OpenRaiser/Intern-Atlas huggingface dataset:
Minimal Convex Decompositions
Venue: arXiv cs.CG 2012
Year: 2012

Let $P$ be a set of $n$ points on the plane in general position. We say that a set $Γ$ of convex polygons with vertices in $P$ is a convex decomposition of $P$ if: Union of all elements in $Γ$ is the convex hull of $P,$ every element in $Γ$ is empty, and for any two different elements of $Γ$ their interiors are disjoint. A minimal convex decomposition of $P$ is a convex decomposition $Γ'$ such that for any two adjacent elements in $Γ'$ its union is a non convex polygon. It is known that $P$ always has a minimal convex decomposition with at most $\frac{3n}{2}$ elements. Here we prove that $P$ always has a minimal convex decomposition with at most $\frac{10n}{7}$ elements.
Secure Channel Simulation
Venue: arXiv cs.IT 2012
Year: 2012

In this paper the Output Statistics of Random Binning (OSRB) framework is used to prove a new inner bound for the problem of secure channel simulation. Our results subsume some recent results on the secure function computation. We also provide an achievability result for the problem of simultaneously simulating a channel and creating a shared secret key. A special case of this result generalizes the lower bound of Gohari and Anantharam on the source model to include constraints on the rates of the public discussion.
Approximate Counting via Correlation Decay on Planar Graphs
Venue: arXiv cs.DS 2012
Year: 2012

We show for a broad class of counting problems, correlation decay (strong spatial mixing) implies FPTAS on planar graphs. The framework for the counting problems considered by us is the Holant problems with arbitrary constant-size domain and symmetric constraint functions. We define a notion of regularity on the constraint functions, which covers a wide range of natural and important counting problems, including all multi-state spin systems, counting graph homomorphisms, counting weighted matchings or perfect matchings, the subgraphs world problem transformed from the ferromagnetic Ising model, and all counting CSPs and Holant problems with symmetric constraint functions of constant arity. The core of our algorithm is a fixed-parameter tractable algorithm which computes the exact values of the Holant problems with regular constraint functions on graphs of bounded treewidth. By utilizing the locally tree-like property of apex-minor-free families of graphs, the parameterized exact algorithm implies an FPTAS for the Holant problem on these graph families whenever the Gibbs measure defined by the problem exhibits strong spatial mixing. We further extend the recursive coupling technique to Holant problems and establish strong spatial mixing for the ferromagnetic Potts model and the subgraphs world problem. As consequences, we have new deterministic approximation algorithms on planar graphs and all apex-minor-free graphs for several counting problems.
On the Equal-Rate Capacity of the AWGN Multiway Relay Channel
Venue: arXiv cs.IT 2012
Year: 2012

The L-user additive white Gaussian noise multiway relay channel is investigated, where L users exchange information at the same rate through a single relay. A new achievable rate region, based on the functional-decode-forward coding strategy, is derived. For the case where there are three or more users, and all nodes transmit at the same power, the capacity is obtained. For the case where the relay power scales with the number of users, it is shown that both compress-forward and functional-decode-forward achieve rates within a constant number of bits of the capacity at all SNR levels; in addition, functional-decode-forward outperforms compress-forward and complete-decode-forward at high SNR levels.
Abelian powers in paper-folding words
Venue: arXiv cs.FL 2012
Year: 2012

We show that paper folding words contain arbitrarily large abelian powers.
CEEC: Centralized Energy Efficient Clustering A New Routing Protocol for WSNs
Venue: arXiv cs.NI 2012
Year: 2012

Energy efficient routing protocol for Wireless Sensor Networks (WSNs) is one of the most challenging task for researcher. Hierarchical routing protocols have been proved more energy efficient routing protocols, as compare to flat and location based routing protocols. Heterogeneity of nodes with respect to their energy level, has also added extra lifespan for sensor network. In this paper, we propose a Centralized Energy Efficient Clustering (CEEC) routing protocol. We design the CEEC for three level heterogeneous network. CEEC can also be implemented in multi-level heterogeneity of networks. For initial practical, we design and analyze CEEC for three level advance heterogeneous network. In CEEC, whole network area is divided into three equal regions, in which nodes with same energy are spread in same region.
Adaptive-Reliable Medium Access Control Protocol for Wireless Body Area Networks
Venue: arXiv cs.NI 2012
Year: 2012

Extensive energy is consumed by Transceiver communication operation [1]. Existing research on MAC layer focuses to maximize battery-powered sensor node's life. Bottleneck of MAC layer protocol design for WBAN is to achieve high reliability and energy minimization. Majority of MAC protocols designed for WBANs are based upon TDMA approach. However, a new protocol needs to be defined to achieve high energy efficiency, fairness and avoid extra energy consumption due to synchronization.
How Random are Online Social Interactions?
Venue: arXiv cs.CY 2012
Year: 2012

The massive amounts of data that social media generates has facilitated the study of online human behavior on a scale unimaginable a few years ago. At the same time, the much discussed apparent randomness with which people interact online makes it appear as if these studies cannot reveal predictive social behaviors that could be used for developing better platforms and services. We use two large social databases to measure the mutual information entropy that both individual and group actions generate as they evolve over time. We show that user's interaction sequences have strong deterministic components, in contrast with existing assumptions and models. In addition, we show that individual interactions are more predictable when users act on their own rather than when attending group activities.
Automated Inference System for End-To-End Diagnosis of Network Performance Issues in Client-Terminal Devices
Venue: arXiv cs.NI 2012
Year: 2012

Traditional network diagnosis methods of Client-Terminal Device (CTD) problems tend to be laborintensive, time consuming, and contribute to increased customer dissatisfaction. In this paper, we propose an automated solution for rapidly diagnose the root causes of network performance issues in CTD. Based on a new intelligent inference technique, we create the Intelligent Automated Client Diagnostic (IACD) system, which only relies on collection of Transmission Control Protocol (TCP) packet traces. Using soft-margin Support Vector Machine (SVM) classifiers, the system (i) distinguishes link problems from client problems and (ii) identifies characteristics unique to the specific fault to report the root cause. The modular design of the system enables support for new access link and fault types. Experimental evaluation demonstrated the capability of the IACD system to distinguish between faulty and healthy links and to diagnose the client faults with 98% accuracy. The system can perform fault diagnosis independent of the user's specific TCP implementation, enabling diagnosis of diverse range of client devices
Performance Analysis of Wavelet Based MC-CDMA System with Implementation of Various Antenna Diversity Schemes
Venue: arXiv cs.IT 2012
Year: 2012

The impact of using wavelet based technique on the performance of a MC-CDMA wireless communication system has been investigated. The system under proposed study incorporates Walsh Hadamard codes to discriminate the message signal for individual user. A computer program written in Mathlab source code is developed and this simulation study is made with implementation of various antenna diversity schemes and fading (Rayleigh and Rician) channel. Computer simulation results demonstrate that the proposed wavelet based MC-CDMA system outperforms in Alamouti (two transmit antenna and one receive antenna) under AWGN and Rician channel.
Top Down Approach: SIMULINK Mixed Hardware / Software Design
Venue: arXiv cs.SE 2012
Year: 2012

System-level design methodologies have been introduced as a solution to handle the design complexity of mixed Hardware / Software systems. In this paper we describe a system-level design flow starting from Simulink specification, focusing on concurrent hardware and software design and verification at four different abstraction levels: System Simulink model, Transaction Simulink model, Macro architecture, and micro architecture. We used the MP3 CodeC application, to validate our approach and methodology.
One-counter verifiers for decidable languages
Venue: arXiv cs.CC 2012
Year: 2012

Condon and Lipton (FOCS 1989) showed that the class of languages having a space-bounded interactive proof system (IPS) is a proper subset of decidable languages, where the verifier is a probabilistic Turing machine. In this paper, we show that if we use architecturally restricted verifiers instead of restricting the working memory, i.e. replacing the working tape(s) with a single counter, we can define some IPS's for each decidable language. Such verifiers are called two-way probabilistic one-counter automata (2pca's). Then, we show that by adding a fixed-size quantum memory to a 2pca, called a two-way one-counter automaton with quantum and classical states (2qcca), the protocol can be space efficient. As a further result, if the 2qcca can use a quantum counter instead of a classical one, then the protocol can even be public, also known as Arthur-Merlin games. We also investigate the computational power of 2pca's and 2qcca's as language recognizers. We show that bounded-error 2pca's can be more powerful than their deterministic counterparts by giving a bounded-error simulation of their nondeterministic counterparts. Then, we present a new programming technique for bounded-error 2qcca's and show that they can recognize a language which seems not to be recognized by any bounded-error 2pca. We also obtain some interesting results for bounded-error 1-pebble quantum finite automata based on this new technique. Lastly, we prove a conjecture posed by Ravikumar (FSTTCS 1992) regarding 1-pebble probabilistic finite automata, i.e. they can recognize some nonstochastic languages with bounded error.
Fast View Frustum Culling of Spatial Object by Analytical Bounding Bin
Venue: arXiv cs.GR 2012
Year: 2012

It is a common sense to apply the VFC (view frustum culling) of spatial object to bounding cube of the object in 3D graphics. The accuracy of VFC can not be guaranteed even in cube rotated three-dimensionally. In this paper is proposed a method which is able to carry out more precise and fast VFC of any spatial object in the image domain of cube by an analytic mapping, and is demonstrated the effect of the method for terrain block on global surface.
A Taxonomy for Congestion Control Algorithms in Vehicular Ad Hoc Networks
Venue: arXiv cs.NI 2012
Year: 2012

One of the main criteria in Vehicular Ad hoc Networks (VANETs) that has attracted the researchers' consideration is congestion control. Accordingly, many algorithms have been proposed to alleviate the congestion problem, although it is hard to find an appropriate algorithm for applications and safety messages among them. Safety messages encompass beacons and event-driven messages. Delay and reliability are essential requirements for event-driven messages. In crowded networks where beacon messages are broadcasted at a high number of frequencies by many vehicles, the Control Channel (CCH), which used for beacons sending, will be easily congested. On the other hand, to guarantee the reliability and timely delivery of event-driven messages, having a congestion free control channel is a necessity. Thus, consideration of this study is given to find a solution for the congestion problem in VANETs by taking a comprehensive look at the existent congestion control algorithms. In addition, the taxonomy for congestion control algorithms in VANETs is presented based on three classes, namely, proactive, reactive and hybrid. Finally, we have found the criteria in which fulfill prerequisite of a good congestion control algorithm.
Computation of the Hausdorff distance between sets of line segments in parallel
Venue: arXiv cs.CG 2012
Year: 2012

We show that the Hausdorff distance for two sets of non-intersecting line segments can be computed in parallel in $O(\log^2 n)$ time using O(n) processors in a CREW-PRAM computation model. We discuss how some parts of the sequential algorithm can be performed in parallel using previously known parallel algorithms; and identify the so-far unsolved part of the problem for the parallel computation, which is the following: Given two sets of $x$-monotone curve segments, red and blue, for each red segment find its extremal intersection points with the blue set, i.e. points with the minimal and maximal $x$-coordinate. Each segment set is assumed to be intersection free. For this intersection problem we describe a parallel algorithm which completes the Hausdorff distance computation within the stated time and processor bounds.
Algebraic Statistics in Model Selection
Venue: arXiv cs.LG 2012
Year: 2012

We develop the necessary theory in computational algebraic geometry to place Bayesian networks into the realm of algebraic statistics. We present an algebra{statistics dictionary focused on statistical modeling. In particular, we link the notion of effiective dimension of a Bayesian network with the notion of algebraic dimension of a variety. We also obtain the independence and non{independence constraints on the distributions over the observable variables implied by a Bayesian network with hidden variables, via a generating set of an ideal of polynomials associated to the network. These results extend previous work on the subject. Finally, the relevance of these results for model selection is discussed.
Region-Based Incremental Pruning for POMDPs
Venue: arXiv cs.AI 2012
Year: 2012

We present a major improvement to the incremental pruning algorithm for solving partially observable Markov decision processes. Our technique targets the cross-sum step of the dynamic programming (DP) update, a key source of complexity in POMDP algorithms. Instead of reasoning about the whole belief space when pruning the cross-sums, our algorithm divides the belief space into smaller regions and performs independent pruning in each region. We evaluate the benefits of the new technique both analytically and experimentally, and show that it produces very significant performance gains. The results contribute to the scalability of POMDP algorithms to domains that cannot be handled by the best existing techniques.
Propositional and Relational Bayesian Networks Associated with Imprecise and Qualitative Probabilistic Assesments
Venue: arXiv cs.AI 2012
Year: 2012

This paper investigates a representation language with flexibility inspired by probabilistic logic and compactness inspired by relational Bayesian networks. The goal is to handle propositional and first-order constructs together with precise, imprecise, indeterminate and qualitative probabilistic assessments. The paper shows how this can be achieved through the theory of credal networks. New exact and approximate inference algorithms based on multilinear programming and iterated/loopy propagation of interval probabilities are presented; their superior performance, compared to existing ones, is shown empirically.
"Ideal Parent" Structure Learning for Continuous Variable Networks
Venue: arXiv cs.LG 2012
Year: 2012

In recent years, there is a growing interest in learning Bayesian networks with continuous variables. Learning the structure of such networks is a computationally expensive procedure, which limits most applications to parameter learning. This problem is even more acute when learning networks with hidden variables. We present a general method for significantly speeding the structure search algorithm for continuous variable networks with common parametric distributions. Importantly, our method facilitates the addition of new hidden variables into the network structure efficiently. We demonstrate the method on several data sets, both for learning structure on fully observable data, and for introducing new hidden variables during structure search.
Case-Factor Diagrams for Structured Probabilistic Modeling
Venue: arXiv cs.AI 2012
Year: 2012

We introduce a probabilistic formalism subsuming Markov random fields of bounded tree width and probabilistic context free grammars. Our models are based on a representation of Boolean formulas that we call case-factor diagrams (CFDs). CFDs are similar to binary decision diagrams (BDDs) but are concise for circuits of bounded tree width (unlike BDDs) and can concisely represent the set of parse trees over a given string undera given context free grammar (also unlike BDDs). A probabilistic model consists of aCFD defining a feasible set of Boolean assignments and a weight (or cost) for each individual Boolean variable. We give an insideoutside algorithm for simultaneously computing the marginal of each Boolean variable, and a Viterbi algorithm for finding the mininum cost variable assignment. Both algorithms run in time proportional to the size of the CFD.
Pre-Selection of Independent Binary Features: An Application to Diagnosing Scrapie in Sheep
Venue: arXiv cs.AI 2012
Year: 2012

Suppose that the only available information in a multi-class problem are expert estimates of the conditional probabilities of occurrence for a set of binary features. The aim is to select a subset of features to be measured in subsequent data collection experiments. In the lack of any information about the dependencies between the features, we assume that all features are conditionally independent and hence choose the Naive Bayes classifier as the optimal classifier for the problem. Even in this (seemingly trivial) case of complete knowledge of the distributions, choosing an optimal feature subset is not straightforward. We discuss the properties and implementation details of Sequential Forward Selection (SFS) as a feature selection procedure for the current problem. A sensitivity analysis was carried out to investigate whether the same features are selected when the probabilities vary around the estimated values. The procedure is illustrated with a set of probability estimates for Scrapie in sheep.
A Generative Bayesian Model for Aggregating Experts' Probabilities
Venue: arXiv cs.LG 2012
Year: 2012

In order to improve forecasts, a decisionmaker often combines probabilities given by various sources, such as human experts and machine learning classifiers. When few training data are available, aggregation can be improved by incorporating prior knowledge about the event being forecasted and about salient properties of the experts. To this end, we develop a generative Bayesian aggregation model for probabilistic classi cation. The model includes an event-specific prior, measures of individual experts' bias, calibration, accuracy, and a measure of dependence betweeen experts. Rather than require absolute measures, we show that aggregation may be expressed in terms of relative accuracy between experts. The model results in a weighted logarithmic opinion pool (LogOps) that satis es consistency criteria such as the external Bayesian property. We derive analytic solutions for independent and for exchangeable experts. Empirical tests demonstrate the model's use, comparing its accuracy with other aggregation methods.
Similarity-Driven Cluster Merging Method for Unsupervised Fuzzy Clustering
Venue: arXiv cs.LG 2012
Year: 2012

In this paper, a similarity-driven cluster merging method is proposed for unsuper-vised fuzzy clustering. The cluster merging method is used to resolve the problem of cluster validation. Starting with an overspecified number of clusters in the data, pairs of similar clusters are merged based on the proposed similarity-driven cluster merging criterion. The similarity between clusters is calculated by a fuzzy cluster similarity matrix, while an adaptive threshold is used for merging. In addition, a modified generalized ob- jective function is used for prototype-based fuzzy clustering. The function includes the p-norm distance measure as well as principal components of the clusters. The number of the principal components is determined automatically from the data being clustered. The properties of this unsupervised fuzzy clustering algorithm are illustrated by several experiments.
An Integrated, Conditional Model of Information Extraction and Coreference with Applications to Citation Matching
Venue: arXiv cs.LG 2012
Year: 2012

Although information extraction and coreference resolution appear together in many applications, most current systems perform them as ndependent steps. This paper describes an approach to integrated inference for extraction and coreference based on conditionally-trained undirected graphical models. We discuss the advantages of conditional probability training, and of a coreference model structure based on graph partitioning. On a data set of research paper citations, we show significant reduction in error by using extraction uncertainty to improve coreference citation matching accuracy, and using coreference to improve the accuracy of the extracted fields.
On the Choice of Regions for Generalized Belief Propagation
Venue: arXiv cs.AI 2012
Year: 2012

Generalized belief propagation (GBP) has proven to be a promising technique for approximate inference tasks in AI and machine learning. However, the choice of a good set of clusters to be used in GBP has remained more of an art then a science until this day. This paper proposes a sequential approach to adding new clusters of nodes and their interactions (i.e. "regions") to the approximation. We first review and analyze the recently introduced region graphs and find that three kinds of operations ("split", "merge" and "death") leave the free energy and (under some conditions) the fixed points of GBP invariant. This leads to the notion of "weakly irreducible" regions as the natural candidates to be added to the approximation. Computational complexity of the GBP algorithm is controlled by restricting attention to regions with small "region-width". Combining the above with an efficient (i.e. local in the graph) measure to predict the improved accuracy of GBP leads to the sequential "region pursuit" algorithm for adding new regions bottom-up to the region graph. Experiments show that this algorithm can indeed perform close to optimally.