OpenRaiser/Intern-Atlas huggingface dataset:
On Large Scale Distributed Compression and Dispersive Information Routing for Networks
Venue: arXiv cs.IT 2013
Year: 2013

This paper considers the problem of distributed source coding for a large network. A major obstacle that poses an existential threat to practical deployment of conventional approaches to distributed coding is the exponential growth of the decoder complexity with the number of sources and the encoding rates. This growth in complexity renders many traditional approaches impractical even for moderately sized networks. In this paper, we propose a new decoding paradigm for large scale distributed compression wherein the decoder complexity is explicitly controlled during the design. Central to our approach is a module called the "bit-subset selector" whose role is to judiciously extract an appropriate subset of the received bits for decoding per individual source. We propose a practical design strategy, based on deterministic annealing (DA) for the joint design of the system components, that enables direct optimization of the decoder complexity-distortion trade-off, and thereby the desired scalability. We also point out the direct connections between the problem of large scale distributed compression and a related problem in sensor networks, namely, dispersive information routing of correlated sources. This allows us to extend the design principles proposed in the context of large scale distributed compression to design efficient routers for minimum cost communication of correlated sources across a network. Experiments on both real and synthetic data-sets provide evidence for substantial gains over conventional approaches.
On the Complexity of Connectivity in Cognitive Radio Networks Through Spectrum Assignment
Venue: arXiv cs.DS 2013
Year: 2013

Cognitive Radio Networks (CRNs) are considered as a promising solution to the spectrum shortage problem in wireless communication. In this paper, we initiate the first systematic study on the algorithmic complexity of the connectivity problem in CRNs through spectrum assignments. We model the network of secondary users (SUs) as a potential graph, where two nodes having an edge between them are connected as long as they choose a common available channel. In the general case, where the potential graph is arbitrary and the SUs may have different number of antennae, we prove that it is NP-complete to determine whether the network is connectable even if there are only two channels. For the special case where the number of channels is constant and all the SUs have the same number of antennae, which is more than one but less than the number of channels, the problem is also NP-complete. For the special cases in which the potential graph is complete, a tree, or a graph with bounded treewidth, we prove the problem is NP-complete and fixed-parameter tractable (FPT) when parameterized by the number of channels. Exact algorithms are also derived to determine the connectability of a given cognitive radio network.
Kadanoff Sand Piles, following the snowball
Venue: arXiv cs.DM 2013
Year: 2013

This paper is about cubic sand grains moving around on nicely packed columns in one dimension (the physical sand pile is two dimensional, but the support of sand columns is one dimensional). The Kadanoff Sand Pile Model is a discrete dynamical system describing the evolution of a finite number of stacked grains --as they would fall from an hourglass-- to a stable configuration. Grains move according to the repeated application of a simple local rule until reaching a stable configuration from which no rule can be applied, namely a fixed point. The main interest of the model relies in the difficulty of understanding its behavior, despite the simplicity of the rule. We are interested in describing the shape of fixed point configurations according to the number of initially stacked sand grains. In this paper, we prove the emergence of a wavy shape on fixed points, i.e., a regular pattern is (nearly) periodically repeated on fixed points. Interestingly, the regular pattern does not cover the entire fixed point, but eventually emerges from a seemingly highly disordered segment. Fortunately, the relative size of the part of fixed points non-covered by the pattern repetition is asymptotically null.
Automatic crosswind flight of tethered wings for airborne wind energy: modeling, control design and experimental results
Venue: arXiv cs.SY 2013
Year: 2013

An approach to control tethered wings for airborne wind energy is proposed. A fixed length of the lines is considered, and the aim of the control system is to obtain figure-eight crosswind trajectories. The proposed technique is based on the notion of the wing's "velocity angle" and, in contrast with most existing approaches, it does not require a measurement of the wind speed or of the effective wind at the wing's location. Moreover, the proposed approach features few parameters, whose effects on the system's behavior are very intuitive, hence simplifying tuning procedures. A simplified model of the steering dynamics of the wing is derived from first-principle laws, compared with experimental data and used for the control design. The control algorithm is divided into a low-level loop for the velocity angle and a high-level guidance strategy to achieve the desired flight patterns. The robustness of the inner loop is verified analytically, and the overall control system is tested experimentally on a small-scale prototype, with varying wind conditions and using different wings.
Direct QR factorizations for tall-and-skinny matrices in MapReduce architectures
Venue: arXiv cs.DC 2013
Year: 2013

The QR factorization and the SVD are two fundamental matrix decompositions with applications throughout scientific computing and data analysis. For matrices with many more rows than columns, so-called "tall-and-skinny matrices," there is a numerically stable, efficient, communication-avoiding algorithm for computing the QR factorization. It has been used in traditional high performance computing and grid computing environments. For MapReduce environments, existing methods to compute the QR decomposition use a numerically unstable approach that relies on indirectly computing the Q factor. In the best case, these methods require only two passes over the data. In this paper, we describe how to compute a stable tall-and-skinny QR factorization on a MapReduce architecture in only slightly more than 2 passes over the data. We can compute the SVD with only a small change and no difference in performance. We present a performance comparison between our new direct TSQR method, a standard unstable implementation for MapReduce (Cholesky QR), and the classic stable algorithm implemented for MapReduce (Householder QR). We find that our new stable method has a large performance advantage over the Householder QR method. This holds both in a theoretical performance model as well as in an actual implementation.
Cooperation Optimized Design for Information Dissemination in Vehicular Networks using Evolutionary Game Theory
Venue: arXiv cs.NI 2013
Year: 2013

We present an evolutionary game theoretic approach to study node cooperation behavior in wireless ad hoc networks. Evolutionary game theory (EGT) has been used to study the conditions governing the growth of cooperation behavior in biological and social networks. We propose a model of node cooperation behavior in dynamic wireless networks such as vehicular networks. Our work is motivated by the fact that, similar to existing EGT studies, node behavior in dynamic wireless networks is characterized by decision making that only depends on the immediate neighborhood. We adapt our model to study cooperation behavior in the context of information dissemination in wireless networks. We obtain conditions that determine whether a network evolves to a state of complete cooperation from all nodes. Finally, we use our model to study the evolution of cooperation behavior and its impact on content downloading in vehicular networks, taking into consideration realistic network conditions.
Polynomial-complexity, Low-delay Scheduling for SCFDMA-based Wireless Uplink Networks (Technical Report)
Venue: arXiv cs.NI 2013
Year: 2013

Uplink scheduling/resource allocation under the single-carrier FDMA constraint is investigated, taking into account the queuing dynamics at the transmitters. Under the single-carrier constraint, the problem of MaxWeight scheduling, as well as that of determining if a given number of packets can be served from all the users, are shown to be NP-complete. Finally, a matching-based scheduling algorithm is presented that requires only a polynomial number of computations per timeslot, and in the case of a system with large bandwidth and user population, provably provides a good delay (small-queue) performance, even under the single-carrier constraint. In summary, the results in first part of the paper support the recent push to remove SCFDMA from the Standards, whereas those in the second part present a way of working around the single-carrier constraint if it remains in the Standards.
A realistic distributed storage system that minimizes data storage and repair bandwidth
Venue: arXiv cs.IT 2013
Year: 2013

In a realistic distributed storage environment, storage nodes are usually placed in racks, a metallic support designed to accommodate electronic equipment. It is known that the communication (bandwidth) cost between nodes within a rack is much lower than the communication (bandwidth) cost between nodes within different racks. In this paper, a new model, where the storage nodes are placed in two racks, is proposed and analyzed. In this model, the storage nodes have different repair costs to repair a node depending on the rack where they are placed. A threshold function, which minimizes the amount of stored data per node and the bandwidth needed to regenerate a failed node, is shown. This threshold function generalizes the threshold function from previous distributed storage models. The tradeoff curve obtained from this threshold function is compared with the ones obtained from the previous models, and it is shown that this new model outperforms the previous ones in terms of repair cost.
Sum-Rate Maximization with Minimum Power Consumption for MIMO DF Two-Way Relaying: Part I - Relay Optimization
Venue: arXiv cs.IT 2013
Year: 2013

The problem of power allocation is studied for a multiple-input multiple-output (MIMO) decode-and-forward (DF) two-way relaying system consisting of two source nodes and one relay. It is shown that achieving maximum sum-rate in such a system does not necessarily demand the consumption of all available power at the relay. Instead, the maximum sum-rate can be achieved through efficient power allocation with minimum power consumption. Deriving such power allocation, however, is nontrivial due to the fact that it generally leads to a nonconvex problem. In Part I of this two-part paper, a sum-rate maximizing power allocation with minimum power consumption is found for MIMO DF two-way relaying, in which the relay optimizes its own power allocation strategy given the power allocation strategies of the source nodes. An algorithm is proposed for efficiently finding the optimal power allocation of the relay based on the proposed idea of relative water-levels. The considered scenario features low complexity due to the fact that the relay optimizes its power allocation without coordinating the source nodes. As a trade-off for the low complexity, it is shown that there can be waste of power at the source nodes because of no coordination between the relay and the source nodes. Simulation results demonstrate the performance of the proposed algorithm and the effect of asymmetry on the considered system.
On the Complexity of $t$-Closeness Anonymization and Related Problems
Venue: arXiv cs.DS 2013
Year: 2013

An important issue in releasing individual data is to protect the sensitive information from being leaked and maliciously utilized. Famous privacy preserving principles that aim to ensure both data privacy and data integrity, such as $k$-anonymity and $l$-diversity, have been extensively studied both theoretically and empirically. Nonetheless, these widely-adopted principles are still insufficient to prevent attribute disclosure if the attacker has partial knowledge about the overall sensitive data distribution. The $t$-closeness principle has been proposed to fix this, which also has the benefit of supporting numerical sensitive attributes. However, in contrast to $k$-anonymity and $l$-diversity, the theoretical aspect of $t$-closeness has not been well investigated. We initiate the first systematic theoretical study on the $t$-closeness principle under the commonly-used attribute suppression model. We prove that for every constant $t$ such that $0\leq t<1$, it is NP-hard to find an optimal $t$-closeness generalization of a given table. The proof consists of several reductions each of which works for different values of $t$, which together cover the full range. To complement this negative result, we also provide exact and fixed-parameter algorithms. Finally, we answer some open questions regarding the complexity of $k$-anonymity and $l$-diversity left in the literature.
How to calculate the practical significance of citation impact differences? An empirical example from evaluative institutional bibliometrics using adjusted predictions and marginal effects
Venue: arXiv cs.DL 2013
Year: 2013

Evaluative bibliometrics is concerned with comparing research units by using statistical procedures. According to Williams (2012) an empirical study should be concerned with the substantive and practical significance of the findings as well as the sign and statistical significance of effects. In this study we will explain what adjusted predictions and marginal effects are and how useful they are for institutional evaluative bibliometrics. As an illustration, we will calculate a regression model using publications (and citation data) produced by four universities in German-speaking countries from 1980 to 2010. We will show how these predictions and effects can be estimated and plotted, and how this makes it far easier to get a practical feel for the substantive meaning of results in evaluative bibliometric studies. We will focus particularly on Average Adjusted Predictions (AAPs), Average Marginal Effects (AMEs), Adjusted Predictions at Representative Values (APRVs) and Marginal Effects at Representative Values (MERVs).
An Extensive Analysis of Query by Singing/Humming System Through Query Proportion
Venue: arXiv cs.MM 2013
Year: 2013

Query by Singing/Humming (QBSH) is a Music Information Retrieval (MIR) system with small audio excerpt as query. The rising availability of digital music stipulates effective music retrieval methods. Further, MIR systems support content based searching for music and requires no musical acquaintance. Current work on QBSH focuses mainly on melody features such as pitch, rhythm, note etc., size of databases, response time, score matching and search algorithms. Even though a variety of QBSH techniques are proposed, there is a dearth of work to analyze QBSH through query excerption. Here, we present an analysis that works on QBSH through query excerpt. To substantiate a series of experiments are conducted with the help of Mel-Frequency Cepstral Coefficients (MFCC), Linear Predictive Coefficients (LPC) and Linear Predictive Cepstral Coefficients (LPCC) to portray the robustness of the knowledge representation. Proposed experiments attempt to reveal that retrieval performance as well as precision diminishes in the snail phase with the growing database size.
A lower bound for constant dimension codes from multi-component lifted MRD codes
Venue: arXiv cs.IT 2013
Year: 2013

In this work we investigate unions of lifted MRD codes of a fixed dimension and minimum distance and derive an explicit formula for the cardinality of such codes. This will then imply a lower bound on the cardinality of constant dimension codes.
Risk-Aversion in Multi-armed Bandits
Venue: arXiv cs.LG 2013
Year: 2013

Stochastic multi-armed bandits solve the Exploration-Exploitation dilemma and ultimately maximize the expected reward. Nonetheless, in many practical problems, maximizing the expected reward is not the most desirable objective. In this paper, we introduce a novel setting based on the principle of risk-aversion where the objective is to compete against the arm with the best risk-return trade-off. This setting proves to be intrinsically more difficult than the standard multi-arm bandit setting due in part to an exploration risk which introduces a regret associated to the variability of an algorithm. Using variance as a measure of risk, we introduce two new algorithms, investigate their theoretical guarantees, and report preliminary empirical results.
Distributed soft thresholding for sparse signal recovery
Venue: arXiv cs.IT 2013
Year: 2013

In this paper, we address the problem of distributed sparse recovery of signals acquired via compressed measurements in a sensor network. We propose a new class of distributed algorithms to solve Lasso regression problems, when the communication to a fusion center is not possible, e.g., due to communication cost or privacy reasons. More precisely, we introduce a distributed iterative soft thresholding algorithm (DISTA) that consists of three steps: an averaging step, a gradient step, and a soft thresholding operation. We prove the convergence of DISTA in networks represented by regular graphs, and we compare it with existing methods in terms of performance, memory, and complexity.
A Forgetting-based Approach to Merging Knowledge Bases
Venue: arXiv cs.AI 2013
Year: 2013

This paper presents a novel approach based on variable forgetting, which is a useful tool in resolving contradictory by filtering some given variables, to merging multiple knowledge bases. This paper first builds a relationship between belief merging and variable forgetting by using dilation. Variable forgetting is applied to capture belief merging operation. Finally, some new merging operators are developed by modifying candidate variables to amend the shortage of traditional merging operators. Different from model selection of traditional merging operators, as an alternative approach, variable selection in those new operators could provide intuitive information about an atom variable among whole knowledge bases.
Artificial Intelligence Framework for Simulating Clinical Decision-Making: A Markov Decision Process Approach
Venue: arXiv cs.AI 2013
Year: 2013

In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (non-disease-specific) computational/artificial intelligence (AI) framework to address these challenges. This serves two potential functions: 1) a simulation environment for exploring various healthcare policies, payment methodologies, etc., and 2) the basis for clinical artificial intelligence - an AI that can think like a doctor. This approach combines Markov decision processes and dynamic decision networks to learn from clinical data and develop complex plans via simulation of alternative sequential decision paths while capturing the sometimes conflicting, sometimes synergistic interactions of various components in the healthcare system. It can operate in partially observable environments (in the case of missing observations or data) by maintaining belief states about patient health status and functions as an online agent that plans and re-plans. This framework was evaluated using real patient data from an electronic health record. Such an AI framework easily outperforms the current treatment-as-usual (TAU) case-rate/fee-for-service models of healthcare (Cost per Unit Change: $189 vs. $497) while obtaining a 30-35% increase in patient outcomes. Tweaking certain model parameters further enhances this advantage, obtaining roughly 50% more improvement for roughly half the costs. Given careful design and problem formulation, an AI simulation framework can approximate optimal decisions even in complex and uncertain environments. Future work is described that outlines potential lines of research and integration of machine learning algorithms for personalized medicine.
Content-Based Video Browsing by Text Region Localization and Classification
Venue: arXiv cs.MM 2013
Year: 2013

The amount of digital video data is increasing over the world. It highlights the need for efficient algorithms that can index, retrieve and browse this data by content. This can be achieved by identifying semantic description captured automatically from video structure. Among these descriptions, text within video is considered as rich features that enable a good way for video indexing and browsing. Unlike most video text detection and extraction methods that treat video sequences as collections of still images, we propose in this paper spatiotemporal. video-text localization and identification approach which proceeds in two main steps: text region localization and text region classification. In the first step we detect the significant appearance of the new objects in a frame by a split and merge processes applied on binarized edge frame pair differences. Detected objects are, a priori, considered as text. They are then filtered according to both local contrast variation and texture criteria in order to get the effective ones. The resulted text regions are classified based on a visual grammar descriptor containing a set of semantic text class regions characterized by visual features. A visual table of content is then generated based on extracted text regions occurring within video sequence enriched by a semantic identification. The experimentation performed on a variety of video sequences shows the efficiency of our approach.
Efficient Approximation for Triangulation of Minimum Treewidth
Venue: arXiv cs.DS 2013
Year: 2013

We present four novel approximation algorithms for finding triangulation of minimum treewidth. Two of the algorithms improve on the running times of algorithms by Robertson and Seymour, and Becker and Geiger that approximate the optimum by factors of 4 and 3 2/3, respectively. A third algorithm is faster than those but gives an approximation factor of 4 1/2. The last algorithm is yet faster, producing factor-O(lg/k) approximations in polynomial time. Finding triangulations of minimum treewidth for graphs is central to many problems in computer science. Real-world problems in artificial intelligence, VLSI design and databases are efficiently solvable if we have an efficient approximation algorithm for them. We report on experimental results confirming the effectiveness of our algorithms for large graphs associated with real-world problems.
Graphical readings of possibilistic logic bases
Venue: arXiv cs.AI 2013
Year: 2013

Possibility theory offers either a qualitive, or a numerical framework for representing uncertainty, in terms of dual measures of possibility and necessity. This leads to the existence of two kinds of possibilistic causal graphs where the conditioning is either based on the minimum, or the product operator. Benferhat et al. (1999) have investigated the connections between min-based graphs and possibilistic logic bases (made of classical formulas weighted in terms of certainty). This paper deals with a more difficult issue : the product-based graphical representations of possibilistic bases, which provides an easy structural reading of possibilistic bases. Moreover, this paper also provides another reading of possibilistic bases in terms of comparative preferences of the form "in the context p, q is preferred to not q". This enables us to explicit preferences underlying a set of goals with different levels of priority.