Structured Learning for Non-Smooth Ranking losses

Learning to rank from relevance judgment is an active research area. Itemwise score regression, pairwise preference satisfaction, and listwise structured learning are the major techniques in use. Listwise structured learning has been applied recently to optimize important non-decomposable ranking criteria like AUC (area under ROC curve) and MAP(mean average precision). We propose new, almost-lineartime algorithms to optimize for two other criteria widely used to evaluate search systems: MRR (mean reciprocal rank) and NDCG (normalized discounted cumulative gain)in the max-margin structured learning framework. We also demonstrate that, for different ranking criteria, one may need to use different feature maps. Search applications should not be optimized in favor of a single criterion, because they need to cater to a variety of queries. E.g., MRR is best for navigational queries, while NDCG is best for informational queries. A key contribution of this paper is to fold multiple ranking loss functions into a multi-criteria max-margin optimization.The result is a single, robust ranking model that is close to the best accuracy of learners trained on individual criteria. In fact, experiments over the popular LETOR and TREC data sets show that, contrary to conventional wisdom, a test criterion is often not best served by training with the same individual criterion.

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.

SPSA Based Feature Relevance Estimation For Video Retrieval

With the availability of a huge amount of video data on various sources, efficient video retrieval tools are increasingly in demand. Video being a multi-modal data, the perceptions of ``relevance'' between the user provided query video (in case of Query-By-Example type of video search) and retrieved video clips are subjective in nature. We present an efficient video retrieval method that takes user's feedback on the relevance of retrieved videos and iteratively reformulates the input query feature vectors (QFV) for improved video retrieval. The QFV reformulation is done by a simple, but powerful feature weight optimization method based on Simultaneous Perturbation Stochastic Approximation (SPSA) technique. A video retrieval system with video indexing, searching and relevance feedback (RF) phases is built for demonstrating the performance of the proposed method. The query and database videos are indexed using the conventional video features like color, texture, etc. However, we use the comprehensive and novel methods of feature representations, and a spatio-temporal distance measure to retrieve the top M videos that are similar to the query. In feedback phase, the user activated iterative on the previously retrieved videos is used to reformulate the QFV weights (measure of importance) that reflect the user's preference, automatically. It is our observation that a few iterations of such feedback are generally sufficient for retrieving the desired video clips. The novel application of SPSA based RF for user-oriented feature weights optimization makes the proposed method to be distinct from the existing ones. The experimental results show that the proposed RF based video retrieval exhibit good performance.

Prediction of Ultimate Capacity of Laterally Loaded Piles in Clay: A Relevance Vector Machine Approach

This study investigates the potential of Relevance Vector Machine (RVM)-based approach to predict the ultimate capacity of laterally loaded pile in clay. RVM is a sparse approximate Bayesian kernel method. It can be seen as a probabilistic version of support vector machine. It provides much sparser regressors without compromising performance, and kernel bases give a small but worthwhile improvement in performance. RVM model outperforms the two other models based on root-mean-square-error (RMSE) and mean-absolute-error (MAE) performance criteria. It also stimates the prediction variance. The results presented in this paper clearly highlight that the RVM is a robust tool for prediction Of ultimate capacity of laterally loaded piles in clay.

Ranking of Prospective New Generation Location for a Power Network in a Deregulated System

In developing countries high rate of growth in demand of electric energy is felt, and so the addition of new generating units becomes necessary. In deregulated power systems private generating stations are encouraged to add new generations. Finding the appropriate location of new generator to be installed can be obtained by running repeated power flows, carrying system studies like analyzing the voltage profile, voltage stability, loss analysis etc. In this paper a new methodology is proposed which will mainly consider the existing network topology into account. A concept of T-index is introduced in this paper, which considers the electrical distances between generator and load nodes.This index is used for ranking significant new generation expansion locations and also indicates the amount of permissible generations that can be installed at these new locations. This concept facilitates for the medium and long term planning of power generation expansions within the available transmission corridors. Studies carried out on a sample 7-bus system, EHV equivalent 24-bus system and IEEE 39 bus system are presented for illustration purpose.

Microbial aspects of acid generation and bioremediation with relevance to indian mining

The role of Acidithiobacillus group of bacteria in acid generation and heavy metal dissolution was studied with relevance to some Indian mines. Microorganisms implicated in acid generation such as Acidithiobacillus Acidithicibacillus thiooxidans and Leptospirillum ferrooxidans were isolated from abandoned mines, waste rocks and tailing dumps. Arsenite oxidizing Thiomonas and Bacillus group of bacteria were isolated and their ability to oxidize As (111) to As (V) established. Mine isolated Sulfate reducing bacteria were used to remove dissolved copper, zinc, iron and arsenic from solutions.

The generalized anomeric effect in the 1,3-thiazolidines: Evidence forboth sulphur and nitrogen as electron donors. Crystal structures of various N-acylthiazolidines including mercury(II) complexes. Possible relevance to penicillin action

Evidence for the generalized anomeric effect (GAE) in the N-acyl-1,3-thiazolidines, an important structural motif in the penicillins, was sought in the crystal structures of N-(4-nitrobenzoyl)-1,3-thiazolidine and its (2:1) complex with mercuric chloride, N-acetyl-2-phenyl-1,3-thiazolidine, and the (2:1) complex of N-benzoyl-1,3-thiazolidine with mercuric bromide. An inverse relationship was generally observed between the. C-2-N and C-2-S bond lengths of the thiazolidine ring, supporting the existence of the GAE. (Maximal bond length changes were similar to 0.04 angstrom for C-2-N-3, S-1-C-2, and similar to 0.08 angstrom for N-3-C-6.) Comparison with N-acylpyrrolidines and tetrahydrothiophenes indicates that both the nitrogen-to-sulphur and sulphur-to-nitrogen GAE's operate simultaneously in the 1,3-thiazolidines, the former being dominant. (This is analogous to the normal and exo-anomeric effects in pyranoses, and also leads to an interesting application of Baldwin's rules.) The nitrogen-to-sulphur GAE is generally enhanced in the mercury(II) complexes (presumably via coordination at the sulphur); a 'competition' between the GAE and the amide resonance of the N-acyl moiety is apparent. There is evidence for a 'push-pull' charge transfer between the thiazolidine moieties in the mercury(II) complexes, and for a 'back-donation' of charge from the bromine atoms to the thiazolidine moieties in the HgBr2 complex. (The sulphur atom appears to be sp(2) hybridised in the mercury(II) complexes, possibly for stereoelectronic reasons.) These results are apparently relevant to the mode of action of the penicillins. (c) 2006 Elsevier B.V. All rights reserved.

Site characterization model using least-square support vector machine and relevance vector machine based on corrected SPT data (N-c)

Statistical learning algorithms provide a viable framework for geotechnical engineering modeling. This paper describes two statistical learning algorithms applied for site characterization modeling based on standard penetration test (SPT) data. More than 2700 field SPT values (N) have been collected from 766 boreholes spread over an area of 220 sqkm area in Bangalore. To get N corrected value (N,), N values have been corrected (Ne) for different parameters such as overburden stress, size of borehole, type of sampler, length of connecting rod, etc. In three-dimensional site characterization model, the function N-c=N-c (X, Y, Z), where X, Y and Z are the coordinates of a point corresponding to N, value, is to be approximated in which N, value at any half-space point in Bangalore can be determined. The first algorithm uses least-square support vector machine (LSSVM), which is related to aridge regression type of support vector machine. The second algorithm uses relevance vector machine (RVM), which combines the strengths of kernel-based methods and Bayesian theory to establish the relationships between a set of input vectors and a desired output. The paper also presents the comparative study between the developed LSSVM and RVM model for site characterization. Copyright (C) 2009 John Wiley & Sons,Ltd.

Positional dependence of material flow in friction stir welding: analysis of joint line remnant and its relevance to dissimilar metal welding

Understanding material flow in friction stir welding is important for production of sound dissimilar metal welding that control the intermixing of two alloys being welded and consequent formation of new constituents which influences the weld properties. In the present experimental investigation material flow patterns are visualised using dissimilar and similar aluminium alloys using a simple innovative ,experiment. The experimental results reveal that only a portion of material transported from the leading edge undergoes chaotic flow and the remaining is deposited systematically in the trailing edge of the weld. Using this information it is shown that the formation of a friction stir welding defect, joint line remnant, does not occur only when the weld interface is on the advancing side. The material flow visualisation study has been utilised to analyse the mechanism of weld formation and its usefulness in improving fatigue properties and for dissimilar metal welds.

A neural network based CBIR system using STI features and relevance feedback

Multimedia mining primarily involves, information analysis and retrieval based on implicit knowledge. The ever increasing digital image databases on the Internet has created a need for using multimedia mining on these databases for effective and efficient retrieval of images. Contents of an image can be expressed in different features such as Shape, Texture and Intensity-distribution(STI). Content Based Image Retrieval(CBIR) is an efficient retrieval of relevant images from large databases based on features extracted from the image. Most of the existing systems either concentrate on a single representation of all features or linear combination of these features. The paper proposes a CBIR System named STIRF (Shape, Texture, Intensity-distribution with Relevance Feedback) that uses a neural network for nonlinear combination of the heterogenous STI features. Further the system is self-adaptable to different applications and users based upon relevance feedback. Prior to retrieval of relevant images, each feature is first clustered independent of the other in its own space and this helps in matching of similar images. Testing the system on a database of images with varied contents and intensive backgrounds showed good results with most relevant images being retrieved for a image query. The system showed better and more robust performance compared to existing CBIR systems

New evidences on Tau-DNA interactions and relevance to neurodegeneration

Tau is mainly distributed in cytoplasm and also found to be localized in the nucleus. There is limited data on DNA binding potential of Tau.We provide novel evidence on nicking of DNA by Tau. Tau nicks the supercoiled DNA leading to open circular and linear forms. The metal ion magnesium (a co-factor for endonuclease) enhanced the Tau DNA nicking ability, while an endonuclease specific inhibitor,aurinetricarboxylic acid (ATA) inhibited the Tau DNA nicking ability Further, we also evidenced that Tau induces B-C-A mixed conformational transition in DNA and also changes DNA stability. Tau-scDNA complex is more sensitive to DNAse I digestion indicating stability changes in DNA caused by Tau. These findings indicate that Tau alters DNA helicity and integrity and also nicks the DNA. The relevance of these novel intriguing findings regarding the role Tau in neuronal dysfunction is discussed. (C) 2010 Elsevier Ltd. All rights reserved.

Curved composite beams—optimum lay-up for buckling by ranking

Instability of laminated curved composite beams made of repeated sublaminate construction is studied using finite element method. In repeated sublaminate construction, a full laminate is obtained by repeating a basic sublaminate which has a smaller number of plies. This paper deals with the determination of optimum lay-up for buckling by ranking of such composite curved beams (which may be solid or sandwich). For this purpose, use is made of a two-noded, 16 degress of freedom curved composite beam finite element. The displacements u, v, w of the element reference axis are expressed in terms of one-dimensional first-order Hermite interpolation polynomials, and line member assumptions are invoked in formulation of the elastic stiffness matrix and geometric stiffness matrix. The nonlinear expressions for the strains, occurring in beams subjected to axial, flexural and torsional loads, are incorporated in a general instability analysis. The computer program developed has been used, after extensive checking for correctness, to obtain optimum orientation scheme of the plies in the sublaminate so as to achieve maximum buckling load for typical curved solid/sandwich composite beams.

Optimum Design of Composite Laminates for Strength by Ranking

In this paper we describe a method for the optimum design of fiber rein forced composite laminates for strength by ranking. The software developed based on this method is capable of designing laminates for strength; which are subjected to inplane and/or bending loads and optionally hygrothermal loads. Symmetric laminates only are considered which are assumed to be made of repeated sublaminate construction. Various layup schemes are evaluated based on the laminated plate theory and quadratic failure cri terion for the given mechanical and hygrothermal loads. The optimum layup sequence in the sublaminate and the number of such sublaminates required are obtained. Further, a ply-drop round-off scheme is adopted to arrive at an optimum laminate thickness. As an example, a family of 0/90/45/ -45 bi-directional lamination schemes are examined for dif ferent types of loads and the gains in optimising the ply orientations in a sublaminate are demonstrated.