Extracting correlated parameters on multicore architectures

16 pagesInternational audienceIn this paper, we present a new approach relevant to the discovery of correlated patterns, based on the use of multicore architectures. Our work rests on a full KDD system and allows one to extract Decision Correlation Rules based on the Chi-squared criterion that include a target column from any database. To achieve this objective, we use a levelwise algorithm as well as contingency vectors, an alternate and more powerful representation of contingency tables, in order to prune the search space. The goal is to parallelize the processing associated with the extraction of relevant rules. The parallelization invokes the PPL (Parallel Patterns Library), which allows a simultaneous access to the whole available cores / processors on modern computers. We nally present rst results on the reached performance gains

Multicore Mining of Correlated Patterns

6 pagesInternational audienceIn this paper, we present a new approach relevant to the discovery of correlated patterns, based on the use of multicore architectures. Our work rests on a full KDD system and allows one to extract Decision Correlation Rules based on the Chi-squared criterion that include a target column from any database. To achieve this objective, we use a levelwise algorithm as well as contingency vectors, an alternate and more powerful representation of contingency tables, in order to prune the search space. The goal is to parallelize the processing associated with the extraction of relevant rules. The parallelization invokes the PPL (Parallel Patterns Library), which allows a simultaneous access to the whole available cores / processors on modern computers. We finally present first results on the reached performance gains

Discovering correlated parameters in Semiconductor Manufacturing processes: a Data Mining approach

International audienceData mining tools are nowadays becoming more and more popular in the semiconductor manufacturing industry, and especially in yield-oriented enhancement techniques. This is because conventional approaches fail to extract hidden relationships between numerous complex process control parameters. In order to highlight correlations between such parameters, we propose in this paper a complete knowledge discovery in databases (KDD) model. The mining heart of the model uses a new method derived from association rules programming, and is based on two concepts: decision correlation rules and contingency vectors. The first concept results from a cross fertilization between correlation and decision rules. It enables relevant links to be highlighted between sets of values of a relation and the values of sets of targets belonging to the same relation. Decision correlation rules are built on the twofold basis of the chi-squared measure and of the support of the extracted values. Due to the very nature of the problem, levelwise algorithms only allow extraction of results with long execution times and huge memory occupation. To offset these two problems, we propose an algorithm based both on the lectic order and contingency vectors, an alternate representation of contingency tables. This algorithm is the basis of our KDD model software, called MineCor. An overall presentation of its other functions, of some significant experimental results, and of associated performances are provided and discussed

Mining Literal Correlation Rules from Itemsets

6 pagesInternational audienceNowadays, data mining tools are becoming more and more popular to extract knowledge from a huge volume of data. In this paper, our aim is to extract Literal Correlation Rules: Correlation Rules admitting literal patterns given a set of items and a binary relation. If a pattern represents a valid Correlation Rule, then any literal belonging to its Canonical Base represents a valid Literal Correlation Rule. Moreover, in order to highlight only relevant Literal Correlation Rules, we add a pruning step based on a support threshold. To extract such rules, we modify the LHS-CHI2 Algorithm and perform some experiments

A Causality Based Feature Selection Approach for Multivariate Time Series Forecasting

International audience—The field of time series forecasting has progressed significantly in recent decades, specially in regards to the need of forecasting economic data. That said, some issues still arise. In particular when we are working with a set of time series that have a large number of variables. Hence, a selection step is usually needed in order to reduce the number of variables that will contribute to forecast each target time series. In this paper, we propose a feature selection and / or dimension reduction algorithm for forecasting multivariate time series, based on (i) the notion of the Granger causality, and (ii) on a selection step based on a clustering strategy. Finally, we carry out experiments on different real data sets, by comparing our proposal and some of the most used feature selection methods. Experiments show that we improved the forecasting accuracy compared with the evaluated methods

Cubes convexes

In various approaches, data cubes are pre-computed in order to answer efficiently OLAP queries. The notion of data cube has been declined in various ways: iceberg cubes, range cubes or differential cubes. In this paper, we introduce the concept of convex cube which captures all the tuples of a datacube satisfying a constraint combination. It can be represented in a very compact way in order to optimize both computation time and required storage space. The convex cube is not an additional structure appended to the list of cube variants but we propose it as a unifying structure that we use to characterize, in a simple, sound and homogeneous way, the other quoted types of cubes. Finally, we introduce the concept of emerging cube which captures the significant trend inversions. characterizations

The Genome of a Pathogenic Rhodococcus: Cooptive Virulence Underpinned by Key Gene Acquisitions

We report the genome of the facultative intracellular parasite Rhodococcus equi, the only animal pathogen within the biotechnologically important actinobacterial genus Rhodococcus. The 5.0-Mb R. equi 103S genome is significantly smaller than those of environmental rhodococci. This is due to genome expansion in nonpathogenic species, via a linear gain of paralogous genes and an accelerated genetic flux, rather than reductive evolution in R. equi. The 103S genome lacks the extensive catabolic and secondary metabolic complement of environmental rhodococci, and it displays unique adaptations for host colonization and competition in the short-chain fatty acid–rich intestine and manure of herbivores—two main R. equi reservoirs. Except for a few horizontally acquired (HGT) pathogenicity loci, including a cytoadhesive pilus determinant (rpl) and the virulence plasmid vap pathogenicity island (PAI) required for intramacrophage survival, most of the potential virulence-associated genes identified in R. equi are conserved in environmental rhodococci or have homologs in nonpathogenic Actinobacteria. This suggests a mechanism of virulence evolution based on the cooption of existing core actinobacterial traits, triggered by key host niche–adaptive HGT events. We tested this hypothesis by investigating R. equi virulence plasmid-chromosome crosstalk, by global transcription profiling and expression network analysis. Two chromosomal genes conserved in environmental rhodococci, encoding putative chorismate mutase and anthranilate synthase enzymes involved in aromatic amino acid biosynthesis, were strongly coregulated with vap PAI virulence genes and required for optimal proliferation in macrophages. The regulatory integration of chromosomal metabolic genes under the control of the HGT–acquired plasmid PAI is thus an important element in the cooptive virulence of R. equi

The MAORY first-light adaptive optics module for E-ELT

The MAORY adaptive optics module is part of the first light instrumentation suite for the E-ELT. The MAORY project phase B is going to start soon. This paper contains a system-level overview of the current instrument design

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570