1 Boolean Logic
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Laced Boolean Functions and Subset Sum Problems in Finite Fields
Laced Boolean functions and subset sum problems in finite fields David Canright1, Sugata Gangopadhyay2 Subhamoy Maitra3, Pantelimon Stanic˘ a˘1 1 Department of Applied Mathematics, Naval Postgraduate School Monterey, CA 93943{5216, USA; fdcanright,[email protected] 2 Department of Mathematics, Indian Institute of Technology Roorkee 247667 INDIA; [email protected] 3 Applied Statistics Unit, Indian Statistical Institute 203 B. T. Road, Calcutta 700 108, INDIA; [email protected] March 13, 2011 Abstract In this paper, we investigate some algebraic and combinatorial properties of a special Boolean function on n variables, defined us- ing weighted sums in the residue ring modulo the least prime p ≥ n. We also give further evidence to a question raised by Shparlinski re- garding this function, by computing accurately the Boolean sensitivity, thus settling the question for prime number values p = n. Finally, we propose a generalization of these functions, which we call laced func- tions, and compute the weight of one such, for every value of n. Mathematics Subject Classification: 06E30,11B65,11D45,11D72 Key Words: Boolean functions; Hamming weight; Subset sum problems; residues modulo primes. 1 1 Introduction Being interested in read-once branching programs, Savicky and Zak [7] were led to the definition and investigation, from a complexity point of view, of a special Boolean function based on weighted sums in the residue ring modulo a prime p. Later on, a modification of the same function was used by Sauerhoff [6] to show that quantum read-once branching programs are exponentially more powerful than classical read-once branching programs. Shparlinski [8] used exponential sums methods to find bounds on the Fourier coefficients, and he posed several open questions, which are the motivation of this work. -
Automated Unbounded Verification of Stateful Cryptographic Protocols
Automated Unbounded Verification of Stateful Cryptographic Protocols with Exclusive OR Jannik Dreier Lucca Hirschi Sasaˇ Radomirovic´ Ralf Sasse Universite´ de Lorraine Department of School of Department of CNRS, Inria, LORIA Computer Science Science and Engineering Computer Science F-54000 Nancy ETH Zurich University of Dundee ETH Zurich France Switzerland UK Switzerland [email protected] [email protected] [email protected] [email protected] Abstract—Exclusive-or (XOR) operations are common in cryp- on widening the scope of automated protocol verification tographic protocols, in particular in RFID protocols and elec- by extending the class of properties that can be verified to tronic payment protocols. Although there are numerous appli- include, e.g., equivalence properties [19], [24], [27], [51], cations, due to the inherent complexity of faithful models of XOR, there is only limited tool support for the verification of [14], extending the adversary model with complex forms of cryptographic protocols using XOR. compromise [13], or extending the expressiveness of protocols, The TAMARIN prover is a state-of-the-art verification tool e.g., by allowing different sessions to update a global, mutable for cryptographic protocols in the symbolic model. In this state [6], [43]. paper, we improve the underlying theory and the tool to deal with an equational theory modeling XOR operations. The XOR Perhaps most significant is the support for user-specified theory can be freely combined with all equational theories equational theories allowing for the modeling of particular previously supported, including user-defined equational theories. cryptographic primitives [21], [37], [48], [24], [35]. -
Analysis of Boolean Functions and Its Applications to Topics Such As Property Testing, Voting, Pseudorandomness, Gaussian Geometry and the Hardness of Approximation
Analysis of Boolean Functions Notes from a series of lectures by Ryan O’Donnell Guest lecture by Per Austrin Barbados Workshop on Computational Complexity February 26th – March 4th, 2012 Organized by Denis Th´erien Scribe notes by Li-Yang Tan arXiv:1205.0314v1 [cs.CC] 2 May 2012 Contents 1 Linearity testing and Arrow’s theorem 3 1.1 TheFourierexpansion ............................. 3 1.2 Blum-Luby-Rubinfeld. .. .. .. .. .. .. .. .. 7 1.3 Votingandinfluence .............................. 9 1.4 Noise stability and Arrow’s theorem . ..... 12 2 Noise stability and small set expansion 15 2.1 Sheppard’s formula and Stabρ(MAJ)...................... 15 2.2 Thenoisyhypercubegraph. 16 2.3 Bonami’slemma................................. 18 3 KKL and quasirandomness 20 3.1 Smallsetexpansion ............................... 20 3.2 Kahn-Kalai-Linial ............................... 21 3.3 Dictator versus Quasirandom tests . ..... 22 4 CSPs and hardness of approximation 26 4.1 Constraint satisfaction problems . ...... 26 4.2 Berry-Ess´een................................... 27 5 Majority Is Stablest 30 5.1 Borell’s isoperimetric inequality . ....... 30 5.2 ProofoutlineofMIST ............................. 32 5.3 Theinvarianceprinciple . 33 6 Testing dictators and UGC-hardness 37 1 Linearity testing and Arrow’s theorem Monday, 27th February 2012 Rn Open Problem [Guy86, HK92]: Let a with a 2 = 1. Prove Prx 1,1 n [ a, x • ∈ k k ∈{− } |h i| ≤ 1] 1 . ≥ 2 Open Problem (S. Srinivasan): Suppose g : 1, 1 n 2 , 1 where g(x) 2 , 1 if • {− } →± 3 ∈ 3 n x n and g(x) 1, 2 if n x n . Prove deg( f)=Ω(n). i=1 i ≥ 2 ∈ − − 3 i=1 i ≤− 2 P P In this workshop we will study the analysis of boolean functions and its applications to topics such as property testing, voting, pseudorandomness, Gaussian geometry and the hardness of approximation. -
Probabilistic Boolean Logic, Arithmetic and Architectures
PROBABILISTIC BOOLEAN LOGIC, ARITHMETIC AND ARCHITECTURES A Thesis Presented to The Academic Faculty by Lakshmi Narasimhan Barath Chakrapani In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the School of Computer Science, College of Computing Georgia Institute of Technology December 2008 PROBABILISTIC BOOLEAN LOGIC, ARITHMETIC AND ARCHITECTURES Approved by: Professor Krishna V. Palem, Advisor Professor Trevor Mudge School of Computer Science, College Department of Electrical Engineering of Computing and Computer Science Georgia Institute of Technology University of Michigan, Ann Arbor Professor Sung Kyu Lim Professor Sudhakar Yalamanchili School of Electrical and Computer School of Electrical and Computer Engineering Engineering Georgia Institute of Technology Georgia Institute of Technology Professor Gabriel H. Loh Date Approved: 24 March 2008 College of Computing Georgia Institute of Technology To my parents The source of my existence, inspiration and strength. iii ACKNOWLEDGEMENTS आचायातर् ्पादमादे पादं िशंयः ःवमेधया। पादं सॄचारयः पादं कालबमेणच॥ “One fourth (of knowledge) from the teacher, one fourth from self study, one fourth from fellow students and one fourth in due time” 1 Many people have played a profound role in the successful completion of this disser- tation and I first apologize to those whose help I might have failed to acknowledge. I express my sincere gratitude for everything you have done for me. I express my gratitude to Professor Krisha V. Palem, for his energy, support and guidance throughout the course of my graduate studies. Several key results per- taining to the semantic model and the properties of probabilistic Boolean logic were due to his brilliant insights. -
CS 50010 Module 1
CS 50010 Module 1 Ben Harsha Apr 12, 2017 Course details ● Course is split into 2 modules ○ Module 1 (this one): Covers basic data structures and algorithms, along with math review. ○ Module 2: Probability, Statistics, Crypto ● Goal for module 1: Review basics needed for CS and specifically Information Security ○ Review topics you may not have seen in awhile ○ Cover relevant topics you may not have seen before IMPORTANT This course cannot be used on a plan of study except for the IS Professional Masters program Administrative details ● Office: HAAS G60 ● Office Hours: 1:00-2:00pm in my office ○ Can also email for an appointment, I’ll be in there often ● Course website ○ cs.purdue.edu/homes/bharsha/cs50010.html ○ Contains syllabus, homeworks, and projects Grading ● Module 1 and module are each 50% of the grade for CS 50010 ● Module 1 ○ 55% final ○ 20% projects ○ 20% assignments ○ 5% participation Boolean Logic ● Variables/Symbols: Can only be used to represent 1 or 0 ● Operations: ○ Negation ○ Conjunction (AND) ○ Disjunction (OR) ○ Exclusive or (XOR) ○ Implication ○ Double Implication ● Truth Tables: Can be defined for all of these functions Operations ● Negation (¬p, p, pC, not p) - inverts the symbol. 1 becomes 0, 0 becomes 1 ● Conjunction (p ∧ q, p && q, p and q) - true when both p and q are true. False otherwise ● Disjunction (p v q, p || q, p or q) - True if at least one of p or q is true ● Exclusive Or (p xor q, p ⊻ q, p ⊕ q) - True if exactly one of p or q is true ● Implication (p → q) - True if p is false or q is true (q v ¬p) ● -
Predicate Logic
Predicate Logic Laura Kovács Recap: Boolean Algebra and Propositional Logic 0, 1 True, False (other notation: t, f) boolean variables a ∈{0,1} atomic formulas (atoms) p∈{True,False} boolean operators ¬, ∧, ∨, fl, ñ logical connectives ¬, ∧, ∨, fl, ñ boolean functions: propositional formulas: • 0 and 1 are boolean functions; • True and False are propositional formulas; • boolean variables are boolean functions; • atomic formulas are propositional formulas; • if a is a boolean function, • if a is a propositional formula, then ¬a is a boolean function; then ¬a is a propositional formula; • if a and b are boolean functions, • if a and b are propositional formulas, then a∧b, a∨b, aflb, añb are boolean functions. then a∧b, a∨b, aflb, añb are propositional formulas. truth value of a boolean function truth value of a propositional formula (truth tables) (truth tables) Recap: Boolean Algebra and Propositional Logic 0, 1 True, False (other notation: t, f) boolean variables a ∈{0,1} atomic formulas (atoms) p∈{True,False} boolean operators ¬, ∧, ∨, fl, ñ logical connectives ¬, ∧, ∨, fl, ñ boolean functions: propositional formulas (propositions, Aussagen ): • 0 and 1 are boolean functions; • True and False are propositional formulas; • boolean variables are boolean functions; • atomic formulas are propositional formulas; • if a is a boolean function, • if a is a propositional formula, then ¬a is a boolean function; then ¬a is a propositional formula; • if a and b are boolean functions, • if a and b are propositional formulas, then a∧b, a∨b, aflb, añb are -
Least Sensitive (Most Robust) Fuzzy “Exclusive Or” Operations
Need for Fuzzy . A Crisp \Exclusive Or" . Need for the Least . Least Sensitive For t-Norms and t- . Definition of a Fuzzy . (Most Robust) Main Result Interpretation of the . Fuzzy \Exclusive Or" Fuzzy \Exclusive Or" . Operations Home Page Title Page 1 2 Jesus E. Hernandez and Jaime Nava JJ II 1Department of Electrical and J I Computer Engineering 2Department of Computer Science Page1 of 13 University of Texas at El Paso Go Back El Paso, TX 79968 [email protected] Full Screen [email protected] Close Quit Need for Fuzzy . 1. Need for Fuzzy \Exclusive Or" Operations A Crisp \Exclusive Or" . Need for the Least . • One of the main objectives of fuzzy logic is to formalize For t-Norms and t- . commonsense and expert reasoning. Definition of a Fuzzy . • People use logical connectives like \and" and \or". Main Result • Commonsense \or" can mean both \inclusive or" and Interpretation of the . \exclusive or". Fuzzy \Exclusive Or" . Home Page • Example: A vending machine can produce either a coke or a diet coke, but not both. Title Page • In mathematics and computer science, \inclusive or" JJ II is the one most frequently used as a basic operation. J I • Fact: \Exclusive or" is also used in commonsense and Page2 of 13 expert reasoning. Go Back • Thus: There is a practical need for a fuzzy version. Full Screen • Comment: \exclusive or" is actively used in computer Close design and in quantum computing algorithms Quit Need for Fuzzy . 2. A Crisp \Exclusive Or" Operation: A Reminder A Crisp \Exclusive Or" . Need for the Least . -
Satisfiability 6 the Decision Problem 7
Satisfiability Difficult Problems Dealing with SAT Implementation Klaus Sutner Carnegie Mellon University 2020/02/04 Finding Hard Problems 2 Entscheidungsproblem to the Rescue 3 The Entscheidungsproblem is solved when one knows a pro- cedure by which one can decide in a finite number of operations So where would be look for hard problems, something that is eminently whether a given logical expression is generally valid or is satis- decidable but appears to be outside of P? And, we’d like the problem to fiable. The solution of the Entscheidungsproblem is of funda- be practical, not some monster from CRT. mental importance for the theory of all fields, the theorems of which are at all capable of logical development from finitely many The Circuit Value Problem is a good indicator for the right direction: axioms. evaluating Boolean expressions is polynomial time, but relatively difficult D. Hilbert within P. So can we push CVP a little bit to force it outside of P, just a little bit? In a sense, [the Entscheidungsproblem] is the most general Say, up into EXP1? problem of mathematics. J. Herbrand Exploiting Difficulty 4 Scaling Back 5 Herbrand is right, of course. As a research project this sounds like a Taking a clue from CVP, how about asking questions about Boolean fiasco. formulae, rather than first-order? But: we can turn adversity into an asset, and use (some version of) the Probably the most natural question that comes to mind here is Entscheidungsproblem as the epitome of a hard problem. Is ϕ(x1, . , xn) a tautology? The original Entscheidungsproblem would presumable have included arbitrary first-order questions about number theory. -
Problems and Comments on Boolean Algebras Rosen, Fifth Edition: Chapter 10; Sixth Edition: Chapter 11 Boolean Functions
Problems and Comments on Boolean Algebras Rosen, Fifth Edition: Chapter 10; Sixth Edition: Chapter 11 Boolean Functions Section 10. 1, Problems: 1, 2, 3, 4, 10, 11, 29, 36, 37 (fifth edition); Section 11.1, Problems: 1, 2, 5, 6, 12, 13, 31, 40, 41 (sixth edition) The notation ""forOR is bad and misleading. Just think that in the context of boolean functions, the author uses instead of ∨.The integers modulo 2, that is ℤ2 0,1, have an addition where 1 1 0 while 1 ∨ 1 1. AsetA is partially ordered by a binary relation ≤, if this relation is reflexive, that is a ≤ a holds for every element a ∈ S,it is transitive, that is if a ≤ b and b ≤ c hold for elements a,b,c ∈ S, then one also has that a ≤ c, and ≤ is anti-symmetric, that is a ≤ b and b ≤ a can hold for elements a,b ∈ S only if a b. The subsets of any set S are partially ordered by set inclusion. that is the power set PS,⊆ is a partially ordered set. A partial ordering on S is a total ordering if for any two elements a,b of S one has that a ≤ b or b ≤ a. The natural numbers ℕ,≤ with their ordinary ordering are totally ordered. A bounded lattice L is a partially ordered set where every finite subset has a least upper bound and a greatest lower bound.The least upper bound of the empty subset is defined as 0, it is the smallest element of L. -
Logic, Proofs
CHAPTER 1 Logic, Proofs 1.1. Propositions A proposition is a declarative sentence that is either true or false (but not both). For instance, the following are propositions: “Paris is in France” (true), “London is in Denmark” (false), “2 < 4” (true), “4 = 7 (false)”. However the following are not propositions: “what is your name?” (this is a question), “do your homework” (this is a command), “this sentence is false” (neither true nor false), “x is an even number” (it depends on what x represents), “Socrates” (it is not even a sentence). The truth or falsehood of a proposition is called its truth value. 1.1.1. Connectives, Truth Tables. Connectives are used for making compound propositions. The main ones are the following (p and q represent given propositions): Name Represented Meaning Negation p “not p” Conjunction p¬ q “p and q” Disjunction p ∧ q “p or q (or both)” Exclusive Or p ∨ q “either p or q, but not both” Implication p ⊕ q “if p then q” Biconditional p → q “p if and only if q” ↔ The truth value of a compound proposition depends only on the value of its components. Writing F for “false” and T for “true”, we can summarize the meaning of the connectives in the following way: 6 1.1. PROPOSITIONS 7 p q p p q p q p q p q p q T T ¬F T∧ T∨ ⊕F →T ↔T T F F F T T F F F T T F T T T F F F T F F F T T Note that represents a non-exclusive or, i.e., p q is true when any of p, q is true∨ and also when both are true. -
Hardware Abstract the Logic Gates References Results Transistors Through the Years Acknowledgements
The Practical Applications of Logic Gates in Computer Science Courses Presenters: Arash Mahmoudian, Ashley Moser Sponsored by Prof. Heda Samimi ABSTRACT THE LOGIC GATES Logic gates are binary operators used to simulate electronic gates for design of circuits virtually before building them with-real components. These gates are used as an instrumental foundation for digital computers; They help the user control a computer or similar device by controlling the decision making for the hardware. A gate takes in OR GATE AND GATE NOT GATE an input, then it produces an algorithm as to how The OR gate is a logic gate with at least two An AND gate is a consists of at least two A NOT gate, also known as an inverter, has to handle the output. This process prevents the inputs and only one output that performs what inputs and one output that performs what is just a single input with rather simple behavior. user from having to include a microprocessor for is known as logical disjunction, meaning that known as logical conjunction, meaning that A NOT gate performs what is known as logical negation, which means that if its input is true, decision this making. Six of the logic gates used the output of this gate is true when any of its the output of this gate is false if one or more of inputs are true. If all the inputs are false, the an AND gate's inputs are false. Otherwise, if then the output will be false. Likewise, are: the OR gate, AND gate, NOT gate, XOR gate, output of the gate will also be false. -
Boolean Algebra
Boolean Algebra ECE 152A – Winter 2012 Reading Assignment Brown and Vranesic 2 Introduction to Logic Circuits 2.5 Boolean Algebra 2.5.1 The Venn Diagram 2.5.2 Notation and Terminology 2.5.3 Precedence of Operations 2.6 Synthesis Using AND, OR and NOT Gates 2.6.1 Sum-of-Products and Product of Sums Forms January 11, 2012 ECE 152A - Digital Design Principles 2 Reading Assignment Brown and Vranesic (cont) 2 Introduction to Logic Circuits (cont) 2.7 NAND and NOR Logic Networks 2.8 Design Examples 2.8.1 Three-Way Light Control 2.8.2 Multiplexer Circuit January 11, 2012 ECE 152A - Digital Design Principles 3 Reading Assignment Roth 2 Boolean Algebra 2.3 Boolean Expressions and Truth Tables 2.4 Basic Theorems 2.5 Commutative, Associative, and Distributive Laws 2.6 Simplification Theorems 2.7 Multiplying Out and Factoring 2.8 DeMorgan’s Laws January 11, 2012 ECE 152A - Digital Design Principles 4 Reading Assignment Roth (cont) 3 Boolean Algebra (Continued) 3.1 Multiplying Out and Factoring Expressions 3.2 Exclusive-OR and Equivalence Operation 3.3 The Consensus Theorem 3.4 Algebraic Simplification of Switching Expressions January 11, 2012 ECE 152A - Digital Design Principles 5 Reading Assignment Roth (cont) 4 Applications of Boolean Algebra Minterm and Maxterm Expressions 4.3 Minterm and Maxterm Expansions 7 Multi-Level Gate Circuits NAND and NOR Gates 7.2 NAND and NOR Gates 7.3 Design of Two-Level Circuits Using NAND and NOR Gates 7.5 Circuit Conversion Using Alternative Gate Symbols January 11, 2012 ECE 152A - Digital Design Principles 6 Boolean Algebra Axioms of Boolean Algebra Axioms generally presented without proof 0 · 0 = 0 1 + 1 = 1 1 · 1 = 1 0 + 0 = 0 0 · 1 = 1 · 0 = 0 1 + 0 = 0 + 1 = 1 if X = 0, then X’ = 1 if X = 1, then X’ = 0 January 11, 2012 ECE 152A - Digital Design Principles 7 Boolean Algebra The Principle of Duality from Zvi Kohavi, Switching and Finite Automata Theory “We observe that all the preceding properties are grouped in pairs.