Presence of dark energy and dark matter: does cosmic acceleration signifies a weak gravitational collapse?

In this work the collapsing process of a spherically symmetric star, made of dust cloud, in the background of dark energy is studied for two different gravity theories separately, i.e., DGP Brane gravity and Loop Quantum gravity. Two types of dark energy fluids, namely, Modified Chaplygin gas and Generalised Cosmic Chaplygin gas are considered for each model. Graphs are drawn to characterize the nature and the probable outcome of gravitational collapse. A comparative study is done between the collapsing process in the two different gravity theories. It is found that in case of dark matter, there is a great possibility of collapse and consequent formation of Black hole. In case of dark energy possibility of collapse is far lesser compared to the other cases, due to the large negative pressure of dark energy component. There is an increase in mass of the cloud in case of dark matter collapse due to matter accumulation. The mass decreases considerably in case of dark energy due to dark energy accretion on the cloud. In case of collapse with a combination of dark energy and dark matter, it is found that in the absence of interaction there is a far better possibility of formation of black hole in DGP brane model compared to Loop quantum cosmology model.

Efficient Algorithms for Linear Summed Error Structural SVMs

Structural Support Vector Machines (SSVMs) have become a popular tool in machine learning for predicting structured objects like parse trees, Part-of-Speech (POS) label sequences and image segments. Various efficient algorithmic techniques have been proposed for training SSVMs for large datasets. The typical SSVM formulation contains a regularizer term and a composite loss term. The loss term is usually composed of the Linear Maximum Error (LME) associated with the training examples. Other alternatives for the loss term are yet to be explored for SSVMs. We formulate a new SSVM with Linear Summed Error (LSE) loss term and propose efficient algorithms to train the new SSVM formulation using primal cutting-plane method and sequential dual coordinate descent method. Numerical experiments on benchmark datasets demonstrate that the sequential dual coordinate descent method is faster than the cutting-plane method and reaches the steady-state generalization performance faster. It is thus a useful alternative for training SSVMs when linear summed error is used.

q-Gaussian based Smoothed Functional Algorithms for Stochastic Optimization

The q-Gaussian distribution results from maximizing certain generalizations of Shannon entropy under some constraints. The importance of q-Gaussian distributions stems from the fact that they exhibit power-law behavior, and also generalize Gaussian distributions. In this paper, we propose a Smoothed Functional (SF) scheme for gradient estimation using q-Gaussian distribution, and also propose an algorithm for optimization based on the above scheme. Convergence results of the algorithm are presented. Performance of the proposed algorithm is shown by simulation results on a queuing model.

Diversity in ranking via resistive graph centers

Users can rarely reveal their information need in full detail to a search engine within 1--2 words, so search engines need to "hedge their bets" and present diverse results within the precious 10 response slots. Diversity in ranking is of much recent interest. Most existing solutions estimate the marginal utility of an item given a set of items already in the response, and then use variants of greedy set cover. Others design graphs with the items as nodes and choose diverse items based on visit rates (PageRank). Here we introduce a radically new and natural formulation of diversity as finding centers in resistive graphs. Unlike in PageRank, we do not specify the edge resistances (equivalently, conductances) and ask for node visit rates. Instead, we look for a sparse set of center nodes so that the effective conductance from the center to the rest of the graph has maximum entropy. We give a cogent semantic justification for turning PageRank thus on its head. In marked deviation from prior work, our edge resistances are learnt from training data. Inference and learning are NP-hard, but we give practical solutions. In extensive experiments with subtopic retrieval, social network search, and document summarization, our approach convincingly surpasses recently-published diversity algorithms like subtopic cover, max-marginal relevance (MMR), Grasshopper, DivRank, and SVMdiv.

Smoothed functional and Quasi-Newton algorithms for routing in multi-stage queueing network with constraints

We consider the problem of optimal routing in a multi-stage network of queues with constraints on queue lengths. We develop three algorithms for probabilistic routing for this problem using only the total end-to-end delays. These algorithms use the smoothed functional (SF) approach to optimize the routing probabilities. In our model all the queues are assumed to have constraints on the average queue length. We also propose a novel quasi-Newton based SF algorithm. Policies like Join Shortest Queue or Least Work Left work only for unconstrained routing. Besides assuming knowledge of the queue length at all the queues. If the only information available is the expected end-to-end delay as with our case such policies cannot be used. We also give simulation results showing the performance of the SF algorithms for this problem.

Combination of similarity measures for time series classification using genetic algorithms

Time series classification deals with the problem of classification of data that is multivariate in nature. This means that one or more of the attributes is in the form of a sequence. The notion of similarity or distance, used in time series data, is significant and affects the accuracy, time, and space complexity of the classification algorithm. There exist numerous similarity measures for time series data, but each of them has its own disadvantages. Instead of relying upon a single similarity measure, our aim is to find the near optimal solution to the classification problem by combining different similarity measures. In this work, we use genetic algorithms to combine the similarity measures so as to get the best performance. The weightage given to different similarity measures evolves over a number of generations so as to get the best combination. We test our approach on a number of benchmark time series datasets and present promising results.

Operator-splitting finite element algorithms for computations of high-dimensional parabolic problems

An operator-splitting finite element method for solving high-dimensional parabolic equations is presented. The stability and the error estimates are derived for the proposed numerical scheme. Furthermore, two variants of fully-practical operator-splitting finite element algorithms based on the quadrature points and the nodal points, respectively, are presented. Both the quadrature and the nodal point based operator-splitting algorithms are validated using a three-dimensional (3D) test problem. The numerical results obtained with the full 3D computations and the operator-split 2D + 1D computations are found to be in a good agreement with the analytical solution. Further, the optimal order of convergence is obtained in both variants of the operator-splitting algorithms. (C) 2012 Elsevier Inc. All rights reserved.

Novel algorithms for distributed sequential hypothesis testing

This paper considers sequential hypothesis testing in a decentralized framework. We start with two simple decentralized sequential hypothesis testing algorithms. One of which is later proved to be asymptotically Bayes optimal. We also consider composite versions of decentralized sequential hypothesis testing. A novel nonparametric version for decentralized sequential hypothesis testing using universal source coding theory is developed. Finally we design a simple decentralized multihypothesis sequential detection algorithm.

Near-optimal large-MIMO detection using randomized MCMC and randomized search algorithms

Low-complexity near-optimal detection of signals in MIMO systems with large number (tens) of antennas is getting increased attention. In this paper, first, we propose a variant of Markov chain Monte Carlo (MCMC) algorithm which i) alleviates the stalling problem encountered in conventional MCMC algorithm at high SNRs, and ii) achieves near-optimal performance for large number of antennas (e.g., 16×16, 32×32, 64×64 MIMO) with 4-QAM. We call this proposed algorithm as randomized MCMC (R-MCMC) algorithm. Second, we propose an other algorithm based on a random selection approach to choose candidate vectors to be tested in a local neighborhood search. This algorithm, which we call as randomized search (RS) algorithm, also achieves near-optimal performance for large number of antennas with 4-QAM. The complexities of the proposed R-MCMC and RS algorithms are quadratic/sub-quadratic in number of transmit antennas, which are attractive for detection in large-MIMO systems. We also propose message passing aided R-MCMC and RS algorithms, which are shown to perform well for higher-order QAM.

Spectral-spatial MODIS image analysis using swarm intelligence algorithms and region based segmentation for flood assessment

This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time-series analysis of satellite images utilizing pixel spectral information for image clustering and region based segmentation for extracting water covered regions. MODIS satellite images are analyzed at two stages: before flood and during flood. Multi-temporal MODIS images are processed in two steps. In the first step, clustering algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to distinguish the water regions from the non-water based on spectral information. These algorithms are chosen since they are quite efficient in solving multi-modal optimization problems. These classified images are then segmented using spatial features of the water region to extract the river. From the results obtained, we evaluate the performance of the methods and conclude that incorporating region based image segmentation along with clustering algorithms provides accurate and reliable approach for the extraction of water covered region.

Cavitation in materials with distributed weak zones: Implications on the origin of brittle fracture in metallic glasses

Several experimental studies have shown that fracture surfaces in brittle metallic glasses (MGs) generally exhibit nanoscale corrugations which may be attributed to the nucleation and coalescence of nanovoids during crack propagation. Recent atomistic simulations suggest that this phenomenon is due to large spatial fluctuations in material properties in a brittle MG, which leads to void nucleation in regions of low atomic density and then catastrophic fracture through void coalescence. To explain this behavior, we propose a model of a heterogeneous solid containing a distribution of weak zones to represent a brittle MG. Plane strain continuum finite element analysis of cavitation in such an elastic-plastic solid is performed with the weak zones idealized as periodically distributed regions having lower yield strength than the background material. It is found that the presence of weak zones can significantly reduce the critical hydrostatic stress for the onset of cavitation which is controlled uniquely by the local yield properties of these zones. Also, the presence of weak zones diminishes the sensitivity of the cavitation stress to the volume fraction of a preexisting void. These results provide plausible explanations for the observations reported in recent atomistic simulations of brittle MGs. An analytical solution for a composite, incompressible elastic-plastic solid with a weak inner core is used to investigate the effect of volume fraction and yield strength of the core on the nature of cavitation bifurcation. It is shown that snap-cavitation may occur, giving rise to sudden formation of voids with finite size, which does not happen in a homogeneous plastic solid. (c) 2012 Elsevier Ltd. All rights reserved.

Bimetallic core-shell nanocomposites using weak reducing agent and their transformation to alloy nanostructures

An in situ seeding growth methodology towards the preparation of core-shell nanoparticles composed of noble metals has been developed by employing trimethylamine borane (TMAB) as the reducing agent. Being a weak reducing agent, TMAB is able to distinguish the smallest reduction potential window of any two metals which renders selective reduction of metal ions thus affording a core-shell architecture of the nanoparticles. A dramatic effect of solvent was noted during the reduction of Ag+ ions: an immediate reduction took place at room temperature when dry THF was used as solvent however, usage of wet THF (THF used directly from the bottle) brings out the reduction only at reflux conditions. In the case of Au and Pd nanoparticles, preparation was found to be independent of the quality of solvent used. Au nanoparticles are realized at room temperature whereas reflux conditions are required in the case of Pd nanoparticles. This difference in behavior of the monometallic nanoparticles was successfully exploited to construct different noble metal nanoparticles with core-shell architectures such as Au@Ag, Ag@Au, and Ag@Pd. Transformation of these core-shell nanoparticles to their thermodynamically stable alloy counterparts is also demonstrated under very mild conditions reported to date.

Power allocation for interference channels

We propose power allocation algorithms for increasing the sum rate of two and three user interference channels. The channels experience fast fading and there is an average power constraint on each transmitter. Our achievable strategies for two and three user interference channels are based on the classification of the interference into very strong, strong and weak interferences. We present numerical results of the power allocation algorithm for two user Gaussian interference channel with Rician fading with mean total power gain of the fade Omega = 3 and Rician factor kappa = 0.5 and compare the sum rate with that obtained from ergodic interference alignment with water-filling. We show that our power allocation algorithm increases the sum rate with a gain of 1.66dB at average transmit SNR of 5dB. For the three user Gaussian interference channel with Rayleigh fading with distribution CN(0, 0.5), we show that our power allocation algorithm improves the sum rate with a gain of 1.5dB at average transmit SNR of 5dB.