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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

物件導向軟體之架構(Object-Oriented Software Construction)探討

■ Abstract Apoptosis is a genetically programmed, morphologically distinct form of cell death that can be triggered by a variety of physiological and pathological stimuli. Studies performed over the past 10 years have demonstrated that proteases play critical roles in initiation and execution of this process. The caspases, a family of cysteine-dependent aspartate-directed proteases, are prominent among the death proteases. Caspases are synthesized as relatively inactive zymogens that become activated by scaffold-mediated transactivation or by cleavage via upstream proteases in an intracellular cascade. Regulation of caspase activation and activity occurs at several different levels: ( a) Zymogen gene transcription is regulated; ( b) antiapoptotic members of the Bcl-2 family and other cellular polypeptides block proximity-induced activation of certain procaspases; and ( c) certain cellular inhibitor of apoptosis proteins (cIAPs) can bind to and inhibit active caspases. Once activated, caspases cleave a variety of intracellular polypeptides, including major structural elements of the cytoplasm and nucleus, components of the DNA repair machinery, and a number of protein kinases. Collectively, these scissions disrupt survival pathways and disassemble important architectural components of the cell, contributing to the stereotypic morphological and biochemical changes that characterize apoptotic cell death.

Mammalian caspases: structure ,a ctivation ,s ubstrates, and functions during apoptosis

Bacopa monniera, a reputed nootropic plant: an overview.

Antioxidant activity of propolis: role of caffeic acid phenethyl ester and galangin.

Flavonoids have recently aroused considerable interest because of their broad pharmacological activity. In fact, flavonoids have been reported to have antiviral, antiallergic, antiplatelet, anti-inflammatory and antitumoral activities. The pharmacological properties of bioflavonoids have been ascribed both to the concomitant inhibition of enzymes involved in the production of free radicals and to their free-radical scavenging and iron chelating capacity. However the antioxidant capacity of bioflavonoids due to free-radical scavenging and/or to iron chelating is still controversial. In this study, we have investigated the free-radical scavenging capacity of bioflavonoids (rutin, catechin, and naringin). In addition, the effects of these polyphenols on xanthine oxidase activity, spontaneous lipid peroxidation, and DNA cleavage were investigated. The bioflavonoids under examination showed a dose-dependent free-radical scavenging effect, a significant inhibition of xanthine oxidase activity, and an antilipoperoxidative capacity. In addition, they showed a protective effect on DNA cleavage.

Bioflavonoids as antiradicals, antioxidants and DNA cleavage protectors.

Phytochemical compounds involved in the anti-inflammatory effect of propolis extract.

Software engineers make use of many descriptions, including analysis models, specifications, designs, program code, user guides, test plans, change requests, style guides, schedules, and process models. But since different developers construct and update these descriptions at various times during development, maintaining consistency among descriptions presents several problems. Descriptions tend to vary considerably. Individual descriptions can be ill-formed or self-contradictory and frequently evolve throughout the life cycle at different rates. Also, checking the consistency of a large, arbitrary set of descriptions is computationally expensive. The authors assert that maintaining consistency at all times is counterproductive. In many cases, it may be desirable to tolerate or even encourage inconsistency to facilitate distributed team-work and prevent premature commitment to design decisions. They advocate using inconsistency to highlight problem areas, using it as a tool to improve the development team's shared understanding, direct the process of requirements elicitation, and assist with verification and validation.

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Alessandra Russo

Papers

Bacopa monniera, a reputed nootropic plant: an overview.

Antioxidant activity of propolis: role of caffeic acid phenethyl ester and galangin.

Bioflavonoids as antiradicals, antioxidants and DNA cleavage protectors.

Phytochemical compounds involved in the anti-inflammatory effect of propolis extract.

Leveraging inconsistency in software development