OpenRaiser/Intern-Atlas huggingface dataset:
On Distributed Internal Model Principle for Output Regulation over Time-Varying Networks of Linear Heterogeneous Agents
Venue: arXiv cs.SY 2016
Year: 2016

We study a multi-agent output regulation problem, where not all agents have access to the exosystem's dynamics. We propose a distributed controller that solves the problem for linear, heterogeneous, and uncertain agent dynamics as well as time-varying directed networks. The distributed controller consists of two parts: (1) an exosystem generator that creates a local copy of the exosystem dynamics by using consensus protocols, and (2) a dynamic compensator that uses (again) consensus to approach the internal model of the exosystem and thereby achieves perfect output regulation. Our approach leverages methods from internal model based controller synthesis, multi-agent consensus over directed networks, and stability of time-varying linear systems; the derived result is an adaptation of the (centralized) internal model principle to the distributed, networked setting.
SWIPT in 3-D Bipolar Ad Hoc Networks with Sectorized Antennas
Venue: arXiv cs.IT 2016
Year: 2016

In this letter, we study the simultaneous wireless information and power transfer (SWIPT) concept in 3-D bipolar ad hoc networks with spatial randomness. Due to three spatial dimensions of the network, we introduce a 3-D antenna sectorization that exploits the horizontal and the vertical spatial separation. The impact of 3-D antenna sectorization on SWIPT performance is evaluated for the power-splitting technique by using stochastic geometry tools. Theoretical and numerical results show that 3-D sectorization achieves a more accurate beam steering in the 3-D space, which can be beneficial for both the information and the power transfer.
An Attentive Neural Architecture for Fine-grained Entity Type Classification
Venue: arXiv cs.CL 2016
Year: 2016

In this work we propose a novel attention-based neural network model for the task of fine-grained entity type classification that unlike previously proposed models recursively composes representations of entity mention contexts. Our model achieves state-of-the-art performance with 74.94% loose micro F1-score on the well-established FIGER dataset, a relative improvement of 2.59%. We also investigate the behavior of the attention mechanism of our model and observe that it can learn contextual linguistic expressions that indicate the fine-grained category memberships of an entity.
Link Capacity Planning for Fault Tolerant Operation in Hybrid SDN/OSPF Networks
Venue: arXiv cs.NI 2016
Year: 2016

Link capacity dimensioning is the periodic task where ISPs have to make provisions for sudden traffic bursts and network failures to assure uninterrupted operations. This provision comes in the form of link working capacities with noticeable amounts of headroom, i.e., spare capacities that are used in case of congestions or network failures. Distributed routing protocols like OSPF provide convergence after network failures and have proven their reliable operation over decades, but require overprovisioning and headroom of over 50%. However, SDN has recently been proposed to either replace or work together with OSPF in routing Internet traffic. This paper addresses the question of how to robustly dimension the link capacities in emerging hybrid SDN/OSPF networks. We analyze the networks with various implementations of hybrid SDN/OSPF control planes, and show that our idea of SDN Partitioning requires less amounts of spare capacity compared to legacy or other hybrid SDN/OSPF schemes, outperformed only by a full SDN deployment.
Using Apache Lucene to Search Vector of Locally Aggregated Descriptors
Venue: arXiv cs.CV 2016
Year: 2016

Surrogate Text Representation (STR) is a profitable solution to efficient similarity search on metric space using conventional text search engines, such as Apache Lucene. This technique is based on comparing the permutations of some reference objects in place of the original metric distance. However, the Achilles heel of STR approach is the need to reorder the result set of the search according to the metric distance. This forces to use a support database to store the original objects, which requires efficient random I/O on a fast secondary memory (such as flash-based storages). In this paper, we propose to extend the Surrogate Text Representation to specifically address a class of visual metric objects known as Vector of Locally Aggregated Descriptors (VLAD). This approach is based on representing the individual sub-vectors forming the VLAD vector with the STR, providing a finer representation of the vector and enabling us to get rid of the reordering phase. The experiments on a publicly available dataset show that the extended STR outperforms the baseline STR achieving satisfactory performance near to the one obtained with the original VLAD vectors.
Approximation Algorithms for the Maximum Carpool Matching Problem
Venue: arXiv cs.DM 2016
Year: 2016

The Maximum Carpool Matching problem is a star packing problem in directed graphs. Formally, given a directed graph $G = (V, A)$, a capacity function $ c: V \to N $, and a weight function $w : A \to R $, a feasible \emph{carpool matching} is a triple $(P, D, M)$, where $P$ (passengers) and $D$ (drivers) form a partition of $V$, and $M$ is a subset of $A \cap (P \times D)$, under the constraints that for every vertex $d \in D$, $din(d) \leq c(d)$, and for every vertex $p \in P$, $dout(p) \leq 1$. In the Maximum Carpool Matching problem we seek for a matching $(P, D, M)$ that maximizes the total weight of $M$. The problem arises when designing an online carpool service, such as Zimride~\cite{zimride}, that tries to connect between passengers and drivers based on (arbitrary) similarity function. The problem is known to be NP-hard, even for uniform weights and without capacity constraints. We present a $3$-approximation algorithm for the problem and $2$-approximation algorithm for the unweighted variant of the problem.
Channel Dynamics and SNR Tracking in Millimeter Wave Cellular Systems
Venue: arXiv cs.NI 2016
Year: 2016

The millimeter wave (mmWave) frequencies are likely to play a significant role in fifth-generation (5G) cellular systems. A key challenge in developing systems in these bands is the potential for rapid channel dynamics: since mmWave signals are blocked by many materials, small changes in the position or orientation of the handset relative to objects in the environment can cause large swings in the channel quality. This paper addresses the issue of tracking the signal to noise ratio (SNR), which is an essential procedure for rate prediction, handover and radio link failure detection. A simple method for estimating the SNR from periodic synchronization signals is considered. The method is then evaluated using real experiments in common blockage scenarios combined with outdoor statistical models.
Formal Design of Robot Integrated Task and Motion Planning
Venue: arXiv cs.RO 2016
Year: 2016

Integrated Task and Motion Planning (ITMP) for mobile robots in a dynamic environment with moving obstacles is a challenging research question and attracts more and more attentions recently. Most existing methods either restrict to static environments or lack performance guarantees. This motivates us to investigate the ITMP problem using formal methods and propose a bottom-up compositional design approach called CoSMoP (Composition of Safe Motion Primitives). Our basic idea is to synthesize a global motion plan through composing simple local moves and actions, and to achieve its performance guarantee through modular and incremental verifications. The design consists of two steps. First, basic motion primitives are designed and verified locally. Then, a global motion path is built upon these certified motion primitives by concatenating them together. In particular, we model the motion primitives as hybrid automata and verify their safety through formulating as Differential Dynamic Logic (d$\mathcal{L}$). Furthermore, these proven safe motion primitives are composed based on an encoding to Satisfiability Modulo Theories (SMT) that takes into account the geometric constraints. Since d$\mathcal{L}$ allows compositional verification, the sequential composition of the safe motion primitives also preserves safety properties. Therefore, the CoSMoP generates correct plans for given task specifications that are formally proven safe even for moving obstacles. Illustrative examples are presented to show the effectiveness of the methods.
Sketching and Neural Networks
Venue: arXiv cs.LG 2016
Year: 2016

High-dimensional sparse data present computational and statistical challenges for supervised learning. We propose compact linear sketches for reducing the dimensionality of the input, followed by a single layer neural network. We show that any sparse polynomial function can be computed, on nearly all sparse binary vectors, by a single layer neural network that takes a compact sketch of the vector as input. Consequently, when a set of sparse binary vectors is approximately separable using a sparse polynomial, there exists a single-layer neural network that takes a short sketch as input and correctly classifies nearly all the points. Previous work has proposed using sketches to reduce dimensionality while preserving the hypothesis class. However, the sketch size has an exponential dependence on the degree in the case of polynomial classifiers. In stark contrast, our approach of using improper learning, using a larger hypothesis class allows the sketch size to have a logarithmic dependence on the degree. Even in the linear case, our approach allows us to improve on the pesky $O({1}/{γ^2})$ dependence of random projections, on the margin $γ$. We empirically show that our approach leads to more compact neural networks than related methods such as feature hashing at equal or better performance.
New Deterministic Approximation Algorithms for Fully Dynamic Matching
Venue: arXiv cs.DS 2016
Year: 2016

We present two deterministic dynamic algorithms for the maximum matching problem. (1) An algorithm that maintains a $(2+ε)$-approximate maximum matching in general graphs with $O(\text{poly}(\log n, 1/ε))$ update time. (2) An algorithm that maintains an $α_K$ approximation of the {\em value} of the maximum matching with $O(n^{2/K})$ update time in bipartite graphs, for every sufficiently large constant positive integer $K$. Here, $1\leq α_K < 2$ is a constant determined by the value of $K$. Result (1) is the first deterministic algorithm that can maintain an $o(\log n)$-approximate maximum matching with polylogarithmic update time, improving the seminal result of Onak et al. [STOC 2010]. Its approximation guarantee almost matches the guarantee of the best {\em randomized} polylogarithmic update time algorithm [Baswana et al. FOCS 2011]. Result (2) achieves a better-than-two approximation with {\em arbitrarily small polynomial} update time on bipartite graphs. Previously the best update time for this problem was $O(m^{1/4})$ [Bernstein et al. ICALP 2015], where $m$ is the current number of edges in the graph.
Multi-agent evolutionary systems for the generation of complex virtual worlds
Venue: arXiv cs.NE 2016
Year: 2016

Modern films, games and virtual reality applications are dependent on convincing computer graphics. Highly complex models are a requirement for the successful delivery of many scenes and environments. While workflows such as rendering, compositing and animation have been streamlined to accommodate increasing demands, modelling complex models is still a laborious task. This paper introduces the computational benefits of an Interactive Genetic Algorithm (IGA) to computer graphics modelling while compensating the effects of user fatigue, a common issue with Interactive Evolutionary Computation. An intelligent agent is used in conjunction with an IGA that offers the potential to reduce the effects of user fatigue by learning from the choices made by the human designer and directing the search accordingly. This workflow accelerates the layout and distribution of basic elements to form complex models. It captures the designer's intent through interaction, and encourages playful discovery.
An algorithm for the optimization of finite element integration loops
Venue: arXiv cs.MS 2016
Year: 2016

We present an algorithm for the optimization of a class of finite element integration loop nests. This algorithm, which exploits fundamental mathematical properties of finite element operators, is proven to achieve a locally optimal operation count. In specified circumstances the optimum achieved is global. Extensive numerical experiments demonstrate significant performance improvements over the state of the art in finite element code generation in almost all cases. This validates the effectiveness of the algorithm presented here, and illustrates its limitations.
Decoding Interleaved Gabidulin Codes using Alekhnovich's Algorithm
Venue: arXiv cs.IT 2016
Year: 2016

We prove that Alekhnovich's algorithm can be used for row reduction of skew polynomial matrices. This yields an $O(\ell^3 n^{(ω+1)/2} \log(n))$ decoding algorithm for $\ell$-Interleaved Gabidulin codes of length $n$, where $ω$ is the matrix multiplication exponent, improving in the exponent of $n$ compared to previous results.
Local Binary Pattern for Word Spotting in Handwritten Historical Document
Venue: arXiv cs.CV 2016
Year: 2016

Digital libraries store images which can be highly degraded and to index this kind of images we resort to word spot- ting as our information retrieval system. Information retrieval for handwritten document images is more challenging due to the difficulties in complex layout analysis, large variations of writing styles, and degradation or low quality of historical manuscripts. This paper presents a simple innovative learning-free method for word spotting from large scale historical documents combining Local Binary Pattern (LBP) and spatial sampling. This method offers three advantages: firstly, it operates in completely learning free paradigm which is very different from unsupervised learning methods, secondly, the computational time is significantly low because of the LBP features which are very fast to compute, and thirdly, the method can be used in scenarios where annotations are not available. Finally we compare the results of our proposed retrieval method with the other methods in the literature.
3D Massive MIMO Systems: Modeling and Performance Analysis
Venue: arXiv cs.IT 2016
Year: 2016

Multiple-input-multiple-output (MIMO) systems of current LTE releases are capable of adaptation in the azimuth only. Recently, the trend is to enhance system performance by exploiting the channel's degrees of freedom in the elevation, which necessitates the characterization of 3D channels. We present an information-theoretic channel model for MIMO systems that supports the elevation dimension. The model is based on the principle of maximum entropy, which enables us to determine the distribution of the channel matrix consistent with the prior information on the angles. Based on this model, we provide analytical expression for the cumulative density function (CDF) of the mutual information (MI) for systems with a single receive and finite number of transmit antennas in the general signal-to-interference-plus-noise-ratio (SINR) regime. The result is extended to systems with finite receive antennas in the low SINR regime. A Gaussian approximation to the asymptotic behavior of MI distribution is derived for the large number of transmit antennas and paths regime. We corroborate our analysis with simulations that study the performance gains realizable through meticulous selection of the transmit antenna downtilt angles, confirming the potential of elevation beamforming to enhance system performance. The results are directly applicable to the analysis of 5G 3D-Massive MIMO-systems.
Comments on "Characteristic matrix of covering and its application to Boolean matrix decomposition[Information Sciences 263(1), 186-197, 2014]"
Venue: arXiv cs.IT 2016
Year: 2016

In this note, we show some improvements for Theorem 7 and Example 8 in Shiping Wang[Information Sciences 263(1), 186-197, 2014]. Concretely, we study further the sixth lower and upper approximations of sets for covering approximation spaces. Furthermore, we present the sixth dual lower and upper approximations of sets for covering approximation spaces. We also construct the sixth dual lower and upper approximations of sets from the view of matrix. Throughout, we use the same notations as Shiping Wang[Information Sciences 263(1), 186-197, 2014].
Jansen-MIDAS: a multi-level photomicrograph segmentation software based on isotropic undecimated wavelets
Venue: arXiv cs.CV 2016
Year: 2016

Image segmentation, the process of separating the elements within an image, is frequently used for obtaining information from photomicrographs. However, segmentation methods should be used with reservations: incorrect segmentation can mislead when interpreting regions of interest (ROI), thus decreasing the success rate of additional procedures. Multi-Level Starlet Segmentation (MLSS) and Multi-Level Starlet Optimal Segmentation (MLSOS) were developed to address the photomicrograph segmentation deficiency on general tools. These methods gave rise to Jansen-MIDAS, an open-source software which a scientist can use to obtain a multi-level threshold segmentation of his/hers photomicrographs. This software is presented in two versions: a text-based version, for GNU Octave, and a graphical user interface (GUI) version, for MathWorks MATLAB. It can be used to process several types of images, becoming a reliable alternative to the scientist.
Subexponential parameterized algorithms for planar and apex-minor-free graphs via low treewidth pattern covering
Venue: arXiv cs.DS 2016
Year: 2016

We prove the following theorem. Given a planar graph $G$ and an integer $k$, it is possible in polynomial time to randomly sample a subset $A$ of vertices of $G$ with the following properties: (i) $A$ induces a subgraph of $G$ of treewidth $\mathcal{O}(\sqrt{k}\log k)$, and (ii) for every connected subgraph $H$ of $G$ on at most $k$ vertices, the probability that $A$ covers the whole vertex set of $H$ is at least $(2^{\mathcal{O}(\sqrt{k}\log^2 k)}\cdot n^{\mathcal{O}(1)})^{-1}$, where $n$ is the number of vertices of $G$. Together with standard dynamic programming techniques for graphs of bounded treewidth, this result gives a versatile technique for obtaining (randomized) subexponential parameterized algorithms for problems on planar graphs, usually with running time bound $2^{\mathcal{O}(\sqrt{k} \log^2 k)} n^{\mathcal{O}(1)}$. The technique can be applied to problems expressible as searching for a small, connected pattern with a prescribed property in a large host graph, examples of such problems include Directed $k$-Path, Weighted $k$-Path, Vertex Cover Local Search, and Subgraph Isomorphism, among others. Up to this point, it was open whether these problems can be solved in subexponential parameterized time on planar graphs, because they are not amenable to the classic technique of bidimensionality. Furthermore, all our results hold in fact on any class of graphs that exclude a fixed apex graph as a minor, in particular on graphs embeddable in any fixed surface.
Constructing error-correcting binary codes using transitive permutation groups
Venue: arXiv cs.IT 2016
Year: 2016

Let $A_2(n,d)$ be the maximum size of a binary code of length $n$ and minimum distance $d$. In this paper we present the following new lower bounds: $A_2(18,4) \ge 5632$, $A_2(21,4) \ge 40960$, $A_2(22,4) \ge 81920$, $A_2(23,4) \ge 163840$, $A_2(24,4) \ge 327680$, $A_2(24,10) \ge 136$, and $A_2(25,6) \ge 17920$. The new lower bounds are a result of a systematic computer search over transitive permutation groups.
Decomposition and Analysis of Technological domains for better understanding of Technological Structure
Venue: arXiv cs.DL 2016
Year: 2016

Patents represent one of the most complete sources of information related to technological change. This paper presents three months of research on U.S. patents in the field of patent analysis. The methodology consists of using search terms to locate the most representative international and US patent classes and determines the overlap of those classes to arrive at the final set of patents and using the prediction model developed by Benson and Magee to calculate the technological improvement rate for the technological domains. My research focused on the Biochemical Pharmacology technological area and selecting relevant patents for technological domains and sub-domains within this area. The goal is to better understand structure of technology domain and understand how fast the domains and their sub-domains progress. The method I used is developed by Benson and Magee which is called the Classification Overlap Method1, it provides a reliable and largely automated way to break the patent database into understandable technological domains where progress can be measured.
Cuckoo Filter: Simplification and Analysis
Venue: arXiv cs.DS 2016
Year: 2016

The cuckoo filter data structure of Fan, Andersen, Kaminsky, and Mitzenmacher (CoNEXT 2014) performs the same approximate set operations as a Bloom filter in less memory, with better locality of reference, and adds the ability to delete elements as well as to insert them. However, until now it has lacked theoretical guarantees on its performance. We describe a simplified version of the cuckoo filter using fewer hash function calls per query. With this simplification, we provide the first theoretical performance guarantees on cuckoo filters, showing that they succeed with high probability whenever their fingerprint length is large enough.
Deep Adaptive Network: An Efficient Deep Neural Network with Sparse Binary Connections
Venue: arXiv cs.LG 2016
Year: 2016

Deep neural networks are state-of-the-art models for understanding the content of images, video and raw input data. However, implementing a deep neural network in embedded systems is a challenging task, because a typical deep neural network, such as a Deep Belief Network using 128x128 images as input, could exhaust Giga bytes of memory and result in bandwidth and computing bottleneck. To address this challenge, this paper presents a hardware-oriented deep learning algorithm, named as the Deep Adaptive Network, which attempts to exploit the sparsity in the neural connections. The proposed method adaptively reduces the weights associated with negligible features to zero, leading to sparse feedforward network architecture. Furthermore, since the small proportion of important weights are significantly larger than zero, they can be robustly thresholded and represented using single-bit integers (-1 and +1), leading to implementations of deep neural networks with sparse and binary connections. Our experiments showed that, for the application of recognizing MNIST handwritten digits, the features extracted by a two-layer Deep Adaptive Network with about 25% reserved important connections achieved 97.2% classification accuracy, which was almost the same with the standard Deep Belief Network (97.3%). Furthermore, for efficient hardware implementations, the sparse-and-binary-weighted deep neural network could save about 99.3% memory and 99.9% computation units without significant loss of classification accuracy for pattern recognition applications.