OpenRaiser/Intern-Atlas huggingface dataset:
Symmetry Based Cluster Approach for Automatic Recognition of the Epileptic Focus in Brain Using PET Scan Image : An Analysis
Venue: arXiv cs.CV 2013
Year: 2013

Recognition of epileptic focal point is the important diagnosis when screening the epilepsy patients for latent surgical cures. The accurate localization is challenging one because of the low spatial resolution images with more noisy data. Positron Emission Tomography (PET) has now replaced the issues and caring a high resolution. This paper focuses the research of automated localization of epileptic seizures in brain functional images using symmetry based cluster approach. This approach presents a fully automated symmetry based brain abnormality detection method for PET sequences. PET images are spatially normalized to Digital Imaging and Communications in Medicine (DICOM) standard and then it has been trained using symmetry based cluster approach using Medical Image Processing, Analysis & Visualization (MIPAV) tool. The performance evolution is considered by the metric like accuracy of diagnosis. The obtained result is surely assists the surgeon for the automated identification of seizures focus.
The Zen of Multidisciplinary Team Recommendation
Venue: arXiv cs.SI 2013
Year: 2013

In order to accomplish complex tasks, it is often necessary to compose a team consisting of experts with diverse competencies. However, for proper functioning, it is also preferable that a team be socially cohesive. A team recommendation system, which facilitates the search for potential team members can be of great help both for (i) individuals who need to seek out collaborators and (ii) managers who need to build a team for some specific tasks. A decision support system which readily helps summarize such metrics, and possibly rank the teams in a personalized manner according to the end users' preferences, can be a great tool to navigate what would otherwise be an information avalanche. In this work we present a general framework of how to compose such subsystems together to build a composite team recommendation system, and instantiate it for a case study of academic teams.
Second Order Asymptotics for Random Number Generation
Venue: arXiv cs.IT 2013
Year: 2013

We treat a random number generation from an i.i.d. probability distribution of $P$ to that of $Q$. When $Q$ or $P$ is a uniform distribution, the problems have been well-known as the uniform random number generation and the resolvability problem respectively, and analyzed not only in the context of the first order asymptotic theory but also that in the second asymptotic theory. On the other hand, when both $P$ and $Q$ are not a uniform distribution, the second order asymptotics has not been treated. In this paper, we focus on the second order asymptotics of a random number generation for arbitrary probability distributions $P$ and $Q$ on a finite set. In particular, we derive the optimal second order generation rate under an arbitrary permissible confidence coefficient.
Approximation Algorithms for Stochastic Boolean Function Evaluation and Stochastic Submodular Set Cover
Venue: arXiv cs.DS 2013
Year: 2013

Stochastic Boolean Function Evaluation is the problem of determining the value of a given Boolean function f on an unknown input x, when each bit of x_i of x can only be determined by paying an associated cost c_i. The assumption is that x is drawn from a given product distribution, and the goal is to minimize the expected cost. This problem has been studied in Operations Research, where it is known as "sequential testing" of Boolean functions. It has also been studied in learning theory in the context of learning with attribute costs. We consider the general problem of developing approximation algorithms for Stochastic Boolean Function Evaluation. We give a 3-approximation algorithm for evaluating Boolean linear threshold formulas. We also present an approximation algorithm for evaluating CDNF formulas (and decision trees) achieving a factor of O(log kd), where k is the number of terms in the DNF formula, and d is the number of clauses in the CNF formula. In addition, we present approximation algorithms for simultaneous evaluation of linear threshold functions, and for ranking of linear functions. Our function evaluation algorithms are based on reductions to the Stochastic Submodular Set Cover (SSSC) problem. This problem was introduced by Golovin and Krause. They presented an approximation algorithm for the problem, called Adaptive Greedy. Our main technical contribution is a new approximation algorithm for the SSSC problem, which we call Adaptive Dual Greedy. It is an extension of the Dual Greedy algorithm for Submodular Set Cover due to Fujito, which is a generalization of Hochbaum's algorithm for the classical Set Cover Problem. We also give a new bound on the approximation achieved by the Adaptive Greedy algorithm of Golovin and Krause.
Multivariate Temporal Dictionary Learning for EEG
Venue: arXiv cs.LG 2013
Year: 2013

This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG signals, this article proposes a data-driven method to obtain an adapted dictionary. To reach an efficient dictionary learning, appropriate spatial and temporal modeling is required. Inter-channels links are taken into account in the spatial multivariate model, and shift-invariance is used for the temporal model. Multivariate learned kernels are informative (a few atoms code plentiful energy) and interpretable (the atoms can have a physiological meaning). Using real EEG data, the proposed method is shown to outperform the classical multichannel matching pursuit used with a Gabor dictionary, as measured by the representative power of the learned dictionary and its spatial flexibility. Moreover, dictionary learning can capture interpretable patterns: this ability is illustrated on real data, learning a P300 evoked potential.
Reasoning about Strategies under Partial Observability and Fairness Constraints
Venue: arXiv cs.LO 2013
Year: 2013

A number of extensions exist for Alternating-time Temporal Logic; some of these mix strategies and partial observability but, to the best of our knowledge, no work provides a unified framework for strategies, partial observability and fairness constraints. In this paper we propose ATLK^F_po, a logic mixing strategies under partial observability and epistemic properties of agents in a system with fairness constraints on states, and we provide a model checking algorithm for it.
Towards an Updatable Strategy Logic
Venue: arXiv cs.LO 2013
Year: 2013

This article is about temporal multi-agent logics. Several of these formalisms have been already presented (ATL-ATL*, ATLsc, SL). They enable to express the capacities of agents in a system to ensure the satisfaction of temporal properties. Particularly, SL and ATLsc enable several agents to interact in a context mixing the different strategies they play in a semantical game. We generalize this possibility by proposing a new formalism, Updating Strategy Logic (USL). In USL, an agent can also refine its own strategy. The gain in expressive power rises the notion of "sustainable capacities" for agents. USL is built from SL. It mainly brings to SL the two following modifications: semantically, the successor of a given state is not uniquely determined by the data of one choice from each agent. Syntactically, we introduce in the language an operator, called an "unbinder", which explicitely deletes the binding of a strategy to an agent. We show that USL is strictly more expressive than SL.
A rewriting point of view on strategies
Venue: arXiv cs.LO 2013
Year: 2013

This paper is an expository contribution reporting on published work. It focusses on an approach followed in the rewriting community to formalize the concept of strategy. Based on rewriting concepts, several definitions of strategy are reviewed and connected: in order to catch the higher-order nature of strategies, a strategy is defined as a proof term expressed in the rewriting logic or in the rewriting calculus; to address in a coherent way deduction and computation, a strategy is seen as a subset of derivations; and to recover the definition of strategy in sequential path-building games or in functional programs, a strategy is considered as a partial function that associates to a reduction-in-progress, the possible next steps in the reduction sequence.
The Complexity of Synthesizing Uniform Strategies
Venue: arXiv cs.GT 2013
Year: 2013

We investigate uniformity properties of strategies. These properties involve sets of plays in order to express useful constraints on strategies that are not μ-calculus definable. Typically, we can state that a strategy is observation-based. We propose a formal language to specify uniformity properties, interpreted over two-player turn-based arenas equipped with a binary relation between plays. This way, we capture e.g. games with winning conditions expressible in epistemic temporal logic, whose underlying equivalence relation between plays reflects the observational capabilities of agents (for example, synchronous perfect recall). Our framework naturally generalizes many other situations from the literature. We establish that the problem of synthesizing strategies under uniformity constraints based on regular binary relations between plays is non-elementary complete.
Structural and Cognitive Bottlenecks to Information Access in Social Networks
Venue: arXiv cs.SI 2013
Year: 2013

Information in networks is non-uniformly distributed, enabling individuals in certain network positions to get preferential access to information. Social scientists have developed influential theories about the role of network structure in information access. These theories were validated through numerous studies, which examined how individuals leverage their social networks for competitive advantage, such as a new job or higher compensation. It is not clear how these theories generalize to online networks, which differ from real-world social networks in important respects, including asymmetry of social links. We address this problem by analyzing how users of the social news aggregator Digg adopt stories recommended by friends, i.e., users they follow. We measure the impact different factors, such as network position and activity rate; have on access to novel information, which in Digg's case means set of distinct news stories. We show that a user can improve his information access by linking to active users, though this becomes less effective as the number of friends, or their activity, grows due to structural network constraints. These constraints arise because users in structurally diverse position within the follower graph have topically diverse interests from their friends. Moreover, though in most cases user's friends are exposed to almost all the information available in the network, after they make their recommendations, the user sees only a small fraction of the available information. Our study suggests that cognitive and structural bottlenecks limit access to novel information in online social networks.
KRAB Algorithm - A Revised Algorithm for Incremental Call Graph Generation
Venue: arXiv cs.PL 2013
Year: 2013

This paper is aimed to present the importance and implementation of an incremental call graph plugin. An algorithm is proposed for the call graph implementation which has better overall performance than the algorithm that has been proposed previously. In addition to this, the algorithm has been empirically proved to have excellent performance on recursive codes. The algorithm also readily checks for function skip and returns exceptions.
Reduced-Rank DOA Estimation based on Joint Iterative Subspace Optimization and Grid Search
Venue: arXiv cs.IT 2013
Year: 2013

In this paper, we propose a novel reduced-rank algorithm for direction of arrival (DOA) estimation based on the minimum variance (MV) power spectral evaluation. It is suitable to DOA estimation with large arrays and can be applied to arbitrary array geometries. The proposed DOA estimation algorithm is formulated as a joint optimization of a subspace projection matrix and an auxiliary reduced-rank parameter vector with respect to the MV and grid search. A constrained least squares method is employed to solve this joint optimization problem for the output power over the grid. The proposed algorithm is described for problems of large number of users' direction finding with or without exact information of the number of sources, and does not require the singular value decomposition (SVD). The spatial smoothing (SS) technique is also employed in the proposed algorithm for dealing with correlated sources problem. Simulations are conducted with comparisons against existent algorithms to show the improved performance of the proposed algorithm in different scenarios.
A Generalized Hybrid Real-Coded Quantum Evolutionary Algorithm Based on Particle Swarm Theory with Arithmetic Crossover
Venue: arXiv cs.NE 2013
Year: 2013

This paper proposes a generalized Hybrid Real-coded Quantum Evolutionary Algorithm (HRCQEA) for optimizing complex functions as well as combinatorial optimization. The main idea of HRCQEA is to devise a new technique for mutation and crossover operators. Using the evolutionary equation of PSO a Single-Multiple gene Mutation (SMM) is designed and the concept of Arithmetic Crossover (AC) is used in the new Crossover operator. In HRCQEA, each triploid chromosome represents a particle and the position of the particle is updated using SMM and Quantum Rotation Gate (QRG), which can make the balance between exploration and exploitation. Crossover is employed to expand the search space, Hill Climbing Selection (HCS) and elitism help to accelerate the convergence speed. Simulation results on Knapsack Problem and five benchmark complex functions with high dimension show that HRCQEA performs better in terms of ability to discover the global optimum and convergence speed.
Discovery of factors in matrices with grades
Venue: arXiv cs.LG 2013
Year: 2013

We present an approach to decomposition and factor analysis of matrices with ordinal data. The matrix entries are grades to which objects represented by rows satisfy attributes represented by columns, e.g. grades to which an image is red, a product has a given feature, or a person performs well in a test. We assume that the grades form a bounded scale equipped with certain aggregation operators and conforms to the structure of a complete residuated lattice. We present a greedy approximation algorithm for the problem of decomposition of such matrix in a product of two matrices with grades under the restriction that the number of factors be small. Our algorithm is based on a geometric insight provided by a theorem identifying particular rectangular-shaped submatrices as optimal factors for the decompositions. These factors correspond to formal concepts of the input data and allow an easy interpretation of the decomposition. We present illustrative examples and experimental evaluation.
Adaptive Spatial Aloha, Fairness and Stochastic Geometry
Venue: arXiv cs.NI 2013
Year: 2013

This work aims at combining adaptive protocol design, utility maximization and stochastic geometry. We focus on a spatial adaptation of Aloha within the framework of ad hoc networks. We consider quasi-static networks in which mobiles learn the local topology and incorporate this information to adapt their medium access probability (MAP) selection to their local environment. We consider the cases where nodes cooperate in a distributed way to maximize the global throughput or to achieve either proportional fair or max-min fair medium access. In the proportional fair case, we show that nodes can compute their optimal MAPs as solutions to certain fixed point equations. In the maximum throughput case, the optimal MAPs are obtained through a Gibbs Sampling based algorithm. In the max min case, these are obtained as the solution of a convex optimization problem. The main performance analysis result of the paper is that this type of distributed adaptation can be analyzed using stochastic geometry in the proportional fair case. In this case, we show that, when the nodes form a homogeneous Poisson point process in the Euclidean plane, the distribution of the optimal MAP can be obtained from that of a certain shot noise process w.r.t. the node Poisson point process and that the mean utility can also be derived from this distribution. We discuss the difficulties to be faced for analyzing the performance of the other cases (maximum throughput and max-min fairness). Numerical results illustrate our findings and quantify the gains brought by spatial adaptation in such networks.
Causality in concurrent systems
Venue: arXiv cs.DC 2013
Year: 2013

Concurrent systems identify systems, either software, hardware or even biological systems, that are characterized by sets of independent actions that can be executed in any order or simultaneously. Computer scientists resort to a causal terminology to describe and analyse the relations between the actions in these systems. However, a thorough discussion about the meaning of causality in such a context has not been developed yet. This paper aims to fill the gap. First, the paper analyses the notion of causation in concurrent systems and attempts to build bridges with the existing philosophical literature, highlighting similarities and divergences between them. Second, the paper analyses the use of counterfactual reasoning in ex-post analysis in concurrent systems (i.e. execution trace analysis).
Inter-Cloud Data Security Strategies
Venue: arXiv cs.CR 2013
Year: 2013

Cloud computing is a complex infrastructure of software, hardware, processing, and storage that is available as a service. Cloud computing offers immediate access to large numbers of the world's most sophisticated supercomputers and their corresponding processing power, interconnected at various locations around the world, proffering speed in the tens of trillions of computations per second. Information in databases and software scattered around the Internet. There are many service providers in the internet, we can call each service as a cloud, each cloud service will exchange data with other cloud, so when the data is exchanged between the clouds, there exist the problem of security. Security is an important issue for cloud computing, both in terms of legal compliance and user trust, and needs to be considered at every phase of design. In contrast to traditional solutions, where the IT services are under proper physical, logical and personnel controls, Cloud Computing moves the application software and databases to the large data centers, where the management of the data and services may not be trustworthy. This unique attribute, however, poses many new security challenges. Cloud computing seems to offer some incredible benefits for communicators.
A Probabilistic Algorithm for Calculating Structure: Borrowing from Simulated Annealing
Venue: arXiv cs.AI 2013
Year: 2013

We have developed a general Bayesian algorithm for determining the coordinates of points in a three-dimensional space. The algorithm takes as input a set of probabilistic constraints on the coordinates of the points, and an a priori distribution for each point location. The output is a maximum-likelihood estimate of the location of each point. We use the extended, iterated Kalman filter, and add a search heuristic for optimizing its solution under nonlinear conditions. This heuristic is based on the same principle as the simulated annealing heuristic for other optimization problems. Our method uses any probabilistic constraints that can be expressed as a function of the point coordinates (for example, distance, angles, dihedral angles, and planarity). It assumes that all constraints have Gaussian noise. In this paper, we describe the algorithm and show its performance on a set of synthetic data to illustrate its convergence properties, and its applicability to domains such ng molecular structure determination.
Tradeoffs in Constructing and Evaluating Temporal Influence Diagrams
Venue: arXiv cs.AI 2013
Year: 2013

This paper addresses the tradeoffs which need to be considered in reasoning using probabilistic network representations, such as Influence Diagrams (IDs). In particular, we examine the tradeoffs entailed in using Temporal Influence Diagrams (TIDs) which adequately capture the temporal evolution of a dynamic system without prohibitive data and computational requirements. Three approaches for TID construction which make different tradeoffs are examined: (1) tailoring the network at each time interval to the data available (rather then just copying the original Bayes Network for all time intervals); (2) modeling the evolution of a parsimonious subset of variables (rather than all variables); and (3) model selection approaches, which seek to minimize some measure of the predictive accuracy of the model without introducing too many parameters, which might cause "overfitting" of the model. Methods of evaluating the accuracy/efficiency of the tradeoffs are proposed.
Causal Modeling
Venue: arXiv cs.AI 2013
Year: 2013

Causal Models are like Dependency Graphs and Belief Nets in that they provide a structure and a set of assumptions from which a joint distribution can, in principle, be computed. Unlike Dependency Graphs, Causal Models are models of hierarchical and/or parallel processes, rather than models of distributions (partially) known to a model builder through some sort of gestalt. As such, Causal Models are more modular, easier to build, more intuitive, and easier to understand than Dependency Graph Models. Causal Models are formally defined and Dependency Graph Models are shown to be a special case of them. Algorithms supporting inference are presented. Parsimonious methods for eliciting dependent probabilities are presented.
Deriving a Minimal I-map of a Belief Network Relative to a Target Ordering of its Nodes
Venue: arXiv cs.AI 2013
Year: 2013

This paper identifies and solves a new optimization problem: Given a belief network (BN) and a target ordering on its variables, how can we efficiently derive its minimal I-map whose arcs are consistent with the target ordering? We present three solutions to this problem, all of which lead to directed acyclic graphs based on the original BN's recursive basis relative to the specified ordering (such a DAG is sometimes termed the boundary DAG drawn from the given BN relative to the said ordering [5]). Along the way, we also uncover an important general principal about arc reversals: when reordering a BN according to some target ordering, (while attempting to minimize the number of arcs generated), the sequence of arc reversals should follow the topological ordering induced by the original belief network's arcs to as great an extent as possible. These results promise to have a significant impact on the derivation of consensus models, as well as on other algorithms that require the reconfiguration and/or combination of BN's.
Intercausal Reasoning with Uninstantiated Ancestor Nodes
Venue: arXiv cs.AI 2013
Year: 2013

Intercausal reasoning is a common inference pattern involving probabilistic dependence of causes of an observed common effect. The sign of this dependence is captured by a qualitative property called product synergy. The current definition of product synergy is insufficient for intercausal reasoning where there are additional uninstantiated causes of the common effect. We propose a new definition of product synergy and prove its adequacy for intercausal reasoning with direct and indirect evidence for the common effect. The new definition is based on a new property matrix half positive semi-definiteness, a weakened form of matrix positive semi-definiteness.
Two Procedures for Compiling Influence Diagrams
Venue: arXiv cs.AI 2013
Year: 2013

Two algorithms are presented for "compiling" influence diagrams into a set of simple decision rules. These decision rules define simple-to-execute, complete, consistent, and near-optimal decision procedures. These compilation algorithms can be used to derive decision procedures for human teams solving time constrained decision problems.