Probabilità e Nondeterminismo nella teoria dei domini
|
|
|
- Ferdinand Robertson
- 10 years ago
- Views:
Transcription
1 Probabilità e Nondeterminismo nella teoria dei domini Daniele Varacca ENS, Paris BRICS, Aarhus Parma, 15 giugno 2004 Probabilità e Nondeterminismo nella teoria dei domini p.1
2 Road Map Motivation Domain theory and denotational semantics Categorical Semantics and Monads Combining monads: Indexed valuations Orders on indexed valuation Probabilità e Nondeterminismo nella teoria dei domini p.2
3 The Nature of Computation It s a wild world out there: complex needs: precision, speed, security complex tools: languages, protocols, networks, channels What do computers do, really? Probabilità e Nondeterminismo nella teoria dei domini p.3
4 Semantics Semantics is a tool to find our way programs are associated to mathematical objects (the meaning) mathematical objects are parts of structures (the models) Goal: study the models to understand the programs Probabilità e Nondeterminismo nella teoria dei domini p.4
5 Semantics Requirements for the models models should be complex enough to reflect interesting properties of real computation models should be simple enough to be studied Right balance between abstraction and complexity Compositionality The meaning of a complex system is built using the meanings of simpler parts of the system. Probabilità e Nondeterminismo nella teoria dei domini p.5
6 Probability A model is probabilistic when different alternatives are represented every alternative is assigned a probability We need probability to model failures and unreliability to produce efficient algorithm to enhance security Probabilità e Nondeterminismo nella teoria dei domini p.6
7 Nondeterminism A model is nondeterministic when different alternatives are represented the way one alternative is chosen over the others is not specified We need nondeterminism to abstract away from details to achieve compositionality to model concurrency (sometimes) Probabilità e Nondeterminismo nella teoria dei domini p.7
8 Road Map Motivation Domain theory and denotational semantics Categorical Semantics and Monads Combining monads: Indexed valuations Orders on indexed valuation Probabilità e Nondeterminismo nella teoria dei domini p.8
9 Denotational semantics Idea: programs take in input values of type X and return values of type Y Programs are represented as function X p Y How do we represent recursive programs? fact(n) := if n == 0 then 1 else n fact(n 1) Probabilità e Nondeterminismo nella teoria dei domini p.9
10 Domain theory (Scott-Strachey late 60): recursive functions are fixed points of operators Ξ(f)(n) := if n == 0 then 1 else n f(n 1) Domain: a framework where fixed points exist Probabilità e Nondeterminismo nella teoria dei domini p.10
11 Domains DCPO: a partial order D, where every directed subset has a least upper bound. Continuous function f : D D - monotone function that preserves least upper bounds of directed sets. The Category of DCPO s and continuoud functions. Fact: every continuous function f : D D has a (least) fixed point. Probabilità e Nondeterminismo nella teoria dei domini p.11
12 Scott topology Continuous function f : D D = Continuous with respect to the Scott Topology. Scott closed sets: downward closed sets, closed by least upper bounds of directed sets. Scott open sets: upward closed sets, inaccessible from below. This topology is T 0 but not T 1. Probabilità e Nondeterminismo nella teoria dei domini p.12
13 Road Map Motivation Domain theory and denotational semantics Categorical Semantics and Monads Combining monads: Indexed valuations Orders on indexed valuation Probabilità e Nondeterminismo nella teoria dei domini p.13
14 Categorical semantics Idea: programs take in input values of type X and return values of type Y Programs are represented as arrows Programs compose: X p Y X p Y,Y q Z X p;q Z Examples: sets and functions DCPOs and continuous functions Probabilità e Nondeterminismo nella teoria dei domini p.14
15 Computational effects Programs can have effects nontermination nondeterminism probability permanent state Idea: programs take in input values of type X and return computations of type Y Probabilità e Nondeterminismo nella teoria dei domini p.15
16 Monads A monad consists of the operator T : if X represents values, T(X) represents computations the unit X η X T(X): values are special kinds of computations the extension ( ) : if X f T(Y ), then T(X) f T(Y ) satisfying some axioms Probabilità e Nondeterminismo nella teoria dei domini p.16
17 Monads The extension models sequential composition: X f T(Y ), Y g T(Z) X f T(Y ) g T(Z) Specific effects have also specific operations Probabilità e Nondeterminismo nella teoria dei domini p.17
18 Monads: examples The nondeterministic monad the operator is the finite nonempty powerset P(X) the unit: x {x} the extension: if X f P(Y ), and if A P(X), then f (A) = x Af(x) A program returns a set of values Choice is modelled by union of sets Probabilità e Nondeterminismo nella teoria dei domini p.18
19 Monads: examples The probabilistic monad the finite valuation operator V (X): functions f : X R + such that f(x) 0 for only finitely many x the unit x δ x the extension is obtained by weighted average A program returns a valuation over values Probabilistic choice x + p y is modelled by convex combination x + p x = x Probabilità e Nondeterminismo nella teoria dei domini p.19
20 Road Map Motivation Domain theory and denotational semantics Categorical Semantics and Monads Combining monads: indexed valuations Orders on indexed valuation Probabilità e Nondeterminismo nella teoria dei domini p.20
21 Combining monads We want a program return a set of valuations. the operator is the the composition of the operators P(V (X)) the unit is the composition of the units the extension Probabilità e Nondeterminismo nella teoria dei domini p.21
22 Combining monads We want a program return a set of valuations. the operator is the the composition of the operators P(V (X)) the unit is the composition of the units the extension does not work! We need to change something Probabilità e Nondeterminismo nella teoria dei domini p.21
23 Solutions Solution 1 (Tix, Mislove): change the sets. Use convex sets of distributions The operator P convex V form a monad Probabilità e Nondeterminismo nella teoria dei domini p.22
24 Solutions Solution 1 (Tix, Mislove): change the sets. Use convex sets of distributions The operator P convex V form a monad Solution 2 (Varacca, Winskel): change the valuations. Use indexed valuations Probabilità e Nondeterminismo nella teoria dei domini p.22
25 Indexed valuations An indexed valuation over a set X is given by a (finite) indexing set I an indexing function ind : I X a valuation over I Notation ν = (x i,p i ) i I Probabilità e Nondeterminismo nella teoria dei domini p.23
26 Indexed valuations Intuition: elements of X represent observations elements of I represent computations the indexing function associates computations to observations The probability on observations is gotten from the computations Like in a... random variable! Probabilità e Nondeterminismo nella teoria dei domini p.24
27 The monad We have a monad: the operator is IV (X) = {indexed valuations over X} the unit: x (x, 1) { } the extension is obtained via dosjoint union of indices The probabilistic choice is modelled by duplicating the indices x + p x x Probabilità e Nondeterminismo nella teoria dei domini p.25
28 Composing the monads We can compose IV and P and get a monad We can now model a programming language with sequential composition nondeterministic choice probabilistic choice Probabilità e Nondeterminismo nella teoria dei domini p.26
29 Ordering Which order on indexed valuations? Idea: the equation x + p x = x is weakened to x + p x x Morally: the more indices, the better This combines well with the Hoare order on sets A B A domain theory Probabilità e Nondeterminismo nella teoria dei domini p.27
30 Semantics A language c ::= skip X := a c;c if b then c else c. States: contents of the locations Semantics [c] : Σ Σ Probabilità e Nondeterminismo nella teoria dei domini p.28
31 Semantics A nondeterministic language c ::=... c or c. Semantics [c] : Σ P(Σ) Probabilità e Nondeterminismo nella teoria dei domini p.29
32 Semantics A probabilistic language c ::=... X := random. Semantics [c] : Σ V (Σ) Probabilità e Nondeterminismo nella teoria dei domini p.30
33 Semantics A nondeterministic and probabilistic language c ::=... c or c X := random. Semantics [c] : Σ P(V (Σ)) Probabilità e Nondeterminismo nella teoria dei domini p.31
34 Semantics A nondeterministic and probabilistic language c ::=... c or c X := random. Semantics [c] : Σ P(V (Σ)) Probabilità e Nondeterminismo nella teoria dei domini p.31
35 Semantics A nondeterministic and probabilistic language c ::=... c or c X := random. Semantics [c] : Σ P(IV (Σ)) Probabilità e Nondeterminismo nella teoria dei domini p.31
36 Semantics A language with recursion c ::=... while b do c. domain theory Probabilità e Nondeterminismo nella teoria dei domini p.32
37 Other issues equational theories other orderings relation with continuous valuations the problem of the concrete characterization operational intuition Probabilità e Nondeterminismo nella teoria dei domini p.33
38 Related work CSP group in Oxford Tix thesis and Mislove s paper Plotkin, Power, Hyland on monads and operations Probabilità e Nondeterminismo nella teoria dei domini p.34
39 Future work concrete characterization presheaf models? Probabilità e Nondeterminismo nella teoria dei domini p.35
40 Grazie Probabilità e Nondeterminismo nella teoria dei domini p.36
The Basics of Graphical Models
The Basics of Graphical Models David M. Blei Columbia University October 3, 2015 Introduction These notes follow Chapter 2 of An Introduction to Probabilistic Graphical Models by Michael Jordan. Many figures
A Framework for the Semantics of Behavioral Contracts
A Framework for the Semantics of Behavioral Contracts Ashley McNeile Metamaxim Ltd, 48 Brunswick Gardens, London W8 4AN, UK [email protected] Abstract. Contracts have proved a powerful concept
Master of Science in Computer Science
Master of Science in Computer Science Background/Rationale The MSCS program aims to provide both breadth and depth of knowledge in the concepts and techniques related to the theory, design, implementation,
Modern Optimization Methods for Big Data Problems MATH11146 The University of Edinburgh
Modern Optimization Methods for Big Data Problems MATH11146 The University of Edinburgh Peter Richtárik Week 3 Randomized Coordinate Descent With Arbitrary Sampling January 27, 2016 1 / 30 The Problem
This asserts two sets are equal iff they have the same elements, that is, a set is determined by its elements.
3. Axioms of Set theory Before presenting the axioms of set theory, we first make a few basic comments about the relevant first order logic. We will give a somewhat more detailed discussion later, but
1 if 1 x 0 1 if 0 x 1
Chapter 3 Continuity In this chapter we begin by defining the fundamental notion of continuity for real valued functions of a single real variable. When trying to decide whether a given function is or
Local periods and binary partial words: An algorithm
Local periods and binary partial words: An algorithm F. Blanchet-Sadri and Ajay Chriscoe Department of Mathematical Sciences University of North Carolina P.O. Box 26170 Greensboro, NC 27402 6170, USA E-mail:
2.1 Complexity Classes
15-859(M): Randomized Algorithms Lecturer: Shuchi Chawla Topic: Complexity classes, Identity checking Date: September 15, 2004 Scribe: Andrew Gilpin 2.1 Complexity Classes In this lecture we will look
A domain of spacetime intervals in general relativity
A domain of spacetime intervals in general relativity Keye Martin Department of Mathematics Tulane University New Orleans, LA 70118 United States of America [email protected] Prakash Panangaden School
Doctor of Philosophy in Computer Science
Doctor of Philosophy in Computer Science Background/Rationale The program aims to develop computer scientists who are armed with methods, tools and techniques from both theoretical and systems aspects
On Probabilistic Distributed Strategies
On Probabilistic Distributed Strategies Glynn Winskel Computer Laboratory, University of Cambridge Abstract. In a distributed game we imagine a team Player engaging a team Opponent in a distributed fashion.
INTRODUCTORY SET THEORY
M.Sc. program in mathematics INTRODUCTORY SET THEORY Katalin Károlyi Department of Applied Analysis, Eötvös Loránd University H-1088 Budapest, Múzeum krt. 6-8. CONTENTS 1. SETS Set, equal sets, subset,
Table of Contents. Preface. Chapter 1 Introduction 1.1 Background. 1.2 Problem description. 1.3 The role of standardization. 1.4 Scope and objectives
Table of Contents Table of Contents Preface Chapter 1 Introduction 1.1 Background 1.2 Problem description 1.3 The role of standardization 1.4 Scope and objectives 1.5 Approach 1.6 Related work 1.7 General
Software Modeling and Verification
Software Modeling and Verification Alessandro Aldini DiSBeF - Sezione STI University of Urbino Carlo Bo Italy 3-4 February 2015 Algorithmic verification Correctness problem Is the software/hardware system
Basic Concepts of Point Set Topology Notes for OU course Math 4853 Spring 2011
Basic Concepts of Point Set Topology Notes for OU course Math 4853 Spring 2011 A. Miller 1. Introduction. The definitions of metric space and topological space were developed in the early 1900 s, largely
WHAT ARE MATHEMATICAL PROOFS AND WHY THEY ARE IMPORTANT?
WHAT ARE MATHEMATICAL PROOFS AND WHY THEY ARE IMPORTANT? introduction Many students seem to have trouble with the notion of a mathematical proof. People that come to a course like Math 216, who certainly
Class notes Program Analysis course given by Prof. Mooly Sagiv Computer Science Department, Tel Aviv University second lecture 8/3/2007
Constant Propagation Class notes Program Analysis course given by Prof. Mooly Sagiv Computer Science Department, Tel Aviv University second lecture 8/3/2007 Osnat Minz and Mati Shomrat Introduction This
Overview Motivating Examples Interleaving Model Semantics of Correctness Testing, Debugging, and Verification
Introduction Overview Motivating Examples Interleaving Model Semantics of Correctness Testing, Debugging, and Verification Advanced Topics in Software Engineering 1 Concurrent Programs Characterized by
A Propositional Dynamic Logic for CCS Programs
A Propositional Dynamic Logic for CCS Programs Mario R. F. Benevides and L. Menasché Schechter {mario,luis}@cos.ufrj.br Abstract This work presents a Propositional Dynamic Logic in which the programs are
P NP for the Reals with various Analytic Functions
P NP for the Reals with various Analytic Functions Mihai Prunescu Abstract We show that non-deterministic machines in the sense of [BSS] defined over wide classes of real analytic structures are more powerful
Introduction to Formal Methods. Các Phương Pháp Hình Thức Cho Phát Triển Phần Mềm
Introduction to Formal Methods Các Phương Pháp Hình Thức Cho Phát Triển Phần Mềm Outline Introduction Formal Specification Formal Verification Model Checking Theorem Proving Introduction Good papers to
PROPERTECHNIQUEOFSOFTWARE INSPECTIONUSING GUARDED COMMANDLANGUAGE
International Journal of Computer ScienceandCommunication Vol. 2, No. 1, January-June2011, pp. 153-157 PROPERTECHNIQUEOFSOFTWARE INSPECTIONUSING GUARDED COMMANDLANGUAGE Neeraj Kumar Singhania University,
Fuzzy Probability Distributions in Bayesian Analysis
Fuzzy Probability Distributions in Bayesian Analysis Reinhard Viertl and Owat Sunanta Department of Statistics and Probability Theory Vienna University of Technology, Vienna, Austria Corresponding author:
BANACH AND HILBERT SPACE REVIEW
BANACH AND HILBET SPACE EVIEW CHISTOPHE HEIL These notes will briefly review some basic concepts related to the theory of Banach and Hilbert spaces. We are not trying to give a complete development, but
Separation Properties for Locally Convex Cones
Journal of Convex Analysis Volume 9 (2002), No. 1, 301 307 Separation Properties for Locally Convex Cones Walter Roth Department of Mathematics, Universiti Brunei Darussalam, Gadong BE1410, Brunei Darussalam
Notes V General Equilibrium: Positive Theory. 1 Walrasian Equilibrium and Excess Demand
Notes V General Equilibrium: Positive Theory In this lecture we go on considering a general equilibrium model of a private ownership economy. In contrast to the Notes IV, we focus on positive issues such
4.2 Description of the Event operation Network (EON)
Integrated support system for planning and scheduling... 2003/4/24 page 39 #65 Chapter 4 The EON model 4. Overview The present thesis is focused in the development of a generic scheduling framework applicable
Chapter ML:IV. IV. Statistical Learning. Probability Basics Bayes Classification Maximum a-posteriori Hypotheses
Chapter ML:IV IV. Statistical Learning Probability Basics Bayes Classification Maximum a-posteriori Hypotheses ML:IV-1 Statistical Learning STEIN 2005-2015 Area Overview Mathematics Statistics...... Stochastics
CHAPTER 7 GENERAL PROOF SYSTEMS
CHAPTER 7 GENERAL PROOF SYSTEMS 1 Introduction Proof systems are built to prove statements. They can be thought as an inference machine with special statements, called provable statements, or sometimes
CPO Semantics of Timed Interactive Actor Networks
CPO Semantics of Timed Interactive Actor Networks Xiaojun Liu Edward A. Lee Electrical Engineering and Computer Sciences University of California at Berkeley Technical Report No. UCB/EECS-2006-67 http://www.eecs.berkeley.edu/pubs/techrpts/2006/eecs-2006-67.html
Kolmogorov Complexity and the Incompressibility Method
Kolmogorov Complexity and the Incompressibility Method Holger Arnold 1. Introduction. What makes one object more complex than another? Kolmogorov complexity, or program-size complexity, provides one of
CS5236 Advanced Automata Theory
CS5236 Advanced Automata Theory Frank Stephan Semester I, Academic Year 2012-2013 Advanced Automata Theory is a lecture which will first review the basics of formal languages and automata theory and then
Rigorous Software Development CSCI-GA 3033-009
Rigorous Software Development CSCI-GA 3033-009 Instructor: Thomas Wies Spring 2013 Lecture 11 Semantics of Programming Languages Denotational Semantics Meaning of a program is defined as the mathematical
6.045: Automata, Computability, and Complexity Or, Great Ideas in Theoretical Computer Science Spring, 2010. Class 4 Nancy Lynch
6.045: Automata, Computability, and Complexity Or, Great Ideas in Theoretical Computer Science Spring, 2010 Class 4 Nancy Lynch Today Two more models of computation: Nondeterministic Finite Automata (NFAs)
Signature-Free Asynchronous Binary Byzantine Consensus with t < n/3, O(n 2 ) Messages, and O(1) Expected Time
Signature-Free Asynchronous Binary Byzantine Consensus with t < n/3, O(n 2 ) Messages, and O(1) Expected Time Achour Mostéfaoui Hamouma Moumen Michel Raynal, LINA, Université de Nantes, 44322 Nantes Cedex,
Address Taken FIAlias, which is equivalent to Steensgaard. CS553 Lecture Alias Analysis II 2
Alias Analysis Last time Alias analysis I (pointer analysis) Address Taken FIAlias, which is equivalent to Steensgaard Today Alias analysis II (pointer analysis) Anderson Emami Next time Midterm review
No: 10 04. Bilkent University. Monotonic Extension. Farhad Husseinov. Discussion Papers. Department of Economics
No: 10 04 Bilkent University Monotonic Extension Farhad Husseinov Discussion Papers Department of Economics The Discussion Papers of the Department of Economics are intended to make the initial results
Semantic Description of Distributed Business Processes
Semantic Description of Distributed Business Processes Authors: S. Agarwal, S. Rudolph, A. Abecker Presenter: Veli Bicer FZI Forschungszentrum Informatik, Karlsruhe Outline Motivation Formalism for Modeling
Data Modeling & Analysis Techniques. Probability & Statistics. Manfred Huber 2011 1
Data Modeling & Analysis Techniques Probability & Statistics Manfred Huber 2011 1 Probability and Statistics Probability and statistics are often used interchangeably but are different, related fields
Is Sometime Ever Better Than Alway?
Is Sometime Ever Better Than Alway? DAVID GRIES Cornell University The "intermittent assertion" method for proving programs correct is explained and compared with the conventional method. Simple conventional
Complete Redundancy Detection in Firewalls
Complete Redundancy Detection in Firewalls Alex X. Liu and Mohamed G. Gouda Department of Computer Sciences, The University of Texas at Austin, Austin, Texas 78712-0233, USA {alex, gouda}@cs.utexas.edu
Real Number Computability and Domain Theory
Real Number Computability and Domain Theory Pietro Di Gianantonio dipartimento di Matematica e Informatica, Università di Udine via delle Scienze 206 I-33100 Udine Italy E-mail: [email protected] Abstract
1. Prove that the empty set is a subset of every set.
1. Prove that the empty set is a subset of every set. Basic Topology Written by Men-Gen Tsai email: [email protected] Proof: For any element x of the empty set, x is also an element of every set since
Unified Language for Network Security Policy Implementation
Unified Language for Network Security Policy Implementation Dmitry Chernyavskiy Information Security Faculty National Research Nuclear University MEPhI Moscow, Russia [email protected] Natalia Miloslavskaya
Random vs. Structure-Based Testing of Answer-Set Programs: An Experimental Comparison
Random vs. Structure-Based Testing of Answer-Set Programs: An Experimental Comparison Tomi Janhunen 1, Ilkka Niemelä 1, Johannes Oetsch 2, Jörg Pührer 2, and Hans Tompits 2 1 Aalto University, Department
Basic Concepts of Set Theory, Functions and Relations
March 1, 2006 p. 1 Basic Concepts of Set Theory, Functions and Relations 1. Basic Concepts of Set Theory...1 1.1. Sets and elements...1 1.2. Specification of sets...2 1.3. Identity and cardinality...3
Repair Checking in Inconsistent Databases: Algorithms and Complexity
Repair Checking in Inconsistent Databases: Algorithms and Complexity Foto Afrati 1 Phokion G. Kolaitis 2 1 National Technical University of Athens 2 UC Santa Cruz and IBM Almaden Research Center Oxford,
Chapter 6: Episode discovery process
Chapter 6: Episode discovery process Algorithmic Methods of Data Mining, Fall 2005, Chapter 6: Episode discovery process 1 6. Episode discovery process The knowledge discovery process KDD process of analyzing
Hint 1. Answer (b) first. Make the set as simple as possible and try to generalize the phenomena it exhibits. [Caution: the next hint is an answer to
Problem. Consider the collection of all subsets A of the topological space X. The operations of osure A Ā and ementation A X A are functions from this collection to itself. (a) Show that starting with
Observational Program Calculi and the Correctness of Translations
Observational Program Calculi and the Correctness of Translations Manfred Schmidt-Schauss 1, David Sabel 1, Joachim Niehren 2, and Jan Schwinghammer 1 Goethe-University, Frankfurt, Germany 2 INRIA Lille,
STORM: Stochastic Optimization Using Random Models Katya Scheinberg Lehigh University. (Joint work with R. Chen and M. Menickelly)
STORM: Stochastic Optimization Using Random Models Katya Scheinberg Lehigh University (Joint work with R. Chen and M. Menickelly) Outline Stochastic optimization problem black box gradient based Existing
FIBER PRODUCTS AND ZARISKI SHEAVES
FIBER PRODUCTS AND ZARISKI SHEAVES BRIAN OSSERMAN 1. Fiber products and Zariski sheaves We recall the definition of a fiber product: Definition 1.1. Let C be a category, and X, Y, Z objects of C. Fix also
Discussion on the paper Hypotheses testing by convex optimization by A. Goldenschluger, A. Juditsky and A. Nemirovski.
Discussion on the paper Hypotheses testing by convex optimization by A. Goldenschluger, A. Juditsky and A. Nemirovski. Fabienne Comte, Celine Duval, Valentine Genon-Catalot To cite this version: Fabienne
Automated Program Behavior Analysis
Automated Program Behavior Analysis Stacy Prowell [email protected] March 2005 SQRL / SEI Motivation: Semantics Development: Most engineering designs are subjected to extensive analysis; software is
4. CLASSES OF RINGS 4.1. Classes of Rings class operator A-closed Example 1: product Example 2:
4. CLASSES OF RINGS 4.1. Classes of Rings Normally we associate, with any property, a set of objects that satisfy that property. But problems can arise when we allow sets to be elements of larger sets
Mechanisms for Fair Attribution
Mechanisms for Fair Attribution Eric Balkanski Yaron Singer Abstract We propose a new framework for optimization under fairness constraints. The problems we consider model procurement where the goal is
SYNTACTIC THEORY FOR BIGRAPHS
SYNTACTIC THEORY FOR BIGRAPHS Troels C. Damgaard ([email protected]) Joint work with Lars Birkedal, Arne John Glenstrup, and Robin Milner Bigraphical Programming Languages (BPL) Group IT University of Copenhagen
Collatz Sequence. Fibbonacci Sequence. n is even; Recurrence Relation: a n+1 = a n + a n 1.
Fibonacci Roulette In this game you will be constructing a recurrence relation, that is, a sequence of numbers where you find the next number by looking at the previous numbers in the sequence. Your job
3 Extending the Refinement Calculus
Building BSP Programs Using the Refinement Calculus D.B. Skillicorn? Department of Computing and Information Science Queen s University, Kingston, Canada [email protected] Abstract. We extend the
Testing LTL Formula Translation into Büchi Automata
Testing LTL Formula Translation into Büchi Automata Heikki Tauriainen and Keijo Heljanko Helsinki University of Technology, Laboratory for Theoretical Computer Science, P. O. Box 5400, FIN-02015 HUT, Finland
Regular Languages and Finite Automata
Regular Languages and Finite Automata 1 Introduction Hing Leung Department of Computer Science New Mexico State University Sep 16, 2010 In 1943, McCulloch and Pitts [4] published a pioneering work on a
Verifying Semantic of System Composition for an Aspect-Oriented Approach
2012 International Conference on System Engineering and Modeling (ICSEM 2012) IPCSIT vol. 34 (2012) (2012) IACSIT Press, Singapore Verifying Semantic of System Composition for an Aspect-Oriented Approach
On a comparison result for Markov processes
On a comparison result for Markov processes Ludger Rüschendorf University of Freiburg Abstract A comparison theorem is stated for Markov processes in polish state spaces. We consider a general class of
Real-time Visual Tracker by Stream Processing
Real-time Visual Tracker by Stream Processing Simultaneous and Fast 3D Tracking of Multiple Faces in Video Sequences by Using a Particle Filter Oscar Mateo Lozano & Kuzahiro Otsuka presented by Piotr Rudol
Formal Languages and Automata Theory - Regular Expressions and Finite Automata -
Formal Languages and Automata Theory - Regular Expressions and Finite Automata - Samarjit Chakraborty Computer Engineering and Networks Laboratory Swiss Federal Institute of Technology (ETH) Zürich March
Low upper bound of ideals, coding into rich Π 0 1 classes
Low upper bound of ideals, coding into rich Π 0 1 classes Antonín Kučera the main part is a joint project with T. Slaman Charles University, Prague September 2007, Chicago The main result There is a low
2. The Language of First-order Logic
2. The Language of First-order Logic KR & R Brachman & Levesque 2005 17 Declarative language Before building system before there can be learning, reasoning, planning, explanation... need to be able to
Classification/Decision Trees (II)
Classification/Decision Trees (II) Department of Statistics The Pennsylvania State University Email: [email protected] Right Sized Trees Let the expected misclassification rate of a tree T be R (T ).
SMT 2014 Algebra Test Solutions February 15, 2014
1. Alice and Bob are painting a house. If Alice and Bob do not take any breaks, they will finish painting the house in 20 hours. If, however, Bob stops painting once the house is half-finished, then the
Convex analysis and profit/cost/support functions
CALIFORNIA INSTITUTE OF TECHNOLOGY Division of the Humanities and Social Sciences Convex analysis and profit/cost/support functions KC Border October 2004 Revised January 2009 Let A be a subset of R m
Credal Networks for Operational Risk Measurement and Management
Credal Networks for Operational Risk Measurement and Management Alessandro Antonucci, Alberto Piatti, and Marco Zaffalon Istituto Dalle Molle di Studi sull Intelligenza Artificiale Via Cantonale, Galleria
Understanding Basic Calculus
Understanding Basic Calculus S.K. Chung Dedicated to all the people who have helped me in my life. i Preface This book is a revised and expanded version of the lecture notes for Basic Calculus and other
Mathematics for Econometrics, Fourth Edition
Mathematics for Econometrics, Fourth Edition Phoebus J. Dhrymes 1 July 2012 1 c Phoebus J. Dhrymes, 2012. Preliminary material; not to be cited or disseminated without the author s permission. 2 Contents
Simulation-Based Security with Inexhaustible Interactive Turing Machines
Simulation-Based Security with Inexhaustible Interactive Turing Machines Ralf Küsters Institut für Informatik Christian-Albrechts-Universität zu Kiel 24098 Kiel, Germany [email protected]
Lecture 15 An Arithmetic Circuit Lowerbound and Flows in Graphs
CSE599s: Extremal Combinatorics November 21, 2011 Lecture 15 An Arithmetic Circuit Lowerbound and Flows in Graphs Lecturer: Anup Rao 1 An Arithmetic Circuit Lower Bound An arithmetic circuit is just like
CHAPTER 2 Estimating Probabilities
CHAPTER 2 Estimating Probabilities Machine Learning Copyright c 2016. Tom M. Mitchell. All rights reserved. *DRAFT OF January 24, 2016* *PLEASE DO NOT DISTRIBUTE WITHOUT AUTHOR S PERMISSION* This is a
Parametric Domain-theoretic models of Linear Abadi & Plotkin Logic
Parametric Domain-theoretic models of Linear Abadi & Plotkin Logic Lars Birkedal Rasmus Ejlers Møgelberg Rasmus Lerchedahl Petersen IT University Technical Report Series TR-00-7 ISSN 600 600 February 00
MA651 Topology. Lecture 6. Separation Axioms.
MA651 Topology. Lecture 6. Separation Axioms. This text is based on the following books: Fundamental concepts of topology by Peter O Neil Elements of Mathematics: General Topology by Nicolas Bourbaki Counterexamples
