OpenRaiser/Intern-Atlas huggingface dataset:
Submodular Functions: Extensions, Distributions, and Algorithms. A Survey
Venue: arXiv cs.DS 2011
Year: 2011

Submodularity is a fundamental phenomenon in combinatorial optimization. Submodular functions occur in a variety of combinatorial settings such as coverage problems, cut problems, welfare maximization, and many more. Therefore, a lot of work has been concerned with maximizing or minimizing a submodular function, often subject to combinatorial constraints. Many of these algorithmic results exhibit a common structure. Namely, the function is extended to a continuous, usually non-linear, function on a convex domain. Then, this relaxation is solved, and the fractional solution rounded to yield an integral solution. Often, the continuous extension has a natural interpretation in terms of distributions on subsets of the ground set. This interpretation is often crucial to the results and their analysis. The purpose of this survey is to highlight this connection between extensions, distributions, relaxations, and optimization in the context of submodular functions. We also present the first constant factor approximation algorithm for minimizing symmetric submodular functions subject to a cardinality constraint.
A Note on the Middle Levels Conjecture
Venue: arXiv cs.DM 2011
Year: 2011

The middle levels conjecture asserts that there is a Hamiltonian cycle in the middle two levels of $2k+1$-dimensional hypercube. The conjecture is known to be true for $k \leq 17$ [I.Shields, B.J.Shields and C.D.Savage, Disc. Math., 309, 5271--5277 (2009)]. In this note, we verify that the conjecture is also true for $k=18$ by constructing a Hamiltonian cycle in the middle two levels of 37-dimensional hypercube with the aid of the computer. We achieve this by introducing a new decomposition technique and an efficient algorithm for ordering the Narayana objects.
Revisiting the Rice Theorem of Cellular Automata
Venue: arXiv cs.DM 2011
Year: 2011

A cellular automaton is a parallel synchronous computing model, which consists in a juxtaposition of finite automata whose state evolves according to that of their neighbors. It induces a dynamical system on the set of configurations, i.e. the infinite sequences of cell states. The limit set of the cellular automaton is the set of configurations which can be reached arbitrarily late in the evolution. In this paper, we prove that all properties of limit sets of cellular automata with binary-state cells are undecidable, except surjectivity. This is a refinement of the classical "Rice Theorem" that Kari proved on cellular automata with arbitrary state sets.
Comparing Distributions and Shapes using the Kernel Distance
Venue: arXiv cs.CG 2011
Year: 2011

Starting with a similarity function between objects, it is possible to define a distance metric on pairs of objects, and more generally on probability distributions over them. These distance metrics have a deep basis in functional analysis, measure theory and geometric measure theory, and have a rich structure that includes an isometric embedding into a (possibly infinite dimensional) Hilbert space. They have recently been applied to numerous problems in machine learning and shape analysis. In this paper, we provide the first algorithmic analysis of these distance metrics. Our main contributions are as follows: (i) We present fast approximation algorithms for computing the kernel distance between two point sets P and Q that runs in near-linear time in the size of (P cup Q) (note that an explicit calculation would take quadratic time). (ii) We present polynomial-time algorithms for approximately minimizing the kernel distance under rigid transformation; they run in time O(n + poly(1/epsilon, log n)). (iii) We provide several general techniques for reducing complex objects to convenient sparse representations (specifically to point sets or sets of points sets) which approximately preserve the kernel distance. In particular, this allows us to reduce problems of computing the kernel distance between various types of objects such as curves, surfaces, and distributions to computing the kernel distance between point sets. These take advantage of the reproducing kernel Hilbert space and a new relation linking binary range spaces to continuous range spaces with bounded fat-shattering dimension.
What's Decidable About Sequences?
Venue: arXiv cs.LO 2011
Year: 2011

We present a first-order theory of sequences with integer elements, Presburger arithmetic, and regular constraints, which can model significant properties of data structures such as arrays and lists. We give a decision procedure for the quantifier-free fragment, based on an encoding into the first-order theory of concatenation; the procedure has PSPACE complexity. The quantifier-free fragment of the theory of sequences can express properties such as sortedness and injectivity, as well as Boolean combinations of periodic and arithmetic facts relating the elements of the sequence and their positions (e.g., "for all even i's, the element at position i has value i+3 or 2i"). The resulting expressive power is orthogonal to that of the most expressive decidable logics for arrays. Some examples demonstrate that the fragment is also suitable to reason about sequence-manipulating programs within the standard framework of axiomatic semantics.
Lecture Notes on Network Information Theory
Venue: arXiv cs.IT 2011
Year: 2011

These lecture notes have been converted to a book titled Network Information Theory published recently by Cambridge University Press. This book provides a significantly expanded exposition of the material in the lecture notes as well as problems and bibliographic notes at the end of each chapter. The authors are currently preparing a set of slides based on the book that will be posted in the second half of 2012. More information about the book can be found at http://www.cambridge.org/9781107008731/. The previous (and obsolete) version of the lecture notes can be found at http://arxiv.org/abs/1001.3404v4/.
The dynamics of message passing on dense graphs, with applications to compressed sensing
Venue: arXiv cs.IT 2011
Year: 2011

Approximate message passing algorithms proved to be extremely effective in reconstructing sparse signals from a small number of incoherent linear measurements. Extensive numerical experiments further showed that their dynamics is accurately tracked by a simple one-dimensional iteration termed state evolution. In this paper we provide the first rigorous foundation to state evolution. We prove that indeed it holds asymptotically in the large system limit for sensing matrices with independent and identically distributed gaussian entries. While our focus is on message passing algorithms for compressed sensing, the analysis extends beyond this setting, to a general class of algorithms on dense graphs. In this context, state evolution plays the role that density evolution has for sparse graphs. The proof technique is fundamentally different from the standard approach to density evolution, in that it copes with large number of short loops in the underlying factor graph. It relies instead on a conditioning technique recently developed by Erwin Bolthausen in the context of spin glass theory.
Capacity Scaling of Wireless Ad Hoc Networks: Shannon Meets Maxwell
Venue: arXiv cs.IT 2011
Year: 2011

In this paper, we characterize the information-theoretic capacity scaling of wireless ad hoc networks with $n$ randomly distributed nodes. By using an exact channel model from Maxwell's equations, we successfully resolve the conflict in the literature between the linear capacity scaling by Özgür et al. and the degrees of freedom limit given as the ratio of the network diameter and the wavelength $λ$ by Franceschetti et al. In dense networks where the network area is fixed, the capacity scaling is given as the minimum of $n$ and the degrees of freedom limit $λ^{-1}$ to within an arbitrarily small exponent. In extended networks where the network area is linear in $n$, the capacity scaling is given as the minimum of $n$ and the degrees of freedom limit $\sqrt{n}λ^{-1}$ to within an arbitrarily small exponent. Hence, we recover the linear capacity scaling by Özgür et al. if $λ=O(n^{-1})$ in dense networks and if $λ=O(n^{-1/2})$ in extended networks. Otherwise, the capacity scaling is given as the degrees of freedom limit characterized by Franceschetti et al. For achievability, a modified hierarchical cooperation is proposed based on a lower bound on the capacity of multiple-input multiple-output channel between two node clusters using our channel model.
Linear Sum Capacity for Gaussian Multiple Access Channels with Feedback
Venue: arXiv cs.IT 2011
Year: 2011

The capacity region of the N-sender Gaussian multiple access channel with feedback is not known in general. This paper studies the class of linear-feedback codes that includes (nonlinear) nonfeedback codes at one extreme and the linear-feedback codes by Schalkwijk and Kailath, Ozarow, and Kramer at the other extreme. The linear-feedback sum-capacity C_L(N,P) under symmetric power constraints P is characterized, the maximum sum-rate achieved by linear-feedback codes when each sender has the equal block power constraint P. In particular, it is shown that Kramer's code achieves this linear-feedback sum-capacity. The proof involves the dependence balance condition introduced by Hekstra and Willems and extended by Kramer and Gastpar, and the analysis of the resulting nonconvex optimization problem via a Lagrange dual formulation. Finally, an observation is presented based on the properties of the conditional maximal correlation---an extension of the Hirschfeld--Gebelein--Renyi maximal correlation---which reinforces the conjecture that Kramer's code achieves not only the linear-feedback sum-capacity, but also the sum-capacity itself (the maximum sum-rate achieved by arbitrary feedback codes).
LP Decoding of Regular LDPC Codes in Memoryless Channels
Venue: arXiv cs.IT 2011
Year: 2011

We study error bounds for linear programming decoding of regular LDPC codes. For memoryless binary-input output-symmetric channels, we prove bounds on the word error probability that are inverse doubly-exponential in the girth of the factor graph. For memoryless binary-input AWGN channel, we prove lower bounds on the threshold for regular LDPC codes whose factor graphs have logarithmic girth under LP-decoding. Specifically, we prove a lower bound of $σ=0.735$ (upper bound of $\frac{Eb}{N_0}=2.67$dB) on the threshold of $(3,6)$-regular LDPC codes whose factor graphs have logarithmic girth. Our proof is an extension of a recent paper of Arora, Daskalakis, and Steurer [STOC 2009] who presented a novel probabilistic analysis of LP decoding over a binary symmetric channel. Their analysis is based on the primal LP representation and has an explicit connection to message passing algorithms. We extend this analysis to any MBIOS channel.
Multiuser Scheduling in a Markov-modeled Downlink using Randomly Delayed ARQ Feedback
Venue: arXiv cs.IT 2011
Year: 2011

We focus on the downlink of a cellular system, which corresponds to the bulk of the data transfer in such wireless systems. We address the problem of opportunistic multiuser scheduling under imperfect channel state information, by exploiting the memory inherent in the channel. In our setting, the channel between the base station and each user is modeled by a two-state Markov chain and the scheduled user sends back an ARQ feedback signal that arrives at the scheduler with a random delay that is i.i.d across users and time. The scheduler indirectly estimates the channel via accumulated delayed-ARQ feedback and uses this information to make scheduling decisions. We formulate a throughput maximization problem as a partially observable Markov decision process (POMDP). For the case of two users in the system, we show that a greedy policy is sum throughput optimal for any distribution on the ARQ feedback delay. For the case of more than two users, we prove that the greedy policy is suboptimal and demonstrate, via numerical studies, that it has near optimal performance. We show that the greedy policy can be implemented by a simple algorithm that does not require the statistics of the underlying Markov channel or the ARQ feedback delay, thus making it robust against errors in system parameter estimation. Establishing an equivalence between the two-user system and a genie-aided system, we obtain a simple closed form expression for the sum capacity of the Markov-modeled downlink. We further derive inner and outer bounds on the capacity region of the Markov-modeled downlink and tighten these bounds for special cases of the system parameters.
Mixing Time and Stationary Expected Social Welfare of Logit Dynamics
Venue: arXiv cs.GT 2011
Year: 2011

We study "logit dynamics" [Blume, Games and Economic Behavior, 1993] for strategic games. This dynamics works as follows: at every stage of the game a player is selected uniformly at random and she plays according to a "noisy" best-response where the noise level is tuned by a parameter $β$. Such a dynamics defines a family of ergodic Markov chains, indexed by $β$, over the set of strategy profiles. We believe that the stationary distribution of these Markov chains gives a meaningful description of the long-term behavior for systems whose agents are not completely rational. Our aim is twofold: On the one hand, we are interested in evaluating the performance of the game at equilibrium, i.e. the expected social welfare when the strategy profiles are random according to the stationary distribution. On the other hand, we want to estimate how long it takes, for a system starting at an arbitrary profile and running the logit dynamics, to get close to its stationary distribution; i.e., the "mixing time" of the chain. In this paper we study the stationary expected social welfare for the 3-player CK game, for 2-player coordination games, and for two simple $n$-player games. For all these games, we also give almost tight upper and lower bounds on the mixing time of logit dynamics. Our results show two different behaviors: in some games the mixing time depends exponentially on $β$, while for other games it can be upper bounded by a function independent of $β$.
The Missing Piece Syndrome in Peer-to-Peer Communication
Venue: arXiv cs.PF 2011
Year: 2011

Typical protocols for peer-to-peer file sharing over the Internet divide files to be shared into pieces. New peers strive to obtain a complete collection of pieces from other peers and from a seed. In this paper we investigate a problem that can occur if the seeding rate is not large enough. The problem is that, even if the statistics of the system are symmetric in the pieces, there can be symmetry breaking, with one piece becoming very rare. If peers depart after obtaining a complete collection, they can tend to leave before helping other peers receive the rare piece. Assuming that peers arrive with no pieces, there is a single seed, random peer contacts are made, random useful pieces are downloaded, and peers depart upon receiving the complete file, the system is stable if the seeding rate (in pieces per time unit) is greater than the arrival rate, and is unstable if the seeding rate is less than the arrival rate. The result persists for any piece selection policy that selects from among useful pieces, such as rarest first, and it persists with the use of network coding.
Additive Asymmetric Quantum Codes
Venue: arXiv cs.IT 2011
Year: 2011

We present a general construction of asymmetric quantum codes based on additive codes under the trace Hermitian inner product. Various families of additive codes over $\F_{4}$ are used in the construction of many asymmetric quantum codes over $\F_{4}$.
Random Access Game in Fading Channels with Capture: Equilibria and Braess-like Paradoxes
Venue: arXiv cs.GT 2011
Year: 2011

The Nash equilibrium point of the transmission probabilities in a slotted ALOHA system with selfish nodes is analyzed. The system consists of a finite number of heterogeneous nodes, each trying to minimize its average transmission probability (or power investment) selfishly while meeting its average throughput demand over the shared wireless channel to a common base station (BS). We use a game-theoretic approach to analyze the network under two reception models: one is called power capture, the other is called signal to interference plus noise ratio (SINR) capture. It is shown that, in some situations, Braess-like paradoxes may occur. That is, the performance of the system may become worse instead of better when channel state information (CSI) is available at the selfish nodes. In particular, for homogeneous nodes, we analytically present that Braess-like paradoxes occur in the power capture model, and in the SINR capture model with the capture ratio larger than one and the noise to signal ratio sufficiently small.
An Effective Dichotomy for the Counting Constraint Satisfaction Problem
Venue: arXiv cs.CC 2011
Year: 2011

Bulatov (2008) gave a dichotomy for the counting constraint satisfaction problem #CSP. A problem from #CSP is characterised by a constraint language, which is a fixed, finite set of relations over a finite domain D. An instance of the problem uses these relations to constrain the variables in a larger set. Bulatov showed that the problem of counting the satisfying assignments of instances of any problem from #CSP is either in polynomial time (FP) or is #P-complete. His proof draws heavily on techniques from universal algebra and cannot be understood without a secure grasp of that field. We give an elementary proof of Bulatov's dichotomy, based on succinct representations, which we call frames, of a class of highly structured relations, which we call strongly rectangular. We show that these are precisely the relations which are invariant under a Mal'tsev polymorphism. En route, we give a simplification of a decision algorithm for strongly rectangular constraint languages, due to Bulatov and Dalmau (2006). We establish a new criterion for the #CSP dichotomy, which we call strong balance, and we prove that this property is decidable. In fact, we establish membership in NP. Thus, we show that the dichotomy is effective, resolving the most important open question concerning the #CSP dichotomy.
Analysis of a CSMA-Based Wireless Network: Feasible Throughput Region and Power Consumption
Venue: arXiv cs.GT 2011
Year: 2011

We analytically study a carrier sense multiple access (CSMA)-based network. In the network, the nodes have their own average throughput demands for transmission to a common base station. The CSMA is based on the request-to-send (RTS)/clear-to-send (CTS) handshake mechanism. Each node individually chooses its probability of transmitting an RTS packet, which specifies the length of its requested data transmission period. The RTS packets transmitted by different nodes in the same time slot interfere with one another, and compete to be received by the base station. If a node's RTS has the received signal to interference plus noise ratio (SINR) higher than the capture ratio, it will be successfully received. The node will then be granted the data transmission period. The transmission probabilities of RTS packets of all nodes will determine the average throughput and power consumption of each node. The set of all possible throughput demands of nodes that can be supported by the network is called the feasible throughput region. We characterize the feasible throughput region and provide an upper bound on the total power consumption for any throughput demands in the feasible throughput region. The upper bound corresponds to one of three points in the feasible throughput region depending on the fraction of time occupied by the RTS packets.
Measuring and Synthesizing Systems in Probabilistic Environments
Venue: arXiv cs.LO 2011
Year: 2011

Often one has a preference order among the different systems that satisfy a given specification. Under a probabilistic assumption about the possible inputs, such a preference order is naturally expressed by a weighted automaton, which assigns to each word a value, such that a system is preferred if it generates a higher expected value. We solve the following optimal-synthesis problem: given an omega-regular specification, a Markov chain that describes the distribution of inputs, and a weighted automaton that measures how well a system satisfies the given specification under the given input assumption, synthesize a system that optimizes the measured value. For safety specifications and measures given by mean-payoff automata, the optimal-synthesis problem amounts to finding a strategy in a Markov decision process (MDP) that is optimal for a long-run average reward objective, which can be done in polynomial time. For general omega-regular specifications, the solution rests on a new, polynomial-time algorithm for computing optimal strategies in MDPs with mean-payoff parity objectives. Our algorithm generates optimal strategies consisting of two memoryless strategies and a counter. This counter is in general not bounded. To obtain a finite-state system, we show how to construct an ε-optimal strategy with a bounded counter for any ε>0. We also show how to decide in polynomial time if we can construct an optimal finite-state system (i.e., a system without a counter) for a given specification. We have implemented our approach in a tool that takes qualitative and quantitative specifications and automatically constructs a system that satisfies the qualitative specification and optimizes the quantitative specification, if such a system exists. We present experimental results showing optimal systems that were generated in this way.
Semi-Automatic Index Tuning: Keeping DBAs in the Loop
Venue: arXiv cs.DB 2011
Year: 2011

To obtain good system performance, a DBA must choose a set of indices that is appropriate for the workload. The system can aid in this challenging task by providing recommendations for the index configuration. We propose a new index recommendation technique, termed semi-automatic tuning, that keeps the DBA "in the loop" by generating recommendations that use feedback about the DBA's preferences. The technique also works online, which avoids the limitations of commercial tools that require the workload to be known in advance. The foundation of our approach is the Work Function Algorithm, which can solve a wide variety of online optimization problems with strong competitive guarantees. We present an experimental analysis that validates the benefits of semi-automatic tuning in a wide variety of conditions.