OpenRaiser/Intern-Atlas huggingface dataset:
Hue Histograms to Spatiotemporal Local Features for Action Recognition
Venue: arXiv cs.CV 2011
Year: 2011

Despite the recent developments in spatiotemporal local features for action recognition in video sequences, local color information has so far been ignored. However, color has been proved an important element to the success of automated recognition of objects and scenes. In this paper we extend the space-time interest point descriptor STIP to take into account the color information on the features' neighborhood. We compare the performance of our color-aware version of STIP (which we have called HueSTIP) with the original one.
Extending and Implementing the Self-adaptive Virtual Processor for Distributed Memory Architectures
Venue: arXiv cs.DC 2011
Year: 2011

Many-core architectures of the future are likely to have distributed memory organizations and need fine grained concurrency management to be used effectively. The Self-adaptive Virtual Processor (SVP) is an abstract concurrent programming model which can provide this, but the model and its current implementations assume a single address space shared memory. We investigate and extend SVP to handle distributed environments, and discuss a prototype SVP implementation which transparently supports execution on heterogeneous distributed memory clusters over TCP/IP connections, while retaining the original SVP programming model.
Understanding Exhaustive Pattern Learning
Venue: arXiv cs.AI 2011
Year: 2011

Pattern learning in an important problem in Natural Language Processing (NLP). Some exhaustive pattern learning (EPL) methods (Bod, 1992) were proved to be flawed (Johnson, 2002), while similar algorithms (Och and Ney, 2004) showed great advantages on other tasks, such as machine translation. In this article, we first formalize EPL, and then show that the probability given by an EPL model is constant-factor approximation of the probability given by an ensemble method that integrates exponential number of models obtained with various segmentations of the training data. This work for the first time provides theoretical justification for the widely used EPL algorithm in NLP, which was previously viewed as a flawed heuristic method. Better understanding of EPL may lead to improved pattern learning algorithms in future.
On the evolution of the instance level of DL-lite knowledge bases
Venue: arXiv cs.AI 2011
Year: 2011

Recent papers address the issue of updating the instance level of knowledge bases expressed in Description Logic following a model-based approach. One of the outcomes of these papers is that the result of updating a knowledge base K is generally not expressible in the Description Logic used to express K. In this paper we introduce a formula-based approach to this problem, by revisiting some research work on formula-based updates developed in the '80s, in particular the WIDTIO (When In Doubt, Throw It Out) approach. We show that our operator enjoys desirable properties, including that both insertions and deletions according to such operator can be expressed in the DL used for the original KB. Also, we present polynomial time algorithms for the evolution of the instance level knowledge bases expressed in the most expressive Description Logics of the DL-lite family.
Power Allocation Based on SEP Minimization in Two-Hop Decode-and-Forward Relay Networks
Venue: arXiv cs.IT 2011
Year: 2011

The problem of optimal power allocation among the relays in a two-hop decode-and-forward cooperative relay network with independent Rayleigh fading channels is considered. It is assumed that only the relays that decode the source message correctly contribute in data transmission. Moreover, only the knowledge of statistical channel state information is available. A new simple closed-form expression for the average symbol error probability is derived. Based on this expression, a new power allocation method that minimizes the average symbol error probability and takes into account the constraints on the total average power of all the relay nodes and maximum instant power of each relay node is developed. The corresponding optimization problem is shown to be a convex problem that can be solved using interior point methods. However, an approximate closed-form solution is obtained and shown to be practically more appealing due to significant complexity reduction. The accuracy of the approximation is discussed. Moreover, the so obtained closed-form solution gives additional insights into the optimal power allocation problem. Simulation results confirm the improved performance of the proposed power allocation scheme as compared to other schemes.
Algorithms and Complexity Results for Persuasive Argumentation
Venue: arXiv cs.AI 2011
Year: 2011

The study of arguments as abstract entities and their interaction as introduced by Dung (Artificial Intelligence 177, 1995) has become one of the most active research branches within Artificial Intelligence and Reasoning. A main issue for abstract argumentation systems is the selection of acceptable sets of arguments. Value-based argumentation, as introduced by Bench-Capon (J. Logic Comput. 13, 2003), extends Dung's framework. It takes into account the relative strength of arguments with respect to some ranking representing an audience: an argument is subjectively accepted if it is accepted with respect to some audience, it is objectively accepted if it is accepted with respect to all audiences. Deciding whether an argument is subjectively or objectively accepted, respectively, are computationally intractable problems. In fact, the problems remain intractable under structural restrictions that render the main computational problems for non-value-based argumentation systems tractable. In this paper we identify nontrivial classes of value-based argumentation systems for which the acceptance problems are polynomial-time tractable. The classes are defined by means of structural restrictions in terms of the underlying graphical structure of the value-based system. Furthermore we show that the acceptance problems are intractable for two classes of value-based systems that where conjectured to be tractable by Dunne (Artificial Intelligence 171, 2007).
Collaboration in computer science: a network science approach. Part II
Venue: arXiv cs.SI 2011
Year: 2011

We represent collaboration of authors in computer science papers in terms of both affiliation and collaboration networks and observe how these networks evolved over time since 1960. We investigate the temporal evolution of bibliometric properties, like size of the discipline, productivity of scholars, and collaboration level in papers, as well as of large-scale network properties, like reachability and average separation distance among scientists, distribution of the number of scholar collaborators, network clustering and network assortativity by number of collaborators.
Quantitative Synthesis for Concurrent Programs
Venue: arXiv cs.PL 2011
Year: 2011

We present an algorithmic method for the quantitative, performance-aware synthesis of concurrent programs. The input consists of a nondeterministic partial program and of a parametric performance model. The nondeterminism allows the programmer to omit which (if any) synchronization construct is used at a particular program location. The performance model, specified as a weighted automaton, can capture system architectures by assigning different costs to actions such as locking, context switching, and memory and cache accesses. The quantitative synthesis problem is to automatically resolve the nondeterminism of the partial program so that both correctness is guaranteed and performance is optimal. As is standard for shared memory concurrency, correctness is formalized "specification free", in particular as race freedom or deadlock freedom. For worst-case (average-case) performance, we show that the problem can be reduced to 2-player graph games (with probabilistic transitions) with quantitative objectives. While we show, using game-theoretic methods, that the synthesis problem is NEXP-complete, we present an algorithmic method and an implementation that works efficiently for concurrent programs and performance models of practical interest. We have implemented a prototype tool and used it to synthesize finite-state concurrent programs that exhibit different programming patterns, for several performance models representing different architectures.
The maximum disjoint paths problem on multi-relations social networks
Venue: arXiv cs.DS 2011
Year: 2011

Motivated by applications to social network analysis (SNA), we study the problem of finding the maximum number of disjoint uni-color paths in an edge-colored graph. We show the NP-hardness and the approximability of the problem, and both approximation and exact algorithms are proposed. Since short paths are much more significant in SNA, we also study the length-bounded version of the problem, in which the lengths of paths are required to be upper bounded by a fixed integer $l$. It is shown that the problem can be solved in polynomial time for $l=3$ and is NP-hard for $l\geq 4$. We also show that the problem can be approximated with ratio $(l-1)/2+ε$ in polynomial time for any $ε>0$. Particularly, for $l=4$, we develop an efficient 2-approximation algorithm.
EMBANKS: Towards Disk Based Algorithms For Keyword-Search In Structured Databases
Venue: arXiv cs.DB 2011
Year: 2011

In recent years, there has been a lot of interest in the field of keyword querying relational databases. A variety of systems such as DBXplorer [ACD02], Discover [HP02] and ObjectRank [BHP04] have been proposed. Another such system is BANKS, which enables data and schema browsing together with keyword-based search for relational databases. It models tuples as nodes in a graph, connected by links induced by foreign key and other relationships. The size of the database graph that BANKS uses is proportional to the sum of the number of nodes and edges in the graph. Systems such as SPIN, which search on Personal Information Networks and use BANKS as the backend, maintain a lot of information about the users' data. Since these systems run on the user workstation which have other demands of memory, such a heavy use of memory is unreasonable and if possible, should be avoided. In order to alleviate this problem, we introduce EMBANKS (acronym for External Memory BANKS), a framework for an optimized disk-based BANKS system. The complexity of this framework poses many questions, some of which we try to answer in this thesis. We demonstrate that the cluster representation proposed in EMBANKS enables in-memory processing of very large database graphs. We also present detailed experiments that show that EMBANKS can significantly reduce database load time and query execution times when compared to the original BANKS algorithms.
The Entropy Rounding Method in Approximation Algorithms
Venue: arXiv cs.DS 2011
Year: 2011

Let A be a matrix, c be any linear objective function and x be a fractional vector, say an LP solution to some discrete optimization problem. Then a recurring task in theoretical computer science (and in approximation algorithms in particular) is to obtain an integral vector y such that Ax is roughly Ay and c*y exceeds c*x by only a moderate factor. We give a new randomized rounding procedure for this task, provided that A has bounded Delta-approximate entropy. This property means that for uniformly chosen random signs chi(j) in {-1,+1} on any subset of the columns, the outcome A*chi can be approximately described using a sub-linear number of bits in expectation. To achieve this result, we modify well-known techniques from the field of discrepancy theory, especially we rely on Beck's entropy method, which to the best of our knowledge has never been used before in the context of approximation algorithms. Our result can be made constructive using the Bansal framework based on semidefinite programming. We demonstrate the versatility of our procedure by rounding fractional solutions to column-based linear programs for some generalizations of Bin Packing. For example we obtain a polynomial time OPT + O(log^2 OPT) approximation for Bin Packing With Rejection and the first AFPTAS for the Train Delivery problem.
Minimum cell connection and separation in line segment arrangements
Venue: arXiv cs.CG 2011
Year: 2011

We study the complexity of the following cell connection and separation problems in segment arrangements. Given a set of straight-line segments in the plane and two points $a$ and $b$ in different cells of the induced arrangement: (i) compute the minimum number of segments one needs to remove so that there is a path connecting $a$ to $b$ that does not intersect any of the remaining segments; (ii) compute the minimum number of segments one needs to remove so that the arrangement induced by the remaining segments has a single cell; (iii) compute the minimum number of segments one needs to retain so that any path connecting $a$ to $b$ intersects some of the retained segments. We show that problems (i) and (ii) are NP-hard and discuss some special, tractable cases. Most notably, we provide a linear-time algorithm for a variant of problem (i) where the path connecting $a$ to $b$ must stay inside a given polygon $P$ with a constant number of holes, the segments are contained in $P$, and the endpoints of the segments are on the boundary of $P$. For problem (iii) we provide a cubic-time algorithm.
Sum Rate Maximized Resource Allocation in Multiple DF Relays Aided OFDM Transmission
Venue: arXiv cs.IT 2011
Year: 2011

In relay-aided wireless transmission systems, one of the key issues is how to decide assisting relays and manage the energy resource at the source and each individual relay, to maximize a certain objective related to system performance. This paper addresses the sum rate maximized resource allocation (RA) problem in a point to point orthogonal frequency division modulation (OFDM) transmission system assisted by multiple decode-and-forward (DF) relays, subject to the individual sum power constraints of the source and the relays. In particular, the transmission at each subcarrier can be in either the direct mode without any relay assisting, or the relay-aided mode with one or several relays assisting. We propose two RA algorithms which optimize the assignment of transmission mode and source power for every subcarrier, as well as the assisting relays and the power allocation to them for every {relay-aided} subcarrier. First, it is shown that the considered RA problem has zero Lagrangian duality gap when there is a big number of subcarriers. In this case, a duality based algorithm that finds a globally optimum RA is developed. Second, a coordinate-ascent based iterative algorithm, which finds a suboptimum RA but is always applicable regardless of the duality gap of the RA problem, is developed. The effectiveness of these algorithms has been illustrated by numerical experiments.
Asymptotic Moments for Interference Mitigation in Correlated Fading Channels
Venue: arXiv cs.IT 2011
Year: 2011

We consider a certain class of large random matrices, composed of independent column vectors with zero mean and different covariance matrices, and derive asymptotically tight deterministic approximations of their moments. This random matrix model arises in several wireless communication systems of recent interest, such as distributed antenna systems or large antenna arrays. Computing the linear minimum mean square error (LMMSE) detector in such systems requires the inversion of a large covariance matrix which becomes prohibitively complex as the number of antennas and users grows. We apply the derived moment results to the design of a low-complexity polynomial expansion detector which approximates the matrix inverse by a matrix polynomial and study its asymptotic performance. Simulation results corroborate the analysis and evaluate the performance for finite system dimensions.
Approximating the Termination Value of One-Counter MDPs and Stochastic Games
Venue: arXiv cs.GT 2011
Year: 2011

One-counter MDPs (OC-MDPs) and one-counter simple stochastic games (OC-SSGs) are 1-player, and 2-player turn-based zero-sum, stochastic games played on the transition graph of classic one-counter automata (equivalently, pushdown automata with a 1-letter stack alphabet). A key objective for the analysis and verification of these games is the termination objective, where the players aim to maximize (minimize, respectively) the probability of hitting counter value 0, starting at a given control state and given counter value. Recently, we studied qualitative decision problems ("is the optimal termination value = 1?") for OC-MDPs (and OC-SSGs) and showed them to be decidable in P-time (in NP and coNP, respectively). However, quantitative decision and approximation problems ("is the optimal termination value ? p", or "approximate the termination value within epsilon") are far more challenging. This is so in part because optimal strategies may not exist, and because even when they do exist they can have a highly non-trivial structure. It thus remained open even whether any of these quantitative termination problems are computable. In this paper we show that all quantitative approximation problems for the termination value for OC-MDPs and OC-SSGs are computable. Specifically, given a OC-SSG, and given epsilon > 0, we can compute a value v that approximates the value of the OC-SSG termination game within additive error epsilon, and furthermore we can compute epsilon-optimal strategies for both players in the game. A key ingredient in our proofs is a subtle martingale, derived from solving certain LPs that we can associate with a maximizing OC-MDP. An application of Azuma's inequality on these martingales yields a computable bound for the "wealth" at which a "rich person's strategy" becomes epsilon-optimal for OC-MDPs.
On Isolating Points Using Disks
Venue: arXiv cs.CG 2011
Year: 2011

In this paper, we consider the problem of choosing disks (that we can think of as corresponding to wireless sensors) so that given a set of input points in the plane, there exists no path between any pair of these points that is not intercepted by some disk. We try to achieve this separation using a minimum number of a given set of unit disks. We show that a constant factor approximation to this problem can be found in polynomial time using a greedy algorithm. To the best of our knowledge we are the first to study this optimization problem.
Reducing Commitment to Tasks with Off-Policy Hierarchical Reinforcement Learning
Venue: arXiv cs.LG 2011
Year: 2011

In experimenting with off-policy temporal difference (TD) methods in hierarchical reinforcement learning (HRL) systems, we have observed unwanted on-policy learning under reproducible conditions. Here we present modifications to several TD methods that prevent unintentional on-policy learning from occurring. These modifications create a tension between exploration and learning. Traditional TD methods require commitment to finishing subtasks without exploration in order to update Q-values for early actions with high probability. One-step intra-option learning and temporal second difference traces (TSDT) do not suffer from this limitation. We demonstrate that our HRL system is efficient without commitment to completion of subtasks in a cliff-walking domain, contrary to a widespread claim in the literature that it is critical for efficiency of learning. Furthermore, decreasing commitment as exploration progresses is shown to improve both online performance and the resultant policy in the taxicab domain, opening a new avenue for research into when it is more beneficial to continue with the current subtask or to replan.
Learning Undirected Graphical Models with Structure Penalty
Venue: arXiv cs.AI 2011
Year: 2011

In undirected graphical models, learning the graph structure and learning the functions that relate the predictive variables (features) to the responses given the structure are two topics that have been widely investigated in machine learning and statistics. Learning graphical models in two stages will have problems because graph structure may change after considering the features. The main contribution of this paper is the proposed method that learns the graph structure and functions on the graph at the same time. General graphical models with binary outcomes conditioned on predictive variables are proved to be equivalent to multivariate Bernoulli model. The reparameterization of the potential functions in graphical model by conditional log odds ratios in multivariate Bernoulli model offers advantage in the representation of the conditional independence structure in the model. Additionally, we impose a structure penalty on groups of conditional log odds ratios to learn the graph structure. These groups of functions are designed with overlaps to enforce hierarchical function selection. In this way, we are able to shrink higher order interactions to obtain a sparse graph structure. Simulation studies show that the method is able to recover the graph structure. The analysis of county data from Census Bureau gives interesting relations between unemployment rate, crime and others discovered by the model.
Doubly Robust Smoothing of Dynamical Processes via Outlier Sparsity Constraints
Venue: arXiv cs.SY 2011
Year: 2011

Coping with outliers contaminating dynamical processes is of major importance in various applications because mismatches from nominal models are not uncommon in practice. In this context, the present paper develops novel fixed-lag and fixed-interval smoothing algorithms that are robust to outliers simultaneously present in the measurements {\it and} in the state dynamics. Outliers are handled through auxiliary unknown variables that are jointly estimated along with the state based on the least-squares criterion that is regularized with the $\ell_1$-norm of the outliers in order to effect sparsity control. The resultant iterative estimators rely on coordinate descent and the alternating direction method of multipliers, are expressed in closed form per iteration, and are provably convergent. Additional attractive features of the novel doubly robust smoother include: i) ability to handle both types of outliers; ii) universality to unknown nominal noise and outlier distributions; iii) flexibility to encompass maximum a posteriori optimal estimators with reliable performance under nominal conditions; and iv) improved performance relative to competing alternatives at comparable complexity, as corroborated via simulated tests.
Large Isolating Cuts Shrink the Multiway Cut
Venue: arXiv cs.DM 2011
Year: 2011

We propose a preprocessing algorithm for the multiway cut problem that establishes its polynomial kernelizability when the difference between the parameter $k$ and the size of the smallest isolating cut is at most $log(k)$. To the best of our knowledge, this is the first progress towards kernelization of the multiway cut problem. We pose two open questions that, if answered affirmatively, would imply, combined with the proposed result, unconditional polynomial kernelizability of the multiway cut problem.
External-Memory Multimaps
Venue: arXiv cs.DS 2011
Year: 2011

Many data structures support dictionaries, also known as maps or associative arrays, which store and manage a set of key-value pairs. A \emph{multimap} is generalization that allows multiple values to be associated with the same key. For example, the inverted file data structure that is used prevalently in the infrastructure supporting search engines is a type of multimap, where words are used as keys and document pointers are used as values. We study the multimap abstract data type and how it can be implemented efficiently online in external memory frameworks, with constant expected I/O performance. The key technique used to achieve our results is a combination of cuckoo hashing using buckets that hold multiple items with a multiqueue implementation to cope with varying numbers of values per key. Our external-memory results are for the standard two-level memory model.
Complex Networks
Venue: arXiv cs.NE 2011
Year: 2011

Introduction to the Special Issue on Complex Networks, Artificial Life journal.