Diagonal Based Feature Extraction for Handwritten Alphabets Recognition System using Neural Network

An off-line handwritten alphabetical character recognition system using multilayer feed forward neural network is described in the paper. A new method, called, diagonal based feature extraction is introduced for extracting the features of the handwritten alphabets. Fifty data sets, each containing 26 alphabets written by various people, are used for training the neural network and 570 different handwritten alphabetical characters are used for testing. The proposed recognition system performs quite well yielding higher levels of recognition accuracy compared to the systems employing the conventional horizontal and vertical methods of feature extraction. This system will be suitable for converting handwritten documents into structural text form and recognizing handwritten names

Pepper yellow leaf curl Indonesia virus, a new bipartite begomovirus species that belongs to distinct clade of Old World geminiviruses

Begomoviruses are currently emerging as a major threat to vegetable production in many tropical and subtropical regions worldwide. Pepper yellow leaf curl disease (PepYLCD) has been noticed in many Capsicum annum L. producing regions from East Asia especially from Indonesia and causes devastating damage to pepper crop production since 2000. In this study we have cloned and sequenced complete nucleotide of begomoviruses from pepper exhibiting leaf curling and bright yellowing symptoms. Besides, we also determined the occurrence of disease on tomato evoking leaf curl symptoms and ageratum with yellow vein type of symptoms. On the basis of genome organization and sequence homology, these viruses were designated as Pepper yellow leaf curl Indonesia virus (PeYLCIV)- new species followed by its two new starins i.e. PeYLCIV-Tomato and PeYLCIV-Ageratum. These viruses have bipartite genomes. Pepper virus DNAs from Indonesia (PepYLCIV, PepYLCIV-Tomato and PepYLCIV-Ageratum DNA-As) were noticeably distinct, forming a separate branch from the other viruses infecting pepper. A considerable divergence is observed in the common region (CR) of the genomic components of PeYLCIV (77%), PeYLCIV-Tomato (82%) and PeYLCIV-Ageratum (75%). A stem-loop forming region and Rep-binding motif are identical in CRs of three viruses. CR of PepYLCIV-Ageratum DNA-A is approximately 10 nucleotides longer than those of PepYLCIV DNA-A and PepYLCIV-Tomato DNA-A. Similar insertion is also found in the common region of PepYLCIV-Ageratum DNA-B. PeYLCIV DNA-A alone is infectious in pepper and N. benthamiana plants and association with DNA-B increases symptom severity

High-dimensional Ising model selection using ℓ1{\ell_1}-regularized logistic regression

We consider the problem of estimating the graph associated with a binary Ising Markov random field. We describe a method based on $\ell_1$-regularized logistic regression, in which the neighborhood of any given node is estimated by performing logistic regression subject to an $\ell_1$-constraint. The method is analyzed under high-dimensional scaling in which both the number of nodes $p$ and maximum neighborhood size $d$ are allowed to grow as a function of the number of observations $n$. Our main results provide sufficient conditions on the triple $(n,p,d)$ and the model parameters for the method to succeed in consistently estimating the neighborhood of every node in the graph simultaneously. With coherence conditions imposed on the population Fisher information matrix, we prove that consistent neighborhood selection can be obtained for sample sizes $n=\Omega(d^3\log p)$ with exponentially decaying error. When these same conditions are imposed directly on the sample matrices, we show that a reduced sample size of $n=\Omega(d^2\log p)$ suffices for the method to estimate neighborhoods consistently. Although this paper focuses on the binary graphical models, we indicate how a generalization of the method of the paper would apply to general discrete Markov random fields.Comment: Published in at http://dx.doi.org/10.1214/09-AOS691 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

An Ensemble Model of QSAR Tools for Regulatory Risk Assessment

Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflicting predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa (κ): 0.63 and 0.62] for both the datasets. The ROC curves demonstrate the utility of the cut-off feature in the predictive ability of the ensemble model. This feature provides an additional control to the regulators in grading a chemical based on the severity of the toxic endpoint under study

Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization

Relative to the large literature on upper bounds on complexity of convex optimization, lesser attention has been paid to the fundamental hardness of these problems. Given the extensive use of convex optimization in machine learning and statistics, gaining an understanding of these complexity-theoretic issues is important. In this paper, we study the complexity of stochastic convex optimization in an oracle model of computation. We improve upon known results and obtain tight minimax complexity estimates for various function classes

Properties of Phase transitions of a Higher Order

The following is a thermodynamic analysis of a III order (and some aspects of a IV order) phase transition. Such a transition can occur in a superconductor if the normal state is a diamagnet. The equation for a phase boundary in an H-T (H is the magnetic field, T, the temperature) plane is derived. by considering two possible forms of the gradient energy, it is possible to construct a field theory which describes a III or a IV order transition and permits a study of thermal fluctuations and inhomogeneous order parameters.Comment: 13 pages, revtex, no figure

Million frames per second infrared imaging system

An infrared imaging system has been developed for measuring the temperature increase during the dynamic deformation of materials. The system consists of an 8×8 HgCdTe focal plane array, each with its own preamplifier. Outputs from the 64 detector/preamplifiers are digitized using a row-parallel scheme. In this approach, all 64 signals are simultaneously acquired and held using a bank of track and hold amplifiers. An array of eight 8:1 multiplexers then routes the signals to eight 10 MHz digitizers, acquiring data from each row of detectors in parallel. The maximum rate is one million frames per second. A fully reflective lens system was developed, consisting of two Schwarszchild objectives operating at infinite conjugation ratio. The ratio of the focal lengths of the objectives determines the lens magnification. The system has been used to image the distribution of temperature rise near the tip of a notch in a high strength steel sample (C-300) subjected to impact loading by a drop weight testing machine. The results show temperature rises at the crack tip up to around 70 K. Localization of temperature, and hence, of deformation into "U" shaped zones emanating from the notch tip is clearly seen, as is the onset of crack propagation