OpenRaiser/Intern-Atlas huggingface dataset:
Orthogonality and Cooperation in Collaborative Spectrum Sensing through MIMO Decision Fusion
Venue: arXiv cs.IT 2013
Year: 2013

This paper deals with spectrum sensing for cognitive radio scenarios where the decision fusion center (DFC) exploits array processing. More specifically, we explore the impact of user cooperation and orthogonal transmissions among secondary users (SUs) on the reporting channel. To this aim four protocols are considered: (i) non-orthogonal and non-cooperative; (ii) orthogonal and non-cooperative; (iii) non-orthogonal and cooperative; (iv) orthogonal and cooperative. The DFC employs maximum ratio combining (MRC) rule and performance are evaluated in terms of complementary receiver operating characteristic (CROC). Analytical results, coupled with Monte Carlo simulations, are presented.
Hardness of Finding Independent Sets in 2-Colorable and Almost 2-Colorable Hypergraphs
Venue: arXiv cs.CC 2013
Year: 2013

This work studies the hardness of finding independent sets in hypergraphs which are either 2-colorable or are almost 2-colorable, i.e. can be 2-colored after removing a small fraction of vertices and the incident hyperedges. To be precise, say that a hypergraph is (1-eps)-almost 2-colorable if removing an eps fraction of its vertices and all hyperedges incident on them makes the remaining hypergraph 2-colorable. In particular we prove the following results. For an arbitrarily small constant gamma > 0, there is a constant xi > 0, such that, given a 4-uniform hypergraph on n vertices which is (1 - eps)-almost 2-colorable for eps = 2^{-(log n)^xi}, it is quasi-NP-hard to find an independent set of n/(2^{(log n)^{1-gamma}}) vertices. For any constants eps, delta > 0, given as input a 3-uniform hypergraph on $n$ vertices which is (1-eps)-almost 2-colorable, it is NP-hard to find an independent set of delta n vertices. Assuming the d-to-1 Games Conjecture the following holds. For any constant delta > 0, given a 2-colorable 3-uniform hypergraph on n vertices, it is NP-hard to find an independent set of delta n vertices. The hardness result on independent set in almost 2-colorable 3-uniform hypergraphs was earlier known only assuming the Unique Games Conjecture. In this work we prove the result unconditionally. For independent sets in 2-colorable 3-uniform hypergaphs we prove the first strong hardness result, albeit assuming the d-to-1 Games Conjecture. Our result on almost 2-colorable 4-uniform hypergraphs gives the first nearly polynomial hardness factor for independent set in hypergraphs which are (almost) colorable with constantly many colors. It partially bridges the gap between the previous best lower bound of poly(log n) and the algorithmic upper bounds of n^{Omega(1)}.
Active Noise Control with Sampled-Data Filtered-x Adaptive Algorithm
Venue: arXiv cs.IT 2013
Year: 2013

Analysis and design of filtered-x adaptive algorithms are conventionally done by assuming that the transfer function in the secondary path is a discrete-time system. However, in real systems such as active noise control, the secondary path is a continuous-time system. Therefore, such a system should be analyzed and designed as a hybrid system including discrete- and continuous- time systems and AD/DA devices. In this article, we propose a hybrid design taking account of continuous-time behavior of the secondary path via lifting (continuous-time polyphase decomposition) technique in sampled-data control theory.
Stochastic Optimization for Machine Learning
Venue: arXiv cs.LG 2013
Year: 2013

It has been found that stochastic algorithms often find good solutions much more rapidly than inherently-batch approaches. Indeed, a very useful rule of thumb is that often, when solving a machine learning problem, an iterative technique which relies on performing a very large number of relatively-inexpensive updates will often outperform one which performs a smaller number of much "smarter" but computationally-expensive updates. In this thesis, we will consider the application of stochastic algorithms to two of the most important machine learning problems. Part i is concerned with the supervised problem of binary classification using kernelized linear classifiers, for which the data have labels belonging to exactly two classes (e.g. "has cancer" or "doesn't have cancer"), and the learning problem is to find a linear classifier which is best at predicting the label. In Part ii, we will consider the unsupervised problem of Principal Component Analysis, for which the learning task is to find the directions which contain most of the variance of the data distribution. Our goal is to present stochastic algorithms for both problems which are, above all, practical--they work well on real-world data, in some cases better than all known competing algorithms. A secondary, but still very important, goal is to derive theoretical bounds on the performance of these algorithms which are at least competitive with, and often better than, those known for other approaches.
Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizations
Venue: arXiv cs.LG 2013
Year: 2013

Control applications often feature tasks with similar, but not identical, dynamics. We introduce the Hidden Parameter Markov Decision Process (HiP-MDP), a framework that parametrizes a family of related dynamical systems with a low-dimensional set of latent factors, and introduce a semiparametric regression approach for learning its structure from data. In the control setting, we show that a learned HiP-MDP rapidly identifies the dynamics of a new task instance, allowing an agent to flexibly adapt to task variations.
A New Distributed DC-Programming Method and its Applications
Venue: arXiv cs.IT 2013
Year: 2013

We propose a novel decomposition framework for the distributed optimization of Difference Convex (DC)-type nonseparable sum-utility functions subject to coupling convex constraints. A major contribution of the paper is to develop for the first time a class of (inexact) best-response-like algorithms with provable convergence, where a suitably convexified version of the original DC program is iteratively solved. The main feature of the proposed successive convex approximation method is its decomposability structure across the users, which leads naturally to distributed algorithms in the primal and/or dual domain. The proposed framework is applicable to a variety of multiuser DC problems in different areas, ranging from signal processing, to communications and networking. As a case study, in the second part of the paper we focus on two examples, namely: i) a novel resource allocation problem in the emerging area of cooperative physical layer security; ii) and the renowned sum-rate maximization of MIMO Cognitive Radio networks. Our contribution in this context is to devise a class of easy-to-implement distributed algorithms with provable convergence to stationary solution of such problems. Numerical results show that the proposed distributed schemes reach performance close to (and sometimes better than) that of centralized methods.
Quantum Gilbert-Varshamov Bound Through Symplectic Self-Orthogonal Codes
Venue: arXiv cs.IT 2013
Year: 2013

It is well known that quantum codes can be constructed through classical symplectic self-orthogonal codes. In this paper, we give a kind of Gilbert-Varshamov bound for symplectic self-orthogonal codes first and then obtain the Gilbert-Varshamov bound for quantum codes. The idea of obtaining the Gilbert-Varshamov bound for symplectic self-orthogonal codes follows from counting arguments.
A Characterization of Ideal Weighted Secret Sharing Schemes
Venue: arXiv cs.CR 2013
Year: 2013

Beimel, Tassa and Weinreb (2008) and Farras and Padro (2010) partially characterized access structures of ideal weighted threshold secret sharing schemes in terms of the operation of composition. They classified indecomposable ideal weighted threshold access structures, and proved that any other ideal weighted threshold access structure is a composition of indecomposable ones. It remained unclear which compositions of indecomposable weighted threshold access structures are weighted. In this paper we fill the gap. Using game-theoretic techniques we determine which compositions of indecomposable ideal access structures are weighted, and obtain an if and only if characterization of ideal weighted threshold secret sharing schemes.
Game Theory with Translucent Players
Venue: arXiv cs.GT 2013
Year: 2013

A traditional assumption in game theory is that players are opaque to one another---if a player changes strategies, then this change in strategies does not affect the choice of other players' strategies. In many situations this is an unrealistic assumption. We develop a framework for reasoning about games where the players may be translucent to one another; in particular, a player may believe that if she were to change strategies, then the other player would also change strategies. Translucent players may achieve significantly more efficient outcomes than opaque ones. Our main result is a characterization of strategies consistent with appropriate analogues of common belief of rationality. Common Counterfactual Belief of Rationality (CCBR) holds if (1) everyone is rational, (2) everyone counterfactually believes that everyone else is rational (i.e., all players i believe that everyone else would still be rational even if $i$ were to switch strategies), (3) everyone counterfactually believes that everyone else is rational, and counterfactually believes that everyone else is rational, and so on. CCBR characterizes the set of strategies surviving iterated removal of minimax dominated strategies, where a strategy s for player i is minimax dominated by s' if the worst-case payoff for i using s' is better than the best possible payoff using s.
Robust Supervisory Control for Uniting Two Output-Feedback Hybrid Controllers with Different Objectives
Venue: arXiv cs.SY 2013
Year: 2013

The problem of robustly, asymptotically stabilizing a point (or a set) with two output-feedback hybrid controllers is considered. These control laws may have different objectives, e.g., the closed-loop systems resulting with each controller may have different attractors. We provide a control algorithm that combines the two hybrid controllers to accomplish the stabilization task. The algorithm consists of a hybrid supervisor that, based on the values of plant's outputs and (norm) state estimates, selects the hybrid controller that should be applied to the plant. The accomplishment of the stabilization task relies on an output-to-state stability property induced by the controllers, which enables the construction of an estimator for the norm of the plant's state. The algorithm is motivated by and applied to robust, semi-global stabilization problems uniting two controllers.
Medial Meshes for Volume Approximation
Venue: arXiv cs.GR 2013
Year: 2013

Volume approximation is an important problem found in many applications of computer graphics, vision, and image processing. The problem is about computing an accurate and compact approximate representation of 3D volumes using some simple primitives. In this study, we propose a new volume representation, called medial meshes, and present an efficient method for its computation. Specifically, we use the union of a novel type of simple volume primitives, which are spheres and the convex hulls of two or three spheres, to approximate a given 3D shape. We compute such a volume approximation based on a new method for medial axis simplification guided by Hausdorff errors. We further demonstrate the superior efficiency and accuracy of our method over existing methods for medial axis simplification.
Analysis and Synthesis of a Subsystem of the Manual Control Loop for Manned Spacecraft
Venue: arXiv cs.HC 2013
Year: 2013

A salient feature of the manned spacecraft is the predominance of discrete information in the manual control loop of the onboard systems. Specifically, command-signaling control panels (CSCP) as a subsystem of the manual control loop are widely used in the Russian manned spacecraft. In this paper CSCP are classified into four types: a) control panels based on multi-channel control; b) control panels based on command-information compression; c) control panels based on command and signaling information compression; d) integrated control consoles (ICC) based on computer and information technology. It is shown that ICC underlies modern information display systems (IDS). ICC first appeared in the Russian manned space program in the IDS of the Soyuz-TMA spacecraft and the Russian modules of the International Space Station. Results of engineering and psychological studies of different types of panels are produced.
Understanding Rulelog Computations in Silk
Venue: arXiv cs.SE 2013
Year: 2013

Rulelog is a knowledge representation and reasoning language based on logic programming under the well-founded semantics. It is an extension of the language of Flora-2 and so supports inheritance and other object-oriented features, as well as the higher-order syntax of Hilog. However, Rulelog rules may also contain quantifiers and may be contra-positional. In addition, these rules are evaluated in the presence of defeasibility mechanisms that include rule cancellation, rule priorities, and other aspects. Rulelog programs are sometimes developed by loosely coordinated teams of knowledge engineers (KEs) who are not necessarily programmers. This requires not only declarative debugging support, but also support for profiling to help KEs understand the overall structure of a computation, including its termination properties. The design of debugging and profiling tools is made more challenging because Rulelog programs undergo a series of transformations into normal programs, so that there is a cognitive distance between how rules are specified and how they are executed. In this paper, we describe the debugging and profiling environment for Rulelog implemented in the integrated development environment of the Silk system. Our approach includes an interface to justification graphs, which treat why-not and defeasibility as well as provenance of the rules supporting answers. It also includes tools for trace-based analysis of computations to permit understanding of erroneous non-termination and of general performance issues. For semantically correct cases of the non-terminating behavior, Silk offers a different approach, which addresses the problem in a formally sound manner by leveraging a form of bounded rationality called restraint.
Patience-aware Scheduling for Cloud Services: Freeing Users from the Chains of Boredom
Venue: arXiv cs.DC 2013
Year: 2013

Scheduling of service requests in Cloud computing has traditionally focused on the reduction of pre-service wait, generally termed as waiting time. Under certain conditions such as peak load, however, it is not always possible to give reasonable response times to all users. This work explores the fact that different users may have their own levels of tolerance or patience with response delays. We introduce scheduling strategies that produce better assignment plans by prioritising requests from users who expect to receive the results earlier and by postponing servicing jobs from those who are more tolerant to response delays. Our analytical results show that the behaviour of users' patience plays a key role in the evaluation of scheduling techniques, and our computational evaluation demonstrates that, under peak load, the new algorithms typically provide better user experience than the traditional FIFO strategy.
A Systematic Mapping Study on Cloud Computing
Venue: arXiv cs.DC 2013
Year: 2013

Cloud Computing emerges from the global economic crisis as an option to use computing resources from a more rational point of view. In other words, a cheaper way to have IT resources. However, issues as security and privacy, SLA (Service Layer Agreement), resource sharing, and billing has left open questions about the real gains of that model. This study aims to investigate state-of-the-art in Cloud Computing, identify gaps, challenges, synthesize available evidences both its use and development, and provides relevant information, clarifying open questions and common discussed issues about that model through literature. The good practices of systematic map- ping study methodology were adopted in order to reach those objectives. Al- though Cloud Computing is based on a business model with over 50 years of existence, evidences found in this study indicate that Cloud Computing still presents limitations that prevent the full use of the proposal on-demand.
Multirate Digital Signal Processing via Sampled-Data H-infinity Optimization
Venue: arXiv cs.IT 2013
Year: 2013

In this thesis, we present a new method for designing multirate signal processing and digital communication systems via sampled-data H-infinity control theory. The difference between our method and conventional ones is in the signal spaces. Conventional designs are executed in the discrete-time domain, while our design takes account of both the discrete-time and the continuous-time signals. Namely, our method can take account of the characteristic of the original analog signal and the influence of the A/D and D/A conversion. While the conventional method often indicates that an ideal digital low-pass filter is preferred, we show that the optimal solution need not be an ideal low-pass when the original analog signal is not completely band-limited. This fact can not be recognized only in the discrete-time domain. Moreover, we consider quantization effects. We discuss the stability and the performance of quantized sampled-data control systems. We justify H-infinity control to reduce distortion caused by the quantizer. Then we apply it to differential pulse code modulation. While the conventional Delta modulator is not optimal and besides not stable, our modulator is stable and optimal with respect to the H-infinity-norm. We also give an LMI (Linear Matrix Inequality) solution to the optimal H-infinity approximation of IIR (Infinite Impulse Response) filters via FIR (Finite Impulse Response) filters. A comparison with the Nehari shuffle is made with a numerical example, and it is observed that the LMI solution generally performs better. Another numerical study also indicates that there is a trade-off between the pass-band and stop-band approximation characteristics.
Birkhoff-von-Neumann Switches with Deflection-Compensated Mechanism
Venue: arXiv cs.NI 2013
Year: 2013

Despite the high throughput and low complexity achieved by input scheduling based on Birkhoff-von-Neumann (BvN) decomposition; the performance of the BvN switch becomes less predictable when the input traffic is bursty. In this paper, we propose a deflection-compensated BvN (D-BvN) switch architecture to enhance the quasi-static scheduling based on BvN decomposition. The D-BvN switches provide capacity guarantee for virtual circuits (VCs) and deflect bursty traffic when overflow occurs. The deflection scheme is devised to offset the excessive buffer requirement of each VC when input traffic is bursty. The design of our conditional deflection mechanism is based on the fact that it is unlikely that the traffic input to VCs is all bursty at the same time; most likely some starving VCs have spare capacities when some other VCs are in the overflow state. The proposed algorithm makes full use of the spare capacities of those starving VCs to deflect the overflow traffic to other inputs and provide bandwidth for the deflected traffic to re-access the desired VC. Our analysis and simulation show that this deflection-compensated mechanism can support BvN switches to achieve close to 100% throughput of offered load even with bursty input traffic, and reduces the average end-to-end delay and delay jitter. Also, our result indicates that the packet out-of-sequence probability due to deflection of overflow traffic is negligible, thus only a small re-sequencing buffer is needed at each output port.
Decentralized Online Big Data Classification - a Bandit Framework
Venue: arXiv cs.LG 2013
Year: 2013

Distributed, online data mining systems have emerged as a result of applications requiring analysis of large amounts of correlated and high-dimensional data produced by multiple distributed data sources. We propose a distributed online data classification framework where data is gathered by distributed data sources and processed by a heterogeneous set of distributed learners which learn online, at run-time, how to classify the different data streams either by using their locally available classification functions or by helping each other by classifying each other's data. Importantly, since the data is gathered at different locations, sending the data to another learner to process incurs additional costs such as delays, and hence this will be only beneficial if the benefits obtained from a better classification will exceed the costs. We assume that the classification functions available to each processing element are fixed, but their prediction accuracy for various types of incoming data are unknown and can change dynamically over time, and thus they need to be learned online. We model the problem of joint classification by the distributed and heterogeneous learners from multiple data sources as a distributed contextual bandit problem where each data is characterized by a specific context. We develop distributed online learning algorithms for which we can prove that they have sublinear regret. Compared to prior work in distributed online data mining, our work is the first to provide analytic regret results characterizing the performance of the proposed algorithms.
On the Zero-freeness of Tall Multirate Linear Systems
Venue: arXiv cs.SY 2013
Year: 2013

In this paper, tall discrete-time linear systems with multirate outputs are studied. In particular, we focus on their zeros. In systems and control literature zeros of multirate systems are defined as those of their corresponding time-invariant blocked systems. Hence, the zeros of tall blocked systems resulting from blocking of linear systems with multirate outputs are mainly explored in this work. We specifically investigate zeros of tall blocked systems formed by blocking tall multirate linear systems with generic parameter matrices. It is demonstrated that tall blocked systems generically have no finite nonzero zeros; however, they may have zeros at the origin or at infinity depending on the choice of blocking delay and the input, state and output dimensions.
Delay Optimal Scheduling for Energy Harvesting Based Communications
Venue: arXiv cs.ET 2013
Year: 2013

Green communication attracts increasing research interest recently. Equipped with a rechargeable battery, a source node can harvest energy from ambient environments and rely on this free and regenerative energy supply to transmit packets. Due to the uncertainty of available energy from harvesting, however, intolerably large latency and packet loss could be induced, if the source always waits for harvested energy. To overcome this problem, one Reliable Energy Source (RES) can be resorted to for a prompt delivery of backlogged packets. Naturally, there exists a tradeoff between the packet delivery delay and power consumption from the RES. In this paper, we address the delay optimal scheduling problem for a bursty communication link powered by a capacity-limited battery storing harvested energy together with one RES. The proposed scheduling scheme gives priority to the usage of harvested energy, and resorts to the RES when necessary based on the data and energy queueing processes, with an average power constraint from the RES. Through twodimensional Markov chain modeling and linear programming formulation, we derive the optimal threshold-based scheduling policy together with the corresponding transmission parameters. Our study includes three exemplary cases that capture some important relations between the data packet arrival process and energy harvesting capability. Our theoretical analysis is corroborated by simulation results.