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Saturday, August 1, 2015
Orioles Highlight: Manny Machado hits 23rd HR, Gerardo Parra 1-for-3 with run in team debut, a 6-2 win over Tigers (ESPN)
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[FD] Vulnerability in VirtueMart for Joomla
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PSG beats Lyon 2-0 in French league curtain raiser
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Wolfsburg beats Bayern on penalties to win German Super Cup
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I have a new follower on Twitter
Paddy
Futurist | Mentor to disruptive companies | Driving Digital Transformation through #IoT and #Data Science | Former CEO @ Aditi
Bellevue, Washington
http://t.co/6L7dU2sMeE
Following: 1974 - Followers: 1614
August 01, 2015 at 03:12PM via Twitter http://twitter.com/pradeeprat
Zenit, CSKA win in Russian Premier League
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Orioles: RHP Mike Wright on 15-day DL with left calf strain; RHP Jorge Rondon recalled (2-1, 2.01 ERA, 40.1 IP Triple-A) (ESPN)
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endotakashi:anonymous
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How Spies Could Unmask Tor Users without Cracking Encryption
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Stephan Beckenbauer, son of Franz, dies at 46
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How to Hack Millions of Android Phones Using Stagefright Bug, Without Sending MMS
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Vue anonymous function variable scope
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AntiVirus Firm BitDefender Hacked; Turns Out Stored Passwords Are UnEncrypted
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[FD] Symantec Endpoint Protection
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The ISS and a Colorful Moon
Friday, July 31, 2015
Orioles Highlight: Adam Jones hits 3-run HR, Machado belts go-ahead 2-run HR, rally from 6 runs down to beat Tigers 8-7 (ESPN)
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Ex-coach rejoins Oklahoma City soccer team after acquittal
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Fired Mexican soccer coach probed for political tweets
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Orlando City unveils new stadium design, completion set for 12 to 14 months
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Orioles: RP Tommy Hunter traded to Cubs for Triple-A OF Junior Lake (ESPN)
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Defendant in FIFA case appears in US court
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Spain replaces women's soccer coach Quereda after 27 years
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Orioles: Buster Olney says team \"could still make a Yoenis Cespedes deal work, even after getting Gerardo Parra\" (ESPN)
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Defendant in FIFA case to appear in US court
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Orioles: Team agrees to trade with Brewers to acquire OF Gerardo Parra for RHP Zach Davies, pending physicals - reports (ESPN)
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Windows 10 Wi-Fi Sense Explained: Actual Security Threat You Need to Know
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ISS Daily Summary Report – 07/30/15
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Mutko set to take control of Russian football union
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Coach Pep Guardiola still undecided about Bayern future
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Most Vulnerable Smart Cities to Cyber Attack on Internet of Things (IoT)
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Anonymous Devil
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New Zealand appeals Olympics disqualification
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Alcoholics Anonymous
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Anonymous donor funding IUD studies and distribution turns out to be Warren Buffett
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Milky Way over Uluru
Thursday, July 30, 2015
Orioles Video: Adam Jones and Chris Davis both homer as rally attempt falls just short in 9-8 loss to Tigers (ESPN)
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Anonymous tip-off sparks Italian car rental investigation
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Optimal estimates for short horizon travel time prediction in urban areas. (arXiv:1507.08444v1 [cs.AI])
Increasing popularity of mobile route planning applications based on GPS technology provides opportunities for collecting traffic data in urban environments. One of the main challenges for travel time estimation and prediction in such a setting is how to aggregate data from vehicles that have followed different routes, and predict travel time for other routes of interest. One approach is to predict travel times for route segments, and sum those estimates to obtain a prediction for the whole route. We study how to obtain optimal predictions in this scenario. It appears that the optimal estimate, minimizing the expected mean absolute error, is a combination of the mean and the median travel times on each segment, where the combination function depends on the number of segments in the route of interest. We present a methodology for obtaining such predictions, and demonstrate its effectiveness with a case study using travel time data from a district of St. Petersburg collected over one year. The proposed methodology can be applied for real-time prediction of expected travel times in an urban road network.
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A Model for Foraging Ants, Controlled by Spiking Neural Networks and Double Pheromones. (arXiv:1507.08467v1 [cs.NE])
A model of an Ant System where ants are controlled by a spiking neural circuit and a second order pheromone mechanism in a foraging task is presented. A neural circuit is trained for individual ants and subsequently the ants are exposed to a virtual environment where a swarm of ants performed a resource foraging task. The model comprises an associative and unsupervised learning strategy for the neural circuit of the ant. The neural circuit adapts to the environment by means of classical conditioning. The initially unknown environment includes different types of stimuli representing food and obstacles which, when they come in direct contact with the ant, elicit a reflex response in the motor neural system of the ant: moving towards or away from the source of the stimulus. The ants are released on a landscape with multiple food sources where one ant alone would have difficulty harvesting the landscape to maximum efficiency. The introduction of a double pheromone mechanism yields better results than traditional ant colony optimization strategies. Traditional ant systems include mainly a positive reinforcement pheromone. This approach uses a second pheromone that acts as a marker for forbidden paths (negative feedback). This blockade is not permanent and is controlled by the evaporation rate of the pheromones. The combined action of both pheromones acts as a collective stigmergic memory of the swarm, which reduces the search space of the problem. This paper explores how the adaptation and learning abilities observed in biologically inspired cognitive architectures is synergistically enhanced by swarm optimization strategies. The model portraits two forms of artificial intelligent behaviour: at the individual level the spiking neural network is the main controller and at the collective level the pheromone distribution is a map towards the solution emerged by the colony.
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Framework for learning agents in quantum environments. (arXiv:1507.08482v1 [quant-ph])
In this paper we provide a broad framework for describing learning agents in general quantum environments. We analyze the types of classically specified environments which allow for quantum enhancements in learning, by contrasting environments to quantum oracles. We show that whether or not quantum improvements are at all possible depends on the internal structure of the quantum environment. If the environments are constructed and the internal structure is appropriately chosen, or if the agent has limited capacities to influence the internal states of the environment, we show that improvements in learning times are possible in a broad range of scenarios. Such scenarios we call luck-favoring settings. The case of constructed environments is particularly relevant for the class of model-based learning agents, where our results imply a near-generic improvement.
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CRISNER: A Practically Efficient Reasoner for Qualitative Preferences. (arXiv:1507.08559v1 [cs.AI])
We present CRISNER (Conditional & Relative Importance Statement Network PrEference Reasoner), a tool that provides practically efficient as well as exact reasoning about qualitative preferences in popular ceteris paribus preference languages such as CP-nets, TCP-nets, CP-theories, etc. The tool uses a model checking engine to translate preference specifications and queries into appropriate Kripke models and verifiable properties over them respectively. The distinguishing features of the tool are: (1) exact and provably correct query answering for testing dominance, consistency with respect to a preference specification, and testing equivalence and subsumption of two sets of preferences; (2) automatic generation of proofs evidencing the correctness of answer produced by CRISNER to any of the above queries; (3) XML inputs and outputs that make it portable and pluggable into other applications. We also describe the extensible architecture of CRISNER, which can be extended to new reference formalisms based on ceteris paribus semantics that may be developed in the future.
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The RatioLog Project: Rational Extensions of Logical Reasoning. (arXiv:1503.06087v2 [cs.AI] UPDATED)
Higher-level cognition includes logical reasoning and the ability of question answering with common sense. The RatioLog project addresses the problem of rational reasoning in deep question answering by methods from automated deduction and cognitive computing. In a first phase, we combine techniques from information retrieval and machine learning to find appropriate answer candidates from the huge amount of text in the German version of the free encyclopedia "Wikipedia". In a second phase, an automated theorem prover tries to verify the answer candidates on the basis of their logical representations. In a third phase - because the knowledge may be incomplete and inconsistent -, we consider extensions of logical reasoning to improve the results. In this context, we work toward the application of techniques from human reasoning: We employ defeasible reasoning to compare the answers w.r.t. specificity, deontic logic, normative reasoning, and model construction. Moreover, we use integrated case-based reasoning and machine learning techniques on the basis of the semantic structure of the questions and answer candidates to learn giving the right answers.
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Quantifying Creativity in Art Networks. (arXiv:1506.00711v1 [cs.AI] CROSS LISTED)
Can we develop a computer algorithm that assesses the creativity of a painting given its context within art history? This paper proposes a novel computational framework for assessing the creativity of creative products, such as paintings, sculptures, poetry, etc. We use the most common definition of creativity, which emphasizes the originality of the product and its influential value. The proposed computational framework is based on constructing a network between creative products and using this network to infer about the originality and influence of its nodes. Through a series of transformations, we construct a Creativity Implication Network. We show that inference about creativity in this network reduces to a variant of network centrality problems which can be solved efficiently. We apply the proposed framework to the task of quantifying creativity of paintings (and sculptures). We experimented on two datasets with over 62K paintings to illustrate the behavior of the proposed framework. We also propose a methodology for quantitatively validating the results of the proposed algorithm, which we call the "time machine experiment".
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James Woods Sues Anonymous Twitter User For $10 Million
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President of CONCACAF to review refereeing department
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President of CONCACAF to review refereeing department
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French soccer referee gets 4-month suspension
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Correction: FIFA-Zimbabwe Fined story
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ISS Daily Summary Report – 07/29/15
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Hackers Can Remotely Hack Self-Aiming Rifles to Change Its Target
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New Android Vulnerability Could Crash your Phones Badly
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Asia football chief praises Platini as potential FIFA boss
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United Airlines Hacked by Sophisticated Hacking Group
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The Deep Lagoon
Wednesday, July 29, 2015
Deportivo Municipal sigue de líder en fútbol peruano
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Orioles Highlight: Jonathan Schoop and J.J. Hardy homer to back Chris Tillman (8.2 IP, 4 H) in 2-0 win over Braves (ESPN)
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Anonymous follows through on a threat
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I have a new follower on Twitter
davd soul
Former practicing attorney and journalist from Chicago who is currently a publicist for an international law firm, Author, Artist and Poet.
Falls Church, VA
http://t.co/JgX8KQe5Cs
Following: 1581 - Followers: 1514
July 29, 2015 at 08:52PM via Twitter http://twitter.com/davdsoul
Anonymous on Twitter: "Canada investigates #Anonymous after release of document showing ...
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[FD] Fwd: CVE_for_Vulnerability_theholidaycalendar
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Document Embedding with Paragraph Vectors. (arXiv:1507.07998v1 [cs.CL])
Paragraph Vectors has been recently proposed as an unsupervised method for learning distributed representations for pieces of texts. In their work, the authors showed that the method can learn an embedding of movie review texts which can be leveraged for sentiment analysis. That proof of concept, while encouraging, was rather narrow. Here we consider tasks other than sentiment analysis, provide a more thorough comparison of Paragraph Vectors to other document modelling algorithms such as Latent Dirichlet Allocation, and evaluate performance of the method as we vary the dimensionality of the learned representation. We benchmarked the models on two document similarity data sets, one from Wikipedia, one from arXiv. We observe that the Paragraph Vector method performs significantly better than other methods, and propose a simple improvement to enhance embedding quality. Somewhat surprisingly, we also show that much like word embeddings, vector operations on Paragraph Vectors can perform useful semantic results.
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Communication: Words and Conceptual Systems. (arXiv:1507.08073v1 [cs.AI])
Words or phrases play a key role in natural language. Word (or phrase) representation is the fundamental problem for knowledge representation and understanding. However, it is always a challenge that how to represent a word (or phrase) can make it easily understdooIn this paper, a new representation for a category is discussed, which defines the inner name, the outer name, inner referring, outer referring and so on. In order to reduce the complexity of category representation, the economy principle of category representation is proposed. Moreover, the conceptual system and communication are also discussed. The conditions for perfect communication are discovered.
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A Gauss-Newton Method for Markov Decision Processes. (arXiv:1507.08271v1 [cs.AI])
Approximate Newton methods are a standard optimization tool which aim to maintain the benefits of Newton's method, such as a fast rate of convergence, whilst alleviating its drawbacks, such as computationally expensive calculation or estimation of the inverse Hessian. In this work we investigate approximate Newton methods for policy optimization in Markov Decision Processes (MDPs). We first analyse the structure of the Hessian of the objective function for MDPs. We show that, like the gradient, the Hessian exhibits useful structure in the context of MDPs and we use this analysis to motivate two Gauss-Newton Methods for MDPs. Like the Gauss-Newton method for non-linear least squares, these methods involve approximating the Hessian by ignoring certain terms in the Hessian which are difficult to estimate. The approximate Hessians possess desirable properties, such as negative definiteness, and we demonstrate several important performance guarantees including guaranteed ascent directions, invariance to affine transformation of the parameter space, and convergence guarantees. We finally provide a unifying perspective of key policy search algorithms, demonstrating that our second Gauss-Newton algorithm is closely related to both the EM-algorithm and natural gradient ascent applied to MDPs, but performs significantly better in practice on a range of challenging domains.
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SAT-based Analysis of Large Real-world Feature Models is Easy. (arXiv:1506.05198v3 [cs.SE] UPDATED)
Modern conflict-driven clause-learning (CDCL) Boolean SAT solvers provide efficient automatic analysis of real-world feature models (FM) of systems ranging from cars to operating systems. It is well-known that solver-based analysis of real-world FMs scale very well even though SAT instances obtained from such FMs are large, and the corresponding analysis problems are known to be NP-complete. To better understand why SAT solvers are so effective, we systematically studied many syntactic and semantic characteristics of a representative set of large real-world FMs. We discovered that a key reason why large real-world FMs are easy-to-analyze is that the vast majority of the variables in these models are unrestricted, i.e., the models are satisfiable for both true and false assignments to such variables under the current partial assignment. Given this discovery and our understanding of CDCL SAT solvers, we show that solvers can easily find satisfying assignments for such models without too many backtracks relative to the model size, explaining why solvers scale so well. Further analysis showed that the presence of unrestricted variables in these real-world models can be attributed to their high-degree of variability. Additionally, we experimented with a series of well-known non-backtracking simplifications that are particularly effective in solving FMs. The remaining variables/clauses after simplifications, called the core, are so few that they are easily solved even with backtracking, further strengthening our conclusions.
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[FD] Assessing The Computer Network Operation (CNO) Capabilities of the Islamic Republic of Iran - Report
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CONMEBOL outlines reforms after FIFA bribery scandal
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TD Naughten: I contacted the Gardaí after anonymous threats
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Platini confirms he will run for FIFA president
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Douglas powers Hiroshima to 4-0 win in J-League
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ISS Daily Summary Report – 07/28/15
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Michel Platini confirms he will run for FIFA president
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FIFA fines Zimbabwe for missing women's Olympic qualifier
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Anonymous on Twitter: "#Snowden case legal, not political: His lawyer http://t.co/k82cjlgsNR http://t ...
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Mob Museum in Las Vegas to open FIFA exhibit
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This $10 Device Can Clone RFID-equipped Access Cards Easily
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Abou Diaby set for Marseille move
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New Zealand faces new complaint over player eligibility
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Anonymous Vows to Keep Leaking Canadian Spy Secrets Over Police Shooting
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Rainbows and Rays over Bryce Canyon
Run-in with reporter triggered Mexico coach Herrera's ouster
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Tuesday, July 28, 2015
Orioles Highlight: Chris Davis hits 3-run HR in 1st inning, 2-run shot in 2nd (10th multi-HR game) in 7-3 win vs. Braves (ESPN)
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Anonymous leaks CSIS document
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I have a new follower on Twitter
The Red Carpet Model
The Red Carpet Modeling & Talent Agency has developed the undisputed path for people seeking to enter the modeling and entertainment business.
3314 Henderson Blvd. Tampa FL
http://t.co/gBnLZdQreq
Following: 3426 - Followers: 3537
July 28, 2015 at 10:36PM via Twitter http://twitter.com/RedCarpetModel
Projected Model Counting. (arXiv:1507.07648v1 [cs.AI])
Model counting is the task of computing the number of assignments to variables V that satisfy a given propositional theory F. Model counting is an essential tool in probabilistic reasoning. In this paper, we introduce the problem of model counting projected on a subset P of original variables that we call 'priority' variables. The task is to compute the number of assignments to P such that there exists an extension to 'non-priority' variables V\P that satisfies F. Projected model counting arises when some parts of the model are irrelevant to the counts, in particular when we require additional variables to model the problem we are counting in SAT. We discuss three different approaches to projected model counting (two of which are novel), and compare their performance on different benchmark problems.
To appear in 18th International Conference on Theory and Applications of Satisfiability Testing, September 24-27, 2015, Austin, Texas, USA
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Computation of Stackelberg Equilibria of Finite Sequential Games. (arXiv:1507.07677v1 [cs.GT])
The Stackelberg equilibrium solution concept describes optimal strategies to commit to: Player 1 (termed the leader) publicly commits to a strategy and Player 2 (termed the follower) plays a best response to this strategy (ties are broken in favor of the leader). We study Stackelberg equilibria in finite sequential games (or extensive-form games) and provide new exact algorithms, approximate algorithms, and hardness results for several classes of these sequential games.
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Belief and Truth in Hypothesised Behaviours. (arXiv:1507.07688v1 [cs.AI])
There is a long history in game theory on the topic of Bayesian or "rational" learning, in which players maintain beliefs over a set of alternative behaviours, or types. This idea has gained increasing interest in the AI community, where it is used to control a single agent in a system composed of multiple agents with unknown behaviours. The idea is to hypothesise a set of types, each specifying a possible behaviour for the other agents, and to plan our own actions with respect to those types which we believe are most likely, based on the observed actions. The game theory literature studies this idea primarily in the context of equilibrium attainment. In contrast, many AI applications have a focus on task completion and payoff maximisation, which renders the game theory literature on this subject of limited applicability. With this perspective in mind, we identify and address a spectrum of questions pertaining to belief and truth in hypothesised types. We formulate three basic ways to incorporate evidence into posterior beliefs and show when the resulting beliefs are correct, and when they may fail to be correct. Moreover, we demonstrate that prior beliefs can have a significant impact on our ability to maximise payoffs in the long-term, and that they can be computed automatically with consistent performance effects. Furthermore, we analyse the conditions under which we are able complete our task optimally, despite inaccuracies in the hypothesised types. Finally, we show how the correctness of hypothesised types can be ascertained during the interaction via an automated statistical analysis.
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Scaling up Greedy Equivalence Search for Continuous Variables. (arXiv:1507.07749v1 [cs.AI])
As standardly implemented in R or in the Tetrad program, causal search algorithms that have been most widely or effectively used in scientific problems have severe dimensionality constraints. However, implementation improvements are possible that extend the feasible dimensionality of search problems by several orders of magnitude. We describe optimizations for the Greedy Equivalence Search (GES) that allow search on 50,000 variable problems in 13 minutes for sparse models with 1000 samples, on a 4 processor 8G laptop computer, and in 18 hours for sparse models with 1000 samples on 1,000,000 variables on a supercomputer node at the Pittsburgh Supercomputing Center with 40 processors and 384 G RAM, on data generated i.i.d. from a linear, Gaussian model.
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Detect & Describe: Deep learning of bank stress in the news. (arXiv:1507.07870v1 [q-fin.CP])
News is a pertinent source of information on financial risks and stress factors, which nevertheless is challenging to harness due to the sparse and unstructured nature of natural text. We propose an approach based on distributional semantics and deep learning with neural networks to model and link text to a scarce set of bank distress events. Through unsupervised training, we learn semantic vector representations of news articles as predictors of distress events. The predictive model that we learn can signal coinciding stress with an aggregated index at bank or European level, while crucially allowing for automatic extraction of text descriptions of the events, based on passages with high stress levels. The method offers insight that models based on other types of data cannot provide, while offering a general means for interpreting this type of semantic-predictive model. We model bank distress with data on 243 events and 6.6M news articles for 101 large European banks.
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GC-SROIQ(C) : Expressive Constraint Modelling and Grounded Circumscription for SROIQ. (arXiv:1411.0406v3 [cs.AI] UPDATED)
Developments in semantic web technologies have promoted ontological encoding of knowledge from diverse domains. However, modelling many practical domains requires more expressive representations schemes than what the standard description logics(DLs) support. We extend the DL SROIQ with constraint networks and grounded circumscription. Applications of constraint modelling include embedding ontologies with temporal or spatial information, while grounded circumscription allows defeasible inference and closed world reasoning. This paper overcomes restrictions on existing constraint modelling approaches by introducing expressive constructs. Grounded circumscription allows concept and role minimization and is decidable for DL. We provide a general and intuitive algorithm for the framework of grounded circumscription that can be applied to a whole range of logics. We present the resulting logic: GC-SROIQ(C), and describe a tableau decision procedure for it.
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Recent advances on inconsistency indices for pairwise comparisons - a commentary. (arXiv:1503.08289v2 [cs.AI] UPDATED)
This paper recalls the definition of consistency for pairwise comparison matrices and briefly presents the concept of inconsistency index in connection to other aspects of the theory of pairwise comparisons. By commenting on a recent contribution by Koczkodaj and Szwarc, it will be shown that the discussion on inconsistency indices is far from being over, and the ground is still fertile for debates.
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Mob Museum in Las Vegas to open FIFA exhibit
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[FD] Reflected XSS in Flickr Justified Gallery could allows unauthenticated attackers to do almost anything an admin can do (WordPress plugin)
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[FD] CSRF and XSS vulnerabilities in D-Link DCS-2103
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Mob Museum in Las Vegas to open FIFA exhibit
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Mexico coach Herrera fired after claim he punched reporter
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Mexico national soccer coach fired after journalist's claim
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Embattled Mexico soccer coach 'El Piojo' Herrera fired after claim he punched journalist
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2-time champion Bellucci advances at Swiss Open
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Atletico Madrid re-signs Brazil defender Felipe Luis
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A way to encourage/enforce constructive comments rather than anonymous down-votes. Please ...
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AP Sources: Michel Platini will run for FIFA president
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ISS Daily Summary Report – 7/27/15
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Bayern Munich signs Chile midfielder Arturo Vidal
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Congolese player remains hospitalized after spinal injury
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Sepp Blatter deserves the Nobel Prize, Vladimir Putin says
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Jovetic undergoing medical with Inter in move from City
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FIFA's Blatter deserves Nobel Prize, says Putin
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NSA to Destroy Bulk Collection of Surveillance Data
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[FD] SEC Consult SA-20150728-0 :: McAfee Application Control Multiple Vulnerabilities
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Critical Persistent Injection Vulnerability in Apple App Store and iTunes
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Milky Way and Aurora over Antarctica
MLB Buzz Video: Jim Bowden says that Orioles are targeting \"outfield bats\" Justin Upton, Jay Bruce and Carlos Gomez (ESPN)
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Monday, July 27, 2015
Orioles Highlight: Matt Wieters hits walk-off HR in 11th (1st HR since June 25) in 2-1 win over Braves; 3rd W in row (ESPN)
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I have a new follower on Twitter
Natalie Hicks
I have a naughty sense of humor, love to laugh! DC United fan.
Washington, DC
Following: 503 - Followers: 148
July 27, 2015 at 09:32PM via Twitter http://twitter.com/MsNatalieHicks
Truth Serums for Massively Crowdsourced Evaluation Tasks. (arXiv:1507.07045v1 [cs.GT])
Incentivizing effort and eliciting truthful responses from agents in the absence of verifiability is a major challenge faced while crowdsourcing many types of evaluation tasks like labeling images, grading assignments in online courses, etc. In this paper, we propose new reward mechanisms for such settings that, unlike most previously studied mechanisms, impose minimal assumptions on the structure and knowledge of the underlying generating model, can account for heterogeneity in the agents' abilities, require no extraneous elicitation from them, and furthermore allow their beliefs to be (almost) arbitrary. Moreover, these mechanisms have the simple and intuitive structure of output agreement mechanisms, which, despite not incentivizing truthful behavior, have nevertheless been quite popular in practice. We achieve this by leveraging a typical characteristic of many of these settings, which is the existence of a large number of similar tasks.
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The Digital Synaptic Neural Substrate: A New Approach to Computational Creativity. (arXiv:1507.07058v1 [cs.AI])
We introduce a new artificial intelligence (AI) approach called, the 'Digital Synaptic Neural Substrate' (DSNS). It uses selected attributes from objects in various domains (e.g. chess problems, classical music, renowned artworks) and recombines them in such a way as to generate new attributes that can then, in principle, be used to create novel objects of creative value to humans relating to any one of the source domains. This allows some of the burden of creative content generation to be passed from humans to machines. The approach was tested in the domain of chess problem composition. We used it to automatically compose numerous sets of chess problems based on attributes extracted and recombined from chess problems and tournament games by humans, renowned paintings, computer-evolved abstract art, photographs of people, and classical music tracks. The quality of these generated chess problems was then assessed automatically using an existing and experimentally-validated computational chess aesthetics model. They were also assessed by human experts in the domain. The results suggest that attributes collected and recombined from chess and other domains using the DSNS approach can indeed be used to automatically generate chess problems of reasonably high aesthetic quality. In particular, a low quality chess source (i.e. tournament game sequences between weak players) used in combination with actual photographs of people was able to produce three-move chess problems of comparable quality or better to those generated using a high quality chess source (i.e. published compositions by human experts), and more efficiently as well. Why information from a foreign domain can be integrated and functional in this way remains an open question for now. The DSNS approach is, in principle, scalable and applicable to any domain in which objects have attributes that can be represented using real numbers.
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Learning (Predictive) Risk Scores in the Presence of Censoring due to Interventions. (arXiv:1507.07295v1 [cs.AI])
A large and diverse set of measurements are regularly collected during a patient's hospital stay to monitor their health status. Tools for integrating these measurements into severity scores, that accurately track changes in illness severity, can improve clinicians ability to provide timely interventions. Existing approaches for creating such scores either 1) rely on experts to fully specify the severity score, or 2) train a predictive score, using supervised learning, by regressing against a surrogate marker of severity such as the presence of downstream adverse events. The first approach does not extend to diseases where an accurate score cannot be elicited from experts. The second approach often produces scores that suffer from bias due to treatment-related censoring (Paxton, 2013). We propose a novel ranking based framework for disease severity score learning (DSSL). DSSL exploits the following key observation: while it is challenging for experts to quantify the disease severity at any given time, it is often easy to compare the disease severity at two different times. Extending existing ranking algorithms, DSSL learns a function that maps a vector of patient's measurements to a scalar severity score such that the resulting score is temporally smooth and consistent with the expert's ranking of pairs of disease states. We apply DSSL to the problem of learning a sepsis severity score using a large, real-world dataset. The learned scores significantly outperform state-of-the-art clinical scores in ranking patient states by severity and in early detection of future adverse events. We also show that the learned disease severity trajectories are consistent with clinical expectations of disease evolution. Further, using simulated datasets, we show that DSSL exhibits better generalization performance to changes in treatment patterns compared to the above approaches.
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A genetic algorithm for autonomous navigation in partially observable domain. (arXiv:1507.07374v1 [cs.LG])
The problem of autonomous navigation is one of the basic problems for robotics. Although, in general, it may be challenging when an autonomous vehicle is placed into partially observable domain. In this paper we consider simplistic environment model and introduce a navigation algorithm based on Learning Classifier System.
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Unification of Fusion Theories, Rules, Filters, Image Fusion and Target Tracking Methods (UFT). (arXiv:1507.07462v1 [cs.AI])
The author has pledged in various papers, conference or seminar presentations, and scientific grant applications (between 2004-2015) for the unification of fusion theories, combinations of fusion rules, image fusion procedures, filter algorithms, and target tracking methods for more accurate applications to our real world problems - since neither fusion theory nor fusion rule fully satisfy all needed applications. For each particular application, one selects the most appropriate fusion space and fusion model, then the fusion rules, and the algorithms of implementation. He has worked in the Unification of the Fusion Theories (UFT), which looks like a cooking recipe, better one could say like a logical chart for a computer programmer, but one does not see another method to comprise/unify all things. The unification scenario presented herein, which is now in an incipient form, should periodically be updated incorporating new discoveries from the fusion and engineering research.
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On the read-once property of branching programs and CNFs of bounded treewidth. (arXiv:1411.0264v3 [cs.CC] UPDATED)
In this paper we prove a space lower bound of $n^{\Omega(k)}$ for non-deterministic (syntactic) read-once branching programs ({\sc nrobp}s) on functions expressible as {\sc cnf}s with treewidth at most $k$ of their primal graphs. This lower bound rules out the possibility of fixed-parameter space complexity of {\sc nrobp}s parameterized by $k$.
We use lower bound for {\sc nrobp}s to obtain a quasi-polynomial separation between Free Binary Decision Diagrams and Decision Decomposable Negation Normal Forms, essentially matching the existing upper bound introduced by Beame et al. and thus proving the tightness of the latter.
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Design Mining VAWT Wind Farms. (arXiv:1506.08781v2 [cs.NE] UPDATED)
The small body of previous work considering the design of wind farms has used arrays of turbines originally intended to operate alone, optimising the layout of homogeneous turbines essentially as an afterthought in the design process. In this paper, we consider designing wind farms composed of collaborating wind turbines. Computational intelligence is combined with rapid prototyping whereby candidate designs are physically instantiated and evaluated under fan-generated wind conditions. It is shown possible to use surrogate-assisted coevolutionary algorithms to aerodynamically optimise the potentially heterogeneous morphology of an array of 6 small scale and closely positioned vertical-axis wind turbines using the total angular kinetic energy of the array as the objective. This approach performs optimisation in the presence of complex inter-turbine wake effects and multi-directional wind flow from nearby obstacles, which is extremely difficult to achieve accurately under high fidelity computational fluid dynamics simulation. The general approach is equally applicable to the design of other forms of sustainable energy where the characteristics of the environment and/or materials involved are too difficult to accurately simulate.
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[FD] Another Snorby 2.6.2 - Stored Cross-site Scripting Vulnerability
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Shannon Boxx and Lauren Holiday to retire after victory tour
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Mexico national team coach allegedly hits TV journalist
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World Cup qualifier for US set for St. Louis
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Anonymous Employer Feedback
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The US government just revealed how Anonymous hackers stole its data
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FIFA tweaks appointment rules for reforms task force
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Juventus to visit Roma in 2nd round of Serie A
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New Parma club approved for Serie D after bankruptcy
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Hacking Air-Gapped Computer With A Simple Cell Phone
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Simple Text Message to Hack Any Android Phone Remotely
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Trinidad extradition hearing delayed for ex-FIFA VP Warner
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Aston Villa signs forward Jordan Ayew from Lorient
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[FD] Apple iTunes & AppStore - Filter Bypass & Persistent Invoice Vulnerability
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App Store | TYP | GEKAUFT BEI | PREIS | |
DuckTales: Remastered Disney Eine Rezension schreiben | Problem melden |
App | [PERSISTENT INJECTED SCRIPT CODE VULNERABILITY!]bkm337">%20 | 9,99 € |