Parameterizing the cost function of Dynamic Time Warping with application to time series classification

Dynamic Time Warping (DTW) is a popular time series distance measure that aligns the points in two series with one another. These alignments support warping of the time dimension to allow for processes that unfold at differing rates. The distance is the minimum sum of costs of the resulting alignments over any allowable warping of the time dimension. The cost of an alignment of two points is a function of the difference in the values of those points. The original cost function was the absolute value of this difference. Other cost functions have been proposed. A popular alternative is the square of the difference. However, to our knowledge, this is the first investigation of both the relative impacts of using different cost functions and the potential to tune cost functions to different tasks. We do so in this paper by using a tunable cost function {\lambda}{\gamma} with parameter {\gamma}. We show that higher values of {\gamma} place greater weight on larger pairwise differences, while lower values place greater weight on smaller pairwise differences. We demonstrate that training {\gamma} significantly improves the accuracy of both the DTW nearest neighbor and Proximity Forest classifiers

A CONSTANS-like gene candidate that could explain most of the genetic variation for flowering date in Medicago truncatula

Flowering is a critical period during a plant’s life cycle, and thus the identification and characterization of genes involved in flowering date control are of great importance in agronomy, breeding, population genetics and ecology. The model species Medicago truncatula can be used to detect genes explaining the variation for flowering date, which could also explain this variation in legume crops. The objective of this study was to identify the most promising candidate gene explaining a major quantitative trait locus (QTL) for flowering date previously found in three M. truncatula mapping populations. Fine mapping and bioinformatic analysis of bacterial artificial chromosomes (BACs) in the confidence interval of the QTL showed six genes potentially involved in the control of flowering date. Two of these genes, similar to CONSTANS and FT of Arabidopsis thaliana respectively, had genomic mutations when compared to the parents. The transcriptomic study of these genes by semi-quantitative RT-PCR in leaves and flowers sampled at two developmental stages showed that the CONSTANS-like gene was differentially expressed in the two parental lines. A gene belonging to the CONSTANS-like family could explain the major QTL for flowering date segregating in M. truncatula progenies

Proximity Forest 2.0: A new effective and scalable similarity-based classifier for time series

Time series classification (TSC) is a challenging task due to the diversity of types of feature that may be relevant for different classification tasks, including trends, variance, frequency, magnitude, and various patterns. To address this challenge, several alternative classes of approach have been developed, including similarity-based, features and intervals, shapelets, dictionary, kernel, neural network, and hybrid approaches. While kernel, neural network, and hybrid approaches perform well overall, some specialized approaches are better suited for specific tasks. In this paper, we propose a new similarity-based classifier, Proximity Forest version 2.0 (PF 2.0), which outperforms previous state-of-the-art similarity-based classifiers across the UCR benchmark and outperforms state-of-the-art kernel, neural network, and hybrid methods on specific datasets in the benchmark that are best addressed by similarity-base methods. PF 2.0 incorporates three recent advances in time series similarity measures -- (1) computationally efficient early abandoning and pruning to speedup elastic similarity computations; (2) a new elastic similarity measure, Amerced Dynamic Time Warping (ADTW); and (3) cost function tuning. It rationalizes the set of similarity measures employed, reducing the eight base measures of the original PF to three and using the first derivative transform with all similarity measures, rather than a limited subset. We have implemented both PF 1.0 and PF 2.0 in a single C++ framework, making the PF framework more efficient

VMAD: a Virtual Machine for Advanced Dynamic Analysis of Programs

International audienceIn this paper, we present a virtual machine, VMAD (Virtual Machine for Advanced Dynamic analysis), enabling an efficient implementation of advanced profiling and analysis of programs. VMAD is organized as a sequence of basic operations where external modules associated to specific profiling strategies are dynamically loaded when required. The program binary files handled by VMAD are previously instrumented at compile time to include necessary data, instrumentation instructions and callbacks to the VM. Dynamic information, such as memory locations of launched modules, are patched at startup in the binary file. The LLVM compiler has been extended to automatically instrument programs according to both VMAD and the handled profiling strategies. VMAD's potential is illustrated by presenting a profiling strategy dedicated to loop nests. It collects all memory addresses that are accessed during a selected number of successive iterations of each loop. The collected addresses are consumed by an analysis process trying to interpolate addresses successively accessed through each memory reference as a linear function of some virtual loop indices. This profiling strategy using VMAD has been run on some of the SPEC2006 and Pointer Intensive benchmark suites, showing a very low time overhead, in most cases

Predictive toxicology using systemic biology and liver microfluidic "on chip" approaches: Application to acetaminophen injury

International audienceWe have analyzed transcriptomic, proteomic and metabolomic profiles of hepatoma cells cultivated inside a microfluidic biochip with or without acetaminophen (APAP). Without APAP, the results show an adaptive cellular response to the microfluidic environment, leading to the induction of anti-oxidative stress and cytoprotective pathways. In presence of APAP, calcium homeostasis perturbation, lipid peroxidation and cell death are observed. These effects can be attributed to APAP metabolism into its highly reactive metabolite. N-acetyl-p-benzoquinone imine (NAPQI). That toxicity pathway was confirmed by the detection of GSH-APAP, the large production of 2-hydroxybutyrate and 3-hydroxybutyrate, and methionine, cystine, and histidine consumption in the treated biochips. Those metabolites have been reported as specific biomarkers of hepatotoxicity and glutathione depletion in the literature. In addition, the integration of the metabolomic, transcriptomic and proteomic collected profiles allowed a more complete reconstruction of the APAP injury pathways. To our knowledge, this work is the first example of a global integration of microfluidic biochip data in toxicity assessment. Our results demonstrate the potential of that new approach to predictive toxicology

BARD : a structured technique for group elicitation of Bayesian networks to support analytic reasoning

In many complex, real-world situations, problem solving and decision making require effective reasoning about causation and uncertainty. However, human reasoning in these cases is prone to confusion and error. Bayesian networks (BNs) are an artificial intelligence technology that models uncertain situations, supporting better probabilistic and causal reasoning and decision making. However, to date, BN methodologies and software require (but do not include) substantial upfront training, do not provide much guidance on either the model building process or on using the model for reasoning and reporting, and provide no support for building BNs collaboratively. Here, we contribute a detailed description and motivation for our new methodology and application, Bayesian ARgumentation via Delphi (BARD). BARD utilizes BNs and addresses these shortcomings by integrating (1) short, high-quality e-courses, tips, and help on demand; (2) a stepwise, iterative, and incremental BN construction process; (3) report templates and an automated explanation tool; and (4) a multiuser web-based software platform and Delphi-style social processes. The result is an end-to-end online platform, with associated online training, for groups without prior BN expertise to understand and analyze a problem, build a model of its underlying probabilistic causal structure, validate and reason with the causal model, and (optionally) use it to produce a written analytic report. Initial experiments demonstrate that, for suitable problems, BARD aids in reasoning and reporting. Comparing their effect sizes also suggests BARD's BN-building and collaboration combine beneficially and cumulatively