OpenRaiser/Intern-Atlas huggingface dataset:
On Some Universally Good Fractional Repetition Codes
Venue: arXiv cs.IT 2016
Year: 2016

Data storage in Distributed Storage Systems (DSSs) is a multidimensional optimization problem. Using network coding, one wants to provide reliability, scalability, security, reduced storage overhead, reduced bandwidth for repair and minimal disk I/O etc. in such systems. Regenerating codes have been used to optimize some of these parameters, where a file can be reconstructed by contacting any k nodes in the system and in case of node failure it can be repaired by using any d nodes in the system. This was further generalized to Fractional repetition (FR) codes (a smart replication of encoded packets) on n nodes which also provides optimized disk I/O and where a node failure can be repaired by contacting some specific set of nodes in the system. Several constructions of FR codes using graphs and combinatorial designs are known. In particular, some constructions of codes for heterogeneous DSSs are given using partial regular graph (where number of packets on each node is different) and ring construction. In this work, we show that the codes constructed using the partial regular graph are universally good code. Further, we found several universally good codes using ring construction and t-construction.
Assembly Sequence Planning for Motion Planning
Venue: arXiv cs.RO 2016
Year: 2016

This paper develops a planner to find an optimal assembly sequence to assemble several objects. The input to the planner is the mesh models of the objects, the relative poses between the objects in the assembly, and the final pose of the assembly. The output is an optimal assembly sequence, namely (1) in which order should one assemble the objects, (2) from which directions should the objects be dropped, and (3) candidate grasps of each object. The proposed planner finds the optimal solution by automatically permuting, evaluating, and searching the possible assembly sequences considering stability, graspability, and assemblability qualities. It is expected to guide robots to do assembly using translational motion. The output provides initial and goal configurations to motion planning algorithms. It is ready to be used by robots and is demonstrated using several simulations and real-world executions.
Optimal User Loading in Massive MIMO Systems with Regularized Zero Forcing Precoding
Venue: arXiv cs.IT 2016
Year: 2016

We consider a downlink multiuser multiple-input multiple output (MIMO) system employing regularized zero-forcing (RZF) precoding. We derive the asymptotic signal-to-leakage-plus-noise ratio (SLNR) as both the number of antennas and the number of users go to infinity at a fixed ratio. Focusing on the symmetric uncorrelated channels, we show that the SLNR is asymptotically equal to signal-to-interference-plus-noise ratio (SINR) which allows us to optimize the user loading for spectral efficiency. The results show that the optimal user loading varies depending on the channel signal-to-noise ratio (SNR) but is equal to one in both the low or high SNR regimes.
Unsupervised Identification of Translationese
Venue: arXiv cs.CL 2016
Year: 2016

Translated texts are distinctively different from original ones, to the extent that supervised text classification methods can distinguish between them with high accuracy. These differences were proven useful for statistical machine translation. However, it has been suggested that the accuracy of translation detection deteriorates when the classifier is evaluated outside the domain it was trained on. We show that this is indeed the case, in a variety of evaluation scenarios. We then show that unsupervised classification is highly accurate on this task. We suggest a method for determining the correct labels of the clustering outcomes, and then use the labels for voting, improving the accuracy even further. Moreover, we suggest a simple method for clustering in the challenging case of mixed-domain datasets, in spite of the dominance of domain-related features over translation-related ones. The result is an effective, fully-unsupervised method for distinguishing between original and translated texts that can be applied to new domains with reasonable accuracy.
Relaxed Binaural LCMV Beamforming
Venue: arXiv cs.SD 2016
Year: 2016

In this paper we propose a new binaural beamforming technique which can be seen as a relaxation of the linearly constrained minimum variance (LCMV) framework. The proposed method can achieve simultaneous noise reduction and exact binaural cue preservation of the target source, similar to the binaural minimum variance distortionless response (BMVDR) method. However, unlike BMVDR, the proposed method is also able to preserve the binaural cues of multiple interferers to a certain predefined accuracy. Specifically, it is able to control the trade-off between noise reduction and binaural cue preservation of the interferers by using a separate trade-off parameter per interferer. Moreover, we provide a robust way of selecting these trade-off parameters in such a way that the preservation accuracy for the binaural cues of the interferers is always better than the corresponding ones of the BMVDR. The relaxation of the constraints in the proposed method achieves approximate binaural cue preservation of more interferers than other previously presented LCMV-based binaural beamforming methods that use strict equality constraints.
Reduced Space and Faster Convergence in Imperfect-Information Games via Regret-Based Pruning
Venue: arXiv cs.GT 2016
Year: 2016

Counterfactual Regret Minimization (CFR) is the most popular iterative algorithm for solving zero-sum imperfect-information games. Regret-Based Pruning (RBP) is an improvement that allows poorly-performing actions to be temporarily pruned, thus speeding up CFR. We introduce Total RBP, a new form of RBP that reduces the space requirements of CFR as actions are pruned. We prove that in zero-sum games it asymptotically prunes any action that is not part of a best response to some Nash equilibrium. This leads to provably faster convergence and lower space requirements. Experiments show that Total RBP results in an order of magnitude reduction in space, and the reduction factor increases with game size.
Delay-Optimal Buffer-Aware Scheduling with Adaptive Transmission
Venue: arXiv cs.IT 2016
Year: 2016

In this work, we aim to obtain the optimal tradeoff between the average delay and the average power consumption in a communication system. In our system, the arrivals occur at each timeslot according to a Bernoulli arrival process and are buffered at the transmitter. The transmitter determines the scheduling policy of how many packets to transmit under an average power constraint. The power is assumed to be an increasing and convex function of the number of packets transmitted in each timeslot to capture the realism in communication systems. We also consider a finite buffer and allow the scheduling decision to depend on the buffer occupancy. This problem is modelled as a Constrained Markov Decision Process (CMDP). We first prove that the optimal policy of the (Lagrangian) relaxation of the CMDP is deterministic and threshold-based. We then show that the optimal delay-power tradeoff curve is convex and piecewise linear, where each of the vertices are obtained by the optimal solution to the relaxed problem. This allows us to show the optimal policies of the CMDP are threshold-based, and hence can be implemented by a proposed efficient algorithm. The theoretical results and the algorithm are validated by Linear Programming and simulations.
Predicting the elliptic curve congruential generator
Venue: arXiv cs.CR 2016
Year: 2016

Let $p$ be a prime and let $\mathbf{E}$ be an elliptic curve defined over the finite field $\mathbb{F}_p$ of $p$ elements. For a point $G\in\mathbf{E}(\mathbb{F}_p)$ the elliptic curve congruential generator (with respect to the first coordinate) is a sequence $(x_n)$ defined by the relation $x_n=x(W_n)=x(W_{n-1}\oplus G)=x(nG\oplus W_0)$, $n=1,2,\dots$, where $\oplus$ denotes the group operation in $\mathbf{E}$ and $W_0$ is an initial point. In this paper, we show that if some consecutive elements of the sequence $(x_n)$ are given as integers, then one can compute in polynomial time an elliptic curve congruential generator (where the curve possibly defined over the rationals or over a residue ring) such that the generated sequence is identical to $(x_n)$ in the revealed segment. It turns out that in practice, all the secret parameters, and thus the whole sequence $(x_n)$, can be computed from eight consecutive elements, even if the prime and the elliptic curve are private.
CompAdaGrad: A Compressed, Complementary, Computationally-Efficient Adaptive Gradient Method
Venue: arXiv cs.LG 2016
Year: 2016

The adaptive gradient online learning method known as AdaGrad has seen widespread use in the machine learning community in stochastic and adversarial online learning problems and more recently in deep learning methods. The method's full-matrix incarnation offers much better theoretical guarantees and potentially better empirical performance than its diagonal version; however, this version is computationally prohibitive and so the simpler diagonal version often is used in practice. We introduce a new method, CompAdaGrad, that navigates the space between these two schemes and show that this method can yield results much better than diagonal AdaGrad while avoiding the (effectively intractable) $O(n^3)$ computational complexity of full-matrix AdaGrad for dimension $n$. CompAdaGrad essentially performs full-matrix regularization in a low-dimensional subspace while performing diagonal regularization in the complementary subspace. We derive CompAdaGrad's updates for composite mirror descent in case of the squared $\ell_2$ norm and the $\ell_1$ norm, demonstrate that its complexity per iteration is linear in the dimension, and establish guarantees for the method independent of the choice of composite regularizer. Finally, we show preliminary results on several datasets.
Knowing Values and Public Inspection
Venue: arXiv cs.LO 2016
Year: 2016

We present a basic dynamic epistemic logic of "knowing the value". Analogous to public announcement in standard DEL, we study "public inspection", a new dynamic operator which updates the agents' knowledge about the values of constants. We provide a sound and strongly complete axiomatization for the single and multi-agent case, making use of the well-known Armstrong axioms for dependencies in databases.
The Generalized Subterm Criterion in TTT2
Venue: arXiv cs.LO 2016
Year: 2016

We present an SMT encoding of a generalized version of the subterm criterion and evaluate its implementation in TTT2.
Math-Aware Search Engines: Physics Applications and Overview
Venue: arXiv cs.DL 2016
Year: 2016

Search engines for equations now exist, which return results matching the query's mathematical meaning or structural presentation. Operating over scientific papers, online encyclopedias, and math discussion forums, their content includes physics, math, and other sciences. They enable physicists to avoid jargon and more easily target mathematical content within and across disciplines. As a natural extension of keyword-based search, they open up a new world for discovering both exact and approximate mathematical solutions; physical systems' analogues and alternative models; and physics' patterns. This review presents the existing math-aware search engines, discusses methods for maximizing their search success, and overviews their math-matching capabilities. Proposed applications to physics are also given, to contribute towards developers' and physicists' exploration of the newly available search horizons.
Technical Report: Timing Abstraction of Perturbed LTI systems with $\mathcal{L}_2$-based Event-Triggering Mechanism
Venue: arXiv cs.SY 2016
Year: 2016

In networked control systems, the advent of event-triggering strategies in the sampling process has resulted in the usage reduction of network capacities, such as communication bandwidth. However, the aperiodic nature of sampling periods generated by event-triggering strategies has hindered the schedulability of such networks. In this study, we propose a framework to construct a timed safety automaton that captures the sampling behavior of perturbed LTI systems with an $\mathcal{L}_2$-based triggering mechanisms proposed in the Literature. In this framework, the state-space is partitioned into a finite number of convex polyhedral cones, each cone representing a discrete mode in the abstracted automaton. Adopting techniques from stability analysis of retarded systems accompanied with a polytopic embedding of time, LMI conditions to characterize the sampling interval associated with each region are derived. Then, using reachability analysis, the transitions in the abstracted automaton are derived.
WaveNet: A Generative Model for Raw Audio
Venue: arXiv cs.SD 2016
Year: 2016

This paper introduces WaveNet, a deep neural network for generating raw audio waveforms. The model is fully probabilistic and autoregressive, with the predictive distribution for each audio sample conditioned on all previous ones; nonetheless we show that it can be efficiently trained on data with tens of thousands of samples per second of audio. When applied to text-to-speech, it yields state-of-the-art performance, with human listeners rating it as significantly more natural sounding than the best parametric and concatenative systems for both English and Mandarin. A single WaveNet can capture the characteristics of many different speakers with equal fidelity, and can switch between them by conditioning on the speaker identity. When trained to model music, we find that it generates novel and often highly realistic musical fragments. We also show that it can be employed as a discriminative model, returning promising results for phoneme recognition.
Examining Representational Similarity in ConvNets and the Primate Visual Cortex
Venue: arXiv cs.CV 2016
Year: 2016

We compare several ConvNets with different depth and regularization techniques with multi-unit macaque IT cortex recordings and assess the impact of the same on representational similarity with the primate visual cortex. We find that with increasing depth and validation performance, ConvNet features are closer to cortical IT representations.
Reasoning about Graph Programs
Venue: arXiv cs.PL 2016
Year: 2016

GP 2 is a non-deterministic programming language for computing by graph transformation. One of the design goals for GP 2 is syntactic and semantic simplicity, to facilitate formal reasoning about programs. In this paper, we demonstrate with four case studies how programmers can prove termination and partial correctness of their solutions. We argue that GP 2's graph transformation rules, together with induction on the length of program executions, provide a convenient framework for program verification.
An Experimental Study of LSTM Encoder-Decoder Model for Text Simplification
Venue: arXiv cs.CL 2016
Year: 2016

Text simplification (TS) aims to reduce the lexical and structural complexity of a text, while still retaining the semantic meaning. Current automatic TS techniques are limited to either lexical-level applications or manually defining a large amount of rules. Since deep neural networks are powerful models that have achieved excellent performance over many difficult tasks, in this paper, we propose to use the Long Short-Term Memory (LSTM) Encoder-Decoder model for sentence level TS, which makes minimal assumptions about word sequence. We conduct preliminary experiments to find that the model is able to learn operation rules such as reversing, sorting and replacing from sequence pairs, which shows that the model may potentially discover and apply rules such as modifying sentence structure, substituting words, and removing words for TS.
Construction of Gray maps for groups of order 16
Venue: arXiv cs.IT 2016
Year: 2016

In this paper we consider Type 1 Gray maps and Type 2 Gray maps for groups of order 16. First, we confirm that we can construct Type 1 Gray maps for all groups of order 16, and we actually construct them. Next, we confirm that we can construct Type 2 Gray maps for several groups of order 16, and we construct all such maps. Finally we reveal why the other groups do not admit Gray maps.
Small Extended Formulation for Knapsack Cover Inequalities from Monotone Circuits
Venue: arXiv cs.DM 2016
Year: 2016

Initially developed for the min-knapsack problem, the knapsack cover inequalities are used in the current best relaxations for numerous combinatorial optimization problems of covering type. In spite of their widespread use, these inequalities yield linear programming (LP) relaxations of exponential size, over which it is not known how to optimize exactly in polynomial time. In this paper we address this issue and obtain LP relaxations of quasi-polynomial size that are at least as strong as that given by the knapsack cover inequalities. For the min-knapsack cover problem, our main result can be stated formally as follows: for any $\varepsilon >0$, there is a $(1/\varepsilon)^{O(1)}n^{O(\log n)}$-size LP relaxation with an integrality gap of at most $2+\varepsilon$, where $n$ is the number of items. Prior to this work, there was no known relaxation of subexponential size with a constant upper bound on the integrality gap. Our construction is inspired by a connection between extended formulations and monotone circuit complexity via Karchmer-Wigderson games. In particular, our LP is based on $O(\log^2 n)$-depth monotone circuits with fan-in~$2$ for evaluating weighted threshold functions with $n$ inputs, as constructed by Beimel and Weinreb. We believe that a further understanding of this connection may lead to more positive results complementing the numerous lower bounds recently proved for extended formulations.
Some Open Problems related to Creative Telescoping
Venue: arXiv cs.SC 2016
Year: 2016

Creative telescoping is the method of choice for obtaining information about definite sums or integrals. It has been intensively studied since the early 1990s, and can now be considered as a classical technique in computer algebra. At the same time, it is still subject of ongoing research. In this paper, we present a selection of open problems in this context. We would be curious to hear about any substantial progress on any of these problems.
Information Theoretic Structure Learning with Confidence
Venue: arXiv cs.IT 2016
Year: 2016

Information theoretic measures (e.g. the Kullback Liebler divergence and Shannon mutual information) have been used for exploring possibly nonlinear multivariate dependencies in high dimension. If these dependencies are assumed to follow a Markov factor graph model, this exploration process is called structure discovery. For discrete-valued samples, estimates of the information divergence over the parametric class of multinomial models lead to structure discovery methods whose mean squared error achieves parametric convergence rates as the sample size grows. However, a naive application of this method to continuous nonparametric multivariate models converges much more slowly. In this paper we introduce a new method for nonparametric structure discovery that uses weighted ensemble divergence estimators that achieve parametric convergence rates and obey an asymptotic central limit theorem that facilitates hypothesis testing and other types of statistical validation.
Model Checking the Logic of Allen's Relations Meets and Started-by is $P^NP$-Complete
Venue: arXiv cs.LO 2016
Year: 2016

In the plethora of fragments of Halpern and Shoham's modal logic of time intervals (HS), the logic AB of Allen's relations Meets and Started-by is at a central position. Statements that may be true at certain intervals, but at no sub-interval of them, such as accomplishments, as well as metric constraints about the length of intervals, that force, for instance, an interval to be at least (resp., at most, exactly) k points long, can be expressed in AB. Moreover, over the linear order of the natural numbers N, it subsumes the (point-based) logic LTL, as it can easily encode the next and until modalities. Finally, it is expressive enough to capture the ω-regular languages, that is, for each ω-regular expression R there exists an AB formula ϕ such that the language defined by R coincides with the set of models of ϕ over N. It has been shown that the satisfiability problem for AB over N is EXPSPACE-complete. Here we prove that, under the homogeneity assumption, its model checking problem is Δ^p_2 = P^NP-complete (for the sake of comparison, the model checking problem for full HS is EXPSPACE-hard, and the only known decision procedure is nonelementary). Moreover, we show that the modality for the Allen relation Met-by can be added to AB at no extra cost (AA'B is P^NP-complete as well).
On the Expressive Power of Sub-Propositional Fragments of Modal Logic
Venue: arXiv cs.LO 2016
Year: 2016

Modal logic is a paradigm for several useful and applicable formal systems in computer science. It generally retains the low complexity of classical propositional logic, but notable exceptions exist in the domains of description, temporal, and spatial logic, where the most expressive formalisms have a very high complexity or are even undecidable. In search of computationally well-behaved fragments, clausal forms and other sub-propositional restrictions of temporal and description logics have been recently studied. This renewed interest on sub-propositional logics, which mainly focus on the complexity of the various fragments, raise natural questions on their the relative expressive power, which we try to answer here for the basic multi-modal logic Kn. We consider the Horn and the Krom restrictions, as well as the combined restriction (known as the core fragment) of modal logic, and, orthogonally, the fragments that emerge by disallowing boxes or diamonds from positive literals. We study the problem in a very general setting, to ease transferring our results to other meaningful cases.
Weighted Linear Dynamic Logic
Venue: arXiv cs.LO 2016
Year: 2016

We introduce a weighted linear dynamic logic (weighted LDL for short) and show the expressive equivalence of its formulas to weighted rational expressions. This adds a new characterization for recognizable series to the fundamental Schützenberger theorem. Surprisingly, the equivalence does not require any restriction to our weighted LDL. Our results hold over arbitrary (resp. totally complete) semirings for finite (resp. infinite) words. As a consequence, the equivalence problem for weighted LDL formulas over fields is decidable in doubly exponential time. In contrast to classical logics, we show that our weighted LDL is expressively incomparable to weighted LTL for finite words. We determine a fragment of the weighted LTL such that series over finite and infinite words definable by LTL formulas in this fragment are definable also by weighted LDL formulas.
The Almost Equivalence by Asymptotic Probabilities for Regular Languages and Its Computational Complexities
Venue: arXiv cs.FL 2016
Year: 2016

We introduce p-equivalence by asymptotic probabilities, which is a weak almost-equivalence based on zero-one laws in finite model theory. In this paper, we consider the computational complexities of p-equivalence problems for regular languages and provide the following details. First, we give an robustness of p-equivalence and a logical characterization for p-equivalence. The characterization is useful to generate some algorithms for p-equivalence problems by coupling with standard results from descriptive complexity. Second, we give the computational complexities for the p-equivalence problems by the logical characterization. The computational complexities are the same as for the (fully) equivalence problems. Finally, we apply the proofs for p-equivalence to some generalized equivalences.