Soft Contract Verification

Behavioral software contracts are a widely used mechanism for governing the flow of values between components. However, run-time monitoring and enforcement of contracts imposes significant overhead and delays discovery of faulty components to run-time. To overcome these issues, we present soft contract verification, which aims to statically prove either complete or partial contract correctness of components, written in an untyped, higher-order language with first-class contracts. Our approach uses higher-order symbolic execution, leveraging contracts as a source of symbolic values including unknown behavioral values, and employs an updatable heap of contract invariants to reason about flow-sensitive facts. We prove the symbolic execution soundly approximates the dynamic semantics and that verified programs can't be blamed. The approach is able to analyze first-class contracts, recursive data structures, unknown functions, and control-flow-sensitive refinements of values, which are all idiomatic in dynamic languages. It makes effective use of an off-the-shelf solver to decide problems without heavy encodings. The approach is competitive with a wide range of existing tools---including type systems, flow analyzers, and model checkers---on their own benchmarks.Comment: ICFP '14, September 1-6, 2014, Gothenburg, Swede

Size-Change Termination as a Contract

Termination is an important but undecidable program property, which has led to a large body of work on static methods for conservatively predicting or enforcing termination. One such method is the size-change termination approach of Lee, Jones, and Ben-Amram, which operates in two phases: (1) abstract programs into "size-change graphs," and (2) check these graphs for the size-change property: the existence of paths that lead to infinite decreasing sequences. We transpose these two phases with an operational semantics that accounts for the run-time enforcement of the size-change property, postponing (or entirely avoiding) program abstraction. This choice has two key consequences: (1) size-change termination can be checked at run-time and (2) termination can be rephrased as a safety property analyzed using existing methods for systematic abstraction. We formulate run-time size-change checks as contracts in the style of Findler and Felleisen. The result compliments existing contracts that enforce partial correctness specifications to obtain contracts for total correctness. Our approach combines the robustness of the size-change principle for termination with the precise information available at run-time. It has tunable overhead and can check for nontermination without the conservativeness necessary in static checking. To obtain a sound and computable termination analysis, we apply existing abstract interpretation techniques directly to the operational semantics, avoiding the need for custom abstractions for termination. The resulting analyzer is competitive with with existing, purpose-built analyzers

Effects of thermal and quantum fluctuations on the phase diagram of a spin-1 87Rb Bose-Einstein condensate

We investigate effects of thermal and quantum fluctuations on the phase diagram of a spin-1 87Rb Bose-Einstein condensate (BEC) under a quadratic Zeeman effect. Due to the large ratio of spinindependent to spin-dependent interactions of 87Rb atoms, the effect of noncondensed atoms on the condensate is much more significant than that in scalar BECs. We find that the condensate and spontaneous magnetization emerge at different temperatures when the ground state is in the brokenaxisymmetry phase. In this phase, a magnetized condensate induces spin coherence of noncondensed atoms in different magnetic sublevels, resulting in temperature-dependent magnetization of the noncondensate. We also examine the effect of quantum fluctuations on the order parameter at absolute zero, and find that the ground-state phase diagram is significantly altered by quantum depletion.Comment: Comment: 21 pages, 7 figures Comment: 20 pages, 7 figures, paper reconstructed, nomenclature changed, references added, grammatical errors correcte

LARES Satellite Thermal Forces and a Test of General Relativity

We summarize a laser-ranged satellite test of frame-dragging, a prediction of General Relativity, and then concentrate on the estimate of thermal thrust, an important perturbation affecting the accuracy of the test. The frame dragging study analysed 3.5 years of data from the LARES satellite and a longer period of time for the two LAGEOS satellites. Using the gravity field GGM05S obtained via the Grace mission, which measures the Earth's gravitational field, the prediction of General Relativity is confirmed with a 1-$\sigma$ formal error of 0.002, and a systematic error of 0.05. The result for the value of the frame dragging around the Earth is $\mu$ = 0.994, compared to $\mu$ = 1 predicted by General Relativity. The thermal force model assumes heat flow from the sun (visual) and from Earth (IR) to the satellite core and to the fused silica reflectors on the satellite, and reradiation into space. For a roughly current epoch (days 1460 - 1580 after launch) we calculate an average along-track drag of -0.50 $pm/s^{2}$.Comment: 6 pages, multiple figures in Proceedings of Metrology for Aerospace (MetroAeroSpace), 2016 IEE

Quantum Chemistry–Machine Learning Approach for Predicting Properties of Lewis Acid–Lewis Base Adducts

Synthetic design allowing predictive control of charge transfer and other optoelectronic properties of Lewis acid adducts remains elusive. This challenge must be addressed through complementary methods combining experimental with computational insights from first principles. Ab initio calculations for optoelectronic properties can be computationally expensive and less straightforward than those sufficient for simple ground-state properties, especially for adducts of large conjugated molecules and Lewis acids. In this contribution, we show that machine learning (ML) can accurately predict density functional theory (DFT)-calculated charge transfer and even properties associated with excited states of adducts from readily obtained molecular descriptors. Seven ML models, built from a dataset of over 1000 adducts, show exceptional performance in predicting charge transfer and other optoelectronic properties with a Pearson correlation coefficient of up to 0.99. More importantly, the influence of each molecular descriptor on predicted properties can be quantitatively evaluated from ML models. This contributes to the optimization of a priori design of Lewis adducts for future applications, especially in organic electronics

TextANIMAR: Text-based 3D Animal Fine-Grained Retrieval

3D object retrieval is an important yet challenging task, which has drawn more and more attention in recent years. While existing approaches have made strides in addressing this issue, they are often limited to restricted settings such as image and sketch queries, which are often unfriendly interactions for common users. In order to overcome these limitations, this paper presents a novel SHREC challenge track focusing on text-based fine-grained retrieval of 3D animal models. Unlike previous SHREC challenge tracks, the proposed task is considerably more challenging, requiring participants to develop innovative approaches to tackle the problem of text-based retrieval. Despite the increased difficulty, we believe that this task has the potential to drive useful applications in practice and facilitate more intuitive interactions with 3D objects. Five groups participated in our competition, submitting a total of 114 runs. While the results obtained in our competition are satisfactory, we note that the challenges presented by this task are far from being fully solved. As such, we provide insights into potential areas for future research and improvements. We believe that we can help push the boundaries of 3D object retrieval and facilitate more user-friendly interactions via vision-language technologies.Comment: arXiv admin note: text overlap with arXiv:2304.0573