There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Machine Learning : A Probabilistic Perspective

Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.

/pdf/survey-of-clustering-algorithms-6zezqhbjyo.pdf

Survey of clustering algorithms

In most libraries, gate parameters such as the pin-to-pin intrinsic delays, load-dependent coefficients, and input pin capacitances have different values for rising and falling signals. Most performance optimization algorithms, however, assume a single value for each parameter. It is known that under the load-independent delay model, the gate assignment (or resizing) problem is solvable in time polynomial in the circuit size when a single value is assumed for each parameter (Kukimoto et al., 1998). We show that, in the presence of different rise and fall parameter values, this problem is NP-complete even for chain and tree topology circuits under the simple load-independent delay model (Murgai, 1999). However, we also show that, for tree circuits, the problem is not NP-complete in the strong sense, and we propose a dynamic programming algorithm that solves it exactly in pseudopolynomial time. More specifically, we show that the problem can be solved in time proportional to the size of the circuit, the number of choices available in the library for each gate and the delay of the circuit. We also present a straightforward way of extending this algorithm to general directed acyclic networks. We present experimental results on a set of benchmark problems using a standard commercial library and show that our algorithm generates provably optimum delays for 69 out of 73 circuits. We also compare our technique with two approaches traditionally used to solve this problem in the industry and academia and show that it performs better than these two. Interestingly, both traditional approaches also yield delays that are, in general, not far from the optimum.

On the problem of gate assignment under different rise and fall delays

The regulatory network underlying the transcriptional up-regulation of the FRL1 gene in mancozeb stressed yeast cells: qualitative modeling and simulation

Discrete models are important to reduce the complexity of the protein folding problem. However, a compromise must be made between the model complexity and the accuracy of the model.

Previous work by Park and Levitt has shown that the protein backbone can be modeled with good accuracy by four state discrete models. Nonetheless, for ab-initio protein folding, the side chains are important to determine if the structure is physically possible and well packed.

We extend the work of Park and Levitt by taking into account the positioning of the side chain in the evaluation of the accuracy. We show that the problem becomes much harder and more dependent on the type of protein being modeled. In fact, the structure fitting method used in their work is no longer adequate to this extended version of the problem. We propose a new method to test the model accuracy.

The presented results show that, for some proteins, the discrete models with side chains cannot achieve the accuracy of the backbone only discrete models. Nevertheless, for the majority of the proteins an RMSD of four angstrom or less is obtained, and, for many of those, we reach an accuracy near the two angstrom limit. These results prove that discrete models can be used in protein folding to obtain low resolution models. Since the side chains are already present in the models, the refinement of these solutions is simpler and more effective.

http://www.inesc-id.pt/pt/indicadores/Ficheiros/4872.pdf

An Evaluation of the Impact of Side Chain Positioning on the Accuracy of Discrete Models of Protein Structures

Welcome to BRAZILIAN JOURNAL OF NEUROSURGERY (JBNC), the official journal of the Brazilian Academy of Neurosurgery, is the oldest Neurosurgery journal in our country. It started in 1973 with the name of “Seara Médica Neurocirúrgica” and, since the beginning, it has been an extremely important information organ for Brazilian neurosurgeons. Brazil is the country with the third largest number of neurosurgeons in the world. Despite this large number of colleagues working in the specialty, we still do not have any journal indexed in PubMedTM, which is considered the most important database for medical journals in the world. Bem-vindo ao Jornal Brasileiro de Neurocirurgia - JBNC. Welcome to Brazilian Journal of Neurosurgery. O JORNAL BRASILEIRO DE NEUROCIRURGIA (JBNC), órgão oficial da Academia Brasileira de Neurocirurgia, é a revista de neurocirurgia mais antiga do nosso país. Iniciou no ano de 1973 com o nome de “Seara Médica Neurocirúrgica” e desde o início tem sido um órgão de informação de extrema importância para os neurocirurgiões brasileiros. 

Perimesencephalic Cerebral Arteriovenous Malformation (AVM): case report

A better understanding of the behavior of a cell, as a system, depends on our ability to model and understand the complex regulatory mechanisms that control gene expression. High level, qualitative models of gene regulatory networks can be used to analyze and characterize the behavior of complex systems, and to provide important insights on the behavior of these systems. In this chapter, we describe a number of additional functionalities that, when supported by a symbolic model checker, make it possible to answer important questions about the nature of the state spaces of gene regulatory networks, such as the nature and size of attractors, and the characteristics of the basins of attraction. We illustrate the type of analysis that can be performed by applying an improved model checker to two well studied gene regulatory models, the network that controls the cell cycle in the yeast S. cerevisiae, and the network that regulates formation of the dorsal-ventral boundary in D. melanogaster. The results show that the insights provided by the analysis can be used to understand and improve the models, and to formulate hypotheses that are biologically relevant and that can be confirmed experimentally. DOI: 10.4018/978-1-60566-685-3.ch016

Arlindo L. Oliveira

Papers

On the problem of gate assignment under different rise and fall delays

The regulatory network underlying the transcriptional up-regulation of the FRL1 gene in mancozeb stressed yeast cells: qualitative modeling and simulation

An Evaluation of the Impact of Side Chain Positioning on the Accuracy of Discrete Models of Protein Structures

Perimesencephalic Cerebral Arteriovenous Malformation (AVM): case report

Improved Model Checking Techniques for State Space Analysis of Gene Regulatory Networks