OpenRaiser/Intern-Atlas huggingface dataset:
Efficient Fully-Compressed Sequence Representations
Venue: arXiv cs.DS 2012
Year: 2012

We present a data structure that stores a sequence $s[1..n]$ over alphabet $[1..σ]$ in $n\Ho(s) + o(n)(\Ho(s){+}1)$ bits, where $\Ho(s)$ is the zero-order entropy of $s$. This structure supports the queries \access, \rank\ and \select, which are fundamental building blocks for many other compressed data structures, in worst-case time $\Oh{\lg\lgσ}$ and average time $\Oh{\lg \Ho(s)}$. The worst-case complexity matches the best previous results, yet these had been achieved with data structures using $n\Ho(s)+o(n\lgσ)$ bits. On highly compressible sequences the $o(n\lgσ)$ bits of the redundancy may be significant compared to the the $n\Ho(s)$ bits that encode the data. Our representation, instead, compresses the redundancy as well. Moreover, our average-case complexity is unprecedented. Our technique is based on partitioning the alphabet into characters of similar frequency. The subsequence corresponding to each group can then be encoded using fast uncompressed representations without harming the overall compression ratios, even in the redundancy. The result also improves upon the best current compressed representations of several other data structures. For example, we achieve $(i)$ compressed redundancy, retaining the best time complexities, for the smallest existing full-text self-indexes; $(ii)$ compressed permutations $π$ with times for $π()$ and $\pii()$ improved to loglogarithmic; and $(iii)$ the first compressed representation of dynamic collections of disjoint sets. We also point out various applications to inverted indexes, suffix arrays, binary relations, and data compressors. ...
Sponsored Search, Market Equilibria, and the Hungarian Method
Venue: arXiv cs.GT 2012
Year: 2012

Matching markets play a prominent role in economic theory. A prime example of such a market is the sponsored search market. Here, as in other markets of that kind, market equilibria correspond to feasible, envy free, and bidder optimal outcomes. For settings without budgets such an outcome always exists and can be computed in polynomial-time by the so-called Hungarian Method. Moreover, every mechanism that computes such an outcome is incentive compatible. We show that the Hungarian Method can be modified so that it finds a feasible, envy free, and bidder optimal outcome for settings with budgets. We also show that in settings with budgets no mechanism that computes such an outcome can be incentive compatible for all inputs. For inputs in general position, however, the presented mechanism---as any other mechanism that computes such an outcome for settings with budgets---is incentive compatible.
On some upper bounds on the fractional chromatic number of weighted graphs
Venue: arXiv cs.IT 2012
Year: 2012

Given a weighted graph $G_\bx$, where $(x(v): v \in V)$ is a non-negative, real-valued weight assigned to the vertices of G, let $B(G_\bx)$ be an upper bound on the fractional chromatic number of the weighted graph $G_\bx$; so $χ_f(G_\bx) \le B(G_\bx)$. To investigate the worst-case performance of the upper bound $B$, we study the graph invariant $$β(G) = \sup_{\bx \ne 0} \frac{B(G_\bx)}{χ_f(G_\bx)}.$$ \noindent This invariant is examined for various upper bounds $B$ on the fractional chromatic number. In some important cases, this graph invariant is shown to be related to the size of the largest star subgraph in the graph. This problem arises in the area of resource estimation in distributed systems and wireless networks; the results presented here have implications on the design and performance of decentralized communication networks.
Approximating the partition function of the ferromagnetic Potts model
Venue: arXiv cs.CC 2012
Year: 2012

We provide evidence that it is computationally difficult to approximate the partition function of the ferromagnetic q-state Potts model when q>2. Specifically we show that the partition function is hard for the complexity class #RHPi_1 under approximation-preserving reducibility. Thus, it is as hard to approximate the partition function as it is to find approximate solutions to a wide range of counting problems, including that of determining the number of independent sets in a bipartite graph. Our proof exploits the first order phase transition of the "random cluster" model, which is a probability distribution on graphs that is closely related to the q-state Potts model.
Optimal Lower Bounds for Projective List Update Algorithms
Venue: arXiv cs.CC 2012
Year: 2012

The list update problem is a classical online problem, with an optimal competitive ratio that is still open, known to be somewhere between 1.5 and 1.6. An algorithm with competitive ratio 1.6, the smallest known to date, is COMB, a randomized combination of BIT and the TIMESTAMP algorithm TS. This and almost all other list update algorithms, like MTF, are projective in the sense that they can be defined by looking only at any pair of list items at a time. Projectivity (also known as "list factoring") simplifies both the description of the algorithm and its analysis, and so far seems to be the only way to define a good online algorithm for lists of arbitrary length. In this paper we characterize all projective list update algorithms and show that their competitive ratio is never smaller than 1.6 in the partial cost model. Therefore, COMB is a best possible projective algorithm in this model.
Solutions to the GSM Security Weaknesses
Venue: arXiv cs.CR 2012
Year: 2012

Recently, the mobile industry has experienced an extreme increment in number of its users. The GSM network with the greatest worldwide number of users succumbs to several security vulnerabilities. Although some of its security problems are addressed in its upper generations, there are still many operators using 2G systems. This paper briefly presents the most important security flaws of the GSM network and its transport channels. It also provides some practical solutions to improve the security of currently available 2G systems.
A Directly Public Verifiable Signcryption Scheme based on Elliptic Curves
Venue: arXiv cs.CR 2012
Year: 2012

A directly public verifiable signcryption scheme is introduced in this paper that provides the security attributes of message confidentiality, authentication, integrity, non-repudiation, unforgeability, and forward secrecy of message confidentiality. It provides the attribute of direct public verifiability so anyone can verify the signcryption without any need for any secret information from the corresponding participants. The proposed scheme is based on elliptic curve cryptography and is so suitable for environments with resource constraints.
A Contextual-Bandit Approach to Personalized News Article Recommendation
Venue: arXiv cs.LG 2012
Year: 2012

Personalized web services strive to adapt their services (advertisements, news articles, etc) to individual users by making use of both content and user information. Despite a few recent advances, this problem remains challenging for at least two reasons. First, web service is featured with dynamically changing pools of content, rendering traditional collaborative filtering methods inapplicable. Second, the scale of most web services of practical interest calls for solutions that are both fast in learning and computation. In this work, we model personalized recommendation of news articles as a contextual bandit problem, a principled approach in which a learning algorithm sequentially selects articles to serve users based on contextual information about the users and articles, while simultaneously adapting its article-selection strategy based on user-click feedback to maximize total user clicks. The contributions of this work are three-fold. First, we propose a new, general contextual bandit algorithm that is computationally efficient and well motivated from learning theory. Second, we argue that any bandit algorithm can be reliably evaluated offline using previously recorded random traffic. Finally, using this offline evaluation method, we successfully applied our new algorithm to a Yahoo! Front Page Today Module dataset containing over 33 million events. Results showed a 12.5% click lift compared to a standard context-free bandit algorithm, and the advantage becomes even greater when data gets more scarce.
Power Loading in Parallel Diversity Channels Based on Statistical Channel Information
Venue: arXiv cs.IT 2012
Year: 2012

In this paper, we show that there exists an arbitrary number of power allocation schemes that achieve capacity in systems operating in parallel channels comprised of single-input multiple-output (SIMO) Nakagami-m fading subchannels when the number of degrees of freedom L (e.g., the number of receive antennas) tends to infinity. Statistical waterfilling -- i.e., waterfilling using channel statistics rather than instantaneous channel knowledge -- is one such scheme. We further prove that the convergence of statistical waterfilling to the optimal power loading scheme is at least O(1/(L log(L))), whereas convergence of other schemes is at worst O(1/log(L)). To validate and demonstrate the practical use of our findings, we evaluate the mutual information of example SIMO parallel channels using simulations as well as new measured ultrawideband channel data.
Delay-rate tradeoff in ergodic interference alignment
Venue: arXiv cs.IT 2012
Year: 2012

Ergodic interference alignment, as introduced by Nazer et al (NGJV), is a technique that allows high-rate communication in n-user interference networks with fast fading. It works by splitting communication across a pair of fading matrices. However, it comes with the overhead of a long time delay until matchable matrices occur: the delay is q^n^2 for field size q. In this paper, we outline two new families of schemes, called JAP and JAP-B, that reduce the expected delay, sometimes at the cost of a reduction in rate from the NGJV scheme. In particular, we give examples of good schemes for networks with few users, and show that in large n-user networks, the delay scales like q^T, where T is quadratic in n for a constant per-user rate and T is constant for a constant sum-rate. We also show that half the single-user rate can be achieved while reducing NGJV's delay from q^n^2 to q^(n-1)(n-2). This extended version includes complete proofs and more details of good schemes for small n.
Opaque sets
Venue: arXiv cs.CG 2012
Year: 2012

The problem of finding "small" sets that meet every straight-line which intersects a given convex region was initiated by Mazurkiewicz in 1916. We call such a set an {\em opaque set} or a {\em barrier} for that region. We consider the problem of computing the shortest barrier for a given convex polygon with $n$ vertices. No exact algorithm is currently known even for the simplest instances such as a square or an equilateral triangle. For general barriers, we present an approximation algorithm with ratio $1/2 + \frac{2 +\sqrt{2}}π=1.5867...$. For connected barriers we achieve the approximation ratio 1.5716, while for single-arc barriers we achieve the approximation ratio $\frac{π+5}{π+2} = 1.5834...$. All three algorithms run in O(n) time. We also show that if the barrier is restricted to the (interior and the boundary of the) input polygon, then the problem admits a fully polynomial-time approximation scheme for the connected case and a quadratic-time exact algorithm for the single-arc case.
The Complexity of Manipulating $k$-Approval Elections
Venue: arXiv cs.AI 2012
Year: 2012

An important problem in computational social choice theory is the complexity of undesirable behavior among agents, such as control, manipulation, and bribery in election systems. These kinds of voting strategies are often tempting at the individual level but disastrous for the agents as a whole. Creating election systems where the determination of such strategies is difficult is thus an important goal. An interesting set of elections is that of scoring protocols. Previous work in this area has demonstrated the complexity of misuse in cases involving a fixed number of candidates, and of specific election systems on unbounded number of candidates such as Borda. In contrast, we take the first step in generalizing the results of computational complexity of election misuse to cases of infinitely many scoring protocols on an unbounded number of candidates. Interesting families of systems include $k$-approval and $k$-veto elections, in which voters distinguish $k$ candidates from the candidate set. Our main result is to partition the problems of these families based on their complexity. We do so by showing they are polynomial-time computable, NP-hard, or polynomial-time equivalent to another problem of interest. We also demonstrate a surprising connection between manipulation in election systems and some graph theory problems.
Minimizing a sum of submodular functions
Venue: arXiv cs.DS 2012
Year: 2012

We consider the problem of minimizing a function represented as a sum of submodular terms. We assume each term allows an efficient computation of {\em exchange capacities}. This holds, for example, for terms depending on a small number of variables, or for certain cardinality-dependent terms. A naive application of submodular minimization algorithms would not exploit the existence of specialized exchange capacity subroutines for individual terms. To overcome this, we cast the problem as a {\em submodular flow} (SF) problem in an auxiliary graph, and show that applying most existing SF algorithms would rely only on these subroutines. We then explore in more detail Iwata's capacity scaling approach for submodular flows (Math. Programming, 76(2):299--308, 1997). In particular, we show how to improve its complexity in the case when the function contains cardinality-dependent terms.
User Partitioning for Less Overhead in MIMO Interference Channels
Venue: arXiv cs.IT 2012
Year: 2012

This paper presents a study on multiple-antenna interference channels, accounting for general overhead as a function of the number of users and antennas in the network. The model includes both perfect and imperfect channel state information based on channel estimation in the presence of noise. Three low complexity methods are proposed for reducing the impact of overhead in the sum network throughput by partitioning users into orthogonal groups. The first method allocates spectrum to the groups equally, creating an imbalance in the sum rate of each group. The second proposed method allocates spectrum unequally among the groups to provide rate fairness. Finally, geographic grouping is proposed for cases where some receivers do not observe significant interference from other transmitters. For each partitioning method, the optimal solution not only requires a brute force search over all possible partitions, but also requires full channel state information, thereby defeating the purpose of partitioning. We therefore propose greedy methods to solve the problems, requiring no instantaneous channel knowledge. Simulations show that the proposed greedy methods switch from time-division to interference alignment as the coherence time of the channel increases, and have a small loss relative to optimal partitioning only at moderate coherence times.
Quickest Detection with Social Learning: Interaction of local and global decision makers
Venue: arXiv cs.GT 2012
Year: 2012

We consider how local and global decision policies interact in stopping time problems such as quickest time change detection. Individual agents make myopic local decisions via social learning, that is, each agent records a private observation of a noisy underlying state process, selfishly optimizes its local utility and then broadcasts its local decision. Given these local decisions, how can a global decision maker achieve quickest time change detection when the underlying state changes according to a phase-type distribution? The paper presents four results. First, using Blackwell dominance of measures, it is shown that the optimal cost incurred in social learning based quickest detection is always larger than that of classical quickest detection. Second, it is shown that in general the optimal decision policy for social learning based quickest detection is characterized by multiple thresholds within the space of Bayesian distributions. Third, using lattice programming and stochastic dominance, sufficient conditions are given for the optimal decision policy to consist of a single linear hyperplane, or, more generally, a threshold curve. Estimation of the optimal linear approximation to this threshold curve is formulated as a simulation-based stochastic optimization problem. Finally, the paper shows that in multi-agent sensor management with quickest detection, where each agent views the world according to its prior, the optimal policy has a similar structure to social learning.
Achievable Error Exponents in the Gaussian Channel with Rate-Limited Feedback
Venue: arXiv cs.IT 2012
Year: 2012

We investigate the achievable error probability in communication over an AWGN discrete time memoryless channel with noiseless delay-less rate-limited feedback. For the case where the feedback rate R_FB is lower than the data rate R transmitted over the forward channel, we show that the decay of the probability of error is at most exponential in blocklength, and obtain an upper bound for increase in the error exponent due to feedback. Furthermore, we show that the use of feedback in this case results in an error exponent that is at least RF B higher than the error exponent in the absence of feedback. For the case where the feedback rate exceeds the forward rate (R_FB \geq R), we propose a simple iterative scheme that achieves a probability of error that decays doubly exponentially with the codeword blocklength n. More generally, for some positive integer L, we show that a L-th order exponential error decay is achievable if R_FB \geq (L-1)R. We prove that the above results hold whether the feedback constraint is expressed in terms of the average feedback rate or per channel use feedback rate. Our results show that the error exponent as a function of R_FB has a strong discontinuity at R, where it jumps from a finite value to infinity.
Social-Aware Forwarding Improves Routing Performance in Pocket Switched Networks
Venue: arXiv cs.NI 2012
Year: 2012

Several social-aware forwarding strategies have been recently introduced in opportunistic networks, and proved effective in considerably in- creasing routing performance through extensive simulation studies based on real-world data. However, this performance improvement comes at the expense of storing a considerable amount of state information (e.g, history of past encounters) at the nodes. Hence, whether the benefits on routing performance comes directly from the social-aware forwarding mechanism, or indirectly by the fact state information is exploited is not clear. Thus, the question of whether social-aware forwarding by itself is effective in improving opportunistic network routing performance remained unaddressed so far. In this paper, we give a first, positive answer to the above question, by investigating the expected message delivery time as the size of the net- work grows larger.
On Two Strong Converse Theorems for Stationary Discrete Memoryless Channels
Venue: arXiv cs.IT 2012
Year: 2012

In 1973, Arimoto proved the strong converse theorem for the discrete memoryless channels stating that when transmission rate $R$ is above channel capacity $C$, the error probability of decoding goes to one as the block length $n$ of code word tends to infinity. He proved the theorem by deriving the exponent function of error probability of correct decoding that is positive if and only if $R>C$. Subsequently, in 1979, Dueck and Körner determined the optimal exponent of correct decoding. Arimoto's bound has been said to be equal to the bound of Dueck and Körner. However its rigorous proof has not been presented so far. In this paper we give a rigorous proof of the equivalence of Arimoto's bound to that of Dueck and Körner.
A* Orthogonal Matching Pursuit: Best-First Search for Compressed Sensing Signal Recovery
Venue: arXiv cs.IT 2012
Year: 2012

Compressed sensing is a developing field aiming at reconstruction of sparse signals acquired in reduced dimensions, which make the recovery process under-determined. The required solution is the one with minimum $\ell_0$ norm due to sparsity, however it is not practical to solve the $\ell_0$ minimization problem. Commonly used techniques include $\ell_1$ minimization, such as Basis Pursuit (BP) and greedy pursuit algorithms such as Orthogonal Matching Pursuit (OMP) and Subspace Pursuit (SP). This manuscript proposes a novel semi-greedy recovery approach, namely A* Orthogonal Matching Pursuit (A*OMP). A*OMP performs A* search to look for the sparsest solution on a tree whose paths grow similar to the Orthogonal Matching Pursuit (OMP) algorithm. Paths on the tree are evaluated according to a cost function, which should compensate for different path lengths. For this purpose, three different auxiliary structures are defined, including novel dynamic ones. A*OMP also incorporates pruning techniques which enable practical applications of the algorithm. Moreover, the adjustable search parameters provide means for a complexity-accuracy trade-off. We demonstrate the reconstruction ability of the proposed scheme on both synthetically generated data and images using Gaussian and Bernoulli observation matrices, where A*OMP yields less reconstruction error and higher exact recovery frequency than BP, OMP and SP. Results also indicate that novel dynamic cost functions provide improved results as compared to a conventional choice.
Location Estimation with Reactive Routing in Resource Constrained Sensor Networks
Venue: arXiv cs.NI 2012
Year: 2012

Routing algorithms for wireless sensor networks can be broadly divided into two classes - proactive and reactive. Proactive routing is suitable for a network with a fixed topology. On the other hand, reactive routing is more suitable for a set of mobile nodes where routes are created on demand and there is not much time to evaluate the worthiness of a route, the prime concern being reachability due to constantly changing node positions. Sensor networks route events of interest from source(s) to destination(s) where appropriate actions could be taken. However, with mobile sensor nodes, it is not only important to know the events but the location of the nodes generating the events. Most sensor nodes are not equipped with expensive GPS or accurate RSSI computation hardware to aid localization. Keeping these in view, we propose a modified reactive routing algorithm, with added support for localization, to localize mobile sensor nodes on the basis of information received from fixed sensor nodes during mutual exchange of routing control packets. The accuracy of localization depends on the ratio of the number of fixed nodes to the number of mobile nodes and the topology of the fixed nodes. A typical application scenario would be a mix of mobile nodes and fixed nodes, where fixed nodes know their absolute location and the location of mobile nodes is derived from the fixed nodes, in step with the reactive routing protocol in action. The modified algorithm would be suitable for deployments where the approximate position of a mobile node (i.e. the event location) is required but there is no external support infrastructure available for localization.