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

In interactive decision-making settings, such as product configuration, users are stating preferences, or foreground constraints, over a set of possible solutions, as defined by background constraints. When the foreground constraints introduce inconsistencies with the background constraints, we wish to find explanations that help the user converge to a solution. In order to provide satisfactory explanations, it can be useful to know one or several subsets of conflicting constraints; such a subset is called a conflict. When computing such conflicts is intractable in an interactive context, we can choose to compile the problem so as to allow faster response times. In this paper we propose a new representation, which implicitly encompasses all conflicts possibly introduced by a user's choices. We claim that it can help in situations where extra information about conflicts is needed, such as when explanations of inconsistency are required.

Compiling all possible conflicts of a CSP

A product family can be regarded as a collection of products which are similar - similarity being defined from a number of perspectives. For example, a collection of products may be regarded as a product family if they provide the same overall function, they have similar properties or they are built from the same parts. The objective of this paper is to demonstrate that constraint based reasoning offers a promising basis for the development of support tools for designers concerned with the design of product families. A constraint-based design environment is currently being developed which assists a designer in designing alternative product structures from an initial statement of required functional and physical properties. Each product structure can be regarded as an intentional specification of a product family - each member of which provides the same functionality and are physically similar in structure. In this paper it is demonstrated how a constraint-based model of a product family can handle the complexity of product family design. In addition, this paper discusses how a constraint-based model of a product family is readily extensible and facilitates the specification of new product families

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Supporting the design of product families through constraint-based reasoning

In this paper, we focus on designing a core optical network. Given a set of Metro-Core (MC) nodes, the problem is to select a set of links such that the maximum distance between any pair of nodes is bounded. The primary objective is to minimise the total length of the links followed by a secondary objective which is to minimise the total length of the paths between the pairs of MC nodes. We present a mixed integer programming (MIP) model for this problem. As the size of the instances of this problem increases, the MIP model does not scale in terms of memory and time. We therefore present an adaptive large neighbourhood search (LNS) algorithm where the idea is to find an initial solution and repeatedly improve it by solving relatively small subproblems. We propose two different ways of selecting subproblems. The experiments are performed using 3 real-world network topologies: Ireland, UK and Italy. Our results demonstrate that the developed approach is suitable for solving large networks and it can compute very high quality solutions which are very close to optimal values.

An adaptive large neighbourhood search for designing transparent optical core network

In this paper we present a Mixed Integer Programming-based (MIP) approach to optimise the workload allocation of geographically distributed Data Centres (DCs) in the face of dynamic DC performances and electricity prices. We reduce the overall electricity cost for running a DC set over an operating horizon by finding a good compromise between: The number of migrations subject to the sovereignty of data, the loads of the servers in DCs and the energy cost reduction possible by following the DCs with best performance and energy efficiencies over time. To model the DC performance we use Power Usage Effectiveness (PUE), with a devoted function per DC dependent on the current outside temperature. We discuss the multiple dimensions of the problem, present a mathematical formulation for it and provide empirical evaluation to claim the improvement on the electricity cost achieved.

Energy cost minimisation of geographically distributed data centres

It is widely recognised that many species adapt to complex and dynamic environments, but it is no longer accepted that an organism passes characteristics acquired during its lifetime to its offspring. However, in evolutionary computation such Lamarckian inheritance can be useful. Simulations of the benefits of Lamarckian inheritance have been reported in the literature. However, in this paper we present the first formal proof that Lamarckian inheritance can dominate more traditional individual (non-inheritable) learning. We present a parameterised model that can demonstrate conditions in which different inheritance types perform best, which we empirically validate.

Barry O'Sullivan

Papers

Compiling all possible conflicts of a CSP

Supporting the design of product families through constraint-based reasoning

An adaptive large neighbourhood search for designing transparent optical core network

Energy cost minimisation of geographically distributed data centres

An analysis of Lamarckian learning in changing environments