OpenRaiser/Intern-Atlas huggingface dataset:
Counting hypergraph matchings up to uniqueness threshold
Venue: arXiv cs.DS 2017
Year: 2017

We study the problem of approximately counting matchings in hypergraphs of bounded maximum degree and maximum size of hyperedges. With an activity parameter $λ$, each matching $M$ is assigned a weight $λ^{|M|}$. The counting problem is formulated as computing a partition function that gives the sum of the weights of all matchings in a hypergraph. This problem unifies two extensively studied statistical physics models in approximate counting: the hardcore model (graph independent sets) and the monomer-dimer model (graph matchings). For this model, the critical activity $λ_c= \frac{d^d}{k (d-1)^{d+1}}$ is the threshold for the uniqueness of Gibbs measures on the infinite $(d+1)$-uniform $(k+1)$-regular hypertree. Consider hypergraphs of maximum degree at most $k+1$ and maximum size of hyperedges at most $d+1$. We show that when $λ< λ_c$, there is an FPTAS for computing the partition function; and when $λ= λ_c$, there is a PTAS for computing the log-partition function. These algorithms are based on the decay of correlation (strong spatial mixing) property of Gibbs distributions. When $λ> 2λ_c$, there is no PRAS for the partition function or the log-partition function unless NP$=$RP. Towards obtaining a sharp transition of computational complexity of approximate counting, we study the local convergence from a sequence of finite hypergraphs to the infinite lattice with specified symmetry. We show a surprising connection between the local convergence and the reversibility of a natural random walk. This leads us to a barrier for the hardness result: The non-uniqueness of infinite Gibbs measure is not realizable by any finite gadgets.
Coloring graphs with no even hole $\geq 6$: the triangle-free case
Venue: arXiv cs.DM 2017
Year: 2017

In this paper, we prove that the class of graphs with no triangle and no induced cycle of even length at least 6 has bounded chromatic number. It is well-known that even-hole-free graphs are $χ$-bounded but we allow here the existence of $C_4$. The proof relies on the concept of Parity Changing Path, an adaptation of Trinity Changing Path which was recently introduced by Bonamy, Charbit and Thomassé to prove that graphs with no induced cycle of length divisible by three have bounded chromatic number.
Binary Adder Circuits of Asymptotically Minimum Depth, Linear Size, and Fan-Out Two
Venue: arXiv cs.AR 2017
Year: 2017

We consider the problem of constructing fast and small binary adder circuits. Among widely-used adders, the Kogge-Stone adder is often considered the fastest, because it computes the carry bits for two $n$-bit numbers (where $n$ is a power of two) with a depth of $2\log_2 n$ logic gates, size $4 n\log_2 n$, and all fan-outs bounded by two. Fan-outs of more than two are avoided, because they lead to the insertion of repeaters for repowering the signal and additional depth in the physical implementation. However, the depth bound of the Kogge-Stone adder is off by a factor of two from the lower bound of $\log_2 n$. This bound is achieved asymptotically in two separate constructions by Brent and Krapchenko. Brent's construction gives neither a bound on the fan-out nor the size, while Krapchenko's adder has linear size, but can have up to linear fan-out. With a fan-out bound of two, neither construction achieves a depth of less than $2 \log_2 n$. In a further approach, Brent and Kung proposed an adder with linear size and fan-out two, but twice the depth of the Kogge-Stone adder. These results are 33-43 years old and no substantial theoretical improvement for has been made since then. In this paper we integrate the individual advantages of all previous adder circuits into a new family of full adders, the first to improve on the depth bound of $2\log_2 n$ while maintaining a fan-out bound of two. Our adders achieve an asymptotically optimum logic gate depth of $\log_2 n + o(\log_2 n)$ and linear size $\mathcal {O}(n)$.
Detecting Falls with X-Factor Hidden Markov Models
Venue: arXiv cs.LG 2017
Year: 2017

Identification of falls while performing normal activities of daily living (ADL) is important to ensure personal safety and well-being. However, falling is a short term activity that occurs infrequently. This poses a challenge to traditional classification algorithms, because there may be very little training data for falls (or none at all). This paper proposes an approach for the identification of falls using a wearable device in the absence of training data for falls but with plentiful data for normal ADL. We propose three `X-Factor' Hidden Markov Model (XHMMs) approaches. The XHMMs model unseen falls using "inflated" output covariances (observation models). To estimate the inflated covariances, we propose a novel cross validation method to remove "outliers" from the normal ADL that serve as proxies for the unseen falls and allow learning the XHMMs using only normal activities. We tested the proposed XHMM approaches on two activity recognition datasets and show high detection rates for falls in the absence of fall-specific training data. We show that the traditional method of choosing a threshold based on maximum of negative of log-likelihood to identify unseen falls is ill-posed for this problem. We also show that supervised classification methods perform poorly when very limited fall data are available during the training phase.
Complexity of Propositional Logics in Team Semantics
Venue: arXiv cs.LO 2017
Year: 2017

We classify the computational complexity of the satisfiability, validity and model-checking problems for propositional independence, inclusion, and team logic. Our main result shows that the satisfiability and validity problems for propositional team logic are complete for alternating exponential-time with polynomially many alternations.
Accelerated kernel discriminant analysis
Venue: arXiv cs.LG 2017
Year: 2017

In this paper, using a novel matrix factorization and simultaneous reduction to diagonal form approach (or in short simultaneous reduction approach), Accelerated Kernel Discriminant Analysis (AKDA) and Accelerated Kernel Subclass Discriminant Analysis (AKSDA) are proposed. Specifically, instead of performing the simultaneous reduction of the between- and within-class or subclass scatter matrices, the nonzero eigenpairs (NZEP) of the so-called core matrix, which is of relatively small dimensionality, and the Cholesky factorization of the kernel matrix are computed, achieving more than one order of magnitude speed up over kernel discriminant analysis (KDA). Moreover, consisting of a few elementary matrix operations and very stable numerical algorithms, AKDA and AKSDA offer improved classification accuracy. The experimental evaluation on various datasets confirms that the proposed approaches provide state-of-the-art performance in terms of both training time and classification accuracy.
Matroids are Immune to Braess Paradox
Venue: arXiv cs.GT 2017
Year: 2017

The famous Braess paradox describes the following phenomenon: It might happen that the improvement of resources, like building a new street within a congested network, may in fact lead to larger costs for the players in an equilibrium. In this paper we consider general nonatomic congestion games and give a characterization of the maximal combinatorial property of strategy spaces for which Braess paradox does not occur. In a nutshell, bases of matroids are exactly this maximal structure. We prove our characterization by two novel sensitivity results for convex separable optimization problems over polymatroid base polyhedra which may be of independent interest.
Algorithms for Non-Linear and Stochastic Resource Constrained Shortest Paths
Venue: arXiv cs.DS 2017
Year: 2017

Resource constrained shortest path problems are usually solved thanks to a smart enumeration of all the non-dominated paths. Recent improvements of these enumeration algorithms rely on the use of bounds on path resources to discard partial solutions. The quality of the bounds determines the performance of the algorithm. The main contribution of this paper is to introduce a standard procedure to generate bounds on paths resources in a general setting which covers most resource constrained shortest path problems, among which stochastic versions. In that purpose, we introduce a generalization of the resource constrained shortest path problem where the resources are taken in a monoid. The resource of a path is the monoid sum of the resources of its arcs. The problem consists in finding a path whose resource minimizes a non-decreasing cost function of the path resource among the paths that respect a given constraint. Enumeration algorithms are generalized to this framework. We use lattice theory to provide polynomial procedures to find good quality bounds. These procedures solve a generalization of the algebraic path problem, where arc resources belong to a lattice ordered monoid. The practical efficiency of the approach is proved through an extensive numerical study on some deterministic and stochastic resource constrained shortest path problems.
Bilinear CNNs for Fine-grained Visual Recognition
Venue: arXiv cs.CV 2017
Year: 2017

We present a simple and effective architecture for fine-grained visual recognition called Bilinear Convolutional Neural Networks (B-CNNs). These networks represent an image as a pooled outer product of features derived from two CNNs and capture localized feature interactions in a translationally invariant manner. B-CNNs belong to the class of orderless texture representations but unlike prior work they can be trained in an end-to-end manner. Our most accurate model obtains 84.1%, 79.4%, 86.9% and 91.3% per-image accuracy on the Caltech-UCSD birds [67], NABirds [64], FGVC aircraft [42], and Stanford cars [33] dataset respectively and runs at 30 frames-per-second on a NVIDIA Titan X GPU. We then present a systematic analysis of these networks and show that (1) the bilinear features are highly redundant and can be reduced by an order of magnitude in size without significant loss in accuracy, (2) are also effective for other image classification tasks such as texture and scene recognition, and (3) can be trained from scratch on the ImageNet dataset offering consistent improvements over the baseline architecture. Finally, we present visualizations of these models on various datasets using top activations of neural units and gradient-based inversion techniques. The source code for the complete system is available at http://vis-www.cs.umass.edu/bcnn.
Push sum with transmission failures
Venue: arXiv cs.DC 2017
Year: 2017

The push-sum algorithm allows distributed computing of the average on a directed graph, and is particularly relevant when one is restricted to one-way and/or asynchronous communications. We investigate its behavior in the presence of unreliable communication channels where messages can be lost. We show that exponential convergence still holds and deduce fundamental properties that implicitly describe the distribution of the final value obtained. We analyze the error of the final common value we get for the essential case of two nodes, both theoretically and numerically. We provide performance comparison with a standard consensus algorithm.
Multidimensional beyond worst-case and almost-sure problems for mean-payoff objectives
Venue: arXiv cs.GT 2017
Year: 2017

The beyond worst-case threshold problem (BWC), recently introduced by Bruyère et al., asks given a quantitative game graph for the synthesis of a strategy that i) enforces some minimal level of performance against any adversary, and ii) achieves a good expectation against a stochastic model of the adversary. They solved the BWC problem for finite-memory strategies and unidimensional mean-payoff objectives and they showed membership of the problem in NP$\cap$coNP. They also noted that infinite-memory strategies are more powerful than finite-memory ones, but the respective threshold problem was left open. We extend these results in several directions. First, we consider multidimensional mean-payoff objectives. Second, we study both finite-memory and infinite-memory strategies. We show that the multidimensional BWC problem is coNP-complete in both cases. Third, in the special case when the worst-case objective is unidimensional (but the expectation objective is still multidimensional) we show that the complexity decreases to NP$\cap$coNP. This solves the infinite-memory threshold problem left open by Bruyère et al., and this complexity cannot be improved without improving the currently known complexity of classical mean-payoff games. Finally, we introduce a natural relaxation of the BWC problem, the beyond almost-sure threshold problem (BAS), which asks for the synthesis of a strategy that ensures some minimal level of performance with probability one and a good expectation against the stochastic model of the adversary. We show that the multidimensional BAS threshold problem is solvable in P.
XTreePath: A generalization of XPath to handle real world structural variation
Venue: arXiv cs.IR 2017
Year: 2017

We discuss a key problem in information extraction which deals with wrapper failures due to changing content templates. A good proportion of wrapper failures are due to HTML templates changing to cause wrappers to become incompatible after element inclusion or removal in a DOM (Tree representation of HTML). We perform a large-scale empirical analyses of the causes of shift and mathematically quantify the levels of domain difficulty based on entropy. We propose the XTreePath annotation method to captures contextual node information from the training DOM. We then utilize this annotation in a supervised manner at test time with our proposed Recursive Tree Matching method which locates nodes most similar in context recursively using the tree edit distance. The search is based on a heuristic function that takes into account the similarity of a tree compared to the structure that was present in the training data. We evaluate XTreePath using 117,422 pages from 75 diverse websites in 8 vertical markets. Our XTreePath method consistently outperforms XPath and a current commercial system in terms of successful extractions in a blackbox test. We make our code and datasets publicly available online.
Characterizing Key Stakeholders in an Online Black-Hat Marketplace
Venue: arXiv cs.CY 2017
Year: 2017

Over the past few years, many black-hat marketplaces have emerged that facilitate access to reputation manipulation services such as fake Facebook likes, fraudulent search engine optimization (SEO), or bogus Amazon reviews. In order to deploy effective technical and legal countermeasures, it is important to understand how these black-hat marketplaces operate, shedding light on the services they offer, who is selling, who is buying, what are they buying, who is more successful, why are they successful, etc. Toward this goal, in this paper, we present a detailed micro-economic analysis of a popular online black-hat marketplace, namely, SEOClerks.com. As the site provides non-anonymized transaction information, we set to analyze selling and buying behavior of individual users, propose a strategy to identify key users, and study their tactics as compared to other (non-key) users. We find that key users: (1) are mostly located in Asian countries, (2) are focused more on selling black-hat SEO services, (3) tend to list more lower priced services, and (4) sometimes buy services from other sellers and then sell at higher prices. Finally, we discuss the implications of our analysis with respect to devising effective economic and legal intervention strategies against marketplace operators and key users.
Approximation of Uplink Inter-Cell Interference in FDMA Small Cell Networks
Venue: arXiv cs.NI 2017
Year: 2017

In this paper, for the first time, we analytically prove that the uplink (UL) inter-cell interference in frequency division multiple access (FDMA) small cell networks (SCNs) can be well approximated by a lognormal distribution under a certain condition. The lognormal approximation is vital because it allows tractable network performance analysis with closed-form expressions. The derived condition, under which the lognormal approximation applies, does not pose particular requirements on the shapes/sizes of user equipment (UE) distribution areas as in previous works. Instead, our results show that if a path loss related random variable (RV) associated with the UE distribution area, has a low ratio of the 3rd absolute moment to the variance, the lognormal approximation will hold. Analytical and simulation results show that the derived condition can be readily satisfied in future dense/ultra-dense SCNs, indicating that our conclusions are very useful for network performance analysis of the 5th generation (5G) systems with more general cell deployment beyond the widely used Poisson deployment.
Decentralized Simultaneous Multi-target Exploration using a Connected Network of Multiple Robots
Venue: arXiv cs.RO 2017
Year: 2017

This paper presents a novel decentralized control strategy for a multi-robot system that enables parallel multi-target exploration while ensuring a time-varying connected topology in cluttered 3D environments. Flexible continuous connectivity is guaranteed by building upon a recent connectivity maintenance method, in which limited range, line-of-sight visibility, and collision avoidance are taken into account at the same time. Completeness of the decentralized multi-target exploration algorithm is guaranteed by dynamically assigning the robots with different motion behaviors during the exploration task. One major group is subject to a suitable downscaling of the main traveling force based on the traveling efficiency of the current leader and the direction alignment between traveling and connectivity force. This supports the leader in always reaching its current target and, on a larger time horizon, that the whole team realizes the overall task in finite time. Extensive Monte~Carlo simulations with a group of several quadrotor UAVs show the scalability and effectiveness of the proposed method and experiments validate its practicability.
The Influence of Context on Dialogue Act Recognition
Venue: arXiv cs.CL 2017
Year: 2017

This article presents an analysis of the influence of context information on dialog act recognition. We performed experiments on the widely explored Switchboard corpus, as well as on data annotated according to the recent ISO 24617-2 standard. The latter was obtained from the Tilburg DialogBank and through the mapping of the annotations of a subset of the Let's Go corpus. We used a classification approach based on SVMs, which had proved successful in previous work and allowed us to limit the amount of context information provided. This way, we were able to observe the influence patterns as the amount of context information increased. Our base features consisted of n-grams, punctuation, and wh-words. Context information was obtained from one to five preceding segments and provided either as n-grams or dialog act classifications, with the latter typically leading to better results and more stable influence patterns. In addition to the conclusions about the importance and influence of context information, our experiments on the Switchboard corpus also led to results that advanced the state-of-the-art on the dialog act recognition task on that corpus. Furthermore, the results obtained on data annotated according to the ISO 24617-2 standard define a baseline for future work and contribute for the standardization of experiments in the area.
The Many Faces of Graph Dynamics
Venue: arXiv cs.SI 2017
Year: 2017

The topological structure of complex networks has fascinated researchers for several decades, resulting in the discovery of many universal properties and reoccurring characteristics of different kinds of networks. However, much less is known today about the network dynamics: indeed, complex networks in reality are not static, but rather dynamically evolve over time. Our paper is motivated by the empirical observation that network evolution patterns seem far from random, but exhibit structure. Moreover, the specific patterns appear to depend on the network type, contradicting the existence of a "one fits it all" model. However, we still lack observables to quantify these intuitions, as well as metrics to compare graph evolutions. Such observables and metrics are needed for extrapolating or predicting evolutions, as well as for interpolating graph evolutions. To explore the many faces of graph dynamics and to quantify temporal changes, this paper suggests to build upon the concept of centrality, a measure of node importance in a network. In particular, we introduce the notion of centrality distance, a natural similarity measure for two graphs which depends on a given centrality, characterizing the graph type. Intuitively, centrality distances reflect the extent to which (non-anonymous) node roles are different or, in case of dynamic graphs, have changed over time, between two graphs. We evaluate the centrality distance approach for five evolutionary models and seven real-world social and physical networks. Our results empirically show the usefulness of centrality distances for characterizing graph dynamics compared to a null-model of random evolution, and highlight the differences between the considered scenarios. Interestingly, our approach allows us to compare the dynamics of very different networks, in terms of scale and evolution speed.
Capacity Bounds for the $K$-User Gaussian Interference Channel
Venue: arXiv cs.IT 2017
Year: 2017

The capacity region of the $K$-user Gaussian interference channel (GIC) is a long-standing open problem and even capacity outer bounds are little known in general. A significant progress on degrees-of-freedom (DoF) analysis, a first-order capacity approximation, for the $K$-user GIC has provided new important insights into the problem of interest in the high signal-to-noise ratio (SNR) limit. However, such capacity approximation has been observed to have some limitations in predicting the capacity at \emph{finite} SNR. In this work, we develop a new upper-bounding technique that utilizes a new type of genie signal and applies \emph{time sharing} to genie signals at $K$ receivers. Based on this technique, we derive new upper bounds on the sum capacity of the three-user GIC with constant, complex channel coefficients and then generalize to the $K$-user case to better understand sum-rate behavior at finite SNR. We also provide closed-form expressions of our upper bounds on the capacity of the $K$-user symmetric GIC easily computable for \emph{any} $K$. From the perspectives of our results, some sum-rate behavior at finite SNR is in line with the insights given by the known DoF results, while some others are not. In particular, the well-known $K/2$ DoF achievable for almost all constant real channel coefficients turns out to be not embodied as a substantial performance gain over a certain range of the cross-channel coefficient in the $K$-user symmetric real case especially for \emph{large} $K$. We further investigate the impact of phase offset between the direct-channel coefficient and the cross-channel coefficients on the sum-rate upper bound for the three-user \emph{complex} GIC. As a consequence, we aim to provide new findings that could not be predicted by the prior works on DoF of GICs.