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chong, Approaching Literature: The Hi

作者:   来源:  热度:5  时间:2021-04-07






This lecture will be co-hosted by the Literature Team of the School of Foreign Studies and the SUFE Centre for the Study of Text and Print Culture 讲

This lecture will be co-hosted by the Literature Team of the School of Foreign Studies and the SUFE Centre for the Study of Text and Print Culture

 讲座摘要/ Abstract 

In this talk, the speaker starts with a review of the “traditional” way of looking at literature, a basically historicist one that focuses more or less on the “historical contexts” of various kinds. Then he contrasts historicist criticisms with “the presentist” way of approaching literature, elaborating on the nature of presentist criticism, the challenge it poses to historicist criticism, its significance for contemporary literary education and study, including some problems of which we have to be aware. Throughout the talk, the speaker will cite cases from contemporary Shakespeare criticism to support his observations and argument.

 主讲人简介/ CV 

Zhang Chong is currently Lecturing Professor of English at Xiamen University. He has taught at Nanjing University and Fudan University. Professor Zhang earned his Ph.D. from Nanjing University in 1989, with Dramatic Irony in Shakespeare and Yuan Drama; post-doc (1991) at Harvard on Renaissance and Shakespeare; Fulbright research scholar (2002) at UPenn & UC Berkeley. He has published extensively on Shakespeare and British-American literature. Major works, in Chinese, include Topics on Shakespeare (2004), Shakespeare in Visual Age (2010), Exploring Shakespeare (2012), From Margin to Center: A History of Native American Literature (2014), and The Dramatic Shakespeare (2019).

More information to get

    本资源整理了自然语言处理相关深度生成模型资源,会议和相关的一些前沿论文,分享给需要的朋友。

    本资源整理自:https://github.com/FranxYao/Deep-Generative-Models-for-Natural-Language-Processing

    当谈到深层生成模型时,通常指三个模型族:变分自动编码器(VAEs)、生成对抗网络(GANs)和归一化流(Normalizing Flows)。

    在这三大模型家族中,我们将更多地关注VAE相关的模型,因为它们更有效。GAN是否真的有效仍然是一个悬而未决的问题。GANs的有效性更像是判别器(discriminator)的正则化,而不是“生成”部分。

    

    自然语言处理的VAE模型涉及许多离散结构。对这些结构的推断既复杂又聪明。本资源整理了相关的一些资源、论文和会议。

    

资源部分

    图形模型基础

    在我们旅程开始之前,DGMs的基础是建立在概率图形模型上的。所以我们首先要了解这些模型。

    推荐三门不错的课程:

    Blei's Foundation of Graphical Models course, STAT 6701 at Columbia 

    Xing's Probabilistic Graphical Models, 10-708 at CMU

    Collins' Natural Language Processing, COMS 4995 at Columbia

    两本不错的书:

    Pattern Recognition and Machine Learning. Christopher M. Bishop. 2006

    Machine Learning: A Probabilistic Perspective. Kevin P. Murphy. 2012

深度生成模型

    分享一些DGMS相关不错的资源:

    Wilker Aziz's DGM Landscape 

    A Tutorial on Deep Latent Variable Models of Natural Language (link), EMNLP 18

    ?Yoon Kim, Sam Wiseman and Alexander M. Rush, Havard

    Deep Generative Models for Natural Language Processing, Ph.D. Thesis 17

  ?Yishu Miao, Oxford

    Stanford CS 236, Deep Generative Models (link)

    NYU Deep Generative Models

    U Toronto CS 2541 Differentiable Inference and Generative Models, CS 2547 Learning Discrete Latent Structures.

    相关知识点思维导图

    不一定全面正确,待补充。

    

NLP相关

    主要关注两个主题:生成和结构推理

    生成部分

    Generating Sentences from a Continuous Space, CoNLL 15

    ?Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Jozefowicz, Samy Bengio

    

    Spherical Latent Spaces for Stable Variational Autoencoders, EMNLP 18

    ?Jiacheng Xu and Greg Durrett, UT Austin

   

    Semi-amortized variational autoencoders, ICML 18

    ?Yoon Kim, Sam Wiseman, Andrew C. Miller, David Sontag, Alexander M. Rush, Havard

    

    Lagging Inference Networks and Posterior Collapse in Variational Autoencoders, ICLR 19

    ?Junxian He, Daniel Spokoyny, Graham Neubig, Taylor Berg-Kirkpatrick

    Avoiding Latent Variable Collapse with Generative Skip Models, AISTATS 19

    ?Adji B. Dieng, Yoon Kim, Alexander M. Rush, David M. Blei

    结构推理

    这部分整理结构推理相关的工作,涉及自然语言处理分块,标记和解析三个部分任务。

    An introduction to Conditional Random Fields. Charles Sutton and Andrew McCallum. 2012

    ?Linear-chain CRFs. Modeling, inference and parameter estimation

    Inside-Outside and Forward-Backward Algorithms Are Just Backprop. Jason Eisner. 2016.

    Differentiable Dynamic Programming for Structured Prediction and Attention. Arthur Mensch and Mathieu Blondel. ICML 2018

    ?To differentiate the max operator in dynamic programming.

    Structured Attention Networks. ICLR 2017

    ?Yoon Kim, Carl Denton, Luong Hoang, Alexander M. Rush

    Recurrent Neural Network Grammars. NAACL 16

    ?Chris Dyer, Adhiguna Kuncoro, Miguel Ballesteros, and Noah Smith.

    Unsupervised Recurrent Neural Network Grammars, NAACL 19

    ?Yoon Kin, Alexander Rush, Lei Yu, Adhiguna Kuncoro, Chris Dyer, and Gabor Melis

    Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder, ICLR 19

    ?Caio Corro, Ivan Titov, Edinburgh

离散Reparamterization的一些技巧

    Categorical Reparameterization with Gumbel-Softmax. ICLR 2017

    ?Eric Jang, Shixiang Gu, Ben Poole

    The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. ICLR 2017

    ?Chris J. Maddison, Andriy Mnih, and Yee Whye Teh

    Reparameterizable Subset Sampling via Continuous Relaxations. IJCAI 2019

    ?Sang Michael Xie and Stefano Ermon

    Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement. ICML 19

    ?Wouter Kool, Herke van Hoof, Max Welling

    

机器学习相关

    机器学习相关部分,首先从VAE开始。

    VAEs

    Auto-Encoding Variational Bayes, Arxiv 13

    ?Diederik P. Kingma, Max Welling

    Variational Inference: A Review for Statisticians, Arxiv 18

    ?David M. Blei, Alp Kucukelbir, Jon D. McAuliffe

   

    Stochastic Backpropagation through Mixture Density Distributions, Arxiv 16

    ?Alex Graves

    Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms. AISTATS 2017

    ?Christian A. Naesseth, Francisco J. R. Ruiz, Scott W. Linderman, David M. Blei

    Reparameterizing the Birkhoff Polytope for Variational Permutation Inference. AISTATS 2018

    ?Scott W. Linderman, Gonzalo E. Mena, Hal Cooper, Liam Paninski, John P. Cunningham.

    Implicit Reparameterization Gradients. NeurIPS 2018.

    ?Michael Figurnov, Shakir Mohamed, and Andriy Mnih

    GANs

    Generative Adversarial Networks, NIPS 14

    ?Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio

    Towards principled methods for training generative adversarial networks, ICLR 2017

    ?Martin Arjovsky and Leon Bottou

 

    Wasserstein GAN

    ?Martin Arjovsky, Soumith Chintala, Léon Bottou

Normalizing Flows相关

    Variational Inference with Normalizing Flows, ICML 15

    ?Danilo Jimenez Rezende, Shakir Mohamed

    Improved Variational Inference with Inverse Autoregressive Flow

    ?Diederik P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, Max Welling

    Learning About Language with Normalizing Flows

    ?Graham Neubig, CMU, slides

    Latent Normalizing Flows for Discrete Sequences. ICML 2019.

    ?Zachary M. Ziegler and Alexander M. Rush

    

Reflections and Critics

    需要补充更多论文

    Do Deep Generative Models Know What They Don't Know? ICLR 2019

    ?Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan

    

更多一些应用

    篇章和多样化

    Paraphrase Generation with Latent Bag of Words. NeurIPS 2019.

    ?Yao Fu, Yansong Feng, and John P. Cunningham

    A Deep Generative Framework for Paraphrase Generation, AAAI 18

    ?Ankush Gupta, Arvind Agarwal, Prawaan Singh, Piyush Rai

    Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization, NIPS 18

    ?Yizhe Zhang, Michel Galley, Jianfeng Gao, Zhe Gan, Xiujun Li, Chris Brockett, Bill Dolan

    主题相关语言生成

    Discovering Discrete Latent Topics with Neural Variational Inference, ICML 17

    ?Yishu Miao, Edward Grefenstette, Phil Blunsom. Oxford

    Topic-Guided Variational Autoencoders for Text Generation, NAACL 19

    ?Wenlin Wang, Zhe Gan, Hongteng Xu, Ruiyi Zhang, Guoyin Wang, Dinghan Shen, Changyou Chen, Lawrence Carin. Duke & MS & Infinia & U Buffalo

    TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency, ICLR 17

    ?Adji B. Dieng, Chong Wang, Jianfeng Gao, John William Paisley

    Topic Compositional Neural Language Model, AISTATS 18

    ?Wenlin Wang, Zhe Gan, Wenqi Wang, Dinghan Shen, Jiaji Huang, Wei Ping, Sanjeev Satheesh, Lawrence Carin

    Topic Aware Neural Response Generation, AAAI 17

    ?Chen Xing, Wei Wu, Yu Wu, Jie Liu, Yalou Huang, Ming Zhou, Wei-Ying Ma

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DeepLearning_NLP

深度学习与NLP

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泥巴团队出品

译者:Pamp

审校:小泥巴

简介

睡眠是保持健康和维护身体机能的基础,研究表明,现有超过50%的人会偶尔失眠,在此之外有10%的人遭受着慢性失眠的困扰(27)。尽管睡眠相关的问题在人群中十分普遍,但年龄似乎是导致老年人睡眠质量下降的显著因素(5,24)。后果是大量美国人通过药物来缓解睡眠问题(6)。

褪黑素已经是成年人的常见的营养补品,预计到2025年其用量将会大幅增长(7,21)。褪黑素俗称“助眠药”,最近经常出现在各种联名产品中,并在市场上作为“天然”助眠剂被大肆推广。本文是探讨补充褪黑素对成年人睡眠的功效和安全性。

了解褪黑素和睡眠循环周期

每天睡眠循环周期是非常复杂的生理互动过程,由昼夜节律系统控制。昼夜节律系统的“节奏”与每天的睡眠周期并行,在我们醒来时调动能量,而在睡眠周期内发出休息和恢复的信号。

褪黑素是松果体分泌的一种激素,是位于下丘脑“生物钟”的主要同步器之一,更为学术的名称为“视交叉上核(SCN)”(31)。褪黑素在昼夜节律系统中有着举足轻重的作用,它可以“调节”人体生理活动,使其与24小时昼夜周期保持同步(8)。亮光会抑制松果体活动和褪黑素的产生;因此,当太阳下山时,对松果体的抑制作用就消除了,提示褪黑素合成的开始(8)。血浆中褪黑素的含量大约在凌晨3:00 – 4:00达到峰值;此后,含量下降,在白天几乎无法检测到褪黑素的含量(8)。

与大多数激素一样,年龄越大,产生的激素越少,但是褪黑素尤其如此。循环褪黑素水平在生命早期(约3-6岁)达到高峰。此后,血浆中的褪黑素下降,直到在某些老年人的血液中可能到无法检测的程度(25)。随年龄增长的褪黑素含量下降,也是随年龄增长而出现的睡眠问题的影响因素(24)。

功效与剂量

自从1959年发现了褪黑素以来,研究界一直对其存在着广泛兴趣。公众对褪黑素的认识几乎都植根于睡眠研究,确实如此,许多使用褪黑素的研究都提到了睡眠质量的改善,包括:睡眠时间、睡眠效率和潜伏期(即入睡所需的时间)(4,24)。然而,考虑到褪黑素作为营养补品的流行性,需要注意的是,文献中仅提到了褪黑素对总体睡眠质量存在适度改善。虽然将外用的褪黑素与大众中表现出多种的个体睡眠障碍关联性超出了本文讨论的范围,尽管如此,本文参考的文献表明仍有大量证据支持褪黑素。以下示例说明了文献中的总体趋势。

有大量证据支持外服褪黑素可以治疗成人睡眠障碍(2,19,31)。一项2005年的整合分析针对失眠和睡眠良好的人发现,服用0.3 – 10 mg褪黑素剂量可提高睡眠质量,包括改善睡眠时间(+12.8分钟),效率(+ 2.2%)和潜伏期(-4.0分钟))(4)。最近几篇综述证明对于成年人的睡眠障碍褪黑素有相似的效果(2,19,31)。

此外,其他综述和整合分析文章也发现,对于那些重症监护室的成人,倒时差的国际旅行者,倒班工人以及其他健康的成年人睡眠指标均得到了改善(9、16、20、32)。因此,外服褪黑素似乎确实能普遍提高的成年人的睡眠参数指标。

但是,在睡眠研究中看到的一致但相对适度的改善提出了“为什么?”的问题。有很多可能性,但是要考虑的是褪黑素研究中通常给予的各种剂量。研究人员通常使用低至0.3 mg或高至10 mg的剂量(4,9,24)。目前尚未达成关于成年人服用褪黑素最佳剂量的共识。然而,2014年的整合分析为了解服用剂量提供了出发点(30)。尽管作者无法在文中确定最佳剂量,但他们建议的“可能最低剂量”为“ 0.3 mg至多1 – 2 mg”(30)。

支持该结论的另一项整合分析发现,尽管外服褪黑素确实改善了成年人和患有睡眠障碍儿童的睡眠参数指标,但目前不存在剂量依赖性,意味着用量更多并不代表效果更好(11)。近期皮尔斯等人指出最小有效剂量可能在1-6毫克之间,但同时强调了指出他们所参照的研究缺乏一致性,并指出需要进行更有针对性的研究(24)。最新的研究证据表明最佳剂量可能为0.3 – 2 mg(30)。

安全性

研究界普遍认为褪黑素是安全的。但是,仍存在一些需要关注的问题,包括褪黑素的用量纯度影响力,出现副作用,药物交互作用以及引发或加剧自身免疫疾病的可能性。在许多国家/地区,禁止在非处方店出售褪黑素,只能通过医疗专业人员的处方获得褪黑素(14)。然而在美国,褪黑素被认为是一种营养补品,在大多数杂货店和营养品商店中都可以买到。结果,就像大多数的国家和地区出售的其他营养品一样,褪黑素几乎没有受到任何监管,而药品的上市则需要通过严格的测试和审查。

营养品掺假,受污染和贴错标签都是普遍存在的问题(10、23、28)。例如,一项关于现成草药产品的研究发现,三分之一的产品含有不在成分清单中的污染物或填料,其中有些是已知的毒性物质(23)。另一项关于营养品的研究发现可能有某些真菌和细菌污染具有潜在毒性(28)。褪黑素当然也逃不开这个问题。事实上,最近对褪黑素的一项研究发现了其功效问题,其中含量值的范围从标记数量的-83%到+ 478%不等(10)。尽管这个问题并非只存在于褪黑素中,但依然值得关注。

外用褪黑素最常见的副作用是头痛和恶心。然而,最近的一项系统性调查研究了外服褪黑素引起的睡眠障碍相关的不良事件(AE),结果表明褪黑素和安慰剂之间的副作用发生频率没有差异(3)。在回顾了37项随机对照试验后,研究人员报告“没有证据表明存在严重的不良事件”(3)。

最近另一项系统评价还得出结论,尽管通常补充褪黑素是安全的,但可能存在一些例外(12)。该系统评价使用了褪黑素进行一系列研究,其中许多与睡眠无关。其中,一些研究方法要求在白天使用褪黑素。该系统评价涵盖了50项研究和3,803名参与者,记录了精神活动和神经认知障碍等其他副作用

作者得出的结论是,尽管褪黑素副作用是短暂的,但这种结果可能也与“日间给药相关”,通过避免日间服用褪黑素可以“轻松地避免”(12)。褪黑素似乎是安全的,因为它副作用发生的概率与安慰剂相似。对褪黑素研究最多的人之一,Russel Reiter博士,将褪黑素描述为具有“异常高的安全性”来表明其安全性(26)。精神运动和神经认知障碍这类严重的副作用似乎与褪黑素的服用时间有关,因此,应该模仿褪黑素的正常昼夜节律性升高(即在就寝时间之前)进行服用补充。(12)。

已知许多药物的交互作用会影响血浆中褪黑素水平(1)。其中非处方药和处方药(例如咖啡因和口服避孕药)都可以与褪黑素发生交互作用,它们可能使得血浆中褪黑素浓度增加(15,17)。还有证据显示抽烟能够提高血浆中褪黑素含量,但仅与外服褪黑素共同使用时才有效(29)。

最后要注意的是外服褪黑素可能与免疫系统发生交互作用,引发或加剧自身免疫反应(13,18)。尽管只是一种推测,但已有少数案例表明了这种可能性。1997年的一份病例报告描述了一位健康的成年女性,在每晚开始服用三毫克的褪黑素后约四个星期,似乎患上了肝炎(18)。作者推测褪黑素可能触发了自身免疫性肝炎的发展(18)。最近,另一例报道称,一名年轻的成年女性在几周内摄入了5毫克褪黑素,疑似自身免疫性肝炎并出现了“生化发作”或恶化(13)。鉴于补充褪黑素的流行,尽管文献仅支持少数说法,褪黑素影响自身免疫反应的可能性必须与其补充进行充分的权衡。

结论

总体来看,对健康成年人来讲,褪黑素在提高常见睡眠指标方面(例如睡眠时长、效率和潜伏期)似乎是有效的。尽管对褪黑素的最佳用量还不确定,但当前的指导用量通常与可用非处方剂量相吻合。研究表明,褪黑素似乎安全性较高,但对于可能患有自身免疫性疾病的人仍需谨慎使用。

5??嘻SFy7XaXADOD嘻 https://m.tb.cn/h.4Nsih1U?sm=640e31  NSCA-CPT备考套餐|备考教材|备考重點|备考题|赠送代报名服务

Source:CARMINE G. MELATONIN SUPPLEMENTATION IN ADULTS— IS IT SAFE AND EFFECTIVE? NSCA PTQ (8.1). Retrieved from nsca.com

参考文献

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