Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userOn Measure Quantifiers in First-Order Arithmetic (Long Version)
125
0
0.0
(
0
)
Added by
Paolo Pistone
Publication date
2021
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
We study the logic obtained by endowing the language of first-order arithmetic with second-order measure quantifiers. This new kind of quantification allows us to express that the argument formula is true in a certain portion of all possible interpretations of the quantified variable. We show that first-order arithmetic with measure quantifiers is capable of formalizing simple results from probability theory and, most importantly, of representing every recursive random function. Moreover, we introduce a realizability interpretation of this logic in which programs have access to an oracle from the Cantor space.
rate research
Read More
Type-two constructions abound in cryptography: adversaries for encryption and authentication schemes, if active, are modeled as algorithms having access to oracles, i.e. as second-order algorithms. But how about making cryptographic schemes themselves higher-order? This paper gives an answer to this question, by first describing why higher-order cryptography is interesting as an object of study, then showing how the concept of probabilistic polynomial time algorithm can be generalized so as to encompass algorithms of order strictly higher than two, and finally proving some positive and negative results about the existence of higher-order cryptographic primitives, namely authentication schemes and pseudorandom functions.
Automated program verification is a difficult problem. It is undecidable even for transition systems over Linear Integer Arithmetic (LIA). Extending the transition system with theory of Arrays, further complicates the problem by requiring inference and reasoning with universally quantified formulas. In this paper, we present a new algorithm, Quic3, that extends IC3 to infer universally quantified invariants over the combined theory of LIA and Arrays. Unlike other approaches that use either IC3 or an SMT solver as a black box, Quic3 carefully manages quantified generalization (to construct quantified invariants) and quantifier instantiation (to detect convergence in the presence of quantifiers). While Quic3 is not guaranteed to converge, it is guaranteed to make progress by exploring longer and longer executions. We have implemented Quic3 within the Constrained Horn Clause solver engine of Z3 and experimented with it by applying Quic3 to verifying a variety of public benchmarks of array manipulating C programs.
We investigate the complexity consequences of adding pointer arithmetic to separation logic. Specifically, we study extensions of the points-to fragment of symbolic-heap separation logic with various forms of Presburger arithmetic constraints. Most significantly, we find that, even in the minimal case when we allow only conjunctions of simple difference constraints (xleq x+k) where k is an integer, polynomial-time decidability is already impossible: satisfiability becomes NP-complete, while quantifier-free entailment becomes coNP-complete and quantified entailment becomes P2-complete (P2 is the second class in the polynomial-time hierarchy) In fact we prove that the upper bound is the same, P2, even for the full pointer arithmetic but with a fixed pointer offset, where we allow any Boolean combinations of the elementary formulas (x=x+k0), (xleq x+k0), and (x<x+k0), and, in addition to the points-to formulas, we allow spatial formulas of the arrays the length of which is bounded by k0 and lists which length is bounded by k0, etc, where k0 is a fixed integer. However, if we allow a significantly more expressive form of pointer arithmetic - namely arbitrary Boolean combinations of elementary formulas over arbitrary pointer sums - then the complexity increase is relatively modest for satisfiability and quantifier-free entailment: they are still NP-complete and coNP-complete respectively, and the complexity appears to increase drastically for quantified entailments.
We graft synchronization onto Girards Geometry of Interaction in its most concrete form, namely token machines. This is realized by introducing proof-nets for SMLL, an extension of multiplicative linear logic with a specific construct modeling synchronization points, and of a multi-token abstract machine model for it. Interestingly, the correctness criterion ensures the absence of deadlocks along reduction and in the underlying machine, this way linking logical and operational properties.
The space complexity of functional programs is not well understood. In particular, traditional implementation techniques are tailored to time efficiency, and space efficiency induces time inefficiencies, as it prefers re-computing to saving. Girards geometry of interaction underlies an alternative approach based on the interaction abstract machine (IAM), claimed as space efficient in the literature. It has also been conjectured to provide a reasonable notion of space for the lambda-calculus, but such an important result seems to be elusive. In this paper we introduce a new intersection type system precisely measuring the space consumption of the IAM on the typed term. Intersection types have been repeatedly used to measure time, which they achieve by dropping idempotency, turning intersections into multisets. Here we show that the space consumption of the IAM is connected to a further structural modification, turning multisets into trees. Tree intersection types lead to a finer understanding of some space complexity results from the literature. They also shed new light on the conjecture about reasonable space: we show that the usual way of encoding Turing machines into the lambda calculus cannot be used to prove that the space of the IAM is a reasonable cost model.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
