Static Safety for an Actor Dedicated Process Calculus by Abstract Interpretation

The actor model eases the definition of concurrent programs with non uniform behaviors. Static analysis of such a model was previously done in a data-flow oriented way, with type systems. This approach was based on constraint set resolution and was not able to deal with precise properties for communications of behaviors. We present here a new approach, control-flow oriented, based on the abstract interpretation framework, able to deal with communication of behaviors. Within our new analyses, we are able to verify most of the previous properties we observed as well as new ones, principally based on occurrence counting

Niektóre zagadnienia ekonomiczne telefonii. Przegląd Zagadnień Łączności, 1964, nr 11 (38)

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Stat3-mediated alterations in lysosomal membrane protein composition.

Lysosome function is essential in cellular homeostasis. In addition to its recycling role, the lysosome has recently been recognized as a cellular signaling hub. We have shown in mammary epithelial cells, both in vivo and in vitro, that signal transducer and activator of transcription 3 (Stat3) modulates lysosome biogenesis and can promote the release of lysosomal proteases that culminates in cell death. To further investigate the impact of Stat3 on lysosomal function, we conducted a proteomic screen of changes in lysosomal membrane protein components induced by Stat3 using an iron nanoparticle enrichment strategy. Our results show that Stat3 activation not only elevates the levels of known membrane proteins but results in the appearance of unexpected factors, including cell surface proteins such as annexins and flotillins. These data suggest that Stat3 may coordinately regulate endocytosis, intracellular trafficking, and lysosome biogenesis to drive lysosome-mediated cell death in mammary epithelial cells. The methodologies described in this study also provide significant improvements to current techniques used for the purification and analysis of the lysosomal proteome

Automatic Verification of Finite Precision Implementations of Linear Controllers

We consider the problem of verifying finite precision implementation of linear time-invariant controllers against mathematical specifications. A specification may have multiple correct implementations which are different from each other in controller state representation, but equivalent from a perspective of input-output behavior (e.g., due to optimization in a code generator). The implementations may use finite precision computations (e.g. floating-point arithmetic) which cause quantization (i.e., roundoff) errors. To address these challenges, we first extract a controller\u27s mathematical model from the implementation via symbolic execution and floating-point error analysis, and then check approximate input-output equivalence between the extracted model and the specification by similarity checking. We show how to automatically verify the correctness of floating-point controller implementation in C language using the combination of techniques such as symbolic execution and convex optimization problem solving. We demonstrate the scalability of our approach through evaluation with randomly generated controller specifications of realistic size

Rate Equations for Graphs

In this paper, we combine ideas from two different scientific traditions: 1) graph transformation systems (GTSs) stemming from the theory of formal languages and concurrency, and 2) mean field approximations (MFAs), a collection of approximation techniques ubiquitous in the study of complex dynamics. Using existing tools from algebraic graph rewriting, as well as new ones, we build a framework which generates rate equations for stochastic GTSs and from which one can derive MFAs of any order (no longer limited to the humanly computable). The procedure for deriving rate equations and their approximations can be automated. An implementation and example models are available online at https://rhz.github.io/fragger. We apply our techniques and tools to derive an expression for the mean velocity of a two-legged walker protein on DNA.Comment: to be presented at the 18th International Conference on Computational Methods in Systems Biology (CMSB 2020

Discrete anisotropic radiative transfer (DART 5) for modeling airborne and satellite spectroradiometer and LIDAR acquisitions of natural and urban landscapes

International audienceSatellite and airborne optical sensors are increasingly used by scientists, and policy makers, and managers for studying and managing forests, agriculture crops, and urban areas. Their data acquired with given instrumental specifications (spectral resolution, viewing direction, sensor field-of-view, etc.) and for a specific experimental configuration (surface and atmosphere conditions, sun direction, etc.) are commonly translated into qualitative and quantitative Earth surface parameters. However, atmosphere properties and Earth surface 3D architecture often confound their interpretation. Radiative transfer models capable of simulating the Earth and atmosphere complexity are, therefore, ideal tools for linking remotely sensed data to the surface parameters. Still, many existing models are oversimplifying the Earth-atmosphere system interactions and their parameterization of sensor specifications is often neglected or poorly considered. The Discrete Anisotropic Radiative Transfer (DART) model is one of the most comprehensive physically based 3D models simulating the Earth-atmosphere radiation interaction from visible to thermal infrared wavelengths. It has been developed since 1992. It models optical signals at the entrance of imaging radiometers and laser scanners on board of satellites and airplanes, as well as the 3D radiative budget, of urban and natural landscapes for any experimental configuration and instrumental specification. It is freely distributed for research and teaching activities. This paper presents DART physical bases and its latest functionality for simulating imaging spectroscopy of natural and urban landscapes with atmosphere, including the perspective projection of airborne acquisitions and LIght Detection And Ranging (LIDAR) waveform and photon counting signals

Measuring beta-diversity by remote sensing: a challenge for biodiversity monitoring

Biodiversity includes multiscalar and multitemporal structures and processes, with different levels of functional organization, from genetic to ecosystemic levels. One of the mostly used methods to infer biodiversity is based on taxonomic approaches and community ecology theories. However, gathering extensive data in the field is difficult due to logistic problems, overall when aiming at modelling biodiversity changes in space and time, which assumes statistically sound sampling schemes. In this view, airborne or satellite remote sensing allow to gather information over wide areas in a reasonable time. Most of the biodiversity maps obtained from remote sensing have been based on the inference of species richness by regression analysis. On the contrary, estimating compositional turnover (beta-diversity) might add crucial information related to relative abundance of different species instead of just richness. Presently, few studies have addressed the measurement of species compositional turnover from space. Extending on previous work, in this manuscript we propose novel techniques to measure beta-diversity from airborne or satellite remote sensing, mainly based on: i) multivariate statistical analysis, ii) the spectral species concept, iii) self-organizing feature maps, iv) multi- dimensional distance matrices, and the v) Rao's Q diversity. Each of these measures allow to solve one or several issues related to turnover measurement. This manuscript is the first methodological example encompassing (and enhancing) most of the available methods for estimating beta-diversity from remotely sensed imagery and potentially relate them to species diversity in the field

Rate Equations for Graphs

International audienceIn this paper, we combine ideas from two different scientifictraditions: 1) graph transformation systems (GTSs) stemming from thetheory of formal languages and concurrency, and 2) mean field approx-imations (MFAs), a collection of approximation techniques ubiquitousin the study of complex dynamics. Using existing tools from algebraicgraph rewriting, as well as new ones, we build a framework which gener-ates rate equations for stochastic GTSs and from which one can deriveMFAs of any order (no longer limited to the humanly computable). Theprocedure for deriving rate equations and their approximations can beautomated. An implementation and example models are available onlineat https://rhz.github.io/fragger. We apply our techniques and tools toderive an expression for the mean velocity of a two-legged walker proteinon DNA