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

Engineering conceptual design can be defined as that phase of the product development process during which the designer takes a specification for a product to be designed and generates many broad solutions to it. This paper presents a constraint-based approach to supporting interactive conceptual design. The approach is based on an expressive and general technique for modeling: the design knowledge that a designer can exploit during a design project; the life-cycle environment that the final product faces; the design specification that defines the set of requirements the product must satisfy; and the structure of the various schemes that are developed by the designer. A computational reasoning environment based on constraint filtering is proposed as the basis of an interactive design support tool. Using such a tool, human designers can be assisted in interactively developing and evaluating a set of schemes that satisfy the various constraints imposed on the design.

Interactive constraint-aided conceptual design

Data centers are a critical and ubiquitous resource for providing infrastructure for banking, Internet and electronic commerce One way of managing data centers efficiently is to minimize a cost function that takes into account the load of the machines, the balance among a set of available resources of the machines, and the costs of moving processes while respecting a set of constraints This problem is called the machine reassignment problem An instance of this online problem can have several tens of thousands of processes Therefore, the challenge is to solve a very large sized instance in a very limited time In this paper, we describe a constraint programming-based Large Neighborhood Search (LNS) approach for solving this problem The values of the parameters of the LNS can have a significant impact on the performance of LNS when solving an instance We, therefore, employ the Instance Specific Algorithm Configuration (ISAC) methodology, where a clustering of the instances is maintained in an offline phase and the parameters of the LNS are automatically tuned for each cluster When a new instance arrives, the values of the parameters of the closest cluster are used for solving the instance in the online phase Results confirm that our CP-based LNS approach, with high quality parameter settings, finds good quality solutions for very large sized instances in very limited time Our results also significantly outperform the hand-tuned settings of the parameters selected by a human expert which were used in the runner-up entry in the 2012 EURO/ROADEF Challenge

Tuning Parameters of Large Neighborhood Search for the Machine Reassignment Problem

We consider the following problem. Given a 2-cnf formula, is it possible to remove at most kclauses so that the resulting 2-cnf formula is satisfiable? This problem is known to different research communities in theoretical computer science under the names Almost 2-SAT, All-but-k2-SAT, 2-cnf deletion, and 2-SAT deletion. The status of the fixed-parameter tractability of this problem is a long-standing open question in the area of parameterized complexity. We resolve this open question by proposing an algorithm that solves this problem in O(15k*k*m3) time showing that this problem is fixed-parameter tractable.

https://www.cs.le.ac.uk/people/ir45/papers/icalp08.pdf

Almost 2-SAT Is Fixed-Parameter Tractable (Extended Abstract)

The state-of-the-art global constraint for bin packing is due to Shaw. We compare two linear continuous relaxations of the bin packing problem, based on the DP-flow and Arc-flow models, with the filtering of the bin packing constraint. Our experiments show that we often obtain significant improvements in runtime. The DP-flow model is a novel formulation of the problem.

/pdf/propagating-the-bin-packing-constraint-using-linear-eqrbobotjz.pdf

Propagating the bin packing constraint using linear programming

Users can often naturally express their preferences in terms of ideal or non-ideal solutions. We show how to reason about logical combinations of distance constraints on ideals and non-ideals using a novel global constraint. We evaluate our approach on both randomly generated and real-world configuration problem instances.

/pdf/distance-constraints-in-constraint-satisfaction-4k2bnofvsf.pdf

Barry O'Sullivan

Papers

Interactive constraint-aided conceptual design

Tuning Parameters of Large Neighborhood Search for the Machine Reassignment Problem

Almost 2-SAT Is Fixed-Parameter Tractable (Extended Abstract)

Propagating the bin packing constraint using linear programming

Distance constraints in constraint satisfaction