Efficient Black-box Checking of Snapshot Isolation in Databases

Snapshot isolation (SI) is a prevalent weak isolation level that avoids the performance penalty imposed by serializability and simultaneously prevents various undesired data anomalies. Nevertheless, SI anomalies have recently been found in production cloud databases that claim to provide the SI guarantee. Given the complex and often unavailable internals of such databases, a black-box SI checker is highly desirable. In this paper we present PolySI, a novel black-box checker that efficiently checks SI and provides understandable counterexamples upon detecting violations. PolySI builds on a novel characterization of SI using generalized polygraphs (GPs), for which we establish its soundness and completeness. PolySI employs an SMT solver and also accelerates SMT solving by utilizing the compact constraint encoding of GPs and domain-specific optimizations for pruning constraints. As demonstrated by our extensive assessment, PolySI successfully reproduces all of 2477 known SI anomalies, detects novel SI violations in three production cloud databases, identifies their causes, outperforms the state-of-the-art black-box checkers under a wide range of workloads, and can scale up to large-sized workloads.Comment: 20 pages, 15 figures, accepted by PVLD

Combustion Chemistry and Decomposition Kinetics of Forest Fuels

AbstractA brief review is given of the studies in combustion chemistry and decomposition kinetics of forest fuels (FF). The methods used in the study to investigate the FF pyrolysis kinetics and the combustion of the Siberian forests are described. The experiments on FF pyrolysis were conducted at high heating rates (150K/s) in a flow reactor by the method of differential mass-spectrometric thermal analysis (DMSTA) in situ using probe molecular-beam mass spectrometry, and at low heating rates (0.17K/s) by the thermogravimetric method. The kinetic parameters of Siberian FF pyrolysis have been determined for oxidative and inert media and simulation of FF pyrolysis has been conducted using the multi-component devolatilization mechanism. The flame structure of a pine branch has been studied by probe molecular-beam mass spectrometry. Species have been identified in the dark and luminous flame zones; the width of the flame zone has been measured

Heterometallic Molecular and Ionic Isomers

Numerous descriptions of structural isomerism in metal complexes do not list any molecular vs ionic isomers. At the same time, one of the most striking examples of structural isomerism in organic chemistry is molecular urea, which has the same atomic composition as the chemically distinct ionic ammonium cyanate. This iconic organic couple now meets its inorganic heterometallic counterpart. We introduce a new class of structural isomers, molecular vs ionic, that can be consummated in complex and coordinatively unsaturated polynuclear/heterometallic compounds. We report inorganic molecular and ionic isomers of the composition [NaCrFe (acac)3(hfac)3] (acac = acetylacetonate; hfac = hexafluoroacetylacetonate). Heterometallic molecular [CrIII(acac)3-Na-FeII(hfac)3] (1m) and ionic {[CrIII(acac)3-Na-CrIII(acac)3]+[FeII(hfac)3-Na-FeII(hfac)3]−} (1i) isomers have been isolated in pure form and characterized. While both ions are heterobimetallic trinuclear entities, the neutral counterpart is a heterotrimetallic trinuclear molecule. The two isomers exhibit distinctly different characteristics in terms of solubility, volatility, mass spectrometry ionization, and thermal behavior. Unambiguous assignment of the positions and oxidation/spin states of the Periodic Table neighbors, Fe and Cr, in both isomers have been made by a combination of characterization techniques that include synchrotron X-ray resonant diffraction, synchrotron X-ray fluorescence spectroscopy, Mössbauer spectroscopy, and DART mass spectrometry. The transformation between the two isomers that does take place in solutions of noncoordinating solvents has also been tested

Parallel mutual information estimation for inferring gene regulatory networks on GPUs

<p>Abstract</p> <p>Background</p> <p>Mutual information is a measure of similarity between two variables. It has been widely used in various application domains including computational biology, machine learning, statistics, image processing, and financial computing. Previously used simple histogram based mutual information estimators lack the precision in quality compared to kernel based methods. The recently introduced B-spline function based mutual information estimation method is competitive to the kernel based methods in terms of quality but at a lower computational complexity.</p> <p>Results</p> <p>We present a new approach to accelerate the B-spline function based mutual information estimation algorithm with commodity graphics hardware. To derive an efficient mapping onto this type of architecture, we have used the Compute Unified Device Architecture (CUDA) programming model to design and implement a new parallel algorithm. Our implementation, called CUDA-MI, can achieve speedups of up to 82 using double precision on a single GPU compared to a multi-threaded implementation on a quad-core CPU for large microarray datasets. We have used the results obtained by CUDA-MI to infer gene regulatory networks (GRNs) from microarray data. The comparisons to existing methods including ARACNE and TINGe show that CUDA-MI produces GRNs of higher quality in less time.</p> <p>Conclusions</p> <p>CUDA-MI is publicly available open-source software, written in CUDA and C++ programming languages. It obtains significant speedup over sequential multi-threaded implementation by fully exploiting the compute capability of commonly used CUDA-enabled low-cost GPUs.</p

Experimental evaluation of ensemble classifiers for imbalance in Big Data

Datasets are growing in size and complexity at a pace never seen before, forming ever larger datasets known as Big Data. A common problem for classification, especially in Big Data, is that the numerous examples of the different classes might not be balanced. Some decades ago, imbalanced classification was therefore introduced, to correct the tendency of classifiers that show bias in favor of the majority class and that ignore the minority one. To date, although the number of imbalanced classification methods have increased, they continue to focus on normal-sized datasets and not on the new reality of Big Data. In this paper, in-depth experimentation with ensemble classifiers is conducted in the context of imbalanced Big Data classification, using two popular ensemble families (Bagging and Boosting) and different resampling methods. All the experimentation was launched in Spark clusters, comparing ensemble performance and execution times with statistical test results, including the newest ones based on the Bayesian approach. One very interesting conclusion from the study was that simpler methods applied to unbalanced datasets in the context of Big Data provided better results than complex methods. The additional complexity of some of the sophisticated methods, which appear necessary to process and to reduce imbalance in normal-sized datasets were not effective for imbalanced Big Data.“la Caixa” Foundation, Spain, under agreement LCF/PR/PR18/51130007. This work was supported by the Junta de Castilla y León, Spain under project BU055P20 (JCyL/FEDER, UE) co-financed through European Union FEDER funds, and by the Consejería de Educación of the Junta de Castilla y León and the European Social Fund, Spain through a pre-doctoral grant (EDU/1100/2017)

An insight into imbalanced Big Data classification: outcomes and challenges

Big Data applications are emerging during the last years, and researchers from many disciplines are aware of the high advantages related to the knowledge extraction from this type of problem. However, traditional learning approaches cannot be directly applied due to scalability issues. To overcome this issue, the MapReduce framework has arisen as a “de facto” solution. Basically, it carries out a “divide-and-conquer” distributed procedure in a fault-tolerant way to adapt for commodity hardware. Being still a recent discipline, few research has been conducted on imbalanced classification for Big Data. The reasons behind this are mainly the difficulties in adapting standard techniques to the MapReduce programming style. Additionally, inner problems of imbalanced data, namely lack of data and small disjuncts, are accentuated during the data partitioning to fit the MapReduce programming style. This paper is designed under three main pillars. First, to present the first outcomes for imbalanced classification in Big Data problems, introducing the current research state of this area. Second, to analyze the behavior of standard pre-processing techniques in this particular framework. Finally, taking into account the experimental results obtained throughout this work, we will carry out a discussion on the challenges and future directions for the topic.This work has been partially supported by the Spanish Ministry of Science and Technology under Projects TIN2014-57251-P and TIN2015-68454-R, the Andalusian Research Plan P11-TIC-7765, the Foundation BBVA Project 75/2016 BigDaPTOOLS, and the National Science Foundation (NSF) Grant IIS-1447795

The promising combination therapy strategy for overcoming resistance to histone deacetylase inhibitors in diffuse large B‐cell lymphoma

Abstract Single histone deacetylases inhibitors (HDACis) or current combination therapies show limited clinical efficiency in facing the striking heterogeneity of diffuse large B‐cell lymphoma (DLBCL). This commentary reviews the research conducted by Wang et al. to demonstrate the mechanism of the highly efficient HDACi LAQ824 and a promising HDCAi‐based combination therapy approach in DLBCL