On Time-sensitive Control Dependencies

We present efficient algorithms for time-sensitive control dependencies (CDs). If statement y is time-sensitively control dependent on statement x , then x decides not only whether y is executed but also how many timesteps after x . If y is not standard control dependent on x , but time-sensitively control dependent, then y will always be executed after x , but the execution time between x and y varies. This allows us to discover, e.g., timing leaks in security-critical software. We systematically develop properties and algorithms for time-sensitive CDs, as well as for nontermination-sensitive CDs. These work not only for standard control flow graphs (CFGs) but also for CFGs lacking a unique exit node (e.g., reactive systems). We show that Cytron’s efficient algorithm for dominance frontiers [ 10 ] can be generalized to allow efficient computation not just of classical CDs but also of time-sensitive and nontermination-sensitive CDs. We then use time-sensitive CDs and time-sensitive slicing to discover cache timing leaks in an AES implementation. Performance measurements demonstrate scalability of the approach.

Conditional Bisimilarity for Reactive Systems

Reactive systems \`a la Leifer and Milner, an abstract categorical framework for rewriting, provide a suitable framework for deriving bisimulation congruences. This is done by synthesizing interactions with the environment in order to obtain a compositional semantics. We enrich the notion of reactive systems by conditions on two levels: first, as in earlier work, we consider rules enriched with application conditions and second, we investigate the notion of conditional bisimilarity. Conditional bisimilarity allows us to say that two system states are bisimilar provided that the environment satisfies a given condition. We present several equivalent definitions of conditional bisimilarity, including one that is useful for concrete proofs and that employs an up-to-context technique, and we compare with related behavioural equivalences. We consider examples based on DPO graph rewriting, an instantiation of reactive systems.

Visualizing Street Pavement Anomalies through Fog Computing V2I Networks and Machine Learning

Analyzing data related to the conditions of city streets and avenues could help to make better decisions about public spending on mobility. Generally, streets and avenues are fixed as soon as they have a citizen report or when a major incident occurs. However, it is uncommon for cities to have real-time reactive systems that detect the different problems they have to fix on the pavement. This work proposes a solution to detect anomalies in streets through state analysis using sensors within the vehicles that travel daily and connecting them to a fog-computing architecture on a V2I network. The system detects and classifies the main road problems or abnormal conditions in streets and avenues using Machine Learning Algorithms (MLA), comparing roughness against a flat reference. An instrumented vehicle obtained the reference through accelerometry sensors and then sent the data through a mid-range communication system. With these data, the system compared an Artificial Neural Network (supervised MLA) and a K-Nearest Neighbor (Supervised MLA) to select the best option to handle the acquired data. This system makes it desirable to visualize the streets’ quality and map the areas with the most significant anomalies.

Active learning of reactive Bayesian force fields: Application to heterogeneous catalysis dynamics of H/Pt

Atomistic modeling of chemically reactive systems has so far relied on either expensive ab initio methods or bond-order force fields requiring arduous parametrization. Here, we describe a Bayesian active learning framework for autonomous ``on-the-fly'' training of fast and accurate reactive many-body force fields during molecular dynamics simulations. At each time step, predictive uncertainties of a sparse Gaussian process are evaluated to automatically determine whether additional ab initio training data are needed. We introduce a general method for mapping trained kernel models onto equivalent polynomial models whose prediction cost is much lower and independent of the training set size. As a demonstration, we perform direct two-phase simulations of heterogeneous H2 turnover on the Pt(111) catalyst surface, at chemical accuracy. The model trains itself in three days and performs at twice the speed of a ReaxFF model, while maintaining much higher fidelity to DFT and excellent agreement with experiment.

On the Satisfiability and Model Checking for one Parameterized Extension of Linear-time Temporal Logic

Sequential reactive systems are computer programs or hardware devices which process the flows of input data or control signals and output the streams of instructions or responses. When designing such systems one needs formal specification languages capable of expressing the relationships between the input and output flows. Previously, we introduced a family of such specification languages based on temporal logics $LTL$, $CTL$ and $CTL^*$ combined with regular languages. A characteristic feature of these new extensions of conventional temporal logics is that temporal operators and basic predicates are parameterized by regular languages. In our early papers, we estimated the expressive power of the new temporal logic $Reg$-$LTL$ and introduced a model checking algorithm for $Reg$-$LTL$, $Reg$-$CTL$, and $Reg$-$CTL^*$. The main issue which still remains unclear is the complexity of decision problems for these logics. In the paper, we give a complete solution to satisfiability checking and model checking problems for $Reg$-$LTL$ and prove that both problems are Pspace-complete. The computational hardness of the problems under consideration is easily proved by reducing to them the intersection emptyness problem for the families of regular languages. The main result of the paper is an algorithm for reducing the satisfiability of checking $Reg$-$LTL$ formulas to the emptiness problem for Buchi automata of relatively small size and a description of a technique that allows one to check the emptiness of the obtained automata within space polynomial of the size of input formulas.

Quantitative X-ray Maps Analaysis of Composition and Microstructure of Permian High-Temperature Relicts in Acidic Rocks from the Sesia-Lanzo Zone Eclogitic Continental Crust, Western Alps

Three samples of meta-acidic rocks with pre-Alpine metamorphic relicts from the Sesia-Lanzo Zone eclogitic continental crust were investigated using stepwise controlled elemental maps by means of the Quantitative X-ray Maps Analyzer (Q-XRMA). Samples were chosen with the aim of analysing the reacting zones along the boundaries between the pre-Alpine and Alpine mineral phases, which developed in low chemically reactive systems. The quantitative data treatment of the X-ray images was based on a former multivariate statistical analytical stage followed by a sequential phase and sub-phase classification and permitted to isolate and to quantitatively investigate the local paragenetic equilibria. The parageneses thus observed were interpreted as related to the pre-Alpine metamorphic or magmatic stages as well as to local Alpine re-equilibrations. On the basis of electron microprobe analysis, specific compositional ranges were defined in micro-domains of the relict and new paragenetic equilibria. In this way calibrated compositional maps were obtained and used to contour different types of reacting boundaries between adjacent solid solution phases. The pre-Alpine and Alpine mineral parageneses thus obtained allowed to perform geothermobarometry on a statistically meaningful and reliable dataset. In general, metamorphic temperatures cluster at 600–700 ∘C and 450–550 ∘C, with lower temperatures referred to a retrograde metamorphic re-equilibration. In all the cases described, pre-Alpine parageneses were overprinted by an Alpine metamorphic mineral assemblage. Pressure-temperature estimates of the Alpine stage averagely range between 420 to 550 ∘C and 12 to 16.5 kbar. The PT constraints permitted to better define the pre-Alpine metamorphic scenario of the western Austroalpine sectors, as well as to better understand the influence of the pre-Alpine metamorphic inheritance on the forthcoming Alpine tectonic evolution.

Verified Lustre Normalization with Node Subsampling

Dataflow languages allow the specification of reactive systems by mutually recursive stream equations, functions, and boolean activation conditions called clocks. Lustre and Scade are dataflow languages for programming embedded systems. Dataflow programs are compiled by a succession of passes. This article focuses on the normalization pass which rewrites programs into the simpler form required for code generation. Vélus is a compiler from a normalized form of Lustre to CompCert’s Clight language. Its specification in the Coq interactive theorem prover includes an end-to-end correctness proof that the values prescribed by the dataflow semantics of source programs are produced by executions of generated assembly code. We describe how to extend Vélus with a normalization pass and to allow subsampled node inputs and outputs. We propose semantic definitions for the unrestricted language, divide normalization into three steps to facilitate proofs, adapt the clock type system to handle richer node definitions, and extend the end-to-end correctness theorem to incorporate the new features. The proofs require reasoning about the relation between static clock annotations and the presence and absence of values in the dynamic semantics. The generalization of node inputs requires adding a compiler pass to ensure the initialization of variables passed in function calls.

A universal framework for featurization of atomistic systems

Molecular dynamics simulations are an invaluable tool in numerous scientific fields. However, the ubiquitous classical force fields cannot describe reactive systems, and quantum molecular dynamics are too computationally demanding to treat large systems or long timescales. Reactive force fields based on physics or machine learning can be used to bridge the gap in time and length scales, but these force fields require substantial effort to construct and are highly specific to a given chemical composition and application. A significant limitation of machine learning models is the use of element-specific features, leading to models that scale poorly with the number of elements. This work introduces the Gaussian multipole (GMP) featurization scheme that utilizes physically-relevant multipole expansions of the electron density around atoms to yield feature vectors that interpolate between element types and have a fixed dimension regardless of the number of elements present. We combine GMP with neural networks to directly compare it to the widely used Behler-Parinello symmetry functions for the MD17 dataset, revealing that it exhibits improved accuracy and computational efficiency. Further, we demonstrate that GMP-based models can achieve chemical accuracy for the QM9 dataset, and their accuracy remains reasonable even when extrapolating to new elements. Finally, we test GMP-based models for the Open Catalysis Project (OCP) dataset, revealing comparable performance to graph convolutional deep learning models. The results indicate that this featurization scheme fills a critical gap in the construction of efficient and transferable machine-learned force fields.