【演講】2019/11/19 (二) @工四816 (智易空間),邀請到Prof. Geoffrey Li(Georgia Tech, USA)與Prof. Li-Chun Wang(NCTU, Taiwan) 演講「Deep Learning based Wireless Resource Allocation/Deep Learning in Physical Layer Communications/Machine Learning Interference Management」
IBM中心特別邀請到Prof. Geoffrey Li(Georgia Tech, USA)與Prof. Li-Chun Wang(NCTU, Taiwan)前來為我們演講,歡迎有興趣的老師與同學報名參加!
演講標題:Deep Learning based Wireless Resource Allocation/Deep Learning in Physical Layer Communications/Machine Learning Interference Management
演 講 者:Prof. Geoffrey Li與Prof. Li-Chun Wang
時 間:2019/11/19(二) 9:00 ~ 12:00
地 點:交大工程四館816 (智易空間)
活動報名網址:https://forms.gle/vUr3kYBDB2vvKtca6
報名方式:
費用:(費用含講義、午餐及茶水)
1.費用:(1) 校內學生免費,校外學生300元/人 (2) 業界人士與老師1500/人
2.人數:60人,依完成報名順序錄取(完成繳費者始完成報名程序)
※報名及繳費方式:
1.報名:請至報名網址填寫資料
2.繳費:
(1)親至交大工程四館813室完成繳費(前來繳費者請先致電)
(2)匯款資訊如下:
戶名: 曾紫玲(國泰世華銀行 竹科分行013)
帳號: 075506235774 (國泰世華銀行 竹科分行013)
匯款後請提供姓名、匯款時間以及匯款帳號後五碼以便對帳
※將於上課日發放課程繳費領據
聯絡方式:曾紫玲 Tel:03-5712121分機54599 Email:tzuling@nctu.edu.tw
Abstract:
1.Deep Learning based Wireless Resource Allocation
【Abstract】
Judicious resource allocation is critical to mitigating interference, improving network efficiency, and ultimately optimizing wireless network performance. The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve it to a certain level of optimality. However, as wireless networks become increasingly diverse and complex, such as high-mobility vehicular networks, the current design methodologies face significant challenges and thus call for rethinking of the traditional design philosophy. Meanwhile, deep learning represents a promising alternative due to its remarkable power to leverage data for problem solving. In this talk, I will present our research progress in deep learning based wireless resource allocation. Deep learning can help solve optimization problems for resource allocation or can be directly used for resource allocation. We will first present our research results in using deep learning to solve linear sum assignment problems (LSAP) and reduce the complexity of mixed integer non-linear programming (MINLP), and introduce graph embedding for wireless link scheduling. We will then discuss how to use deep reinforcement learning directly for wireless resource allocation with application in vehicular networks.
2.Deep Learning in Physical Layer Communications
【Abstract】
It has been demonstrated recently that deep learning (DL) has great potentials to break the bottleneck of the conventional communication systems. In this talk, we present our recent work in DL in physical layer communications. DL can improve the performance of each individual (traditional) block in the conventional communication systems or jointly optimize the whole transmitter or receiver. Therefore, we can categorize the applications of DL in physical layer communications into with and without block processing structures. For DL based communication systems with block structures, we present joint channel estimation and signal detection based on a fully connected deep neural network, model-drive DL for signal detection, and some experimental results. For those without block structures, we provide our recent endeavors in developing end-to-end learning communication systems with the help of deep reinforcement learning (DRL) and generative adversarial net (GAN). At the end of the talk, we provide some potential research topics in the area.
3.Machine Learning Interference Management
【Abstract】
In this talk, we discuss how machine learning algorithms can address the performance issues of high-capacity ultra-dense small cells in an environment with dynamical traffic patterns and time-varying channel conditions. We introduce a bi adaptive self-organizing network (Bi-SON) to exploit the power of data-driven resource management in ultra-dense small cells (UDSC). On top of the Bi-SON framework, we further develop an affinity propagation unsupervised learning algorithm to improve energy efficiency and reduce interference of the operator deployed and the plug-and-play small cells, respectively. Finally, we discuss the opportunities and challenges of reinforcement learning and deep reinforcement learning (DRL) in more decentralized, ad-hoc, and autonomous modern networks, such as Internet of things (IoT), vehicle -to-vehicle networks, and unmanned aerial vehicle (UAV) networks.
Bio:
Dr. Geoffrey Li is a Professor with the School of Electrical and Computer Engineering at Georgia Institute of Technology. He was with AT&T Labs – Research for five years before joining Georgia Tech in 2000. His general research interests include statistical signal processing and machine learning for wireless communications. In these areas, he has published around 500 referred journal and conference papers in addition to over 40 granted patents. His publications have cited by 37,000 times and he has been listed as the World’s Most Influential Scientific Mind, also known as a Highly-Cited Researcher, by Thomson Reuters almost every year since 2001. He has been an IEEE Fellow since 2006. He received 2010 IEEE ComSoc Stephen O. Rice Prize Paper Award, 2013 IEEE VTS James Evans Avant Garde Award, 2014 IEEE VTS Jack Neubauer Memorial Award, 2017 IEEE ComSoc Award for Advances in Communication, and 2017 IEEE SPS Donald G. Fink Overview Paper Award. He also won the 2015 Distinguished Faculty Achievement Award from the School of Electrical and Computer Engineering, Georgia Tech.
Li-Chun Wang (M'96 -- SM'06 -- F'11) received Ph. D. degree from the Georgia Institute of Technology, Atlanta, in 1996. From 1996 to 2000, he was with AT&T Laboratories, where he was a Senior Technical Staff Member in the Wireless Communications Research Department. Currently, he is the Chair Professor of the Department of Electrical and Computer Engineering and the Director of Big Data Research Center of of National Chiao Tung University in Taiwan. Dr. Wang was elected to the IEEE Fellow in 2011 for his contributions to cellular architectures and radio resource management in wireless networks. He was the co-recipients of IEEE Communications Society Asia-Pacific Board Best Award (2015), Y. Z. Hsu Scientific Paper Award (2013), and IEEE Jack Neubauer Best Paper Award (1997). He won the Distinguished Research Award of Ministry of Science and Technology in Taiwan twice (2012 and 2016). He is currently the associate editor of IEEE Transaction on Cognitive Communications and Networks. His current research interests are in the areas of software-defined mobile networks, heterogeneous networks, and data-driven intelligent wireless communications. He holds 23 US patents, and have published over 300 journal and conference papers, and co-edited a book, “Key Technologies for 5G Wireless Systems,” (Cambridge University Press 2017).
「ibm power 11」的推薦目錄:
ibm power 11 在 君子馬蘭頭 - Ivan Li 李聲揚 Facebook 的最佳解答
[香港人好富貴—你身家係咩冧把?]今日今日仲講百萬富翁實在太out.所以彭博又有新搞作 (https://bloom.bg/2odR9Az)
首先一個gag,神版友(唔tag佢,雖則佢係medium作者,仲係深烤我介紹佢用。我其實好驚佢的)成廿年前已經講:Millionaire譯百萬富翁,冇問題。但根本中文係譯唔到billionaire 呢個字。億萬富翁?但billion明明係十億(即係一千個million,一後面九個零)
事實係,百萬富翁已成為笑話。一百萬港紙?首期都未夠畀。小弟都一早係百萬富翁(搞移民台灣都要百萬五港紙啦大佬)。一百萬美金?好啲,但其實應該都唔多夠退休。有數得計,好進取當你收五厘息一年,一個月三皮嘢左右,都勉勉強強咋。冇得即時拍枱起身嘅,有乜可能係「富翁」?真係老夫子咁中頭獎都返去餵猩猩,秦先生咁中頭獎仲返去拖地咩。
百萬富翁呢個詞成為笑話,又畀你見到通脹有幾緊要,所以唔投資真係死路一條。但今日重點唔係講呢個。
彭博話,millionaire 上一級就係billionaire(以下所有都講美金,除非另有注明),實在太闊。入面已經一條歧視鏈。剛好百萬嘅,真係仲要Benz for the mouth為口奔馳,中端打工仔咋,但去到幾億(several hundred million)身家嘅,仲係millionaire,未到billionaire,但肯定已係唔使做嘅富人級。
但去到billionaire,一樣都係又分好多種,又係歧視鏈。Billionaire入門嘅,十億美金身家,當然係富豪。但只係普通富豪。Jay-Z,趙薇嗰啲明星入面嘅富豪咪十億美金身家水平(當然呢啲榜多數低估,但講故唔好駁故),係富,但唔係去到流傳千古嘅水平。你去到另一端嘅,標基呀光頭佬呀,過百Billion,千億美金身家,何止富可敵國,直頭more money than god,就唔係Jay-Z,趙薇或者NBA英超球星嗰啲級數
而在低端嘅,我地又唔會叫人做thousandnaire 或者even worse,hunderdnaire.但明明正係我地好多人嘅寫照
(埃汾按:原文咁寫咋,我肯定我啲讀者唔止咁,個撚個都一年去幾次日本,食米芝蓮,電話又成皮嘢。所以你睇美國佬幾咁窮。舊文都有講,幾千銀都拎唔到出嚟呀)(http://bit.ly/2oi4ZSk)(http://bit.ly/2mDE9DU)
正如我講,用字係好講究。我到而家都認為講「年薪二十萬」十分滑稽,正如我冇可能講我間屋「佔地四百尺」,埃汾FB Page「坐擁六千粉絲」,恒指亦冇得「勁跌300點」。用字咁極端,等同廚師勁落味精,係缺乏自信嘅表現。
彭博就話,好簡單,用scientific notation。你副身家,10 to the power幾多。就係你嘅Wealth Score
由十分忘恩嘅負2,去到湯大狀最愛嘅11(如是我聞,其實我都唔知係乜),應該包晒全人類咁滯。入門嘅百萬富翁,6分。光頭佬同標基,11分,全宇宙就係佢兩個。另一極端嘅,一蚊雞美金身家嘅小朋友,咪1分。一毫子美金身家嘅(我啲勞蘇?),咪-1。去到十分忘恩嘅-2,人類極限了。再少啲?你都唔會得閒睇文。
負資產嗰啲點算?係undefined的.10嘅幾多次方都唔會變成負數(你唔知?又證明香港地講STEM真係算鳩數)。log負數計數機只會出error.《那些年》話你知唔識log 都可以好好生活下話,可能。但負資產嘅,唯有計入忘恩嘅-2級,冇計,你突破晒人類底線。
其實,講咁多嘢,好撚似開心大發現咁。我地華文傳媒一早有講啦,身家幾多位數字嘛。嘻。根本就係運媒去紐卡素,輪子再發明。所以唔好以為鬼文一定好。不過我冇去譯(算係啦),你邊有得笑。仆街,仲要畀錢彭博先有得睇架。但你就唔使畀錢。係咪好爽先。記得畀個讚同share。未like我FB Page嘅快去,等我唔好再「坐擁六千粉絲」
不過唔止係呢類士哥嘅。彭博下面個表,有啲意思。除咗睇個士哥,負由十分忘恩嘅負2,去到湯大狀最愛嘅11外,仲有係,有幾多人,例子係乜,同埋你可以負擔到乜。大約講下
忘恩嘅負2去到你個個網友最鍾意嘅正2:少過一百蚊身家。十五億人。大鑊。基本上係一無所有,貧農。或者玩到負資產嘅人。記住,你已經幸福過十五億人。咩話?你負資產?咁只有祝你好運。我知唔啱聽,但咁嘅樓市都可以負資產,其實應該真係抵撚死的
3,一千蚊身家:十七億人。已經幾好架啦。不過香港啲小朋友一部電話都唔止。代表你唔使借錢都可以應急(有幾急?一千蚊美金做到幾多嘢)。典型嘅美國租樓者就係咁。係呀。
4,一萬蚊身家:十三億人。夠買架新車(唔係法拉利啦下),如果冇讀大學嘅美國中產家庭,就係咁上下。
5,十萬身家:得返四億人。代表你可以供樓了,嘻。典型嘅?就係美國左翼(膠?)民主黨明星(都新咗好耐)AOC,Alexandria Ocasio-Cortez。當然係佢出咗國會糧之後。之前仲係冇錢交租要做侍應的。
6,百萬身家millionaire:四千萬人。典型嘅? Boris Johnson。代表你可以在海邊買多間屋了。又, Boris Johnson都係講緊百萬美金身家咋?你睇香港人幾咁富貴。普通一個公務員退休金都有。做食環都住千幾萬樓 (http://bit.ly/2ogR3rO)
7,千萬身家:百幾萬人咋。基本上鍾意邊度買多層樓都得(但唔係山頂掛)。典型嘅,Ginni Rometty,IBM CEO.大企業CEO頂級打公仔大約就係呢啲級數。好勁嘅可以勉強突破,例如霍建寧
8,億億聲美金:五萬個。可以在大學有楝樓係你個名。典型?Rex Tillerson。美國國務卿,已被炒。ExxonMobil蓬佩奧上手。
9,十億美金,billionaire:幾千個,夠你有間大學用自己命名。但我懷疑,得咁少?例子係意大利政棍,AC米蘭前(?)班主,萬惡嘅貝魯斯科尼 Silvio Berlusconi。或者頭先上面講嘅,Jay-Z,趙薇
10,百億美金身家:百五個,全部係富豪榜上數得出嘅名人。例如真人Iron Man,Tesla 嘅Elon Musk嗰種級數。可以買隊波(唔係西甲愛斯賓奴之類啦大佬)返嚟自己玩。但其實達拉斯小牛班主(?)Mark Cuban都講緊係9分咋下。「不過幾十億美金身家」。
11,千億美金:就係兩條友,光頭佬同標基。可以上太空呀,或者消滅天花呀咁。能人所不能,神奇頂級超卓。
12?冇了。仍需努力。
留意,企業就有four comma club(http://bit.ly/2JISKXf),講過啦,one thousand 一撇,one million 兩撇,one billion三撇,one trillion市值(美金啦)咪四撇,four comma.有幾間企業先後上過去four comma club,一萬億市值。呢刻微軟同蘋果都仲係。但人,暫時冇人接近呢個four comma club。光頭佬同標基都「只係」千億美金,要多九倍十倍先去到,似乎冇乜希望,亦冇乜必要。
睇完,都冇乜得著。不過都係睇你點睇。知足嘅,咪對下睇。香港地,人工點都皮幾兩皮嘢(洗碗都有啦),儲到一萬蚊(美金啦頂)應該唔難。當然有人飯都冇得開執紙皮,但唔係你啲網友啦。咁已經至少好過地球上45億人,一大半。但我話你知你幸福過北韓啲小朋友,唔會令你對個社會少啲不滿架,對不?因為人只會同身邊嘅人比
唔知足嘅,望上去,仲有成百萬人有千萬美金身家。你估你幾時先追到?
咁你估都估到,個分佈梗係一個金字塔啦,窮人最多,底下三層幾十億人,11分嘅得兩個。而每個士哥之間,係爭10倍。對數嘛,叫咗你唔好睇咁多《那些年》。正如早排推特有人講 7.2級同8.1級地震「等級接近」,我就唯有話佢知 振幅爭差不多10倍!強度爭30倍!十年一遇同百年一萬嘅分別。學好數學好重要的說。
最後引返原文最後一句,唔譯,原汁原味,畀你嗒下
It’s a bit appalling that disparities in wealth have gotten so big that we need logarithms to describe them. But that’s the world we live in.
記住聽日準時起身返工啦
ibm power 11 在 口袋財經 Pocket Money Facebook 的最佳解答
【微軟和IBM合作壟斷1990年代的個人電腦市場】
當我們一打開電腦,最常見的就是Microsoft Windows的作業系統,我們幾乎每天都會使用到的Word、Power Point、Excel等軟體,這些都是來自於微軟的產品,他們默默的存在於我們的生活中,陪伴我們進行許多大大小小的任務,這一週,就帶大家來看看,我們生活中的好夥伴—微軟的創業故事吧。Let’s go!
網頁好讀版 ➡️ http://bit.ly/2urvVzA
👉 從創辦人比爾.蓋茲談起…
提到微軟這家公司,相信大家也會馬上聯想到他的創辦人比爾.蓋茲。
比爾.蓋茲從小就是位出色聰明的人,在他13歲時候他就開始了學習電腦程式設計,而且以個人自由和維護自己的智慧財產權而聞名。
1969 年,十幾歲的比爾.蓋茲和保羅.艾倫第一次相遇,他們都對開發程序和個人計算機的新興工業很感興趣,於是從此成為好友。後來比爾.蓋茲進入哈佛大學念書,在哈佛的時間裡,他和艾倫開發了一個程式語言版本,並為第一家微型電腦公司MITS的Altair牛郎星8800設計了BASIC編譯器。在大三那年他決定離開哈佛,找了好友艾倫一起創業,於是在 1975 年微軟誕生了。
■ 微軟的關鍵技術
剛剛有提到,比爾.蓋茲和艾倫共同開發替Altair開發了編譯器,在當時,Altair是第一台在商業上獲得成功的個人電腦,而比爾.蓋茲所設計的BASIC語言又是相當易用易學的程式語言,於是這個BASIC版本就是後來的Microsoft BASIC,也是MS-DOS(Microsoft Disk Operating System)作業系統的基礎,而MS-DOS作業系統可以說是微軟早期成功的關鍵。
👉 沒有IBM就沒有現在的微軟
IBM這家公司相信大家也不陌生,他是美國最老牌的計算科技公司,IBM的輝煌時代是上世紀六七十年代,當時IBM推出了一個劃時代的產品「大型機」,身為一個創新的產品,他不僅體積非常巨大,價格也不斐,因此只有大公司和國家政府才會購買,儘管如此,1975年IBM生產的計算機總數量仍是其他廠商生產數量總和的4倍,還因此接到許多政府的反壟斷起訴;此外,當年美國登月時,阿波羅11號使用的很多資料和數據庫全靠IBM支持。
說到這裡,大家可能還很疑惑這樣像IBM這樣龐大的公司會和小小的微軟有什麼關係,但是這個世界就是充滿許多意外的,在1980年的時候,IBM和微軟都迎來了自己歷史上的轉折點。
■ IBM主動與微軟合作
1980年左右的IBM正好在開發個人電腦,他們發現想要開發個人電腦,就需要一個操作系統,非常巧的是,它們正好找上了微軟,想要向它們購買操作系統。
微軟在當時只成立了四年多,雖然在BASIC編譯器的技術上表現不錯,但總體來說還是個不夠完備的企業,見到這樣一個厲害的公司找上門來,比爾.蓋茲和艾倫在驚訝之餘,也決定無論如何都要接下這筆生意。結果他們花了幾萬美元買了西雅圖電腦產品公司的86-DOS系統,自己完善改造之後,命名為MS-DOS系統,然後以便宜的5萬美元的價格授權給IBM使用。後來隨著IBM的個人電腦開始稱霸整個市場,MS-DOS系統以及後來的的Windows系統也隨之稱霸市場。
👉 微軟迎向全盛時期
從微軟的股價趨勢可以看到,和IBM合作之後,微軟在1990年代隨著IBM電腦壟斷市場快速成長。
在當時,整個個人電腦行業,基本可以是一個企業級市場,從收入來說,四分之三是企業用戶貢獻的。
對於企業來說,他們想要的是可以方便和其他企業交換資料形式的系統,而當時大多數企業都購買IBM的電腦、使用微軟的軟體,若有其他廠商想要推出不同的作業軟體進入這塊市場,其實是很有難度的。
然而,在這樣風光的一面的背後,也由於微軟壟斷了太長一段時間,讓他沒有查覺到產業細微的產業細微的變化,在2000年後因為個人手機的出現,微軟面臨龐大的危機,這個部分就等下週再向大家介紹囉!
ibm power 11 在 ibm-power-utilities/powerpc-utils: Suite of utilities for Linux on ... 的推薦與評價
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ibm power 11 在 Why IBM POWER Systems - YouTube 的推薦與評價
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