OpenRaiser/Intern-Atlas huggingface dataset:
Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination
Venue: arXiv cs.LG 2014
Year: 2014

CANDECOMP/PARAFAC (CP) tensor factorization of incomplete data is a powerful technique for tensor completion through explicitly capturing the multilinear latent factors. The existing CP algorithms require the tensor rank to be manually specified, however, the determination of tensor rank remains a challenging problem especially for CP rank. In addition, existing approaches do not take into account uncertainty information of latent factors, as well as missing entries. To address these issues, we formulate CP factorization using a hierarchical probabilistic model and employ a fully Bayesian treatment by incorporating a sparsity-inducing prior over multiple latent factors and the appropriate hyperpriors over all hyperparameters, resulting in automatic rank determination. To learn the model, we develop an efficient deterministic Bayesian inference algorithm, which scales linearly with data size. Our method is characterized as a tuning parameter-free approach, which can effectively infer underlying multilinear factors with a low-rank constraint, while also providing predictive distributions over missing entries. Extensive simulations on synthetic data illustrate the intrinsic capability of our method to recover the ground-truth of CP rank and prevent the overfitting problem, even when a large amount of entries are missing. Moreover, the results from real-world applications, including image inpainting and facial image synthesis, demonstrate that our method outperforms state-of-the-art approaches for both tensor factorization and tensor completion in terms of predictive performance.
On the Power of Cooperation: Can a Little Help a Lot? (Extended Version)
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we propose a new cooperation model for discrete memoryless multiple access channels. Unlike in prior cooperation models (e.g., conferencing encoders), where the transmitters cooperate directly, in this model the transmitters cooperate through a larger network. We show that under this indirect cooperation model, there exist channels for which the increase in sum-capacity resulting from cooperation is significantly larger than the rate shared by the transmitters to establish the cooperation. This result contrasts both with results on the benefit of cooperation under prior models and results in the network coding literature, where attempts to find examples in which similar small network modifications yield large capacity benefits have to date been unsuccessful.
Transmitter Optimization in MISO Broadcast Channel with Common and Secret Messages
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we consider transmitter optimization in multiple-input single-output (MISO) broadcast channel with common and secret messages. The secret message is intended for $K$ users and it is transmitted with perfect secrecy with respect to $J$ eavesdroppers which are also assumed to be legitimate users in the network. The common message is transmitted at a fixed rate $R_{0}$ and it is intended for all $K$ users and $J$ eavesdroppers. The source operates under a total power constraint. It also injects artificial noise to improve the secrecy rate. We obtain the optimum covariance matrices associated with the common message, secret message, and artificial noise, which maximize the achievable secrecy rate and simultaneously meet the fixed rate $R_{0}$ for the common message.
Achievable Degrees of Freedom in MIMO Correlatively Changing Fading Channels
Venue: arXiv cs.IT 2014
Year: 2014

The relationship between the transmitted signal and the noiseless received signals in correlatively changing fading channels is modeled as a nonlinear mapping over manifolds of different dimensions. Dimension counting argument claims that the dimensionality of the neighborhood in which this mapping is bijective with probability one is achievable as the degrees of freedom of the system.We call the degrees of freedom achieved by the nonlinear decoding methods the nonlinear degrees of freedom.
A Machine Learning Approach for the Identification of Bengali Noun-Noun Compound Multiword Expressions
Venue: arXiv cs.CL 2014
Year: 2014

This paper presents a machine learning approach for identification of Bengali multiword expressions (MWE) which are bigram nominal compounds. Our proposed approach has two steps: (1) candidate extraction using chunk information and various heuristic rules and (2) training the machine learning algorithm called Random Forest to classify the candidates into two groups: bigram nominal compound MWE or not bigram nominal compound MWE. A variety of association measures, syntactic and linguistic clues and a set of WordNet-based similarity features have been used for our MWE identification task. The approach presented in this paper can be used to identify bigram nominal compound MWE in Bengali running text.
Keyword and Keyphrase Extraction Using Centrality Measures on Collocation Networks
Venue: arXiv cs.CL 2014
Year: 2014

Keyword and keyphrase extraction is an important problem in natural language processing, with applications ranging from summarization to semantic search to document clustering. Graph-based approaches to keyword and keyphrase extraction avoid the problem of acquiring a large in-domain training corpus by applying variants of PageRank algorithm on a network of words. Although graph-based approaches are knowledge-lean and easily adoptable in online systems, it remains largely open whether they can benefit from centrality measures other than PageRank. In this paper, we experiment with an array of centrality measures on word and noun phrase collocation networks, and analyze their performance on four benchmark datasets. Not only are there centrality measures that perform as well as or better than PageRank, but they are much simpler (e.g., degree, strength, and neighborhood size). Furthermore, centrality-based methods give results that are competitive with and, in some cases, better than two strong unsupervised baselines.
Completion Time Reduction in Instantly Decodable Network Coding Through Decoding Delay Control
Venue: arXiv cs.IT 2014
Year: 2014

For several years, the completion time and decoding delay problems in Instantly Decodable Network Coding (IDNC) were considered separately and were thought to completely act against each other. Recently, some works aimed to balance the effects of these two important IDNC metrics but none of them studied a further optimization of one by controlling the other. In this paper, we study the effect of controlling the decoding delay to reduce the completion time below its currently best known solution. We first derive the decoding-delay-dependent expressions of the users' and overall completion times. Although using such expressions to find the optimal overall completion time is NP-hard, we design a novel heuristic that minimizes the probability of increasing the maximum of these decoding-delay-dependent completion time expressions after each transmission through a layered control of their decoding delays. Simulation results show that this new algorithm achieves both a lower mean completion time and mean decoding delay compared to the best known heuristic for completion time reduction. The gap in performance becomes significant for harsh erasure scenarios.
Coinduction up to in a fibrational setting
Venue: arXiv cs.LO 2014
Year: 2014

Bisimulation up-to enhances the coinductive proof method for bisimilarity, providing efficient proof techniques for checking properties of different kinds of systems. We prove the soundness of such techniques in a fibrational setting, building on the seminal work of Hermida and Jacobs. This allows us to systematically obtain up-to techniques not only for bisimilarity but for a large class of coinductive predicates modelled as coalgebras. By tuning the parameters of our framework, we obtain novel techniques for unary predicates and nominal automata, a variant of the GSOS rule format for similarity, and a new categorical treatment of weak bisimilarity.
Capacity Region of the Broadcast Channel with Two Deterministic Channel State Components
Venue: arXiv cs.IT 2014
Year: 2014

This paper establishes the capacity region of a class of broadcast channels with random state in which each channel component is selected from two possible functions and each receiver knows its state sequence. This channel model does not fit into any class of broadcast channels for which the capacity region was previously known and is useful in studying wireless communication channels when the fading state is known only at the receivers. The capacity region is shown to coincide with the UV outer bound and is achieved via Marton coding.
Modeling the behavior of reinforced concrete walls under fire, considering the impact of the span on firewalls
Venue: arXiv cs.CE 2014
Year: 2014

Numerical modeling using computers is known to present several advantages compared to experimental testing. The high cost and the amount of time required to prepare and to perform a test were among the main problems on the table when the first tools for modeling structures in fire were developed. The discipline structures-in-fire modeling is still currently the subject of important research efforts around the word, those research efforts led to develop many software. In this paper, our task is oriented to the study of fire behavior and the impact of the span reinforced concrete walls with different sections belonging to a residential building braced by a system composed of porticoes and sails. Regarding the design and mechanical loading (compression forces and moments) exerted on the walls in question, we are based on the results of a study conducted at cold. We use on this subject the software Safir witch obeys to the Eurocode laws, to realize this study. It was found that loading, heating, and sizing play a capital role in the state of failed walls. Our results justify well the use of reinforced concrete walls, acting as a firewall. Their role is to limit the spread of fire from one structure to another structure nearby, since we get fire resistance reaching more than 10 hours depending on the loading considered.
Slotted Aloha for Networked Base Stations with Spatial and Temporal Diversity
Venue: arXiv cs.IT 2014
Year: 2014

We consider framed slotted Aloha where $m$ base stations cooperate to decode messages from $n$ users. Users and base stations are placed uniformly at random over an area. At each frame, each user sends multiple replicas of its packet according to a prescribed distribution, and it is heard by all base stations within the communication radius $r$. Base stations employ a decoding algorithm that utilizes the successive interference cancellation mechanism, both in space--across neighboring base stations, and in time--across different slots, locally at each base station. We show that there exists a threshold on the normalized load $G=n/(τm)$, where $τ$ is the number of slots per frame, below which decoding probability converges asymptotically (as $n,m,τ\rightarrow \infty$, $r\rightarrow 0$) to the maximal possible value--the probability that a user is heard by at least one base station, and we find a lower bound on the threshold. Further, we give a heuristic evaluation of the decoding probability based on the and-or-tree analysis. Finally, we show that the peak throughput increases linearly in the number of base stations.
Context-based Word Acquisition for Situated Dialogue in a Virtual World
Venue: arXiv cs.CL 2014
Year: 2014

To tackle the vocabulary problem in conversational systems, previous work has applied unsupervised learning approaches on co-occurring speech and eye gaze during interaction to automatically acquire new words. Although these approaches have shown promise, several issues related to human language behavior and human-machine conversation have not been addressed. First, psycholinguistic studies have shown certain temporal regularities between human eye movement and language production. While these regularities can potentially guide the acquisition process, they have not been incorporated in the previous unsupervised approaches. Second, conversational systems generally have an existing knowledge base about the domain and vocabulary. While the existing knowledge can potentially help bootstrap and constrain the acquired new words, it has not been incorporated in the previous models. Third, eye gaze could serve different functions in human-machine conversation. Some gaze streams may not be closely coupled with speech stream, and thus are potentially detrimental to word acquisition. Automated recognition of closely-coupled speech-gaze streams based on conversation context is important. To address these issues, we developed new approaches that incorporate user language behavior, domain knowledge, and conversation context in word acquisition. We evaluated these approaches in the context of situated dialogue in a virtual world. Our experimental results have shown that incorporating the above three types of contextual information significantly improves word acquisition performance.
Unsupervised Visual and Textual Information Fusion in Multimedia Retrieval - A Graph-based Point of View
Venue: arXiv cs.IR 2014
Year: 2014

Multimedia collections are more than ever growing in size and diversity. Effective multimedia retrieval systems are thus critical to access these datasets from the end-user perspective and in a scalable way. We are interested in repositories of image/text multimedia objects and we study multimodal information fusion techniques in the context of content based multimedia information retrieval. We focus on graph based methods which have proven to provide state-of-the-art performances. We particularly examine two of such methods : cross-media similarities and random walk based scores. From a theoretical viewpoint, we propose a unifying graph based framework which encompasses the two aforementioned approaches. Our proposal allows us to highlight the core features one should consider when using a graph based technique for the combination of visual and textual information. We compare cross-media and random walk based results using three different real-world datasets. From a practical standpoint, our extended empirical analysis allow us to provide insights and guidelines about the use of graph based methods for multimodal information fusion in content based multimedia information retrieval.
An N-Body Solution to the Problem of Fock Exchange
Venue: arXiv cs.DS 2014
Year: 2014

We report an N-Body approach to computing the Fock exchange matrix with and without permutational symmetry. The method achieves an O(N lg N) computational complexity through an embedded metric-query, allowing hierarchical application of direct SCF criteria. The advantages of permutational symmetry are found to be 4-fold for small systems, but decreasing with increasing system size and/or more permissive neglect criteria. This work sets the stage for: (1) the introduction of range queries in multi-level multipole schemes for rank reduction, and (2) recursive task parallelism.
Performance Analysis of Spin Transfer Torque Random Access Memory with cross shaped free layer using Heusler Alloys by using micromagnetic studies
Venue: arXiv cs.ET 2014
Year: 2014

We investigated the performance of spin transfer torque random access memory (STT-RAM) cell with cross shaped Heusler compound based free layer using micromagnetic simulations. We designed the free layer using Cobalt based Heusler compounds. Here in this paper, simulation results predict that switching time from one state to other state is reduced. Also it is examined that critical switching current density to switch the magnetization of free layer of STT RAM cell is reduced.
A decoding algorithm for CSS codes using the X/Z correlations
Venue: arXiv cs.IT 2014
Year: 2014

We propose a simple decoding algorithm for CSS codes taking into account the correlations between the X part and the Z part of the error. Applying this idea to surface codes, we derive an improved version of the perfect matching decoding algorithm which uses these X/Z correlations.
Fast and energy-efficient technique for jammed region mapping in wireless sensor networks
Venue: arXiv cs.NI 2014
Year: 2014

Wireless sensor networks (WSNs) have great practical importance for surveillance systems to perform monitoring by acquiring and sending information on any intrusion in a secured area. Requirement of very little human intervention is one of the most desirable features of WSNs, thus making it a cheaper and safer alternative for securing large areas such as international borders. Jamming attacks in WSNs can be applied to disrupt communications among the sensor nodes in the network. Since it is difficult to prevent jamming attacks, detection and mapping out the jammed regions is critical to overcome this problem. In a security monitoring scenario, the network operators will be able to take proper measures against jamming once the jammed regions in the network are known to them. It is also desirable to keep the interactions of the sensor nodes in the network minimal, as they are low powered devices and need to conserve their resources. In this paper we propose a light-weight technique for faster mapping of the jammed regions. We minimize the load on the sensors by removing the actual responsibility of mapping from the network to the central base station (BS). After a few nodes report to the BS, it carries out the task of mapping of the jammed regions in the network. We use our simulation results to compare our proposed system with the existing techniques and also to measure the performance of our system. Our results show that the jammed regions in a network can be mapped from fewer nodes reporting to the base station.
Randomized Minmax Regret for Combinatorial Optimization Under Uncertainty
Venue: arXiv cs.DM 2014
Year: 2014

The minmax regret problem for combinatorial optimization under uncertainty can be viewed as a zero-sum game played between an optimizing player and an adversary, where the optimizing player selects a solution and the adversary selects costs with the intention of maximizing the regret of the player. The existing minmax regret model considers only deterministic solutions/strategies, and minmax regret versions of most polynomial solvable problems are NP-hard. In this paper, we consider a randomized model where the optimizing player selects a probability distribution (corresponding to a mixed strategy) over solutions and the adversary selects costs with knowledge of the player's distribution, but not its realization. We show that under this randomized model, the minmax regret version of any polynomial solvable combinatorial problem becomes polynomial solvable. This holds true for both the interval and discrete scenario representations of uncertainty. Using the randomized model, we show new proofs of existing approximation algorithms for the deterministic model based on primal-dual approaches. Finally, we prove that minmax regret problems are NP-hard under general convex uncertainty.
Acquisition of a Project-Specific Process
Venue: arXiv cs.SE 2014
Year: 2014

Currently, proposed development processes are often considered too generic for operational use. This often leads to a misunderstanding of the project-specific processes and its refuse. One reason for non-appropriate project-specific processes is insufficient support for the tailoring of generic processes to project characteristics and context constraints. To tackle this problem, we propose a method for the acquisition of a project-specific process. This method uses a domain-specific process line for top-down process tailoring and supports bottom-up refinement of the defined generic process based on tracking process activities. The expected advantage of the method is tailoring efficiency gained by usage of a process line and higher process adherence gained by bottom-up adaptation of the process. The work described was conducted in the automotive domain. This article presents an overview of the so-called Emergent Process Acquisition method (EPAc) and sketches an initial validation study.
Fundamental Limits in Correlated Fading MIMO Broadcast Channels: Benefits of Transmit Correlation Diversity
Venue: arXiv cs.IT 2014
Year: 2014

We investigate asymptotic capacity limits of the Gaussian MIMO broadcast channel (BC) with spatially correlated fading to understand when and how much transmit correlation helps the capacity. By imposing a structure on channel covariances (equivalently, transmit correlations at the transmitter side) of users, also referred to as \emph{transmit correlation diversity}, the impact of transmit correlation on the power gain of MIMO BCs is characterized in several regimes of system parameters, with a particular interest in the large-scale array (or massive MIMO) regime. Taking the cost for downlink training into account, we provide asymptotic capacity bounds of multiuser MIMO downlink systems to see how transmit correlation diversity affects the system multiplexing gain. We make use of the notion of joint spatial division and multiplexing (JSDM) to derive the capacity bounds. It is advocated in this paper that transmit correlation diversity may be of use to significantly increase multiplexing gain as well as power gain in multiuser MIMO systems. In particular, the new type of diversity in wireless communications is shown to improve the system multiplexing gain up to by a factor of the number of degrees of such diversity. Finally, performance limits of conventional large-scale MIMO systems not exploiting transmit correlation are also characterized.
Towards a Resolution of P = NP Conjecture
Venue: arXiv cs.NA 2014
Year: 2014

In this research paper, the problem of optimization of a quadratic form over the convex hull generated by the corners of hypercube is attempted and solved. It is reasoned that under some conditions, the optimum occurs at the corners of hypercube. Results related to the computation of global optimum stable state (an NP hard problem) are discussed. An algorithm is proposed. It is hoped that the results shed light on resolving the P not equal to NP problem.
SSthreshless Start: A Sender-Side TCP Intelligence for Long Fat Network
Venue: arXiv cs.NI 2014
Year: 2014

Measurement shows that 85% of TCP flows in the internet are short-lived flows that stay most of their operation in the TCP startup phase. However, many previous studies indicate that the traditional TCP Slow Start algorithm does not perform well, especially in long fat networks. Two obvious problems are known to impact the Slow Start performance, which are the blind initial setting of the Slow Start threshold and the aggressive increase of the probing rate during the startup phase regardless of the buffer sizes along the path. Current efforts focusing on tuning the Slow Start threshold and/or probing rate during the startup phase have not been considered very effective, which has prompted an investigation with a different approach. In this paper, we present a novel TCP startup method, called threshold-less slow start or SSthreshless Start, which does not need the Slow Start threshold to operate. Instead, SSthreshless Start uses the backlog status at bottleneck buffer to adaptively adjust probing rate which allows better seizing of the available bandwidth. Comparing to the traditional and other major modified startup methods, our simulation results show that SSthreshless Start achieves significant performance improvement during the startup phase. Moreover, SSthreshless Start scales well with a wide range of buffer size, propagation delay and network bandwidth. Besides, it shows excellent friendliness when operating simultaneously with the currently popular TCP NewReno connections.