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Saturday, September 17, 2016
Ravens: Sr. defensive assistant Clarence Brooks, 65, dies following battle with cancer; was team's longest-tenured coach (ESPN)
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British Court rules Hacktivist 'Lauri Love' can be extradited to USA
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Friday, September 16, 2016
Orioles Video: Michael Bourn, Manny Machado and Matt Wieters combine to get the final out at home in 5-4 win vs. Rays (ESPN)
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Orioles: Mark Trumbo (back spasms) out of lineup Friday vs. Rays; Buck Showalter hopeful he will start Saturday (ESPN)
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Paper retracts anonymous quotation after PatientsLikeMe able to identify speaker
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Flagging a node as an anonymous user doesn't work with Rules
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Instead of spending $1.3 million, FBI could have Hacked iPhone in just $100
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Heitkamp Condemns Anonymous Online Threats against Officials, Law Enforcement
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Anonymous (Original Mix)
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Configure anonymous sessions
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ISS Daily Summary Report – 09/15/2016
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Using 'Signal' for Encrypted Chats? You Shouldn't Skip Its Next Update
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Retrograde Mars and Saturn
Arctic Sea Ice from March to September 2016
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Thursday, September 15, 2016
Bayesian Reinforcement Learning: A Survey. (arXiv:1609.04436v1 [cs.AI])
Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. In this survey, we provide an in-depth review of the role of Bayesian methods for the reinforcement learning (RL) paradigm. The major incentives for incorporating Bayesian reasoning in RL are: 1) it provides an elegant approach to action-selection (exploration/exploitation) as a function of the uncertainty in learning; and 2) it provides a machinery to incorporate prior knowledge into the algorithms. We first discuss models and methods for Bayesian inference in the simple single-step Bandit model. We then review the extensive recent literature on Bayesian methods for model-based RL, where prior information can be expressed on the parameters of the Markov model. We also present Bayesian methods for model-free RL, where priors are expressed over the value function or policy class. The objective of the paper is to provide a comprehensive survey on Bayesian RL algorithms and their theoretical and empirical properties.
from cs.AI updates on arXiv.org http://ift.tt/2cSeb5O
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Column Networks for Collective Classification. (arXiv:1609.04508v1 [cs.AI])
Relational learning deals with data that are characterized by relational structures. An important task is collective classification, which is to jointly classify networked objects. While it holds a great promise to produce a better accuracy than non-collective classifiers, collective classification is computational challenging and has not leveraged on the recent breakthroughs of deep learning. We present Column Network (CLN), a novel deep learning model for collective classification in multi-relational domains. CLN has many desirable theoretical properties: (i) it encodes multi-relations between any two instances; (ii) it is deep and compact, allowing complex functions to be approximated at the network level with a small set of free parameters; (iii) local and relational features are learned simultaneously; (iv) long-range, higher-order dependencies between instances are supported naturally; and (v) crucially, learning and inference are efficient, linear in the size of the network and the number of relations. We evaluate CLN on multiple real-world applications: (a) delay prediction in software projects, (b) PubMed Diabetes publication classification and (c) film genre classification. In all applications, CLN demonstrates a higher accuracy than state-of-the-art rivals.
from cs.AI updates on arXiv.org http://ift.tt/2cJv6KQ
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Context Aware Nonnegative Matrix Factorization Clustering. (arXiv:1609.04628v1 [cs.CV])
In this article we propose a method to refine the clustering results obtained with the nonnegative matrix factorization (NMF) technique, imposing consistency constraints on the final labeling of the data. The research community focused its effort on the initialization and on the optimization part of this method, without paying attention to the final cluster assignments. We propose a game theoretic framework in which each object to be clustered is represented as a player, which has to choose its cluster membership. The information obtained with NMF is used to initialize the strategy space of the players and a weighted graph is used to model the interactions among the players. These interactions allow the players to choose a cluster which is coherent with the clusters chosen by similar players, a property which is not guaranteed by NMF, since it produces a soft clustering of the data. The results on common benchmarks show that our model is able to improve the performances of many NMF formulations.
from cs.AI updates on arXiv.org http://ift.tt/2cSe1eP
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Sequencing Chess. (arXiv:1609.04648v1 [cs.AI])
We analyze the structure of the state space of chess by means of transition path sampling Monte Carlo simulation. Based on the typical number of moves required to transpose a given configuration of chess pieces into another, we conclude that the state space consists of several pockets between which transitions are rare. Skilled players explore an even smaller subset of positions that populate some of these pockets only very sparsely. These results suggest that the usual measures to estimate both, the size of the state space and the size of the tree of legal moves, are not unique indicators of the complexity of the game, but that topological considerations are equally important.
from cs.AI updates on arXiv.org http://ift.tt/2cJw9dM
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Concordance and the Smallest Covering Set of Preference Orderings. (arXiv:1609.04722v1 [cs.AI])
Preference orderings are orderings of a set of items according to the preferences (of judges). Such orderings arise in a variety of domains, including group decision making, consumer marketing, voting and machine learning. Measuring the mutual information and extracting the common patterns in a set of preference orderings are key to these areas. In this paper we deal with the representation of sets of preference orderings, the quantification of the degree to which judges agree on their ordering of the items (i.e. the concordance), and the efficient, meaningful description of such sets.
We propose to represent the orderings in a subsequence-based feature space and present a new algorithm to calculate the size of the set of all common subsequences - the basis of a quantification of concordance, not only for pairs of orderings but also for sets of orderings. The new algorithm is fast and storage efficient with a time complexity of only $O(Nn^2)$ for the orderings of $n$ items by $N$ judges and a space complexity of only $O(\min\{Nn,n^2\})$.
Also, we propose to represent the set of all $N$ orderings through a smallest set of covering preferences and present an algorithm to construct this smallest covering set.
from cs.AI updates on arXiv.org http://ift.tt/2cSdzgv
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The multi-vehicle covering tour problem: building routes for urban patrolling. (arXiv:1309.5502v2 [cs.AI] UPDATED)
In this paper we study a particular aspect of the urban community policing: routine patrol route planning. We seek routes that guarantee visibility, as this has a sizable impact on the community perceived safety, allowing quick emergency responses and providing surveillance of selected sites (e.g., hospitals, schools). The planning is restricted to the availability of vehicles and strives to achieve balanced routes. We study an adaptation of the model for the multi-vehicle covering tour problem, in which a set of locations must be visited, whereas another subset must be close enough to the planned routes. It constitutes an NP-complete integer programming problem. Suboptimal solutions are obtained with several heuristics, some adapted from the literature and others developed by us. We solve some adapted instances from TSPLIB and an instance with real data, the former being compared with results from literature, and latter being compared with empirical data.
from cs.AI updates on arXiv.org http://ift.tt/18m44gL
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Learning from networked examples. (arXiv:1405.2600v2 [cs.AI] UPDATED)
Many machine learning algorithms are based on the assumption that training examples are drawn identically and independently. However, this assumption does not hold anymore when learning from a networked sample because two or more training examples may share some common objects, and hence share the features of these shared objects. We first show that the classic approach of ignoring this problem potentially can have a disastrous effect on the accuracy of statistics, and then consider alternatives. One of these is to only use independent examples, discarding other information. However, this is clearly suboptimal. We analyze sample error bounds in a networked setting, providing both improved and new results. Next, we propose an efficient weighting method which achieves a better sample error bound than those of previous methods. Our approach is based on novel concentration inequalities for networked variables.
from cs.AI updates on arXiv.org http://ift.tt/1ggs7qe
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A Discrete and Bounded Envy-Free Cake Cutting Protocol for Any Number of Agents. (arXiv:1604.03655v9 [cs.DS] UPDATED)
We consider the well-studied cake cutting problem in which the goal is to find an envy-free allocation based on queries from $n$ agents. The problem has received attention in computer science, mathematics, and economics. It has been a major open problem whether there exists a discrete and bounded envy-free protocol. We resolve the problem by proposing a discrete and bounded envy-free protocol for any number of agents. The maximum number of queries required by the protocol is $n^{n^{n^{n^{n^n}}}}$. We additionally show that even if we do not run our protocol to completion, it can find in at most $n^{n+1}$ queries a partial allocation of the cake that achieves proportionality (each agent gets at least $1/n$ of the value of the whole cake) and envy-freeness. Finally we show that an envy-free partial allocation can be computed in $n^{n+1}$ queries such that each agent gets a connected piece that gives the agent at least $1/(3n)$ of the value of the whole cake.
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[FD] BINOM3 Electric Power Quality Meter Vulnerabilities
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[FD] Oxwall 1.8.0: XSS & Open Redirect
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[FD] MyBB 1.8.6: Improper validation of data passed to eval
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[FD] MyBB 1.8.6: SQL Injection
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[FD] MyBB 1.8.6: CSRF, Weak Hashing, Plaintext Passwords
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[FD] Peel Shopping 8.0.2: Object Injection
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[FD] Insecure transmission of data in Android applications developed with Adobe AIR [CVE-2016-6936]
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[FD] Multiple vulnerabilities in ASUS RT-N10
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[FD] Keypatch v2.0 is out!
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Re: [FD] CVE-2016-6662 - MySQL Remote Root Code Execution / Privilege Escalation ( 0day )
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[FD] Security Advisory -- Multiple Vulnerabilities - MuM Map Edit
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[FD] APPLE-SA-2016-09-14-1 iOS 10.0.1
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[FD] APPLE-SA-2016-09-13-3 watchOS 3
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Ravens: WR Breshad Perriman returns to practice Thursday after missing Wednesday's workouts to deal with calf issue (ESPN)
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leave original anonymous author's IP
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ISS Daily Summary Report – 09/14/2016
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Xiaomi Can Silently Install Any App On Your Android Phone Using A Backdoor
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Provide support for anonymous checkouts.
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Enable anonymous-or properly secure- reporting of DBS code violations
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FBI Director — You Should Cover Your Webcam With Tape
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The Rivers of the Mississippi Watershed
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Wednesday, September 14, 2016
Ravens: WR Derrick Mason among the first-time eligible nominees for the 2017 Pro Football Hall of Fame class (ESPN)
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I have a new follower on Twitter
Openwave Mobility
Openwave Mobility empowers operators to manage and monetize the growth in mobile video and web traffic.
Redwood City, CA
http://t.co/Ya3XD9Kvc7
Following: 2732 - Followers: 3265
September 14, 2016 at 09:50PM via Twitter http://twitter.com/owMobility
Orioles: Steve Pearce gets PRP injection in elbow and is out indefinitely; Buck Showalter hopes he'll return this season (ESPN)
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"Flow Size Difference" Can Make a Difference: Detecting Malicious TCP Network Flows Based on Benford's Law. (arXiv:1609.04214v1 [cs.CR])
Statistical characteristics of network traffic have attracted a significant amount of research for automated network intrusion detection, some of which looked at applications of natural statistical laws such as Zipf's law, Benford's law and the Pareto distribution. In this paper, we present the application of Benford's law to a new network flow metric "flow size difference", which have not been studied by other researchers, to build an unsupervised flow-based intrusion detection system (IDS). The method was inspired by our observation on a large number of TCP flow datasets where normal flows tend to follow Benford's law closely but malicious flows tend to deviate significantly from it. The proposed IDS is unsupervised so no training is needed thus can be easily deployed. It has two simple parameters with a clear semantic meaning, allowing the human operator to set and adapt their values intuitively to adjust the overall performance of the IDS. We tested the proposed IDS on one closed and two public datasets and proved its efficiency in terms of AUC (area under the ROC curve). Being a simple and fast standalone IDS itself, the proposed method can also be easily combined with other network IDSs e.g. added as an additional component into another existing IDS to enhance its performance.
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Quick and energy-efficient Bayesian computing of binocular disparity using stochastic digital signals. (arXiv:1609.04337v1 [cs.CV])
Reconstruction of the tridimensional geometry of a visual scene using the binocular disparity information is an important issue in computer vision and mobile robotics, which can be formulated as a Bayesian inference problem. However, computation of the full disparity distribution with an advanced Bayesian model is usually an intractable problem, and proves computationally challenging even with a simple model. In this paper, we show how probabilistic hardware using distributed memory and alternate representation of data as stochastic bitstreams can solve that problem with high performance and energy efficiency. We put forward a way to express discrete probability distributions using stochastic data representations and perform Bayesian fusion using those representations, and show how that approach can be applied to diparity computation. We evaluate the system using a simulated stochastic implementation and discuss possible hardware implementations of such architectures and their potential for sensorimotor processing and robotics.
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Finite LTL Synthesis is EXPTIME-complete. (arXiv:1609.04371v1 [cs.LO])
LTL synthesis -- the construction of a function to satisfy a logical specification formulated in Linear Temporal Logic -- is a 2EXPTIME-complete problem with relevant applications in controller synthesis and a myriad of artificial intelligence applications. In this research note we consider De Giacomo and Vardi's variant of the synthesis problem for LTL formulas interpreted over finite rather than infinite traces. Rather surprisingly, given the existing claims on complexity, we establish that LTL synthesis is EXPTIME-complete for the finite interpretation, and not 2EXPTIME-complete as previously reported. Our result coincides nicely with the planning perspective where non-deterministic planning with full observability is EXPTIME-complete and partial observability increases the complexity to 2EXPTIME-complete; a recent related result for LTL synthesis shows that in the finite case with partial observability, the problem is 2EXPTIME-complete.
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Complexity Classification in Infinite-Domain Constraint Satisfaction. (arXiv:1201.0856v8 [cs.CC] UPDATED)
A constraint satisfaction problem (CSP) is a computational problem where the input consists of a finite set of variables and a finite set of constraints, and where the task is to decide whether there exists a satisfying assignment of values to the variables. Depending on the type of constraints that we allow in the input, a CSP might be tractable, or computationally hard. In recent years, general criteria have been discovered that imply that a CSP is polynomial-time tractable, or that it is NP-hard. Finite-domain CSPs have become a major common research focus of graph theory, artificial intelligence, and finite model theory. It turned out that the key questions for complexity classification of CSPs are closely linked to central questions in universal algebra.
This thesis studies CSPs where the variables can take values from an infinite domain. This generalization enhances dramatically the range of computational problems that can be modeled as a CSP. Many problems from areas that have so far seen no interaction with constraint satisfaction theory can be formulated using infinite domains, e.g. problems from temporal and spatial reasoning, phylogenetic reconstruction, and operations research.
It turns out that the universal-algebraic approach can also be applied to study large classes of infinite-domain CSPs, yielding elegant complexity classification results. A new tool in this thesis that becomes relevant particularly for infinite domains is Ramsey theory. We demonstrate the feasibility of our approach with two complete complexity classification results: one on CSPs in temporal reasoning, the other on a generalization of Schaefer's theorem for propositional logic to logic over graphs. We also study the limits of complexity classification, and present classes of computational problems provably do not exhibit a complexity dichotomy into hard and easy problems.
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Spacetimes with Semantics (III) - The Structure of Functional Knowledge Representation and Artificial Reasoning. (arXiv:1608.02193v2 [cs.AI] UPDATED)
Using the previously developed concepts of semantic spacetime, I explore the interpretation of knowledge representations, and their structure, as a semantic system, within the framework of promise theory. By assigning interpretations to phenomena, from observers to observed, we may approach a simple description of knowledge-based functional systems, with direct practical utility. The focus is especially on the interpretation of concepts, associative knowledge, and context awareness. The inference seems to be that most if not all of these concepts emerge from purely semantic spacetime properties, which opens the possibility for a more generalized understanding of what constitutes a learning, or even `intelligent' system.
Some key principles emerge for effective knowledge representation: 1) separation of spacetime scales, 2) the recurrence of four irreducible types of association, by which intent propagates: aggregation, causation, cooperation, and similarity, 3) the need for discrimination of identities (discrete), which is assisted by distinguishing timeline simultaneity from sequential events, and 4) the ability to learn (memory). It is at least plausible that emergent knowledge abstraction capabilities have their origin in basic spacetime structures.
These notes present a unified view of mostly well-known results; they allow us to see information models, knowledge representations, machine learning, and semantic networking (transport and information base) in a common framework. The notion of `smart spaces' thus encompasses artificial systems as well as living systems, across many different scales, e.g. smart cities and organizations.
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RETAIN: Interpretable Predictive Model in Healthcare using Reverse Time Attention Mechanism. (arXiv:1608.05745v3 [cs.LG] UPDATED)
Accuracy and interpretation are two goals of any successful predictive models. Most existing works have to suffer the tradeoff between the two by either picking complex black box models such as recurrent neural networks (RNN) or relying on less accurate traditional models with better interpretation such as logistic regression. To address this dilemma, we present REverse Time AttentIoN model (RETAIN) for analyzing Electronic Health Records (EHR) data that achieves high accuracy while remaining clinically interpretable. RETAIN is a two-level neural attention model that can find influential past visits and significant clinical variables within those visits (e.g,. key diagnoses). RETAIN mimics physician practice by attending the EHR data in a reverse time order so that more recent clinical visits will likely get higher attention. Experiments on a large real EHR dataset of 14 million visits from 263K patients over 8 years confirmed the comparable predictive accuracy and computational scalability to the state-of-the-art methods such as RNN. Finally, we demonstrate the clinical interpretation with concrete examples from RETAIN.
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MLB Video: Orioles' Mark Trumbo demolishes a ball well over the Green Monster, traveling 448 feet into the Boston night (ESPN)
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MLB: Rick Porcello (20-3) and Red Sox look to extend AL East lead over Orioles (2 GB); watch live in the ESPN App (ESPN)
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MLB: Tony La Russa questions Adam Jones calling baseball a "white man's sport," asks "how much wronger can he be?" (ESPN)
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Orioles will earn an AL wild-card spot because of their improved rotation and high-scoring offense - Eddie Matz (ESPN)
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Ravens Image: LB Terrell Suggs shows off one of the 15 Joe Flacco "elite" T-shirts that players purchased (ESPN)
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Regression in overload resolution of a parameterless anonymous delegate
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Ravens: WR Breshad Perriman, who missed practice Wednesday, dealing with a "minor calf issue" - source (ESPN)
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Massive Data Breach Exposes 6.6 Million Plaintext Passwords from Ad Company
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ISS Daily Summary Report – 09/13/2016
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The Project Zero Contest — Google will Pay you $200,000 to Hack Android OS
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Microsoft and Adobe Rolls Out Critical Security Updates - Patch Now!
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Tuesday, September 13, 2016
Orioles Video: J.J. Hardy destroys a ball off the billboard over the Green Monster for a 3-run HR in 6-3 win at Red Sox (ESPN)
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Joint Extraction of Events and Entities within a Document Context. (arXiv:1609.03632v1 [cs.CL])
Events and entities are closely related; entities are often actors or participants in events and events without entities are uncommon. The interpretation of events and entities is highly contextually dependent. Existing work in information extraction typically models events separately from entities, and performs inference at the sentence level, ignoring the rest of the document. In this paper, we propose a novel approach that models the dependencies among variables of events, entities, and their relations, and performs joint inference of these variables across a document. The goal is to enable access to document-level contextual information and facilitate context-aware predictions. We demonstrate that our approach substantially outperforms the state-of-the-art methods for event extraction as well as a strong baseline for entity extraction.
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Graph Aggregation. (arXiv:1609.03765v1 [cs.AI])
Graph aggregation is the process of computing a single output graph that constitutes a good compromise between several input graphs, each provided by a different source. One needs to perform graph aggregation in a wide variety of situations, e.g., when applying a voting rule (graphs as preference orders), when consolidating conflicting views regarding the relationships between arguments in a debate (graphs as abstract argumentation frameworks), or when computing a consensus between several alternative clusterings of a given dataset (graphs as equivalence relations). In this paper, we introduce a formal framework for graph aggregation grounded in social choice theory. Our focus is on understanding which properties shared by the individual input graphs will transfer to the output graph returned by a given aggregation rule. We consider both common properties of graphs, such as transitivity and reflexivity, and arbitrary properties expressible in certain fragments of modal logic. Our results establish several connections between the types of properties preserved under aggregation and the choice-theoretic axioms satisfied by the rules used. The most important of these results is a powerful impossibility theorem that generalises Arrow's seminal result for the aggregation of preference orders to a large collection of different types of graphs.
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Instrumenting an SMT Solver to Solve Hybrid Network Reachability Problems. (arXiv:1609.03847v1 [cs.AI])
PDDL+ planning has its semantics rooted in hybrid automata (HA) and recent work has shown that it can be modeled as a network of HAs. Addressing the complexity of nonlinear PDDL+ planning as HAs requires both space and time efficient reasoning. Unfortunately, existing solvers either do not address nonlinear dynamics or do not natively support networks of automata.
We present a new algorithm, called HNSolve, which guides the variable selection of the dReal Satisfiability Modulo Theories (SMT) solver while reasoning about network encodings of nonlinear PDDL+ planning as HAs. HNSolve tightly integrates with dReal by solving a discrete abstraction of the HA network. HNSolve finds composite runs on the HA network that ignore continuous variables, but respect mode jumps and synchronization labels. HNSolve admissibly detects dead-ends in the discrete abstraction, and posts conflict clauses that prune the SMT solver's search. We evaluate the benefits of our HNSolve algorithm on PDDL+ benchmark problems and demonstrate its performance with respect to prior work.
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Feynman Machine: The Universal Dynamical Systems Computer. (arXiv:1609.03971v1 [cs.NE])
Efforts at understanding the computational processes in the brain have met with limited success, despite their importance and potential uses in building intelligent machines. We propose a simple new model which draws on recent findings in Neuroscience and the Applied Mathematics of interacting Dynamical Systems. The Feynman Machine is a Universal Computer for Dynamical Systems, analogous to the Turing Machine for symbolic computing, but with several important differences. We demonstrate that networks and hierarchies of simple interacting Dynamical Systems, each adaptively learning to forecast its evolution, are capable of automatically building sensorimotor models of the external and internal world. We identify such networks in mammalian neocortex, and show how existing theories of cortical computation combine with our model to explain the power and flexibility of mammalian intelligence. These findings lead directly to new architectures for machine intelligence. A suite of software implementations has been built based on these principles, and applied to a number of spatiotemporal learning tasks.
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A Generic Bet-and-run Strategy for Speeding Up Traveling Salesperson and Minimum Vertex Cover. (arXiv:1609.03993v1 [cs.AI])
A common strategy for improving optimization algorithms is to restart the algorithm when it is believed to be trapped in an inferior part of the search space. However, while specific restart strategies have been developed for specific problems (and specific algorithms), restarts are typically not regarded as a general tool to speed up an optimization algorithm. In fact, many optimization algorithms do not employ restarts at all.
Recently, "bet-and-run" was introduced in the context of mixed-integer programming, where first a number of short runs with randomized initial conditions is made, and then the most promising run of these is continued. In this article, we consider two classical NP-complete combinatorial optimization problems, traveling salesperson and minimum vertex cover, and study the effectiveness of different bet-and-run strategies. In particular, our restart strategies do not take any problem knowledge into account, nor are tailored to the optimization algorithm. Therefore, they can be used off-the-shelf. We observe that state-of-the-art solvers for these problems can benefit significantly from restarts on standard benchmark instances.
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An Evolutionary Algorithm to Learn SPARQL Queries for Source-Target-Pairs: Finding Patterns for Human Associations in DBpedia. (arXiv:1607.07249v3 [cs.AI] UPDATED)
Efficient usage of the knowledge provided by the Linked Data community is often hindered by the need for domain experts to formulate the right SPARQL queries to answer questions. For new questions they have to decide which datasets are suitable and in which terminology and modelling style to phrase the SPARQL query.
In this work we present an evolutionary algorithm to help with this challenging task. Given a training list of source-target node-pair examples our algorithm can learn patterns (SPARQL queries) from a SPARQL endpoint. The learned patterns can be visualised to form the basis for further investigation, or they can be used to predict target nodes for new source nodes.
Amongst others, we apply our algorithm to a dataset of several hundred human associations (such as "circle - square") to find patterns for them in DBpedia. We show the scalability of the algorithm by running it against a SPARQL endpoint loaded with > 7.9 billion triples. Further, we use the resulting SPARQL queries to mimic human associations with a Mean Average Precision (MAP) of 39.9 % and a Recall@10 of 63.9 %.
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Orioles: Steve Pearce not in lineup Tuesday because right forearm is bothering him again; will see orthopedic specialist (ESPN)
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Orioles Image: Ubaldo Jimenez takes a photo next to a replica of the Statue of Liberty after becoming a U.S. citizen (ESPN)
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The Founded Semantics and Constraint Semantics of Logic Rules. (arXiv:1606.06269v2 [cs.LO] UPDATED)
This paper describes a simple new semantics for logic rules, the founded semantics, and its straightforward extension to another simple new semantics, the constraint semantics. The new semantics support unrestricted negation, as well as unrestricted existential and universal quantifications. They are uniquely expressive and intuitive by allowing assumptions about the predicates and rules to be specified explicitly, are completely declarative and easy to understand, and relate cleanly to prior semantics. In addition, founded semantics can be computed in linear time in the size of the ground program.
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Teen offspring of anonymous sperm donor find each other through online registry
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324,000 Financial Records with CVV Numbers Stolen From A Payment Gateway
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Anonymous user ffee8e
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Ravens Image: C.J. Mosley shows off all-purple Nike Color Rush uniforms for Nov. 10th Thursday night game vs. Browns (ESPN)
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Orioles OF Adam Jones joins SportsCenter, addresses comments on national anthem protests; watch live in the ESPN App (ESPN)
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SportsCenter Video: Raul Ibanez \"not surprised\" that Orioles OF Adam Jones spoke out, calls him a \"great kid\" (ESPN)
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An Integrated Classification Model for Financial Data Mining. (arXiv:1609.02976v1 [cs.AI])
Nowadays, financial data analysis is becoming increasingly important in the business market. As companies collect more and more data from daily operations, they expect to extract useful knowledge from existing collected data to help make reasonable decisions for new customer requests, e.g. user credit category, churn analysis, real estate analysis, etc. Financial institutes have applied different data mining techniques to enhance their business performance. However, simple ap-proach of these techniques could raise a performance issue. Besides, there are very few general models for both understanding and forecasting different finan-cial fields. We present in this paper a new classification model for analyzing fi-nancial data. We also evaluate this model with different real-world data to show its performance.
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Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks. (arXiv:1609.02993v1 [cs.AI])
We consider scenarios from the real-time strategy game StarCraft as new benchmarks for reinforcement learning algorithms. We propose micromanagement tasks, which present the problem of the short-term, low-level control of army members during a battle. From a reinforcement learning point of view, these scenarios are challenging because the state-action space is very large, and because there is no obvious feature representation for the state-action evaluation function. We describe our approach to tackle the micromanagement scenarios with deep neural network controllers from raw state features given by the game engine. In addition, we present a heuristic reinforcement learning algorithm which combines direct exploration in the policy space and backpropagation. This algorithm allows for the collection of traces for learning using deterministic policies, which appears much more efficient than, for example, {\epsilon}-greedy exploration. Experiments show that with this algorithm, we successfully learn non-trivial strategies for scenarios with armies of up to 15 agents, where both Q-learning and REINFORCE struggle.
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A Tube-and-Droplet-based Approach for Representing and Analyzing Motion Trajectories. (arXiv:1609.03058v1 [cs.CV])
Trajectory analysis is essential in many applications. In this paper, we address the problem of representing motion trajectories in a highly informative way, and consequently utilize it for analyzing trajectories. Our approach first leverages the complete information from given trajectories to construct a thermal transfer field which provides a context-rich way to describe the global motion pattern in a scene. Then, a 3D tube is derived which depicts an input trajectory by integrating its surrounding motion patterns contained in the thermal transfer field. The 3D tube effectively: 1) maintains the movement information of a trajectory, 2) embeds the complete contextual motion pattern around a trajectory, 3) visualizes information about a trajectory in a clear and unified way. We further introduce a droplet-based process. It derives a droplet vector from a 3D tube, so as to characterize the high-dimensional 3D tube information in a simple but effective way. Finally, we apply our tube-and-droplet representation to trajectory analysis applications including trajectory clustering, trajectory classification & abnormality detection, and 3D action recognition. Experimental comparisons with state-of-the-art algorithms demonstrate the effectiveness of our approach.
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