OpenRaiser/Intern-Atlas huggingface dataset:
Strategic Learning and the Topology of Social Networks
Venue: arXiv cs.GT 2015
Year: 2015

We consider a group of strategic agents who must each repeatedly take one of two possible actions. They learn which of the two actions is preferable from initial private signals, and by observing the actions of their neighbors in a social network. We show that the question of whether or not the agents learn efficiently depends on the topology of the social network. In particular, we identify a geometric "egalitarianism" condition on the social network that guarantees learning in infinite networks, or learning with high probability in large finite networks, in any equilibrium. We also give examples of non-egalitarian networks with equilibria in which learning fails.
Simple and Optimal Randomized Fault-Tolerant Rumor Spreading
Venue: arXiv cs.DS 2015
Year: 2015

We revisit the classic problem of spreading a piece of information in a group of $n$ fully connected processors. By suitably adding a small dose of randomness to the protocol of Gasienic and Pelc (1996), we derive for the first time protocols that (i) use a linear number of messages, (ii) are correct even when an arbitrary number of adversarially chosen processors does not participate in the process, and (iii) with high probability have the asymptotically optimal runtime of $O(\log n)$ when at least an arbitrarily small constant fraction of the processors are working. In addition, our protocols do not require that the system is synchronized nor that all processors are simultaneously woken up at time zero, they are fully based on push-operations, and they do not need an a priori estimate on the number of failed nodes. Our protocols thus overcome the typical disadvantages of the two known approaches, algorithms based on random gossip (typically needing a large number of messages due to their unorganized nature) and algorithms based on fair workload splitting (which are either not {time-efficient} or require intricate preprocessing steps plus synchronization).
A pair of universal sequence-set betting strategies
Venue: arXiv cs.DS 2015
Year: 2015

We introduce the sequence-set betting game, a generalization of An. A. Muchnik's non-monotonic betting game. Instead of successively partitioning the infinite binary strings by their value of a bit at a chosen position, as in the non-monotonic game, the player is allowed to partition the strings into any two clopen sets with equal measure. We show that, while there is no single computable sequence-set betting strategy that predicts all non-Martin-Löf random strings, we can construct two strategies such that every non-Martin-Löf random string is predicted by at least one of them.
Subspace Fuzzy Vault
Venue: arXiv cs.IT 2015
Year: 2015

Fuzzy vault is a scheme providing secure authentication based on fuzzy matching of sets. A major application is the use of biometric features for authentication, whereby unencrypted storage of these features is not an option because of security concerns. While there is still ongoing research around the practical implementation of such schemes, we propose and analyze here an alternative construction based on subspace codes. This offers some advantages in terms of security, as an eventual discovery of the key does not provide an obvious access to the features. Crucial for an efficient implementation are the computational complexity and the choice of good code parameters. The parameters depend on the particular application, e.g. the biometric feature to be stored and the rate one wants to allow for false acceptance. The developed theory is closely linked to constructions of subspace codes studied in the area of random network coding.
Polynomial-time solutions of computational problems in noncommutative-algebraic cryptography
Venue: arXiv cs.CR 2015
Year: 2015

We introduce the \emph{linear centralizer method}, and use it to devise a provable polynomial time solution of the Commutator Key Exchange Problem, the computational problem on which, in the passive adversary model, the security of the Anshel--Anshel--Goldfeld 1999 \emph{Commutator} key exchange protocol is based. We also apply this method to the computational problem underlying the \emph{Centralizer} key exchange protocol, introduced by Shpilrain and Ushakov in 2006. This is the first provable polynomial time cryptanalysis of the Commutator key exchange protocol, hitherto the most important key exchange protocol in the realm of noncommutative-algebraic cryptography, and the first cryptanalysis (of any kind) of the Centralizer key exchange protocol. Unlike earlier cryptanalyses of the Commutator key exchange protocol, our cryptanalyses cannot be foiled by changing the distributions used in the protocol.
Enabling Social Applications via Decentralized Social Data Management
Venue: arXiv cs.SI 2015
Year: 2015

An unprecedented information wealth produced by online social networks, further augmented by location/collocation data, is currently fragmented across different proprietary services. Combined, it can accurately represent the social world and enable novel socially-aware applications. We present Prometheus, a socially-aware peer-to-peer service that collects social information from multiple sources into a multigraph managed in a decentralized fashion on user-contributed nodes, and exposes it through an interface implementing non-trivial social inferences while complying with user-defined access policies. Simulations and experiments on PlanetLab with emulated application workloads show the system exhibits good end-to-end response time, low communication overhead and resilience to malicious attacks.
Finite and infinite basis in P and NP
Venue: arXiv cs.CC 2015
Year: 2015

This article provide new approach to solve P vs NP problem by using cardinality of bases function. About NP-Complete problems, we can divide to infinite disjunction of P-Complete problems. These P-Complete problems are independent of each other in disjunction. That is, NP-Complete problem is in infinite dimension function space that bases are P-Complete. The other hand, any P-Complete problem have at most a finite number of P-Complete basis. The reason is that each P problems have at most finite number of Least fixed point operator. Therefore, we cannot describe NP-Complete problems in P. We can also prove this result from incompleteness of P.
New Invariants for the Graph Isomorphism Problem
Venue: arXiv cs.DS 2015
Year: 2015

In this paper we introduce a novel polynomial-time algorithm to compute graph invariants based on the modified random walk idea on graphs. However not proved to be a full graph invariant by now, our method gives the right answer for the graph instances other well-known methods could not compute (such as special Furer Gadgets and point-line incidence graphs of finite projective planes of higher degrees
Relational Foundations For Functorial Data Migration
Venue: arXiv cs.DB 2015
Year: 2015

We study the data transformation capabilities associated with schemas that are presented by directed multi-graphs and path equations. Unlike most approaches which treat graph-based schemas as abbreviations for relational schemas, we treat graph-based schemas as categories. A schema $S$ is a finitely-presented category, and the collection of all $S$-instances forms a category, $S$-inst. A functor $F$ between schemas $S$ and $T$, which can be generated from a visual mapping between graphs, induces three adjoint data migration functors, $Σ_F:S$-inst$\to T$-inst, $Π_F: S$-inst $\to T$-inst, and $Δ_F:T$-inst $\to S$-inst. We present an algebraic query language FQL based on these functors, prove that FQL is closed under composition, prove that FQL can be implemented with the select-project-product-union relational algebra (SPCU) extended with a key-generation operation, and prove that SPCU can be implemented with FQL.
CFW: A Collaborative Filtering System Using Posteriors Over Weights Of Evidence
Venue: arXiv cs.IR 2015
Year: 2015

We describe CFW, a computationally efficient algorithm for collaborative filtering that uses posteriors over weights of evidence. In experiments on real data, we show that this method predicts as well or better than other methods in situations where the size of the user query is small. The new approach works particularly well when the user s query CONTAINS low frequency(unpopular) items.The approach complements that OF dependency networks which perform well WHEN the size OF the query IS large.Also IN this paper, we argue that the USE OF posteriors OVER weights OF evidence IS a natural way TO recommend similar items collaborative - filtering task.
Structure and Parameter Learning for Causal Independence and Causal Interaction Models
Venue: arXiv cs.AI 2015
Year: 2015

This paper discusses causal independence models and a generalization of these models called causal interaction models. Causal interaction models are models that have independent mechanisms where a mechanism can have several causes. In addition to introducing several particular types of causal interaction models, we show how we can apply the Bayesian approach to learning causal interaction models obtaining approximate posterior distributions for the models and obtain MAP and ML estimates for the parameters. We illustrate the approach with a simulation study of learning model posteriors.
Adaptive Metric Dimensionality Reduction
Venue: arXiv cs.LG 2015
Year: 2015

We study adaptive data-dependent dimensionality reduction in the context of supervised learning in general metric spaces. Our main statistical contribution is a generalization bound for Lipschitz functions in metric spaces that are doubling, or nearly doubling. On the algorithmic front, we describe an analogue of PCA for metric spaces: namely an efficient procedure that approximates the data's intrinsic dimension, which is often much lower than the ambient dimension. Our approach thus leverages the dual benefits of low dimensionality: (1) more efficient algorithms, e.g., for proximity search, and (2) more optimistic generalization bounds.
A Bayesian Approach to Learning Causal Networks
Venue: arXiv cs.AI 2015
Year: 2015

Whereas acausal Bayesian networks represent probabilistic independence, causal Bayesian networks represent causal relationships. In this paper, we examine Bayesian methods for learning both types of networks. Bayesian methods for learning acausal networks are fairly well developed. These methods often employ assumptions to facilitate the construction of priors, including the assumptions of parameter independence, parameter modularity, and likelihood equivalence. We show that although these assumptions also can be appropriate for learning causal networks, we need additional assumptions in order to learn causal networks. We introduce two sufficient assumptions, called {em mechanism independence} and {em component independence}. We show that these new assumptions, when combined with parameter independence, parameter modularity, and likelihood equivalence, allow us to apply methods for learning acausal networks to learn causal networks.
A Weight-coded Evolutionary Algorithm for the Multidimensional Knapsack Problem
Venue: arXiv cs.NE 2015
Year: 2015

A revised weight-coded evolutionary algorithm (RWCEA) is proposed for solving multidimensional knapsack problems. This RWCEA uses a new decoding method and incorporates a heuristic method in initialization. Computational results show that the RWCEA performs better than a weight-coded evolutionary algorithm proposed by Raidl (1999) and to some existing benchmarks, it can yield better results than the ones reported in the OR-library.
Non-overlapping codes
Venue: arXiv cs.DM 2015
Year: 2015

We say that a $q$-ary length $n$ code is \emph{non-overlapping} if the set of non-trivial prefixes of codewords and the set of non-trivial suffices of codewords are disjoint. These codes were first studied by Levenshtein in 1964, motivated by applications in synchronisation. More recently these codes were independently invented (under the name \emph{cross-bifix-free} codes) by Bajić and Stojanović. We provide a simple construction for a class of non-overlapping codes which has optimal cardinality whenever $n$ divides $q$. Moreover, for all parameters $n$ and $q$ we show that a code from this class is close to optimal, in the sense that it has cardinality within a constant factor of an upper bound due to Levenshtein from 1970. Previous constructions have cardinality within a constant factor of the upper bound only when $q$ is fixed. Chee, Kiah, Purkayastha and Wang showed that a $q$-ary length $n$ non-overlapping code contains at most $q^n/(2n-1)$ codewords; this bound is weaker than the Levenshtein bound. Their proof appealed to the application in synchronisation: we provide a direct combinatorial argument to establish the bound of Chee \emph{et al}. We also consider codes of short length, finding the leading term of the maximal cardinality of a non-overlapping code when $n$ is fixed and $q\rightarrow \infty$. The largest cardinality of non-overlapping codes of lengths $3$ or less is determined exactly.
A Bayesian Approach to Data Fusion in Sensor Networks
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we address the fusion problem in wireless sensor networks, where the cross-correlation between the estimates is unknown. To solve the problem within the Bayesian framework, we assume that the covariance matrix has a prior distribution. We also assume that we know the covariance of each estimate, i.e., the diagonal block of the entire covariance matrix (of the random vector consisting of the two estimates). We then derive the conditional distribution of the off-diagonal blocks, which is the cross-correlation of our interest. We show that when there are two nodes, the conditional distribution happens to be the inverted matrix variate $t$-distribution, from which we can readily sample. For more than two nodes, the conditional distribution is no longer the inverted matrix variate $t$-distribution. But we show that we can decompose it into several sampling problems, each of which is the inverted matrix variate $t$-distribution and therefore we can still sample from it. Since we can sample from this distribution, it enables us to use the Monte Carlo method to compute the minimum mean square error estimate for the fusion problem. We use two models to generate experiment data and demonstrate the generality of our method. Simulation results show that the proposed method works better than the popular covariance intersection method.
Linear under-determined systems with sparse solutions: Redirecting a challenge?
Venue: arXiv cs.IT 2015
Year: 2015

Seminal works \cite{CRT,DonohoUnsigned,DonohoPol} generated a massive interest in studying linear under-determined systems with sparse solutions. In this paper we give a short mathematical overview of what was accomplished in last 10 years in a particular direction of such a studying. We then discuss what we consider were the main challenges in last 10 years and give our own view as to what are the main challenges that lie ahead. Through the presentation we arrive to a point where the following natural rhetoric question arises: is it a time to redirect the main challenges? While we can not provide the answer to such a question we hope that our small discussion will stimulate further considerations in this direction.
Average Case and Distributional Analysis of Dual-Pivot Quicksort
Venue: arXiv cs.DS 2015
Year: 2015

In 2009, Oracle replaced the long-serving sorting algorithm in its Java 7 runtime library by a new dual-pivot Quicksort variant due to Vladimir Yaroslavskiy. The decision was based on the strikingly good performance of Yaroslavskiy's implementation in running time experiments. At that time, no precise investigations of the algorithm were available to explain its superior performance - on the contrary: Previous theoretical studies of other dual-pivot Quicksort variants even discouraged the use of two pivots. Only in 2012, two of the authors gave an average case analysis of a simplified version of Yaroslavskiy's algorithm, proving that savings in the number of comparisons are possible. However, Yaroslavskiy's algorithm needs more swaps, which renders the analysis inconclusive. To force the issue, we herein extend our analysis to the fully detailed style of Knuth: We determine the exact number of executed Java Bytecode instructions. Surprisingly, Yaroslavskiy's algorithm needs sightly more Bytecode instructions than a simple implementation of classic Quicksort - contradicting observed running times. Like in Oracle's library implementation we incorporate the use of Insertionsort on small subproblems and show that it indeed speeds up Yaroslavskiy's Quicksort in terms of Bytecodes; but even with optimal Insertionsort thresholds the new Quicksort variant needs slightly more Bytecode instructions on average. Finally, we show that the (suitably normalized) costs of Yaroslavskiy's algorithm converge to a random variable whose distribution is characterized by a fixed-point equation. From that, we compute variances of costs and show that for large n, costs are concentrated around their mean.
Similarity Networks for the Construction of Multiple-Faults Belief Networks
Venue: arXiv cs.AI 2015
Year: 2015

A similarity network is a tool for constructing belief networks for the diagnosis of a single fault. In this paper, we examine modifications to the similarity-network representation that facilitate the construction of belief networks for the diagnosis of multiple coexisting faults.
Cutting corners cheaply, or how to remove Steiner points
Venue: arXiv cs.DS 2015
Year: 2015

Our main result is that the Steiner Point Removal (SPR) problem can always be solved with polylogarithmic distortion, which answers in the affirmative a question posed by Chan, Xia, Konjevod, and Richa (2006). Specifically, we prove that for every edge-weighted graph $G = (V,E,w)$ and a subset of terminals $T \subseteq V$, there is a graph $G'=(T,E',w')$ that is isomorphic to a minor of $G$, such that for every two terminals $u,v\in T$, the shortest-path distances between them in $G$ and in $G'$ satisfy $d_{G,w}(u,v) \le d_{G',w'}(u,v) \le O(\log^5|T|) \cdot d_{G,w}(u,v)$. Our existence proof actually gives a randomized polynomial-time algorithm. Our proof features a new variant of metric decomposition. It is well-known that every $n$-point metric space $(X,d)$ admits a $β$-separating decomposition for $β=O(\log n)$, which roughly means for every desired diameter bound $Δ>0$ there is a randomized partitioning of $X$, which satisfies the following separation requirement: for every $x,y \in X$, the probability they lie in different clusters of the partition is at most $β\,d(x,y)/Δ$. We introduce an additional requirement, which is the following tail bound: for every shortest-path $P$ of length $d(P) \leq Δ/β$, the number of clusters of the partition that meet the path $P$, denoted $Z_P$, satisfies $\Pr[Z_P > t] \le 2e^{-Ω(t)}$ for all $t>0$.
Time Invariant Error Bounds for Modified-CS based Sparse Signal Sequence Recovery
Venue: arXiv cs.IT 2015
Year: 2015

In this work, we obtain performance guarantees for modified-CS and for its improved version, modified-CS-Add-LS-Del, for recursive reconstruction of a time sequence of sparse signals from a reduced set of noisy measurements available at each time. Under mild assumptions, we show that the support recovery error of both algorithms is bounded by a time-invariant and small value at all times. The same is also true for the reconstruction error. Under a slow support change assumption, (i) the support recovery error bound is small compared to the support size; and (ii) our results hold under weaker assumptions on the number of measurements than what $\ell_1$ minimization for noisy data needs. We first give a general result that only assumes a bound on support size, number of support changes and number of small magnitude nonzero entries at each time. Later, we specialize the main idea of these results for two sets of signal change assumptions that model the class of problems in which a new element that is added to the support either gets added at a large initial magnitude or its magnitude slowly increases to a large enough value within a finite delay. Simulation experiments are shown to back up our claims.
PetIGA: A Framework for High-Performance Isogeometric Analysis
Venue: arXiv cs.MS 2015
Year: 2015

We present PetIGA, a code framework to approximate the solution of partial differential equations using isogeometric analysis. PetIGA can be used to assemble matrices and vectors which come from a Galerkin weak form, discretized with Non-Uniform Rational B-spline basis functions. We base our framework on PETSc, a high-performance library for the scalable solution of partial differential equations, which simplifies the development of large-scale scientific codes, provides a rich environment for prototyping, and separates parallelism from algorithm choice. We describe the implementation of PetIGA, and exemplify its use by solving a model nonlinear problem. To illustrate the robustness and flexibility of PetIGA, we solve some challenging nonlinear partial differential equations that include problems in both solid and fluid mechanics. We show strong scaling results on up to 4096 cores, which confirm the suitability of PetIGA for large scale simulations.
Clustered planarity testing revisited
Venue: arXiv cs.CG 2015
Year: 2015

The Hanani--Tutte theorem is a classical result proved for the first time in the 1930s that characterizes planar graphs as graphs that admit a drawing in the plane in which every pair of edges not sharing a vertex cross an even number of times. We generalize this result to clustered graphs with two disjoint clusters, and show that a straightforward extension to flat clustered graphs with three or more disjoint clusters is not possible. For general clustered graphs we show a variant of the Hanani--Tutte theorem in the case when each cluster induces a connected subgraph. Di Battista and Frati proved that clustered planarity of embedded clustered graphs whose every face is incident with at most five vertices can be tested in polynomial time. We give a new and short proof of this result, using the matroid intersection algorithm.
Computations by fly-automata beyond monadic second-order logic
Venue: arXiv cs.LO 2015
Year: 2015

We present logically based methods for constructing XP and FPT graph algorithms, parametrized by tree-width or clique-width. We will use fly-automata introduced in a previous article. They make possible to check properties that are not monadic second-order expressible because their states may include counters, so that their sets of states may be infinite. We equip these automata with output functions, so that they can compute values associated with terms or graphs. Rather than new algorithmic results we present tools for constructing easily certain dynamic programming algorithms by combining predefined automata for basic functions and properties.
Algorithms for detecting dependencies and rigid subsystems for CAD
Venue: arXiv cs.CG 2015
Year: 2015

Geometric constraint systems underly popular Computer Aided Design soft- ware. Automated approaches for detecting dependencies in a design are critical for developing robust solvers and providing informative user feedback, and we provide algorithms for two types of dependencies. First, we give a pebble game algorithm for detecting generic dependencies. Then, we focus on identifying the "special positions" of a design in which generically independent constraints become dependent. We present combinatorial algorithms for identifying subgraphs associated to factors of a particular polynomial, whose vanishing indicates a special position and resulting dependency. Further factoring in the Grassmann- Cayley algebra may allow a geometric interpretation giving conditions (e.g., "these two lines being parallel cause a dependency") determining the special position.