The book is outstanding and admirable in many respects. ... is necessary reading for all kinds of readers from undergraduate students to top authorities in the field. Journal of Symbolic Logic Written by two experts in the field, this is the only comprehensive and unified treatment of the central ideas and their applications of Kolmogorov complexity. The book presents a thorough treatment of the subject with a wide range of illustrative applications. Such applications include the randomness of finite objects or infinite sequences, Martin-Loef tests for randomness, information theory, computational learning theory, the complexity of algorithms, and the thermodynamics of computing. It will be ideal for advanced undergraduate students, graduate students, and researchers in computer science, mathematics, cognitive sciences, philosophy, artificial intelligence, statistics, and physics. The book is self-contained in that it contains the basic requirements from mathematics and computer science. Included are also numerous problem sets, comments, source references, and hints to solutions of problems. New topics in this edition include Omega numbers, KolmogorovLoveland randomness, universal learning, communication complexity, Kolmogorov's random graphs, time-limited universal distribution, Shannon information and others.

An Introduction to Kolmogorov Complexity and Its Applications

Introduction to linear and nonlinear programming

During the year 2006, the work at the Excimer Laser Laboratory was mainly focused on the development and applications of a laser-driven plasma source emitting in the extreme ultraviolet (EUV) spectrum, on laser irradiation of textiles, on the design and test of optical systems, and on the know-how transfer of the ENEA-patented laser beam homogeniser. Since early ’90, we have developed an Laser-Plasma source emitting in the spectral range EUV/soft X-rays which has been applied to atomic spectroscopy, radiobiology, in vivo microscopy and photonics. More recently the source has been addressed for use in EUV lithography, integrating it in the EGERIA (Extreme-ultraviolet-radiation Generation for Experimental Research and Industrial Applications) facility, which was described in previous Annual Reports. The development of a 14nm Exposure Tool is the main objective of a National project (FIRB-EUVL) ending 2007, while an efficient debris mitigation system has been developed within the frame of the European Integrated Project More Moore.

Annual Report for 2006

We address the problem of learning classifiers when observations have multiple views, some of which may not be observed for all examples. We assume the existence of view generating functions which may complete the missing views in an approximate way. This situation corresponds for example to learning text classifiers from multilingual collections where documents are not available in all languages. In that case, Machine Translation (MT) systems may be used to translate each document in the missing languages. We derive a generalization error bound for classifiers learned on examples with multiple artificially created views. Our result uncovers a trade-off between the size of the training set, the number of views, and the quality of the view generating functions. As a consequence, we identify situations where it is more interesting to use multiple views for learning instead of classical single view learning. An extension of this framework is a natural way to leverage unlabeled multi-view data in semi-supervised learning. Experimental results on a subset of the Reuters RCV1/RCV2 collections support our findings by showing that additional views obtained from MT may significantly improve the classification performance in the cases identified by our trade-off.

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Learning from Multiple Partially Observed Views - an Application to Multilingual Text Categorization

1. Mathematical Preliminaries. 2. Elements of Computability Theory. 4. The Class P. 5. The Glass NP. 6. The Complexity of Optiimzation Problems. 7. Beyond NP. 8. Space-Complexity Classes. 9. Probabiillistic. 10. Algorithms and Complexity Classes. 11. Interactivite Proof. 12. Systems. 13. Models of Parallel Computer. 14. Parallel Algorithms.

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Introduction to the theory of complexity

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New Computational Paradigms

The many types of resource-bounded reductions that are both an object of study and a research tool in structural complexity theory have given rise to a large variety of completeness notions. A complete set in a complexity class is a manageable object that represents the structure of the entire class. The study of its structure can reveal properties that are general in that the complexity class, and the study of the structure of complete sets in different classes, can reveal secrets about the relation between these classes. Research into all sorts of aspects and properties of complete sets has been and will be a major topic in structural complexity theory. In this expository paper, we review the progress that has been made in recent years on selected topics in the study of complete sets. >

On the structure of complete sets

It is shown that any p-selective and self-reducible sets is in P. As the converse is also true, the authors obtain a new characterization of the class P. A generalization and several consequences of this theorem are discussed. Among other consequences, it is shown that under reasonable assumptions autoreducibility and self-reducibility differ on NP, and there are non-p-T-mitotic sets in NP. >

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P-selective self-reducibles sets: a new characterization of P

Nonapproximability of the normalized information distance

We demonstrate differences between reducibilities and corresponding completeness notions for nondeterministic time and space classes. For time classes the studied completeness notions under polynomial-time bounded (even logarithmic-space bounded) reducibilities turn out to be different for any class containingNE. For space classes the completeness notions under logspace reducibilities can be separated for any class properly containingLOGSPACE. Key observation in obtaining the separations is the honesty property of reductions, which was recently observed to hold for the time classes and can be shown to hold for space classes.

Leen Torenvliet

Papers

New Computational Paradigms

On the structure of complete sets

P-selective self-reducibles sets: a new characterization of P

Nonapproximability of the normalized information distance

Completeness for nondeterministic complexity classes