OpenRaiser/Intern-Atlas huggingface dataset:
Complexity-Style Resources in Cryptography
Venue: arXiv cs.CR 2013
Year: 2013

Previously, the author has developed a framework within which to quantify and compare the resources consumed during computational-especially unconventional computational-processes (adding to the familiar resources of run-time and memory space such non-standard resources as precision and energy); it is natural and beneficial in this framework to employ various complexity-theoretic tools and techniques. Here, we seek an analogous treatment not of computational processes but of cryptographic protocols and similar, so as to be able to apply the existing arsenal of complexity-theoretic methods in new ways, in the derivation and verification of protocols in a wider, cryptographic context. Accordingly, we advocate a framework in which one may view as resources the costs-which may be related to computation, communication, information (including side-channel information) or availability of primitives, for example-incurred when executing cryptographic protocols, coin-tossing schemes, etc. The ultimate aim is to formulate as a resource, and be able to analyse complexity-theoretically, the security of these protocols and schemes.
Towards Automated Proof Strategy Generalisation
Venue: arXiv cs.LO 2013
Year: 2013

The ability to automatically generalise (interactive) proofs and use such generalisations to discharge related conjectures is a very hard problem which remains unsolved. Here, we develop a notion of goal types to capture key properties of goals, which enables abstractions over the specific order and number of sub-goals arising when composing tactics. We show that the goal types form a lattice, and utilise this property in the techniques we develop to automatically generalise proof strategies in order to reuse it for proofs of related conjectures. We illustrate our approach with an example.
Ergodic Interference Alignment with Delayed Feedback
Venue: arXiv cs.IT 2013
Year: 2013

We propose new ergodic interference alignment techniques for $K$-user interference channels with delayed feedback. Two delayed feedback scenarios are considered -- delayed channel information at transmitter (CIT) and delayed output feedback. It is proved that the proposed techniques achieve total $2K/(K+2)$ DoF which is higher than that by the retrospective interference alignment for the delayed feedback scenarios.
Controling the number of focal elements
Venue: arXiv cs.IT 2013
Year: 2013

A basic belief assignment can have up to 2^n focal elements, and combining them with a simple conjunctive operator will need O(2^2n) operations. This article proposes some techniques to limit the size of the focal sets of the bbas to be combined while preserving a large part of the information they carry. The first section revisits some well-known definitions with an algorithmic point of vue. The second section proposes a matrix way of building the least committed isopignistic, and extends it to some other bodies of evidence. The third section adapts the k-means algorithm for an unsupervized clustering of the focal elements of a given bba.
Hybrid Evolutionary Computation for Continuous Optimization
Venue: arXiv cs.NE 2013
Year: 2013

Hybrid optimization algorithms have gained popularity as it has become apparent there cannot be a universal optimization strategy which is globally more beneficial than any other. Despite their popularity, hybridization frameworks require more detailed categorization regarding: the nature of the problem domain, the constituent algorithms, the coupling schema and the intended area of application. This report proposes a hybrid algorithm for solving small to large-scale continuous global optimization problems. It comprises evolutionary computation (EC) algorithms and a sequential quadratic programming (SQP) algorithm; combined in a collaborative portfolio. The SQP is a gradient based local search method. To optimize the individual contributions of the EC and SQP algorithms for the overall success of the proposed hybrid system, improvements were made in key features of these algorithms. The report proposes enhancements in: i) the evolutionary algorithm, ii) a new convergence detection mechanism was proposed; and iii) in the methods for evaluating the search directions and step sizes for the SQP local search algorithm. The proposed hybrid design aim was to ensure that the two algorithms complement each other by exploring and exploiting the problem search space. Preliminary results justify that an adept hybridization of evolutionary algorithms with a suitable local search method, could yield a robust and efficient means of solving wide range of global optimization problems. Finally, a discussion of the outcomes of the initial investigation and a review of the associated challenges and inherent limitations of the proposed method is presented to complete the investigation. The report highlights extensive research, particularly, some potential case studies and application areas.
Optimal Receding Horizon Control for Finite Deterministic Systems with Temporal Logic Constraints
Venue: arXiv cs.RO 2013
Year: 2013

In this paper, we develop a provably correct optimal control strategy for a finite deterministic transition system. By assuming that penalties with known probabilities of occurrence and dynamics can be sensed locally at the states of the system, we derive a receding horizon strategy that minimizes the expected average cumulative penalty incurred between two consecutive satisfactions of a desired property. At the same time, we guarantee the satisfaction of correctness specifications expressed as Linear Temporal Logic formulas. We illustrate the approach with a persistent surveillance robotics application.
Implicit Simulation Methods for Stochastic Chemical Kinetics
Venue: arXiv cs.CE 2013
Year: 2013

In biochemical systems some of the chemical species are present with only small numbers of molecules. In this situation discrete and stochastic simulation approaches are more relevant than continuous and deterministic ones. The fundamental Gillespie's stochastic simulation algorithm (SSA) accounts for every reaction event, which occurs with a probability determined by the configuration of the system. This approach requires a considerable computational effort for models with many reaction channels and chemical species. In order to improve efficiency, tau-leaping methods represent multiple firings of each reaction during a simulation step by Poisson random variables. For stiff systems the mean of this variable is treated implicitly in order to ensure numerical stability. This paper develops fully implicit tau-leaping-like algorithms that treat implicitly both the mean and the variance of the Poisson variables. The construction is based on adapting weakly convergent discretizations of stochastic differential equations to stochastic chemical kinetic systems. Theoretical analyses of accuracy and stability of the new methods are performed on a standard test problem. Numerical results demonstrate the performance of the proposed tau-leaping methods.
Distributed Optimal Rate-Reliability-Lifetime Tradeoff in Wireless Sensor Networks
Venue: arXiv cs.NI 2013
Year: 2013

The transmission rate, delivery reliability and network lifetime are three fundamental but conflicting design objectives in energy-constrained wireless sensor networks. In this paper, we address the optimal rate-reliability-lifetime tradeoff with link capacity constraint, reliability constraint and energy constraint. By introducing the weight parameters, we combine the objectives at rate, reliability, and lifetime into a single objective to characterize the tradeoff among them. However, the optimization formulation of the rate-reliability-reliability tradeoff is neither separable nor convex. Through a series of transformations, a separable and convex problem is derived, and an efficient distributed Subgradient Dual Decomposition algorithm (SDD) is proposed. Numerical examples confirm its convergence. Also, numerical examples investigate the impact of weight parameters on the rate utility, reliability utility and network lifetime, which provide a guidance to properly set the value of weight parameters for a desired performance of WSNs according to the realistic application's requirements.
Low-Complexity Adaptive Set-Membership Reduced-rank LCMV Beamforming
Venue: arXiv cs.IT 2013
Year: 2013

This paper proposes a new adaptive algorithm for the implementation of the linearly constrained minimum variance (LCMV) beamformer. The proposed algorithm utilizes the set-membership filtering (SMF) framework and the reduced-rank joint iterative optimization (JIO) scheme. We develop a stochastic gradient (SG) based algorithm for the beamformer design. An effective time-varying bound is employed in the proposed method to adjust the step sizes, avoid the misadjustment and the risk of overbounding or underbounding. Simulations are performed to show the improved performance of the proposed algorithm in comparison with existing full-rank and reduced-rank methods.
A Last-Step Regression Algorithm for Non-Stationary Online Learning
Venue: arXiv cs.LG 2013
Year: 2013

The goal of a learner in standard online learning is to maintain an average loss close to the loss of the best-performing single function in some class. In many real-world problems, such as rating or ranking items, there is no single best target function during the runtime of the algorithm, instead the best (local) target function is drifting over time. We develop a novel last-step minmax optimal algorithm in context of a drift. We analyze the algorithm in the worst-case regret framework and show that it maintains an average loss close to that of the best slowly changing sequence of linear functions, as long as the total of drift is sublinear. In some situations, our bound improves over existing bounds, and additionally the algorithm suffers logarithmic regret when there is no drift. We also build on the H_infinity filter and its bound, and develop and analyze a second algorithm for drifting setting. Synthetic simulations demonstrate the advantages of our algorithms in a worst-case constant drift setting.
Joint Maximum Sum-Rate Receiver Design and Power Adjustment for Multihop Wireless Sensor Networks
Venue: arXiv cs.IT 2013
Year: 2013

In this paper, we consider a multihop wireless sensor network (WSN) with multiple relay nodes for each hop where the amplify-and-forward (AF) scheme is employed. We present a strategy to jointly design the linear receiver and the power allocation parameters via an alternating optimization approach that maximizes the sum rate of the WSN. We derive constrained maximum sum-rate (MSR) expressions along with an algorithm to compute the linear receiver and the power allocation parameters with the optimal complex amplification coefficients for each relay node. Computer simulations show good performance of our proposed methods in terms of sum rate compared to the method with equal power allocation.
Compressive Demodulation of Mutually Interfering Signals
Venue: arXiv cs.IT 2013
Year: 2013

Multi-User Detection is fundamental not only to cellular wireless communication but also to Radio-Frequency Identification (RFID) technology that supports supply chain management. The challenge of Multi-user Detection (MUD) is that of demodulating mutually interfering signals, and the two biggest impediments are the asynchronous character of random access and the lack of channel state information. Given that at any time instant the number of active users is typically small, the promise of Compressive Sensing (CS) is the demodulation of sparse superpositions of signature waveforms from very few measurements. This paper begins by unifying two front-end architectures proposed for MUD by showing that both lead to the same discrete signal model. Algorithms are presented for coherent and noncoherent detection that are based on iterative matching pursuit. Noncoherent detection is all that is needed in the application to RFID technology where it is only the identity of the active users that is required. The coherent detector is also able to recover the transmitted symbols. It is shown that compressive demodulation requires $\mathcal{O}(K\log N(τ+1))$ samples to recover $K$ active users whereas standard MUD requires $N(τ+1)$ samples to process $N$ total users with a maximal delay $τ$. Performance guarantees are derived for both coherent and noncoherent detection that are identical in the way they scale with number of active users. The power profile of the active users is shown to be less important than the SNR of the weakest user. Gabor frames and Kerdock codes are proposed as signature waveforms and numerical examples demonstrate the superior performance of Kerdock codes - the same probability of error with less than half the samples.
On the Locality of Codeword Symbols in Non-Linear Codes
Venue: arXiv cs.IT 2013
Year: 2013

Consider a possibly non-linear (n,K,d)_q code. Coordinate i has locality r if its value is determined by some r other coordinates. A recent line of work obtained an optimal trade-off between information locality of codes and their redundancy. Further, for linear codes meeting this trade-off, structure theorems were derived. In this work we give a new proof of the locality / redundancy trade-off and generalize structure theorems to non-linear codes.
Optimal Vaccine Allocation to Control Epidemic Outbreaks in Arbitrary Networks
Venue: arXiv cs.SI 2013
Year: 2013

We consider the problem of controlling the propagation of an epidemic outbreak in an arbitrary contact network by distributing vaccination resources throughout the network. We analyze a networked version of the Susceptible-Infected-Susceptible (SIS) epidemic model when individuals in the network present different levels of susceptibility to the epidemic. In this context, controlling the spread of an epidemic outbreak can be written as a spectral condition involving the eigenvalues of a matrix that depends on the network structure and the parameters of the model. We study the problem of finding the optimal distribution of vaccines throughout the network to control the spread of an epidemic outbreak. We propose a convex framework to find cost-optimal distribution of vaccination resources when different levels of vaccination are allowed. We also propose a greedy approach with quality guarantees for the case of all-or-nothing vaccination. We illustrate our approaches with numerical simulations in a real social network.
Planar graphs with maximum degree D at least 8 are (D+1)-edge-choosable
Venue: arXiv cs.DM 2013
Year: 2013

We consider the problem of list edge coloring for planar graphs. Edge coloring is the problem of coloring the edges while ensuring that two edges that are incident receive different colors. A graph is k-edge-choosable if for any assignment of k colors to every edge, there is an edge coloring such that the color of every edge belongs to its color assignment. Vizing conjectured in 1965 that every graph is (D+1)-edge-choosable, where D is the maximum degree. In 1990, Borodin solved the conjecture for planar graphs with maximum degree at least 9, and asked whether the bound could be lowered to 8. We prove here that planar graphs with maximum degree D at least 8 are (D+1)-edge-choosable.
Two exact algorithms for the generalized assignment problem
Venue: arXiv cs.DS 2013
Year: 2013

Let A={a_1,a_2,...,a_s} and B={b_1,b_2,...,b_t} be two sets of objects with s+r=n, the generalized assignment problem assigns each element a_i in A to at least alpha_i and at most alpha '_i elements in B, and each element b_j in B to at least beta_j and at most beta '_j elements in A for all 1 <= i <= s and 1 <= j <= t. In this paper, we present an O(n^4) time and O(n) space algorithm for this problem using the well known Hungarian algorithm. We also present an O(n^3) algorithm for a special case of the generalized assignment, called the limited-capacity assignment problem, where alpha_i,beta_j=1 for all i,j.
PAPR Reduction of OFDM System Through Iterative Selection of Input Sequences
Venue: arXiv cs.IT 2013
Year: 2013

Orthogonal Frequency Division Multiplexing (OFDM) based multi-carrier systems can support high data rate wireless transmission without the requirement of any extensive equalization and yet offer excellent immunity against fading and inter-symbol interference. But one of the major drawbacks of these systems is the large Peak-to-Average Power Ratio (PAPR) of the transmit signal which renders a straightforward implementation costly and inefficient. In this paper, a new PAPR reduction scheme is introduced where a number of sequences from the original data sequence is generated by changing the position of each symbol and the sequence with lowest PAPR is selected for transmission. A comparison of performance of this proposed technique with an existing PAPR reduction scheme, i.e., the Selective Mapping (SLM) is performed. It is shown that considerable reduction in PAPR along with higher throughput can be achieved at the expense of some additional computational complexity.
Sparsity-Exploiting Anchor Placement for Localization in Sensor Networks
Venue: arXiv cs.IT 2013
Year: 2013

We consider the anchor placement problem in localization based on one-way ranging, in which either the sensor or the anchors send the ranging signals. The number of anchors deployed over a geographical area is generally sparse, and we show that the anchor placement can be formulated as the design of a sparse selection vector. Interestingly, the case in which the anchors send the ranging signals, results in a joint ranging energy optimization and anchor placement problem. We make abstraction of the localization algorithm and instead use the Cramér-Rao lower bound (CRB) as the performance constraint. The anchor placement problem is formulated as an elegant convex optimization problem which can be solved efficiently.
Device-Free Person Detection and Ranging in UWB Networks
Venue: arXiv cs.OH 2013
Year: 2013

We present a novel device-free stationary person detection and ranging method, that is applicable to ultra-wide bandwidth (UWB) networks. The method utilizes a fixed UWB infrastructure and does not require a training database of template waveforms. Instead, the method capitalizes on the fact that a human presence induces small low-frequency variations that stand out against the background signal, which is mainly affected by wideband noise. We analyze the detection probability, and validate our findings with numerical simulations and experiments with off-the-shelf UWB transceivers in an indoor environment.
Robust Decentralized State Estimation and Tracking for Power Systems via Network Gossiping
Venue: arXiv cs.DC 2013
Year: 2013

This paper proposes a fully decentralized adaptive re-weighted state estimation (DARSE) scheme for power systems via network gossiping. The enabling technique is the proposed Gossip-based Gauss-Newton (GGN) algorithm, which allows to harness the computation capability of each area (i.e. a database server that accrues data from local sensors) to collaboratively solve for an accurate global state. The DARSE scheme mitigates the influence of bad data by updating their error variances online and re-weighting their contributions adaptively for state estimation. Thus, the global state can be estimated and tracked robustly using near-neighbor communications in each area. Compared to other distributed state estimation techniques, our communication model is flexible with respect to reconfigurations and resilient to random failures as long as the communication network is connected. Furthermore, we prove that the Jacobian of the power flow equations satisfies the Lipschitz condition that is essential for the GGN algorithm to converge to the desired solution. Simulations of the IEEE-118 system show that the DARSE scheme can estimate and track online the global power system state accurately, and degrades gracefully when there are random failures and bad data.
Markov Chain Monte Carlo for Arrangement of Hyperplanes in Locality-Sensitive Hashing
Venue: arXiv cs.LG 2013
Year: 2013

Since Hamming distances can be calculated by bitwise computations, they can be calculated with less computational load than L2 distances. Similarity searches can therefore be performed faster in Hamming distance space. The elements of Hamming distance space are bit strings. On the other hand, the arrangement of hyperplanes induce the transformation from the feature vectors into feature bit strings. This transformation method is a type of locality-sensitive hashing that has been attracting attention as a way of performing approximate similarity searches at high speed. Supervised learning of hyperplane arrangements allows us to obtain a method that transforms them into feature bit strings reflecting the information of labels applied to higher-dimensional feature vectors. In this p aper, we propose a supervised learning method for hyperplane arrangements in feature space that uses a Markov chain Monte Carlo (MCMC) method. We consider the probability density functions used during learning, and evaluate their performance. We also consider the sampling method for learning data pairs needed in learning, and we evaluate its performance. We confirm that the accuracy of this learning method when using a suitable probability density function and sampling method is greater than the accuracy of existing learning methods.
Network Non-Neutrality through Preferential Signaling
Venue: arXiv cs.GT 2013
Year: 2013

One of the central issues in the debate on network neutrality has been whether one should allow or prevent preferential treatment by an internet service provider (ISP) of traffic according to its origin. This raised the question of whether to allow an ISP to have exclusive agreement with a content provider (CP). In this paper we consider discrimination in the opposite direction. We study the impact that a CP can have on the benefits of several competing ISPs by sharing private information concerning the demand for its content. More precisely, we consider ISPs that compete over access to one common CP. Each ISP selects the price that it charges its subscribers for accessing the content. The CP is assumed to have private information about demand for its content, and in particular, about the inverse demand function corresponding to the content. The competing ISPs are assumed to have knowledge on only the statistical distribution of these functions. We derive in this paper models for studying the impact that the CP can have on the utilities of the ISPs by favoring one of them by exclusively revealing its private information. We also consider the case where CP can charge ISPs for providing such information. We propose two mechanisms based on {\em weighted proportional fairness} for payment between ISPs and CP. Finally, we compare the social utility resulting from these mechanisms with the optimal social utility by introducing a performance metric termed as {\em price of partial bargaining}
On the Design of Channel Estimators for given Signal Estimators and Detectors
Venue: arXiv cs.IT 2013
Year: 2013

The fundamental task of a digital receiver is to decide the transmitted symbols in the best possible way, i.e., with respect to an appropriately defined performance metric. Examples of usual performance metrics are the probability of error and the Mean Square Error (MSE) of a symbol estimator. In a coherent receiver, the symbol decisions are made based on the use of a channel estimate. This paper focuses on examining the optimality of usual estimators such as the minimum variance unbiased (MVU) and the minimum mean square error (MMSE) estimators for these metrics and on proposing better estimators whenever it is necessary. For illustration purposes, this study is performed on a toy channel model, namely a single input single output (SISO) flat fading channel with additive white Gaussian noise (AWGN). In this way, this paper highlights the design dependencies of channel estimators on target performance metrics.