OpenRaiser/Intern-Atlas huggingface dataset:
Rate-Distortion Region of a Gray-Wyner Model with Side Information
Venue: arXiv cs.IT 2017
Year: 2017

In this work, we establish a full single-letter characterization of the rate-distortion region of an instance of the Gray-Wyner model with side information at the decoders. Specifically, in this model an encoder observes a pair of memoryless, arbitrarily correlated, sources $(S^n_1,S^n_2)$ and communicates with two receivers over an error-free rate-limited link of capacity $R_0$, as well as error-free rate-limited individual links of capacities $R_1$ to the first receiver and $R_2$ to the second receiver. Both receivers reproduce the source component $S^n_2$ losslessly; and Receiver $1$ also reproduces the source component $S^n_1$ lossily, to within some prescribed fidelity level $D_1$. Also, Receiver $1$ and Receiver $2$ are equipped respectively with memoryless side information sequences $Y^n_1$ and $Y^n_2$. Important in this setup, the side information sequences are arbitrarily correlated among them, and with the source pair $(S^n_1,S^n_2)$; and are not assumed to exhibit any particular ordering. Furthermore, by specializing the main result to two Heegard-Berger models with successive refinement and scalable coding, we shed light on the roles of the common and private descriptions that the encoder should produce and what they should carry optimally. We develop intuitions by analyzing the developed single-letter optimal rate-distortion regions of these models, and discuss some insightful binary examples.
Secure Lossy Source Coding for Some Classes of Helper and Gray-Wyner Models
Venue: arXiv cs.IT 2017
Year: 2017

In this work, we investigate two source coding models, a \emph{Helper} problem and a \emph{Gray-Wyner} problem, under equivocation constraints. Specifically, in the Helper problem, an encoder communicates with a legitimate receiver through noise-free rate-limited public and private links; and an external passive eavesdropper intercepts every information that is sent on the public link. We study two classes of this model: i) when a pair of arbitrarily correlated discrete memoryless sources is to be encoded such that one component has to be recovered lossily at the legitimate receiver while the equivocation about both components at the eavesdropper must be maintained no smaller than some prescribed level; and ii) when the legitimate receiver reproduces both components, one of which, that is recovered losslessly, has to be concealed from the eavesdropper to some equivocation level. For both classes problems, we establish single-letter characterizations of optimal rate-distortion-equivocation tradeoffs in the discrete memoryless case. Next, we extend our results to the case of two legitimate receivers, i.e., Gray-Wyner network with equivocation constraints. Here, two legitimate receivers are connected to the encoder each through a dedicated error-free private link as well as a common error-free public link; and an external passive eavesdropper overhears on the public link. We study two classes of this model that are extensions of the aforementioned instances of Helper problems to the case of two receivers. For each of the two classes, we establish a single-letter characterization of the optimal rate-distortion-equivocation region. Throughout the paper, the analysis sheds light on the role of the private links, and we illustrate the results by computing them for some binary examples. Also, we make some meaningful connections, e.g., with problems of secret-sharing and encryption.
Multivariate Analysis for Computing Maxima in High Dimensions
Venue: arXiv cs.CG 2017
Year: 2017

We study the problem of computing the \textsc{Maxima} of a set of $n$ $d$-dimensional points. For dimensions 2 and 3, there are algorithms to solve the problem with order-oblivious instance-optimal running time. However, in higher dimensions there is still room for improvements. We present an algorithm sensitive to the structural entropy of the input set, which improves the running time, for large classes of instances, on the best solution for \textsc{Maxima} to date for $d \ge 4$.
Truncation-free Hybrid Inference for DPMM
Venue: arXiv cs.LG 2017
Year: 2017

Dirichlet process mixture models (DPMM) are a cornerstone of Bayesian non-parametrics. While these models free from choosing the number of components a-priori, computationally attractive variational inference often reintroduces the need to do so, via a truncation on the variational distribution. In this paper we present a truncation-free hybrid inference for DPMM, combining the advantages of sampling-based MCMC and variational methods. The proposed hybridization enables more efficient variational updates, while increasing model complexity only if needed. We evaluate the properties of the hybrid updates and their empirical performance in single- as well as mixed-membership models. Our method is easy to implement and performs favorably compared to existing schemas.
The 2-adic complexity of a class of binary sequences with almost optimal autocorrelation
Venue: arXiv cs.IT 2017
Year: 2017

Pseudo-random sequences with good statistical property, such as low autocorrelation, high linear complexity and large 2-adic complexity, have been applied in stream cipher. In general, it is difficult to give both the linear complexity and 2-adic complexity of a periodic binary sequence. Cai and Ding \cite{Cai Ying} gave a class of sequences with almost optimal autocorrelation by constructing almost difference sets. Wang \cite{Wang Qi} proved that one type of those sequences by Cai and Ding has large linear complexity. Sun et al. \cite{Sun Yuhua} showed that another type of sequences by Cai and Ding has also large linear complexity. Additionally, Sun et al. also generalized the construction by Cai and Ding using $d$-form function with difference-balanced property. In this paper, we first give the detailed autocorrelation distribution of the sequences was generalized from Cai and Ding \cite{Cai Ying} by Sun et al. \cite{Sun Yuhua}. Then, inspired by the method of Hu \cite{Hu Honggang}, we analyse their 2-adic complexity and give a lower bound on the 2-adic complexity of these sequences. Our result show that the 2-adic complexity of these sequences is at least $N-\mathrm{log}_2\sqrt{N+1}$ and that it reach $N-1$ in many cases, which are large enough to resist the rational approximation algorithm (RAA) for feedback with carry shift registers (FCSRs).
Performance Analysis of Low-Density Parity-Check Codes over 2D Interference Channels via Density Evolution
Venue: arXiv cs.IT 2017
Year: 2017

The theoretical analysis of detection and decoding of low-density parity-check (LDPC) codes transmitted over channels with two-dimensional (2D) interference and additive white Gaussian noise (AWGN) is provided in this paper. The detection and decoding system adopts the joint iterative detection and decoding scheme (JIDDS) in which the log-domain sum-product algorithm is adopted to decode the LDPC codes. The graph representations of the JIDDS are explained. Using the graph representations, we prove that the message-flow neighborhood of the detection and decoding system will be tree-like for a sufficiently long code length. We further confirm that the performance of the JIDDS will concentrate around the performance in which message-flow neighborhood is tree-like. Based on the tree-like message-flow neighborhood, we employ a modified density evolution algorithm to track the message densities during the iterations. A threshold is calculated using the density evolution algorithm which can be considered as the theoretical performance limit of the system. Simulation results demonstrate that the modified density evolution is effective in analyzing the performance of 2D interference systems.
Learning Linear Dynamical Systems with High-Order Tensor Data for Skeleton based Action Recognition
Venue: arXiv cs.CV 2017
Year: 2017

In recent years, there has been renewed interest in developing methods for skeleton-based human action recognition. A skeleton sequence can be naturally represented as a high-order tensor time series. In this paper, we model and analyze tensor time series with Linear Dynamical System (LDS) which is the most common for encoding spatio-temporal time-series data in various disciplines dut to its relative simplicity and efficiency. However, the traditional LDS treats the latent and observation state at each frame of video as a column vector. Such a vector representation fails to take into account the curse of dimensionality as well as valuable structural information with human action. Considering this fact, we propose generalized Linear Dynamical System (gLDS) for modeling tensor observation in the time series and employ Tucker decomposition to estimate the LDS parameters as action descriptors. Therefore, an action can be represented as a subspace corresponding to a point on a Grassmann manifold. Then we perform classification using dictionary learning and sparse coding over Grassmann manifold. Experiments on MSR Action3D Dataset, UCF Kinect Dataset and Northwestern-UCLA Multiview Action3D Dataset demonstrate that our proposed method achieves superior performance to the state-of-the-art algorithms.
Computer-aided molecular design: An introduction and review of tools, applications, and solution techniques
Venue: arXiv cs.CE 2017
Year: 2017

This article provides an introduction to and review of the field of computer-aided molecular design (CAMD). It is intended to be approachable for the absolute beginner as well as useful to the seasoned CAMD practitioner. We begin by discussing various quantitative structure-property relationships (QSPRs) which have been demonstrated to work well with CAMD problems. The methods discussed in this article are (1) group contribution methods, (2) topological indices, and (3) signature descriptors. Next, we present general optimization formulations for various forms of the CAMD problem. Common design constraints are discussed and structural feasibility constraints are provided for the three types of QSPRs addressed. We then detail useful techniques for approaching CAMD optimization problems, including decomposition methods, heuristic approaches, and mathematical programming strategies. Finally, we discuss many applications that have been addressed using CAMD.
Making compression algorithms for Unicode text
Venue: arXiv cs.IT 2017
Year: 2017

The majority of online content is written in languages other than English, and is most commonly encoded in UTF-8, the world's dominant Unicode character encoding. Traditional compression algorithms typically operate on individual bytes. While this approach works well for the single-byte ASCII encoding, it works poorly for UTF-8, where characters often span multiple bytes. Previous research has focused on developing Unicode compressors from scratch, which often failed to outperform established algorithms such as bzip2. We develop a technique to modify byte-based compressors to operate directly on Unicode characters, and implement variants of LZW and PPM that apply this technique. We find that our method substantially improves compression effectiveness on a UTF-8 corpus, with our PPM variant outperforming the state-of-the-art PPMII compressor. On ASCII and binary files, our variants perform similarly to the original unmodified compressors.
Cooperative Transmissions in Ultra-Dense Networks under a Bounded Dual-Slope Path Loss Model
Venue: arXiv cs.NI 2017
Year: 2017

In an Ultra-dense network (UDN) where there are more base stations (BSs) than active users, it is possible that many BSs are instantaneously left idle. Thus, how to utilize these dormant BSs by means of cooperative transmission is an interesting question. In this paper, we investigate the performance of a UDN with two types of cooperation schemes: non-coherent joint transmission (JT) without channel state information (CSI) and coherent JT with full CSI knowledge. We consider a bounded dual-slope path loss model to describe UDN environments where a user has several BSs in the near-field and the rest in the far-field. Numerical results show that non-coherent JT cannot improve the user spectral efficiency (SE) due to the simultaneous increment in signal and interference powers. For coherent JT, the achievable SE gain depends on the range of near-field, the relative densities of BSs and users, and the CSI accuracy. Finally, we assess the energy efficiency (EE) of cooperation in UDN. Despite costing extra energy consumption, cooperation can still improve EE under certain conditions.
Probabilistic Termination by Monadic Affine Sized Typing (Long Version)
Venue: arXiv cs.PL 2017
Year: 2017

We introduce a system of monadic affine sized types, which substantially generalise usual sized types, and allows this way to capture probabilistic higher-order programs which terminate almost surely. Going beyond plain, strong normalisation without losing soundness turns out to be a hard task, which cannot be accomplished without a richer, quantitative notion of types, but also without imposing some affinity constraints. The proposed type system is powerful enough to type classic examples of probabilistically terminating programs such as random walks. The way typable programs are proved to be almost surely terminating is based on reducibility, but requires a substantial adaptation of the technique.
Optimal Repair Schemes for Some Families of Full-Length Reed-Solomon Codes
Venue: arXiv cs.IT 2017
Year: 2017

Reed-Solomon codes have found many applications in practical storage systems, but were until recently considered unsuitable for distributed storage applications due to the widely-held belief that they have poor repair bandwidth. The work of Guruswami and Wootters (STOC'16) has shown that one can actually perform bandwidth-efficient linear repair with Reed-Solomon codes: When the codes are over the field $\mathbb{F}_{q^t}$ and the number of parities $r \geq q^s$, where $(t-s)$ divides $t$, there exists a linear scheme that achieves a repair bandwidth of $(n-1)(t-s)\log_2 q$ bits. We extend this result by showing the existence of such a linear repair scheme for every $1 \leq s < t$. Moreover, our new schemes are optimal among all linear repair schemes for Reed-Solomon codes when $n = q^t$ and $r = q^s$. Additionally, we improve the lower bound on the repair bandwidth for Reed-Solomon codes, also established in the work of Guruswami and Wootters.
Taxi-based Emergency Medical System
Venue: arXiv cs.OH 2017
Year: 2017

In case of a severe accident, the key to saving lives is the time between the incident and when the victim receives treatment from the first-responders. In areas with well designed emergency medical systems, the time for an ambulance to arrive at the accident location is often not too long. However, in many low and middle income countries, it usually takes much longer for an ambulance to arrive at the accident location due to lack of proper services. On the other hand, with ubiquitous wireless connectivity, and emergence of radio based taxis, it seems feasible to build a low-cost emergency response system based on taxi service. In this report, we explore one such solution for deployment of a taxi-based emergency response systems using reinforcement learning.
Automatic SDF-based Code Generation from Simulink Models for Embedded Software Development
Venue: arXiv cs.DC 2017
Year: 2017

Matlab/Simulink is a wide-spread tool for model-based design of embedded systems. Supporting hierarchy, domain specific building blocks, functional simulation and automatic code-generation, makes it well-suited for the design of control and signal processing systems. In this work, we propose an automated translation methodology for a subset of Simulink models to Synchronous dataflow Graphs (SDFGs) including the automatic code-generation of SDF-compatible embedded code. A translation of Simulink models to SDFGs, is very suitable due to Simulink actor-oriented modeling nature, allowing the application of several optimization techniques from the SDFG domain. Because of their well-defined semantics, SDFGs can be analyzed at compiling phase to obtain deadlock-free and memory-efficient schedules. In addition, several real-time analysis methods exist which allow throughput-optimal mappings of SDFGs to Multiprocessor on Chip (MPSoC) while guaranteeing upper-bounded latencies. The correctness of our translation is justified by integrating the SDF generated code as a software-in-the-loop (SIL) and comparing its results with the results of the model-in-the-loop (MIL) simulation of reference Simulink models. The translation is demonstrated with the help of two case studies: a Transmission Controller Unit (TCU) and an Automatic Climate Control.
It's Always April Fools' Day! On the Difficulty of Social Network Misinformation Classification via Propagation Features
Venue: arXiv cs.SI 2017
Year: 2017

Given the huge impact that Online Social Networks (OSN) had in the way people get informed and form their opinion, they became an attractive playground for malicious entities that want to spread misinformation, and leverage their effect. In fact, misinformation easily spreads on OSN and is a huge threat for modern society, possibly influencing also the outcome of elections, or even putting people's life at risk (e.g., spreading "anti-vaccines" misinformation). Therefore, it is of paramount importance for our society to have some sort of "validation" on information spreading through OSN. The need for a wide-scale validation would greatly benefit from automatic tools. In this paper, we show that it is difficult to carry out an automatic classification of misinformation considering only structural properties of content propagation cascades. We focus on structural properties, because they would be inherently difficult to be manipulated, with the the aim of circumventing classification systems. To support our claim, we carry out an extensive evaluation on Facebook posts belonging to conspiracy theories (as representative of misinformation), and scientific news (representative of fact-checked content). Our findings show that conspiracy content actually reverberates in a way which is hard to distinguish from the one scientific content does: for the classification mechanisms we investigated, classification F1-score never exceeds 0.65 during content propagation stages, and is still less than 0.7 even after propagation is complete.
Polynomial-Time Proactive Synthesis of Tree-to-String Functions from Examples
Venue: arXiv cs.FL 2017
Year: 2017

Synthesis from examples enables non-expert users to generate programs by specifying examples of their behavior. A domain-specific form of such synthesis has been recently deployed in a widely used spreadsheet software product. In this paper we contribute to foundations of such techniques and present a complete algorithm for synthesis of a class of recursive functions defined by structural recursion over a given algebraic data type definition. The functions we consider map an algebraic data type to a string; they are useful for, e.g., pretty printing and serialization of programs and data. We formalize our problem as learning deterministic sequential top-down tree-to-string transducers with a single state. The first problem we consider is learning a tree-to-string transducer from any set of input/output examples provided by the user. We show that this problem is NP-complete in general, but can be solved in polynomial time under a (practically useful) closure condition that each subtree of a tree in the input/output example set is also part of the input/output examples. Because coming up with relevant input/output examples may be difficult for the user while creating hard constraint problems for the synthesizer, we also study a more automated active learning scenario in which the algorithm chooses the inputs for which the user provides the outputs. Our algorithm asks a worst-case linear number of queries as a function of the size of the algebraic data type definition to determine a unique transducer.
An Object-oriented approach to Robotic planning using Taxi domain
Venue: arXiv cs.RO 2017
Year: 2017

This paper aims to implement Object-Oriented Markov Decision Process (OO-MDPs) for goal planning and navigation of robot in an indoor environment. We use the OO-MDP representation of the environment which is a natural way of modeling the environment based on objects and their interactions. The paper aims to extend the well known Taxi domain example which has been tested on grid world environment to robotics domain with larger state-spaces. For the purpose of this project we have created simulation of the environment and robot in ROS with Gazebo and Rviz as visualization tools.The mobile robot uses a 2D LIDAR module to perform SLAM in the unknown environment. The goal of this project is to be able to make an autonomous agent capable of performing planning and navigation in an indoor environment to deliver boxes (passengers in Taxi domain) placed at random locations to a particular location (warehouse). The approach can be extended to a wide variety of mobile and manipulative robots
On Estimating Maximum Matching Size in Graph Streams
Venue: arXiv cs.DS 2017
Year: 2017

We study the problem of estimating the maximum matching size in graphs whose edges are revealed in a streaming manner. We consider both insertion-only streams and dynamic streams and present new upper and lower bound results for both models. On the upper bound front, we show that an $α$-approximate estimate of the matching size can be computed in dynamic streams using $\widetilde{O}({n^2/α^4})$ space, and in insertion-only streams using $\widetilde{O}(n/α^2)$-space. On the lower bound front, we prove that any $α$-approximation algorithm for estimating matching size in dynamic graph streams requires $Ω(\sqrt{n}/α^{2.5})$ bits of space, even if the underlying graph is both sparse and has arboricity bounded by $O(α)$. We further improve our lower bound to $Ω(n/α^2)$ in the case of dense graphs. Furthermore, we prove that a $(1+ε)$-approximation to matching size in insertion-only streams requires RS$(n) \cdot n^{1-O(ε)}$ space; here, RS${n}$ denotes the maximum number of edge-disjoint induced matchings of size $Θ(n)$ in an $n$-vertex graph. It is a major open problem to determine the value of RS$(n)$, and current results leave open the possibility that RS$(n)$ may be as large as $n/\log n$. We also show how to avoid the dependency on the parameter RS$(n)$ in proving lower bound for dynamic streams and present a near-optimal lower bound of $n^{2-O(ε)}$ for $(1+ε)$-approximation in this model. Using a well-known connection between matching size and matrix rank, all our lower bounds also hold for the problem of estimating matrix rank. In particular our results imply a near-optimal $n^{2-O(ε)}$ bit lower bound for $(1+ε)$-approximation of matrix ranks for dense matrices in dynamic streams, answering an open question of Li and Woodruff (STOC 2016).
Automatic Knot Adjustment Using Dolphin Echolocation Algorithm for B-Spline Curve Approximation
Venue: arXiv cs.GR 2017
Year: 2017

In this paper, a new approach to solve the cubic B-spline curve fitting problem is presented based on a meta-heuristic algorithm called " dolphin echolocation ". The method minimizes the proximity error value of the selected nodes that measured using the least squares method and the Euclidean distance method of the new curve generated by the reverse engineering. The results of the proposed method are compared with the genetic algorithm. As a result, this new method seems to be successful.