OpenRaiser/Intern-Atlas huggingface dataset:
A New Vision for Smart Objects and the Internet of Things: Mobile Robots and Long-Range UHF RFID Sensor Tags
Venue: arXiv cs.RO 2015
Year: 2015

We present a new vision for smart objects and the Internet of Things wherein mobile robots interact with wirelessly-powered, long-range, ultra-high frequency radio frequency identification (UHF RFID) tags outfitted with sensing capabilities. We explore the technology innovations driving this vision by examining recently-commercialized sensor tags that could be affixed-to or embedded-in objects or the environment to yield true embodied intelligence. Using a pair of autonomous mobile robots outfitted with UHF RFID readers, we explore several potential applications where mobile robots interact with sensor tags to perform tasks such as: soil moisture sensing, remote crop monitoring, infrastructure monitoring, water quality monitoring, and remote sensor deployment.
Maximum matching width: new characterizations and a fast algorithm for dominating set
Venue: arXiv cs.DS 2015
Year: 2015

We give alternative definitions for maximum matching width, e.g. a graph $G$ has $\operatorname{mmw}(G) \leq k$ if and only if it is a subgraph of a chordal graph $H$ and for every maximal clique $X$ of $H$ there exists $A,B,C \subseteq X$ with $A \cup B \cup C=X$ and $|A|,|B|,|C| \leq k$ such that any subset of $X$ that is a minimal separator of $H$ is a subset of either $A, B$ or $C$. Treewidth and branchwidth have alternative definitions through intersections of subtrees, where treewidth focuses on nodes and branchwidth focuses on edges. We show that mm-width combines both aspects, focusing on nodes and on edges. Based on this we prove that given a graph $G$ and a branch decomposition of mm-width $k$ we can solve Dominating Set in time $O^*({8^k})$, thereby beating $O^*(3^{\operatorname{tw}(G)})$ whenever $\operatorname{tw}(G) > \log_3{8} \times k \approx 1.893 k$. Note that $\operatorname{mmw}(G) \leq \operatorname{tw}(G)+1 \leq 3 \operatorname{mmw}(G)$ and these inequalities are tight. Given only the graph $G$ and using the best known algorithms to find decompositions, maximum matching width will be better for solving Dominating Set whenever $\operatorname{tw}(G) > 1.549 \times \operatorname{mmw}(G)$.
Planar Ultrametric Rounding for Image Segmentation
Venue: arXiv cs.DS 2015
Year: 2015

We study the problem of hierarchical clustering on planar graphs. We formulate this in terms of an LP relaxation of ultrametric rounding. To solve this LP efficiently we introduce a dual cutting plane scheme that uses minimum cost perfect matching as a subroutine in order to efficiently explore the space of planar partitions. We apply our algorithm to the problem of hierarchical image segmentation.
Research Approaches on Energy-aware Cognitive Radio Networks and Cloud based Infrastructures
Venue: arXiv cs.NI 2015
Year: 2015

As years passes wireless networks were rapidly improved, introducing new applications and services, as well as important challenges for mobility support. This research field is new with many researchers and scientists making their proposals to optimize the provision of multiple services to the mobile users. In this context, this survey paper studies research approaches from the following topics: Cognitive Radio Networks, Interactive Broadcasting, Energy Efficient Networks, Cloud Computing and Resource Management.
Managing Autonomous Mobility on Demand Systems for Better Passenger Experience
Venue: arXiv cs.AI 2015
Year: 2015

Autonomous mobility on demand systems, though still in their infancy, have very promising prospects in providing urban population with sustainable and safe personal mobility in the near future. While much research has been conducted on both autonomous vehicles and mobility on demand systems, to the best of our knowledge, this is the first work that shows how to manage autonomous mobility on demand systems for better passenger experience. We introduce the Expand and Target algorithm which can be easily integrated with three different scheduling strategies for dispatching autonomous vehicles. We implement an agent-based simulation platform and empirically evaluate the proposed approaches with the New York City taxi data. Experimental results demonstrate that the algorithm significantly improve passengers' experience by reducing the average passenger waiting time by up to 29.82% and increasing the trip success rate by up to 7.65%.
Effect of Strategic Grading and Early Offers in Matching Markets
Venue: arXiv cs.GT 2015
Year: 2015

Strategic suppression of grades, as well as early offers and contracts, are well-known phenomena in the matching process where graduating students apply to jobs or further education. In this paper, we consider a game theoretic model of these phenomena introduced by Ostrovsky and Schwarz, and study the loss in social welfare resulting from strategic behavior of the schools, employers, and students. We model grading of students as a game where schools suppress grades in order to improve their students' placements. We also consider the quality loss due to unraveling of the matching market, the strategic behavior of students and employers in offering early contracts with the goal to improve the quality. Our goal is to evaluate if strategic grading or unraveling of the market (or a combination of the two) can cause significant welfare loss compared to the optimal assignment of students to jobs. To measure welfare of the assignment, we assume that welfare resulting from a job -- student pair is a separable and monotone function of student ability and the quality of the jobs. Assuming uniform student quality distribution, we show that the quality loss from the above strategic manipulation is bounded by at most a factor of 2, and give improved bounds for some special cases of welfare functions.
Meshfree C^2-Weighting for Shape Deformation
Venue: arXiv cs.GR 2015
Year: 2015

Handle-driven deformation based on linear blending is widely used in many applications because of its merits in intuitiveness, efficiency and easiness of implementation. We provide a meshfree method to compute the smooth weights of linear blending for shape deformation. The C2-continuity of weighting is guaranteed by the carefully formulated basis functions, with which the computation of weights is in a closed-form. Criteria to ensure the quality of deformation are preserved by the basis functions after decomposing the shape domain according to the Voronoi diagram of handles. The cost of inserting a new handle is only the time to evaluate the distances from the new handle to all sample points in the space of deformation. Moreover, a virtual handle insertion algorithm has been developed to allow users freely placing handles while preserving the criteria on weights. Experimental examples for real-time 2D/3D deformations are shown to demonstrate the effectiveness of this method.
Recovery of Signals with Low Density
Venue: arXiv cs.IT 2015
Year: 2015

Sparse signals (i.e., vectors with a small number of non-zero entries) build the foundation of most kernel (or nullspace) results, uncertainty relations, and recovery guarantees in the sparse signal processing and compressive sensing literature. In this paper, we introduce a novel signal-density measure that extends the common notion of sparsity to non-sparse signals whose entries' magnitudes decay rapidly. By taking into account such magnitude information, we derive a more general and less restrictive kernel result and uncertainty relation. Furthermore, we demonstrate the effectiveness of the proposed signal-density measure by showing that orthogonal matching pursuit (OMP) provably recovers low-density signals with up to 2$\times$ more non-zero coefficients compared to that guaranteed by standard results for sparse signals.
Role Identification of Social Networkers
Venue: arXiv cs.SI 2015
Year: 2015

A social network consists of a set of actors and a set of relationships between them which describe certain patterns of communication. Most current networks are huge and difficult to analyze and visualize. One of the methods frequently used is to extract the most important features, namely to create a certain abstraction, that is the transformation of a large network to a much smaller one, so the latter is a useful summary of the original one, still keeping the most important characteristics. In the case of a social network it can be achieved in two ways. One is to find groups of actors and present only them and relationships between them. The other is to find actors who play similar roles and to construct a smaller network in which the connection between the actors would be replaced with connections between the roles. Classifying actors by the roles they are playing in the network can help to understand 'who is who' in a social network. This classification can be very useful, because it gives us a comprehensive view of the network and helps to understand how the network is organized, and to predict how it could behave in the case of certain events (internal or external).
Pareto Optimal Matchings in Many-to-Many Markets with Ties
Venue: arXiv cs.GT 2015
Year: 2015

We consider Pareto-optimal matchings (POMs) in a many-to-many market of applicants and courses where applicants have preferences, which may include ties, over individual courses and lexicographic preferences over sets of courses. Since this is the most general setting examined so far in the literature, our work unifies and generalizes several known results. Specifically, we characterize POMs and introduce the \emph{Generalized Serial Dictatorship Mechanism with Ties (GSDT)} that effectively handles ties via properties of network flows. We show that GSDT can generate all POMs using different priority orderings over the applicants, but it satisfies truthfulness only for certain such orderings. This shortcoming is not specific to our mechanism; we show that any mechanism generating all POMs in our setting is prone to strategic manipulation. This is in contrast to the one-to-one case (with or without ties), for which truthful mechanisms generating all POMs do exist.
Deep Perceptual Mapping for Thermal to Visible Face Recognition
Venue: arXiv cs.CV 2015
Year: 2015

Cross modal face matching between the thermal and visible spectrum is a much de- sired capability for night-time surveillance and security applications. Due to a very large modality gap, thermal-to-visible face recognition is one of the most challenging face matching problem. In this paper, we present an approach to bridge this modality gap by a significant margin. Our approach captures the highly non-linear relationship be- tween the two modalities by using a deep neural network. Our model attempts to learn a non-linear mapping from visible to thermal spectrum while preserving the identity in- formation. We show substantive performance improvement on a difficult thermal-visible face dataset. The presented approach improves the state-of-the-art by more than 10% in terms of Rank-1 identification and bridge the drop in performance due to the modality gap by more than 40%.
Space Filling Curves for 3D Sensor Networks with Complex Topology
Venue: arXiv cs.CG 2015
Year: 2015

Several aspects of managing a sensor network (e.g., motion planning for data mules, serial data fusion and inference) benefit once the network is linearized to a path. The linearization is often achieved by constructing a space filling curve in the domain. However, existing methods cannot handle networks distributed on surfaces of complex topology. This paper presents a novel method for generating space filling curves for 3D sensor networks that are distributed densely on some two-dimensional geometric surface. Our algorithm is completely distributed and constructs a path which gets uniformly, progressively denser as it becomes longer. We analyze the algorithm mathematically and prove that the curve we obtain is dense. Our method is based on the Hodge decomposition theorem and uses holomorphic differentials on Riemann surfaces. The underlying high genus surface is conformally mapped to a union of flat tori and then a proportionally-dense space filling curve on this union is constructed. The pullback of this curve to the original network gives us the desired curve.
Overcoming data scarcity of Twitter: using tweets as bootstrap with application to autism-related topic content analysis
Venue: arXiv cs.SI 2015
Year: 2015

Notwithstanding recent work which has demonstrated the potential of using Twitter messages for content-specific data mining and analysis, the depth of such analysis is inherently limited by the scarcity of data imposed by the 140 character tweet limit. In this paper we describe a novel approach for targeted knowledge exploration which uses tweet content analysis as a preliminary step. This step is used to bootstrap more sophisticated data collection from directly related but much richer content sources. In particular we demonstrate that valuable information can be collected by following URLs included in tweets. We automatically extract content from the corresponding web pages and treating each web page as a document linked to the original tweet show how a temporal topic model based on a hierarchical Dirichlet process can be used to track the evolution of a complex topic structure of a Twitter community. Using autism-related tweets we demonstrate that our method is capable of capturing a much more meaningful picture of information exchange than user-chosen hashtags.
Estimation of Laplacian spectra of direct and strong product graphs
Venue: arXiv cs.DM 2015
Year: 2015

Calculating a product of multiple graphs has been studied in mathematics, engineering, computer science, and more recently in network science, particularly in the context of multilayer networks. One of the important questions to be addressed in this area is how to characterize spectral properties of a product graph using those of its factor graphs. While several such characterizations have already been obtained analytically (mostly for adjacency spectra), characterization of Laplacian spectra of direct product and strong product graphs has remained an open problem. Here we develop practical methods to estimate Laplacian spectra of direct and strong product graphs from spectral properties of their factor graphs using a few heuristic assumptions. Numerical experiments showed that the proposed methods produced reasonable estimation with percentage errors confined within a +/-10% range for most eigenvalues.
Markov Logic Networks for Natural Language Question Answering
Venue: arXiv cs.AI 2015
Year: 2015

Our goal is to answer elementary-level science questions using knowledge extracted automatically from science textbooks, expressed in a subset of first-order logic. Given the incomplete and noisy nature of these automatically extracted rules, Markov Logic Networks (MLNs) seem a natural model to use, but the exact way of leveraging MLNs is by no means obvious. We investigate three ways of applying MLNs to our task. In the first, we simply use the extracted science rules directly as MLN clauses. Unlike typical MLN applications, our domain has long and complex rules, leading to an unmanageable number of groundings. We exploit the structure present in hard constraints to improve tractability, but the formulation remains ineffective. In the second approach, we instead interpret science rules as describing prototypical entities, thus mapping rules directly to grounded MLN assertions, whose constants are then clustered using existing entity resolution methods. This drastically simplifies the network, but still suffers from brittleness. Finally, our third approach, called Praline, uses MLNs to align the lexical elements as well as define and control how inference should be performed in this task. Our experiments, demonstrating a 15\% accuracy boost and a 10x reduction in runtime, suggest that the flexibility and different inference semantics of Praline are a better fit for the natural language question answering task.
An efficient tree decomposition method for permanents and mixed discriminants
Venue: arXiv cs.DM 2015
Year: 2015

We present an efficient algorithm to compute permanents, mixed discriminants and hyperdeterminants of structured matrices and multidimensional arrays (tensors). We describe the sparsity structure of an array in terms of a graph, and we assume that its treewidth, denoted as $ω$, is small. Our algorithm requires $O(n 2^ω)$ arithmetic operations to compute permanents, and $O(n^2 + n 3^ω)$ for mixed discriminants and hyperdeterminants. We finally show that mixed volume computation continues to be hard under bounded treewidth assumptions.
A Gauss-Seidel Iterative Thresholding Algorithm for lq Regularized Least Squares Regression
Venue: arXiv cs.NA 2015
Year: 2015

In recent studies on sparse modeling, $l_q$ ($0<q<1$) regularized least squares regression ($l_q$LS) has received considerable attention due to its superiorities on sparsity-inducing and bias-reduction over the convex counterparts. In this paper, we propose a Gauss-Seidel iterative thresholding algorithm (called GAITA) for solution to this problem. Different from the classical iterative thresholding algorithms using the Jacobi updating rule, GAITA takes advantage of the Gauss-Seidel rule to update the coordinate coefficients. Under a mild condition, we can justify that the support set and sign of an arbitrary sequence generated by GAITA will converge within finite iterations. This convergence property together with the Kurdyka-Łojasiewicz property of ($l_q$LS) naturally yields the strong convergence of GAITA under the same condition as above, which is generally weaker than the condition for the convergence of the classical iterative thresholding algorithms. Furthermore, we demonstrate that GAITA converges to a local minimizer under certain additional conditions. A set of numerical experiments are provided to show the effectiveness, particularly, much faster convergence of GAITA as compared with the classical iterative thresholding algorithms.
Dynamic Topological Mapping with Biobotic Swarms
Venue: arXiv cs.RO 2015
Year: 2015

In this paper, we present an approach for dynamic exploration and mapping of unknown environments using a swarm of biobotic sensing agents, with a stochastic natural motion model and a leading agent (e.g., an unmanned aerial vehicle). The proposed robust mapping technique constructs a topological map of the environment using only encounter information from the swarm. A sliding window strategy is adopted in conjunction with a topological mapping strategy based on local interactions among the swarm in a coordinate-free fashion to obtain local maps of the environment. These maps are then merged into a global topological map which can be visualized using a graphical representation that integrates geometric as well as topological feature of the environment. Localized robust topological features are extracted using tools from topological data analysis. Simulation results have been presented to illustrate and verify the correctness of our dynamic mapping algorithm.
More Than 1700 Years of Word Equations
Venue: arXiv cs.LO 2015
Year: 2015

Geometry and Diophantine equations have been ever-present in mathematics. Diophantus of Alexandria was born in the 3rd century (as far as we know), but a systematic mathematical study of word equations began only in the 20th century. So, the title of the present article does not seem to be justified at all. However, a linear Diophantine equation can be viewed as a special case of a system of word equations over a unary alphabet, and, more importantly, a word equation can be viewed as a special case of a Diophantine equation. Hence, the problem WordEquations: "Is a given word equation solvable?" is intimately related to Hilbert's 10th problem on the solvability of Diophantine equations. This became clear to the Russian school of mathematics at the latest in the mid 1960s, after which a systematic study of that relation began. Here, we review some recent developments which led to an amazingly simple decision procedure for WordEquations, and to the description of the set of all solutions as an EDT0L language.
Tensor principal component analysis via sum-of-squares proofs
Venue: arXiv cs.LG 2015
Year: 2015

We study a statistical model for the tensor principal component analysis problem introduced by Montanari and Richard: Given a order-$3$ tensor $T$ of the form $T = τ\cdot v_0^{\otimes 3} + A$, where $τ\geq 0$ is a signal-to-noise ratio, $v_0$ is a unit vector, and $A$ is a random noise tensor, the goal is to recover the planted vector $v_0$. For the case that $A$ has iid standard Gaussian entries, we give an efficient algorithm to recover $v_0$ whenever $τ\geq ω(n^{3/4} \log(n)^{1/4})$, and certify that the recovered vector is close to a maximum likelihood estimator, all with high probability over the random choice of $A$. The previous best algorithms with provable guarantees required $τ\geq Ω(n)$. In the regime $τ\leq o(n)$, natural tensor-unfolding-based spectral relaxations for the underlying optimization problem break down (in the sense that their integrality gap is large). To go beyond this barrier, we use convex relaxations based on the sum-of-squares method. Our recovery algorithm proceeds by rounding a degree-$4$ sum-of-squares relaxations of the maximum-likelihood-estimation problem for the statistical model. To complement our algorithmic results, we show that degree-$4$ sum-of-squares relaxations break down for $τ\leq O(n^{3/4}/\log(n)^{1/4})$, which demonstrates that improving our current guarantees (by more than logarithmic factors) would require new techniques or might even be intractable. Finally, we show how to exploit additional problem structure in order to solve our sum-of-squares relaxations, up to some approximation, very efficiently. Our fastest algorithm runs in nearly-linear time using shifted (matrix) power iteration and has similar guarantees as above. The analysis of this algorithm also confirms a variant of a conjecture of Montanari and Richard about singular vectors of tensor unfoldings.
Unboundedness and Downward Closures of Higher-Order Pushdown Automata
Venue: arXiv cs.FL 2015
Year: 2015

We show the diagonal problem for higher-order pushdown automata (HOPDA), and hence the simultaneous unboundedness problem, is decidable. From recent work by Zetzsche this means that we can construct the downward closure of the set of words accepted by a given HOPDA. This also means we can construct the downward closure of the Parikh image of a HOPDA. Both of these consequences play an important role in verifying concurrent higher-order programs expressed as HOPDA or safe higher-order recursion schemes.
Aren't we all nearest neighbors: Spatial trees, high dimensional reductions and batch nearest neighbor search
Venue: arXiv cs.CG 2015
Year: 2015

We start with a review of the pervasiveness of the nearest neighbor search problem and techniques used to solve it along with some experimental results. In the second chapter, we show reductions between two different classes of geo- metric proximity problems: the nearest neighbor problems to solve the Euclidean minimum spanning tree problem and the farthest neighbor problems to solve the k-centers problem. In the third chapter, we unify spatial partitioning trees un- der one framework the meta-tree. Finally, we propose a dual tree algorithm for Bichromatic Closest Pair and measure the complexity of batch nearest neighbor search.