OpenRaiser/Intern-Atlas huggingface dataset:
Efficient Regularized Regression for Variable Selection with L0 Penalty
Venue: arXiv cs.LG 2014
Year: 2014

Variable (feature, gene, model, which we use interchangeably) selections for regression with high-dimensional BIGDATA have found many applications in bioinformatics, computational biology, image processing, and engineering. One appealing approach is the L0 regularized regression which penalizes the number of nonzero features in the model directly. L0 is known as the most essential sparsity measure and has nice theoretical properties, while the popular L1 regularization is only a best convex relaxation of L0. Therefore, it is natural to expect that L0 regularized regression performs better than LASSO. However, it is well-known that L0 optimization is NP-hard and computationally challenging. Instead of solving the L0 problems directly, most publications so far have tried to solve an approximation problem that closely resembles L0 regularization. In this paper, we propose an efficient EM algorithm (L0EM) that directly solves the L0 optimization problem. $L_0$EM is efficient with high dimensional data. It also provides a natural solution to all Lp p in [0,2] problems. The regularized parameter can be either determined through cross-validation or AIC and BIC. Theoretical properties of the L0-regularized estimator are given under mild conditions that permit the number of variables to be much larger than the sample size. We demonstrate our methods through simulation and high-dimensional genomic data. The results indicate that L0 has better performance than LASSO and L0 with AIC or BIC has similar performance as computationally intensive cross-validation. The proposed algorithms are efficient in identifying the non-zero variables with less-bias and selecting biologically important genes and pathways with high dimensional BIGDATA.
On multiply-exponential write-once Turing machines
Venue: arXiv cs.CC 2014
Year: 2014

In this work we analyze the multiply-exponential complexity classes for write-once Turing machines, i.e. machines that can write to a given tape cell at most once. We show that $k$-DExpWOSpace = $k$-DExpWOTime = $k$-ExpTime and the nondeterministic counterpart. For alternating machines we show that $k$-AExpWOTime = $k$-AExpTime = $k-1$-ExpSpace.
Uniform recovery of fusion frame structured sparse signals
Venue: arXiv cs.IT 2014
Year: 2014

We consider the problem of recovering fusion frame sparse signals from incomplete measurements. These signals are composed of a small number of nonzero blocks taken from a family of subspaces. First, we show that, by using a-priori knowledge of a coherence parameter associated with the angles between the subspaces, one can uniformly recover fusion frame sparse signals with a significantly reduced number of vector-valued (sub-)Gaussian measurements via mixed l^1/l^2-minimization. We prove this by establishing an appropriate version of the restricted isometry property. Our result complements previous nonuniform recovery results in this context, and provides stronger stability guarantees for noisy measurements and approximately sparse signals. Second, we determine the minimal number of scalar-valued measurements needed to uniformly recover all fusion frame sparse signals via mixed l^1/l^2-minimization. This bound is achieved by scalar-valued subgaussian measurements. In particular, our result shows that the number of scalar-valued subgaussian measurements cannot be further reduced using knowledge of the coherence parameter. As a special case it implies that the best known uniform recovery result for block sparse signals using subgaussian measurements is optimal.
Spectral and Energy Spectral Efficiency Optimization of Joint Transmit and Receive Beamforming Based Multi-Relay MIMO-OFDMA Cellular Networks
Venue: arXiv cs.IT 2014
Year: 2014

We first conceive a novel transmission protocol for a multi-relay multiple-input--multiple-output orthogonal frequency-division multiple-access (MIMO-OFDMA) cellular network based on joint transmit and receive beamforming. We then address the associated network-wide spectral efficiency (SE) and energy spectral efficiency (ESE) optimization problems. More specifically, the network's MIMO channels are mathematically decomposed into several effective multiple-input--single-output (MISO) channels, which are essentially spatially multiplexed for transmission. Hence, these effective MISO channels are referred to as spatial multiplexing components (SMCs). For the sake of improving the SE/ESE performance attained, the SMCs are grouped using a pair of proposed grouping algorithms. The first is optimal in the sense that it exhaustively evaluates all the possible combinations of SMCs satisfying both the semi-orthogonality criterion and other relevant system constraints, whereas the second is a lower-complexity alternative. Corresponding to each of the two grouping algorithms, the pair of SE and ESE maximization problems are formulated, thus the optimal SMC groups and optimal power control variables can be obtained for each subcarrier block. These optimization problems are proven to be concave, and the dual decomposition approach is employed for obtaining their solutions. Relying on these optimization solutions, the impact of various system parameters on both the attainable SE and ESE is characterized. In particular, we demonstrate that under certain conditions the lower-complexity SMC grouping algorithm achieves 90% of the SE/ESE attained by the exhaustive-search based optimal grouping algorithm, while imposing as little as 3.5% of the latter scheme's computational complexity.
Cooperation and Storage Tradeoffs in Power-Grids with Renewable Energy Resources
Venue: arXiv cs.IT 2014
Year: 2014

One of the most important challenges in smart grid systems is the integration of renewable energy resources into its design. In this work, two different techniques to mitigate the time varying and intermittent nature of renewable energy generation are considered. The first one is the use of storage, which smooths out the fluctuations in the renewable energy generation across time. The second technique is the concept of distributed generation combined with cooperation by exchanging energy among the distributed sources. This technique averages out the variation in energy production across space. This paper analyzes the trade-off between these two techniques. The problem is formulated as a stochastic optimization problem with the objective of minimizing the time average cost of energy exchange within the grid. First, an analytical model of the optimal cost is provided by investigating the steady state of the system for some specific scenarios. Then, an algorithm to solve the cost minimization problem using the technique of Lyapunov optimization is developed and results for the performance of the algorithm are provided. These results show that in the presence of limited storage devices, the grid can benefit greatly from cooperation, whereas in the presence of large storage capacity, cooperation does not yield much benefit. Further, it is observed that most of the gains from cooperation can be obtained by exchanging energy only among a few energy harvesting sources.
Chance Constrained Optimization for Targeted Internet Advertising
Venue: arXiv cs.CE 2014
Year: 2014

We introduce a chance constrained optimization model for the fulfillment of guaranteed display Internet advertising campaigns. The proposed formulation for the allocation of display inventory takes into account the uncertainty of the supply of Internet viewers. We discuss and present theoretical and computational features of the model via Monte Carlo sampling and convex approximations. Theoretical upper and lower bounds are presented along with a numerical substantiation.
Information Theoretic Approach to Social Networks
Venue: arXiv cs.IT 2014
Year: 2014

We propose an information theoretic model for sociological networks. The model is a microcanonical ensemble of states and particles. The states are the possible pairs of nodes (i.e. people, sites and alike) which exchange information. The particles are the energetic information bits. With analogy to bosons gas, we define for these networks model: entropy, volume, pressure and temperature. We show that these definitions are consistent with Carnot efficiency (the second law) and ideal gas law. Therefore, if we have two large networks: hot and cold having temperatures TH and TC and we remove Q energetic bits from the hot network to the cold network we can save W profit bits. The profit will be calculated from W equal or smaller than Q (1-TH/TC), namely, Carnot formula. In addition it is shown that when two of these networks are merged the entropy increases. This explains the tendency of economic and social networks to merge.
How Helpful is Colour-Cueing of PIN Entry?
Venue: arXiv cs.HC 2014
Year: 2014

21st Century citizens are faced with the need to remember numbers of PINs (Personal Identification Numbers) in order to do their daily business, and they often have difficulties due to human memory limitations. One way of helping them could be by providing cues during the PIN entry process. The provision of cues that would only be helpful to the PIN owner is challenging because the cue should only make sense to the legitimate user, and not to a random observer. In this paper we report on an empirical study where we added colour to the PINpad to provide an implicit memory cue to PINpad users. We compared the impact of colour PINpads as opposed to grey ones. As expected, the ability to recall a PIN deteriorated significantly over time irrespective of the type of PINpad used. However, there was ultimately no improvement in the ability to recall PINs when using colour PINpads.
Genus Distributions of cubic series-parallel graphs
Venue: arXiv cs.DM 2014
Year: 2014

We derive a quadratic-time algorithm for the genus distribution of any 3-regular, biconnected series-parallel graph, which we extend to any biconnected series-parallel graph of maximum degree at most 3. Since the biconnected components of every graph of treewidth 2 are series-parallel graphs, this yields, by use of bar-amalgamation, a quadratic-time algorithm for every graph of treewidth at most 2 and maximum degree at most 3.
Clustering Approach Towards Image Segmentation: An Analytical Study
Venue: arXiv cs.CV 2014
Year: 2014

Image processing is an important research area in computer vision. Image segmentation plays the vital rule in image processing research. There exist so many methods for image segmentation. Clustering is an unsupervised study. Clustering can also be used for image segmentation. In this paper, an in-depth study is done on different clustering techniques that can be used for image segmentation with their pros and cons. An experiment for color image segmentation based on clustering with K-Means algorithm is performed to observe the accuracy of clustering technique for the segmentation purpose.
People are Strange when you're a Stranger: Impact and Influence of Bots on Social Networks
Venue: arXiv cs.SI 2014
Year: 2014

Bots are, for many Web and social media users, the source of many dangerous attacks or the carrier of unwanted messages, such as spam. Nevertheless, crawlers and software agents are a precious tool for analysts, and they are continuously executed to collect data or to test distributed applications. However, no one knows which is the real potential of a bot whose purpose is to control a community, to manipulate consensus, or to influence user behavior. It is commonly believed that the better an agent simulates human behavior in a social network, the more it can succeed to generate an impact in that community. We contribute to shed light on this issue through an online social experiment aimed to study to what extent a bot with no trust, no profile, and no aims to reproduce human behavior, can become popular and influential in a social media. Results show that a basic social probing activity can be used to acquire social relevance on the network and that the so-acquired popularity can be effectively leveraged to drive users in their social connectivity choices. We also register that our bot activity unveiled hidden social polarization patterns in the community and triggered an emotional response of individuals that brings to light subtle privacy hazards perceived by the user base.
DuSK: A Dual Structure-preserving Kernel for Supervised Tensor Learning with Applications to Neuroimages
Venue: arXiv cs.LG 2014
Year: 2014

With advances in data collection technologies, tensor data is assuming increasing prominence in many applications and the problem of supervised tensor learning has emerged as a topic of critical significance in the data mining and machine learning community. Conventional methods for supervised tensor learning mainly focus on learning kernels by flattening the tensor into vectors or matrices, however structural information within the tensors will be lost. In this paper, we introduce a new scheme to design structure-preserving kernels for supervised tensor learning. Specifically, we demonstrate how to leverage the naturally available structure within the tensorial representation to encode prior knowledge in the kernel. We proposed a tensor kernel that can preserve tensor structures based upon dual-tensorial mapping. The dual-tensorial mapping function can map each tensor instance in the input space to another tensor in the feature space while preserving the tensorial structure. Theoretically, our approach is an extension of the conventional kernels in the vector space to tensor space. We applied our novel kernel in conjunction with SVM to real-world tensor classification problems including brain fMRI classification for three different diseases (i.e., Alzheimer's disease, ADHD and brain damage by HIV). Extensive empirical studies demonstrate that our proposed approach can effectively boost tensor classification performances, particularly with small sample sizes.
A Novel C2C E-Commerce Recommender System Based on Link Prediction: Applying Social Network Analysis
Venue: arXiv cs.SI 2014
Year: 2014

Social network analysis emerged as an important research topic in sociology decades ago, and it has also attracted scientists from various fields of study like psychology, anthropology, geography and economics. In recent years, a significant number of researches has been conducted on using social network analysis to design e-commerce recommender systems. Most of the current recommender systems are designed for B2C e-commerce websites. This paper focuses on building a recommendation algorithm for C2C e-commerce business model by considering special features of C2C e-commerce websites. In this paper, we consider users and their transactions as a network; by this mapping, link prediction technique which is an important task in social network analysis could be used to build the recommender system. The proposed tow-level recommendation algorithm, rather than topology of the network, uses nodes features like: category of items, ratings of users, and reputation of sellers. The results show that the proposed model can be used to predict a portion of future trades between users in a C2C commercial network.
Integrated ERP System for Improving the Functional efficiency of the organization by Customized Architecture
Venue: arXiv cs.DB 2014
Year: 2014

An ERP is a kind of package which consist front end and backend as DBMS like a collection of DBMSs. You can create DBMS to manage one aspect of your business. For example, a publishing house has a database of books that keeps information about books such as Author Name, Title, Translator Name, etc. But this database app only helps enter books data and search them. It doesn't help them, for example, sell books. They get or develop another DBMS database that has all the Books data plus prices, discount formulas, names of common clients, etc. Now they connect the Books database to Sales database and maybe also the inventory database. Now its DBMS slowly turning into an ERP. They may add payroll database and connect it to this ERP. They may develop sales staff and commissions database and connect it to this ERP and so on. In the traditional Database management system the different databases are used for the various Campuses of the JSPM Group of Education like Wagholi Campus, Tathwade Campus, Narhe Campus, Hadpsar Campuses, Bhavdhan Campus as well as Corporate office at Katraj of same organization so it is not possible to keep different databases for the same so in this paper proposed the use of Integrated Database for the Entire organization using ERP system. The Proposed ERP system applied on the existing Architecture of the JSPM Group; the marginal difference observed in the Databases need to be accessed to generate the same number of Reports when use the Traditional DBMS which end up with improvement in the Functional efficiency of Organizational Architecture.
Architecture of a Web-based Predictive Editor for Controlled Natural Language Processing
Venue: arXiv cs.CL 2014
Year: 2014

In this paper, we describe the architecture of a web-based predictive text editor being developed for the controlled natural language PENG$^{ASP)$. This controlled language can be used to write non-monotonic specifications that have the same expressive power as Answer Set Programs. In order to support the writing process of these specifications, the predictive text editor communicates asynchronously with the controlled natural language processor that generates lookahead categories and additional auxiliary information for the author of a specification text. The text editor can display multiple sets of lookahead categories simultaneously for different possible sentence completions, anaphoric expressions, and supports the addition of new content words to the lexicon.
Conditional Restricted Boltzmann Machines for Cold Start Recommendations
Venue: arXiv cs.IR 2014
Year: 2014

Restricted Boltzman Machines (RBMs) have been successfully used in recommender systems. However, as with most of other collaborative filtering techniques, it cannot solve cold start problems for there is no rating for a new item. In this paper, we first apply conditional RBM (CRBM) which could take extra information into account and show that CRBM could solve cold start problem very well, especially for rating prediction task. CRBM naturally combine the content and collaborative data under a single framework which could be fitted effectively. Experiments show that CRBM can be compared favourably with matrix factorization models, while hidden features learned from the former models are more easy to be interpreted.
Linear Locally Repairable Codes with Random Matrices
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, locally repairable codes with all-symbol locality are studied. Methods to modify already existing codes are presented. Also, it is shown that with high probability, a random matrix with a few extra columns guaranteeing the locality property, is a generator matrix for a locally repairable code with a good minimum distance. The proof of this gives also a constructive method to find locally repairable codes.
Towards a Domain Specific Language for a Scene Graph based Robotic World Model
Venue: arXiv cs.RO 2014
Year: 2014

Robot world model representations are a vital part of robotic applications. However, there is no support for such representations in model-driven engineering tool chains. This work proposes a novel Domain Specific Language (DSL) for robotic world models that are based on the Robot Scene Graph (RSG) approach. The RSG-DSL can express (a) application specific scene configurations, (b) semantic scene structures and (c) inputs and outputs for the computational entities that are loaded into an instance of a world model.
Matrix Factorization with Explicit Trust and Distrust Relationships
Venue: arXiv cs.SI 2014
Year: 2014

With the advent of online social networks, recommender systems have became crucial for the success of many online applications/services due to their significance role in tailoring these applications to user-specific needs or preferences. Despite their increasing popularity, in general recommender systems suffer from the data sparsity and the cold-start problems. To alleviate these issues, in recent years there has been an upsurge of interest in exploiting social information such as trust relations among users along with the rating data to improve the performance of recommender systems. The main motivation for exploiting trust information in recommendation process stems from the observation that the ideas we are exposed to and the choices we make are significantly influenced by our social context. However, in large user communities, in addition to trust relations, the distrust relations also exist between users. For instance, in Epinions the concepts of personal "web of trust" and personal "block list" allow users to categorize their friends based on the quality of reviews into trusted and distrusted friends, respectively. In this paper, we propose a matrix factorization based model for recommendation in social rating networks that properly incorporates both trust and distrust relationships aiming to improve the quality of recommendations and mitigate the data sparsity and the cold-start users issues. Through experiments on the Epinions data set, we show that our new algorithm outperforms its standard trust-enhanced or distrust-enhanced counterparts with respect to accuracy, thereby demonstrating the positive effect that incorporation of explicit distrust information can have on recommender systems.
Time to timestamp: opportunistic cooperative localization from reception time measurements
Venue: arXiv cs.NI 2014
Year: 2014

We present a general framework for improving and extending GNSS-based positioning by leveraging opportunistic measurements from legacy terrestrial radio signals. The proposed approach requires only that participating nodes collect and share reception timestamps of incoming packets and/or other reference signals transmitted by other fixed or mobile nodes, with no need of inter-node synchronization. The envisioned scheme couples the idea of cooperative GNSS augmentation with recent pioneering work in the field of time-based localization in asynchronous networks. In this contribution we present the fundamental principles of the proposed approach and discuss the system-level aspects that make it particularly appealing and timely for Cooperative ITS applications, with the goal of motivating further research and experimentation in this direction.