We consider the isotropic or anisotropic XY spin chain in the presence of a transversal random magnetic field, with parameters given by random variables. It is shown that eigenfunction correlator localization of the corresponding effective one-particle Hamiltonian implies a uniform area law bound in expectation for the bipartite entanglement entropy of all eigenstates of the XY chain, i.e., a form of many-body localization at all energies. Here, entanglement with respect to arbitrary connected subchains of the chain can be considered. Applications where the required eigenfunction correlator bounds are known include the isotropic XY chain in random field as well as the anisotropic chain in strong or strongly disordered random field.
Equipped with these tools we can then complete the proof of the area law ) in . This is accomplished by using an extension of an argument from @cite_50 to reduce the claim to the eigencorrelator localization bound ).
@cite_50
1511737804 1986909116 1963772255 171295454
Motivated by applications to program verification, we study a decision procedure for satisfiability in an expressive fragment of a theory of arrays, which is parameterized by the theories of the array elements. The decision procedure reduces satisfiability of a formula of the fragment to satisfiability of an equisatisfiable quantifier-free formula in the combined theory of equality with uninterpreted functions (EUF), Presburger arithmetic, and the element theories. This fragment allows a constrained use of universal quantification, so that one quantifier alternation is allowed, with some syntactic restrictions. It allows expressing, for example, that an assertion holds for all elements in a given index range, that two arrays are equal in a given range, or that an array is sorted. We demonstrate its expressiveness through applications to verification of sorting algorithms and parameterized systems. We also prove that satisfiability is undecidable for several natural extensions to the fragment. Finally, we describe our implementation in the πVC verifying compiler. In this paper we study the global localization problem in SLAM: the determination of the vehicle location in a previously mapped environment with no other prior information. We show that, using a grid sampling representation of the configuration space, it is possible to evaluate all vehicle location hypotheses in the environment (up to a certain resolution) with a computational cost that is bilinear: linear both in the number of map features and in the number of sensor measurements. We propose a pairing-driven algorithm that considers only individual measurement-feature pairings and thus, in contrast with current correspondence space algorithms, it avoids searching in the exponential correspondence space. It uses a voting strategy that accumulates evidence for each vehicle location hypothesis, assuring robustness to noise in the sensor measurements and environment models. The general nature of the proposed strategy allows the consideration of different types of features and sensor measurements. Using the popular Victoria Park dataset, we compare its performance with location-driven algorithms where the solution space is usually randomly sampled. We show that the proposed pairing-driven technique is computationally more efficient in proportion to the density of features in the environment. Starting with Moggi's work on monads as refined to Lawvere theories, we give a general construct that extends denotational semantics for a global computational effect canonically to yield denotational semantics for a corresponding local computational effect. Our leading example yields a construction of the usual denotational semantics for local state from that for global state. Given any Lawvere theory L, possibly countable and possibly enriched, we first give a universal construction that extends L, hence the global operations and equations of a given effect, to incorporate worlds of arbitrary finite size. Then, making delicate use of the final comodel of the ordinary Lawvere theory L, we give a construct that uniformly allows us to model block, the universality of the final comodel yielding a universal property of the construct. We illustrate both the universal extension of L and the canonical construction of block by seeing how they work in the case of state. We present a technique for using infeasible program paths to automatically infer Range Predicates that describe properties of unbounded array segments. First, we build proofs showing the infeasibility of the paths, using axioms that precisely encode the high-level (but informal) rules with which programmers reason about arrays. Next, we mine the proofs for Craig Interpolants which correspond to predicates that refute the particular counterexample path. By embedding the predicate inference technique within a Counterexample-Guided Abstraction-Refinement (CEGAR) loop, we obtain a method for verifying data-sensitive safety properties whose precision is tailored in a program- and property-sensitive manner. Though the axioms used are simple, we show that the method suffices to prove a variety of array-manipulating programs that were previously beyond automatic model checkers.