QRAT+: Generalizing QRAT by a More Powerful QBF Redundancy Property

The QRAT (quantified resolution asymmetric tautology) proof system simulates virtually all inference rules applied in state of the art quantified Boolean formula (QBF) reasoning tools. It consists of rules to rewrite a QBF by adding and deleting clauses and universal literals that have a certain redundancy property. To check for this redundancy property in QRAT, propositional unit propagation (UP) is applied to the quantifier free, i.e., propositional part of the QBF. We generalize the redundancy property in the QRAT system by QBF specific UP (QUP). QUP extends UP by the universal reduction operation to eliminate universal literals from clauses. We apply QUP to an abstraction of the QBF where certain universal quantifiers are converted into existential ones. This way, we obtain a generalization of QRAT we call QRAT+. The redundancy property in QRAT+ based on QUP is more powerful than the one in QRAT based on UP. We report on proof theoretical improvements and experimental results to illustrate the benefits of QRAT+ for QBF preprocessing.Comment: preprint of a paper to be published at IJCAR 2018, LNCS, Springer, including appendi

Efficient Certified Resolution Proof Checking

We present a novel propositional proof tracing format that eliminates complex processing, thus enabling efficient (formal) proof checking. The benefits of this format are demonstrated by implementing a proof checker in C, which outperforms a state-of-the-art checker by two orders of magnitude. We then formalize the theory underlying propositional proof checking in Coq, and extract a correct-by-construction proof checker for our format from the formalization. An empirical evaluation using 280 unsatisfiable instances from the 2015 and 2016 SAT competitions shows that this certified checker usually performs comparably to a state-of-the-art non-certified proof checker. Using this format, we formally verify the recent 200 TB proof of the Boolean Pythagorean Triples conjecture

Entanglement dynamics of two qubits under the influence of external kicks and Gaussian pulses

We have investigated the dynamics of entanglement between two spin-1/2 qubits that are subject to independent kick and Gaussian pulse type external magnetic fields analytically as well as numerically. Dyson time ordering effect on the dynamics is found to be important for the sequence of kicks. We show that "almost-steady" high entanglement can be created between two initially unentangled qubits by using carefully designed kick or pulse sequences

Encoding Redundancy for Satisfaction-Driven Clause Learning

Satisfaction-Driven Clause Learning (SDCL) is a recent SAT solving paradigm that aggressively trims the search space of possible truth assignments. To determine if the SAT solver is currently exploring a dispensable part of the search space, SDCL uses the so-called positive reduct of a formula: The positive reduct is an easily solvable propositional formula that is satisfiable if the current assignment of the solver can be safely pruned from the search space. In this paper, we present two novel variants of the positive reduct that allow for even more aggressive pruning. Using one of these variants allows SDCL to solve harder problems, in particular the well-known Tseitin formulas and mutilated chessboard problems. For the first time, we are able to generate and automatically check clausal proofs for large instances of these problems

Maximum Causal Entropy Specification Inference from Demonstrations

In many settings (e.g., robotics) demonstrations provide a natural way to specify tasks; however, most methods for learning from demonstrations either do not provide guarantees that the artifacts learned for the tasks, such as rewards or policies, can be safely composed and/or do not explicitly capture history dependencies. Motivated by this deficit, recent works have proposed learning Boolean task specifications, a class of Boolean non-Markovian rewards which admit well-defined composition and explicitly handle historical dependencies. This work continues this line of research by adapting maximum causal entropy inverse reinforcement learning to estimate the posteriori probability of a specification given a multi-set of demonstrations. The key algorithmic insight is to leverage the extensive literature and tooling on reduced ordered binary decision diagrams to efficiently encode a time unrolled Markov Decision Process. This enables transforming a naive exponential time algorithm into a polynomial time algorithm.Comment: Computer Aided Verification, 202

Nonexistence Certificates for Ovals in a Projective Plane of Order Ten

In 1983, a computer search was performed for ovals in a projective plane of order ten. The search was exhaustive and negative, implying that such ovals do not exist. However, no nonexistence certificates were produced by this search, and to the best of our knowledge the search has never been independently verified. In this paper, we rerun the search for ovals in a projective plane of order ten and produce a collection of nonexistence certificates that, when taken together, imply that such ovals do not exist. Our search program uses the cube-and-conquer paradigm from the field of satisfiability (SAT) checking, coupled with a programmatic SAT solver and the nauty symbolic computation library for removing symmetries from the search.Comment: Appears in the Proceedings of the 31st International Workshop on Combinatorial Algorithms (IWOCA 2020

Somatostatin receptor scintigraphy in cutaneous malignant lymphomas

Background: Lymphoid cells may express somatostatin receptors (SS-Rs) on their cell surface. Therefore radiolabeled somatostalin analogues may be used to visualize SS-R-positive lymphoid neoplasms in vivo. Exact staging is the basis for treatment decisions in cutaneous malignant lymphoma. We considered the possibility that SS-R scintigraphy might offer a clinically useful method of diagnostic imaging in patients with cutaneous malignant lymphoma. Objective: We evaluated SS-R scintigraphy in comparison with conventional staging methods in the staging of cutaneous malignant lymphoma. Methods: We conducted a prospective study in 14 consecutive patients with histologically proven cutaneous malignant lymphoma. SS-R scintigraphy was compared with physical, radiologic, and bone marrow examinations. Lymph node excisions were performed in patients with palpable lymph nodes. Results: SS-R scintigraphy was positive in the lymph nodes in all four patients with malignant lymph node infiltration and negative in the three patients with dermatopathic lymphadenopathy. In two patients, previously unsuspected lymphoma localizations were visualized by SS-R scintigraphy. In only three patients all skin lesions were visualized by SS-R scintigraphy; these three patients had not been treated with topical corticosteroids. SS-R scintigraphy failed to detect an adrenal mass in one patient and bone marrow infiltration in two patients. Conclusion: SS-R scintigraphy may help distinguish dermatopathic lymphadenopathy from malignant lymph node infiltration in patients with cutaneous malignant lymphoma

Automating Deductive Verification for Weak-Memory Programs

Writing correct programs for weak memory models such as the C11 memory model is challenging because of the weak consistency guarantees these models provide. The first program logics for the verification of such programs have recently been proposed, but their usage has been limited thus far to manual proofs. Automating proofs in these logics via first-order solvers is non-trivial, due to reasoning features such as higher-order assertions, modalities and rich permission resources. In this paper, we provide the first implementation of a weak memory program logic using existing deductive verification tools. We tackle three recent program logics: Relaxed Separation Logic and two forms of Fenced Separation Logic, and show how these can be encoded using the Viper verification infrastructure. In doing so, we illustrate several novel encoding techniques which could be employed for other logics. Our work is implemented, and has been evaluated on examples from existing papers as well as the Facebook open-source Folly library.Comment: Extended version of TACAS 2018 publicatio

Learning Moore Machines from Input-Output Traces

The problem of learning automata from example traces (but no equivalence or membership queries) is fundamental in automata learning theory and practice. In this paper we study this problem for finite state machines with inputs and outputs, and in particular for Moore machines. We develop three algorithms for solving this problem: (1) the PTAP algorithm, which transforms a set of input-output traces into an incomplete Moore machine and then completes the machine with self-loops; (2) the PRPNI algorithm, which uses the well-known RPNI algorithm for automata learning to learn a product of automata encoding a Moore machine; and (3) the MooreMI algorithm, which directly learns a Moore machine using PTAP extended with state merging. We prove that MooreMI has the fundamental identification in the limit property. We also compare the algorithms experimentally in terms of the size of the learned machine and several notions of accuracy, introduced in this paper. Finally, we compare with OSTIA, an algorithm that learns a more general class of transducers, and find that OSTIA generally does not learn a Moore machine, even when fed with a characteristic sample

Diffuse reflection of ultracold neutrons from low-roughness surfaces

We report a measurement of the reflection of ultracold neutrons from flat, large-area plates of different Fermi potential materials with low surface roughness. The results were used to test two diffuse reflection models, the well-known Lambert model and the micro-roughness model which is based on wave scattering. The Lambert model fails to reproduce the diffuse reflection data. The surface roughness b and correlation length w , obtained by fitting the micro-roughness model to the data are in the range 1$\le$ b $\le$3 nm and 10$\le$ w $\le$120 nm, in qualitative agreement with independent measurements using atomic force microscop