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

The Analysis of Time Series: An Introduction, 4th edn. By C. Chatfield. ISBN 0 412 31820 2. Chapman and Hall, London, 1989. 242 pp. £13.50.

The Analysis of Time Series: An Introduction

This comprehensive textbook presents a clean and coherent account of most fundamental tools and techniques in Parameterized Algorithms and is a self-contained guide to the area. The book covers many of the recent developments of the field, including application of important separators, branching based on linear programming, Cut & Count to obtain faster algorithms on tree decompositions, algorithms based on representative families of matroids, and use of the Strong Exponential Time Hypothesis. A number of older results are revisited and explained in a modern and didactic way. The book provides a toolbox of algorithmic techniques. Part I is an overview of basic techniques, each chapter discussing a certain algorithmic paradigm. The material covered in this part can be used for an introductory course on fixed-parameter tractability. Part II discusses more advanced and specialized algorithmic ideas, bringing the reader to the cutting edge of current research. Part III presents complexity results and lower bounds, giving negative evidence by way of W[1]-hardness, the Exponential Time Hypothesis, and kernelization lower bounds. All the results and concepts are introduced at a level accessible to graduate students and advanced undergraduate students. Every chapter is accompanied by exercises, many with hints, while the bibliographic notes point to original publications and related work.

Parameterized Algorithms

This paper outlines an online approach for algorithm configuration which uses the power of modern multicore system to evaluate multiple parameters configurations in parallel.

Online search algorithm configuration

Tradeoffs have been proposed in the literature as an approach to resolving over-constrainedness in interactive constraint-based tools, such as product configurators. It has been reported how tradeoffs can be modeled as additional constraints. This paper presents a formal framework for tradeoff generation based on the semiring approach to soft constraints. In particular, user preferences and tradeoffs are, respectively, represented as soft constraints and as an entailment operator. The entailment operator is used to interactively generate new constraints representing tradeoffs. The framework we present is well-motivated by real-world approaches that exploit tradeoff generation in online buying and configuration processes.

Tradeoff generation using soft constraints

COMPUTATIONAL sustainability is concerned with the development and application of computational methods for balancing environmental, economic, and societal needs for a sustainable future [1]. Specifically, it considers the major problem domains that impact global sustainability, those technologies and processes that offer the greatest opportunity to increase sustainability in these domains, and the fundamental computational methods that support these technologies and processes. The literature demonstrates that key sustainability issues translate into decision and optimization problems that fall within the realm of computing and information science, but generally they have not been studied by computer scientists. Computational sustainability encompasses problems in disciplines as diverse as ecology, natural resources, atmospheric science, materials science, renewable energy, and biological and environmental engineering. According to the Brundtland Commission [2], sustainable development is development that meets the needs of the present generation without compromising the ability of future generations to meet their own needs. Computational sustainability is a new interdisciplinary field [1] that aims to apply techniques from computer science and related fields, namely information science, operations research, applied mathematics, and statistics, to applications related to sustainable development. The range of problems that fall under computational sustainability is rather wide, encompassing computational challenges in disciplines as diverse as ecology, natural resources, atmospheric science, biological and environmental engineering, and land use, conservation, or transportation planning. Research in computational sustainability is necessarily interdisciplinary. The objective of this special section is to promote awareness and deepen understanding of the critical role computer science and computational methods can play in studying and providing solutions to sustainability-related problems. The special section also aims to provide a resource to the research community that we hope will assist in developing the expertise that society will need to address sustainability challenges by inspiring scientists to pursue sustainability-related research. Finally, this special section showcases a variety of cutting-edge techniques and methods that address the scale and complexity of the challenges facing societal efforts to move towards sustainability. Collaboration between computer scientists and fields more traditionally associated with sustainability-related research provides an opportunity to introduce enhanced or new computational methods and techniques to advance work in numerous disciplines. We hope that this special section will also appeal to those working outside computer science, demonstrating what that discipline has to offer to the broader sustainability agenda. We have selected seven papers to be included in this special section, covering a variety of computational sustainability topics. In “Nationwide Prediction of Drough Conditions in Iran Based on Remote Sensing Data,” Mahdi Jalili, Joobin Gharibshah, Seyed Morsal Ghavami, Mohammadreza Beheshtifar, and Reza Farshi, propose the use of artificial neural networks to model and predict the drough conditions based on satellite imagery collecting indexes on vegetation and land cover as well as the temperature. The paper applies multi-layer neural networks, radial-base function networks and support vector machines to the drough forecasting. The three models have been trained with time series and predict the drough conditions in terms of Standardized Precipitation Index. The accuracy of the model achieves up to the 90 percent and the multi-layer perception model is the best performing predictor. Marco Chiarandini, Niels H. Kjeldsen, and Napoleao Nepomuceno, in their paper entitled “Integrated Planning of Biomass Inventory and Energy Production,” essentially merge two problems that have been traditionally kept separate, namely biomass provisioning and its use for heating or energy production of each power plant. The paper proposes a stochastic 0-1 MILP to model the problem. Due to the large instance size, a relaxation of the problem and a Benders decomposition approach are compared in terms of solution quality, ease of implementation, and scalability, showing good accuracy of the relaxed model, but a simpler implementation and higher scalability for the Benders decomposition approach. Sensing and monitoring of environmental phenomena is an important part of computational sustainability; a promising approach is community sensing, where measurements are gathered by individual agents, and aggregated into publicly available maps by a public authority. In their paper entitled “Incentive Mechanisms for Community Sensing,” Boi Faltings, Jason Jingshi Li, and Radu Jurca, present a novel, game theoretic incentive mechanism that rewards accurate and truthful measurements in a community sensing scenario, providing the necessary quality control, and ensuring that the results are valid despite the absence of a centralized control. The scheme is analyzed and evaluated in a testbed of 88 IEEE TRANSACTIONS ON COMPUTERS, VOL. 63, NO. 1, JANUARY 2014

Guest Editors' Introduction: Special Section on Computational Sustainability: Where Computer Science meets Sustainable Development

Many network design problems arising in areas as diverse as VLSI circuit design, QoS routing, traffic engineering, and computational sustainability require clients to be connected to a facility under path-length constraints and budget limits. These problems can be seen as instances of the Rooted Distance-Constrained Minimum Spanning-Tree problem (RDCMST), which is NP-hard. An inherent feature of these networks is that they are vulnerable to a failure. Therefore, it is often important to ensure that all clients are connected to two or more facilities via edge-disjoint paths.We call this problem the Edge-disjoint RDCMST (ERDCMST). Previous work on RDCMST has focused on dedicated algorithms and, therefore, it is difficult to use these algorithms to tackle ERDCMST. We present a constraint-based parallel local search algorithm for solving ERDCMST. Traditional ways of extending a sequential algorithm to run in parallel perform either portfolio-based search in parallel or parallel neighbourhood search. Instead, we exploit the semantics of the constraints of the problem to perform multiple moves in parallel by ensuring that they are mutually independent. The ideas presented in this chapter are general and can be adapted to other problems as well. The effectiveness of our approach is demonstrated by experimenting with a set of problem instances taken from real-world passive optical network deployments in Ireland, Italy, and the UK. Our results show that performing moves in parallel can significantly reduce the elapsed time and improve the quality of the solutions of our local search approach.

Parallel Constraint-Based Local Search: An Application to Designing Resilient Long-Reach Passive Optical Networks

We propose a constraint programming approach to the popular matching problem. We show that one can use the Global Cardinality Constraint to encode the problem even in cases that involve ties in the ordinal preferences of the applicants.

https://hal.archives-ouvertes.fr/hal-01689926/document

Barry O'Sullivan

Papers

Online search algorithm configuration

Tradeoff generation using soft constraints

Guest Editors' Introduction: Special Section on Computational Sustainability: Where Computer Science meets Sustainable Development

Parallel Constraint-Based Local Search: An Application to Designing Resilient Long-Reach Passive Optical Networks

A CP-based approach to popular matching