A Unified Model for Context-Sensitive Program Analyses:

Context-sensitive methods of program analysis increase the precision of interprocedural analysis by achieving the effect of call inlining. These methods have been defined using different formalisms and hence appear as algorithms that are very different from each other. Some methods traverse a call graph top-down, whereas some others traverse it bottom-up first and then top-down. Some define contexts explicitly, whereas some do not. Some of them directly compute data flow values, while some first compute summary functions and then use them to compute data flow values. Further, different methods place different kinds of restrictions on the data flow frameworks supported by them. As a consequence, it is difficult to compare the ideas behind these methods in spite of the fact that they solve essentially the same problem. We argue that these incomparable views are similar to those of blind men describing an elephant, called context sensitivity, and make it difficult for a non-expert reader to form a coherent picture of context-sensitive data flow analysis. We bring out this whole-elephant view of context sensitivity in program analysis by proposing a unified model of context sensitivity that provides a clean separation between computation of contexts and computation of data flow values. Our model captures the essence of context sensitivity and defines simple soundness and precision criteria for context-sensitive methods. It facilitates declarative specifications of context-sensitive methods, insightful comparisons between them, and reasoning about their soundness and precision. We demonstrate this by instantiating our model to many known context-sensitive methods.

Role of Electronic Excited State in Kinetics of the CH2OO + SO2 ! HCHO + SO3 Reaction

In this work, kinetics of the CH2OO + SO2 ! HCHO + SO3 reaction was studied by ring-polymer molecular dynamics (RPMD). To perform RPMD calculations, multi-reference configuration interaction (MRCI) was first carried out to compute data for constructing potential energy surface (PES) through a kernel regression method. On the basis of the present MRCI calculations, the statics multi-state mechanism involving the lowest-lying singlet excited state (denoted by S 1) was proposed, which is di?erent from the previously proposed mechanism with the lowest-lying triplet state (denoted by T1). Moreover, the present RPMD calculations predicted the rate coe?cient of 3:95?10􀀀11cm3 molecule􀀀1s􀀀1 at the room temperature (namely 298 K), agreeing with the previously reported experimental values. Finally, based on the present calculations, a probable dynamics mechanism was discussed, where the produced HCHO molecule was proposed to be in a vibrationally excited state. This needs further experimental and theoretical observation in the future.<br>

Role of Electronic Excited State in Kinetics of the CH2OO + SO2 ! HCHO + SO3 Reaction

In this work, kinetics of the CH2OO + SO2 ! HCHO + SO3 reaction was studied by ring-polymer molecular dynamics (RPMD). To perform RPMD calculations, multi-reference configuration interaction (MRCI) was first carried out to compute data for constructing potential energy surface (PES) through a kernel regression method. On the basis of the present MRCI calculations, the statics multi-state mechanism involving the lowest-lying singlet excited state (denoted by S 1) was proposed, which is di?erent from the previously proposed mechanism with the lowest-lying triplet state (denoted by T1). Moreover, the present RPMD calculations predicted the rate coe?cient of 3:95?10􀀀11cm3 molecule􀀀1s􀀀1 at the room temperature (namely 298 K), agreeing with the previously reported experimental values. Finally, based on the present calculations, a probable dynamics mechanism was discussed, where the produced HCHO molecule was proposed to be in a vibrationally excited state. This needs further experimental and theoretical observation in the future.<br>

How and Why the Cerebellum Recodes Input Signals: An Alternative to Machine Learning

Mossy fiber input to the cerebellum is received by granule cells where it is thought to be recoded into internal signals received by Purkinje cells, which alone carry the output of the cerebellar cortex. In any neural network, variables are contained in groups of signals as well as signals themselves—which cells are active and how many, for example, and statistical variables coded in rates, such as the mean and range, and which rates are strongly represented, in a defined population. We argue that the primary function of recoding is to confine translation to an effect of some variables and not others—both where input is recoded into internal signals and the translation downstream of internal signals into an effect on Purkinje cells. The cull of variables is harsh. Internal signaling is group coded. This allows coding to exploit statistics for a reliable and precise effect despite needing to work with high-dimensional input which is a highly unpredictably variable. An important effect is to normalize eclectic input signals, so that the basic, repeating cerebellar circuit, preserved across taxa, does not need to specialize (within regional variations). With this model, there is no need to slavishly conserve or compute data coded in single signals. If we are correct, a learning algorithm—for years, a mainstay of cerebellar modeling—would be redundant.

European Radiometry Buoy and Infrastructure (EURYBIA): A Contribution to the Design of the European Copernicus Infrastructure for Ocean Colour System Vicarious Calibration

In the context of the Copernicus Program, EUMETSAT prioritizes the creation of an ocean color infrastructure for system vicarious calibration (OC-SVC). This work aims to reply to this need by proposing the European Radiometry Buoy and Infrastructure (EURYBIA). EURYBIA is designed as an autonomous European infrastructure operating within the Marine Optical Network (MarONet) established by University of Miami (Miami, FL, USA) based on the Marine Optical Buoy (MOBY) experience and NASA support. MarONet addresses SVC requirements in different sites, consistently and in a traceable way. The selected EURYBIA installation is close to the Lampedusa Island in the central Mediterranean Sea. This area is widely studied and hosts an Atmospheric and Oceanographic Observatory for long-term climate monitoring. The EURYBIA field segment comprises off-shore and on-shore infrastructures to manage the observation system and perform routine sensors calibrations. The ground segment includes the telemetry center for data communication and the processing center to compute data products and uncertainty budgets. The study shows that the overall uncertainty of EURYBIA SVC gains computed for the Sentinel-3 OLCI mission under EUMETSAT protocols is of about 0.05% in the blue-green wavelengths after a decade of measurements, similar to that of the reference site in Hawaii and in compliance with requirements for climate studies.

Advances in Geometric Techniques for Analyzing Blebbing in Chemotaxing Dictyostelium Cells

We present a technical platform that allows us to monitor and measure cortex and membrane dynamics during bleb-based chemotaxis. Using D. discoideum cells expressing LifeAct-GFP and crawling under agarose containing RITC-dextran, we were able to simultaneously visualize the actin cortex and the cell membrane throughout bleb formation. Using these images, we then applied edge detect to generate points on the cell boundary with coordinates in a coordinate plane. Then we fitted these points to a curve with known x and y coordinate functions. The result was to parameterize the cell outline. With the parameterization, we demonstrate how to compute data for geometric features such as cell area, bleb area and edge curvature. This allows us to collect vital data for the analysis of blebbing.

Data Analysis and Visualization for High Fidelity CFD Models for Medical Device Realization

To reduce the development time-line of stent grafts, this project focuses on software tools that will automate and simplify execution of Computational Fluid Design (CFD) simulations on a High Performance Computing (HPC) platform, as well as postprocessing CFD results. This program is a Java-based Graphical User Interface (GUI), which enables the end user to automatically calculate and visualize the CFD results. Its portability and design allow for ease of use across multiple workstations. A visual element is included to reconstruct simulation images into other video formats. In addition, Scripting languages, such as Perl and R, were used to indicate the appropriate allocation of resources within an HPC environment. Each simulation would receive the optimal assets for each test conducted, and potentially need less time to perform. The projected outcome is to allow the user to compute data and quickly produce results for predicting the flow mechanics and near-wall hemodynamics of endovascular stent grafts in the design process.

Massively parallel motion planning algorithms under uncertainty using POMDP

We present new parallel algorithms that solve continuous-state partially observable Markov decision process (POMDP) problems using the GPU (gPOMDP) and a hybrid of the GPU and CPU (hPOMDP). We choose the Monte Carlo value iteration (MCVI) method as our base algorithm and parallelize this algorithm using the multi-level parallel formulation of MCVI. For each parallel level, we propose efficient algorithms to utilize the massive data parallelism available on modern GPUs. Our GPU-based method uses the two workload distribution techniques, compute/data interleaving and workload balancing, in order to obtain the maximum parallel performance at the highest level. Here we also present a CPU–GPU hybrid method that takes advantage of both CPU and GPU parallelism in order to solve highly complex POMDP planning problems. The CPU is responsible for data preparation, while the GPU performs Monte Cacrlo simulations; these operations are performed concurrently using the compute/data overlap technique between the CPU and GPU. To the best of the authors’ knowledge, our algorithms are the first parallel algorithms that efficiently execute POMDP in a massively parallel fashion utilizing the GPU or a hybrid of the GPU and CPU. Our algorithms outperform the existing CPU-based algorithm by a factor of 75–99 based on the chosen benchmark.

Coalescent: an Open-Science framework for Importance Sampling in Coalescent theory

Importance sampling is widely used in coalescent theory to compute data likelihood. Efficient importance sampling requires a trial distribution close to the target distribution of the genealogies conditioned on the data. Moreover, an efficient proposal requires intuition about how the data influence the target distribution. Different proposals might work under similar conditions, and sometimes the corresponding concepts overlap extensively. Currently, there is no framework available for coalescent theory that evaluates proposals in an integrated manner. Typically, problems are not modeled, optimization is performed vigorously on limited datasets, user interaction requires thorough knowledge, and programs are not aligned with the current demands of open science. We have designed a general framework (http://coalescent.sourceforge.net) for importance sampling, to compute data likelihood under the infinite sites model of mutation. The framework models the necessary core concepts, comes integrated with several data sets of varying size, implements the standard competing proposals, and integrates tightly with our previous framework for calculating exact probabilities. The framework computes the data likelihood and provides maximum likelihood estimates of the mutation parameter. Well-known benchmarks in the coalescent literature validate the framework’s accuracy. We evaluate several proposals in the coalescent literature, to discover that the order of efficiency among three standard proposals changes when running time is considered along with the effective sample size. The framework provides an intuitive user interface with minimal clutter. For speed, the framework switches automatically to modern multicore hardware, if available. It runs on three major platforms (Windows, Mac and Linux). Extensive tests and coverage make the framework accessible to a large community.