Institute of Statistical Science Academia Sinica [Seminar Feed] http://www.stat.sinica.edu.tw Statistics, Stat, Edu en-us Sat, 29 Apr 2017 09:46:31 +0800 http://www.stat.sinica.edu.tw/statnewsite/seminar/rss/ PHP admin@stat.sinica.edu.tw admin@stat.sinica.edu.tw 區塊鏈與金融科技 http://www.stat.sinica.edu.tw/statnewsite/seminar/show/2199/

Abstract

    傳統金融產業正面臨互聯網金融及金融科技(FinTech)的衝擊,而其中又以數位金融相關的區塊鏈(Blockchain)技術為新興顯學,吸引全球政府機關與金融業巨頭相繼投入研究。目前區塊鏈技術在西方國家研發可謂如火如荼,光是投資到新創公司的資金就超過 10 億美金,是 1990 年代中期對網際網路的新創投資的 4 倍金額。區塊鏈重金投資不只是在區塊鏈基礎建設而已,也在上層應用,諸如金融業的結算系統,數字憑證如股票的發行,及日常生活中的應用比比皆是。在這個演講,我會以五個面向,討論區塊鏈在金融科技中扮演的角色。

 

一、區塊鏈與金融科技變革

二、區塊鏈的技術創新

三、區塊鏈與比特幣

四、區塊鏈技術應用範疇

五、區塊鏈未來發展趨勢

 

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Wed, 26 Apr 2017 09:37:16 +0800 http://www.stat.sinica.edu.tw/statnewsite/seminar/show/2199/
A Bayesian Procedure for Copy Number Variations Detection from DNA-Sequencing http://www.stat.sinica.edu.tw/statnewsite/seminar/show/2197/

Abstract

  Copy number variations (CNVs) are genomic structural mutations with abnormal gene fragment copies. CNV detection algorithms for next generation sequencing (NGS) data could be classified by genome targets including whole genome sequencing (WGS) and targeted exome sequencing (TES) with different suppositions. Some tools have been published to predict CNVs by NGS data, but most of them just apply to a specific data type. Many whole genome tools assume that the continuity of search space and reads uniform coverage across the genome. These assumptions break down in the exome capture because of discontinuous segments and exome specific functional biases. We specify the large unconsidered genomic fragments as gaps to preserve the truly location information. The gap labels will get great help for expressing the information from the discontinuous regions and it will adapt to detect CNV for both WGS and TES with the following Bayesian procedure. We built a Bayesian hierarchical model and an efficient reversible jump Markov chain Monte Carlo inference algorithm for analyzing NGS read depths. The performance of the Bayesian procedure was evaluated and compared with competing approaches using both simulations and real data.

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Tue, 25 Apr 2017 14:47:35 +0800 http://www.stat.sinica.edu.tw/statnewsite/seminar/show/2197/
The Carbon Club: Measuring and mapping carbon dioxide from remote sensing satellite data http://www.stat.sinica.edu.tw/statnewsite/seminar/show/2198/

Abstract

  This talk is about environmental informatics for global remote sensing of atmospheric carbon dioxide, a leading greenhouse gas. An important compartment of the carbon cycle is atmospheric carbon dioxide (CO2), where it (and other gases) contribute to climate change through a greenhouse effect. There are CO2 observational programs where measurements are made at a small number of ground-based locations at somewhat regular time intervals. In contrast, satellite-based programs are spatially global but do not have the temporal richness. A recent satellite launched to measure CO2 is NASA's Orbiting Carbon Observatory-2 (OCO-2), whose principal objective is to retrieve a geographical distribution of CO2 sources and sinks. OCO-2’s measurement of column-averaged mole fraction, XCO2, is designed to achieve this, through a data-assimilation procedure that is statistical at its basis. Consequently, uncertainty quantification is key, starting with retrieval of the atmospheric state from each individual sounding, to mapping the state with spatial-statistical models, to flux-inversion using flux-process models.

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Tue, 25 Apr 2017 14:50:41 +0800 http://www.stat.sinica.edu.tw/statnewsite/seminar/show/2198/