Computing Reeb Graphs as a Union of Contour Trees

The Reeb graph of a scalar function tracks the evolution of the topology of its level sets. This paper describes a fast algorithm to compute the Reeb graph of a piecewise-linear (PL) function defined over manifolds and non-manifolds. The key idea in the proposed approach is to maximally leverage the efficient contour tree algorithm to compute the Reeb graph. The algorithm proceeds by dividing the input into a set of subvolumes that have loop-free Reeb graphs using the join tree of the scalar function and computes the Reeb graph by combining the contour trees of all the subvolumes. Since the key ingredient of this method is a series of union-find operations, the algorithm is fast in practice. Experimental results demonstrate that it outperforms current generic algorithms by a factor of up to two orders of magnitude, and has a performance on par with algorithms that are catered to restricted classes of input. The algorithm also extends to handle large data that do not fit in memory.

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.

Eulerian two-phase flow simulation and experimental validation of semisolid slurry generation process using cooling slope

Experimental and numerical studies of slurry generation using a cooling slope are presented in the paper. The slope having stainless steel body has been designed and constructed to produce semisolid A356 Al alloy slurry. The pouring temperature of molten metal, slope angle of the cooling slope and slope wall temperature were varied during the experiment. A multiphase numerical model, considering liquid metal and air, has been developed to simulate the liquid metal flow along the cooling channel using an Eulerian two-phase flow approach. Solid fraction evolution of the solidifying melt is tracked at different locations of the cooling channel following Schiel's equation. The continuity, momentum and energy equations are solved considering thin wall boundary condition approach. During solidification of the melt, based on the liquid fraction and latent heat of the alloy, temperature of the alloy is modified continuously by introducing a modified temperature recovery method. Numerical simulations has been carried out for semisolid slurry formation by varying the process parameters such as angle of the cooling slope, cooling slope wall temperature and melt superheat temperature, to understand the effect of process variables on cooling slope semisolid slurry generation process such as temperature distribution, velocity distribution and solid fraction of the solidifying melt. Experimental validation performed for some chosen cases reveals good agreement with the numerical simulations.

Threshold voltage model for accumulation mode polycrystalline SOI MOSFETs and comparisons with experimental results

In this paper, a simple but accurate semi analytical charge sheet model is presented for threshold voltage of accumulation mode polycrystalline silicon on insulator (PSOI) MOSFETs. In this model, we define the threshold voltage (V-T) of the polysilicon accumulation mode MOSFET as the gate voltage required to raise the surface potential (phi(s)) to a value phi(sT) necessary to overcome the charge trapping in the grain boundary and to create channel accumulation charge that is equal to the channel accumulation charge available in the case of single crystal silicon accumulation mode MOSFET at that phi(sT). The correctness of the model is demonstrated by comparing the theoretically estimated values of threshold voltage with the experimentally measured threshold voltages on the accumulation mode PSOI MOSFETs fabricated in the laboratory using LPCVD polysilicon layers doped with boron to achieve dopant densities in the range 3.3 x 10(-15)-5 x 10(17)/cm(3). Further, it is shown that the threshold voltage values of accumulation mode PSOI MOSFETs predicted by the present model match very closely with the experimental results, better than those obtained with the models previously reported in the literature. (C) 2012 Elsevier B.V. All rights reserved.

Exploiting coherence for the simultaneous discovery of latent facets and associated sentiments

Facet-based sentiment analysis involves discovering the latent facets, sentiments and their associations. Traditional facet-based sentiment analysis algorithms typically perform the various tasks in sequence, and fail to take advantage of the mutual reinforcement of the tasks. Additionally,inferring sentiment levels typically requires domain knowledge or human intervention. In this paper, we propose aseries of probabilistic models that jointly discover latent facets and sentiment topics, and also order the sentiment topics with respect to a multi-point scale, in a language and domain independent manner. This is achieved by simultaneously capturing both short-range syntactic structure and long range semantic dependencies between the sentiment and facet words. The models further incorporate coherence in reviews, where reviewers dwell on one facet or sentiment level before moving on, for more accurate facet and sentiment discovery. For reviews which are supplemented with ratings, our models automatically order the latent sentiment topics, without requiring seed-words or domain-knowledge. To the best of our knowledge, our work is the first attempt to combine the notions of syntactic and semantic dependencies in the domain of review mining. Further, the concept of facet and sentiment coherence has not been explored earlier either. Extensive experimental results on real world review data show that the proposed models outperform various state of the art baselines for facet-based sentiment analysis.

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.

Improving query focused summarization using look-ahead strategy

Query focused summarization is the task of producing a compressed text of original set of documents based on a query. Documents can be viewed as graph with sentences as nodes and edges can be added based on sentence similarity. Graph based ranking algorithms which use 'Biased random surfer model' like topic-sensitive LexRank have been successfully applied to query focused summarization. In these algorithms, random walk will be biased towards the sentences which contain query relevant words. Specifically, it is assumed that random surfer knows the query relevance score of the sentence to where he jumps. However, neighbourhood information of the sentence to where he jumps is completely ignored. In this paper, we propose look-ahead version of topic-sensitive LexRank. We assume that random surfer not only knows the query relevance of the sentence to where he jumps but he can also look N-step ahead from that sentence to find query relevance scores of future set of sentences. Using this look ahead information, we figure out the sentences which are indirectly related to the query by looking at number of hops to reach a sentence which has query relevant words. Then we make the random walk biased towards even to the indirect query relevant sentences along with the sentences which have query relevant words. Experimental results show 20.2% increase in ROUGE-2 score compared to topic-sensitive LexRank on DUC 2007 data set. Further, our system outperforms best systems in DUC 2006 and results are comparable to state of the art systems.

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.

AN EXPERIMENTAL STUDY ON EFFERVESCENT ATOMIZATION OF BIO-OIL FUELS

This paper presents the work on detailed characterization of effervescent spray of Jatropha and Pongamia pure plant oils. The spray characteristics of these biofuels are compared with those of diesel. Both macroscopic and microscopic spray characteristics at different injection pressures and gas-to-liquid ratio (GLR) have been studied. The particle/droplet imaging analysis (PDIA) technique along with direct imaging methods are used for the purpose of spray characterization. Due to their higher viscosity, pure plant oils showed poor atomization compared to diesel and a blend of diesel and pure plant oil at a given GLR. Pure plant oil sprays showed a lower spray cone angle when compared to diesel and blends at lower GLRs. However, the difference is not significant at higher GLRs. Droplet size measurements at 100 mm downstream of the exit orifice showed reduction in Sauter mean diameter (SMD) diameter with increase in GLR. A radial variation in the SMD is observed for the blend and pure plant oils. Pure oils showed a larger variation when compared to the blend. Spray unsteadiness has been characterized based on the image-to-image variation in the mean droplet diameter and fluctuations in the spray cone angle. Results showed that pure plant oil sprays are more unsteady at lower GLRs when compared to diesel and blend. A critical GLR is identified at which the spray becomes steady. The three regimes of spray operation, namely ``steady spray,'' ``pulsating spray,'' and ``spray and unbroken liquid jet'' are identified in the injection pressure-GLR parameter space for these pure plant oils. Two-phase flow imaging inside the exit orifice shows that for the pure plant oils, the flow is highly transient at low GLRs and the bubbly, slug, and annular two-phase flow regimes are all observed. However, at higher GLRs where the spray is steady, only the annular flow regime is observed.

Experimental investigation on switching characteristics of IGBTs for traction application

In traction application, inverters need to have high reliability on account of wide variation in operating conditions, extreme ambient conditions, thermal cycling and varying DC link voltage. Hence it is important to have a good knowledge of switching characteristics of the devices used. The focus of this paper is to investigate and compare switching characteristics and losses of IGBT modules for traction application. Dependence of device transition times and switching energy losses on dc link voltage, device current and operating temperature is studied experimentally.