47 Matching Annotations
  1. Jul 2020
  2. Jun 2020
    1. Kempfert, K., Martinez, K., Siraj, A., Conrad, J., Fairchild, G., Ziemann, A., Parikh, N., Osthus, D., Generous, N., Del Valle, S., & Manore, C. (2020). Time Series Methods and Ensemble Models to Nowcast Dengue at the State Level in Brazil. ArXiv:2006.02483 [q-Bio, Stat]. http://arxiv.org/abs/2006.02483

  3. May 2020
  4. Nov 2019
    1. This booklet itells you how to use the R statistical software to carry out some simple analyses that are common in analysing time series data.

      what is time series?

  5. Sep 2019
    1. Variation in Rates of Fatal Police Shootings across US States:the Role of Firearm Availability

      Hello! This article is about the relationship between firearm prevalence (the IV) and the rates of fatal police shootings (the DV). The authors hypothesize that the greater the prevalence of firearms, the grater the rates of fatal police shooting.

      This article follows the classical structure of social science research -- abstract, introduction, literature review/theory, research design, findings, and conclusions.

  6. Jul 2019
    1. It seems to me that in considering the purpose of a subset of the entire series of transactions, Justice Paris had in mind the correct test. As this Court said in MacKay v. Canada, 2008 FCA 105 (CanLII) at paragraph 25: The existence of a bona fide non-tax purpose for a series of transactions does not exclude the possibility that the primary purpose of one or more transactions within the series is to obtain a tax benefit.

      Existence of a bona fide non-tax purpose for a series of transactions does not exclude possibility that the primary purpose of one of the transactions in the series is to obtain a tax benefit.

  7. Jun 2019
    1. MelNet: A Generative Model for Audio in the Frequency Domain

      本文的主要贡献如下:

      • 提出了 MelNet。一个语谱图的生成模型,它结合了细粒度的自回归模型和多尺度生成过程,能够同时捕获局部和全局的结构。

      • 展示了 MelNet 在长程依赖性上卓越的性能。

      • 展示了 MelNet 在多种音频生成任务上优秀的能力:无条件语音生成任务、音乐生成任务、文字转语音合成任务。而且在这些任务上,MelNet 都是端到端的实现。

  8. Feb 2019
    1. 0 \0$aH

      The value in the original 490 is a 4XX in the provided series statement; it is incorrect, but that's not really Marcive's fault

  9. Jan 2019
    1. MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks

      这 paper 挺神的,用 GAN 做时序数据异常检测。主要神在 G 和 D 都仅用 LSTM-RNN 来构造的!不仅因此值得我关注,更因为该模型可以为自己思考“非模板引力波探测”带来启发!

  10. Dec 2018
    1. Deep Neural Networks for Automatic Classification of Anesthetic-Induced Unconsciousness

      spatio-temporo-spectral features.

    2. Using Convolutional Neural Networks to Classify Audio Signal in Noisy Sound Scenes

      先辨别信号位置,再过滤出信号,这和 LIGO 找event波形的套路很像~ ;又看到 RNN与CNN 结合起来的应用~

    3. Sound Event Detection Using Spatial Features and Convolutional Recurrent Neural Network.

      输入数据是多通道音频信号,网络是结合了CNN 和 LSTM。

  11. Nov 2018
    1. Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis

      初步扫了一眼,感觉这篇文章应该可以给我一些 idea,内含我感兴趣(看得懂)的方法/机制,另外综述的参考文献对我来时也应该很有帮助。

      本文是北京航空航天大学发表于KDD 2018的文章,作者提出了将小波变换和深度神经网络进行完美结合,克服了融合的损失,对时间序列数据的分析起到了很好的启发性研究。

      Summary

    2. Foundations of Sequence-to-Sequence Modeling for Time Series

      利用序列到序列模型来做时序数据预测的理论研究 paper~

    3. Interpretable Convolutional Filters with SincNet

      一篇值得我高度关注的 paper,来自 AI 三巨头之一 Yoshua Bengio!其背后的核心是将数字信号处理DSP中卷积的激励函数(滤波器)进行了重新设计,不仅会保留了卷积的特性(线性性+时间平移不变性)还在滤波器上添加待学习参数来学习合适的高低频截断位置。

    4. A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data

      虽然数据特点为多变量时序信号+噪声少还频率低,不过作者提出的 Multi-Scale Convolutional Recurrent Encoder-Decoder (MSCRED) 网络很有趣,可见基于注意力机制的 ConvLSTM 在模式识别上是大为有用的!

      此外paper里的数学表述和实验讨论也很值得参考学习,算是非常标准的基于新model的 paper 样板~

    5. Deep Convolutional Neural Networks On Multichannel Time Series For Human Activity Recognition.

      这个文章的研究对象是 HAR 问题。不过这里的多通道时序信号是排成 长 x 宽维度,模拟图片数据来解决的,并不是图像的多通道。不过最后的效果貌似很不错,远胜过 SVM,KNN,MV,以及 DBN(深度置信网络)。

      文章对网络结构的英文描述还是值得借鉴的~

    6. Towards a universal neural network encoder for time series

      数据任务是“时序序列的分类”,这是我感兴趣的问题。Universal 代表不需要额外设置和训练,从某数据集训练后,就可以拿到另一个新训练集类型去搞事情~ 另一个特点是用了 encoder 得到了低维不变的表示。

    7. Time Series Classification Using Multi-Channels Deep Convolutional Neural Networks

      这是一个三通道并列输入的时序信号模型。效果貌似不错,还针对不同模型算法的预测时间在不同训练规模数据上的模型表现做了对比,

      另外文章的模型图示也很有启发性。

    8. Stochastic Adaptive Neural Architecture Search for Keyword Spotting

      一篇讲 identifying keywords in a real-time audio stream 的 paper。这和引力波探测中的数据处理很接近哦~!此文提出 end-end 的“随机自适应神经构架搜寻” (SANAS) 实现高效准确的训练效果。这显然对 real-time 特点的类型数据应用带来启发。FYI:人家源码还开放了。。。

    9. WaveGlow: A Flow-based Generative Network for Speech Synthesis

      一篇来自 NVIDIA 的小文。提出的实时生成网络 WaveGlow 结合了 Glow 和 WaveNet 的特点,实现了更快速高效准确的语音合成。

    10. Deep learning for time series classification: a review

      这个文与我的课题貌似相当相关!

      准备好好写一个 Paper Summary 为好~

    11. Deep Learning for Time-Series Analysis

      一个比较简洁的关于时序序列的 DL 应用的综述文章。

      其中也有谈论时序序列的分类问题。

    12. Whispered-to-voiced Alaryngeal Speech Conversion with Generative Adversarial Networks

      这是一篇用 GAN 来做 Voiced Speech Restoration 的,并且使用了作者自己提出的 speech enhancement using GANs (SEGAN) 。

      于我而言,亮点有二:

      1. 数据是时序语音
      2. 利用 GAN 对语音的增强效果似乎对降噪有些启发
      3. 网络结构图画的蛮好看的:

    13. Model Selection Techniques -- An Overview

      一篇关于模型选择的综述文章。涉及信号处理,图像处理等等多方面数据信息的处理。发表在信号处理的期刊杂志上。

      文中关于模型选择的大概念方向,和数学表示,是值得好好阅读的。

    14. End-to-end music source separation: is it possible in the waveform domain?

      讨论的是 Music source separation 问题。

      作者认为前人基于spectrogram的输入数据都忽略了相位信息,所以提出了直接waveform-based的模型得到了明显更好的效果。

    15. Unifying Probabilistic Models for Time-Frequency Analysis

      文章涉及好些自己还没搞懂的概念和方法:

      • 时频分析
      • Gaussian processes
      • ...

      文中的 review 给的是很不错的~

  12. Oct 2018
  13. Sep 2018
    1. showRSS tracks most TV shows, so you don't have to. RSS integration lets you automate your set up.
  14. Nov 2017
    1. Trending Videos Top YouTube Channels Marketing Guides Guest Blogging Video Marketing YouTube Marketing Facebook Video Marketing Tips & Tricks YouTube Analysis Infographics Video Marketing webinar Video Trends Branded Content YouTube Series Online Video News YouTuber Interviews Platforms Facebook Vine Instagram Netflix YouTube Industry Insights Entertainment Beauty & Cosmetics Lifestyle MCN Resources More Vidooly Product Updates Video Technology Social Video Online Video Streaming Video Production Weekly Roundup Video Marketing YouTube Marketing Facebook Video Marketing Tips & Tricks YouTube Analysis Infographics Video Marketing webinar Video Trends Branded Content YouTube Series Online Video News YouTuber Interviews Platforms Facebook Vine Instagram Netflix YouTube Industry Insights Entertainment Beauty & Cosmetics Lifestyle MCN Resources More Vidooly Product Updates Video Technology Social Video Online Video Streaming Video Production Weekly Roundup _pe.subscribe(); HomeVideo TrendsYouTube Series Most watched Indian Web series on YouTube. Akshay ChandraNovember 22, 2017YouTube Series If you look back 10 years from now, almost every engineering or management student was thorough with western telly soaps like “How I Met Your Mother, FRIENDS, The Walking Dead, Mad Men and Breaking Bad.” Ask them about Indian television soaps and you will be judged on your watching habits and T.V series you like. It was and it is obvious that millions of young Indian millennials don’t have anything to watch on Indian television except watching mother-in-laws who loves overthinking and daughters-in-laws who are classified as typical Indian women from the 19th century. With this age-old scripted clone shows of one another, who needs television right now? If these shows still generate viewership from adults between the age of 30 to 65, what are Indian millennials are watching now? The rise of Netflix in India paved the way for various competitors trying to create content that is inclined towards young adults and millennials of India. Amazon Prime came up with some fresh web series like Inside Edge featuring established actors like Vivek Oberoi. But the one platform that stood out of the competition luring young viewers in India was YouTube. This year, YouTube reported a massive surge in India with 180 million monthly active users on mobile devices alone where handsets account for 80% of total watch time. Since 2011, YouTube has worked with regional studios to help them get a wider distribution. In 2014, content creators from Mumbai including TVF and AIB were making waves on YouTube and gaining traction. We can say that TVF is the pioneer of Indian Web series on YouTube. A large portion of this content was a mix of Hindi and English, which proved to be popular in the metropolitan cities including Mumbai, New Delhi, and Bengaluru. Thus, in order to check the effectivity of these Indian YouTube web series, Vidooly took the responsibility to check the total viewership of every YouTube web series ever uploaded on YouTube platform. After documenting more than 130 + Indian web series playlists on YouTube, here are the most watched Indian web series on YouTube aligned as per their viewership. Channel TitleWeb series Title Viewership BeingIndianMumbai On41,070,790 Shitty Ideas TrendingMen The Real Victims36,864,336 TheViralFeverVideosTVF's Permanent Roommates31,322,645 YFilmsSex Chat with Pappu & Papa30,310,109 VB On The WebTwisted28,082,221 YFilmsBang Baaja Baaraat25,112,907 YFilmsLadies Room24,832,571 VB On The WebMaaya22,193,313 East India ComedyEIC Vs Bollywood21,677,800 TheViralFeverVideosTVF Tripling19,297,772 Purani Dili TalkiesPDT Bewadey18,853,230 Dice MediaLittle Things18,556,856 The TimelinersThe Aam Aadmi Family18,275,268 Purani Dili TalkiesPDT GyANDUpanti18,102,590 YFilmsMan's World17,877,347 NOTE: You have to understand the difference between a sketch and a webseries in India. A web series is a series of scripted or non-scripted videos, generally in episodic form, released on the Internet. Most of the playlist we jotted down have “Webseries” in their title. A sketch, on the other hand, is a short form of video without any continuation. For example, YouTube channels like Filter Copy, BB Ki Vines, and Ashish Chanchlani Vines are the pioneers of the short form of comedy sketches. Also, we avoided the content that was posted on TV first followed by their YouTube channel. Bindas YouTube channel possess. The ranking is based on overall viewership and not average views per episode. HERE ARE THE INSIGHTS The most viewed web series playlist – Mumbai On | YouTube channel – BeingIndian YouTube channel with most web series – TheViralFeverVideos | 12 web series Most disliked Indian YouTube web series – What If | YouTube channel – SnG Comedy Most liked and engaged Indian YouTube webseries – EIC Outrage | YouTube channel – East India Comedy Most watched Indian Web series on YouTube. 2017-11-22T17:32:09+00:00 2017-11-27T17:57:10+00:00 Akshay Chandra People Also ReadBranded content on Webseries – YouTube IndiaIndian web shows in 2016 – Beginning of a new era of entertainment in the countryAnalysis of All TVF Original Web SeriesSacred Games- The First Netflix Original Series from India13 Indian Web Series that you can Binge Watch for Free on YouTubeSex Chat with Pappu & Papa Finale Review + Web Series AnalysisYouTube Red originals – Ghostmates (2016) with T-pain and SmoshWill online video eventually kill TV in India? Comments comments indian web series on youtubeAkshay ChandraContent SpecialistBeing an artist, movie buff and a media enthusiast, content writing is my career train. I am a proud alumni of Symbiosis Institute of Media Communication (Pune) and currently working for Vidooly. All about online video(function() { if (!window.mc4wp) { window.mc4wp = { listeners: [], forms : { on: function (event, callback) { window.mc4wp.listeners.push({ event : event, callback: callback }); } } } } })(); Name Email address: Leave this field empty if you're human: Follow us (function() { var cx = '000567304743394344014:ijucnstgltm'; var gcse = document.createElement('script'); gcse.type = 'text/javascript'; gcse.async = true; gcse.src = 'https://cse.google.com/cse.js?cx=' + cx; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(gcse, s); })();  powered by Learn More Beauty & Makeup (3) Blogs em português (1) Blogs en Español (66) Branded Content (9) Entertainment (54) Facebook (21) Facebook Video Marketing (8) Industry Insights (55) Infographics (12) Instagram (5) Lifestyle (2) MCN Resources (9) Netflix (3) Online Video News (68) Online Video Streaming (3) Periscope (1) Platforms (22) Social Video (3) Tips & Tricks (89) Video Marketing (122) Video Marketing webinar (2) Video SEO (8) Video Trends (30) Vidooly Product Updates (23) Vine (2) Weekly Roundup (15) YouTube (10) YouTube Analysis (19) YouTube Marketing (27) YouTube Series (11) YouTube Success Stories (7) YouTuber Interviews (10) Insights The Late Show with Stephen Colbert wins the 100 days of Trump – Full Report May 3, 2017 How Airline safety has evolved through Video Marketing November 17, 2017 Vidooly Predicts Ittefaq’s box office collection November 2, 2017 VIDOOLY YOUTUBE MONTHLY REPORT – 2017 September 21, 2017 YOUTUBE INDUSTRY ANALYSIS – DUBAI September 7, 2017 The Late Show with Stephen Colbert wins the 100 days of Trump – Full Report May 3, 2017 How Airline safety has evolved through Video Marketing November 17, 2017 Recent Top 10 Best Netflix Original Shows To Binge-Watch In 2017 Branded content on Webseries – YouTube India Most watched Indian Web series on YouTube. 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      If you look back 10 years from now, almost every engineering or management student was thorough with western telly soaps like “How I Met Your Mother, FRIENDS, The Walking Dead, Mad Men and Breaking Bad.” Ask them about Indian television soaps and you will be judged on your watching habits and T.V series you like.

  15. Apr 2017
    1. Ye say that the interest of the master is a sufficient protection to the slave. In the fury of man’s mad will, he will wittingly and with open eyes sell his own soul to the Devil to get his ends; and will he be more careful of his neighbor’s body?

      Having a master's favor could mean a much better life, and since Legree hated Tom it meant that he wanted to kill him even though it likened him to sin.