OpenRaiser/Intern-Atlas huggingface dataset:
Discriminating Lambda-Terms Using Clocked Boehm Trees
Venue: arXiv cs.LO 2014
Year: 2014

As observed by Intrigila, there are hardly techniques available in the lambda-calculus to prove that two lambda-terms are not beta-convertible. Techniques employing the usual Boehm Trees are inadequate when we deal with terms having the same Boehm Tree (BT). This is the case in particular for fixed point combinators, as they all have the same BT. Another interesting equation, whose consideration was suggested by Scott, is BY = BYS, an equation valid in the classical model P-omega of lambda-calculus, and hence valid with respect to BT-equality but nevertheless the terms are beta-inconvertible. To prove such beta-inconvertibilities, we employ `clocked' BT's, with annotations that convey information of the tempo in which the data in the BT are produced. Boehm Trees are thus enriched with an intrinsic clock behaviour, leading to a refined discrimination method for lambda-terms. The corresponding equality is strictly intermediate between beta-convertibility and Boehm Tree equality, the equality in the model P-omega. An analogous approach pertains to Levy-Longo and Berarducci Trees. Our refined Boehm Trees find in particular an application in beta-discriminating fixed point combinators (fpc's). It turns out that Scott's equation BY = BYS is the key to unlocking a plethora of fpc's, generated by a variety of production schemes of which the simplest was found by Boehm, stating that new fpc's are obtained by postfixing the term SI, also known as Smullyan's Owl. We prove that all these newly generated fpc's are indeed new, by considering their clocked BT's. Even so, not all pairs of new fpc's can be discriminated this way. For that purpose we increase the discrimination power by a precision of the clock notion that we call `atomic clock'.
A Combinatorial Polynomial Algorithm for the Linear Arrow-Debreu Market
Venue: arXiv cs.DS 2014
Year: 2014

We present the first combinatorial polynomial time algorithm for computing the equilibrium of the Arrow-Debreu market model with linear utilities.
Nonanticipative Rate Distortion Function and Filtering Theory: A weak Convergence Approach
Venue: arXiv cs.IT 2014
Year: 2014

In this paper the relation between nonanticipative rate distortion function (RDF) and Bayesian filtering theory is further investigated on general Polish spaces. The relation is established via an optimization on the space of conditional distributions of the so-called directed information subject to fidelity constraints. Existence of the optimal reproduction distribution of the nonanticipative RDF is shown using the topology of weak convergence of probability measures. Subsequently, we use the solution of the nonanticipative RDF to present the realization of a multidimensional partially observable source over a scalar Gaussian channel. We show that linear encoders are optimal, establishing joint source-channel coding in real-time.
On Randomized Memoryless Algorithms for the Weighted $k$-server Problem
Venue: arXiv cs.DS 2014
Year: 2014

The weighted $k$-server problem is a generalization of the $k$-server problem in which the cost of moving a server of weight $β_i$ through a distance $d$ is $β_i\cdot d$. The weighted server problem on uniform spaces models caching where caches have different write costs. We prove tight bounds on the performance of randomized memoryless algorithms for this problem on uniform metric spaces. We prove that there is an $α_k$-competitive memoryless algorithm for this problem, where $α_k=α_{k-1}^2+3α_{k-1}+1$; $α_1=1$. On the other hand we also prove that no randomized memoryless algorithm can have competitive ratio better than $α_k$. To prove the upper bound of $α_k$ we develop a framework to bound from above the competitive ratio of any randomized memoryless algorithm for this problem. The key technical contribution is a method for working with potential functions defined implicitly as the solution of a linear system. The result is robust in the sense that a small change in the probabilities used by the algorithm results in a small change in the upper bound on the competitive ratio. The above result has two important implications. Firstly this yields an $α_k$-competitive memoryless algorithm for the weighted $k$-server problem on uniform spaces. This is the first competitive algorithm for $k>2$ which is memoryless. Secondly, this helps us prove that the Harmonic algorithm, which chooses probabilities in inverse proportion to weights, has a competitive ratio of $kα_k$.
On Randomized Fictitious Play for Approximating Saddle Points Over Convex Sets
Venue: arXiv cs.GT 2014
Year: 2014

Given two bounded convex sets $X\subseteq\RR^m$ and $Y\subseteq\RR^n,$ specified by membership oracles, and a continuous convex-concave function $F:X\times Y\to\RR$, we consider the problem of computing an $\eps$-approximate saddle point, that is, a pair $(x^*,y^*)\in X\times Y$ such that $\sup_{y\in Y} F(x^*,y)\le \inf_{x\in X}F(x,y^*)+\eps.$ Grigoriadis and Khachiyan (1995) gave a simple randomized variant of fictitious play for computing an $\eps$-approximate saddle point for matrix games, that is, when $F$ is bilinear and the sets $X$ and $Y$ are simplices. In this paper, we extend their method to the general case. In particular, we show that, for functions of constant "width", an $\eps$-approximate saddle point can be computed using $O^*(\frac{(n+m)}{\eps^2}\ln R)$ random samples from log-concave distributions over the convex sets $X$ and $Y$. It is assumed that $X$ and $Y$ have inscribed balls of radius $1/R$ and circumscribing balls of radius $R$. As a consequence, we obtain a simple randomized polynomial-time algorithm that computes such an approximation faster than known methods for problems with bounded width and when $\eps \in (0,1)$ is a fixed, but arbitrarily small constant. Our main tool for achieving this result is the combination of the randomized fictitious play with the recently developed results on sampling from convex sets.
Binary Jumbled Pattern Matching on Trees and Tree-Like Structures
Venue: arXiv cs.DS 2014
Year: 2014

Binary jumbled pattern matching asks to preprocess a binary string $S$ in order to answer queries $(i,j)$ which ask for a substring of $S$ that is of length $i$ and has exactly $j$ 1-bits. This problem naturally generalizes to vertex-labeled trees and graphs by replacing "substring" with "connected subgraph". In this paper, we give an $O(n^2 / \log^2 n)$-time solution for trees, matching the currently best bound for (the simpler problem of) strings. We also give an $\Oh{g^{2 / 3} n^{4 / 3}/(\log n)^{4/3}}$-time solution for strings that are compressed by a grammar of size $g$. This solution improves the known bounds when the string is compressible under many popular compression schemes. Finally, we prove that the problem is fixed-parameter tractable with respect to the treewidth $w$ of the graph, thus improving the previous best $n^{O(w)}$ algorithm [ICALP'07].
Sample Complexity of Bayesian Optimal Dictionary Learning
Venue: arXiv cs.LG 2014
Year: 2014

We consider a learning problem of identifying a dictionary matrix D (M times N dimension) from a sample set of M dimensional vectors Y = N^{-1/2} DX, where X is a sparse matrix (N times P dimension) in which the density of non-zero entries is 0<rho< 1. In particular, we focus on the minimum sample size P_c (sample complexity) necessary for perfectly identifying D of the optimal learning scheme when D and X are independently generated from certain distributions. By using the replica method of statistical mechanics, we show that P_c=O(N) holds as long as alpha = M/N >rho is satisfied in the limit of N to infinity. Our analysis also implies that the posterior distribution given Y is condensed only at the correct dictionary D when the compression rate alpha is greater than a certain critical value alpha_M(rho). This suggests that belief propagation may allow us to learn D with a low computational complexity using O(N) samples.
Efficient Algorithms for One-Dimensional k-Center Problems
Venue: arXiv cs.CG 2014
Year: 2014

We consider the problem of finding k centers for n weighted points on a real line. This (weighted) k-center problem was solved in O(n log n) time previously by using Cole's parametric search and other complicated approaches. In this paper, we present an easier O(n log n) time algorithm that avoids the parametric search, and in certain special cases our algorithm solves the problem in O(n) time. In addition, our techniques involve developing interesting data structures for processing queries that find a lowest point in the common intersection of a certain subset of half-planes. This subproblem is interesting in its own right and our solution for it may find other applications as well.
Peak-to-average power ratio of good codes for Gaussian channel
Venue: arXiv cs.IT 2014
Year: 2014

Consider a problem of forward error-correction for the additive white Gaussian noise (AWGN) channel. For finite blocklength codes the backoff from the channel capacity is inversely proportional to the square root of the blocklength. In this paper it is shown that codes achieving this tradeoff must necessarily have peak-to-average power ratio (PAPR) proportional to logarithm of the blocklength. This is extended to codes approaching capacity slower, and to PAPR measured at the output of an OFDM modulator. As a by-product the convergence of (Smith's) amplitude-constrained AWGN capacity to Shannon's classical formula is characterized in the regime of large amplitudes. This converse-type result builds upon recent contributions in the study of empirical output distributions of good channel codes.
Large-Scale Fading Behavior for a Cellular Network with Uniform Spatial Distribution
Venue: arXiv cs.IT 2014
Year: 2014

Large-scale fading (LSF) between interacting nodes is a fundamental element in radio communications, responsible for weakening the propagation, and thus worsening the service quality. Given the importance of channel-losses in general, and the inevitability of random spatial geometry in real-life wireless networks, it was then natural to merge these two paradigms together in order to obtain an improved stochastical model for the LSF indicator. Therefore, in exact closed-form notation, we generically derived the LSF distribution between a prepositioned reference base-station and an arbitrary node for a multi-cellular random network model. In fact, we provided an explicit and definitive formulation that considered at once: the lattice profile, the users' random geometry, the effect of the far-field phenomenon, the path-loss behavior, and the stochastic impact of channel scatters. The veracity and accuracy of the theoretical analysis were also confirmed through Monte Carlo simulations.
Non-monetary fair scheduling---a cooperative game theory approach
Venue: arXiv cs.DC 2014
Year: 2014

We consider a multi-organizational system in which each organization contributes processors to the global pool but also jobs to be processed on the common resources. The fairness of the scheduling algorithm is essential for the stability and even for the existence of such systems (as organizations may refuse to join an unfair system). We consider on-line, non-clairvoyant scheduling of sequential jobs. The started jobs cannot be stopped, canceled, preempted, or moved to other processors. We consider identical processors, but most of our results can be extended to related or unrelated processors. We model the fair scheduling problem as a cooperative game and we use the Shapley value to determine the ideal fair schedule. In contrast to the current literature, we do not use money to assess the relative utilities of jobs. Instead, to calculate the contribution of an organization, we determine how the presence of this organization influences the performance of other organizations. Our approach can be used with arbitrary utility function (e.g., flow time, tardiness, resource utilization), but we argue that the utility function should be strategy resilient. The organizations should be discouraged from splitting, merging or delaying their jobs. We present the unique (to within a multiplicative and additive constants) strategy resilient utility function. We show that the problem of fair scheduling is NP-hard and hard to approximate. However, for unit-size jobs, we present an FPRAS. Also, we show that the problem parametrized with the number of organizations is FPT. Although for the large number of the organizations the problem is computationally hard, the presented exponential algorithm can be used as a fairness benchmark.
Wi-Fi & WiMAX: A Comparative Study
Venue: arXiv cs.NI 2014
Year: 2014

Usually broadband wireless access networks are considered to be enterprise level networks providing us with more capacity as well as coverage. We have seen that in remote inaccessible areas wired networks are not at all cost effective. Wireless networking has offered us an alternative solution for such problem of information access. They have definitely changed the way people communicate and share information among themselves by overcoming problems nowadays associated with distance and location. This paper provides a comparison and technical analysis of alternatives for implementing last-mile wireless broadband services. It provides detailed technical differences between 802.11 (Wi-FI) wireless networks with 802.16 (WiMAX), a new technology that solves many of the difficulties in last-mile implementations.
Benchmarking Usability and Performance of Multicore Languages
Venue: arXiv cs.DC 2014
Year: 2014

Developers face a wide choice of programming languages and libraries supporting multicore computing. Ever more diverse paradigms for expressing parallelism and synchronization become available while their influence on usability and performance remains largely unclear. This paper describes an experiment comparing four markedly different approaches to parallel programming: Chapel, Cilk, Go, and Threading Building Blocks (TBB). Each language is used to implement sequential and parallel versions of six benchmark programs. The implementations are then reviewed by notable experts in the language, thereby obtaining reference versions for each language and benchmark. The resulting pool of 96 implementations is used to compare the languages with respect to source code size, coding time, execution time, and speedup. The experiment uncovers strengths and weaknesses in all approaches, facilitating an informed selection of a language under a particular set of requirements. The expert review step furthermore highlights the importance of expert knowledge when using modern parallel programming approaches.
Learning Equilibria of Games via Payoff Queries
Venue: arXiv cs.GT 2014
Year: 2014

A recent body of experimental literature has studied empirical game-theoretical analysis, in which we have partial knowledge of a game, consisting of observations of a subset of the pure-strategy profiles and their associated payoffs to players. The aim is to find an exact or approximate Nash equilibrium of the game, based on these observations. It is usually assumed that the strategy profiles may be chosen in an on-line manner by the algorithm. We study a corresponding computational learning model, and the query complexity of learning equilibria for various classes of games. We give basic results for bimatrix and graphical games. Our focus is on symmetric network congestion games. For directed acyclic networks, we can learn the cost functions (and hence compute an equilibrium) while querying just a small fraction of pure-strategy profiles. For the special case of parallel links, we have the stronger result that an equilibrium can be identified while only learning a small fraction of the cost values.
Reasoning about Independence in Probabilistic Models of Relational Data
Venue: arXiv cs.AI 2014
Year: 2014

We extend the theory of d-separation to cases in which data instances are not independent and identically distributed. We show that applying the rules of d-separation directly to the structure of probabilistic models of relational data inaccurately infers conditional independence. We introduce relational d-separation, a theory for deriving conditional independence facts from relational models. We provide a new representation, the abstract ground graph, that enables a sound, complete, and computationally efficient method for answering d-separation queries about relational models, and we present empirical results that demonstrate effectiveness.
Unions of Onions: Preprocessing Imprecise Points for Fast Onion Decomposition
Venue: arXiv cs.CG 2014
Year: 2014

Let $\mathcal{D}$ be a set of $n$ pairwise disjoint unit disks in the plane. We describe how to build a data structure for $\mathcal{D}$ so that for any point set $P$ containing exactly one point from each disk, we can quickly find the onion decomposition (convex layers) of $P$. Our data structure can be built in $O(n \log n)$ time and has linear size. Given $P$, we can find its onion decomposition in $O(n \log k)$ time, where $k$ is the number of layers. We also provide a matching lower bound. Our solution is based on a recursive space decomposition, combined with a fast algorithm to compute the union of two disjoint onion
Dual Lower Bounds for Approximate Degree and Markov-Bernstein Inequalities
Venue: arXiv cs.CC 2014
Year: 2014

The $ε$-approximate degree of a Boolean function $f: \{-1, 1\}^n \to \{-1, 1\}$ is the minimum degree of a real polynomial that approximates $f$ to within $ε$ in the $\ell_\infty$ norm. We prove several lower bounds on this important complexity measure by explicitly constructing solutions to the dual of an appropriate linear program. Our first result resolves the $ε$-approximate degree of the two-level AND-OR tree for any constant $ε> 0$. We show that this quantity is $Θ(\sqrt{n})$, closing a line of incrementally larger lower bounds. The same lower bound was recently obtained independently by Sherstov using related techniques. Our second result gives an explicit dual polynomial that witnesses a tight lower bound for the approximate degree of any symmetric Boolean function, addressing a question of Špalek. Our final contribution is to reprove several Markov-type inequalities from approximation theory by constructing explicit dual solutions to natural linear programs. These inequalities underly the proofs of many of the best-known approximate degree lower bounds, and have important uses throughout theoretical computer science.
Fixed point theorems for Boolean networks expressed in terms of forbidden subnetworks
Venue: arXiv cs.DM 2014
Year: 2014

We are interested in fixed points in Boolean networks, {\em i.e.} functions $f$ from $\{0,1\}^n$ to itself. We define the subnetworks of $f$ as the restrictions of $f$ to the subcubes of $\{0,1\}^n$, and we characterizes a class $\mathcal{F}$ of Boolean networks satisfying the following property: Every subnetwork of $f$ has a unique fixed point if and only if $f$ has no subnetwork in $\mathcal{F}$. This characterization generalizes the fixed point theorem of Shih and Dong, which asserts that if for every $x$ in $\{0,1\}^n$ there is no directed cycle in the directed graph whose the adjacency matrix is the discrete Jacobian matrix of $f$ evaluated at point $x$, then $f$ has a unique fixed point. Then, denoting by $\mathcal{C}^+$ (resp. $\mathcal{C}^-$) the networks whose the interaction graph is a positive (resp. negative) cycle, we show that the non-expansive networks of $\mathcal{F}$ are exactly the networks of $\mathcal{C}^+\cup \mathcal{C}^-$; and for the class of non-expansive networks we get a "dichotomization" of the previous forbidden subnetwork theorem: Every subnetwork of $f$ has at most (resp. at least) one fixed point if and only if $f$ has no subnetworks in $\mathcal{C}^+$ (resp. $\mathcal{C}^-$) subnetwork. Finally, we prove that if $f$ is a conjunctive network then every subnetwork of $f$ has at most one fixed point if and only if $f$ has no subnetwork in $\mathcal{C}^+$.
Online Convex Optimization Against Adversaries with Memory and Application to Statistical Arbitrage
Venue: arXiv cs.LG 2014
Year: 2014

The framework of online learning with memory naturally captures learning problems with temporal constraints, and was previously studied for the experts setting. In this work we extend the notion of learning with memory to the general Online Convex Optimization (OCO) framework, and present two algorithms that attain low regret. The first algorithm applies to Lipschitz continuous loss functions, obtaining optimal regret bounds for both convex and strongly convex losses. The second algorithm attains the optimal regret bounds and applies more broadly to convex losses without requiring Lipschitz continuity, yet is more complicated to implement. We complement our theoretic results with an application to statistical arbitrage in finance: we devise algorithms for constructing mean-reverting portfolios.
Accuracy of Homology based Coverage Hole Detection for Wireless Sensor Networks on Sphere
Venue: arXiv cs.NI 2014
Year: 2014

Homology theory has attracted great attention because it can provide novel and powerful solutions to address coverage problems in wireless sensor networks. They usually use an easily computable algebraic object, Rips complex, to detect coverage holes. But Rips complex may miss some coverage holes in some cases. In this paper, we investigate homology-based coverage hole detection for wireless sensor networks on sphere. The situations when Rips complex may miss coverage holes are first presented. Then we choose the proportion of the area of coverage holes missed by Rips complex as a metric to evaluate the accuracy of homology-based coverage hole detection approaches. Three different cases are considered for the computation of accuracy. For each case, closed-form expressions for lower and upper bounds of the accuracy are derived. Simulation results are well consistent with the analytical lower and upper bounds, with maximum differences of 0.5% and 3% respectively. Furthermore, it is shown that the radius of sphere has little impact on the accuracy if it is much larger than communication and sensing radii of each sensor.
Computing Minimum Cycle Bases in Weighted Partial 2-Trees in Linear Time
Venue: arXiv cs.DS 2014
Year: 2014

We present a linear time algorithm for computing an implicit linear space representation of a minimum cycle basis (MCB) in weighted partial 2-trees, i.e., graphs of treewidth two. The implicit representation can be made explicit in a running time that is proportional to the size of the MCB. Our algorithm improves the result of Borradaile, Sankowski, and Wulff-Nilsen [Min $st$-cut Oracle for Planar Graphs with Near-Linear Preprocessing Time, FOCS 2010]---which computes for all planar graphs an implicit $O(n \log n)$ space representation of an MCB in $O(n \log^5 n)$ time---by a polylog factor for the special case of partial 2-trees. Such an improvement was achieved previously only for outerplanar graphs [Liu and Lu: Minimum Cycle Bases of Weighted Outerplanar Graphs, IPL 110:970--974, 2010].
Reachability via Compositionality in Petri nets
Venue: arXiv cs.LO 2014
Year: 2014

We introduce a novel technique for checking reachability in Petri nets that relies on a recently introduced compositional algebra of nets. We prove that the technique is correct, and discuss our implementation. We report promising experimental results on some well-known examples.