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Saturday, October 17, 2015
Berisha hits late winner for Melbourne Victory in A-League
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Neymar scores 4 times in Spain; Bayern remains perfect
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Olympiakos trounces AEK 4-0, keeps perfect league record
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Fabian Johnson plays full game for 'Gladbach, hits post
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Sanchez, Giroud and Ramsey guide Arsenal to win at Watford
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De Rossi scores in 500th game to help Roma beat Empoli 3-1
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Germany bid: From 'spirit of fair play' to FIFA bribes probe
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Ibrahimovic scores 2 as league leader PSG wins 2-0 at Bastia
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Vardy scores 2 as Leicester rescues 2-2 draw at Southampton
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West Ham scores 2 late goals in 3-1 win vs Palace
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Chelsea, Manchester United back to form in Premier League
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Man City beats Bournemouth 5-1 in Premier League
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Ronaldo puts Real Madrid atop Spanish league standings
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Bayern Munich claims 9th Bundesliga win for best ever start
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I have a new follower on Twitter
Alex Hibbert
29. Leads polar journeys, especially long, cold & dark ones. Supported by @BridgedaleSocks. Speaker, anti-theist, meritocrat, writer & photographer.
London, UK
http://t.co/llwFVDyhhC
Following: 2353 - Followers: 67319
October 17, 2015 at 11:20AM via Twitter http://twitter.com/alexhibbert
Klopp's 1st Liverpool game ends 0-0 vs Tottenham
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Anonymous John
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Former Everton manager Howard Kendall dies at age 69
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Emergency Patch released for Latest Flash Zero-Day Vulnerability
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[FD] ERPSCAN Research Advisory [ERPSCAN-15-017] SAP NetWeaver J2EE DAS service - Unauthorized Access
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Kashima beats Reysol to keep pace with Hiroshima in J-League
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ISIS Hacker who Passed U.S. Military Data to Terrorists Arrested in Malaysia
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Court stops Botswana deporting Eritrean soccer players
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Night Hides the World
Friday, October 16, 2015
Sara Andreasson to illustrate next issue of The Anonymous Sex Journal
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CONMEBOL head backs Platini as FIFA leader despite scandal
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Import content as anonymous user if user does not exist
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Mkhitaryan helps Dortmund to 2-0 win at Mainz in Bundesliga
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Rafael helps Lyon to 1-1 draw with Monaco in French league
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Ex-Trinidad & Tobago captain submits poll candidacy to FIFA
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Ravens: Pro Bowl G Marshal Yanda signs 4-year extension - reports; 9th season in Baltimore, started 109 career games (ESPN)
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How NSA successfully Broke Trillions of Encrypted Connections
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Strachan stays in charge of Scotland
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FIFA suspends Kuwaiti association in dispute over sports law
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FIFA will investigate 'serious allegations' that Germany secured 2006 World Cup with bribes
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Di Maria left out of PSG squad for Bastia game
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NEWS RELEASE!
News Release!
The Wait is OVER! Bob Ross Now on iTunes!
Go To:
http://bit.ly/iTunesBobRoss
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UEFA punishes Lokomotiv Moscow over racism
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Bilbao striker Aduriz extends contract until 2017
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Blatter: Payment to Platini based on 'gentleman's agreement'
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Report: Germany paid FIFA members bribes for 2006 World Cup
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ISS Daily Summary Report – 10/15/15
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Former Russia player Filippenkov dies during friendly game
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Suspended Platini loses England's support in FIFA campaign
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FIFA analyzing claim of irregularities in Neymar's transfer
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Barcelona, Madrid start Copa del Rey against 3rd-tier teams
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German football association investigating payment to FIFA
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Windows 10 Upgrade Become More Creepy, No Option to Opt-Out
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I have a new follower on Twitter
FreeDyn
DNS updater for iPhone, iPad and Macbook
http://t.co/RWGpMSd1zU
Following: 442 - Followers: 259
October 16, 2015 at 05:48AM via Twitter http://twitter.com/FreeDynApp
10 Eritrea soccer players seek asylum in Botswana after game
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I have a new follower on Twitter
XtremePush
Mobile marketing automation made simple. We provide actionable app analytics, layered with location tech to create a highly contextualized mobile experience
Born in Dublin, Growing Global
http://t.co/LQEM6haJeq
Following: 2705 - Followers: 3373
October 16, 2015 at 03:16AM via Twitter http://twitter.com/XtremePush
M16 and the Eagle Nebula
[FD] Qualys Security Advisory - LibreSSL (CVE-2015-5333 and CVE-2015-5334)
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The Chesapeake Bay in 661 Million Pixels
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How Did We Tile Greenland?
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Nebraska Water Usage
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Thursday, October 15, 2015
I have a new follower on Twitter
Data WH Automation
Following: 1983 - Followers: 1894
October 15, 2015 at 11:37PM via Twitter http://twitter.com/DWAutomation
Narrative Science Systems: A Review. (arXiv:1510.04420v1 [cs.AI])
Automatic narration of events and entities is the need of the hour, especially when live reporting is critical and volume of information to be narrated is huge. This paper discusses the challenges in this context, along with the algorithms used to build such systems. From a systematic study, we can infer that most of the work done in this area is related to statistical data. It was also found that subjective evaluation or contribution of experts is also limited for narration context.
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Layer-Specific Adaptive Learning Rates for Deep Networks. (arXiv:1510.04609v1 [cs.CV])
The increasing complexity of deep learning architectures is resulting in training time requiring weeks or even months. This slow training is due in part to vanishing gradients, in which the gradients used by back-propagation are extremely large for weights connecting deep layers (layers near the output layer), and extremely small for shallow layers (near the input layer); this results in slow learning in the shallow layers. Additionally, it has also been shown that in highly non-convex problems, such as deep neural networks, there is a proliferation of high-error low curvature saddle points, which slows down learning dramatically. In this paper, we attempt to overcome the two above problems by proposing an optimization method for training deep neural networks which uses learning rates which are both specific to each layer in the network and adaptive to the curvature of the function, increasing the learning rate at low curvature points. This enables us to speed up learning in the shallow layers of the network and quickly escape high-error low curvature saddle points. We test our method on standard image classification datasets such as MNIST, CIFAR10 and ImageNet, and demonstrate that our method increases accuracy as well as reduces the required training time over standard algorithms.
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Language Models for Image Captioning: The Quirks and What Works. (arXiv:1505.01809v3 [cs.CL] UPDATED)
Two recent approaches have achieved state-of-the-art results in image captioning. The first uses a pipelined process where a set of candidate words is generated by a convolutional neural network (CNN) trained on images, and then a maximum entropy (ME) language model is used to arrange these words into a coherent sentence. The second uses the penultimate activation layer of the CNN as input to a recurrent neural network (RNN) that then generates the caption sequence. In this paper, we compare the merits of these different language modeling approaches for the first time by using the same state-of-the-art CNN as input. We examine issues in the different approaches, including linguistic irregularities, caption repetition, and data set overlap. By combining key aspects of the ME and RNN methods, we achieve a new record performance over previously published results on the benchmark COCO dataset. However, the gains we see in BLEU do not translate to human judgments.
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Distributed Deep Q-Learning. (arXiv:1508.04186v2 [cs.LG] UPDATED)
We propose a distributed deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is based on the deep Q-network, a convolutional neural network trained with a variant of Q-learning. Its input is raw pixels and its output is a value function estimating future rewards from taking an action given a system state. To distribute the deep Q-network training, we adapt the DistBelief software framework to the context of efficiently training reinforcement learning agents. As a result, the method is completely asynchronous and scales well with the number of machines. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to achieve reasonable success on a simple game with minimal parameter tuning.
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Mining Combined Causes in Large Data Sets. (arXiv:1508.07092v2 [cs.AI] UPDATED)
In recent years, many methods have been developed for detecting causal relationships in observational data. Some of them have the potential to tackle large data sets. However, these methods fail to discover a combined cause, i.e. a multi-factor cause consisting of two or more component variables which individually are not causes. A straightforward approach to uncovering a combined cause is to include both individual and combined variables in the causal discovery using existing methods, but this scheme is computationally infeasible due to the huge number of combined variables. In this paper, we propose a novel approach to address this practical causal discovery problem, i.e. mining combined causes in large data sets. The experiments with both synthetic and real world data sets show that the proposed method can obtain high-quality causal discoveries with a high computational efficiency.
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[FD] APPLE-SA-2015-10-15-1 Keynote 6.6, Pages 5.6, Numbers 3.6, and iWork for iOS 2.6
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[FD] CakePHP Xml class SSRF Vulnerability
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[FD] UISGCON11 CFP
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[FD] hackercon berlin: hack4 the year is 2015
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[FD] Unicorn CPU Emulator Framework is out!
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Mourinho criticizes FA over fine and suspended stadium ban
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AP Source: Sheikh Salman closes in on potential FIFA bid
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Moenchengladbach head coach backs Johnson over thigh injury
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Spanish star Raul to retire after NASL season with Cosmos
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Partial stadium closure for Marseille after crowd trouble
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Eddie Howe signs contract extension with Bournemouth
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UEFA statement after meeting to discuss Platini case
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Column: In defeat, is Jose Mourinho still worth our time?
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Acting FIFA president Issa Hayatou arrives in Zurich
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World Cup host city Nizhny Novgorod wants extra $110 million
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Russia says UEFA executive committee backs suspended Platini
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Bayern Munich defender Holger Badstuber returns to training
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More than Meets the Eye with FDM Printers/"The Martian" Gets it Right
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I have a new follower on Twitter
KPanel
Monitor all your Kerio Connect, Control and Operator servers at the same time.
http://t.co/fvcEVXVJOl
Following: 341 - Followers: 276
October 15, 2015 at 10:18AM via Twitter http://twitter.com/KPanelApp
Milan forward Icardi eager to take on Juventus again
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[FD] Freemake Video Downloader 3.7.1 - Code Execution Vulnerability
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[FD] PayPal Inc Bug Bounty #117 - Session Fixation Vulnerability
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Jobs from Anonymous
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Swiss agree to extradite FIFA staffer in bribery case
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ISS Daily Summary Report – 10/14/15
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As usual, Real Madrid looks to Ronaldo for goals
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Bayern aiming for Bundesliga record 9-game winning start
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First Ever Anti-Drone Weapon that Shoots Down UAVs with Radio Waves
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Presentation: Social Media
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Injuries complicate ideal situation for PSG in French league
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UEFA leaders demand answers on Platini payment at meeting
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Untethered Jailbreak for iOS 9.0, 9.0.1 and 9.0.2 Released
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Klopp era at Liverpool starts with trip to Spurs
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Nepal captain, players arrested over match fixing
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Prince Ali lodges candidacy for FIFA presidency
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A Gegenschein Lunar Eclipse
El Nino: Disrupting the Marine Food Web
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Wednesday, October 14, 2015
Structured Memory for Neural Turing Machines. (arXiv:1510.03931v1 [cs.AI])
Neural Turing Machines (NTM) contain memory component that simulates "working memory" in the brain to store and retrieve information to ease simple algorithms learning. So far, only linearly organized memory is proposed, and during experiments, we observed that the model does not always converge, and overfits easily when handling certain tasks. We think memory component is key to some faulty behaviors of NTM, and better organization of memory component could help fight those problems. In this paper, we propose several different structures of memory for NTM, and we proved in experiments that two of our proposed structured-memory NTMs could lead to better convergence, in term of speed and prediction accuracy on copy task and associative recall task as in (Graves et al. 2014).
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