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

An approach to complexity theory which offers a means of analysing algorithms in terms of their tractability. The authors consider the problem in terms of parameterized languages and taking "k-slices" of the language, thus introducing readers to new classes of algorithms which may be analysed more precisely than was the case until now. The book is as self-contained as possible and includes a great deal of background material. As a result, computer scientists, mathematicians, and graduate students interested in the design and analysis of algorithms will find much of interest.

Parameterized Complexity

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

PART I: FOUNDATIONS 1. Introduction to Fixed-Parameter Algorithms 2. Preliminaries and Agreements 3. Parameterized Complexity Theory - A Primer 4. Vertex Cover - An Illustrative Example 5. The Art of Problem Parameterization 6. Summary and Concluding Remarks PART II: ALGORITHMIC METHODS 7. Data Reduction and Problem Kernels 8. Depth-Bounded Search Trees 9. Dynamic Programming 10. Tree Decompositions of Graphs 11. Further Advanced Techniques 12. Summary and Concluding Remarks PART III: SOME THEORY, SOME CASE STUDIES 13. Parameterized Complexity Theory 14. Connections to Approximation Algorithms 15. Selected Case Studies 16. Zukunftsmusik References Index

Invitation to fixed-parameter algorithms

In this paper, we give for constant $k$ a linear-time algorithm that, given a graph $G=(V,E)$, determines whether the treewidth of $G$ is at most $k$ and, if so, finds a tree-decomposition of $G$ with treewidth at most $k$. A consequence is that every minor-closed class of graphs that does not contain all planar graphs has a linear-time recognition algorithm. Another consequence is that a similar result holds when we look instead for path-decompositions with pathwidth at most some constant $k$.

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A Linear-Time Algorithm for Finding Tree-Decompositions of Small Treewidth

Cayley graph techniques are introduced for the problem of constructing networks having the maximum possible number of nodes, among networks that satisfy prescribed bounds on the parameters maximum node degree and broadcast diameter. The broadcast diameter of a network is the maximum time required for a message originating at a node of the network to be relayed to all other nodes, under the restriction that in a single time step any node can communicate with only one neighboring node. For many parameter values these algebraic methods yield the largest known constructions, improving on previous graph-theoretic approaches. It has previously been shown that hypercubes are optimal for degree $k$ and broadcast diameter $k$. A construction employing dihedral groups is shown to be optimal for degree $k$ and broadcast diameter $k+1$.

Algebraic constructions of efficient broadcast networks

Based on the framework of parameterized complexity theory, we derive tight lower bounds on the computational complexity for a number of well-known NP-hard problems. We start by proving a general result, namely that the parameterized weighted satisfiability problem on depth-t circuits cannot be solved in time n/sup o(k)/poly(m), where n is the circuit input length, m is the circuit size, and k is the parameter, unless the (t - l)-st level W[t $1] of the W-hierarchy collapses to FPT. By refining this technique, we prove that a group of parameterized NP-hard problems, including weighted SAT, dominating set, hitting set, set cover, and feature set, cannot be solved in time n/sup o(k)/poly(m), where n is the size of the universal set from which the k elements are to be selected and m is the instance size, unless the first level W[l] of the W-hierarchy collapses to FPT. We also prove that another group of parameterized problems which includes weighted q-SAT (for any fixed q /spl ges/ 2), clique, and independent set, cannot be solved in time n/sup o(k)/ unless all search problems in the syntactic class SNP, introduced by Papadimitriou and Yannakakis, are solvable in subexponential time. Note that all these parameterized problems have trivial algorithms of running time either n/sup k/ poly(m) or O(n/sup k/).

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Tight lower bounds for certain parameterized NP-hard problems

It is well known that for a fixed relational database query φ in m free variables, it can be determined in time polynomial in the size n of the database whether there exists an m-tuple x that belongs to the relation defined by the query. For the best known algorithms, however, the exponent of the polynomial is proportional to the size of the query. We study the data complexity of this problem parameterized by the size k = |φ| of the query, and answer a question recently raised by Yannakakis [Yan95]. Our main results show: (1) the general problem is complete for the parametric complexity class AW [∗], and (2) when restricted to monotone queries, the problem is complete for the fundamental parametric complexity class W [1]. The practical significance of these results is that unless the parameterized complexity hierarchy collapses, there are unlikely to be algorithms that solve this problem (even under the restriction to monotone queries) in time f(k)n where f is an arbitrary function of k and c is a constant independent of k. An important consequence of the proof of (2) is a significantly improved characterization of the parameterized complexity class W [1]. Previous results by Downey and Fellows characterize W [1] in terms of the kWeighted Circuit Satisfiability problem, for families of circuits that satisfy: (1) the depth of the circuits is bounded by a constant c, (2) on any input-output path there is at most one gate having unbounded fanin (termed a large gate), with all other gates having fan-in bounded by c (that is, small gates). We show that the definition can be broadened by allowing circuits of depth bounded by an arbitrary function f(k). If we denote this parameterized complexity class W ∗[1], then our corollary ⋆ Research supported by the New Zealand Marsden Fund for Basic Science ⋆⋆ Research supported by the National Science and Engineering Research Council of Canada. is: W ∗[1] = W [1]. As a consequence, the k-Weighted Satisfiability problem for Boolean expressions that are products of k-sums is complete for W [1]. Part of our argument establishes a general normalization lemma for W ∗[t] for all t.

The Parameterized Complexity of Relational Database Queries and an Improved Characterization of W[1].

Introduction Computers are everywhere. We all need to learn how to use them, and many of us use them every day. But how do they work? How do they think? And how can people write software that is fast and easy to use? Computer science is a fascinating subject that explores these very questions. The easy and fun activities in this book, designed for studentren of all ages, introduce you to some of the building blocks of how computers work—without using a computer at all! This book can be effectively used in enrichment and extension programmes, or even in the regular classroom. You don't have to be a computer expert to enjoy learning these principles with your students. The book contains a range of activities, with background information explained simply. Answers to all problems are provided, and each activity ends with a 'what's it all about?' section that explains the relevance of the activities. Many of the activities are mathematically based, e.g. exploring binary numbers, mapping and graphs, patterns and sorting problems, and cryptography. Others link in well with the technology curriculum, and the knowledge and understanding of how computers work. The studentren are actively involved in communication, problem solving, creativity, and thinking skills in a meaningful context. The activities also provide a very engaging way to explore " computational thinking " , which is gaining traction in school curricula. In addition to this book, the " Unplugged " project has as lot of free, online resources including videos, pictures and extra material at csunplugged.org. As part of the 2015 revision of this book, we have also released a brand new website, with more resources, better access to the open source material, and stronger curriculum links to match the appearance of computer science and computational thinking in school curricula. This book was written by three computer science lecturers and two school teachers, and is based on our experience in classrooms as well as feedback from hundreds of educators over two decades. We have found that many important concepts can be taught without using a computer—in fact, sometimes the computer is just a distraction from learning. Often computer science is taught using programming first, but not every student finds this motivating, and it can be a significant barrier to getting into the really interesting ideas in computer science. So unplug your computer, and get ready to learn what computer science is …

Computer Science Unplugged: An enrichment and extension programme for primary-aged children

We consider graph drawings in which vertices are assigned to layers and edges are drawn as straight line-segments between vertices on adjacent layers. We prove that graphs admitting crossing-free h-layer drawings (for fixed h) have bounded pathwidth. We then use a path decomposition as the basis for a linear-time algorithm to decide if a graph has a crossing-free h-layer drawing (for fixed h). This algorithm is extended to solve a large number of related problems, including allowing at most k crossings, or removing at most r edges to leave a crossing-free drawing (for fixed k or r). If the number of crossings or deleted edges is a non-fixed parameter then these problems are NP-complete. For each setting, we can also permit downward drawings of directed graphs and drawings in which edges may span multiple layers, in which case the total span or the maximum span of edges can be minimized. In contrast to the so-called Sugiyama method for layered graph drawing, our algorithms do not assume a preassignment of the vertices to layers.

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Michael R. Fellows

Papers

Algebraic constructions of efficient broadcast networks

Tight lower bounds for certain parameterized NP-hard problems

The Parameterized Complexity of Relational Database Queries and an Improved Characterization of W[1].

Computer Science Unplugged: An enrichment and extension programme for primary-aged children

On the Parameterized Complexity of Layered Graph Drawing