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.

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Survey of clustering algorithms

We describe the research being undertaken by the ALGOS/KDBIO and Biological Sciences groups of Instituto Superior Tecnico on the field of bioinformatics and computational biology, with emphasis on the efforts under way to develop new approaches, methods and algorithms for the determination of gene regulatory networks. We put the field in perspective by first looking at recent developments in the field of bioinformatics, and how these developments contributed to the advance of science. We then describe the approach that is being followed, based on the development of algorithms and information systems for the problems of motif detection, gene expression analysis and inference of gene regulatory networks. We conclude by pointing out possible directions for future research in the fields of systems biology and synthetic biology, two critical areas for the development of science in the coming years.

Bioinformatics: A New Approach for the Challenges of Molecular Biology

Europe was hit hard by the COVID-19 pandemic and Portugal was one of the most affected countries, having suffered three waves in the first twelve months. Approximately between Jan 19th and Feb 5th 2021 Portugal was the country in the world with the largest incidence rate, with 14-days incidence rates per 100,000 inhabitants in excess of 1000. Despite its importance, accurate prediction of the geospatial evolution of COVID-19 remains a challenge, since existing analytical methods fail to capture the complex dynamics that result from both the contagion within a region and the spreading of the infection from infected neighboring regions. 
We use a previously developed methodology and official municipality level data from the Portuguese Directorate-General for Health (DGS), relative to the first twelve months of the pandemic, to compute an estimate of the incidence rate in each location of mainland Portugal. The resulting sequence of incidence rate maps was then used as a gold standard to test the effectiveness of different approaches in the prediction of the spatial-temporal evolution of the incidence rate. Four different methods were tested: a simple cell level autoregressive moving average (ARMA) model, a cell level vector autoregressive (VAR) model, a municipality-by-municipality compartmental SIRD model followed by direct block sequential simulation and a convolutional sequence-to-sequence neural network model based on the STConvS2S architecture. We conclude that the convolutional sequence-to-sequence neural network is the best performing method, when predicting the medium-term future incidence rate, using the available information.

Modeling the geospatial evolution of COVID-19 using spatio-temporal convolutional sequence-to-sequence neural networks

We present a new quick algorithm for the solution of the well-known point location problem and for the more specific problem of point-in-polygon determination. Previous approaches to this problem are presented in the first sections of this paper. In the remainder of the paper, we present a new quick location algorithm based on a quaternary partition of the space, as well as its associated cost and data structures.

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A New Quick Point Location Algorithm

Multidrug resistance (MDR), a phenomenon with impact in Human Health and in Agro-Food and Environmental Biotechnology, often results from the activation of drug efflux pumps, many times controlled at the transcriptional level. The complex transcriptional control of these genes has been on the focus of our research, guided by the information gathered in the YEASTRACT database. In this paper, the approach used to elucidate the transcriptional control of FLR1, encoding a Saccharomyces cerevisiae Drug:H+ Antiporter, in response to stress induced by the fungicide mancozeb is explained. The transcription regulatory network underlying FLR1 activation was defined based on experimental data. Subsequently, a mathematical model describing this network was built and its response to mancozeb stress in different genetic backgrounds was simulated, using the Genetic Network Analyzer (GNA) software. This approach allowed the identification of essential features of the transition from unstressed to fungicide stressed cells and to make new predictions on the dynamical behavior of the system, which were validated experimentally. This work provides a good example of the successful combination of experimental and computational approaches in a systems biology perspective.

Using systems biology approaches to study a multidrug resistance network

Keywords: Lempel-Ziv, Lossless Data Compression, Sufﬁx Arrays, Sufﬁx Tre es, String Matching.Abstract: Lossless compression algorithms of the Lempel-Ziv (LZ) family are widely used in a variety of applications.The LZ encoder and decoder exhibit a high asymmetry, regarding time and memory requirements, with theformer being much more demanding. Several techniques have been used to speed up the encoding process;among them is the use of sufﬁx trees. In this paper, we explore the use of a simple data structure, namedsufﬁx array , to hold the dictionary of the LZ encoder, and propose an algorithm to search the dictionary.A comparison with the sufﬁx tree based LZ encoder is carried out, showin g that the compression ratios areroughly the same. The ammount of memory required by the sufﬁx arra y is ﬁxed, being much lower than thevariable memory requirements of the sufﬁx tree encoder, which depen ds on the text to encode. We concludethat sufﬁx arrays are a very interesting option regarding the tradeoff b etween time, memory, and compressionratio, when compared with sufﬁx trees, that make them preferable in som e compression scenarios.

Arlindo L. Oliveira

Papers

Bioinformatics: A New Approach for the Challenges of Molecular Biology

Modeling the geospatial evolution of COVID-19 using spatio-temporal convolutional sequence-to-sequence neural networks

A New Quick Point Location Algorithm

Using systems biology approaches to study a multidrug resistance network

Suffix Arrays - A Competitive Choice for Fast Lempel-Ziv Compressions.