A regularized linear classifier for effective text classification

In document community support vector machines and naïve bayes classifier are known for their simplistic yet excellent performance. Normally the feature subsets used by these two approaches complement each other, however a little has been done to combine them. The essence of this paper is a linear classifier, very similar to these two. We propose a novel way of combining these two approaches, which synthesizes best of them into a hybrid model. We evaluate the proposed approach using 20ng dataset, and compare it with its counterparts. The efficacy of our results strongly corroborate the effectiveness of our approach.

Discriminative feature analysis and selection for document classification

Classification of a large document collection involves dealing with a huge feature space where each distinct word is a feature. In such an environment, classification is a costly task both in terms of running time and computing resources. Further it will not guarantee optimal results because it is likely to overfit by considering every feature for classification. In such a context, feature selection is inevitable. This work analyses the feature selection methods, explores the relations among them and attempts to find a minimal subset of features which are discriminative for document classification.

Influence of rock depth on seismic site classification for shallow bedrock regions

Seismic site classifications are used to represent site effects for estimating hazard parameters (response spectral ordinates) at the soil surface. Seismic site classifications have generally been carried out using average shear wave velocity and/or standard penetration test n-values of top 30-m soil layers, according to the recommendations of the National Earthquake Hazards Reduction Program (NEHRP) or the International Building Code (IBC). The site classification system in the NEHRP and the IBC is based on the studies carried out in the United States where soil layers extend up to several hundred meters before reaching any distinct soil-bedrock interface and may not be directly applicable to other regions, especially in regions having shallow geological deposits. This paper investigates the influence of rock depth on site classes based on the recommendations of the NEHRP and the IBC. For this study, soil sites having a wide range of average shear wave velocities (or standard penetration test n-values) have been collected from different parts of Australia, China, and India. Shear wave velocities of rock layers underneath soil layers have also been collected at depths from a few meters to 180 m. It is shown that a site classification system based on the top 30-m soil layers often represents stiffer site classes for soil sites having shallow rock depths (rock depths less than 25 m from the soil surface). A new site classification system based on average soil thickness up to engineering bedrock has been proposed herein, which is considered more representative for soil sites in shallow bedrock regions. It has been observed that response spectral ordinates, amplification factors, and site periods estimated using one-dimensional shear wave analysis considering the depth of engineering bedrock are different from those obtained considering top 30-m soil layers.

Efficient classification using phrases generated by topic models

There are many popular models available for classification of documents like Naïve Bayes Classifier, k-Nearest Neighbors and Support Vector Machine. In all these cases, the representation is based on the “Bag of words” model. This model doesn't capture the actual semantic meaning of a word in a particular document. Semantics are better captured by proximity of words and their occurrence in the document. We propose a new “Bag of Phrases” model to capture this discriminative power of phrases for text classification. We present a novel algorithm to extract phrases from the corpus using the well known topic model, Latent Dirichlet Allocation(LDA), and to integrate them in vector space model for classification. Experiments show a better performance of classifiers with the new Bag of Phrases model against related representation models.

Effect of lipopolysaccharide on alteration of phospholipids and their fatty acid composition in spleen and thymus by in vitro metabolic labeling

Lipopolysaccharide (LPS) is an endotoxin, a potent stimulator of immune response and induction of LPS leads to acute lung injury (ALI)/acute respiratory distress syndrome (ARDS). ARDS is a life-threatening disease worldwide with a high mortality rate. The immunological effect of LPS with spleen and thymus is well documented; however the impact on membrane phospholipid during endotoxemia has not yet been studied. Hence we aimed to investigate the influence of LPS on spleen and thymus phospholipid and fatty acid composition by 32P]orthophosphate labeling in rats. The in vitro labeling was carried out with phosphate-free medium (saline). Time course, LPS concentration-dependent, pre- and post-labeling with LPS and fatty acid analysis of phospholipid were performed. Labeling studies showed that 50 mu g LPS specifically altered the major phospholipids, phosphatidylcholine and phosphatidylglycerol in spleen and phosphatidylcholine in thymus. Fatty acid analysis showed a marked alteration of unsaturated fatty acids/saturated fatty acids in spleen and thymus leading to immune impairment via the fatty acid remodeling pathway. Our present in vitro lipid metabolic labeling study could open up new vistas for exploring LPS-induced immune impairment in spleen and thymus, as well as the underlying mechanism.

Noise tolerance under risk minimization

In this paper, we explore noise-tolerant learning of classifiers. We formulate the problem as follows. We assume that there is an unobservable training set that is noise free. The actual training set given to the learning algorithm is obtained from this ideal data set by corrupting the class label of each example. The probability that the class label of an example is corrupted is a function of the feature vector of the example. This would account for most kinds of noisy data one encounters in practice. We say that a learning method is noise tolerant if the classifiers learnt with noise-free data and with noisy data, both have the same classification accuracy on the noise-free data. In this paper, we analyze the noise-tolerance properties of risk minimization (under different loss functions). We show that risk minimization under 0-1 loss function has impressive noise-tolerance properties and that under squared error loss is tolerant only to uniform noise; risk minimization under other loss functions is not noise tolerant. We conclude this paper with some discussion on the implications of these theoretical results.

Crop stage classification of hyperspectral data using unsupervised techniques

The presence of a large number of spectral bands in the hyperspectral images increases the capability to distinguish between various physical structures. However, they suffer from the high dimensionality of the data. Hence, the processing of hyperspectral images is applied in two stages: dimensionality reduction and unsupervised classification techniques. The high dimensionality of the data has been reduced with the help of Principal Component Analysis (PCA). The selected dimensions are classified using Niche Hierarchical Artificial Immune System (NHAIS). The NHAIS combines the splitting method to search for the optimal cluster centers using niching procedure and the merging method is used to group the data points based on majority voting. Results are presented for two hyperspectral images namely EO-1 Hyperion image and Indian pines image. A performance comparison of this proposed hierarchical clustering algorithm with the earlier three unsupervised algorithms is presented. From the results obtained, we deduce that the NHAIS is efficient.

Gene expression programming-fuzzy logic method for crop type classification

Crop type classification using remote sensing data plays a vital role in planning cultivation activities and for optimal usage of the available fertile land. Thus a reliable and precise classification of agricultural crops can help improve agricultural productivity. Hence in this paper a gene expression programming based fuzzy logic approach for multiclass crop classification using Multispectral satellite image is proposed. The purpose of this work is to utilize the optimization capabilities of GEP for tuning the fuzzy membership functions. The capabilities of GEP as a classifier is also studied. The proposed method is compared to Bayesian and Maximum likelihood classifier in terms of performance evaluation. From the results we can conclude that the proposed method is effective for classification.

Robust formulations for clustering-based large-scale classification

Chebyshev-inequality-based convex relaxations of Chance-Constrained Programs (CCPs) are shown to be useful for learning classifiers on massive datasets. In particular, an algorithm that integrates efficient clustering procedures and CCP approaches for computing classifiers on large datasets is proposed. The key idea is to identify high density regions or clusters from individual class conditional densities and then use a CCP formulation to learn a classifier on the clusters. The CCP formulation ensures that most of the data points in a cluster are correctly classified by employing a Chebyshev-inequality-based convex relaxation. This relaxation is heavily dependent on the second-order statistics. However, this formulation and in general such relaxations that depend on the second-order moments are susceptible to moment estimation errors. One of the contributions of the paper is to propose several formulations that are robust to such errors. In particular a generic way of making such formulations robust to moment estimation errors is illustrated using two novel confidence sets. An important contribution is to show that when either of the confidence sets is employed, for the special case of a spherical normal distribution of clusters, the robust variant of the formulation can be posed as a second-order cone program. Empirical results show that the robust formulations achieve accuracies comparable to that with true moments, even when moment estimates are erroneous. Results also illustrate the benefits of employing the proposed methodology for robust classification of large-scale datasets.

Loss and gain signals in broadband stimulated-Raman spectra: Theoretical analysis

Stimulated optical signals obtained by subjecting the system to a narrow band and a broadband pulse show both gain and loss Raman features at the red and blue side of the narrow beam, respectively. Recently observed temperature-dependent asymmetry in these features Mallick et al., J. Raman Spectrosc. 42, 1883 (2011); Dang et al., Phys. Rev. Lett. 107, 043001 (2011)] has been attributed to the Stokes and anti-Stokes components of the third-order susceptibility, chi((3)). By treating the setup as a steady state of an open system coupled to four quantum radiation field modes, we show that Stokes and anti-Stokes processes contribute to both the loss and gain resonances. chi((3)) predicts loss and gain signals with equal intensity for electronically off-resonant excitation. Some asymmetry may exist for resonant excitation. However, this is unrelated to the Stokes vs anti-Stokes processes. Any observed temperature-dependent asymmetry must thus originate from effects lying outside the chi((3)) regime.

Homocysteine alters the osteoprotegerin/RANKL system in the osteoblast to promote bone loss: pivotal role of the redox regulator forkhead O1

In this study we determined the molecular mechanisms of how homocysteine differentially affects receptor activator of nuclear factor-kappa B ligand (RANKL) and osteoprotegerin (OPG) synthesis in the bone. The results showed that oxidative stress induced by homocysteine deranges insulin-sensitive FOXO1 and MAP kinase signaling cascades to decrease OPG and increase RANKL synthesis in osteoblast cultures. We observed that downregulation of insulin/FOXO1 and p38 MAP kinase signaling mechanisms due to phosphorylation of protein phosphatase 2 A (PP2A) was the key event that inhibited OPG synthesis in homocysteine-treated osteoblast cultures. siRNA knockdown experiments confirmed that FOXO1 is integral to OPG and p38 synthesis. Conversely homocysteine increased RANKL synthesis in osteoblasts through c-Jun/JNK MAP kinase signaling mechanisms independent of FOXO1. In the rat bone milieu, high-methionine diet-induced hyperhomocysteinemia lowered FOXO1 and OPG expression and increased synthesis of proresorptive and inflammatory cytokines such as RANKL, M-CSF, IL-1 alpha, IL-1 beta, G-CSF, GM-CSF, MIP-1 alpha, IFN-gamma, IL-17, and TNF-alpha. Such pathophysiological conditions were exacerbated by ovariectomy. Lowering the serum homocysteine level by a simultaneous supplementation with N-acetylcysteine improved OPG and FOXO1 expression and partially antagonized RANKL and proresorptive cytokine synthesis in the bone milieu. These results emphasize that hyperhomocysteinemia alters the redox regulatory mechanism in the osteoblast by activating PP2A and deranging FOXO1 and MAPK signaling cascades, eventually shifting the OPG:RANKL ratio toward increased osteoclast activity and decreased bone quality (C) 2013 Elsevier Inc. All rights reserved.

Efficient method of moving shadow detection and vehicle classification

Moving shadow detection and removal from the extracted foreground regions of video frames, aim to limit the risk of misconsideration of moving shadows as a part of moving objects. This operation thus enhances the rate of accuracy in detection and classification of moving objects. With a similar reasoning, the present paper proposes an efficient method for the discrimination of moving object and moving shadow regions in a video sequence, with no human intervention. Also, it requires less computational burden and works effectively under dynamic traffic road conditions on highways (with and without marking lines), street ways (with and without marking lines). Further, we have used scale-invariant feature transform-based features for the classification of moving vehicles (with and without shadow regions), which enhances the effectiveness of the proposed method. The potentiality of the method is tested with various data sets collected from different road traffic scenarios, and its superiority is compared with the existing methods. (C) 2013 Elsevier GmbH. All rights reserved.

Classification of Vehicles Using Magnetic Field Angle Model

This paper presents an efficient approach to the modeling and classification of vehicles using the magnetic signature of the vehicle. A database was created using the magnetic signature collected over a wide range of vehicles(cars). A sensor dependent approach called as Magnetic Field Angle Model is proposed for modeling the obtained magnetic signature. Based on the data model, we present a novel method to extract the feature vector from the magnetic signature. In the classification of vehicles, a linear support vector machine configuration is used to classify the vehicles based on the obtained feature vectors.

Classification, representation, and automatic extraction of deformation features in sheet metal parts

This paper presents classification, representation and extraction of deformation features in sheet-metal parts. The thickness is constant for these shape features and hence these are also referred to as constant thickness features. The deformation feature is represented as a set of faces with a characteristic arrangement among the faces. Deformation of the base-sheet or forming of material creates Bends and Walls with respect to a base-sheet or a reference plane. These are referred to as Basic Deformation Features (BDFs). Compound deformation features having two or more BDFs are defined as characteristic combinations of Bends and Walls and represented as a graph called Basic Deformation Features Graph (BDFG). The graph, therefore, represents a compound deformation feature uniquely. The characteristic arrangement of the faces and type of bends belonging to the feature decide the type and nature of the deformation feature. Algorithms have been developed to extract and identify deformation features from a CAD model of sheet-metal parts. The proposed algorithm does not require folding and unfolding of the part as intermediate steps to recognize deformation features. Representations of typical features are illustrated and results of extracting these deformation features from typical sheet metal parts are presented and discussed. (C) 2013 Elsevier Ltd. All rights reserved.

A unified view of energetic efficiency in active drag reduction, thrust generation and self-propulsion through a loss coefficient with some applications

An analysis of the energy budget for the general case of a body translating in a stationary fluid under the action of an external force is used to define a power loss coefficient. This universal definition of power loss coefficient gives a measure of the energy lost in the wake of the translating body and, in general, is applicable to a variety of flow configurations including active drag reduction, self-propulsion and thrust generation. The utility of the power loss coefficient is demonstrated on a model bluff body flow problem concerning a two-dimensional elliptical cylinder in a uniform cross-flow. The upper and lower boundaries of the elliptic cylinder undergo continuous motion due to a prescribed reflectionally symmetric constant tangential surface velocity. It is shown that a decrease in drag resulting from an increase in the strength of tangential surface velocity leads to an initial reduction and eventual rise in the power loss coefficient. A maximum in energetic efficiency is attained for a drag reducing tangential surface velocity which minimizes the power loss coefficient. The effect of the tangential surface velocity on drag reduction and self-propulsion of both bluff and streamlined bodies is explored through a variation in the thickness ratio (ratio of the minor and major axes) of the elliptical cylinders.