Leveraging the Crowd for Complex Analytics Koushik Sinha HP Labs India koushik.sinha@hp

1. Continuous medial representation in image shape analysis and classificationBy : Leonid MestetskiyProfessor, Department of Computational Mathematics and Cybernetics, Moscow State University, Moscow, Russia. Email :mestlm@mail.ru • Medial representation (skeleton + radial function) is an important descriptor of the image shape. It is a powerful and widely used tool for image shape analysis. Methods based on the computation of medial representation are commonly used in pattern recognition and image shape classification. • Originally, the concept of medial representation was denoted for continuous objects. The skeleton of a closed region in Euclidean plane is a locus of centers of maximum inscribed circles in this region. Radial function sets at each point of the skeleton radius of the inscribed circle centered at this point. However, this concept became the most popular in shape analysis and classification of discrete bitmap digital images. Therefore, the need to generalize the concept of the medial representation for discrete images was raised. • Approach, which we call continuous, is based on the approximation of a discrete object (left figure) by the geometrical figure in terms of a continuous geometry (middle figure) and the construction of the skeleton and radial function for this figure (right figure). The resulting object is considered as a continuous medial representation of discrete objects. Continuous medial representation allows the use of graph theory and computational geometry algorithms for image shape analysis and recognition. Another advantage of the continuous approach is its high computational efficiency.

2. We propose an original method for discrete image shape description by a planar graph whose edges are “lines with width”: segments of straight lines and quadratic Bezier curves with radial functions and describes the full implementation of the continuous approach. • Medial axis representation finds applications in the practical problems of analysis, comparison and transformation the image shapes. We consider biometric identification, leafs classification, gesture recognition.

3. Projective Morphologies and Morphological SpectrumBy : Yury V. Vizilter State Research Institute of Aviation Systems” (GosNIIAS), Moscow, RussiaEmail : viz@gosniias.ru • Projective Morphology scheme is developed based on Serra’s Mathematical Morphology (MM), Pavel’s Shape Theory and Pytiev’s Morphological Analysis. This morphological framework utilizes the structural image modeling with regularization constrains and decides some image segmentation and image comparison problems. Following common aspects of morphological approaches are addressed: image algebra with projectors, projection to the shape, projection as a combination of segmentation and reconstruction procedures, morphological shape model as a combination of shape elements, morphological complexity of shape models (generalization of granulometry idea), shape model fitting by “precision vs. complexity” criterion. Following common instruments are considered: morphological filtering as a projection to the shape, morphological segmentation as a regularization of shape model, morphological image matching based on morphological shape correlation, morphological feature extraction using morphological background normalization (hit-miss-transform), morphological decomposition, morphological skeletons, morphological spectrum.

4. One tool from morphological toolbox is described more detail: morphological spectrum proposed by P. Maragos and originally based on granulometry – a set of sieves of different grades, each allowing details of certain size classes to pass. There are two types of morphological spectra: based on structuring elements (S.E.) and based on connected filters. Both types are used for classification problems (basically, texture analysis, content-based image retrieval). • S.E. morphological spectrum describes the area density distribution of a union of figures according to figure detail size. Main problems of morphological spectrum in practical applications are following. It is too slow: practical machine vision applications require the real-time spectrum computation. It is hard to compare: there is no effective spectrum comparison metrics, so, pattern recognition applications of morphological spectrum are weak enough. Morphology is wider than spectrum: known morphological spectra are based on Serra MM, but spectra for other projective morphologies could be designed too.

5. Some answers for these questions were proposed in our last works: Morphological spectrum real-time computation using continuous skeletons, spectrum comparison using EMD-metrics, generalized morphological complexity spectra for all criterion-based projective morphologies. First experiments and theoretical analysis allows making some conclusions about perspectives of morphological spectrum in shape recognition tasks. Morphological spectrum is computed in real time and successfully compared with the EMD-metrics. Morphological spectrum is robust relative to area and contour shape distortion, shift and rotation invariant (if a structuring element is a disk), and it is informative enough (if the figure shape varies greatly in thickness). So, it could be of use for shape recognition tasks.

6. Evolutionary optimization techniques for clusteringSriparna SahaDepartment of Computer Science and EngineeringIndian Institute of Technology PatnaPatna, BiharEmail: sriparna@iitp.ac.in, sriparna.saha@gmail.com • Clustering is an important unsupervised classification technique where a number of patterns, usually vectors in a multidimensional space, are grouped into clusters in such a way that patterns in the same cluster are similar in some sense and patterns in different clusters are dissimilar in the same sense. • Cluster analysis is a difficult problem due to the variety of ways of measuring the similarity and dissimilarity concepts • It may be noted that, in general, one of the fundamental features of shapes and objects is symmetry, which is considered to be important for enhancing their recognition (16). As symmetry is commonly found in the natural world, it may be interesting to exploit this property while clustering a data set. • Clustering is considered to be a difficult task as no unambiguous partitioning of the data exists for many data sets. • Most of the existing clustering techniques are based on only one criterion which reflects a single measure of goodness of a partitioning. However, a single cluster quality measure is seldom equally applicable for different kinds of data sets with different characteristics. • Hence, it may become necessary to simultaneously optimize several cluster quality measures that can capture different data characteristics. Many evolutionary optimization based clustering techniques have been developed in recent past (26)(27)(28). In this talk I will mainly focus on different single • objective and multi objective based evolutionary clustering techniques.

7. A New Algorithm to Determine he Ground State of Quantum Coulomb GlassTribikram GuptaAssistant Professor, RVCE, Bangalore • Doped Rare Earth Manganites exhibit Colossal Magneto Resistance (CMR) , a phenomena that is of immense use to industry and engineers. • The attempt to understand these CMR materials poses a huge challenge to physicists as these systems are very complicated with a very rich phase diagram, having a zoo of phases. • This talk deals with what these complications are and how we try to understand them. • In the process we have to solve a highly frustrated system with a glassy groundstate . This is called the “Quantum Coulomb Glass”. • We have developed a new algorithm to solve the above mentioned problem. • For strongly correlated systems, a particular site cannot be doubly occupied and hence the lattice points that will support the hopping around of the itinerant electrons will be only those sites that are unoccupied. • Our algorithm runs around the key idea of studying and enumerating these lattice animals(non regular shaped lattice point clumps that have no electrons on them) and solving the quantum mechanics only on these small clumps. I will highlight the strengths of our new approach and how we may use it to perform calculation on much larger sized systems with proper finite size scaling.

8. Computing Cutoff frequencies for transverse waves along solar flux tubesSwati Routhe-mail: routhswati@rvce.edu.in • It is a well known that the propagation of linear transverse waves along a thin but isothermal magnetic flux tube is affected by the existence of the global cutoff frequency, which separates the propagating and non-propagating waves. • In this presentation, the wave propagation along a thin and non-isothermal flux tube is considered. • The effects of different temperature profiles on the derived local cutoff frequency are studied by considering different power-law temperature distributions as well as the semi-empirical VAL C model of the solar atmosphere. • The obtained results show that the conditions for wave propagation strongly depend on the temperature gradients. • Moreover, the local cutoff frequency calculated for the VAL C model gives constraints on the range of wave frequencies that are propagating in different parts of the solar atmosphere. • These theoretically predicted constraints are compared to observational data and are used to discuss the role played by transverse tube waves in the atmospheric heating and dynamics, and in the excitation of solar atmospheric oscillations.

9. External Confinement: A Key Factor In The Dynamics Of Bose-Einstein CondensateUtpal RoyIndian Institute of Technology Patna, Patliputra Colony, Patna 800013, IndiaEmail: uroy@iitp.ac.in • The experimental discovery of BEC [1], in atomic clouds under external confinement, has opened up the rapidly developing field of the dynamics and characterization of BECs. • The said dynamics is represented by the Gross-Pitaevskii equation, which under inhomogeneous external trapping potential becomes either cigar-shaped or disc shaped BECs. • Interatomic interactions or the nonlinearity of the systems strongly affect the dynamics. • There have been several experiments in these systems with cubic, quintic or higher order nonlinearities [2,3]. • On the other hand, external potential plays a crucial role for the stability of solitons in BEC. Solitons are generated under the balance of nonlinearity and dispersion in a given external confinement[4]. These can be manipulated by controlling both nonlinearity [5] as well as external trap. • We emphasis on various external confinements in BECs and discuss about the coherent control of soliton profile and trajectory. • We reveal the emergence of various interesting phenomena by using different potential functions, consistent with experiments. • We show that the potential parameters are the most important for the dynamics of this nonlinear system.

10. Human Activity Classification Based on Space-Time Interest PointsSoumitra Samanta and Bhabatosh ChandaIndian Statistical Institute203, B. T. Road, Kolkata - 700108Email: soumitra_r@isical.ac.in and chanda@isical.ac.in • This paper describes the development of a system for human activity recognition based on the local features computed at space time interest points. • We propose a novel method to detect space time interest points (STIP) from video data using three dimensional facet model and call it a facet space-time interest point or FaSTIP. • The proposed algorithm detects all the desired interest points efficiently at different scales compared to other existing methods. • A video clip is then described as a collection of 3D wavelet and time derivative base features computed at these interest points. • Finally, multi-channel SVM with chi-square kernel is used to classify human actions. Our contributions here are twofold: • First, we present a new algorithm for interest point detection in video data, and second, we propose a new descriptor for general human activity classification. Experimental result reveals the accuracy of the detected interest points and the power of descriptor compared to the state-of-the-art.

11. Multiscale techniques to find signatures of image classesGowri Srinivasa Dept. of ISE, PESIT Bangalore South Campus, Bangalore. • This talk focuses on a few multiscale techniques that can be used to extract features at multiple scales that prove to be useful for classification. • An example of such a tool is the two-channel filter bank that helps us zoom in on singularities that may not be obvious to the human eye from the original image. • With an appropriate selection of filters and computation of statistical features (in the case of fingerprint images) or intuitive descriptors (in the case of histopathology images) or a combination of the two and a suitable cost function, we investigate the existence of a signature for each class and the mechanism of classifying images from such datasets.

12. Information Extraction and Bio-text MiningAsif EkbalDepartment of Computer Science and EngineeringIndian Institute of Technology PatnaPatna, Bihar, India-800 013Email: asif@iitp.ac.in,asif.ekbal@gmail.com • Information Extraction is the task of extracting relevant information from a huge collection of documents. The idea is to extract information that fits the pre-defined database schemas or templates, specifying the output formats. • Entity Recognition (NER) is a well-established task in information extraction that involves identification and classification of every word/term in a document into some predefined categories • The existing approaches of NER can be grouped into three main categories, namely rule based, machine learning (ML) based and hybrid approach. • Works related to NER in Indian languages have started to emerge only very recently. • The combination of multiple classifiers could be more effective compared to any individual one for NER. • The main idea behind classifier ensemble creation is that ensembles are often much more accurate than the individual classifiers that make them up. • Usually the members of an ensemble are generated either by applying a single learning algorithm [7] or using different learning algorithms over a dataset 8]. Two basic approaches to combine the outputs of several classifiers are majority voting and weighted voting [7]. • In my talk, I will focus on our recent activities on evolutionary optimization based classifier ensemble techniques [14, 1] for solving IE problems. In the second part of my talk I will briefly introduce bio-text mining and then present our work on event extraction from biomedical Text.

13. Incompressible flow computations using infinitely smooth RBFs with optimum shape parameterSanyasiraju YedidaProfessor, Department of Mathematics, IIT Madras, Chennai 600036 • In the recent years, radial basis functions (RBF) , where, for multiquadrics (MQ) for Thin plate splines (TPS) have been very successfully used to solve linear • partial differential equations over scattered data points and also in image processing and neural networks. • Domain decomposition, preconditioning and the use of compactly supported RBFs are some of the alternatives investigated to condition the RBF systems however, the success was very limited. In RBF based local grid free scheme for any differential operator L, the operator values of the unknown u are expressed as a linear combination of the values of u at some neighboring centers ni, where ni << N (the number of total centers), as • and then shown that the weights ci can be obtained by solving • where dummy vector corresponding to the added polynomial base and • In the present lecture, the optimization of the shape parameter is discussed and its validation results by applying the scheme to some benchmark problems in incompressible flow are also presented.

14. Leveraging the Crowd for Complex AnalyticsKoushik SinhaHP Labs Indiakoushik.sinha@hp.com The proliferation of mobile devices and massive amount of unstructured data in the form of social media and video data has disrupted the traditional approaches to analytics that work on structured data. Modern analytics often require extensive collection and deep analysis/fusion of different types of unstructured data captured in the form of text, images, audio and video. In general, such analytics may require understanding sentiment and opinion from various types of text data sources like product reviews, customer feedbacks and tweets, social network and media analysis, natural language speech understanding and translation, event spotting in audiovisual streams, handwritten document analysis and others. Unfortunately, the resulting demands of scale and more intelligent (human-like) analysis are often hard (if not impossible) and/or expensive to meet using purely manual processing or purely machine computation. Therefore, using humans as computing agents holds great promise for enabling such sophisticated analytics, provided it is possible to address the various challenges associated with enabling seamless, cost-effective and intelligent orchestration of automated processing with on-demand human intelligence. In order to address these challenges, crowdsourcing is rapidly emerging as an active area of exploration. In this talk, we shall discuss about the essence and power of crowdsourcing along with the various challenges involved in effectively combining human and machine computation agents to deliver practical solutions for (human-like) data-analytics.

15. Sub-Planck scale structures and their relevance for Quantum MetrologySuranjana Ghosh* Indian Institute of Technology Patna email : sghosh@iitp.ac.in • Quantum mechanical systems can in principle play a crucial role to produce greater sensitivity over classical methods. Quantum metrology is the field which deals with the fundamental limits to measurement. Improvement in quantum parameter estimation has always led to scientific breakthrough and technological advancement. More generally, quantum metrology deals with the measurement and discrimination procedures that receive some kind of enhancement through the use of quantum effects. We point out a general framework that includes some physical systems in which quantum effects enable an increase in precision. We illustrate the origin of sub-Planck scale structures in quantum interference region and their relevance in sensitivity analysis. Past few years have witnessed an outbreak in the study of sub-Planck scale structures. These highly nonclassical structures are particularly very sensitive to decoherence or perturbation. The significance of sub-Planck structures in relation to quantum sensitivity issue is explored in different physical systems. We discuss a general scheme for measuring small displacements and rotations in phase space by using nonclassical states. Moreover, maximum quantum limit is achieved specially for a diatomic molecular system.

16. Confined fluid flow in nanoscale: A simulation study using molecular dynamics methods.Dr. Antina Ghosh, Postdoctoral Fellow, Tel Aviv University, Israel • Fluid flow in confined geometries plays an important role over a range of physical phenomena and technological applications starting from physics to biology. Some of the examples are lab on a chip devices, micro cooling, translocation of bio-components through nano pores . Such emerging technology has essentially propelled a strong interest in understanding Nano fluidic systems and how different the flow behavior would be compared to its bulk counterpart. There are many effects, in particular surface effects plays a dominant role in nanoscale and often the continuum equations are not adequate to describe the flow behavior. Thus numerical simulations and experimental techniques are the main tools to investigate such nanoscale flows. In the present study, we use molecular dynamics simulation methods to study driven flow of an atomic liquid interacting via 12-6 Lennard-Jones potential in a nano-sized channel. The channel is unbounded in x-y direction and bounded by walls along z direction. We first obtain the static density profile that exhibits an oscillatory variation indicating the existence of distinct fluid layers across the channel. When the channel height is sufficiently small such density variation usually occurs all over the channel resulting an highly inhomogeneous fluid structure. To simulate Poiseuillle flow in the system we impose a constant body force on each fluid atom along x direction. We then obtain various macroscopic fields like velocity, stress, strain and viscosity profiles from the microscopic data over a range of body forces describing the effect of such inhomogeneity on flow behavior and rheology of the system.