OpenRaiser/Intern-Atlas huggingface dataset:
Maximizing Social Influence in Nearly Optimal Time
Venue: arXiv cs.DS 2016
Year: 2016

Diffusion is a fundamental graph process, underpinning such phenomena as epidemic disease contagion and the spread of innovation by word-of-mouth. We address the algorithmic problem of finding a set of k initial seed nodes in a network so that the expected size of the resulting cascade is maximized, under the standard independent cascade model of network diffusion. Runtime is a primary consideration for this problem due to the massive size of the relevant input networks. We provide a fast algorithm for the influence maximization problem, obtaining the near-optimal approximation factor of (1 - 1/e - epsilon), for any epsilon > 0, in time O((m+n)k log(n) / epsilon^2). Our algorithm is runtime-optimal (up to a logarithmic factor) and substantially improves upon the previously best-known algorithms which run in time Omega(mnk POLY(1/epsilon)). Furthermore, our algorithm can be modified to allow early termination: if it is terminated after O(beta(m+n)k log(n)) steps for some beta < 1 (which can depend on n), then it returns a solution with approximation factor O(beta). Finally, we show that this runtime is optimal (up to logarithmic factors) for any beta and fixed seed size k.
Superlinear lower bounds for multipass graph processing
Venue: arXiv cs.CC 2016
Year: 2016

We prove $n^{1+Ω(1/p)}/p^{O(1)}$ lower bounds for the space complexity of $p$-pass streaming algorithms solving the following problems on $n$-vertex graphs: * testing if an undirected graph has a perfect matching (this implies lower bounds for computing a maximum matching or even just the maximum matching size), * testing if two specific vertices are at distance at most $2(p+1)$ in an undirected graph, * testing if there is a directed path from $s$ to $t$ for two specific vertices $s$ and $t$ in a directed graph. Prior to our result, it was known that these problems require $Ω(n^2)$ space in one pass, but no $n^{1+Ω(1)}$ lower bound was known for any $p\ge 2$. These streaming results follow from a communication complexity lower bound for a communication game in which the players hold two graphs on the same set of vertices. The task of the players is to find out whether the sets of vertices at distance exactly $p+1$ from a specific vertex intersect. The game requires a significant amount of communication only if the players are forced to speak in a specific difficult order. This is reminiscent of lower bounds for communication problems such as indexing and pointer chasing. Among other things, our line of attack requires proving an information cost lower bound for a decision version of the classic pointer chasing problem and a direct sum type theorem for the disjunction of several instances of this problem.
Load-aware Channel Selection for 802.11 WLANs with Limited Measurement
Venue: arXiv cs.NI 2016
Year: 2016

It has been known that load unaware channel selection in 802.11 networks results in high level interference, and can significantly reduce the network throughput. In current implementation, the only way to determine the traffic load on a channel is to measure that channel for a certain duration of time. Therefore, in order to find the best channel with the minimum load all channels have to be measured, which is costly and can cause unacceptable communication interruptions between the AP and the stations. In this paper, we propose a learning based approach which aims to find the channel with the minimum load by measuring only limited number of channels. Our method uses Gaussian Process Regressing to accurately track the traffic load on each channel based on the previous measured load. We confirm the performance of our algorithm by using experimental data, and show that the time consumed for the load measurement can be reduced up to 46% compared to the case where all channels are monitored.
The Future of Mobile Security
Venue: arXiv cs.CR 2016
Year: 2016

Mobile devices are more than just phones, they are a lifeline to the outdoor world, entertainment platform, GPS system, a little black book and a shopping and banking tool. What is not well known is that these devices are also gateways. Mobile devices can be used by a hacker as an access point into many other aspects of your digital life as well the lives of others in your network, making mobile security about more than just protecting your phone. This is an overview of technologies supporting mobile data cryptography and two-step protection mechanisms that may be used to secure online transactions and user authentication.
On Isodual Cyclic Codes over Finite Fields and Finite Chain Rings: Monomial Equivalence
Venue: arXiv cs.IT 2016
Year: 2016

This paper present the construction cyclic isodual codes over finite fields and finite chain rings. These codes are monomially equivalent to their dual. Conditions are given for the existence of cyclic isodual codes. In addition, the concept of duadic codes over finite fields is extended to finite chain rings. Several constructions of isodual cyclic codes and self-dual codes are given.
Linear complexity of generalized cyclotomic sequences of order 4 over F_l
Venue: arXiv cs.IT 2016
Year: 2016

Generalized cyclotomic sequences of period pq have several desirable randomness properties if the two primes p and q are chosen properly. In particular,Ding deduced the exact formulas for the autocorrelation and the linear complexity of these sequences of order 2. In this paper, we consider the generalized sequences of order 4. Under certain conditions, the linear complexity of these sequences of order 4 is developed over a finite field F_l. Results show that in many cases they have high linear complexity.
Fast Approximate Polynomial Multipoint Evaluation and Applications
Venue: arXiv cs.NA 2016
Year: 2016

It is well known that, using fast algorithms for polynomial multiplication and division, evaluation of a polynomial $F \in \mathbb{C}[x]$ of degree $n$ at $n$ complex-valued points can be done with $\tilde{O}(n)$ exact field operations in $\mathbb{C},$ where $\tilde{O}(\cdot)$ means that we omit polylogarithmic factors. We complement this result by an analysis of approximate multipoint evaluation of $F$ to a precision of $L$ bits after the binary point and prove a bit complexity of $\tilde{O}(n(L + τ+ nΓ)),$ where $2^τ$ and $2^Γ,$ with $τ, Γ\in \mathbb{N}_{\ge 1},$ are bounds on the magnitude of the coefficients of $F$ and the evaluation points, respectively. In particular, in the important case where the precision demand dominates the other input parameters, the complexity is soft-linear in $n$ and $L$. Our result on approximate multipoint evaluation has some interesting consequences on the bit complexity of further approximation algorithms which all use polynomial evaluation as a key subroutine. Of these applications, we discuss in detail an algorithm for polynomial interpolation and for computing a Taylor shift of a polynomial. Furthermore, our result can be used to derive improved complexity bounds for algorithms to refine isolating intervals for the real roots of a polynomial. For all of the latter algorithms, we derive near-optimal running times.
On the automorphism groups of some AG-codes based on $C_{a, b}$ curves
Venue: arXiv cs.IT 2016
Year: 2016

We study $C_{a, b}$ curves and their applications to coding theory. Recently, Joyner and Ksir have suggested a decoding algorithm based on the automorphisms of the code. We show how $C_{a, b}$ curves can be used to construct MDS codes and focus on some $C_{a, b}$ curves with extra automorphisms, namely $y^3=x^4+1, y^3=x^4-x, y^3-y=x^4$. The automorphism groups of such codes are determined in most characteristics.
New Results on Equilibria in Strategic Candidacy
Venue: arXiv cs.GT 2016
Year: 2016

We consider a voting setting where candidates have preferences about the outcome of the election and are free to join or leave the election. The corresponding candidacy game, where candidates choose strategically to participate or not, has been studied %initially by Dutta et al., who showed that no non-dictatorial voting procedure satisfying unanimity is candidacy-strategyproof, that is, is such that the joint action where all candidates enter the election is always a pure strategy Nash equilibrium. Dutta et al. also showed that for some voting tree procedures, there are candidacy games with no pure Nash equilibria, and that for the rule that outputs the sophisticated winner of voting by successive elimination, all games have a pure Nash equilibrium. No results were known about other voting rules. Here we prove several such results. For four candidates, the message is, roughly, that most scoring rules (with the exception of Borda) do not guarantee the existence of a pure Nash equilibrium but that Condorcet-consistent rules, for an odd number of voters, do. For five candidates, most rules we study no longer have this guarantee. Finally, we identify one prominent rule that guarantees the existence of a pure Nash equilibrium for any number of candidates (and for an odd number of voters): the Copeland rule. We also show that under mild assumptions on the voting rule, the existence of strong equilibria cannot be guaranteed.
A strongly polynomial algorithm for generalized flow maximization
Venue: arXiv cs.DS 2016
Year: 2016

A strongly polynomial algorithm is given for the generalized flow maximization problem. It uses a new variant of the scaling technique, called continuous scaling. The main measure of progress is that within a strongly polynomial number of steps, an arc can be identified that must be tight in every dual optimal solution, and thus can be contracted. As a consequence of the result, we also obtain a strongly polynomial algorithm for the linear feasibility problem with at most two nonzero entries per column in the constraint matrix.
Optimal Quantization for Distribution Synthesis
Venue: arXiv cs.IT 2016
Year: 2016

Finite precision approximations of discrete probability distributions are considered, applicable for distribution synthesis, e.g., probabilistic shaping. Two algorithms are presented that find the optimal $M$-type approximation $Q$ of a distribution $P$ in terms of the variational distance $| Q-P|_1$ and the informational divergence $\mathbb{D}(Q| P)$. Bounds on the approximation errors are derived and shown to be asymptotically tight. Several examples illustrate that the variational distance optimal approximation can be quite different from the informational divergence optimal approximation.
Union of Low-Rank Subspaces Detector
Venue: arXiv cs.IT 2016
Year: 2016

The problem of signal detection using a flexible and general model is considered. Due to applicability and flexibility of sparse signal representation and approximation, it has attracted a lot of attention in many signal processing areas. In this paper, we propose a new detection method based on sparse decomposition in a union of subspaces (UoS) model. Our proposed detector uses a dictionary that can be interpreted as a bank of matched subspaces. This improves the performance of signal detection, as it is a generalization for detectors. Low-rank assumption for the desired signals implies that the representations of these signals in terms of some proper bases would be sparse. Our proposed detector exploits sparsity in its decision rule. We demonstrate the high efficiency of our method in the cases of voice activity detection in speech processing.
On Finding a Subset of Healthy Individuals from a Large Population
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we derive mutual information based upper and lower bounds on the number of nonadaptive group tests required to identify a given number of "non defective" items from a large population containing a small number of "defective" items. We show that a reduction in the number of tests is achievable compared to the approach of first identifying all the defective items and then picking the required number of non-defective items from the complement set. In the asymptotic regime with the population size $N \rightarrow \infty$, to identify $L$ non-defective items out of a population containing $K$ defective items, when the tests are reliable, our results show that $\frac{C_s K}{1-o(1)} (Φ(α_0, β_0) + o(1))$ measurements are sufficient, where $C_s$ is a constant independent of $N, K$ and $L$, and $Φ(α_0, β_0)$ is a bounded function of $α_0 \triangleq \lim_{N\rightarrow \infty} \frac{L}{N-K}$ and $β_0 \triangleq \lim_{N\rightarrow \infty} \frac{K} {N-K}$. Further, in the nonadaptive group testing setup, we obtain rigorous upper and lower bounds on the number of tests under both dilution and additive noise models. Our results are derived using a general sparse signal model, by virtue of which, they are also applicable to other important sparse signal based applications such as compressive sensing.
A Parallel Data Distribution Management Algorithm
Venue: arXiv cs.DS 2016
Year: 2016

Identifying intersections among a set of d-dimensional rectangular regions (d-rectangles) is a common problem in many simulation and modeling applications. Since algorithms for computing intersections over a large number of regions can be computationally demanding, an obvious solution is to take advantage of the multiprocessing capabilities of modern multicore processors. Unfortunately, many solutions employed for the Data Distribution Management service of the High Level Architecture are either inefficient, or can only partially be parallelized. In this paper we propose the Interval Tree Matching (ITM) algorithm for computing intersections among d-rectangles. ITM is based on a simple Interval Tree data structure, and exhibits an embarrassingly parallel structure. We implement the ITM algorithm, and compare its sequential performance with two widely used solutions (brute force and sort-based matching). We also analyze the scalability of ITM on shared-memory multicore processors. The results show that the sequential implementation of ITM is competitive with sort-based matching; moreover, the parallel implementation provides good speedup on multicore processors.
Parameterized Exact and Approximation Algorithms for Maximum $k$-Set Cover and Related Satisfiability Problems
Venue: arXiv cs.CC 2016
Year: 2016

Given a family of subsets $\mathcal S$ over a set of elements~$X$ and two integers~$p$ and~$k$, Max k-Set Cover consists of finding a subfamily~$\mathcal T \subseteq \mathcal S$ of cardinality at most~$k$, covering at least~$p$ elements of~$X$. This problem is W[2]-hard when parameterized by~$k$, and FPT when parameterized by $p$. We investigate the parameterized approximability of the problem with respect to parameters~$k$ and~$p$. Then, we show that Max Sat-k, a satisfiability problem generalizing Max k-Set Cover, is also FPT with respect to parameter~$p$.
Analyse des rôles dans les communautés virtuelles : définitions et premières expérimentations sur IMDb
Venue: arXiv cs.SI 2016
Year: 2016

Role analysis in online communities allows us to understand and predict users behavior. Though several approaches have been followed, there is still lack of generalization of their methods and their results. In this paper, we discuss about the ground theory of roles and search for a consistent and computable definition that allows the automatic detection of roles played by users in forum threads on the internet. We analyze the web site IMDb to illustrate the discussion.
Semantic Measures for the Comparison of Units of Language, Concepts or Instances from Text and Knowledge Base Analysis
Venue: arXiv cs.CL 2016
Year: 2016

Semantic measures are widely used today to estimate the strength of the semantic relationship between elements of various types: units of language (e.g., words, sentences, documents), concepts or even instances semantically characterized (e.g., diseases, genes, geographical locations). Semantic measures play an important role to compare such elements according to semantic proxies: texts and knowledge representations, which support their meaning or describe their nature. Semantic measures are therefore essential for designing intelligent agents which will for example take advantage of semantic analysis to mimic human ability to compare abstract or concrete objects. This paper proposes a comprehensive survey of the broad notion of semantic measure for the comparison of units of language, concepts or instances based on semantic proxy analyses. Semantic measures generalize the well-known notions of semantic similarity, semantic relatedness and semantic distance, which have been extensively studied by various communities over the last decades (e.g., Cognitive Sciences, Linguistics, and Artificial Intelligence to mention a few).
Coalitions in Routing Games: A Worst-Case Perspective
Venue: arXiv cs.NI 2016
Year: 2016

We investigate a routing game that allows for the creation of coalitions, within the framework of cooperative game theory. Specifically, we describe the cost of each coalition as its maximin value. This represents the performance that the coalition can guarantee itself, under any (including worst) conditions. We then investigate fundamental solution concepts of the considered cooperative game, namely the core and a variant of the min-max fair nucleolus. We consider two types of routing games based on the agents' Performance Objectives, namely bottleneck routing games and additive routing games. For bottleneck games we establish that the core includes all system-optimal flow profiles and that the nucleolus is system-optimal or disadvantageous for the smallest agent in the system. Moreover, we describe an interesting set of scenarios for which the nucleolus is always system-optimal. For additive games, we focus on the fundamental load balancing game of routing over parallel links. We establish that, in contrary to bottleneck games, not all system-optimal flow profiles lie in the core. However, we describe a specific system-optimal flow profile that does lie in the core and, under assumptions of symmetry, is equal to the nucleolus.
Forwarding, Caching and Congestion Control in Named Data Networks
Venue: arXiv cs.NI 2016
Year: 2016

Emerging information-centric networking architectures seek to optimally utilize both bandwidth and storage for efficient content distribution. This highlights the need for joint design of traffic engineering and caching strategies, in order to optimize network performance in view of both current traffic loads and future traffic demands. We present a systematic framework for joint dynamic interest request forwarding and dynamic cache placement and eviction, within the context of the Named Data Networking (NDN) architecture. The framework employs a virtual control plane which operates on the user demand rate for data objects in the network, and an actual plane which handles Interest Packets and Data Packets. We develop distributed algorithms within the virtual plane to achieve network load balancing through dynamic forwarding and caching, thereby maximizing the user demand rate that the NDN network can satisfy. Next, we show that congestion control can be optimally combined with forwarding and caching within this framework to maximize user utilities subject to network stability. Numerical experiments within a number of network settings demonstrate the superior performance of the resulting algorithms for the actual plane in terms of high user utilities, low user delay, and high rate of cache hits.
Clustering Markov Decision Processes For Continual Transfer
Venue: arXiv cs.AI 2016
Year: 2016

We present algorithms to effectively represent a set of Markov decision processes (MDPs), whose optimal policies have already been learned, by a smaller source subset for lifelong, policy-reuse-based transfer learning in reinforcement learning. This is necessary when the number of previous tasks is large and the cost of measuring similarity counteracts the benefit of transfer. The source subset forms an `$ε$-net' over the original set of MDPs, in the sense that for each previous MDP $M_p$, there is a source $M^s$ whose optimal policy has $<ε$ regret in $M_p$. Our contributions are as follows. We present EXP-3-Transfer, a principled policy-reuse algorithm that optimally reuses a given source policy set when learning for a new MDP. We present a framework to cluster the previous MDPs to extract a source subset. The framework consists of (i) a distance $d_V$ over MDPs to measure policy-based similarity between MDPs; (ii) a cost function $g(\cdot)$ that uses $d_V$ to measure how good a particular clustering is for generating useful source tasks for EXP-3-Transfer and (iii) a provably convergent algorithm, MHAV, for finding the optimal clustering. We validate our algorithms through experiments in a surveillance domain.
Kernelizations for the hybridization number problem on multiple nonbinary trees
Venue: arXiv cs.DM 2016
Year: 2016

Given a finite set $X$, a collection $\mathcal{T}$ of rooted phylogenetic trees on $X$ and an integer $k$, the Hybridization Number problem asks if there exists a phylogenetic network on $X$ that displays all trees from $\mathcal{T}$ and has reticulation number at most $k$. We show two kernelization algorithms for Hybridization Number, with kernel sizes $4k(5k)^t$ and $20k^2(Δ^+-1)$ respectively, with $t$ the number of input trees and $Δ^+$ their maximum outdegree. Experiments on simulated data demonstrate the practical relevance of these kernelization algorithms. In addition, we present an $n^{f(k)}t$-time algorithm, with $n=|X|$ and $f$ some computable function of $k$.
Near Optimal Compressed Sensing of a Class of Sparse Low-Rank Matrices via Sparse Power Factorization
Venue: arXiv cs.IT 2016
Year: 2016

Compressed sensing of simultaneously sparse and low-rank matrices enables recovery of sparse signals from a few linear measurements of their bilinear form. One important question is how many measurements are needed for a stable reconstruction in the presence of measurement noise. Unlike conventional compressed sensing for sparse vectors, where convex relaxation via the $\ell_1$-norm achieves near optimal performance, for compressed sensing of sparse low-rank matrices, it has been shown recently Oymak et al. that convex programmings using the nuclear norm and the mixed norm are highly suboptimal even in the noise-free scenario. We propose an alternating minimization algorithm called sparse power factorization (SPF) for compressed sensing of sparse rank-one matrices. For a class of signals whose sparse representation coefficients are fast-decaying, SPF achieves stable recovery of the rank-1 matrix formed by their outer product and requires number of measurements within a logarithmic factor of the information-theoretic fundamental limit. For the recovery of general sparse low-rank matrices, we propose subspace-concatenated SPF (SCSPF), which has analogous near optimal performance guarantees to SPF in the rank-1 case. Numerical results show that SPF and SCSPF empirically outperform convex programmings using the best known combinations of mixed norm and nuclear norm.
Complexity Hierarchies Beyond Elementary
Venue: arXiv cs.CC 2016
Year: 2016

We introduce a hierarchy of fast-growing complexity classes and show its suitability for completeness statements of many non elementary problems. This hierarchy allows the classification of many decision problems with a non-elementary complexity, which occur naturally in logic, combinatorics, formal languages, verification, etc., with complexities ranging from simple towers of exponentials to Ackermannian and beyond.