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1012.4404
Multicolored Dynamos on Toroidal Meshes
cs.DC cs.CC cs.DS cs.SI
Detecting on a graph the presence of the minimum number of nodes (target set) that will be able to "activate" a prescribed number of vertices in the graph is called the target set selection problem (TSS) proposed by Kempe, Kleinberg, and Tardos. In TSS's settings, nodes have two possible states (active or non-active) and the threshold triggering the activation of a node is given by the number of its active neighbors. Dealing with fault tolerance in a majority based system the two possible states are used to denote faulty or non-faulty nodes, and the threshold is given by the state of the majority of neighbors. Here, the major effort was in determining the distribution of initial faults leading the entire system to a faulty behavior. Such an activation pattern, also known as dynamic monopoly (or shortly dynamo), was introduced by Peleg in 1996. In this paper we extend the TSS problem's settings by representing nodes' states with a "multicolored" set. The extended version of the problem can be described as follows: let G be a simple connected graph where every node is assigned a color from a finite ordered set C = {1, . . ., k} of colors. At each local time step, each node can recolor itself, depending on the local configurations, with the color held by the majority of its neighbors. Given G, we study the initial distributions of colors leading the system to a k monochromatic configuration in toroidal meshes, focusing on the minimum number of initial k-colored nodes. We find upper and lower bounds to the size of a dynamo, and then special classes of dynamos, outlined by means of a new approach based on recoloring patterns, are characterized.
1012.4485
An Experimental Approach for Optimising Mobile Agent Migrations
cs.NI cs.MA
The field of mobile agent (MA) technology has been intensively researched during the past few years, resulting in the phenomenal proliferation of available MA platforms, all sharing several common design characteristics. Research projects have mainly focused on identifying applications where the employment of MAs is preferable compared to centralised or alternative distributed computing models. Very little work has been made on examining how MA platforms design can be optimised so as the network traffic and latency associated with MA transfers are minimised. The work presented in this paper addresses these issues by investigating the effect of several optimisation ideas applied on our MA platform prototype. Furthermore, we discuss the results of a set of timing experiments that offers a better understanding of the agent migration process and recommend new techniques for reducing MA transfers delay.
1012.4519
Belief-propagation-based joint channel estimation and decoding for spectrally efficient communication over unknown sparse channels
cs.IT math.IT
We consider spectrally-efficient communication over a Rayleigh N-block-fading channel with a K- sparse L-length discrete-time impulse response (for 0<K<L<N), where neither the transmitter nor receiver know the channel's coefficients nor its support. Since the high-SNR ergodic capacity of this channel has been shown to obey C(SNR) = (1-K/N)log2(SNR)+O(1), any pilot-aided scheme that sacrifices more than K dimensions per fading block to pilots will be spectrally inefficient. This causes concern about the conventional "compressed channel sensing" approach, which uses O(K polylog L) pilots. In this paper, we demonstrate that practical spectrally-efficient communication is indeed possible. For this, we propose a novel belief-propagation-based reception scheme to use with a standard bit- interleaved coded orthogonal frequency division multiplexing (OFDM) transmitter. In particular, we leverage the "relaxed belief propagation" methodology, which allows us to perform joint sparse-channel estimation and data decoding with only O(LN) complexity. Empirical results show that our receiver achieves the desired capacity pre-log factor of 1 - K/N and performs near genie-aided bounds at both low and high SNR.
1012.4521
Characterizing Structure Through Shape Matching and Applications to Self Assembly
cond-mat.soft cs.CV
Structural quantities such as order parameters and correlation functions are often employed to gain insight into the physical behavior and properties of condensed matter systems. While standard quantities for characterizing structure exist, often they are insufficient for treating problems in the emerging field of nano and microscale self-assembly, where the structures encountered may be complex and unusual. The computer science field of "shape matching" offers a robust solution to this problem by defining diverse methods for quantifying the similarity between arbitrarily complex shapes. Most order parameters and correlation functions used in condensed matter apply a specific measure of structural similarity within the context of a broader scheme. By substituting shape matching quantities for traditional quantities, we retain the essence of the broader scheme, but extend its applicability to more complex structures. Here we review some standard shape matching techniques and discuss how they might be used to create highly flexible structural metrics for diverse systems such as self-assembled matter. We provide three proof-of-concept example problems applying shape matching methods to identifying local and global structures, and tracking structural transitions in complex assembled systems. The shape matching methods reviewed here are applicable to a wide range of condensed matter systems, both simulated and experimental, provided particle positions are known or can be accurately imaged.
1012.4524
The interplay of microscopic and mesoscopic structure in complex networks
cond-mat.stat-mech cs.SI physics.soc-ph q-bio.MN
Not all nodes in a network are created equal. Differences and similarities exist at both individual node and group levels. Disentangling single node from group properties is crucial for network modeling and structural inference. Based on unbiased generative probabilistic exponential random graph models and employing distributive message passing techniques, we present an efficient algorithm that allows one to separate the contributions of individual nodes and groups of nodes to the network structure. This leads to improved detection accuracy of latent class structure in real world data sets compared to models that focus on group structure alone. Furthermore, the inclusion of hitherto neglected group specific effects in models used to assess the statistical significance of small subgraph (motif) distributions in networks may be sufficient to explain most of the observed statistics. We show the predictive power of such generative models in forecasting putative gene-disease associations in the Online Mendelian Inheritance in Man (OMIM) database. The approach is suitable for both directed and undirected uni-partite as well as for bipartite networks.
1012.4527
Harmonic Order Parameters for Characterizing Complex Particle Morphologies
cond-mat.soft cs.CV physics.chem-ph
Order parameters based on spherical harmonics and Fourier coefficients already play a significant role in condensed matter research in the context of systems of spherical or point particles. Here, we extend these types of order parameter to more complex shapes, such as those encountered in nanoscale self-assembly applications. To do so, we build on a powerful set of techniques that originate in the computer science field of "shape matching." We demonstrate how shape matching techniques can be applied to identify unknown structures and create highly-specialized \textit{ad hoc} order parameters. Additionally, we investigate the special symmetry properties of harmonic descriptors, and demonstrate how they can be exploited to provide optimal solutions to certain classes of problems. Our techniques can be applied to particle systems in general, both simulated and experimental, provided the particle positions are known.
1012.4542
Impact of Mistiming on the Achievable Information Rate of Rake Receivers in DS-UWB Systems
cs.IT math.IT
In this paper, we investigate the impact of mistiming on the performance of Rake receivers in direct-sequence ultra-wideband (DS-UWB) systems from the perspective of the achievable information rate. A generalized expression for the performance degradation due to mistiming is derived. Monte Carlo simulations based on this expression are then conducted, which demonstrate that the performance loss has little relationship with the target achievable information rate, but varies significantly with the system bandwidth and the multipath diversity order, which reflects design trade-offs among the system timing requirement, the bandwidth and the implementation complexity. In addition, the performance degradations of Rake receivers with different multipath component selection schemes and combining techniques are compared. Among these receivers, the widely used maximal ratio combining (MRC) selective-Rake (S-Rake) suffers the largest performance loss in the presence of mistiming.
1012.4552
On the Throughput Cost of Physical Layer Security in Decentralized Wireless Networks
cs.IT math.IT
This paper studies the throughput of large-scale decentralized wireless networks with physical layer security constraints. In particular, we are interested in the question of how much throughput needs to be sacrificed for achieving a certain level of security. We consider random networks where the legitimate nodes and the eavesdroppers are distributed according to independent two-dimensional Poisson point processes. The transmission capacity framework is used to characterize the area spectral efficiency of secure transmissions with constraints on both the quality of service (QoS) and the level of security. This framework illustrates the dependence of the network throughput on key system parameters, such as the densities of legitimate nodes and eavesdroppers, as well as the QoS and security constraints. One important finding is that the throughput cost of achieving a moderate level of security is quite low, while throughput must be significantly sacrificed to realize a highly secure network. We also study the use of a secrecy guard zone, which is shown to give a significant improvement on the throughput of networks with high security requirements.
1012.4571
How I won the "Chess Ratings - Elo vs the Rest of the World" Competition
cs.LG
This article discusses in detail the rating system that won the kaggle competition "Chess Ratings: Elo vs the rest of the world". The competition provided a historical dataset of outcomes for chess games, and aimed to discover whether novel approaches can predict the outcomes of future games, more accurately than the well-known Elo rating system. The winning rating system, called Elo++ in the rest of the article, builds upon the Elo rating system. Like Elo, Elo++ uses a single rating per player and predicts the outcome of a game, by using a logistic curve over the difference in ratings of the players. The major component of Elo++ is a regularization technique that avoids overfitting these ratings. The dataset of chess games and outcomes is relatively small and one has to be careful not to draw "too many conclusions" out of the limited data. Many approaches tested in the competition showed signs of such an overfitting. The leader-board was dominated by attempts that did a very good job on a small test dataset, but couldn't generalize well on the private hold-out dataset. The Elo++ regularization takes into account the number of games per player, the recency of these games and the ratings of the opponents. Finally, Elo++ employs a stochastic gradient descent scheme for training the ratings, and uses only two global parameters (white's advantage and regularization constant) that are optimized using cross-validation.
1012.4583
Constructing Quantum Network Coding Schemes from Classical Nonlinear Protocols
quant-ph cs.IT math.IT
The k-pair problem in network coding theory asks to send k messages simultaneously between k source-target pairs over a directed acyclic graph. In a previous paper [ICALP 2009, Part I, pages 622--633] the present authors showed that if a classical k-pair problem is solvable by means of a linear coding scheme, then the quantum k-pair problem over the same graph is also solvable, provided that classical communication can be sent for free between any pair of nodes of the graph. Here we address the main case that remained open in our previous work, namely whether nonlinear classical network coding schemes can also give rise to quantum network coding schemes. This question is motivated by the fact that there are networks for which there are no linear solutions to the k-pair problem, whereas nonlinear solutions exist. In the present paper we overcome the limitation to linear protocols and describe a new communication protocol for perfect quantum network coding that improves over the previous one as follows: (i) the new protocol does not put any condition on the underlying classical coding scheme, that is, it can simulate nonlinear communication protocols as well, and (ii) the amount of classical communication sent in the protocol is significantly reduced.
1012.4621
Self-organized Emergence of Navigability on Small-World Networks
cs.SI physics.soc-ph
This paper mainly investigates why small-world networks are navigable and how to navigate small-world networks. We find that the navigability can naturally emerge from self-organization in the absence of prior knowledge about underlying reference frames of networks. Through a process of information exchange and accumulation on networks, a hidden metric space for navigation on networks is constructed. Navigation based on distances between vertices in the hidden metric space can efficiently deliver messages on small-world networks, in which long range connections play an important role. Numerical simulations further suggest that high cluster coefficient and low diameter are both necessary for navigability. These interesting results provide profound insights into scalable routing on the Internet due to its distributed and localized requirements.
1012.4623
Fitness-driven deactivation in network evolution
physics.soc-ph cond-mat.dis-nn cs.SI
Individual nodes in evolving real-world networks typically experience growth and decay --- that is, the popularity and influence of individuals peaks and then fades. In this paper, we study this phenomenon via an intrinsic nodal fitness function and an intuitive aging mechanism. Each node of the network is endowed with a fitness which represents its activity. All the nodes have two discrete stages: active and inactive. The evolution of the network combines the addition of new active nodes randomly connected to existing active ones and the deactivation of old active nodes with possibility inversely proportional to their fitnesses. We obtain a structured exponential network when the fitness distribution of the individuals is homogeneous and a structured scale-free network with heterogeneous fitness distributions. Furthermore, we recover two universal scaling laws of the clustering coefficient for both cases, $C(k) \sim k^{-1}$ and $C \sim n^{-1}$, where $k$ and $n$ refer to the node degree and the number of active individuals, respectively. These results offer a new simple description of the growth and aging of networks where intrinsic features of individual nodes drive their popularity, and hence degree.
1012.4668
Distributed Detection over Random Networks: Large Deviations Performance Analysis
cs.IT math.IT
We study the large deviations performance, i.e., the exponential decay rate of the error probability, of distributed detection algorithms over random networks. At each time step $k$ each sensor: 1) averages its decision variable with the neighbors' decision variables; and 2) accounts on-the-fly for its new observation. We show that distributed detection exhibits a "phase change" behavior. When the rate of network information flow (the speed of averaging) is above a threshold, then distributed detection is asymptotically equivalent to the optimal centralized detection, i.e., the exponential decay rate of the error probability for distributed detection equals the Chernoff information. When the rate of information flow is below a threshold, distributed detection achieves only a fraction of the Chernoff information rate; we quantify this achievable rate as a function of the network rate of information flow. Simulation examples demonstrate our theoretical findings on the behavior of distributed detection over random networks.
1012.4715
Joint Unitary Triangularization for MIMO Networks
cs.IT math.IT
This work considers communication networks where individual links can be described as MIMO channels. Unlike orthogonal modulation methods (such as the singular-value decomposition), we allow interference between sub-channels, which can be removed by the receivers via successive cancellation. The degrees of freedom earned by this relaxation are used for obtaining a basis which is simultaneously good for more than one link. Specifically, we derive necessary and sufficient conditions for shaping the ratio vector of sub-channel gains of two broadcast-channel receivers. We then apply this to two scenarios: First, in digital multicasting we present a practical capacity-achieving scheme which only uses scalar codes and linear processing. Then, we consider the joint source-channel problem of transmitting a Gaussian source over a two-user MIMO channel, where we show the existence of non-trivial cases, where the optimal distortion pair (which for high signal-to-noise ratios equals the optimal point-to-point distortions of the individual users) may be achieved by employing a hybrid digital-analog scheme over the induced equivalent channel. These scenarios demonstrate the advantage of choosing a modulation basis based upon multiple links in the network, thus we coin the approach "network modulation".
1012.4752
Semantic Web: Who is who in the field - A bibliometric analysis
cs.DL cs.IR
The Semantic Web is one of the main efforts aiming to enhance human and machine interaction by representing data in an understandable way for machines to mediate data and services. It is a fast-moving and multidisciplinary field. This study conducts a thorough bibliometric analysis of the field by collecting data from Web of Science (WOS) and Scopus for the period of 1960-2009. It utilizes a total of 44,157 papers with 651,673 citations from Scopus, and 22,951 papers with 571,911 citations from WOS. Based on these papers and citations, it evaluates the research performance of the Semantic Web (SW) by identifying the most productive players, major scholarly communication media, highly cited authors, influential papers and emerging stars.
1012.4755
Mutual information, matroids and extremal dependencies
cs.IT math.IT
In this paper, it is shown that the rank function of a matroid can be represented by a "mutual information function" if and only if the matroid is binary. The mutual information function considered is the one measuring the amount of information between the inputs (binary uniform) and the output of a multiple access channel (MAC). Moreover, it is shown that a MAC whose mutual information function is integer valued is "equivalent" to a linear deterministic MAC, in the sense that it essentially contains at the output no more information than some linear forms of the inputs. These notes put emphasis on the connection between mutual information functionals and rank functions in matroid theory, without assuming prior knowledge on these two subjects. The first section introduces mutual information functionals, the second section introduces basic notions of matroid theory, and the third section connects these two subjects. It is also shown that entropic matroids studied in the literature correspond to specific cases of MAC matroids.
1012.4759
Chem2Bio2RDF: A Linked Open Data Portal for Chemical Biology
cs.IR q-bio.OT
The Chem2Bio2RDF portal is a Linked Open Data (LOD) portal for systems chemical biology aiming for facilitating drug discovery. It converts around 25 different datasets on genes, compounds, drugs, pathways, side effects, diseases, and MEDLINE/PubMed documents into RDF triples and links them to other LOD bubbles, such as Bio2RDF, LODD and DBPedia. The portal is based on D2R server and provides a SPARQL endpoint, but adds on few unique features like RDF faceted browser, user-friendly SPARQL query generator, MEDLINE/PubMed cross validation service, and Cytoscape visualization plugin. Three use cases demonstrate the functionality and usability of this portal.
1012.4776
Automatic Estimation of the Exposure to Lateral Collision in Signalized Intersections using Video Sensors
cs.AI
Intersections constitute one of the most dangerous elements in road systems. Traffic signals remain the most common way to control traffic at high-volume intersections and offer many opportunities to apply intelligent transportation systems to make traffic more efficient and safe. This paper describes an automated method to estimate the temporal exposure of road users crossing the conflict zone to lateral collision with road users originating from a different approach. This component is part of a larger system relying on video sensors to provide queue lengths and spatial occupancy that are used for real time traffic control and monitoring. The method is evaluated on data collected during a real world experiment.
1012.4795
On the Equivalence of the General Covariance Union (GCU) and Minimum Enclosing Ellipsoid (MEE) Problems
math.OC cs.SY
In this paper we describe General Covariance Union (GCU) and show that solutions to GCU and the Minimum Enclosing Ellipsoid (MEE) problems are equivalent. This is a surprising result because GCU is defined over positive semidefinite (PSD) matrices with statistical interpretations while MEE involves PSD matrices with geometric interpretations. Their equivalence establishes an intersection between the seemingly disparate methodologies of covariance-based (e.g., Kalman) filtering and bounded region approaches to data fusion.
1012.4814
Noisy channel coding via privacy amplification and information reconciliation
quant-ph cs.IT math.IT
We show that optimal protocols for noisy channel coding of public or private information over either classical or quantum channels can be directly constructed from two more primitive information-theoretic tools: privacy amplification and information reconciliation, also known as data compression with side information. We do this in the one-shot scenario of structureless resources, and formulate our results in terms of the smooth min- and max-entropy. In the context of classical information theory, this shows that essentially all two-terminal protocols can be reduced to these two primitives, which are in turn governed by the smooth min- and max-entropies, respectively. In the context of quantum information theory, the recently-established duality of these two protocols means essentially all two-terminal protocols can be constructed using just a single primitive.
1012.4824
Input Parameters Optimization in Swarm DS-CDMA Multiuser Detectors
cs.AI math.CO stat.CO
In this paper, the uplink direct sequence code division multiple access (DS-CDMA) multiuser detection problem (MuD) is studied into heuristic perspective, named particle swarm optimization (PSO). Regarding different system improvements for future technologies, such as high-order modulation and diversity exploitation, a complete parameter optimization procedure for the PSO applied to MuD problem is provided, which represents the major contribution of this paper. Furthermore, the performance of the PSO-MuD is briefly analyzed via Monte-Carlo simulations. Simulation results show that, after convergence, the performance reached by the PSO-MuD is much better than the conventional detector, and somewhat close to the single user bound (SuB). Rayleigh flat channel is initially considered, but the results are further extend to diversity (time and spatial) channels.
1012.4845
Directed factor graph based fault diagnosis model construction for mode switching satellite power system
cs.SY
Satellite power system is a complex, highly interconnected hybrid system that exhibit nonlinear and mode switching behaviors. Directed factor graph is an inference model for fault diagnosis using probabilistic reasoning techniques. A novel approach for constructing the directed factor graph structure based on hybrid bond graph model is proposed. The system components status and their fault symptoms are treated as hypothesis and evidences respectively. The cause-effect relations between hypothesis and evidences are identified and concluded though qualitative equations and causal path analysis on hybrid bond graph model. A power supply module of a satellite power system is provided as case study to show the feasibility and validity of the proposed method.
1012.4855
Target-driven merging of Taxonomies
cs.DB
The proliferation of ontologies and taxonomies in many domains increasingly demands the integration of multiple such ontologies. The goal of ontology integration is to merge two or more given ontologies in order to provide a unified view on the input ontologies while maintaining all information coming from them. We propose a new taxonomy merging algorithm that, given as input two taxonomies and an equivalence matching between them, can generate an integrated taxonomy in a fully automatic manner. The approach is target-driven, i.e. we merge a source taxonomy into the target taxonomy and preserve the structure of the target ontology as much as possible. We also discuss how to extend the merge algorithm providing auxiliary information, like additional relationships between source and target concepts, in order to semantically improve the final result. The algorithm was implemented in a working prototype and evaluated using synthetic and real-world scenarios.
1012.4875
Upper Tag Ontology (UTO) For Integrating Social Tagging Data
cs.IR cs.SI
Data integration and mediation have become central concerns of information technology over the past few decades. With the advent of the Web and the rapid increases in the amount of data and the number of Web documents and users, researchers have focused on enhancing the interoperability of data through the development of metadata schemes. Other researchers have looked to the wealth of metadata generated by bookmarking sites on the Social Web. While several existing ontologies capitalize on the semantics of metadata created by tagging activities, the Upper Tag Ontology (UTO) emphasizes the structure of tagging activities to facilitate modeling of tagging data and the integration of data from different bookmarking sites as well as the alignment of tagging ontologies. UTO is described and its utility in harvesting, modeling, integrating, searching and analyzing data is demonstrated with metadata harvested from three major social tagging systems (Delicious, Flickr and YouTube).
1012.4889
Tight Bounds for Lp Samplers, Finding Duplicates in Streams, and Related Problems
cs.DS cs.CC cs.DB
In this paper, we present near-optimal space bounds for Lp-samplers. Given a stream of updates (additions and subtraction) to the coordinates of an underlying vector x \in R^n, a perfect Lp sampler outputs the i-th coordinate with probability |x_i|^p/||x||_p^p. In SODA 2010, Monemizadeh and Woodruff showed polylog space upper bounds for approximate Lp-samplers and demonstrated various applications of them. Very recently, Andoni, Krauthgamer and Onak improved the upper bounds and gave a O(\epsilon^{-p} log^3 n) space \epsilon relative error and constant failure rate Lp-sampler for p \in [1,2]. In this work, we give another such algorithm requiring only O(\epsilon^{-p} log^2 n) space for p \in (1,2). For p \in (0,1), our space bound is O(\epsilon^{-1} log^2 n), while for the $p=1$ case we have an O(log(1/\epsilon)\epsilon^{-1} log^2 n) space algorithm. We also give a O(log^2 n) bits zero relative error L0-sampler, improving the O(log^3 n) bits algorithm due to Frahling, Indyk and Sohler. As an application of our samplers, we give better upper bounds for the problem of finding duplicates in data streams. In case the length of the stream is longer than the alphabet size, L1 sampling gives us an O(log^2 n) space algorithm, thus improving the previous O(log^3 n) bound due to Gopalan and Radhakrishnan. In the second part of our work, we prove an Omega(log^2 n) lower bound for sampling from 0, \pm 1 vectors (in this special case, the parameter p is not relevant for Lp sampling). This matches the space of our sampling algorithms for constant \epsilon > 0. We also prove tight space lower bounds for the finding duplicates and heavy hitters problems. We obtain these lower bounds using reductions from the communication complexity problem augmented indexing.
1012.4905
Convolutional Goppa codes defined on fibrations
cs.IT math.IT
We define a new class of Convolutional Codes in terms of fibrations of algebraic varieties generalizaing our previous constructions of Convolutional Goppa Codes. Using this general construction we can give several examples of Maximum Distance Separable (MDS) Convolutional Codes.
1012.4924
Information-Theoretic Capacity and Error Exponents of Stationary Point Processes under Random Additive Displacements
cs.IT math.IT math.PR
This paper studies the Shannon regime for the random displacement of stationary point processes. Let each point of some initial stationary point process in $\R^n$ give rise to one daughter point, the location of which is obtained by adding a random vector to the coordinates of the mother point, with all displacement vectors independently and identically distributed for all points. The decoding problem is then the following one: the whole mother point process is known as well as the coordinates of some daughter point; the displacements are only known through their law; can one find the mother of this daughter point? The Shannon regime is that where the dimension $n$ tends to infinity and where the logarithm of the intensity of the point process is proportional to $n$. We show that this problem exhibits a sharp threshold: if the sum of the proportionality factor and of the differential entropy rate of the noise is positive, then the probability of finding the right mother point tends to 0 with $n$ for all point processes and decoding strategies. If this sum is negative, there exist mother point processes, for instance Poisson, and decoding strategies, for instance maximum likelihood, for which the probability of finding the right mother tends to 1 with $n$. We then use large deviations theory to show that in the latter case, if the entropy spectrum of the noise satisfies a large deviation principle, then the error probability goes exponentially fast to 0 with an exponent that is given in closed form in terms of the rate function of the noise entropy spectrum. This is done for two classes of mother point processes: Poisson and Mat\'ern. The practical interest to information theory comes from the explicit connection that we also establish between this problem and the estimation of error exponents in Shannon's additive noise channel with power constraints on the codewords.
1012.4928
Calibration Using Matrix Completion with Application to Ultrasound Tomography
cs.LG cs.IT math.IT
We study the calibration process in circular ultrasound tomography devices where the sensor positions deviate from the circumference of a perfect circle. This problem arises in a variety of applications in signal processing ranging from breast imaging to sensor network localization. We introduce a novel method of calibration/localization based on the time-of-flight (ToF) measurements between sensors when the enclosed medium is homogeneous. In the presence of all the pairwise ToFs, one can easily estimate the sensor positions using multi-dimensional scaling (MDS) method. In practice however, due to the transitional behaviour of the sensors and the beam form of the transducers, the ToF measurements for close-by sensors are unavailable. Further, random malfunctioning of the sensors leads to random missing ToF measurements. On top of the missing entries, in practice an unknown time delay is also added to the measurements. In this work, we incorporate the fact that a matrix defined from all the ToF measurements is of rank at most four. In order to estimate the missing ToFs, we apply a state-of-the-art low-rank matrix completion algorithm, OPTSPACE . To find the correct positions of the sensors (our ultimate goal) we then apply MDS. We show analytic bounds on the overall error of the whole process in the presence of noise and hence deduce its robustness. Finally, we confirm the functionality of our method in practice by simulations mimicking the measurements of a circular ultrasound tomography device.
1012.4981
Local Minima of a Quadratic Binary Functional with a Quasi-Hebbian Connection Matrix
cond-mat.dis-nn cs.NE
The local minima of a quadratic functional depending on binary variables are discussed. An arbitrary connection matrix can be presented in the form of quasi-Hebbian expansion where each pattern is supplied with its own individual weight. For such matrices statistical physics methods allow one to derive an equation describing local minima of the functional. A model where only one weight differs from other ones is discussed in detail. In this case the equation can be solved analytically. The critical values of the weight, for which the energy landscape is reconstructed, are obtained. Obtained results are confirmed by computer simulations.
1012.5041
Jensen divergence based on Fisher's information
cs.IT math.IT physics.data-an
The measure of Jensen-Fisher divergence between probability distributions is introduced and its theoretical grounds set up. This quantity, in contrast to the remaining Jensen divergences, is very sensitive to the fluctuations of the probability distributions because it is controlled by the (local) Fisher information, which is a gradient functional of the distribution. So, it is appropriate and informative when studying the similarity of distributions, mainly for those having oscillatory character. The new Jensen-Fisher divergence shares with the Jensen-Shannon divergence the following properties: non-negativity, additivity when applied to an arbitrary number of probability densities, symmetry under exchange of these densities, vanishing if and only if all the densities are equal, and definiteness even when these densities present non-common zeros. Moreover, the Jensen-Fisher divergence is shown to be expressed in terms of the relative Fisher information as the Jensen-Shannon divergence does in terms of the Kullback-Leibler or relative Shannon entropy. Finally the Jensen-Shannon and Jensen-Fisher divergences are compared for the following three large, non-trivial and qualitatively different families of probability distributions: the sinusoidal, generalized gamma-like and Rakhmanov-Hermite distributions.
1012.5071
Extension of the Blahut-Arimoto algorithm for maximizing directed information
cs.IT math.IT
We extend the Blahut-Arimoto algorithm for maximizing Massey's directed information. The algorithm can be used for estimating the capacity of channels with delayed feedback, where the feedback is a deterministic function of the output. In order to do so, we apply the ideas from the regular Blahut-Arimoto algorithm, i.e., the alternating maximization procedure, onto our new problem. We provide both upper and lower bound sequences that converge to the optimum value. Our main insight in this paper is that in order to find the maximum of the directed information over causal conditioning probability mass function (PMF), one can use a backward index time maximization combined with the alternating maximization procedure. We give a detailed description of the algorithm, its complexity, the memory needed, and several numerical examples.
1012.5074
Power-Rate Allocation in DS/CDMA Based on Discretized Verhulst Equilibrium
cs.CE
This paper proposes to extend the discrete Verhulst power equilibrium approach, previously suggested in [1], to the power-rate optimal allocation problem. Multirate users associated to different types of traffic are aggregated to distinct user' classes, with the assurance of minimum rate allocation per user and QoS. Herein, Verhulst power allocation algorithm was adapted to the single-input-single-output DS/CDMA jointly power-rate control problem. The analysis was carried out taking into account the convergence time, quality of solution, in terms of the normalized squared error (NSE), when compared with the analytical solution based on interference matrix inverse, and computational complexity. Numerical results demonstrate the validity of the proposed resource allocation methodology.
1012.5113
Timed Game Abstraction of Control Systems
cs.SY
This paper proposes a method for abstracting control systems by timed game automata, and is aimed at obtaining automatic controller synthesis. The proposed abstraction is based on partitioning the state space of a control system using positive and negative invariant sets, generated by Lyapunov functions. This partitioning ensures that the vector field of the control system is transversal to the facets of the cells, which induces some desirable properties of the abstraction. To allow a rich class of control systems to be abstracted, the update maps of the timed game automaton are extended. Conditions on the partitioning of the state space and the control are set up to obtain sound abstractions. Finally, an example is provided to demonstrate the method applied to a control problem related to navigation.
1012.5174
SNEED: Enhancing Network Security Services Using Network Coding and Joint Capacity
cs.NI cs.CR cs.IT math.IT
Traditional network security protocols depend mainly on developing cryptographic schemes and on using biometric methods. These have led to several network security protocols that are unbreakable based on difficulty of solving untractable mathematical problems such as factoring large integers. In this paper, Security of Networks Employing Encoding and Decoding (SNEED) is developed to mitigate single and multiple link attacks. Network coding and shared capacity among the working paths are used to provide data protection and data integrity against network attackers and eavesdroppers. SNEED can be incorporated into various applications in on-demand TV, satellite communications and multimedia security. Finally, It is shown that SNEED can be implemented easily where there are k edge disjoint paths between two core nodes (routers or switches) in an enterprize network.
1012.5197
Accessible Capacity of Secondary Users
cs.IT math.IT
A new problem formulation is presented for the Gaussian interference channels (GIFC) with two pairs of users, which are distinguished as primary users and secondary users, respectively. The primary users employ a pair of encoder and decoder that were originally designed to satisfy a given error performance requirement under the assumption that no interference exists from other users. In the scenario when the secondary users attempt to access the same medium, we are interested in the maximum transmission rate (defined as {\em accessible capacity}) at which secondary users can communicate reliably without affecting the error performance requirement by the primary users under the constraint that the primary encoder (not the decoder) is kept unchanged. By modeling the primary encoder as a generalized trellis code (GTC), we are then able to treat the secondary link and the cross link from the secondary transmitter to the primary receiver as finite state channels (FSCs). Based on this, upper and lower bounds on the accessible capacity are derived. The impact of the error performance requirement by the primary users on the accessible capacity is analyzed by using the concept of interference margin. In the case of non-trivial interference margin, the secondary message is split into common and private parts and then encoded by superposition coding, which delivers a lower bound on the accessible capacity. For some special cases, these bounds can be computed numerically by using the BCJR algorithm. Numerical results are also provided to gain insight into the impacts of the GTC and the error performance requirement on the accessible capacity.
1012.5208
Texture feature extraction in the spatial-frequency domain for content-based image retrieval
cs.CV cs.IR cs.MM
The advent of large scale multimedia databases has led to great challenges in content-based image retrieval (CBIR). Even though CBIR is considered an emerging field of research, however it constitutes a strong background for new methodologies and systems implementations. Therefore, many research contributions are focusing on techniques enabling higher image retrieval accuracy while preserving low level of computational complexity. Image retrieval based on texture features is receiving special attention because of the omnipresence of this visual feature in most real-world images. This paper highlights the state-of-the-art and current progress relevant to texture-based image retrieval and spatial-frequency image representations. In particular, it gives an overview of statistical methodologies and techniques employed for texture feature extraction using most popular spatial-frequency image transforms, namely discrete wavelets, Gabor wavelets, dual-tree complex wavelet and contourlets. Indications are also given about used similarity measurement functions and most important achieved results.
1012.5224
Max-Flow Min-Cut Theorems for Multi-User Communication Networks
cs.IT cs.LO math.CO math.IT
The paper presents four distinct new ideas and results for communication networks: 1) We show that relay-networks (i.e. communication networks where different nodes use the same coding functions) can be used to model dynamic networks. 2) We introduce {\em the term model}, which is a simple, graph-free symbolic approach to communication networks. 3) We state and prove variants of a theorem concerning the dispersion of information in single-receiver communications. 4) We show that the solvability of an abstract multi-user communication problem is equivalent to the solvability of a single-target communication in a suitable relay network. In the paper, we develop a number of technical ramifications of these ideas and results. One technical result is a max-flow min-cut theorem for the R\'enyi entropy with order less than one, given that the sources are equiprobably distributed; conversely, we show that the max-flow min-cut theorem fails for the R\'enyi entropy with order greater than one. We leave the status of the theorem with regards to the ordinary Shannon Entropy measure (R\'enyi entropy of order one and the limit case between validity or failure of the theorem) as an open question. In non-dynamic static communication networks with a single receiver, a simple application of Menger's theorem shows that the optimal throughput can be achieved without proper use of network coding i.e. just by using ordinary packet-switching. This fails dramatically in relay networks with a single receiver. We show that even a powerful method like linear network coding fails miserably for relay networks. With that in mind, it is noticeable that our rather weak form of network coding (routing with dynamic headers) is asymptotically sufficient to reach capacity.
1012.5240
Exploring Grid Polygons Online
cs.CG cs.RO
We investigate the exploration problem of a short-sighted mobile robot moving in an unknown cellular room. To explore a cell, the robot must enter it. Once inside, the robot knows which of the 4 adjacent cells exist and which are boundary edges. The robot starts from a specified cell adjacent to the room's outer wall; it visits each cell, and returns to the start. Our interest is in a short exploration tour; that is, in keeping the number of multiple cell visits small. For abitrary environments containing no obstacles we provide a strategy producing tours of length S <= C + 1/2 E - 3, and for environments containing obstacles we provide a strategy, that is bound by S <= C + 1/2 E + 3H + WCW - 2, where C denotes the number of cells-the area-, E denotes the number of boundary edges-the perimeter-, and H is the number of obstacles, and WCW is a measure for the sinuosity of the given environment.
1012.5248
Matrix Insertion-Deletion Systems
cs.FL cs.CC cs.CL cs.DM
In this article, we consider for the first time the operations of insertion and deletion working in a matrix controlled manner. We show that, similarly as in the case of context-free productions, the computational power is strictly increased when using a matrix control: computational completeness can be obtained by systems with insertion or deletion rules involving at most two symbols in a contextual or in a context-free manner and using only binary matrices.
1012.5253
Exploring Simple Triangular and Hexagonal Grid Polygons Online
cs.CG cs.RO
We investigate the online exploration problem (aka covering) of a short-sighted mobile robot moving in an unknown cellular environment with hexagons and triangles as types of cells. To explore a cell, the robot must enter it. Once inside, the robot knows which of the 3 or 6 adjacent cells exist and which are boundary edges. The robot's task is to visit every cell in the given environment and to return to the start. Our interest is in a short exploration tour; that is, in keeping the number of multiple cell visits small. For arbitrary environments containing no obstacles we provide a strategy producing tours of length S <= C + 1/4 E - 2.5 for hexagonal grids, and S <= C + E - 4 for triangular grids. C denotes the number of cells-the area-, E denotes the number of boundary edges-the perimeter-of the given environment. Further, we show that our strategy is 4/3-competitive in both types of grids, and we provide lower bounds of 14/13 for hexagonal grids and 7/6 for triangular grids.
1012.5306
An optimization strategy on prion AGAAAAGA amyloid fibril molecular modeling
cs.CE physics.bio-ph q-bio.BM q-bio.QM
X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy are two powerful tools to determine the protein 3D structure. However, not all proteins can be successfully crystallized, particularly for membrane proteins. Although NMR spectroscopy is indeed very powerful in determining the 3D structures of membrane proteins, same as X-ray crystallography, it is still very time-consuming and expensive. Under many circumstances, due to the noncrystalline and insoluble nature of some proteins, X-ray and NMR cannot be used at all. Computational approaches, however, allow us to obtain a description of the protein 3D structure at a submicroscopic level. To the best of the author's knowledge, there is little structural data available to date on the AGAAAAGA palindrome in the hydrophobic region (113--120) of prion proteins, which falls just within the N-terminal unstructured region (1--123) of prion proteins. Many experimental studies have shown that the AGAAAAGA region has amyloid fibril forming properties and plays an important role in prion diseases. However, due to the noncrystalline and insoluble nature of the amyloid fibril, little structural data on the AGAAAAGA is available. This paper introduces a simple optimization strategy approach to address the 3D atomic-resolution structure of prion AGAAAAGA amyloid fibrils. Atomic-resolution structures of prion AGAAAAGA amyloid fibrils got in this paper are useful for the drive to find treatments for prion diseases in the field of medicinal chemistry.
1012.5318
Condensation into ground state in binary string models
cs.IT math.IT
The ensemble of binary strings defined via strong-interaction model exhibits enhanced condensation (collapse) into ground state below certain temperature. The non-interaction model shows gradual accumulation into ground state as temperature approaches zero
1012.5327
Computationally Efficient Modulation Level Classification Based on Probability Distribution Distance Functions
cs.IT cs.PF math.IT stat.ML
We present a novel modulation level classification (MLC) method based on probability distribution distance functions. The proposed method uses modified Kuiper and Kolmogorov-Smirnov distances to achieve low computational complexity and outperforms the state of the art methods based on cumulants and goodness-of-fit tests. We derive the theoretical performance of the proposed MLC method and verify it via simulations. The best classification accuracy, under AWGN with SNR mismatch and phase jitter, is achieved with the proposed MLC method using Kuiper distances.
1012.5339
Efficient Generation of Random Bits from Finite State Markov Chains
cs.IT math.IT
The problem of random number generation from an uncorrelated random source (of unknown probability distribution) dates back to von Neumann's 1951 work. Elias (1972) generalized von Neumann's scheme and showed how to achieve optimal efficiency in unbiased random bits generation. Hence, a natural question is what if the sources are correlated? Both Elias and Samuelson proposed methods for generating unbiased random bits in the case of correlated sources (of unknown probability distribution), specifically, they considered finite Markov chains. However, their proposed methods are not efficient or have implementation difficulties. Blum (1986) devised an algorithm for efficiently generating random bits from degree-2 finite Markov chains in expected linear time, however, his beautiful method is still far from optimality on information-efficiency. In this paper, we generalize Blum's algorithm to arbitrary degree finite Markov chains and combine it with Elias's method for efficient generation of unbiased bits. As a result, we provide the first known algorithm that generates unbiased random bits from an arbitrary finite Markov chain, operates in expected linear time and achieves the information-theoretic upper bound on efficiency.
1012.5340
Relations between $\beta$ and $\delta$ for QP and LP in Compressed Sensing Computations
cs.IT math.IT
In many compressed sensing applications, linear programming (LP) has been used to reconstruct a sparse signal. When observation is noisy, the LP formulation is extended to allow an inequality constraint and the solution is dependent on a parameter $\delta$, related to the observation noise level. Recently, some researchers also considered quadratic programming (QP) for compressed sensing signal reconstruction and the solution in this case is dependent on a Lagrange multiplier $\beta$. In this work, we investigated the relation between $\delta$ and $\beta$ and derived an upper and a lower bound on $\beta$ in terms of $\delta$. For a given $\delta$, these bounds can be used to approximate $\beta$. Since $\delta$ is a physically related quantity and easy to determine for an application while there is no easy way in general to determine $\beta$, our results can be used to set $\beta$ when the QP is used for compressed sensing. Our results and experimental verification also provide some insight into the solutions generated by compressed sensing.
1012.5357
Quasirandom Rumor Spreading: An Experimental Analysis
cs.DS cs.SI
We empirically analyze two versions of the well-known "randomized rumor spreading" protocol to disseminate a piece of information in networks. In the classical model, in each round each informed node informs a random neighbor. In the recently proposed quasirandom variant, each node has a (cyclic) list of its neighbors. Once informed, it starts at a random position of the list, but from then on informs its neighbors in the order of the list. While for sparse random graphs a better performance of the quasirandom model could be proven, all other results show that, independent of the structure of the lists, the same asymptotic performance guarantees hold as for the classical model. In this work, we compare the two models experimentally. This not only shows that the quasirandom model generally is faster, but also that the runtime is more concentrated around the mean. This is surprising given that much fewer random bits are used in the quasirandom process. These advantages are also observed in a lossy communication model, where each transmission does not reach its target with a certain probability, and in an asynchronous model, where nodes send at random times drawn from an exponential distribution. We also show that typically the particular structure of the lists has little influence on the efficiency.
1012.5430
Trajectory Codes for Flash Memory
cs.IT math.IT
Flash memory is well-known for its inherent asymmetry: the flash-cell charge levels are easy to increase but are hard to decrease. In a general rewriting model, the stored data changes its value with certain patterns. The patterns of data updates are determined by the data structure and the application, and are independent of the constraints imposed by the storage medium. Thus, an appropriate coding scheme is needed so that the data changes can be updated and stored efficiently under the storage-medium's constraints. In this paper, we define the general rewriting problem using a graph model. It extends many known rewriting models such as floating codes, WOM codes, buffer codes, etc. We present a new rewriting scheme for flash memories, called the trajectory code, for rewriting the stored data as many times as possible without block erasures. We prove that the trajectory code is asymptotically optimal in a wide range of scenarios. We also present randomized rewriting codes optimized for expected performance (given arbitrary rewriting sequences). Our rewriting codes are shown to be asymptotically optimal.
1012.5454
Compressed Sensing for Feedback Reduction in MIMO Broadcast Channels
cs.IT math.IT
We propose a generalized feedback model and compressive sensing based opportunistic feedback schemes for feedback resource reduction in MIMO Broadcast Channels under the assumption that both uplink and downlink channels undergo block Rayleigh fading. Feedback resources are shared and are opportunistically accessed by users who are strong, i.e. users whose channel quality information is above a certain fixed threshold. Strong users send the same feedback information on all shared channels. They are identified by the base station via compressive sensing. Both analog and digital feedbacks are considered. The proposed analog & digital opportunistic feedback schemes are shown to achieve the same sum-rate throughput as that achieved by dedicated feedback schemes, but with feedback channels growing only logarithmically with number of users. Moreover, there is also a reduction in the feedback load. In the analog feedback case, we show that the proposed scheme reduces the feedback noise which eventually results in better throughput, whereas in the digital feedback case the proposed scheme in a noisy scenario achieves almost the throughput obtained in a noiseless dedicated feedback scenario. We also show that for a given fixed budget of feedback bits, there exists a trade-off between the number of shared channels and thresholds accuracy of the fed back SNR.
1012.5464
Classification of self-dual codes of length 36
math.CO cs.IT math.IT
A complete classification of binary self-dual codes of length 36 is given.
1012.5498
Checkable Codes from Group Rings
cs.IT math.AC math.IT
We study codes with a single check element derived from group rings, namely, checkable codes. The notion of a code-checkable group ring is introduced. Necessary and sufficient conditions for a group ring to be code-checkable are given in the case where the group is a finite abelian group and the ring is a finite field. This characterization leads to many good examples, among which two checkable codes and two shortened codes have minimum distance better than the lower bound given in Grassl's online table. Furthermore, when a group ring is code-checkable, it is shown that every code in such a group ring admits a generator, and that its dual is also generated by an element which may be deduced directly from a check element of the original code. These are analogous to the generator and parity-check polynomials of cyclic codes. In addition, the structures of reversible and complementary dual checkable codes are established as generalizations of reversible and complementary dual cyclic codes.
1012.5499
Integrating neighborhoods in the evaluation of fitness promotes cooperation in the spatial prisoner's dilemma game
physics.soc-ph cs.SI
A fundamental question of human society is the evolution of cooperation. Many previous studies explored this question via setting spatial background, where players obtain their payoffs by playing game with their nearest neighbors. Another undoubted fact is that environment plays an important role in the individual development. Inspired by these phenomena, we reconsider the definition of individual fitness which integrates the environment, denoted by the average payoff of all individual neighbors, with the traditional individual payoffs by introducing a selection parameter $u$. Tuning $u$ equal to zero returns the traditional version, while increasing $u$ bears the influence of environment. We find that considering the environment, i.e. integrating neighborhoods in the evaluation of fitness, promotes cooperation. If we enhance the value of $u$, the invasion of defection could be resisted better. We also provide quantitative explanations and complete phase diagrams presenting the influence of environment on the evolution of cooperation. Finally, the universality of this mechanism is testified for different neighborhood sizes, different topology structures and different game models. Our work may shed a light on the emergence and persistence of cooperation in our life.
1012.5506
Ontology-based Queries over Cancer Data
cs.AI cs.DB cs.IR
The ever-increasing amount of data in biomedical research, and in cancer research in particular, needs to be managed to support efficient data access, exchange and integration. Existing software infrastructures, such caGrid, support access to distributed information annotated with a domain ontology. However, caGrid's current querying functionality depends on the structure of individual data resources without exploiting the semantic annotations. In this paper, we present the design and development of an ontology-based querying functionality that consists of: the generation of OWL2 ontologies from the underlying data resources metadata and a query rewriting and translation process based on reasoning, which converts a query at the domain ontology level into queries at the software infrastructure level. We present a detailed analysis of our approach as well as an extensive performance evaluation. While the implementation and evaluation was performed for the caGrid infrastructure, the approach could be applicable to other model and metadata-driven environments for data sharing.
1012.5546
Mining Multi-Level Frequent Itemsets under Constraints
cs.DB cs.AI cs.DS
Mining association rules is a task of data mining, which extracts knowledge in the form of significant implication relation of useful items (objects) from a database. Mining multilevel association rules uses concept hierarchies, also called taxonomies and defined as relations of type 'is-a' between objects, to extract rules that items belong to different levels of abstraction. These rules are more useful, more refined and more interpretable by the user. Several algorithms have been proposed in the literature to discover the multilevel association rules. In this article, we are interested in the problem of discovering multi-level frequent itemsets under constraints, involving the user in the research process. We proposed a technique for modeling and interpretation of constraints in a context of use of concept hierarchies. Three approaches for discovering multi-level frequent itemsets under constraints were proposed and discussed: Basic approach, "Test and Generate" approach and Pruning based Approach.
1012.5553
Cyclic-Coded Integer-Forcing Equalization
cs.IT math.IT
A discrete-time intersymbol interference channel with additive Gaussian noise is considered, where only the receiver has knowledge of the channel impulse response. An approach for combining decision-feedback equalization with channel coding is proposed, where decoding precedes the removal of intersymbol interference. This is accomplished by combining the recently proposed integer-forcing equalization approach with cyclic block codes. The channel impulse response is linearly equalized to an integer-valued response. This is then utilized by leveraging the property that a cyclic code is closed under (cyclic) integer-valued convolution. Explicit bounds on the performance of the proposed scheme are also derived.
1012.5585
Symmetry Breaking with Polynomial Delay
cs.AI
A conservative class of constraint satisfaction problems CSPs is a class for which membership is preserved under arbitrary domain reductions. Many well-known tractable classes of CSPs are conservative. It is well known that lexleader constraints may significantly reduce the number of solutions by excluding symmetric solutions of CSPs. We show that adding certain lexleader constraints to any instance of any conservative class of CSPs still allows us to find all solutions with a time which is polynomial between successive solutions. The time is polynomial in the total size of the instance and the additional lexleader constraints. It is well known that for complete symmetry breaking one may need an exponential number of lexleader constraints. However, in practice, the number of additional lexleader constraints is typically polynomial number in the size of the instance. For polynomially many lexleader constraints, we may in general not have complete symmetry breaking but polynomially many lexleader constraints may provide practically useful symmetry breaking -- and they sometimes exclude super-exponentially many solutions. We prove that for any instance from a conservative class, the time between finding successive solutions of the instance with polynomially many additional lexleader constraints is polynomial even in the size of the instance without lexleaderconstraints.
1012.5594
The Ethics of Robotics
cs.AI cs.RO
The three laws of Robotics first appeared together in Isaac Asimov's story 'Runaround' after being mentioned in some form or the other in previous works by Asimov. These three laws commonly known as the three laws of robotics are the earliest forms of depiction for the needs of ethics in Robotics. In simplistic language Isaac Asimov is able to explain what rules a robot must confine itself to in order to maintain societal sanctity. However, even though they are outdated they still represent some of our innate fears which are beginning to resurface in present day 21st Century. Our society is on the advent of a new revolution; a revolution led by advances in Computer Science, Artificial Intelligence & Nanotechnology. Some of our advances have been so phenomenal that we surpassed what was predicted by the Moore's law. With these advancements comes the fear that our future may be at the mercy of these androids. Humans today are scared that we, ourselves, might create something which we cannot control. We may end up creating something which can not only learn much faster than anyone of us can, but also evolve faster than what the theory of evolution has allowed us to. The greatest fear is not only that we might lose our jobs to these intelligent beings, but that these beings might end up replacing us at the top of the cycle. The public hysteria has been heightened more so by a number of cultural works which depict annihilation of the human race by robots. Right from Frankenstein to I, Robot mass media has also depicted such issues. This paper is an effort to understand the need for ethics in Robotics or simply termed as Roboethics. This is achieved by the study of artificial beings and the thought being put behind them. By the end of the paper, however, it is concluded that there isn't a need for ethical robots but more so ever a need for ethical roboticists.
1012.5625
Free and Open-Source Software is not an Emerging Property but Rather the Result of Studied Design
cs.CY cs.SI
Free and open source software (FOSS) is considered by many, along with Wikipedia, the proof of an ongoing paradigm shift from hierarchically-managed and market-driven production of knowledge to heterarchical, collaborative and commons-based production styles. In such perspective, it has become common place to refer to FOSS as a manifestation of collective intelligence where deliverables and artefacts emerge by virtue of mere cooperation, with no need for supervising leadership. The paper argues that this assumption is based on limited understanding of the software development process, and may lead to wrong conclusions as to the potential of peer production. The development of a less than trivial piece of software, irrespective of whether it be FOSS or proprietary, is a complex cooperative effort requiring the participation of many (often thousands of) individuals. A subset of the participants always play the role of leading system and subsystem designers, determining architecture and functionality; the rest of the people work "underneath" them in a logical, functional sense. While new and powerful forces, including FOSS, are clearly at work in the post-industrial, networked econ-omy, the currently ingenuous stage of research in the field of collective intelligence and networked cooperation must give way to a deeper level of consciousness, which requires an understanding of the software development process.
1012.5693
On the Asymptotic Connectivity of Random Networks under the Random Connection Model
cs.NI cs.IT math.IT
Consider a network where all nodes are distributed on a unit square following a Poisson distribution with known density $\rho$ and a pair of nodes separated by an Euclidean distance $x$ are directly connected with probability $g(\frac{x}{r_{\rho}})$, where $g:[0,\infty)\rightarrow[0,1]$ satisfies three conditions: rotational invariance, non-increasing monotonicity and integral boundedness, $r_{\rho}=\sqrt{\frac{\log\rho+b}{C\rho}}$, $C=\int_{\Re^{2}}g(\Vert \boldsymbol{x}\Vert)d\boldsymbol{x}$ and $b$ is a constant, independent of the event that another pair of nodes are directly connected. In this paper, we analyze the asymptotic distribution of the number of isolated nodes in the above network using the Chen-Stein technique and the impact of the boundary effect on the number of isolated nodes as $\rho\rightarrow\infty$. On that basis we derive a necessary condition for the above network to be asymptotically almost surely connected. These results form an important link in expanding recent results on the connectivity of the random geometric graphs from the commonly used unit disk model to the more generic and more practical random connection model.
1012.5696
Fast and Tiny Structural Self-Indexes for XML
cs.DB
XML document markup is highly repetitive and therefore well compressible using dictionary-based methods such as DAGs or grammars. In the context of selectivity estimation, grammar-compressed trees were used before as synopsis for structural XPath queries. Here a fully-fledged index over such grammars is presented. The index allows to execute arbitrary tree algorithms with a slow-down that is comparable to the space improvement. More interestingly, certain algorithms execute much faster over the index (because no decompression occurs). E.g., for structural XPath count queries, evaluating over the index is faster than previous XPath implementations, often by two orders of magnitude. The index also allows to serialize XML results (including texts) faster than previous systems, by a factor of ca. 2-3. This is due to efficient copy handling of grammar repetitions, and because materialization is totally avoided. In order to compare with twig join implementations, we implemented a materializer which writes out pre-order numbers of result nodes, and show its competitiveness.
1012.5705
Looking for plausibility
cs.AI
In the interpretation of experimental data, one is actually looking for plausible explanations. We look for a measure of plausibility, with which we can compare different possible explanations, and which can be combined when there are different sets of data. This is contrasted to the conventional measure for probabilities as well as to the proposed measure of possibilities. We define what characteristics this measure of plausibility should have. In getting to the conception of this measure, we explore the relation of plausibility to abductive reasoning, and to Bayesian probabilities. We also compare with the Dempster-Schaefer theory of evidence, which also has its own definition for plausibility. Abduction can be associated with biconditionality in inference rules, and this provides a platform to relate to the Collins-Michalski theory of plausibility. Finally, using a formalism for wiring logic onto Hopfield neural networks, we ask if this is relevant in obtaining this measure.
1012.5723
Towards a Better Understanding of Large Scale Network Models
cs.NI cs.IT math.IT
Connectivity and capacity are two fundamental properties of wireless multi-hop networks. The scalability of these properties has been a primary concern for which asymptotic analysis is a useful tool. Three related but logically distinct network models are often considered in asymptotic analyses, viz. the dense network model, the extended network model and the infinite network model, which consider respectively a network deployed in a fixed finite area with a sufficiently large node density, a network deployed in a sufficiently large area with a fixed node density, and a network deployed in $\Re^{2}$ with a sufficiently large node density. The infinite network model originated from continuum percolation theory and asymptotic results obtained from the infinite network model have often been applied to the dense and extended networks. In this paper, through two case studies related to network connectivity on the expected number of isolated nodes and on the vanishing of components of finite order k>1 respectively, we demonstrate some subtle but important differences between the infinite network model and the dense and extended network models. Therefore extra scrutiny has to be used in order for the results obtained from the infinite network model to be applicable to the dense and extended network models. Asymptotic results are also obtained on the expected number of isolated nodes, the vanishingly small impact of the boundary effect on the number of isolated nodes and the vanishing of components of finite order k>1 in the dense and extended network models using a generic random connection model.
1012.5752
Increasing risk behavior can outweigh the benefits of anti-retroviral drug treatment on the HIV incidence among men-having-sex-with-men in Amsterdam
cs.SI physics.med-ph q-bio.PE
The transmission through contacts among MSM (men who have sex with men) is one of the dominating contributors to HIV prevalence in industrialized countries. In Amsterdam, the capital of the Netherlands, the MSM risk group has been traced for decades. This has motivated studies which provide detailed information about MSM's risk behavior statistically, psychologically and sociologically. Despite the era of potent antiretroviral therapy, the incidence of HIV among MSM increases. In the long term the contradictory effects of risk behavior and effective therapy are still poorly understood. Using a previously presented Complex Agent Network model, we describe steady and casual partnerships to predict the HIV spreading among MSM. Behavior-related parameters and values, inferred from studies on Amsterdam MSM, are fed into the model; we validate the model using historical yearly incidence data. Subsequently, we study scenarios to assess the contradictory effects of risk behavior and effective therapy, by varying corresponding values of parameters. Finally, we conduct quantitative analysis based on the resulting incidence data. The simulated incidence reproduces the ACS historical incidence well and helps to predict the HIV epidemic among MSM in Amsterdam. Our results show that in the long run the positive influence of effective therapy can be outweighed by an increase in risk behavior of at least 30% for MSM. Conclusion: We recommend, based on the model predictions, that lowering risk behavior is the prominent control mechanism of HIV incidence even in the presence of effective therapy.
1012.5754
Software Effort Estimation with Ridge Regression and Evolutionary Attribute Selection
cs.SE cs.AI cs.LG
Software cost estimation is one of the prerequisite managerial activities carried out at the software development initiation stages and also repeated throughout the whole software life-cycle so that amendments to the total cost are made. In software cost estimation typically, a selection of project attributes is employed to produce effort estimations of the expected human resources to deliver a software product. However, choosing the appropriate project cost drivers in each case requires a lot of experience and knowledge on behalf of the project manager which can only be obtained through years of software engineering practice. A number of studies indicate that popular methods applied in the literature for software cost estimation, such as linear regression, are not robust enough and do not yield accurate predictions. Recently the dual variables Ridge Regression (RR) technique has been used for effort estimation yielding promising results. In this work we show that results may be further improved if an AI method is used to automatically select appropriate project cost drivers (inputs) for the technique. We propose a hybrid approach combining RR with a Genetic Algorithm, the latter evolving the subset of attributes for approximating effort more accurately. The proposed hybrid cost model has been applied on a widely known high-dimensional dataset of software project samples and the results obtained show that accuracy may be increased if redundant attributes are eliminated.
1012.5755
DD-EbA: An algorithm for determining the number of neighbors in cost estimation by analogy using distance distributions
cs.SE cs.AI
Case Based Reasoning and particularly Estimation by Analogy, has been used in a number of problem-solving areas, such as cost estimation. Conventional methods, despite the lack of a sound criterion for choosing nearest projects, were based on estimation using a fixed and predetermined number of neighbors from the entire set of historical instances. This approach puts boundaries to the estimation ability of such algorithms, for they do not take into consideration that every project under estimation is unique and requires different handling. The notion of distributions of distances together with a distance metric for distributions help us to adapt the proposed method (we call it DD-EbA) each time to a specific case that is to be estimated without loosing in prediction power or computational cost. The results of this paper show that the proposed technique achieves the above idea in a very efficient way.
1012.5774
Towards the Capacity Region of Multiplicative Linear Operator Broadcast Channels
cs.IT math.AG math.IT
Recent research indicates that packet transmission employing random linear network coding can be regarded as transmitting subspaces over a linear operator channel (LOC). In this paper we propose the framework of linear operator broadcast channels (LOBCs) to model packet broadcasting over LOCs, and we do initial work on the capacity region of constant-dimension multiplicative LOBCs(CMLOBCs), a generalization of broadcast erasure channels. Two fundamental problems regarding CMLOBCs are addressed-finding necessary and sufficient conditions for degradation and deciding whether time sharing suffices to achieve the boundary of the capacity region in the degraded case.
1012.5813
Neural Network Influence in Group Technology: A Chronological Survey and Critical Analysis
cs.AI nlin.AO
This article portrays a chronological review of the influence of Artificial Neural Network in group technology applications in the vicinity of Cellular Manufacturing Systems. The research trend is identified and the evolvement is captured through a critical analysis of the literature accessible from the very beginning of its practice in the early 90's till the 2010. Analysis of the diverse ANN approaches, spotted research pattern, comparison of the clustering efficiencies, the solutions obtained and the tools used make this study exclusive in its class.
1012.5815
SAPFOCS: a metaheuristic based approach to part family formation problems in group technology
cs.AI
This article deals with Part family formation problem which is believed to be moderately complicated to be solved in polynomial time in the vicinity of Group Technology (GT). In the past literature researchers investigated that the part family formation techniques are principally based on production flow analysis (PFA) which usually considers operational requirements, sequences and time. Part Coding Analysis (PCA) is merely considered in GT which is believed to be the proficient method to identify the part families. PCA classifies parts by allotting them to different families based on their resemblances in: (1) design characteristics such as shape and size, and/or (2) manufacturing characteristics (machining requirements). A novel approach based on simulated annealing namely SAPFOCS is adopted in this study to develop effective part families exploiting the PCA technique. Thereafter Taguchi's orthogonal design method is employed to solve the critical issues on the subject of parameters selection for the proposed metaheuristic algorithm. The adopted technique is therefore tested on 5 different datasets of size 5 {\times} 9 to 27 {\times} 9 and the obtained results are compared with C-Linkage clustering technique. The experimental results reported that the proposed metaheuristic algorithm is extremely effective in terms of the quality of the solution obtained and has outperformed C-Linkage algorithm in most instances.
1012.5846
Improvement of the Han-Kobayashi Rate Region for General Interference Channel-v2
cs.IT math.IT
Allowing the input auxiliary random variables to be correlated and using the binning scheme, the Han-Kobayashi (HK) rate region for general interference channel is partially improved. The obtained partially new achievable rate region (i) is compared to the HK region and its simplified description, i.e., Chong-Motani-Garg (CMG) region, in a detailed and favorable manner, by considering different versions of the regions, and (ii) has an interesting and easy interpretation: as expected, any rate in our region has generally two additional terms in comparison with the HK region (one due to the input correlation and the other as a result of the binning scheme). Keywords. Interference channel, Input correlation, Binning scheme
1012.5847
On Elementary Loops of Logic Programs
cs.AI
Using the notion of an elementary loop, Gebser and Schaub refined the theorem on loop formulas due to Lin and Zhao by considering loop formulas of elementary loops only. In this article, we reformulate their definition of an elementary loop, extend it to disjunctive programs, and study several properties of elementary loops, including how maximal elementary loops are related to minimal unfounded sets. The results provide useful insights into the stable model semantics in terms of elementary loops. For a nondisjunctive program, using a graph-theoretic characterization of an elementary loop, we show that the problem of recognizing an elementary loop is tractable. On the other hand, we show that the corresponding problem is {\sf coNP}-complete for a disjunctive program. Based on the notion of an elementary loop, we present the class of Head-Elementary-loop-Free (HEF) programs, which strictly generalizes the class of Head-Cycle-Free (HCF) programs due to Ben-Eliyahu and Dechter. Like an HCF program, an HEF program can be turned into an equivalent nondisjunctive program in polynomial time by shifting head atoms into the body.
1012.5883
On sub-ideal causal smoothing filters
math.OC cs.SY math.CA math.SP
Smoothing causal linear time-invariant filters are studied for continuous time processes. The paper suggests a family of causal filters with almost exponential damping of the energy on the higher frequencies. These filters are sub-ideal meaning that a faster decay of the frequency response would lead to the loss of causality.
1012.5913
All liaisons are dangerous when all your friends are known to us
cs.SI cs.CY cs.DM
Online Social Networks (OSNs) are used by millions of users worldwide. Academically speaking, there is little doubt about the usefulness of demographic studies conducted on OSNs and, hence, methods to label unknown users from small labeled samples are very useful. However, from the general public point of view, this can be a serious privacy concern. Thus, both topics are tackled in this paper: First, a new algorithm to perform user profiling in social networks is described, and its performance is reported and discussed. Secondly, the experiments --conducted on information usually considered sensitive-- reveal that by just publicizing one's contacts privacy is at risk and, thus, measures to minimize privacy leaks due to social graph data mining are outlined.
1012.5933
Affine-invariant diffusion geometry for the analysis of deformable 3D shapes
cs.CV
We introduce an (equi-)affine invariant diffusion geometry by which surfaces that go through squeeze and shear transformations can still be properly analyzed. The definition of an affine invariant metric enables us to construct an invariant Laplacian from which local and global geometric structures are extracted. Applications of the proposed framework demonstrate its power in generalizing and enriching the existing set of tools for shape analysis.
1012.5936
Affine-invariant geodesic geometry of deformable 3D shapes
cs.CV
Natural objects can be subject to various transformations yet still preserve properties that we refer to as invariants. Here, we use definitions of affine invariant arclength for surfaces in R^3 in order to extend the set of existing non-rigid shape analysis tools. In fact, we show that by re-defining the surface metric as its equi-affine version, the surface with its modified metric tensor can be treated as a canonical Euclidean object on which most classical Euclidean processing and analysis tools can be applied. The new definition of a metric is used to extend the fast marching method technique for computing geodesic distances on surfaces, where now, the distances are defined with respect to an affine invariant arclength. Applications of the proposed framework demonstrate its invariance, efficiency, and accuracy in shape analysis.
1012.5947
Orthogonal symmetric Toeplitz matrices for compressed sensing: Statistical isometry property
cs.IT math.IT
Recently, the statistical restricted isometry property (RIP) has been formulated to analyze the performance of deterministic sampling matrices for compressed sensing. In this paper, we propose the usage of orthogonal symmetric Toeplitz matrices (OSTM) for compressed sensing and study their statistical RIP by taking advantage of Stein's method. In particular, we derive the statistical RIP performance bound in terms of the largest value of the sampling matrix and the sparsity level of the input signal. Based on such connections, we show that OSTM can satisfy the statistical RIP for an overwhelming majority of signals with given sparsity level, if a Golay sequence used to generate the OSTM. Such sensing matrices are deterministic, Toeplitz, and efficient to implement. Simulation results show that OSTM can offer reconstruction performance similar to that of random matrices.
1012.5956
A New Noncoherent Decoder for Wireless Network Coding
cs.IT math.IT
This work deals with the decoding aspect of wireless network coding in the canonical two-way relay channel where two senders exchange messages via a common relay and they receive the mixture of two messages. One of the recent works on wireless network coding was well explained by Katti \textit{et al.} in SIGCOMM'07. In this work, we analyze the issue with one of their decoders when minimum-shift keying (MSK) is employed as the modulation format, and propose a new noncoherent decoder in the presence of two interfering signals.
1012.5960
Extending Binary Qualitative Direction Calculi with a Granular Distance Concept: Hidden Feature Attachment
cs.AI
In this paper we introduce a method for extending binary qualitative direction calculi with adjustable granularity like OPRAm or the star calculus with a granular distance concept. This method is similar to the concept of extending points with an internal reference direction to get oriented points which are the basic entities in the OPRAm calculus. Even if the spatial objects are from a geometrical point of view infinitesimal small points locally available reference measures are attached. In the case of OPRAm, a reference direction is attached. The same principle works also with local reference distances which are called elevations. The principle of attaching references features to a point is called hidden feature attachment.
1012.5961
Vulnerability of Networks Against Critical Link Failures
physics.soc-ph cond-mat.other cs.SI
Networks are known to be prone to link failures. In this paper we set out to investigate how networks of varying connectivity patterns respond to different link failure schemes in terms of connectivity, clustering coefficient and shortest path lengths. We then propose a measure, which we call the vulnerability of a network, for evaluating the extent of the damage these failures can cause. Accepting the disconnections of node pairs as a damage indicator, vulnerability simply represents how quickly the failure of the critical links cause the network to undergo a specified damage extent. Analyzing the vulnerabilities under varying damage specifications shows that scale free networks are relatively more vulnerable for small failures, but more efficient; whereas Erd\"os-R\'enyi networks are the least vulnerable despite lacking any clustered structure.
1012.5962
Annotated English
cs.CL
This document presents Annotated English, a system of diacritical symbols which turns English pronunciation into a precise and unambiguous process. The annotations are defined and located in such a way that the original English text is not altered (not even a letter), thus allowing for a consistent reading and learning of the English language with and without annotations. The annotations are based on a set of general rules that make the frequency of annotations not dramatically high. This makes the reader easily associate annotations with exceptions, and makes it possible to shape, internalise and consolidate some rules for the English language which otherwise are weakened by the enormous amount of exceptions in English pronunciation. The advantages of this annotation system are manifold. Any existing text can be annotated without a significant increase in size. This means that we can get an annotated version of any document or book with the same number of pages and fontsize. Since no letter is affected, the text can be perfectly read by a person who does not know the annotation rules, since annotations can be simply ignored. The annotations are based on a set of rules which can be progressively learned and recognised, even in cases where the reader has no access or time to read the rules. This means that a reader can understand most of the annotations after reading a few pages of Annotated English, and can take advantage from that knowledge for any other annotated document she may read in the future.
1012.5994
Toward Emerging Topic Detection for Business Intelligence: Predictive Analysis of `Meme' Dynamics
cs.SI
Detecting and characterizing emerging topics of discussion and consumer trends through analysis of Internet data is of great interest to businesses. This paper considers the problem of monitoring the Web to spot emerging memes - distinctive phrases which act as "tracers" for topics - as a means of early detection of new topics and trends. We present a novel methodology for predicting which memes will propagate widely, appearing in hundreds or thousands of blog posts, and which will not, thereby enabling discovery of significant topics. We begin by identifying measurables which should be predictive of meme success. Interestingly, these metrics are not those traditionally used for such prediction but instead are subtle measures of meme dynamics. These metrics form the basis for learning a classifier which predicts, for a given meme, whether or not it will propagate widely. The utility of the prediction methodology is demonstrated through analysis of memes that emerged online during the second half of 2008.
1012.5997
Protection Over Asymmetric Channels, S-MATE: Secure Multipath Adaptive Traffic Engineering
cs.IT cs.CR cs.NI math.IT
Several approaches have been proposed to the problem of provisioning traffic engineering between core network nodes in Internet Service Provider (ISP) networks. Such approaches aim to minimize network delay, increase capacity, and enhance security services between two core (relay) network nodes, an ingress node and an egress node. MATE (Multipath Adaptive Traffic Engineering) has been proposed for multipath adaptive traffic engineering between an ingress node (source) and an egress node (destination) to distribute the network flow among multiple disjoint paths. Its novel idea is to avoid network congestion and attacks that might exist in edge and node disjoint paths between two core network nodes. This paper proposes protection schemes over asymmetric channels. Precisely, the paper aims to develop an adaptive, robust, and reliable traffic engineering scheme to improve performance and reliability of communication networks. This scheme will also provision Quality of Server (QoS) and protection of traffic engineering to maximize network efficiency. Specifically, S-MATE (secure MATE) is proposed to protect the network traffic between two core nodes (routers, switches, etc.) in a cloud network. S-MATE secures against a single link attack/failure by adding redundancy in one of the operational redundant paths between the sender and receiver nodes. It is also extended to secure against multiple attacked links. The proposed scheme can be applied to secure core networks such as optical and IP networks.
1012.6009
Cluster Evaluation of Density Based Subspace Clustering
cs.DB
Clustering real world data often faced with curse of dimensionality, where real world data often consist of many dimensions. Multidimensional data clustering evaluation can be done through a density-based approach. Density approaches based on the paradigm introduced by DBSCAN clustering. In this approach, density of each object neighbours with MinPoints will be calculated. Cluster change will occur in accordance with changes in density of each object neighbours. The neighbours of each object typically determined using a distance function, for example the Euclidean distance. In this paper SUBCLU, FIRES and INSCY methods will be applied to clustering 6x1595 dimension synthetic datasets. IO Entropy, F1 Measure, coverage, accurate and time consumption used as evaluation performance parameters. Evaluation results showed SUBCLU method requires considerable time to process subspace clustering; however, its value coverage is better. Meanwhile INSCY method is better for accuracy comparing with two other methods, although consequence time calculation was longer.
1012.6012
On the Capacity of the Discrete Memoryless Broadcast Channel with Feedback
cs.IT math.IT
A coding scheme for the discrete memoryless broadcast channel with {noiseless, noisy, generalized} feedback is proposed, and the associated achievable region derived. The scheme is based on a block-Markov strategy combining the Marton scheme and a lossy version of the Gray-Wyner scheme with side-information. In each block the transmitter sends fresh data and update information that allows the receivers to improve the channel outputs observed in the previous block. For a generalization of Dueck's broadcast channel our scheme achieves the noiseless-feedback capacity, which is strictly larger than the no-feedback capacity. For a generalization of Blackwell's channel and when the feedback is noiseless our new scheme achieves rate points that are outside the no-feedback capacity region. It follows by a simple continuity argument that for both these channels and when the feedback noise is sufficiently low, our scheme improves on the no-feedback capacity even when the feedback is noisy.
1012.6018
Learning a Representation of a Believable Virtual Character's Environment with an Imitation Algorithm
cs.AI
In video games, virtual characters' decision systems often use a simplified representation of the world. To increase both their autonomy and believability we want those characters to be able to learn this representation from human players. We propose to use a model called growing neural gas to learn by imitation the topology of the environment. The implementation of the model, the modifications and the parameters we used are detailed. Then, the quality of the learned representations and their evolution during the learning are studied using different measures. Improvements for the growing neural gas to give more information to the character's model are given in the conclusion.
1101.0011
Packet Scheduling in Switches with Target Outflow Profiles
cs.NI cs.MM cs.SY
The problem of packet scheduling for traffic streams with target outflow profiles traversing input queued switches is formulated in this paper. Target outflow profiles specify the desirable inter-departure times of packets leaving the switch from each traffic stream. The goal of the switch scheduler is to dynamically select service configurations of the switch, so that actual outflow streams ("pulled" through the switch) adhere to their desired target profiles as accurately as possible. Dynamic service controls (schedules) are developed to minimize deviation of actual outflow streams from their targets and suppress stream "distortion". Using appropriately selected subsets of service configurations of the switch, efficient schedules are designed, which deliver high performance at relatively low complexity. Some of these schedules are provably shown to achieve 100% pull-throughput. Moreover, simulations demonstrate that for even substantial contention of streams through the switch, due to stringent/intense target outflow profiles, the proposed schedules achieve closely their target profiles and suppress stream distortion. The switch model investigated here deviates from the classical switching paradigm. In the latter, the goal of packet scheduling is primarily to "push" as much traffic load through the switch as possible, while controlling delay to traverse the switch and keeping congestion/backlogs from exploding. In the model presented here, however, the goal of packet scheduling is to "pull" traffic streams through the switch, maintaining desirable (target) outflow profiles.
1101.0064
Dual universality of hash functions and its applications to quantum cryptography
quant-ph cs.IT math.IT
In this paper, we introduce the concept of dual universality of hash functions and present its applications to quantum cryptography. We begin by establishing the one-to-one correspondence between a linear function family {\cal F} and a code family {\cal C}, and thereby defining \varepsilon-almost dual universal_2 hash functions, as a generalization of the conventional universal_2 hash functions. Then we show that this generalized (and thus broader) class of hash functions is in fact sufficient for the security of quantum cryptography. This result can be explained in two different formalisms. First, by noting its relation to the \delta-biased family introduced by Dodis and Smith, we demonstrate that Renner's two-universal hashing lemma is generalized to our class of hash functions. Next, we prove that the proof technique by Shor and Preskill can be applied to quantum key distribution (QKD) systems that use our generalized class of hash functions for privacy amplification. While Shor-Preskill formalism requires an implementer of a QKD system to explicitly construct a linear code of the Calderbank-Shor-Steane type, this result removes the existing difficulty of the construction a linear code of CSS code by replacing it by the combination of an ordinary classical error correcting code and our proposed hash function. We also show that a similar result applies to the quantum wire-tap channel. Finally we compare our results in the two formalisms and show that, in typical QKD scenarios, the Shor-Preskill--type argument gives better security bounds in terms of the trace distance and Holevo information, than the method based on the \delta-biased family.
1101.0085
Linear Codes, Target Function Classes, and Network Computing Capacity
cs.IT cs.DC math.CO math.IT
We study the use of linear codes for network computing in single-receiver networks with various classes of target functions of the source messages. Such classes include reducible, injective, semi-injective, and linear target functions over finite fields. Computing capacity bounds and achievability are given with respect to these target function classes for network codes that use routing, linear coding, or nonlinear coding.
1101.0133
Enabling Node Repair in Any Erasure Code for Distributed Storage
cs.IT cs.DC cs.NI math.IT
Erasure codes are an efficient means of storing data across a network in comparison to data replication, as they tend to reduce the amount of data stored in the network and offer increased resilience in the presence of node failures. The codes perform poorly though, when repair of a failed node is called for, as they typically require the entire file to be downloaded to repair a failed node. A new class of erasure codes, termed as regenerating codes were recently introduced, that do much better in this respect. However, given the variety of efficient erasure codes available in the literature, there is considerable interest in the construction of coding schemes that would enable traditional erasure codes to be used, while retaining the feature that only a fraction of the data need be downloaded for node repair. In this paper, we present a simple, yet powerful, framework that does precisely this. Under this framework, the nodes are partitioned into two 'types' and encoded using two codes in a manner that reduces the problem of node-repair to that of erasure-decoding of the constituent codes. Depending upon the choice of the two codes, the framework can be used to avail one or more of the following advantages: simultaneous minimization of storage space and repair-bandwidth, low complexity of operation, fewer disk reads at helper nodes during repair, and error detection and correction.
1101.0139
A Fast Statistical Method for Multilevel Thresholding in Wavelet Domain
nlin.CD cs.CV
An algorithm is proposed for the segmentation of image into multiple levels using mean and standard deviation in the wavelet domain. The procedure provides for variable size segmentation with bigger block size around the mean, and having smaller blocks at the ends of histogram plot of each horizontal, vertical and diagonal components, while for the approximation component it provides for finer block size around the mean, and larger blocks at the ends of histogram plot coefficients. It is found that the proposed algorithm has significantly less time complexity, achieves superior PSNR and Structural Similarity Measurement Index as compared to similar space domain algorithms[1]. In the process it highlights finer image structures not perceptible in the original image. It is worth emphasizing that after the segmentation only 16 (at threshold level 3) wavelet coefficients captures the significant variation of image.
1101.0198
Link Spam Detection based on DBSpamClust with Fuzzy C-means Clustering
cs.IR cs.IT cs.SI math.IT
Search engine became omnipresent means for ingoing to the web. Spamming Search engine is the technique to deceiving the ranking in search engine and it inflates the ranking. Web spammers have taken advantage of the vulnerability of link based ranking algorithms by creating many artificial references or links in order to acquire higher-than-deserved ranking n search engines' results. Link based algorithms such as PageRank, HITS utilizes the structural details of the hyperlinks for ranking the content in the web. In this paper an algorithm DBSpamClust is proposed for link spam detection. As showing through experiments such a method can filter out web spam effectively
1101.0211
Spectral Properties of Directed Random Networks with Modular Structure
cond-mat.dis-nn cs.SI physics.soc-ph q-bio.MN
We study spectra of directed networks with inhibitory and excitatory couplings. We investigate in particular eigenvector localization properties of various model networks for different value of correlation among their entries. Spectra of random networks, with completely uncorrelated entries show a circular distribution with delocalized eigenvectors, where as networks with correlated entries have localized eigenvectors. In order to understand the origin of localization we track the spectra as a function of connection probability and directionality. As connections are made directed, eigenstates start occurring in complex conjugate pairs and the eigenvalue distribution combined with the localization measure shows a rich pattern. Moreover, for a very well distinguished community structure, the whole spectrum is localized except few eigenstates at boundary of the circular distribution. As the network deviates from the community structure there is a sudden change in the localization property for a very small value of deformation from the perfect community structure. We search for this effect for the whole range of correlation strengths and for different community configurations. Furthermore, we investigate spectral properties of a metabolic network of zebrafish, and compare them with those of the model networks.
1101.0237
A Framework for Real-Time Face and Facial Feature Tracking using Optical Flow Pre-estimation and Template Tracking
cs.CV
This work presents a framework for tracking head movements and capturing the movements of the mouth and both the eyebrows in real-time. We present a head tracker which is a combination of a optical flow and a template based tracker. The estimation of the optical flow head tracker is used as starting point for the template tracker which fine-tunes the head estimation. This approach together with re-updating the optical flow points prevents the head tracker from drifting. This combination together with our switching scheme, makes our tracker very robust against fast movement and motion-blur. We also propose a way to reduce the influence of partial occlusion of the head. In both the optical flow and the template based tracker we identify and exclude occluded points.
1101.0242
Binary and nonbinary description of hypointensity in human brain MR images
cs.CV
Accumulating evidence has shown that iron is involved in the mechanism underlying many neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease and Huntington's disease. Abnormal (higher) iron accumulation has been detected in the brains of most neurodegenerative patients, especially in the basal ganglia region. Presence of iron leads to changes in MR signal in both magnitude and phase. Accordingly, tissues with high iron concentration appear hypo-intense (darker than usual) in MR contrasts. In this report, we proposed an improved binary hypointensity description and a novel nonbinary hypointensity description based on principle components analysis. Moreover, Kendall's rank correlation coefficient was used to compare the complementary and redundant information provided by the two methods in order to better understand the individual descriptions of iron accumulation in the brain.
1101.0245
Use of Python and Phoenix-M Interface in Robotics
cs.RO cs.AI
In this paper I will show how to use Python programming with a computer interface such as Phoenix-M 1 to drive simple robots. In my quest towards Artificial Intelligence(AI) I am experimenting with a lot of different possibilities in Robotics. This one will try to mimic the working of a simple insect's nervous system using hard wiring and some minimal software usage. This is the precursor to my advanced robotics and AI integration where I plan to use a new paradigm of AI based on Machine Learning and Self Consciousness via Knowledge Feedback and Update Process.
1101.0255
Conditional information and definition of neighbor in categorical random fields
math.ST cs.LG stat.TH
We show that the definition of neighbor in Markov random fields as defined by Besag (1974) when the joint distribution of the sites is not positive is not well-defined. In a random field with finite number of sites we study the conditions under which giving the value at extra sites will change the belief of an agent about one site. Also the conditions under which the information from some sites is equivalent to giving the value at all other sites is studied. These concepts provide an alternative to the concept of neighbor for general case where the positivity condition of the joint does not hold.
1101.0270
"On the engineers' new toolbox" or Analog Circuit Design, using Symbolic Analysis, Computer Algebra, and Elementary Network Transformations
cs.SC cs.CE cs.DM
In this paper, by way of three examples - a fourth order low pass active RC filter, a rudimentary BJT amplifier, and an LC ladder - we show, how the algebraic capabilities of modern computer algebra systems can, or in the last example, might be brought to use in the task of designing analog circuits.
1101.0272
Social Norms for Online Communities
cs.SI cs.NI physics.soc-ph
Sustaining cooperation among self-interested agents is critical for the proliferation of emerging online social communities, such as online communities formed through social networking services. Providing incentives for cooperation in social communities is particularly challenging because of their unique features: a large population of anonymous agents interacting infrequently, having asymmetric interests, and dynamically joining and leaving the community; operation errors; and low-cost reputation whitewashing. In this paper, taking these features into consideration, we propose a framework for the design and analysis of a class of incentive schemes based on a social norm, which consists of a reputation scheme and a social strategy. We first define the concept of a sustainable social norm under which every agent has an incentive to follow the social strategy given the reputation scheme. We then formulate the problem of designing an optimal social norm, which selects a social norm that maximizes overall social welfare among all sustainable social norms. Using the proposed framework, we study the structure of optimal social norms and the impacts of punishment lengths and whitewashing on optimal social norms. Our results show that optimal social norms are capable of sustaining cooperation, with the amount of cooperation varying depending on the community characteristics.
1101.0275
Asynchronous Interference Alignment
cs.IT math.IT
A constant K-user interference channel in which the users are not symbol-synchronous is considered. It is shown that the asynchronism among the users facilitates aligning interfering signals at each receiver node while it does not affect the total number of degrees of freedom (DoF) of the channel. To achieve the total K/2 DoF of the channel when single antenna nodes are used, a novel practical interference alignment scheme is proposed wherein the alignment task is performed with the help of asynchronous delays which inherently exist among the received signals at each receiver node. When each node is equipped with M > 1 antennas, it is argued that the same alignment scheme is sufficient to achieve the total MK/2 DoF of the medium when all links between collocated antennas experience the same asynchronous delay.
1101.0287
On the Capacity of the Heat Channel, Waterfilling in the Time-Frequency Plane, and a C-NODE Relationship
cs.IT math.IT
The heat channel is defined by a linear time-varying (LTV) filter with additive white Gaussian noise (AWGN) at the filter output. The continuous-time LTV filter is related to the heat kernel of the quantum mechanical harmonic oscillator, so the name of the channel. The channel's capacity is given in closed form by means of the Lambert W function. Also a waterfilling theorem in the time-frequency plane for the capacity is derived. It relies on a specific Szego theorem for which an essentially self-contained proof is provided. Similarly, the rate distortion function for a related nonstationary source is given in closed form and a (reverse) waterfilling theorem in the time-frequency plane is derived. Finally, a second closed-form expression for the capacity of the heat channel based on the detected perturbed filter output signals is presented. In this context, a precise differential connection between channel capacity and the normalized optimal detection error (NODE) is revealed. This C-NODE relationship is compared with the well-known I-MMSE relationship connecting mutual information with the minimum mean-square error (MMSE) of estimation theory.
1101.0294
Virtual Full Duplex Wireless Broadcasting via Compressed Sensing
cs.IT cs.NI math.IT
A novel solution is proposed to undertake a frequent task in wireless networks, which is to let all nodes broadcast information to and receive information from their respective one-hop neighboring nodes. The contribution is two-fold. First, as each neighbor selects one message-bearing codeword from its unique codebook for transmission, it is shown that decoding their messages based on a superposition of those codewords through the multiaccess channel is fundamentally a problem of compressed sensing. In the case where each message consists of a small number of bits, an iterative algorithm based on belief propagation is developed for efficient decoding. Second, to satisfy the half-duplex constraint, each codeword consists of randomly distributed on-slots and off-slots. A node transmits during its on-slots, and listens to its neighbors only through its own off-slots. Over one frame interval, each node broadcasts a message to neighbors and simultaneously decodes neighbors' messages based on the superposed signals received through its own off-slots. Thus the solution fully exploits the multiaccess nature of the wireless medium and addresses the half-duplex constraint at the fundamental level. In a network consisting of Poisson distributed nodes, numerical results demonstrate that the proposed scheme often achieves several times the rate of slotted ALOHA and CSMA with the same packet error rate.
1101.0302
Mutual Information, Relative Entropy, and Estimation in the Poisson Channel
cs.IT math.IT
Let $X$ be a non-negative random variable and let the conditional distribution of a random variable $Y$, given $X$, be ${Poisson}(\gamma \cdot X)$, for a parameter $\gamma \geq 0$. We identify a natural loss function such that: 1) The derivative of the mutual information between $X$ and $Y$ with respect to $\gamma$ is equal to the \emph{minimum} mean loss in estimating $X$ based on $Y$, regardless of the distribution of $X$. 2) When $X \sim P$ is estimated based on $Y$ by a mismatched estimator that would have minimized the expected loss had $X \sim Q$, the integral over all values of $\gamma$ of the excess mean loss is equal to the relative entropy between $P$ and $Q$. For a continuous time setting where $X^T = \{X_t, 0 \leq t \leq T \}$ is a non-negative stochastic process and the conditional law of $Y^T=\{Y_t, 0\le t\le T\}$, given $X^T$, is that of a non-homogeneous Poisson process with intensity function $\gamma \cdot X^T$, under the same loss function: 1) The minimum mean loss in \emph{causal} filtering when $\gamma = \gamma_0$ is equal to the expected value of the minimum mean loss in \emph{non-causal} filtering (smoothing) achieved with a channel whose parameter $\gamma$ is uniformly distributed between 0 and $\gamma_0$. Bridging the two quantities is the mutual information between $X^T$ and $Y^T$. 2) This relationship between the mean losses in causal and non-causal filtering holds also in the case where the filters employed are mismatched, i.e., optimized assuming a law on $X^T$ which is not the true one. Bridging the two quantities in this case is the sum of the mutual information and the relative entropy between the true and the mismatched distribution of $Y^T$. Thus, relative entropy quantifies the excess estimation loss due to mismatch in this setting. These results parallel those recently found for the Gaussian channel.