括透過搜索引擎和數碼廣告宣傳，但有關內容的製作費（即市場推廣用之影像、文字和影片）並不會獲資助。
雖然疫情打擊各行業市道，但今次政府推出D-Biz計劃是個良機，讓更多企業有誘因使用雲端服務和其他網上遙距工具強化營運，進行數碼轉型和幫助企業扭轉運作模式，迎合網購潮流和對網上服務的殷切需求。
政府設立遙距營商計劃評審委員會，當中包括業界、商界等代表。筆者希望政府可以和業界保持密切溝通和監察計劃成效，視乎市場反應、申請情況、用家意見等，考慮日後是否有需要為資助額5億元加碼、延長可使用資助的時期和擴大申請機構的資格等，進一步刺激資訊科技行業增長，推動本港企業使用科技。
莫乃光 立法會（資訊科技界）議員
What it is, where it comes from, how it hurts us, and how we fight it.
by Neel V. Patel archive page April 15, 2020
SAIMAN CHOW What is it?
A SARS-CoV-2 virion (a single virus particle) is about 80 nanometers in diameter. The pathogen is a member of the coronavirus family, which includes the viruses responsible for SARS and MERS infections. Each virion is a sphere of protein protecting a ball of RNA, the virus’s genetic code. It’s covered by spiky protrusions, which are in turn enveloped in a layer of fat (the reason soap does a good job of destroying the virus).
Where does it come from?
Covid-19, like SARS, MERS, AIDS, and Ebola, is a zoonotic disease—it jumped from another species to human hosts. This probably happened in late 2019 in Wuhan, China. Scientists believe bats are the likeliest reservoir; SARS-CoV-2’s closest relative is a bat virus that shares 96% of its genome. It might have jumped from bats to pangolins, an endangered species sometimes eaten as a delicacy, and then to humans.
How does it get into human cells?
The coronavirus issue This story was part of our May 2020 issue
See the rest of the issue Subscribe The virus’s protein spikes attach to a protein on the surface of cells, called ACE2. Normally, ACE2 plays a role in regulating blood pressure. But when the coronavirus binds to it, it sets off chemical changes that effectively fuse the membranes around the cell and the virus together, allowing the virus’s RNA to enter the cell.
The virus then hijacks the host cell’s protein-making machinery to translate its RNA into new copies of the virus. In just hours, a single cell can be forced to produce tens of thousands of new virions, which then infect other healthy cells.
Parts of the virus’s RNA also code for proteins that stay in the host cell. At least three are known. One prevents the host cell from sending out signals to the immune system that it’s under attack. Another encourages the host cell to release the newly created virions. And another helps the virus resist the host cell’s innate immunity.
How does the immune system fight it off?
As with most viral infections, the body’s temperature rises in an effort to kill off the virus. Additionally, white blood cells pursue the infection: some ingest and destroy infected cells, others create antibodies that prevent virions from infecting host cells, and still others make chemicals that are toxic to infected cells.
But different people’s immune systems respond differently. Like the flu or common cold, covid-19 is easy to get over if it infects only the upper respiratory tract—everything above the vocal cords. It can lead to complications like bronchitis or pneumonia if it takes hold further down. People without a history of respiratory illness often have only mild symptoms, but there are many reports of severe infections in young, healthy people, as well as milder infections in people who were expected to be vulnerable.
If the virus can infect the lower airway (as its close cousin, SARS, does more aggressively), it creates havoc in the lungs, making it hard to breathe. Anything that weakens the immune system—even heavy drinking, missed meals, or a lack of sleep—could encourage a more severe infection.
How does it make people sick?
Infection is a race between the virus and the immune system. The outcome of that race depends on where it starts: the milder the initial dose, the more chance the immune system has of overcoming the infection before the virus multiplies out of control. The relationship between symptoms and the number of virions in the body, though, remains unclear.
If an infection sufficiently damages the lungs, they will be unable to deliver oxygen to the rest of the body, and a patient will require a ventilator. The CDC estimates that this happens to between 3% and 17% percent of all covid-19 patients. Secondary infections that take advantage of weakened immune systems are another major cause of death.
Sometimes it is the body’s response that is most damaging. Fevers are intended to cook the virus to death, but prolonged fevers also degrade the body’s own proteins. In addition, the immune system creates small proteins called cytokines that are meant to hinder the virus’s ability to replicate. Overzealous production of these, in what is called a cytokine storm, can result in deadly hyper-inflammation
How do treatments and vaccines work?
There are about a half-dozen basic types of vaccines, including killed viruses, weakened viruses, and parts of viruses, or viral proteins. All aim to expose the body to components of the virus so specialized blood cells can make antibodies. Then, if a real infection happens, a person’s immune system will be primed to halt it.
In the past it has been difficult to manufacture vaccines for new zoonotic diseases quickly. A lot of trial and error is involved. A new approach being taken by Moderna Pharmaceuticals, which recently began clinical trials of a vaccine, is to copy genetic material from a virus and add it to artificial nanoparticles. This makes it possible to create a vaccine based purely on the genetic sequence rather than the virus itself. The idea has been around for a while, but it is unclear if such RNA vaccines are potent enough to provoke a sufficient response from the immune system. That’s what clinical trials will establish, if they first prove that the proposed vaccine isn’t toxic.
Other antiviral treatments use various tactics to slow down the virus’s spread, though it is not yet clear how effective any of these are. Chloroquine and hydroxychloroquine, typically used to fight malaria, might inhibit the release of the viral RNA into host cells. Favipiravir, a drug from Japan, could keep viruses from replicating their genomes. A combination therapy of lopinavir and ritonavir, a common HIV treatment that has been successful against MERS, prevents cells from creating viral proteins. Some believe the ACE2 protein that the coronavirus latches onto could be targeted using hypertension drugs.
Another promising approach is to take blood serum from people who have recovered from the virus and use it—and the antibodies it contains—as a drug. It could be useful either to confer a sort of temporary immunity to health-care workers or to combat the virus’s spread in infected people. This approach has worked against other viral diseases in the past, but it remains unclear how effective it is against SARS-CoV-2.
With additional reporting from Antonio Regalado.
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Category: Artificial intelligence Posted May 01
The news: In a fresh spin on manufactured pop, OpenAI has released a neural network called Jukebox that can generate catchy songs in a variety of different styles, from teenybop and country to hip-hop and heavy metal. It even sings—sort of.
How it works: Give it a genre, an artist, and lyrics, and Jukebox will produce a passable pastiche in the style of well-known performers, such as Katy Perry, Elvis Presley or Nas. You can also give it the first few seconds of a song and it will autocomplete the rest.
Old songs, new tricks: Computer-generated music has been a thing for 50 years or more, and AIs already have impressive examples of orchestral classical and ambient electronic compositions in their back catalogue. Video games often use computer-generated music in the background, which loops and crescendos on the fly depending on what the player is doing at the time. But it is much easier for a machine to generate something that sounds a bit like Bach than the Beatles. That’s because the mathematical underpinning of much classical music lends itself to the symbolic representation of music that AI composers often use. Despite being simpler, pop songs are different.
OpenAI trained Jukebox on 1.2 million songs, using the raw audio data itself rather than an abstract representation of pitch, instrument, or timing. But this required a neural network that could track so-called dependencies—a repeating melody, say—across the three or four minutes of a typical pop song, which is hard for an AI to do. To give a sense of the task, Jukebox keeps track of millions of time stamps per song, compared with the thousand time stamps that OpenAI’s language generator GPT-2 uses when keeping track of a piece of writing.
Chatbot sing-alongs: To be honest, it’s not quite there yet. You will notice that the results, while technically impressive, are pretty deep in the uncanny valley. But while we are still a long way from artificial general intelligence (OpenAI’s stated goal), Jukebox shows once again just how good neural networks are getting at imitating humans, blurring the line between what’s real and what’s not. This week, rapper Jay-Z started legal action to remove deepfakes of him singing Billy Joel songs, for example. OpenAI says it plans to conduct research into the implications of AI for intellectual -property rights.
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Categorized in Artificial intelligence4 days Facebook claims its new chatbot beats Google’s as the best in the world It has also open-sourced the AI system to spur further research.
Categorized in Opinion5 days The US already has the technology to test millions of people a day Advances in biology in the past decade can provide the tools we need to revolutionize the testing process.
Sean RossUpdated Feb 20, 2020 It is not entirely understood just how much, or even in what direction, the Federal Reserve's quantitative easing, or QE, program affected the bond market.
Simple market theory, based on increased demand from homogeneous buyers, predicts that the Fed's purchase programs suppressed bond yields below their natural market-clearing level. This assumption also suggests that bond prices were too high, given that yield and price were inverted, to the point of even creating a bubble in the bond market.
Key Takeaways
Quantitative easing was used by the Federal Reserve from 2008 through 2014 to alleviate the financial effects of the Great Recession. The strategy was to buy bonds in order to suppress their prices and correct a skewed yield curve. Did it work? No one can say definitively. Quantitative easing is a non-traditional approach to boosting an economy, used only when other measures fail. In the United States, the Federal Reserve employed the strategy to alleviate the financial effects of the Great Recession. Quantitative easing was employed in several rounds beginning in late 2008 and continuing periodically through late 2014. The central bank eventually accumulated more than $4 trillion in financial assets.
Quantitative Easing and Bond Prices
As noted, a campaign to suppress bond yields implies that bond prices are too high.
Operating under this assumption, traditional and conservative buy-and-hold bond strategies become riskier. In fact, both opportunity cost risks and actual default risks escalate in circumstances when bond prices are artificially high. Bondholders receive a lower return for their investments and become exposed to inflation, losing yield when they might have been better off pursuing instruments with higher upside.
Pros and Cons
This perceived risk was so strong that, during the deliberations about quantitative easing in the European Union, economists from the World Pensions Council warned that artificially low government bond interest rates could compromise the underfunding condition of pension funds. They argued that diminished returns from QE could force negative real savings rates on retirees.
Many economists and bond market analysts worry that too much QE pushes bond prices too high due to artificially low interest rates. However, all of the money creation from QE could lead to rising inflation.
The European Union has also grappled with the pros and cons of quantitative easing.
The conventional weapon used by the Federal Reserve and other central banks to fight inflation is to raise interest rates. Rising rates could cause massive losses in principal value for bondholders.
However, there are some factors at play that call into question this seemingly logical analysis. Bond buyers are not homogeneous, and the incentives to purchase bonds and other financial assets are different for the Federal Reserve than for other market participants.
Risks and Expectations
In other words, the Fed does not necessarily purchase bonds on a marginal basis, and fully backed debt obligations of the U.S. government are not exposed to the same default risks as other assets.
In addition, market expectations may be priced into the bond market ahead of time, creating a situation in which prices reflect anticipated future conditions rather than current conditions.
This can be seen in historical bond yields when yields rose for several months after the start of QE1. After the QE ended, prices rose and yields fell. This is the opposite of what many assumed would occur.
Does this prove the bond market is improved by quantitative easing? Certainly not. Circumstances never repeat in exactly the same way, and no economic policy can be evaluated in a vacuum.
It is still entirely possible that market expectations will shift again and future QE strategies will have different effects on the bond market.
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