RacerD: compositional static race detection

Automatic static detection of data races is one of the most basic problems in reasoning about concurrency. We present RacerD—a static program analysis for detecting data races in Java programs which is fast, can scale to large code, and has proven effective in an industrial software engineering scenario. To our knowledge, RacerD is the first inter-procedural, compositional data race detector which has been empirically shown to have non-trivial precision and impact. Due to its compositionality, it can analyze code changes quickly, and this allows it to perform continuous reasoning about a large, rapidly changing codebase as part of deployment within a continuous integration ecosystem. In contrast to previous static race detectors, its design favors reporting high-confidence bugs over ensuring their absence. RacerD has been in deployment for over a year at Facebook, where it has flagged over 2500 issues that have been fixed by developers before reaching production. It has been important in enabling the development of new code as well as fixing old code: it helped support the conversion of part of the main Facebook Android app from a single-threaded to a multi-threaded architecture. In this paper we describe RacerD’s design, implementation, deployment and impact

Alternative mechanisms of structuring biomembranes: Self-assembly vs. self-organization

We study two mechanisms for the formation of protein patterns near membranes of living cells by mathematical modelling. Self-assembly of protein domains by electrostatic lipid-protein interactions is contrasted with self-organization due to a nonequilibrium biochemical reaction cycle of proteins near the membrane. While both processes lead eventually to quite similar patterns, their evolution occurs on very different length and time scales. Self-assembly produces periodic protein patterns on a spatial scale below 0.1 micron in a few seconds followed by extremely slow coarsening, whereas self-organization results in a pattern wavelength comparable to the typical cell size of 100 micron within a few minutes suggesting different biological functions for the two processes.Comment: 4 pages, 5 figure

SmartTrack: Efficient Predictive Race Detection

Widely used data race detectors, including the state-of-the-art FastTrack algorithm, incur performance costs that are acceptable for regular in-house testing, but miss races detectable from the analyzed execution. Predictive analyses detect more data races in an analyzed execution than FastTrack detects, but at significantly higher performance cost. This paper presents SmartTrack, an algorithm that optimizes predictive race detection analyses, including two analyses from prior work and a new analysis introduced in this paper. SmartTrack's algorithm incorporates two main optimizations: (1) epoch and ownership optimizations from prior work, applied to predictive analysis for the first time; and (2) novel conflicting critical section optimizations introduced by this paper. Our evaluation shows that SmartTrack achieves performance competitive with FastTrack-a qualitative improvement in the state of the art for data race detection.Comment: Extended arXiv version of PLDI 2020 paper (adds Appendices A-E) #228 SmartTrack: Efficient Predictive Race Detectio

Algorithms for optimizing drug therapy

BACKGROUND: Drug therapy has become increasingly efficient, with more drugs available for treatment of an ever-growing number of conditions. Yet, drug use is reported to be sub optimal in several aspects, such as dosage, patient's adherence and outcome of therapy. The aim of the current study was to investigate the possibility to optimize drug therapy using computer programs, available on the Internet. METHODS: One hundred and ten officially endorsed text documents, published between 1996 and 2004, containing guidelines for drug therapy in 246 disorders, were analyzed with regard to information about patient-, disease- and drug-related factors and relationships between these factors. This information was used to construct algorithms for identifying optimum treatment in each of the studied disorders. These algorithms were categorized in order to define as few models as possible that still could accommodate the identified factors and the relationships between them. The resulting program prototypes were implemented in HTML (user interface) and JavaScript (program logic). RESULTS: Three types of algorithms were sufficient for the intended purpose. The simplest type is a list of factors, each of which implies that the particular patient should or should not receive treatment. This is adequate in situations where only one treatment exists. The second type, a more elaborate model, is required when treatment can by provided using drugs from different pharmacological classes and the selection of drug class is dependent on patient characteristics. An easily implemented set of if-then statements was able to manage the identified information in such instances. The third type was needed in the few situations where the selection and dosage of drugs were depending on the degree to which one or more patient-specific factors were present. In these cases the implementation of an established decision model based on fuzzy sets was required. Computer programs based on one of these three models could be constructed regarding all but one of the studied disorders. The single exception was depression, where reliable relationships between patient characteristics, drug classes and outcome of therapy remain to be defined. CONCLUSION: Algorithms for optimizing drug therapy can, with presumably rare exceptions, be developed for any disorder, using standard Internet programming methods

The Transcription Factor Rfx3 Regulates β-Cell Differentiation, Function, and Glucokinase Expression

OBJECTIVE: Pancreatic islets of perinatal mice lacking the transcription factor Rfx3 exhibit a marked reduction in insulin-producing beta-cells. The objective of this work was to unravel the cellular and molecular mechanisms underlying this deficiency. RESEARCH DESIGN AND METHODS: Immunofluorescence studies and quantitative RT-PCR experiments were used to study the emergence of insulin-positive cells, the expression of transcription factors implicated in the differentiation of beta-cells from endocrine progenitors, and the expression of mature beta-cell markers during development in Rfx3(-/-) and pancreas-specific Rfx3-knockout mice. RNA interference experiments were performed to document the consequences of downregulating Rfx3 expression in Min6 beta-cells. Quantitative chromatin immunoprecipitation (ChIP), ChIP sequencing, and bandshift experiments were used to identify Rfx3 target genes. RESULTS: Reduced development of insulin-positive cells in Rfx3(-/-) mice was not due to deficiencies in endocrine progenitors or beta-lineage specification, but reflected the accumulation of insulin-positive beta-cell precursors and defective beta-cells exhibiting reduced insulin, Glut-2, and Gck expression. Similar incompletely differentiated beta-cells developed in pancreas-specific Rfx3-deficient embryos. Defective beta-cells lacking Glut-2 and Gck expression dominate in Rfx3-deficent adults, leading to glucose intolerance. Attenuated Glut-2 and glucokinase expression, and impaired glucose-stimulated insulin secretion, were also induced by RNA interference-mediated inhibition of Rfx3 expression in Min6 cells. Finally, Rfx3 was found to bind in Min6 cells and human islets to two well-known regulatory sequences, Pal-1 and Pal-2, in the neuroendocrine promoter of the glucokinase gene. CONCLUSIONS: Our results show that Rfx3 is required for the differentiation and function of mature beta-cells and regulates the beta-cell promoter of the glucokinase gene