OpenRaiser/Intern-Atlas huggingface dataset:
Optimal non-coherent data detection for massive SIMO wireless systems: A polynomial complexity solution
Venue: arXiv cs.IT 2014
Year: 2014

Massive MIMO systems have made significant progress in increasing spectral and energy efficiency over traditional MIMO systems by exploiting large antenna arrays. In this paper we consider the joint maximum likelihood (ML) channel estimation and data detection problem for massive SIMO (single input multiple output) wireless systems. Despite the large number of unknown channel coefficients for massive SIMO systems, we improve an algorithm to achieve the exact ML non-coherent data detection with a low expected complexity. We show that the expected computational complexity of this algorithm is linear in the number of receive antennas and polynomial in channel coherence time. Simulation results show the performance gain of the optimal non-coherent data detection with a low computational complexity.
Conditional probability calculations for the nonlinear Schrödinger equation with additive noise
Venue: arXiv cs.IT 2014
Year: 2014

The method for computation of conditional probability density function for the nonlinear Schrödinger equation with additive noise is developed. We present in a constructive form the conditional probability density function in the limit of a small noise and analytically derive it in a weakly nonlinear case. The general theory results are illustrated using fibre-optic communications as a particular, albeit practically very important, example.
Discretization of Planar Geometric Cover Problems
Venue: arXiv cs.CG 2014
Year: 2014

We consider discretization of the 'geometric cover problem' in the plane: Given a set $P$ of $n$ points in the plane and a compact planar object $T_0$, find a minimum cardinality collection of planar translates of $T_0$ such that the union of the translates in the collection contains all the points in $P$. We show that the geometric cover problem can be converted to a form of the geometric set cover, which has a given finite-size collection of translates rather than the infinite continuous solution space of the former. We propose a reduced finite solution space that consists of distinct canonical translates and present polynomial algorithms to find the reduce solution space for disks, convex/non-convex polygons (including holes), and planar objects consisting of finite Jordan curves.
Proportional Topology Optimization: A new non-gradient method for solving stress constrained and minimum compliance problems and its implementation in MATLAB
Venue: arXiv cs.CE 2014
Year: 2014

A new topology optimization method called the Proportional Topology Optimization (PTO) is presented. As a non-gradient method, PTO is simple to understand, easy to implement, and is also efficient and accurate at the same time. It is implemented into two MATLAB programs to solve the stress constrained and minimum compliance problems. Descriptions of the algorithm and computer programs are provided in detail. The method is applied to solve three numerical examples for both types of problems. The method shows comparable efficiency and accuracy with an existing gradient optimality criteria method. Also, the PTO stress constrained algorithm and minimum compliance algorithm are compared by feeding output from one algorithm to the other in an alternative manner, where the former yields lower maximum stress and volume fraction but higher compliance compared to the latter. Advantages and disadvantages of the proposed method and future works are discussed. The computer programs are self-contained and publicly shared in the website www.ptomethod.org.
A canonical form for the continuous piecewise polynomial functions
Venue: arXiv cs.SC 2014
Year: 2014

We present in this paper a canonical form for the elements in the ring of continuous piecewise polynomial functions. This new representation is based on the use of a particular class of functions $$\{C_i(P):P\in\Q[x],i=0,\ldots,°(P)\}$$ defined by $$C_i(P)(x)= \left\{ \begin{array}{cll}0 & \mbox{ if } & x \leq α\\ P(x) & \mbox{ if } & x \geq α\end{array} \right.$$ where $α$ is the $i$-th real root of the polynomial $P$. These functions will allow us to represent and manipulate easily every continuous piecewise polynomial function through the use of the corresponding canonical form. It will be also shown how to produce a "rational" representation of each function $C_{i}(P)$ allowing its evaluation by performing only operations in $\Q$ and avoiding the use of any real algebraic number.
Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data
Venue: arXiv cs.LG 2014
Year: 2014

We propose an efficient family of algorithms to learn the parameters of a Bayesian network from incomplete data. In contrast to textbook approaches such as EM and the gradient method, our approach is non-iterative, yields closed form parameter estimates, and eliminates the need for inference in a Bayesian network. Our approach provides consistent parameter estimates for missing data problems that are MCAR, MAR, and in some cases, MNAR. Empirically, our approach is orders of magnitude faster than EM (as our approach requires no inference). Given sufficient data, we learn parameters that can be orders of magnitude more accurate.
Two-Stage Beamformer Design for Massive MIMO Downlink By Trace Quotient Formulation
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, the problem of outer beamformer design based only on channel statistic information is considered for two-stage beamforming for multi-user massive MIMO downlink, and the problem is approached based on signal-to-leakage-plus-noise ratio (SLNR). To eliminate the dependence on the instantaneous channel state information, a lower bound on the average SLNR is derived by assuming zero-forcing (ZF) inner beamforming, and an outer beamformer design method that maximizes the lower bound on the average SLNR is proposed. It is shown that the proposed SLNR-based outer beamformer design problem reduces to a trace quotient problem (TQP), which is often encountered in the field of machine learning. An iterative algorithm is presented to obtain an optimal solution to the proposed TQP. The proposed method has the capability of optimally controlling the weighting factor between the signal power to the desired user and the interference leakage power to undesired users according to different channel statistics. Numerical results show that the proposed outer beamformer design method yields significant performance gain over existing methods.
Collision Avoidance in TV White Spaces: A Cross-layer Design Approach for Cognitive Radio Networks
Venue: arXiv cs.NI 2014
Year: 2014

One of the most promising applications of cognitive radio networks (CRNs)is the efficient exploitation of TV white spaces (TVWSs) for enhancing the performance of wireless networks. In this paper, we propose a cross-layer design (CLD) of carrier sense multiple access with collision avoidance (CSMA/CA) mechanism at the medium access control (MAC) layer with spectrum sensing (SpSe) at the physical layer, for identifying the occupancy status of TV bands. The proposed CLD relies on a Markov chain model with a state pair containing both the SpSe and the CSMA/CA from which we derive the collision probability and the achievable throughput. Analytical and simulation results are obtained for different collision avoidance and spectrum sensing implementation scenarios by varying the contention window, backoff stage and probability of detection. The obtained results depict the achievable throughput under different collision avoidance and spectrum sensing implementation scenarios indicating thereby the performance of collision avoidance in TVWSs based cognitive radio networks.
NewSQL: Towards Next-Generation Scalable RDBMS for Online Transaction Processing (OLTP) for Big Data Management
Venue: arXiv cs.DB 2014
Year: 2014

One of the key advances in resolving the big-data problem has been the emergence of an alternative database technology. Today, classic RDBMS are complemented by a rich set of alternative Data Management Systems (DMS) specially designed to handle the volume, variety, velocity and variability ofBig Data collections; these DMS include NoSQL, NewSQL and Search-based systems. NewSQL is a class of modern relational database management systems (RDBMS) that provide the same scalable performance of NoSQL systems for online transaction processing (OLTP) read-write workloads while still maintaining the ACID guarantees of a traditional database system. This paper discusses about NewSQL data management system; and compares with NoSQL and with traditional database system. This paper covers architecture, characteristics, classification of NewSQL databases for online transaction processing (OLTP) for Big data management. It also provides the list ofpopular NoSQL as well as NewSQL databases in separate categorized tables. This paper compares SQL based RDBMS, NoSQL and NewSQL databases with set of metrics; as well as, addressed some research issues ofNoSQL and NewSQL.
Quality versus quantity in scientific impact
Venue: arXiv cs.DL 2014
Year: 2014

Citation metrics are becoming pervasive in the quantitative evaluation of scholars, journals and institutions. More then ever before, hiring, promotion, and funding decisions rely on a variety of impact metrics that cannot disentangle quality from quantity of scientific output, and are biased by factors such as discipline and academic age. Biases affecting the evaluation of single papers are compounded when one aggregates citation-based metrics across an entire publication record. It is not trivial to compare the quality of two scholars that during their careers have published at different rates in different disciplines in different periods of time. We propose a novel solution based on the generation of a statistical baseline specifically tailored on the academic profile of each researcher. Our method can decouple the roles of quantity and quality of publications to explain how a certain level of impact is achieved. The method is flexible enough to allow for the evaluation of, and fair comparison among, arbitrary collections of papers --- scholar publication records, journals, and entire institutions; and can be extended to simultaneously suppresses any source of bias. We show that our method can capture the quality of the work of Nobel laureates irrespective of number of publications, academic age, and discipline, even when traditional metrics indicate low impact in absolute terms. We further apply our methodology to almost a million scholars and over six thousand journals to measure the impact that cannot be explained by the volume of publications alone.
Algorithms in the Ultra-Wide Word Model
Venue: arXiv cs.DS 2014
Year: 2014

The effective use of parallel computing resources to speed up algorithms in current multi-core parallel architectures remains a difficult challenge, with ease of programming playing a key role in the eventual success of various parallel architectures. In this paper we consider an alternative view of parallelism in the form of an ultra-wide word processor. We introduce the Ultra-Wide Word architecture and model, an extension of the word-RAM model that allows for constant time operations on thousands of bits in parallel. Word parallelism as exploited by the word-RAM model does not suffer from the more difficult aspects of parallel programming, namely synchronization and concurrency. For the standard word-RAM algorithms, the speedups obtained are moderate, as they are limited by the word size. We argue that a large class of word-RAM algorithms can be implemented in the Ultra-Wide Word model, obtaining speedups comparable to multi-threaded computations while keeping the simplicity of programming of the sequential RAM model. We show that this is the case by describing implementations of Ultra-Wide Word algorithms for dynamic programming and string searching. In addition, we show that the Ultra-Wide Word model can be used to implement a nonstandard memory architecture, which enables the sidestepping of lower bounds of important data structure problems such as priority queues and dynamic prefix sums. While similar ideas about operating on large words have been mentioned before in the context of multimedia processors [Thorup 2003], it is only recently that an architecture like the one we propose has become feasible and that details can be worked out.
Output Feedback Stabilization of Switched Linear Systems with Limited Information
Venue: arXiv cs.SY 2014
Year: 2014

We propose an encoding and control strategy for the stabilization of switched systems with limited information, supposing the controller is given for each mode. Only the quantized output and the active mode of the plant at each sampling time are transmitted to the controller. Due to switching, the active mode of the plant may be different from that of the controller in the closed-loop system. Hence if switching occurs, the quantizer must recalculate a bounded set containing the estimation error for quantization at the next sampling time. We establish the global asymptotic stability under a slow-switching assumption on dwell time and average dwell time. To this end, we construct multiple discrete-time Lyapunov functions with respect to the estimated state and the size of the bounded set.
Worst-Case Linear Discriminant Analysis as Scalable Semidefinite Feasibility Problems
Venue: arXiv cs.LG 2014
Year: 2014

In this paper, we propose an efficient semidefinite programming (SDP) approach to worst-case linear discriminant analysis (WLDA). Compared with the traditional LDA, WLDA considers the dimensionality reduction problem from the worst-case viewpoint, which is in general more robust for classification. However, the original problem of WLDA is non-convex and difficult to optimize. In this paper, we reformulate the optimization problem of WLDA into a sequence of semidefinite feasibility problems. To efficiently solve the semidefinite feasibility problems, we design a new scalable optimization method with quasi-Newton methods and eigen-decomposition being the core components. The proposed method is orders of magnitude faster than standard interior-point based SDP solvers. Experiments on a variety of classification problems demonstrate that our approach achieves better performance than standard LDA. Our method is also much faster and more scalable than standard interior-point SDP solvers based WLDA. The computational complexity for an SDP with $m$ constraints and matrices of size $d$ by $d$ is roughly reduced from $\mathcal{O}(m^3+md^3+m^2d^2)$ to $\mathcal{O}(d^3)$ ($m>d$ in our case).
SmartConnect: A System for the Design and Deployment of Wireless Sensor Networks
Venue: arXiv cs.NI 2014
Year: 2014

We have developed SmartConnect, a tool that addresses the growing need for the design and deployment of multihop wireless relay networks for connecting sensors to a control center. Given the locations of the sensors, the traffic that each sensor generates, the quality of service (QoS) requirements, and the potential locations at which relays can be placed, SmartConnect helps design and deploy a low- cost wireless multihop relay network. SmartConnect adopts a field interactive, iterative approach, with model based network design, field evaluation and relay augmentation per- formed iteratively until the desired QoS is met. The design process is based on approximate combinatorial optimization algorithms. In the paper, we provide the design choices made in SmartConnect and describe the experimental work that led to these choices. We provide results from some experimental deployments. Finally, we conduct an experimental study of the robustness of the network design over long time periods (as channel conditions slowly change), in terms of the relay augmentation and route adaptation required.
A Parallel Genetic Algorithm for Generalized Vertex Cover Problem
Venue: arXiv cs.DC 2014
Year: 2014

This paper presents a parallel genetic algorithm for generalised vertex cover problem (GVCP) using Hadoop Map-Reduce framework. The proposed Map-Reduce implementation helps to run the genetic algorithm for generalized vertex cover problem (GVCP) on multiple machines parallely and computes the solution in relatively short time.
Magic coins are useful for small-space quantum machines
Venue: arXiv cs.CC 2014
Year: 2014

Although polynomial-time probabilistic Turing machines can utilize uncomputable transition probabilities to recognize uncountably many languages with bounded error when allowed to use logarithmic space, it is known that such "magic coins" give no additional computational power to constant-space versions of those machines. We show that adding a few quantum bits to the model changes the picture dramatically. For every language $L$, there exists such a two-way quantum finite automaton that recognizes a language of the same Turing degree as $L$ with bounded error in polynomial time. When used as verifiers in public-coin interactive proof systems, such automata can verify membership in all languages with bounded error, outperforming their classical counterparts, which are known to fail for the palindromes language.
Analysis of Topology Based Routing Protocols for Vehicular Ad-Hoc Network (VANET)
Venue: arXiv cs.NI 2014
Year: 2014

Now-a-days vehicles are one of the most important parts of our life. We need them to cross distances in our everyday life. In this paper we discuss Vehicular AdHoc Network (VANET) technology that can ensure the maintenance of traffic rules and regulation. By applying this technology we can save life, save time, corruption, vehicle security, avoid collision and so on. Vehicular Ad Hoc Network (VANET) is a part of Mobile Ad Hoc Network (MANET). Every node or vehicle can move freely and they will communicate each other by wireless technology in coverage. The main goal of this research is to study the existing routing protocols for ad-hoc network system and compared between AODV (Reactive) and DSDV (Proactive). We have studied different types of routing protocols such as topology based, position based, cluster based, geo-cast based and broadcast based. We have simulated and compared AODV (Reactive) and DSDV (Proactive) to find out their efficiency and detect their flaws.
Learning Face Representation from Scratch
Venue: arXiv cs.CV 2014
Year: 2014

Pushing by big data and deep convolutional neural network (CNN), the performance of face recognition is becoming comparable to human. Using private large scale training datasets, several groups achieve very high performance on LFW, i.e., 97% to 99%. While there are many open source implementations of CNN, none of large scale face dataset is publicly available. The current situation in the field of face recognition is that data is more important than algorithm. To solve this problem, this paper proposes a semi-automatical way to collect face images from Internet and builds a large scale dataset containing about 10,000 subjects and 500,000 images, called CASIAWebFace. Based on the database, we use a 11-layer CNN to learn discriminative representation and obtain state-of-theart accuracy on LFW and YTF. The publication of CASIAWebFace will attract more research groups entering this field and accelerate the development of face recognition in the wild.
Solving Games with Functional Regret Estimation
Venue: arXiv cs.AI 2014
Year: 2014

We propose a novel online learning method for minimizing regret in large extensive-form games. The approach learns a function approximator online to estimate the regret for choosing a particular action. A no-regret algorithm uses these estimates in place of the true regrets to define a sequence of policies. We prove the approach sound by providing a bound relating the quality of the function approximation and regret of the algorithm. A corollary being that the method is guaranteed to converge to a Nash equilibrium in self-play so long as the regrets are ultimately realizable by the function approximator. Our technique can be understood as a principled generalization of existing work on abstraction in large games; in our work, both the abstraction as well as the equilibrium are learned during self-play. We demonstrate empirically the method achieves higher quality strategies than state-of-the-art abstraction techniques given the same resources.
Paradigm shifts. Part I. Collagen. Confirming and complementing the work of Henry Small
Venue: arXiv cs.DL 2014
Year: 2014

The paradigm shift in collagen research during the early 1970s marked by the discovery of the collagen precursor molecule procollagen was traced using co-citation analysis and title word frequency determination, confirming previous work performed by Henry Small.
ScreenAvoider: Protecting Computer Screens from Ubiquitous Cameras
Venue: arXiv cs.CR 2014
Year: 2014

We live and work in environments that are inundated with cameras embedded in devices such as phones, tablets, laptops, and monitors. Newer wearable devices like Google Glass, Narrative Clip, and Autographer offer the ability to quietly log our lives with cameras from a `first person' perspective. While capturing several meaningful and interesting moments, a significant number of images captured by these wearable cameras can contain computer screens. Given the potentially sensitive information that is visible on our displays, there is a need to guard computer screens from undesired photography. People need protection against photography of their screens, whether by other people's cameras or their own cameras. We present ScreenAvoider, a framework that controls the collection and disclosure of images with computer screens and their sensitive content. ScreenAvoider can detect images with computer screens with high accuracy and can even go so far as to discriminate amongst screen content. We also introduce a ScreenTag system that aids in the identification of screen content, flagging images with highly sensitive content such as messaging applications or email webpages. We evaluate our concept on realistic lifelogging datasets, showing that ScreenAvoider provides a practical and useful solution that can help users manage their privacy.
Preset Distinguishing Sequences and Diameter of Transformation Semigroups
Venue: arXiv cs.FL 2014
Year: 2014

We investigate the length $\ell(n,k)$ of a shortest preset distinguishing sequence (PDS) in the worst case for a $k$-element subset of an $n$-state Mealy automaton. It was mentioned by Sokolovskii that this problem is closely related to the problem of finding the maximal subsemigroup diameter $\ell(\mathbf{T}_n)$ for the full transformation semigroup $\mathbf{T}_n$ of an $n$-element set. We prove that $\ell(\mathbf{T}_n)=2^n\exp\{\sqrt{\frac{n}{2}\ln n}(1+ o(1))\}$ as $n\to\infty$ and, using approach of Sokolovskii, find the asymptotics of $\log_2 \ell(n,k)$ as $n,k\to\infty$ and $k/n\to a\in (0,1)$.
Analysis of the Effects of Failure and Noise in the Distributed Connectivity Maintenance of a Multi-robot System
Venue: arXiv cs.MA 2014
Year: 2014

To perform cooperative tasks in a decentralized manner, multi-robot systems are often required to communicate with each other. Therefore, maintaining the communication graph connectivity is a fundamental issue when roaming a territory with obstacles. However, when dealing with real-robot systems, several sources of data corruption can appear in the agent interaction. In this paper, the effects of failure and noise in the communication between agents are analyzed upon a connectivity maintenance control strategy. The results show that the connectivity strategy is resilient to the negative effects of such disturbances under realistic settings that consider a bandwidth limit for the control effort. This opens the perspective of applying the connectivity maintenance strategy in adaptive schemes that consider, for instance, autonomous adaptation to constraints other than connectivity itself, e.g. communication efficiency and energy harvesting.