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Saturday, August 27, 2016
Ravens: Joe Flacco "answered all the questions" with solid performance in return from 2015 injury - Jamison Hensley (ESPN)
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Ravens: RB Kenneth Dixon leaves game vs. Lions with apparent left knee injury; 41 rush yards on 6 carries in 1st half (ESPN)
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Ravens: TE Benjamin Watson suffers a torn Achilles on the first play of Saturday's game vs. Lions, is out for season (ESPN)
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Opera Browser Sync Service Hacked; Users' Data and Saved Passwords Compromised
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Orioles: OF Adam Jones not in starting lineup Saturday vs. Yankees after straining his left hamstring in Friday's game (ESPN)
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Conservative impl trait does not seem to work with anonymous types
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Megaupload Domains Seized by FBI 'Hijacked' to Host Porn Ads
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Better Call Jay: The Lawyer Who Defends Anonymous
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Hacker reveals How He Could have Hacked Multiple Facebook Accounts
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[InsideNothing] nikkyevans liked your post "[FD] PLANET IP Surveillance camera Multiple Vulnerabilities"
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Friday, August 26, 2016
Can Racism Be Stopped If We All Attend 'Racist Anonymous'
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Orioles: OF Adam Jones leaves Friday's game vs. the Yankees in the 2nd inning with a left hamstring strain (ESPN)
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D8 How to get rid of homepage field on comment for anonymous user
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Sorry Philly Mag, Bobby Henon might love to comment; here's why he won't
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Anonymous move for Daughters
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Church Offers a 'Racists Anonymous' Counseling Meetings
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De La Soul's Album 'and the Anonymous Nobody' Has Arrived
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network-anonymous-i2p
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ISS Daily Summary Report – 08/25/2016
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[FD] Apple libc incomplete fix of Security Update for OS X El Capitan 10.11.2
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This Open Source 25-Core Processor Chip Scaled Up to 200,000-Core Computer
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Closest Star has Potentially Habitable Planet
Thursday, August 25, 2016
I have a new follower on Twitter
DΔN
Bits of #Cyber, with the pump of #Fitness, fistful of #MMA and pips of #Forex.
BROHIO
Following: 5345 - Followers: 14558
August 25, 2016 at 11:13PM via Twitter http://twitter.com/Zer0DayDan
I have a new follower on Twitter
Ian Metzke
Managing Director of IMA Management and Technology, sometime fencer, average cyclist.
ÜT: -37.814173,144.962008
http://t.co/mSvCjZUOKU
Following: 7426 - Followers: 8201
August 25, 2016 at 10:58PM via Twitter http://twitter.com/metzke
State Duration and Interval Modeling in Hidden Semi-Markov Model for Sequential Data Analysis. (arXiv:1608.06954v1 [cs.AI])
Sequential data modeling and analysis have become indispensable tools for analyzing sequential data such as time-series data because a larger amount of sensed event data have become available. These methods capture the sequential structure of data of interest, such as input- output relationship and correlation among datasets. However, since most studies in this area are specialized or limited for their respective applications, rigorous requirement analysis on such a model has not been examined in a general point of view. Hence, we particularly examine the structure of sequential data, and extract the necessity of "state duration" and "state duration" of events for efficient and rich representation of sequential data. Specifically addressing the hidden semi-Markov model (HSMM) that represents such state duration inside a model, we attempt to newly add representational capability of state interval of events onto HSMM. To this end, we propose two extended models; one is interval state hidden semi-Markov model (IS-HSMM) to express the length of state interval with a special state node designated as "interval state node". The other is interval length probability hidden semi-Markov model (ILP-HSMM) which repre- sents the length of state interval with a new probabilistic parameter "interval length probability." From exhaustive simulations, we show superior performances of the proposed models in comparison with HSMM. To the best of our knowledge, our proposed models are the first extensions of HMM to support state interval representation as well as state duration representation.
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Incremental Minimax Optimization based Fuzzy Clustering for Large Multi-view Data. (arXiv:1608.07001v1 [cs.AI])
Incremental clustering approaches have been proposed for handling large data when given data set is too large to be stored. The key idea of these approaches is to find representatives to represent each cluster in each data chunk and final data analysis is carried out based on those identified representatives from all the chunks. However, most of the incremental approaches are used for single view data. As large multi-view data generated from multiple sources becomes prevalent nowadays, there is a need for incremental clustering approaches to handle both large and multi-view data. In this paper we propose a new incremental clustering approach called incremental minimax optimization based fuzzy clustering (IminimaxFCM) to handle large multi-view data. In IminimaxFCM, representatives with multiple views are identified to represent each cluster by integrating multiple complementary views using minimax optimization. The detailed problem formulation, updating rules derivation, and the in-depth analysis of the proposed IminimaxFCM are provided. Experimental studies on several real world multi-view data sets have been conducted. We observed that IminimaxFCM outperforms related incremental fuzzy clustering in terms of clustering accuracy, demonstrating the great potential of IminimaxFCM for large multi-view data analysis.
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Multi-View Fuzzy Clustering with Minimax Optimization for Effective Clustering of Data from Multiple Sources. (arXiv:1608.07005v1 [cs.AI])
Multi-view data clustering refers to categorizing a data set by making good use of related information from multiple representations of the data. It becomes important nowadays because more and more data can be collected in a variety of ways, in different settings and from different sources, so each data set can be represented by different sets of features to form different views of it. Many approaches have been proposed to improve clustering performance by exploring and integrating heterogeneous information underlying different views. In this paper, we propose a new multi-view fuzzy clustering approach called MinimaxFCM by using minimax optimization based on well-known Fuzzy c means. In MinimaxFCM the consensus clustering results are generated based on minimax optimization in which the maximum disagreements of different weighted views are minimized. Moreover, the weight of each view can be learned automatically in the clustering process. In addition, there is only one parameter to be set besides the fuzzifier. The detailed problem formulation, updating rules derivation, and the in-depth analysis of the proposed MinimaxFCM are provided here. Experimental studies on nine multi-view data sets including real world image and document data sets have been conducted. We observed that MinimaxFCM outperforms related multi-view clustering approaches in terms of clustering accuracy, demonstrating the great potential of MinimaxFCM for multi-view data analysis.
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Title Generation for User Generated Videos. (arXiv:1608.07068v1 [cs.CV])
A great video title describes the most salient event compactly and captures the viewer's attention. In contrast, video captioning tends to generate sentences that describe the video as a whole. Although generating a video title automatically is a very useful task, it is much less addressed than video captioning. We address video title generation for the first time by proposing two methods that extend state-of-the-art video captioners to this new task. First, we make video captioners highlight sensitive by priming them with a highlight detector. Our framework allows for jointly training a model for title generation and video highlight localization. Second, we induce high sentence diversity in video captioners, so that the generated titles are also diverse and catchy. This means that a large number of sentences might be required to learn the sentence structure of titles. Hence, we propose a novel sentence augmentation method to train a captioner with additional sentence-only examples that come without corresponding videos. We collected a large-scale Video Titles in the Wild (VTW) dataset of 18100 automatically crawled user-generated videos and titles. On VTW, our methods consistently improve title prediction accuracy, and achieve the best performance in both automatic and human evaluation. Finally, our sentence augmentation method also outperforms the baselines on the M-VAD dataset.
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Modelling Chemical Reasoning to Predict Reactions. (arXiv:1608.07117v1 [cs.AI])
The ability to reason beyond established knowledge allows Organic Chemists to solve synthetic problems and to invent novel transformations. Here, we propose a model which mimics chemical reasoning and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outperforms a rule-based expert system in the reaction prediction task for 180,000 randomly selected binary reactions. We show that our data-driven model generalises even beyond known reaction types, and is thus capable of effectively (re-) discovering novel transformations (even including transition-metal catalysed reactions). Our model enables computers to infer hypotheses about reactivity and reactions by only considering the intrinsic local structure of the graph, and because each single reaction prediction is typically achieved in a sub-second time frame, our model can be used as a high-throughput generator of reaction hypotheses for reaction discovery.
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Semantics derived automatically from language corpora necessarily contain human biases. (arXiv:1608.07187v1 [cs.AI])
Artificial intelligence and machine learning are in a period of astounding growth. However, there are concerns that these technologies may be used, either with or without intention, to perpetuate the prejudice and unfairness that unfortunately characterizes many human institutions. Here we show for the first time that human-like semantic biases result from the application of standard machine learning to ordinary language---the same sort of language humans are exposed to every day. We replicate a spectrum of standard human biases as exposed by the Implicit Association Test and other well-known psychological studies. We replicate these using a widely used, purely statistical machine-learning model---namely, the GloVe word embedding---trained on a corpus of text from the Web. Our results indicate that language itself contains recoverable and accurate imprints of our historic biases, whether these are morally neutral as towards insects or flowers, problematic as towards race or gender, or even simply veridical, reflecting the status quo for the distribution of gender with respect to careers or first names. These regularities are captured by machine learning along with the rest of semantics. In addition to our empirical findings concerning language, we also contribute new methods for evaluating bias in text, the Word Embedding Association Test (WEAT) and the Word Embedding Factual Association Test (WEFAT). Our results have implications not only for AI and machine learning, but also for the fields of psychology, sociology, and human ethics, since they raise the possibility that mere exposure to everyday language can account for the biases we replicate here.
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Is a good offensive always the best defense?. (arXiv:1608.07223v1 [cs.AI])
A checkers-like model game with a simplified set of rules is studied through extensive simulations of agents with different expertise and strategies. The introduction of complementary strategies, in a quite general way, provides a tool to mimic the basic ingredients of a wide scope of real games. We find that only for the player having the higher offensive expertise (the dominant player ), maximizing the offensive always increases the probability to win. For the non-dominant player, interestingly, a complete minimization of the offensive becomes the best way to win in many situations, depending on the relative values of the defense expertise. Further simulations on the interplay of defense expertise were done separately, in the context of a fully-offensive scenario, offering a starting point for analytical treatments. In particular, we established that in this scenario the total number of moves is defined only by the player with the lower defensive expertise. We believe that these results stand for a first step towards a new way to improve decisions-making in a large number of zero-sum real games.
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On Simulated Annealing Dedicated to Maximin Latin Hypercube Designs. (arXiv:1608.07225v1 [cs.AI])
The goal of our research was to enhance local search heuristics used to construct Latin Hypercube Designs. First, we introduce the \textit{1D-move} perturbation to improve the space exploration performed by these algorithms. Second, we propose a new evaluation function $\psi_{p,\sigma}$ specifically targeting the Maximin criterion.
Exhaustive series of experiments with Simulated Annealing, which we used as a typically well-behaving local search heuristics, confirm that our goal was reached as the result we obtained surpasses the best scores reported in the literature. Furthermore, the $\psi_{p,\sigma}$ function seems very promising for a wide spectrum of optimization problems through the Maximin criterion.
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Learning Latent Vector Spaces for Product Search. (arXiv:1608.07253v1 [cs.IR])
We introduce a novel latent vector space model that jointly learns the latent representations of words, e-commerce products and a mapping between the two without the need for explicit annotations. The power of the model lies in its ability to directly model the discriminative relation between products and a particular word. We compare our method to existing latent vector space models (LSI, LDA and word2vec) and evaluate it as a feature in a learning to rank setting. Our latent vector space model achieves its enhanced performance as it learns better product representations. Furthermore, the mapping from words to products and the representations of words benefit directly from the errors propagated back from the product representations during parameter estimation. We provide an in-depth analysis of the performance of our model and analyze the structure of the learned representations.
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Social and Business Intelligence Analysis Using PSO. (arXiv:1407.6090v2 [cs.AI] UPDATED)
The goal of this paper is to elaborate swarm intelligence for business intelligence decision making and the business rules management improvement. .The swarm optimization, which is highly influenced by the behavior of creature, performs in group. The Spatial data is defined as data that is represented by 2D or 3D images. SQL Server supports only 2D images till now. As we know that location is an essential part of any organizational data as well as business data enterprises maintain customer address lists, own property, ship goods from and to warehouses, manage transport flows among their workforce, and perform many other activities. By means to say a lot of spatial data is used and processed by enterprises, organizations and other bodies in order to make the things more visible and self descriptive. From the experiments, we found that PSO is can facilitate the intelligence in social and business behavior.
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Load Disaggregation Based on Aided Linear Integer Programming. (arXiv:1603.07417v2 [cs.AI] UPDATED)
Load disaggregation based on aided linear integer programming (ALIP) is proposed. We start with a conventional linear integer programming (IP) based disaggregation and enhance it in several ways. The enhancements include additional constraints, correction based on a state diagram, median filtering, and linear programming-based refinement. With the aid of these enhancements, the performance of IP-based disaggregation is significantly improved. The proposed ALIP system relies only on the instantaneous load samples instead of waveform signatures, and hence does not crucially depend on high sampling frequency. Experimental results show that the proposed ALIP system performs better than the conventional IP-based load disaggregation system.
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Evaluation and selection of Medical Tourism sites: A rough AHP based MABAC approach. (arXiv:1606.08962v2 [cs.AI] UPDATED)
In this paper, a novel multiple criteria decision making (MCDM) methodology is presented for assessing and prioritizing medical tourism destinations in uncertain environment. A systematic evaluation and assessment method is proposed by integrating rough number based AHP (Analytic Hierarchy Process) and rough number based MABAC (Multi-Attributive Border Approximation area Comparison). Rough number is used to aggregate individual judgments and preferences to deal with vagueness in decision making due to limited data. Rough AHP analyzes the relative importance of criteria based on their preferences given by experts. Rough MABAC evaluates the alternative sites based on the criteria weights. The proposed methodology is explained through a case study considering different cities for healthcare service in India. The validity of the obtained ranking for the given decision making problem is established by testing criteria proposed by Wang and Triantaphyllou (2008) along with further analysis and discussion.
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Verveling op verkansie
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USCB Fine Arts Department Presents Anonymous Ancestors
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Apple releases 'Emergency' Patch after Advanced Spyware Targets Human Rights Activist
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[FD] Onapsis Security Advisory ONAPSIS-2016-00171: JD Edwards Server Manager Password Disclosure
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[FD] APPLE-SA-2016-08-25-1 iOS 9.3.5
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Ravens: QB Joe Flacco (knee) expecting to play about one half Saturday in his 2016 preseason debut against the Lions (ESPN)
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[FD] Onapsis Security Advisory ONAPSIS-2016-00171: JD Edwards Server Manager Password Disclosure
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[FD] Onapsis Security Advisory ONAPSIS-2016-014: JD Edwards JDENET function DoS
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[FD] Onapsis Security Advisory ONAPSIS-2016-012: JD Edwards JDENET function DoS
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[FD] Onapsis Security Advisory ONAPSIS-2016-011: JD Edwards Server Manager Create users
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[FD] Onapsis Security Advisory ONAPSIS-2016-010: JD Edwards Server Manager Shutdown
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WhatsApp to Share Your Data with Facebook — You have 30 Days to Stop It
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De La Soul
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Self-tallying quantum anonymous voting
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[FD] Onapsis Security Advisory ONAPSIS-2016-009: JD Edwards JDENet Password Disclosure
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ANONYMOUS SAYS HIS GIRL DOESN'T WANT HIS LAST NAME
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homebox_build_title() creates error for anonymous user
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Re: [ethereum/go-ethereum] Too many open files , leading to panic (#2358)
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Wrong redirect path for disabled anonymous checkout
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MONUMENTO SIMÓN BOLÍVAR [Material gráfico] | Anonymous
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ISS Daily Summary Report – 08/24/2016
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Germany and France declare War on Encryption to Fight Terrorism
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[FD] SEC Consult SA-20160825-0 :: Multiple vulnerabilities in Micro Focus (Novell) GroupWise
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Curiosity at Murray Buttes on Mars
Wednesday, August 24, 2016
OPP: Not-So-Anonymous Alcoholic (Audio)
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Orioles: Closer Zach Britton's streak of 43 straight scoreless appearances snapped, allowed 9th-inning run at Nationals (ESPN)
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Orioles: Closer Zach Britton's streak of 43 straight scoreless appearances snapped, allowed 9th-inning run at Nationals (ESPN)
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OPP: Not-So-Anonymous Alcoholic 8/24
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[FD] [RCESEC-2016-005][CVE-2016-6913] AlienVault USM/OSSIM 5.2 conf/reload.php "back" DOM-based Cross-Site Scripting
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[FD] Dotclear 2.9.1 SSRF/XSPA Vulnerability
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[FD] Dotclear 2.9.1 Malicious File Upload Restriction Bypass
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[FD] Dotclear 2.9.1 Directory Download Vulnerability
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Unsupervised, Efficient and Semantic Expertise Retrieval. (arXiv:1608.06651v1 [cs.IR])
We introduce an unsupervised discriminative model for the task of retrieving experts in online document collections. We exclusively employ textual evidence and avoid explicit feature engineering by learning distributed word representations in an unsupervised way. We compare our model to state-of-the-art unsupervised statistical vector space and probabilistic generative approaches. Our proposed log-linear model achieves the retrieval performance levels of state-of-the-art document-centric methods with the low inference cost of so-called profile-centric approaches. It yields a statistically significant improved ranking over vector space and generative models in most cases, matching the performance of supervised methods on various benchmarks. That is, by using solely text we can do as well as methods that work with external evidence and/or relevance feedback. A contrastive analysis of rankings produced by discriminative and generative approaches shows that they have complementary strengths due to the ability of the unsupervised discriminative model to perform semantic matching.
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Expressibility of norms in temporal logic. (arXiv:1608.06787v1 [cs.AI])
In this short note we address the issue of expressing norms (such as obligations and prohibitions) in temporal logic. In particular, we address the argument from [Governatori 2015] that norms cannot be expressed in Linear Time Temporal Logic (LTL).
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Effect of Incomplete Meta-dataset on Average Ranking Method. (arXiv:1608.06845v1 [cs.AI])
One of the simplest metalearning methods is the average ranking method. This method uses metadata in the form of test results of a given set of algorithms on given set of datasets and calculates an average rank for each algorithm. The ranks are used to construct the average ranking. We investigate the problem of how the process of generating the average ranking is affected by incomplete metadata including fewer test results. This issue is relevant, because if we could show that incomplete metadata does not ?affect the final results much, we could explore it in future design. We could simply conduct fewer tests and save thus computation time. In this paper we describe an upgraded average ranking method that is capable of dealing with incomplete metadata. Our results show that the proposed method is relatively robust to omission in test results in the meta datasets.
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A Parallel Memory-efficient Epistemic Logic Program Solver: Harder, Better, Faster. (arXiv:1608.06910v1 [cs.AI])
As the practical use of answer set programming (ASP) has grown with the development of efficient solvers, we expect a growing interest in extensions of ASP as their semantics stabilize and solvers supporting them mature. Epistemic Specifications, which adds modal operators K and M to the language of ASP, is one such extension. We call a program in this language an epistemic logic program (ELP). Solvers have thus far been practical for only the simplest ELPs due to exponential growth of the memory required. We describe a solver that is able to solve harder problems better (without exponentially-growing memory needs w.r.t. K and M occurrences) and faster than any other known ELP solver.
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DISCO Nets: DISsimilarity COefficient Networks. (arXiv:1606.02556v4 [cs.CV] UPDATED)
We present a new type of probabilistic model which we call DISsimilarity COefficient Networks (DISCO Nets). DISCO Nets allow us to efficiently sample from a posterior distribution parametrised by a neural network. During training, DISCO Nets are learned by minimising the dissimilarity coefficient between the true distribution and the estimated distribution. This allows us to tailor the training to the loss related to the task at hand. We empirically show that (i) by modeling uncertainty on the output value, DISCO Nets outperform equivalent non-probabilistic predictive networks and (ii) DISCO Nets accurately model the uncertainty of the output, outperforming existing probabilistic models based on deep neural networks.
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Marshal Yanda (No. 38) is the lone Ravens player on ESPN's #NFLRank Top 100 list (ESPN)
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Orioles: P Chris Tillman officially placed on the 15-day DL due to right shoulder inflammation; P Mike Wright called up (ESPN)
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De La Soul on new album 'Anonymous Nobody,' Frank Ocean's
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Our Anonymous Donor Has Issued A Challenge
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Happy Birthday! LINUX Turns 25 Years Old Today
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ATMs in Thailand Hacked; 12 Million Baht Stolen; 10,000 ATMs Prone to Hackers
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SportsCenter Video: Joe Flacco "looks strong in camp," ready for first action since Nov. 2015 ACL injury - Adam Schefter (ESPN)
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Have an anonymous TED Talk? We want to hear it.
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User permissions for comments
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MIT Researchers Solve the Spectrum Crunch to make Wi-Fi 10 times Faster
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Make user anonymous in activity list
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I have a new follower on Twitter
Drupe Paul
We laugh we cry we dominate in our constant search to master Drupal. #drupal
Following: 633 - Followers: 1287
August 24, 2016 at 07:58AM via Twitter http://twitter.com/drupepaul
ISS Daily Summary Report – 08/23/2016
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