Erik Paul. Leipzig University. August 22, QuantLA

Σχετικά έγγραφα
Fractional Colorings and Zykov Products of graphs

Π Ο Λ Ι Τ Ι Κ Α Κ Α Ι Σ Τ Ρ Α Τ Ι Ω Τ Ι Κ Α Γ Ε Γ Ο Ν Ο Τ Α

LTL to Buchi. Overview. Buchi Model Checking LTL Translating LTL into Buchi. Ralf Huuck. Buchi Automata. Example

Homomorphism in Intuitionistic Fuzzy Automata

Α Ρ Ι Θ Μ Ο Σ : 6.913

ibemo Kazakhstan Republic of Kazakhstan, West Kazakhstan Oblast, Aksai, Pramzone, BKKS office complex Phone: ; Fax:

Overview. Transition Semantics. Configurations and the transition relation. Executions and computation

2. Α ν ά λ υ σ η Π ε ρ ι ο χ ή ς. 3. Α π α ι τ ή σ ε ι ς Ε ρ γ ο δ ό τ η. 4. Τ υ π ο λ ο γ ί α κ τ ι ρ ί ω ν. 5. Π ρ ό τ α σ η. 6.

Elements of Information Theory

Τομέας Επιστήμης Υπολογιστών και Αριθμητικής Ανάλυσης

ω ω ω ω ω ω+2 ω ω+2 + ω ω ω ω+2 + ω ω+1 ω ω+2 2 ω ω ω ω ω ω ω ω+1 ω ω2 ω ω2 + ω ω ω2 + ω ω ω ω2 + ω ω+1 ω ω2 + ω ω+1 + ω ω ω ω2 + ω

Oscillatory integrals

D Alembert s Solution to the Wave Equation

CHAPTER (2) Electric Charges, Electric Charge Densities and Electric Field Intensity

Lecture 21: Scattering and FGR

ΕΛΛΗΝΙΚΗ ΔΗΜΟΚΡΑΤΙΑ Ανώτατο Εκπαιδευτικό Ίδρυμα Πειραιά Τεχνολογικού Τομέα. Ξένη Ορολογία. Ενότητα 5 : Financial Ratios


HOMEWORK 4 = G. In order to plot the stress versus the stretch we define a normalized stretch:

Σχολή Εφαρμοσμένων Μαθηματικών και Φυσικών Επιστημών. Εθνικό Μετσόβιο Πολυτεχνείο. Thales Workshop, 1-3 July 2015.

P AND P. P : actual probability. P : risk neutral probability. Realtionship: mutual absolute continuity P P. For example:

Chap. 6 Pushdown Automata

Fourier Series. MATH 211, Calculus II. J. Robert Buchanan. Spring Department of Mathematics

ΗΜΥ 210 ΣΧΕΔΙΑΣΜΟΣ ΨΗΦΙΑΚΩΝ ΣΥΣΤΗΜΑΤΩΝ. Χειµερινό Εξάµηνο ΔΙΑΛΕΞΗ 3: Αλγοριθµική Ελαχιστοποίηση (Quine-McCluskey, tabular method)

Anti-Corrosive Thin Film Precision Chip Resistor (PR Series)

ΠΕΔΙΟ_ΑΓΟΡΑ: Stakeholder Analysis Questionnaire

Iterated trilinear fourier integrals with arbitrary symbols

Every set of first-order formulas is equivalent to an independent set

Ordinal Arithmetic: Addition, Multiplication, Exponentiation and Limit

Chapter 3: Ordinal Numbers

ΟΜΑΔΕΣ ΑΣΚΗΣΕΩΝ

Math 446 Homework 3 Solutions. (1). (i): Reverse triangle inequality for metrics: Let (X, d) be a metric space and let x, y, z X.

λρ-calculus 1. each λ-variable is a λρ-term, called an atom or atomic term; 2. if M and N are λρ-term then (MN) is a λρ-term called an application;

ΠΟΡΤΑ ΤΗΛΕΣΚΟΠΙΚΗ 4ΦΥΛΛΗ 4 PANEL SIDE OPENING DOOR

ΠΟΡΤΑ ΚΕΝΤΡΙΚΗ 2ΦΥΛΛΗ 2 PANEL CENTRE PARTING DOOR

Wavelet based matrix compression for boundary integral equations on complex geometries

Anti-Corrosive Thin Film Precision Chip Resistor-SMDR Series. official distributor of

Solutions to Exercise Sheet 5

From the finite to the transfinite: Λµ-terms and streams

ΚΥΠΡΙΑΚΟΣ ΣΥΝΔΕΣΜΟΣ ΠΛΗΡΟΦΟΡΙΚΗΣ CYPRUS COMPUTER SOCIETY 21 ος ΠΑΓΚΥΠΡΙΟΣ ΜΑΘΗΤΙΚΟΣ ΔΙΑΓΩΝΙΣΜΟΣ ΠΛΗΡΟΦΟΡΙΚΗΣ Δεύτερος Γύρος - 30 Μαρτίου 2011

6.642, Continuum Electromechanics, Fall 2004 Prof. Markus Zahn Lecture 8: Electrohydrodynamic and Ferrohydrodynamic Instabilities

Λογική Πρώτης Τάξης. Γιώργος Κορφιάτης. Νοέµβριος Εθνικό Μετσόβιο Πολυτεχνείο

SPECIAL FUNCTIONS and POLYNOMIALS

Σύνοψη Προηγούµενου. Ισοδυναµίες, Μερικές ιατάξεις. Σχέσεις Ισοδυναµίας. Σχέσεις, Ιδιότητες, Αναπαράσταση. Ανακλαστικές (a, a) R

ENGR 691/692 Section 66 (Fall 06): Machine Learning Assigned: August 30 Homework 1: Bayesian Decision Theory (solutions) Due: September 13

Spherical Coordinates

6.642 Continuum Electromechanics

Αναερόβια Φυσική Κατάσταση

14 Lesson 2: The Omega Verb - Present Tense

Affine Weyl Groups. Gabriele Nebe. Summerschool GRK 1632, September Lehrstuhl D für Mathematik

Module 5. February 14, h 0min

Solutions - Chapter 4

Gradient Descent for Optimization Problems With Sparse Solutions

Approximation of distance between locations on earth given by latitude and longitude

EE512: Error Control Coding

Thick Film Array Chip Resistor

Σειρά Προβλημάτων 1 Λύσεις

Formal Semantics. 1 Type Logic

ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΥΠΡΟΥ ΤΜΗΜΑ ΠΛΗΡΟΦΟΡΙΚΗΣ EPL035: ΔΟΜΕΣ ΔΕΔΟΜΕΝΩΝ ΚΑΙ ΑΛΓΟΡΙΘΜΟΙ

ΑΛΓΟΡΙΘΜΟΙ Άνοιξη I. ΜΗΛΗΣ

SCITECH Volume 13, Issue 2 RESEARCH ORGANISATION Published online: March 29, 2018

Thin Film Chip Resistors

Thin Film Chip Resistors

Other Test Constructions: Likelihood Ratio & Bayes Tests

Representing Relations Using Digraph

T : g r i l l b a r t a s o s Α Γ Ί Α Σ Σ Ο Φ Ί Α Σ 3, Δ Ρ Α Μ Α. Δ ι α ν ο μ έ ς κ α τ ο ί κ ο ν : 1 2 : 0 0 έ ω ς 0 1 : 0 0 π μ

ΗΥ-150. Προγραμματισμός

ΗΥ-150. Προγραμματισμός

Ψηφιακή Επεξεργασία Φωνής

Section 8.3 Trigonometric Equations

Part III - Pricing A Down-And-Out Call Option

Dong Liu State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China

ο ό Α αφ ο ι α ι οί οι Α αφο ο ι Α αφ ο α ά ο ι αβ Α αφ α Α αφ ί α ό Α αφο ο ι ά ι Α αφ ο α ια ι α ι ο ι ά αι,, ό ι ι ά ι ά α α Ευφυής Έλεγχος 4

Verification. Lecture 12. Martin Zimmermann

b. Use the parametrization from (a) to compute the area of S a as S a ds. Be sure to substitute for ds!

Finite Field Problems: Solutions

Ηλεκτρονικοί Υπολογιστές IV

ΜΑΘΗΜΑΤΙΚΗ ΠΡΟΣΟΜΟΙΩΣΗ ΤΗΣ ΔΥΝΑΜΙΚΗΣ ΤΟΥ ΕΔΑΦΙΚΟΥ ΝΕΡΟΥ ΣΤΗΝ ΠΕΡΙΠΤΩΣΗ ΑΡΔΕΥΣΗΣ ΜΕ ΥΠΟΓΕΙΟΥΣ ΣΤΑΛΑΚΤΗΦΟΡΟΥΣ ΣΩΛΗΝΕΣ ΣΕ ΔΙΑΣΤΡΩΜΕΝΑ ΕΔΑΦΗ

MINIMAL INTUITIONISTIC GENERAL L-FUZZY AUTOMATA

Reminders: linear functions

Example Sheet 3 Solutions

Αποθήκες εδοµένων και Εξόρυξη Γνώσης (Data Warehousing & Data Mining)

HW 3 Solutions 1. a) I use the auto.arima R function to search over models using AIC and decide on an ARMA(3,1)

Δίκτυα Επικοινωνιών ΙΙ: OSPF Configuration

Introduction to Risk Parity and Budgeting

Chapter 1 Fundamentals in Elasticity

Data sheet Thick Film Chip Resistor 5% - RS Series 0201/0402/0603/0805/1206

5.4 The Poisson Distribution.

Copernicus for Local and Regional Authorities

Finitary proof systems for Kozen s µ

ΕΛΛΗΝΙΚΗ ΔΗΜΟΚΡΑΤΙΑ ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΡΗΤΗΣ. Ψηφιακή Οικονομία. Διάλεξη 7η: Consumer Behavior Mαρίνα Μπιτσάκη Τμήμα Επιστήμης Υπολογιστών

The Spiral of Theodorus, Numerical Analysis, and Special Functions

Integrals in cylindrical, spherical coordinates (Sect. 15.7)

Bayesian statistics. DS GA 1002 Probability and Statistics for Data Science.

This is a repository copy of Persistent poverty and children's cognitive development: Evidence from the UK Millennium Cohort Study.

The mass and anisotropy profiles of nearby galaxy clusters from the projected phase-space density

the total number of electrons passing through the lamp.

Κοστολόγηση και Τιμολόγηση των νοσοκομειακών υπηρεσιών, ως εργαλείο για την αποδοτική λειτουργία των Νοσοκομείων. Μπαλασοπούλου Αναστασία, MSc

FORTRAN & Αντικειμενοστραφής Προγραμματισμός ΣΝΜΜ 2017

Μη γράφετε στο πίσω μέρος της σελίδας

Keystroke-Level Model

Transcript:

MONITOR LOGICS FOR QUANTITATIVE MONITOR AUTOMATA Erik Pul Leipzig University August 22, 2017 QuntLA

MOTIVATION 0% 1 item x restocked in shop) every Mondy

MOTIVATION 2% 1 item x restocked in shop) every Mondy purchse events between Mondys

MOTIVATION 5% 1 item x restocked in shop) every Mondy purchse events between Mondys modeled s infinite) sequence over lphbet {restock, demnd}

MOTIVATION 8% 1 item x restocked in shop) every Mondy purchse events between Mondys modeled s infinite) sequence over lphbet {restock, demnd} How mny purchses of x ech week?

MOTIVATION 11% 1 item x restocked in shop) every Mondy purchse events between Mondys modeled s infinite) sequence over lphbet {restock, demnd} How mny purchses of x ech week? miniml demnd

MOTIVATION 14% 1 item x restocked in shop) every Mondy purchse events between Mondys modeled s infinite) sequence over lphbet {restock, demnd} How mny purchses of x ech week? miniml demnd mximl demnd

MOTIVATION 17% 1 item x restocked in shop) every Mondy purchse events between Mondys modeled s infinite) sequence over lphbet {restock, demnd} How mny purchses of x ech week? miniml demnd mximl demnd long-term verge demnd

MOTIVATION 20% 1 item x restocked in shop) every Mondy purchse events between Mondys modeled s infinite) sequence over lphbet {restock, demnd} How mny purchses of x ech week? miniml demnd mximl demnd long-term verge demnd Quntittive Monitor Automt [Chtterjee, Henzinger, Otop 16]

QUANTITATIVE MONITOR AUTOMATA 23% 2 A = Σ, Q, I, F, n, δ, Vl) Quntittive Monitor Automton

QUANTITATIVE MONITOR AUTOMATA 26% 2 A = Σ, Q, I, F, n, δ, Vl) Quntittive Monitor Automton Σ finite lphbet

QUANTITATIVE MONITOR AUTOMATA 29% 2 A = Σ, Q, I, F, n, δ, Vl) Quntittive Monitor Automton Σ finite lphbet Q, I, F sttes initil,finl)

QUANTITATIVE MONITOR AUTOMATA 32% 2 A = Σ, Q, I, F, n, δ, Vl) Quntittive Monitor Automton Σ finite lphbet Q, I, F sttes initil,finl) n number of monitor counters

QUANTITATIVE MONITOR AUTOMATA 35% 2 A = Σ, Q, I, F, n, δ, Vl) Quntittive Monitor Automton Σ finite lphbet Q, I, F sttes initil,finl) n δ Q Σ Q Z {s, t}) n number of monitor counters trnsitions

QUANTITATIVE MONITOR AUTOMATA 38% 2 A = Σ, Q, I, F, n, δ, Vl) Quntittive Monitor Automton Σ finite lphbet Q, I, F sttes initil,finl) n δ Q Σ Q Z {s, t}) n Vl: Z N R { } number of monitor counters trnsitions vlution function

QUANTITATIVE MONITOR AUTOMATA 41% 2 A = Σ, Q, I, F, n, δ, Vl) Quntittive Monitor Automton Σ finite lphbet Q, I, F sttes initil,finl) n δ Q Σ Q Z {s, t}) n Vl: Z N R { } number of monitor counters trnsitions vlution function e.g. minimum, mximum, long-term verge

QUANTITATIVE MONITOR AUTOMATA 44% 3 A = Σ, Q, I, F, n, δ, Vl) Quntittive Monitor Automton δ Q Σ Q Z {s, t}) n trnsitions ) s ) 1 b ) 1 ) t b ) 0 ) s q 0 0 0 s 2 3 t q 1 q 2 q 3 q 4 q 5...

QUANTITATIVE MONITOR AUTOMATA 47% 3 A = Σ, Q, I, F, n, δ, Vl) Quntittive Monitor Automton δ Q Σ Q Z {s, t}) n trnsitions ) s ) 1 b ) 1 ) t b ) 0 ) s q 0 0 q 1 2 0 s 2 3 t q 2 q 3 q 4 q 5...

QUANTITATIVE MONITOR AUTOMATA 50% 3 A = Σ, Q, I, F, n, δ, Vl) Quntittive Monitor Automton δ Q Σ Q Z {s, t}) n trnsitions ) s ) 1 b ) 1 ) t b ) 0 ) s q 0 0 q 1 2 0 q 2 s q 3 5 2 3 t q 4 q 5...

QUANTITATIVE MONITOR AUTOMATA 52% 3 A = Σ, Q, I, F, n, δ, Vl) Quntittive Monitor Automton δ Q Σ Q Z {s, t}) n trnsitions ) s ) 1 b ) 1 ) t b ) 0 ) s q 0 0 q 1 2 0 q 2 s q 3 5 2 q 4 3 q 5 t... 3

QUANTITATIVE MONITOR AUTOMATA 55% 3 A = Σ, Q, I, F, n, δ, Vl) Quntittive Monitor Automton δ Q Σ Q Z {s, t}) n trnsitions ) s ) 1 b ) 1 ) t b ) 0 ) s q 0 0 q 1 2 0 q 2 s q 3 5 2 q 4 3 q 5 t... 3 Weight of run: Vlz i ) i 1 )

QUANTITATIVE MONITOR AUTOMATA 58% 3 A = Σ, Q, I, F, n, δ, Vl) Quntittive Monitor Automton δ Q Σ Q Z {s, t}) n trnsitions ) s ) 1 b ) 1 ) t b ) 0 ) s q 0 0 q 1 2 0 q 2 s q 3 5 2 q 4 3 q 5 t... 3 Weight of run: Vlz i ) i 1 ) Weight of ω-word: infimum over ll runs

EXAMPLE 61% 5 demnd, 1, 0) restock, t, s) restock, s, 0) q 0 q 1 q 2 restock, s, t) demnd, 0, 1)

EXAMPLE 64% 5 demnd, 1, 0) restock, t, s) restock, s, 0) q 0 q 1 q 2 restock, s, t) demnd, 0, 1) sequence 5, 3, 7, 4,... of demnds per week

EXAMPLE 67% 5 demnd, 1, 0) restock, t, s) restock, s, 0) q 0 q 1 q 2 restock, s, t) demnd, 0, 1) sequence 5, 3, 7, 4,... of demnds per week vlution function to compute long-time verge, minimum,...

MONITOR LOGICS FOR QUANTITATIVE MONITOR AUTOMATA 70% 6 β ::= P x) x y x X β β β x.β X.β

MONITOR LOGICS FOR QUANTITATIVE MONITOR AUTOMATA 73% 6 β ::= P x) x y x X β β β x.β X.β ψ ::= k β? ψ : ψ β? ψ 1 : ψ 2 w) = { ψ 1 w) ψ 2 w) if w = β otherwise

MONITOR LOGICS FOR QUANTITATIVE MONITOR AUTOMATA 76% 6 β ::= P x) x y x X β β β x.β X.β ψ ::= k β? ψ : ψ ζ x ::= β? ζ x : ζ x x,z y.ψ β? ψ 1 : ψ 2 w) = { ψ 1 w) ψ 2 w) if w = β otherwise

MONITOR LOGICS FOR QUANTITATIVE MONITOR AUTOMATA 79% 6 β ::= P x) x y x X β β β x.β X.β ψ ::= k β? ψ : ψ ζ x ::= β? ζ x : ζ x x,z y.ψ ϕ ::= β? ϕ : ϕ minϕ, ϕ) inf x.ϕ inf X.ϕ Vl x.ζ x β? ψ 1 : ψ 2 w) = { ψ 1 w) ψ 2 w) if w = β otherwise Vl x.ζ x w) = Vl ζ x w[x i])) i 1 )

MONITOR LOGICS FOR QUANTITATIVE MONITOR AUTOMATA 82% 6 β ::= P x) x y x X β β β x.β X.β ψ ::= k β? ψ : ψ ζ x ::= β? ζ x : ζ x x,z y.ψ ϕ ::= β? ϕ : ϕ minϕ, ϕ) inf x.ϕ inf X.ϕ Vl x.ζ x x,z y.ψ w) =

MONITOR LOGICS FOR QUANTITATIVE MONITOR AUTOMATA 85% 6 β ::= P x) x y x X β β β x.β X.β ψ ::= k β? ψ : ψ ζ x ::= β? ζ x : ζ x x,z y.ψ ϕ ::= β? ϕ : ϕ minϕ, ϕ) inf x.ϕ inf X.ϕ Vl x.ζ x x 1 i=x+1 ψ w[y i]) x,z y.ψ w) = if x Z nd x Z : x > x otherwise

MONITOR LOGICS FOR QUANTITATIVE MONITOR AUTOMATA 88% 6 β ::= P x) x y x X β β β x.β X.β ψ ::= k β? ψ : ψ ζ x ::= β? ζ x : ζ x x,z y.ψ ϕ ::= β? ϕ : ϕ minϕ, ϕ) inf x.ϕ inf X.ϕ Vl x.ζ x x 1 i=x+1 ψ w[y i]) x,z y.ψ w) = if x Z nd x Z : x > x otherwise x,z ) ) ϕ = inf Z. z.z Z P restock z))? Vl x. y.1 :

MONITOR LOGICS FOR QUANTITATIVE MONITOR AUTOMATA 91% 6 β ::= P x) x y x X β β β x.β X.β ψ ::= k β? ψ : ψ ζ x ::= β? ζ x : ζ x x,z y.ψ ϕ ::= β? ϕ : ϕ minϕ, ϕ) inf x.ϕ inf X.ϕ Vl x.ζ x Muller utomt: Vl x.ψ z i = ψ w[x i]) Vl x.ψ w) = Vlz 0, z 1, z 2,...)

MONITOR LOGICS FOR QUANTITATIVE MONITOR AUTOMATA 94% 6 β ::= P x) x y x X β β β x.β X.β ψ ::= k β? ψ : ψ ζ x ::= β? ζ x : ζ x x,z y.ψ ϕ ::= β? ϕ : ϕ minϕ, ϕ) inf x.ϕ inf X.ϕ Vl x.ζ x Muller utomt: Vl x.ψ z i = ψ w[x i]) Vl x.ψ w) = Vlz 0, z 1, z 2,...) w = 0 1 2 3 4... 0 z 0 ) ) ) ) ) 1 2 3 4... z 1 z 2 z 3 z 4

MONITOR LOGICS FOR QUANTITATIVE MONITOR AUTOMATA 97% 6 β ::= P x) x y x X β β β x.β X.β ψ ::= k β? ψ : ψ ζ x ::= β? ζ x : ζ x x,z y.ψ ϕ ::= β? ϕ : ϕ minϕ, ϕ) inf x.ϕ inf X.ϕ Vl x.ζ x Muller utomt: Vl x.ψ z i = ψ w[x i]) Vl x.ψ w) = Vlz 0, z 1, z 2,...) w = 0 1 2 3 4... 0 z 0 ) ) ) ) ) 1 2 3 4... z 1 z 2 z 3 z 4 correct weights is recognizble property

MONITOR LOGICS FOR QUANTITATIVE MONITOR AUTOMATA β ::= P x) x y x X β β β x.β X.β ψ ::= k β? ψ : ψ ζ x ::= β? ζ x : ζ x x,z y.ψ ϕ ::= β? ϕ : ϕ minϕ, ϕ) inf x.ϕ inf X.ϕ Vl x.ζ x QMA: Vl x.ζ x ϕ = Vl x. x,z y.1 ) restock s 1 demnd 1 0 demnd 1 0 restock t s 1 demnd 1 0 6 100%