Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Cause, conseguenze e strategie di mitigazione Proponiamo il primo di una serie di articoli in cui affronteremo l’attuale problema dei mutamenti climatici. Presentiamo il documento redatto, votato e pubblicato dall’Ipcc - Comitato intergovernativo sui cambiamenti climatici - che illustra la sintesi delle ricerche svolte su questo tema rilevante.

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Climate Change 2007: The Physical Science Basis.

This review concentrates on advances in nitric oxide synthase (NOS) structure, function and inhibition made in the last seven years, during which time substantial advances have been made in our understanding of this enzyme family. There is now information on the enzyme structure at all levels from primary (amino acid sequence) to quaternary (dimerization, association with other proteins) structure. The crystal structures of the oxygenase domains of inducible NOS (iNOS) and vascular endothelial NOS (eNOS) allow us to interpret other information in the context of this important part of the enzyme, with its binding sites for iron protoporphyrin IX (haem), biopterin, L-arginine, and the many inhibitors which interact with them. The exact nature of the NOS reaction, its mechanism and its products continue to be sources of controversy. The role of the biopterin cofactor is now becoming clearer, with emerging data implicating one-electron redox cycling as well as the multiple allosteric effects on enzyme activity. Regulation of the NOSs has been described at all levels from gene transcription to covalent modification and allosteric regulation of the enzyme itself. A wide range of NOS inhibitors have been discussed, interacting with the enzyme in diverse ways in terms of site and mechanism of inhibition, time-dependence and selectivity for individual isoforms, although there are many pitfalls and misunderstandings of these aspects. Highly selective inhibitors of iNOS versus eNOS and neuronal NOS have been identified and some of these have potential in the treatment of a range of inflammatory and other conditions in which iNOS has been implicated.

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Nitric oxide synthases: structure, function and inhibition

I read this book the same weekend that the Packers took on the Rams, and the experience of the latter event, obviously, colored my judgment. Although I abhor anything that smacks of being a handbook (like, \"How to Earn a Merit Badge in Neurosurgery\") because too many volumes in biomedical science already evince a boyscout-like approach, I must confess that parts of this volume are fast, scholarly, and significant, with certain reservations. I like parts of this well-illustrated book because Dr. Sj6strand, without so stating, develops certain subjects on technique in relation to the acquisition of judgment and sophistication. And this is important! So, given that the author (like all of us) is somewhat deficient in some areas, and biased in others, the book is still valuable if the uninitiated reader swallows it in a general fashion, realizing full well that what will be required from the reader is a modulation to fit his vision, propreception, adaptation and response, and the kind of problem he is undertaking. A major deficiency of this book is revealed by comparison of its use of physics and of chemistry to provide understanding and background for the application of high resolution electron microscopy to problems in biology. Since the volume is keyed to high resolution electron microscopy, which is a sophisticated form of structural analysis, but really morphology in a modern guise, the physical and mechanical background of The instrument and its ancillary tools are simply and well presented. The potential use of chemical or cytochemical information as it relates to biological fine structure , however, is quite deficient. I wonder when even sophisticated morphol-ogists will consider fixation a reaction and not a technique; only then will the fundamentals become self-evident and predictable and this sine qua flon will become less mystical. Staining reactions (the most inadequate chapter) ought to be something more than a technique to selectively enhance contrast of morphological elements; it ought to give the structural addresses of some of the chemical residents of cell components. Is it pertinent that auto-radiography gets singled out for more complete coverage than other significant aspects of cytochemistry by a high resolution microscopist, when it has a built-in minimal error of 1,000 A in standard practice? I don't mean to blind-side (in strict football terminology) Dr. Sj6strand's efforts for what is \"routinely used in our laboratory\"; what is done is usually well done. It's just that …

Methods in Enzymology.

Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image-processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non-parametric classifiers such as neural network, decision tree classifier, and knowledge-based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image-processing chain to improve classification accuracy.

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A survey of image classification methods and techniques for improving classification performance

A special class of nonlinear non differentiable programming problem containing square root term in the objective function as well as in the constraints is considered. Necessary and sufficient conditions of optimality are given under strong pseudoinvexity assumption on the functions involved. These conditions are then used to establish duality theorems.

A Non-differentiable Programming Problem under Strong Pseudoinvexity

Objective:The present study was undertaken to find out the different causative factors relating to the prognosis of various types of schizophrenic patients. Method: To accomplish the objectives of the current study all the subjects were taken from SCARF hospital and a total of 80 subjects were taken using the purposive sampling method which will involve taking samples from the population which is easily available or are easy to contact and were matched in age, sex and education and occupation. All the subject were the inhabitant of Chennai. General health questionnaire (GHQ-12), Mental Health Battery (MHB), Eysenck Personality Questionnaire (EPQ), and Multiphasic Personality Questionnaire (MPQ) were employed with each of the subjects in two sessions. Result:From Results of GHQ, it seen then scored above average and expressed less variability in scores. In EPQ subjects showed high score in psychoticism and in neuroticism, but average in introversion-extroversion. t-Test was also conducted and was found not significant. In MHB they score below average in all dimension. Correlation was done and was found that score of different dimensions is not related to each other. 

comprehensive study to find out the different factors affecting the prognosis of schizophrenic patients

This study was undertaken to study the Impact of Menstruation on Physical and Mental Health of young adolescent girls. Two different phases ( Pre-menstrual phase and post menstrual phase) were taken to assess the impact of the premenstrual symptoms screening tool revised for adolescents (PSST-A),   Measure of Adolescent Coping Strategies (MACS), Well-Being Index (WBI–CVSV)andAdjustment Inventory for School Students (AISS–SS) . The age of the subjects ranges from 13 to 16 years ( grade 8th to 10th). Results revealed that the subjects of pre phase of menstruation showed deterioration in premenstrual symptoms screening tool revised for adolescents (PSST-A), Measure of adolescent coping strategies, emotional well-being, psychological well-being, spiritual well-being. The subjects of post menstrual phases showed stronger psychological characteristics in measure of adolescent coping strategies and levels of adjustment. It was also confirmed by the study that the magnitude of the problem comes down during the post phase of menstruation. Physical health was proved a significant factor during menstruation from which other associated symptoms and problems may generate. So, physical and mental health are very much associated with menstruation for the females during their adolescence.

Impact of menstruation on physical and mental health of young adolescent girls

The patterns of gene expression in the fission yeast Schizosaccharomyces pombe under various experimental conditions form the basis of any transcriptomic study. We describe a method involving reverse transcription of the mRNA, Polymerase Chain Reaction (PCR), and the subsequent separation of the products onto Urea-Polyacrylamide gel that can be used to study the gene expression patterns in the fission yeast. The method described is cost effective and reproducible with satisfactory resolution of expressed transcripts in the gel. The method has the following essential steps: total RNA isolation and purification, cDNA synthesis from mRNAs, PCR amplification of cDNAs, visualization of PCR products, re-amplification and cloning of the differentially expressed PCR products, sequencing the confirmed clones, and finally cDNA library screening to isolate the genes of interest. The technique is also popularly known as Differential Display Reverse Transcription (DDRT-PCR). After its invention in 1992, a number of modifications have been introduced to optimize the technique and specifically to reduce the major problem of "false positives." Since understanding of specific gene expression patterns that regulate developmental and stress responses is a major concern of biology, DDRT-PCR has become a very popular molecular technique during the past two decades.

Sanjay Ghosh

Papers

A Non-differentiable Programming Problem under Strong Pseudoinvexity

comprehensive study to find out the different factors affecting the prognosis of schizophrenic patients

Impact of menstruation on physical and mental health of young adolescent girls

Analysis of Reverse Transcribed mRNA Using PCR and Polyacrylamide Gel Electrophoresis.

Image downscaling via co-occurrence learning