OpenRaiser/Intern-Atlas huggingface dataset:
Free Space Optical Communication: Challenges and Mitigation Techniques
Venue: arXiv cs.IT 2015
Year: 2015

In recent years, free space optical (FSO) communication has gained significant importance owing to its unique features: large bandwidth, license free spectrum, high data rate, easy and quick deployability, less power and low mass requirement. FSO communication uses optical carrier in the near infrared (IR) and visible band to establish either terrestrial links within the Earths atmosphere or inter-satellite or deep space links or ground to satellite or satellite to ground links. However, despite of great potential of FSO communication, its performance is limited by the adverse effects (viz., absorption, scattering and turbulence) of the atmospheric channel. Out of these three effects, the atmospheric turbulence is a major challenge that may lead to serious degradation in the bit error rate (BER) performance of the system and make the communication link infeasible. This paper presents a comprehensive survey on various challenges faced by FSO communication system for both terrestrial and space links. It will provide details of various performance mitigation techniques in order to have high link availability and reliability of FSO system. The first part of the paper will focus on various types of impairments that poses a serious challenge to the performance of FSO system for both terrestrial and space links. The latter part of the paper will provide the reader with an exhaustive review of various techniques used in FSO system both at physical layer as well as at the upper layers (transport, network or link layer) to combat the adverse effects of the atmosphere. Further, this survey uniquely offers the current literature on FSO coding and modulation schemes using various channel models and detection techniques. It also presents a recently developed technique in FSO system using orbital angular momentum to combat the effect of atmospheric turbulence.
PCA with Gaussian perturbations
Venue: arXiv cs.LG 2015
Year: 2015

Most of machine learning deals with vector parameters. Ideally we would like to take higher order information into account and make use of matrix or even tensor parameters. However the resulting algorithms are usually inefficient. Here we address on-line learning with matrix parameters. It is often easy to obtain online algorithm with good generalization performance if you eigendecompose the current parameter matrix in each trial (at a cost of $O(n^3)$ per trial). Ideally we want to avoid the decompositions and spend $O(n^2)$ per trial, i.e. linear time in the size of the matrix data. There is a core trade-off between the running time and the generalization performance, here measured by the regret of the on-line algorithm (total gain of the best off-line predictor minus the total gain of the on-line algorithm). We focus on the key matrix problem of rank $k$ Principal Component Analysis in $\mathbb{R}^n$ where $k \ll n$. There are $O(n^3)$ algorithms that achieve the optimum regret but require eigendecompositions. We develop a simple algorithm that needs $O(kn^2)$ per trial whose regret is off by a small factor of $O(n^{1/4})$. The algorithm is based on the Follow the Perturbed Leader paradigm. It replaces full eigendecompositions at each trial by the problem finding $k$ principal components of the current covariance matrix that is perturbed by Gaussian noise.
The Discrete Fréchet Gap
Venue: arXiv cs.CG 2015
Year: 2015

We introduce the discrete Fréchet gap and its variants as an alternative measure of similarity between polygonal curves. We believe that for some applications the new measure (and its variants) may better reflect our intuitive notion of similarity than the discrete Fréchet distance (and its variants), since the latter measure is indifferent to (matched) pairs of points that are relatively close to each other. Referring to the frogs analogy by which the discrete Fréchet distance is often described, the discrete Fréchet gap is the minimum difference between the longest and shortest positions of the leash needed for the frogs to traverse their point sequences. We present an optimization scheme, which is suitable for any monotone function defined for pairs of distances such as the gap and ratio functions. We apply this scheme to two variants of the discrete Fréchet gap, namely, the one-sided discrete Fréchet gap with shortcuts and the weak discrete Fréchet gap, to obtain $O(n^2 \log^2 n)$-time algorithms for computing them.
Several new classes of Boolean functions with few Walsh transform values
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, several new classes of Boolean functions with few Walsh transform values, including bent, semi-bent and five-valued functions, are obtained by adding the product of two or three linear functions to some known bent functions.Numerical results show that the proposed class contains cubic bent functions that are affinely inequivalent to all known quadratic ones. Meanwhile, we determine the distribution of the Walsh spectrum of five-valued functions constructed in this paper.
On relative weighted entropies with central moments weight functions
Venue: arXiv cs.IT 2015
Year: 2015

Following [1], the aim of this paper is to analyze the relative weighted entropy involving the central moments weight functions. We compare the standard relative entropy with the weighted case in two particular forms of Gaussian distributions. As an application, the weighted deviance information criterion is proposed.
The Scope and Limits of Simulation in Cognitive Models
Venue: arXiv cs.AI 2015
Year: 2015

It has been proposed that human physical reasoning consists largely of running "physics engines in the head" in which the future trajectory of the physical system under consideration is computed precisely using accurate scientific theories. In such models, uncertainty and incomplete knowledge is dealt with by sampling probabilistically over the space of possible trajectories ("Monte Carlo simulation"). We argue that such simulation-based models are too weak, in that there are many important aspects of human physical reasoning that cannot be carried out this way, or can only be carried out very inefficiently; and too strong, in that humans make large systematic errors that the models cannot account for. We conclude that simulation-based reasoning makes up at most a small part of a larger system that encompasses a wide range of additional cognitive processes.
Numeric Input Relations for Relational Learning with Applications to Community Structure Analysis
Venue: arXiv cs.LG 2015
Year: 2015

Most work in the area of statistical relational learning (SRL) is focussed on discrete data, even though a few approaches for hybrid SRL models have been proposed that combine numerical and discrete variables. In this paper we distinguish numerical random variables for which a probability distribution is defined by the model from numerical input variables that are only used for conditioning the distribution of discrete response variables. We show how numerical input relations can very easily be used in the Relational Bayesian Network framework, and that existing inference and learning methods need only minor adjustments to be applied in this generalized setting. The resulting framework provides natural relational extensions of classical probabilistic models for categorical data. We demonstrate the usefulness of RBN models with numeric input relations by several examples. In particular, we use the augmented RBN framework to define probabilistic models for multi-relational (social) networks in which the probability of a link between two nodes depends on numeric latent feature vectors associated with the nodes. A generic learning procedure can be used to obtain a maximum-likelihood fit of model parameters and latent feature values for a variety of models that can be expressed in the high-level RBN representation. Specifically, we propose a model that allows us to interpret learned latent feature values as community centrality degrees by which we can identify nodes that are central for one community, that are hubs between communities, or that are isolated nodes. In a multi-relational setting, the model also provides a characterization of how different relations are associated with each community.
PROP - Patronage of PHP Web Applications
Venue: arXiv cs.CR 2015
Year: 2015

PHP is one of the most commonly used languages to develop web sites because of its simplicity, easy to learn and it can be easily embedded with any of the databases. A web developer with his basic knowledge developing an application without practising secure guidelines, improper validation of user inputs leads to various source code vulnerabilities. Logical flaws while designing, implementing and hosting the web application causes work flow deviation attacks. In this paper, we are analyzing the complete behaviour of a web application through static and dynamic analysis methodologies.
An Entropy Maximizing Geohash for Distributed Spatiotemporal Database Indexing
Venue: arXiv cs.DB 2015
Year: 2015

We present a modification of the standard geohash algorithm based on maximum entropy encoding in which the data volume is approximately constant for a given hash prefix length. Distributed spatiotemporal databases, which typically require interleaving spatial and temporal elements into a single key, reap large benefits from a balanced geohash by creating a consistent ratio between spatial and temporal precision even across areas of varying data density. This property is also useful for indexing purely spatial datasets, where the load distribution of large range scans is an important aspect of query performance. We apply our algorithm to data generated proportional to population as given by census block population counts provided from the US Census Bureau.
Partial Network Alignment with Anchor Meta Path and Truncated Generic Stable Matching
Venue: arXiv cs.SI 2015
Year: 2015

To enjoy more social network services, users nowadays are usually involved in multiple online social networks simultaneously. The shared users between different networks are called anchor users, while the remaining unshared users are named as non-anchor users. Connections between accounts of anchor users in different networks are defined as anchor links and networks partially aligned by anchor links can be represented as partially aligned networks. In this paper, we want to predict anchor links between partially aligned social networks, which is formally defined as the partial network alignment problem. The partial network alignment problem is very difficult to solve because of the following two challenges: (1) the lack of general features for anchor links, and (2) the "one-to-one$_\le$" (one to at most one) constraint on anchor links. To address these two challenges, a new method PNA (Partial Network Aligner) is proposed in this paper. PNA (1) extracts a set of explicit anchor adjacency features and latent topological features for anchor links based on the anchor meta path concept and tensor decomposition techniques, and (2) utilizes the generic stable matching to identify the non-anchor users to prune the redundant anchor links attached to them. Extensive experiments conducted on two real-world partially aligned social networks demonstrate that PNA can solve the partial network alignment problem very well and outperform all the other comparison methods with significant advantages.
SAT-based Analysis of Large Real-world Feature Models is Easy
Venue: arXiv cs.SE 2015
Year: 2015

Modern conflict-driven clause-learning (CDCL) Boolean SAT solvers provide efficient automatic analysis of real-world feature models (FM) of systems ranging from cars to operating systems. It is well-known that solver-based analysis of real-world FMs scale very well even though SAT instances obtained from such FMs are large, and the corresponding analysis problems are known to be NP-complete. To better understand why SAT solvers are so effective, we systematically studied many syntactic and semantic characteristics of a representative set of large real-world FMs. We discovered that a key reason why large real-world FMs are easy-to-analyze is that the vast majority of the variables in these models are unrestricted, i.e., the models are satisfiable for both true and false assignments to such variables under the current partial assignment. Given this discovery and our understanding of CDCL SAT solvers, we show that solvers can easily find satisfying assignments for such models without too many backtracks relative to the model size, explaining why solvers scale so well. Further analysis showed that the presence of unrestricted variables in these real-world models can be attributed to their high-degree of variability. Additionally, we experimented with a series of well-known non-backtracking simplifications that are particularly effective in solving FMs. The remaining variables/clauses after simplifications, called the core, are so few that they are easily solved even with backtracking, further strengthening our conclusions.
Fast Multiple Order-Preserving Matching Algorithms
Venue: arXiv cs.DS 2015
Year: 2015

Given a text $T$ and a pattern $P$, the order-preserving matching problem is to find all substrings in $T$ which have the same relative orders as $P$. Order-preserving matching has been an active research area since it was introduced by Kubica et al. \cite{kubica2013linear} and Kim et al. \cite{kim2014order}. In this paper we present two algorithms for the multiple order-preserving matching problem, one of which runs in sublinear time on average and the other in linear time on average. Both algorithms run much faster than the previous algorithms.
Stochastic Geometry Modeling and Performance Evaluation of mmWave Cellular Communications
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, a new mathematical framework to the analysis of millimeter wave cellular networks is introduced. Its peculiarity lies in considering realistic path-loss and blockage models, which are derived from experimental data recently reported in the literature. The path-loss model accounts for different distributions for line-of-sight and non-line-of-sight propagation conditions and the blockage model includes an outage state that provides a better representation of the outage possibilities of millimeter wave communications. By modeling the locations of the base stations as points of a Poisson point process and by relying upon a noise-limited approximation for typical millimeter wave network deployments, exact integral expressions for computing the coverage probability and the average rate are obtained. With the aid of Monte Carlo simulations, the noise-limited approximation is shown to be sufficiently accurate for typical network densities. Furthermore, it is shown that sufficiently dense millimeter wave cellular networks are capable of outperforming micro wave cellular networks, both in terms of coverage probability and average rate.
Asymptotic Analysis of Multi-Antenna Cognitive Radio Systems Using Extreme Value Theory
Venue: arXiv cs.IT 2015
Year: 2015

We consider a spectrum-sharing cognitive radio system with antenna selection applied at the secondary transmitter (ST). Based on the extreme value theory, we deduce a simple and accurate expression for the asymptotic distribution of the signal to interference plus noise ratio at the secondary receiver. Using this result, the asymptotic mean capacity and the outage capacity for the secondary user (SU) are derived. The obtained asymptotic capacities approach the exact results as the number of transmit antennas $N$ increases. Results indicate that the rate of the SU scales as $\log(N)$ when the transmit power of the ST is limited by the maximum allowable interference level, while rate scales as $\log(\log(N))$ if ST is limited by the maximum transmit power.
On the Depth of Deep Neural Networks: A Theoretical View
Venue: arXiv cs.LG 2015
Year: 2015

People believe that depth plays an important role in success of deep neural networks (DNN). However, this belief lacks solid theoretical justifications as far as we know. We investigate role of depth from perspective of margin bound. In margin bound, expected error is upper bounded by empirical margin error plus Rademacher Average (RA) based capacity term. First, we derive an upper bound for RA of DNN, and show that it increases with increasing depth. This indicates negative impact of depth on test performance. Second, we show that deeper networks tend to have larger representation power (measured by Betti numbers based complexity) than shallower networks in multi-class setting, and thus can lead to smaller empirical margin error. This implies positive impact of depth. The combination of these two results shows that for DNN with restricted number of hidden units, increasing depth is not always good since there is a tradeoff between positive and negative impacts. These results inspire us to seek alternative ways to achieve positive impact of depth, e.g., imposing margin-based penalty terms to cross entropy loss so as to reduce empirical margin error without increasing depth. Our experiments show that in this way, we achieve significantly better test performance.
On the Adoption of Multi-Agent Systems for the Development of Industrial Control Networks
Venue: arXiv cs.SY 2015
Year: 2015

Multi-Agent Systems (MAS) are adopted and tested with many complex and critical industrial applications, which are required to be adaptive, scalable, context-aware, and include real-time constraints. Industrial Control Networks (ICN) are examples of these applications. An ICN is considered a system that contains a variety of interconnected industrial equipments, such as physical control processes, control systems, computers, and communication networks. It is built to supervise and control industrial processes. This paper presents a development case study on building a multi-layered agent-based ICN in which agents cooperate to provide an effective supervision and control of a set of control processes, basically controlled by a set of legacy control systems with limited computing capabilities. The proposed ICN is designed to add an intelligent layer on top of legacy control systems to compensate their limited capabilities using a cost-effective agent-based approach, and also to provide global synchronization and safety plans. It is tested and evaluated within a simulation environment. The main conclusion of this research is that agents and MAS can provide an effective, flexible, and cost-effective solution to handle the emerged limitations of legacy control systems if they are properly integrated with these systems.
Optimized Asynchronous Passive Multi-Channel Discovery of Beacon-Enabled Networks
Venue: arXiv cs.NI 2015
Year: 2015

Neighbor discovery is a fundamental task for wireless networks deployment. It is essential for setup and maintenance of networks and is typically a precondition for further communication. In this work we focus on passive discovery of networks operating in multi-channel environments, performed by listening for periodically transmitted beaconing messages. It is well-known that performance of such discovery approaches strongly depends on the structure of the adopted Beacon Interval (BI) set, that is, set of intervals between individual beaconing messages. However, although imposing constraints on this set has the potential to make the discovery process more efficient, there is demand for high-performance discovery strategies for BI sets that are as general as possible. They would allow to cover a broad range of wireless technologies and deployment scenarios, and enable network operators to select BI's that are best suited for the targeted application and/or device characteristics. In the present work, we introduce a family of novel low-complexity discovery algorithms that minimize both the Expected Mean Discovery Time (EMDT) and the makespan, for a quite general family of BI sets. Notably, this family of BI sets completely includes BI's supported by IEEE 802.15.4 and a large part of BI's supported by IEEE 802.11. Furthermore, we present another novel discovery algorithm, based on an Integer Linear Program (ILP), that minimizes EMDT for arbitrary BI sets. In addition to analytically proving optimality, we numerically evaluate the proposed algorithms using different families of BI sets and compare their performance with the passive scan of IEEE 802.15.4 w.r.t. various performance metrics, such as makespan, EMDT, energy usage, etc.
On Dress Codes with Flowers
Venue: arXiv cs.IT 2015
Year: 2015

Fractional Repetition (FR) codes are well known class of Distributed Replication-based Simple Storage (Dress) codes for the Distributed Storage Systems (DSSs). In such systems, the replicas of data packets encoded by Maximum Distance Separable (MDS) code, are stored on distributed nodes. Most of the available constructions for the FR codes are based on combinatorial designs and Graph theory. In this work, we propose an elegant sequence based approach for the construction of the FR code. In particular, we propose a beautiful class of codes known as Flower codes and study its basic properties.
Simultaneous Orthogonal Matching Pursuit With Noise Stabilization: Theoretical Analysis
Venue: arXiv cs.IT 2015
Year: 2015

This paper studies the joint support recovery of similar sparse vectors on the basis of a limited number of noisy linear measurements, i.e., in a multiple measurement vector (MMV) model. The additive noise signals on each measurement vector are assumed to be Gaussian and to exhibit different variances. The simultaneous orthogonal matching pursuit (SOMP) algorithm is generalized to weight the impact of each measurement vector on the choice of the atoms to be picked according to their noise levels. The new algorithm is referred to as SOMP-NS where NS stands for noise stabilization. To begin with, a theoretical framework to analyze the performance of the proposed algorithm is developed. This framework is then used to build conservative lower bounds on the probability of partial or full joint support recovery. Numerical simulations show that the proposed algorithm outperforms SOMP and that the theoretical lower bound provides a great insight into how SOMP-NS behaves when the weighting strategy is modified.
Self-assembling interactive modules: A research programme
Venue: arXiv cs.FL 2015
Year: 2015

In this paper we propose a research programme for getting structural characterisations for 2-dimensional languages generated by self-assembling tiles. This is part of a larger programme on getting a formal foundation of parallel, interactive, distributed systems.
Learning Contextualized Semantics from Co-occurring Terms via a Siamese Architecture
Venue: arXiv cs.IR 2015
Year: 2015

One of the biggest challenges in Multimedia information retrieval and understanding is to bridge the semantic gap by properly modeling concept semantics in context. The presence of out of vocabulary (OOV) concepts exacerbates this difficulty. To address the semantic gap issues, we formulate a problem on learning contextualized semantics from descriptive terms and propose a novel Siamese architecture to model the contextualized semantics from descriptive terms. By means of pattern aggregation and probabilistic topic models, our Siamese architecture captures contextualized semantics from the co-occurring descriptive terms via unsupervised learning, which leads to a concept embedding space of the terms in context. Furthermore, the co-occurring OOV concepts can be easily represented in the learnt concept embedding space. The main properties of the concept embedding space are demonstrated via visualization. Using various settings in semantic priming, we have carried out a thorough evaluation by comparing our approach to a number of state-of-the-art methods on six annotation corpora in different domains, i.e., MagTag5K, CAL500 and Million Song Dataset in the music domain as well as Corel5K, LabelMe and SUNDatabase in the image domain. Experimental results on semantic priming suggest that our approach outperforms those state-of-the-art methods considerably in various aspects.