Etude expé́rimentale du transport hydraulique de grandes particules en conduite horizontale et en forme de S

We study the pressure drop and flow regimes for large spheres with respect to the diameter of the pipe (5, 10 and 15%) by differential pressure measurements and visualizations. Two densities are used. The losses are smaller for large grain sizes, and density has a strong effect on the transition point between regimes with a stationary bed flow and dispersed flows. Models based on the Froude number are tested. Finally, we study mixtures of size and / or density. We are also interested in plugging and unplugging transients

Etude expé́rimentale du transport hydraulique de grandes particules en conduite horizontale et en forme de S

We study the pressure drop and flow regimes for large spheres with respect to the diameter of the pipe (5, 10 and 15%) by differential pressure measurements and visualizations. Two densities are used. The losses are smaller for large grain sizes, and density has a strong effect on the transition point between regimes with a stationary bed flow and dispersed flows. Models based on the Froude number are tested. Finally, we study mixtures of size and / or density. We are also interested in plugging and unplugging transients

An initialization scheme for supervized K-means

Over the last years, researchers have focused their attention on a new approach, supervised clustering, that combines the main characteristics of both traditional clustering and supervised classification tasks. Motivated by the importance of the initialization in the traditional clustering context, this paper explores to what extent supervised initialization step could help traditional clustering to obtain better performances on supervised clustering tasks. This paper reports experiments which show that the simple proposed approach yields a good solution together with significant reduction of the computational cost

Ampelovirus and Vitivirus relationships in grapevine

Ampelovirus and Vitivirus relationships in grapevine. 16e Rencontres de Virologie Végétal

Biquality Learning: a Framework to Design Algorithms Dealing with Closed-Set Distribution Shifts

Training machine learning models from data with weak supervision and dataset shifts is still challenging. Designing algorithms when these two situations arise has not been explored much, and existing algorithms cannot always handle the most complex distributional shifts. We think the biquality data setup is a suitable framework for designing such algorithms. Biquality Learning assumes that two datasets are available at training time: a trusted dataset sampled from the distribution of interest and the untrusted dataset with dataset shifts and weaknesses of supervision (aka distribution shifts). The trusted and untrusted datasets available at training time make designing algorithms dealing with any distribution shifts possible. We propose two methods, one inspired by the label noise literature and another by the covariate shift literature for biquality learning. We experiment with two novel methods to synthetically introduce concept drift and class-conditional shifts in real-world datasets across many of them. We opened some discussions and assessed that developing biquality learning algorithms robust to distributional changes remains an interesting problem for future research

ECOTS: Early Classification in Open Time Series

Learning to predict ahead of time events in open time series is challenging. While Early Classification of Time Series (ECTS) tackles the problem of balancing online the accuracy of the prediction with the cost of delaying the decision when the individuals are time series of finite length with a unique label for the whole time series. Surprisingly, this trade-off has never been investigated for open time series with undetermined length and with different classes for each subsequence of the same time series. In this paper, we propose a principled method to adapt any technique for ECTS to the Early Classification in Open Time Series (ECOTS). We show how the classifiers must be constructed and what the decision triggering system becomes in this new scenario. We address the challenge of decision making in the predictive maintenance field. We illustrate our methodology by transforming two state-of-the-art ECTS algorithms for the ECOTS scenario and report numerical experiments on a real dataset for predictive maintenance that demonstrate the practicality of the novel approach

Open challenges for Machine Learning based Early Decision-Making research

More and more applications require early decisions, i.e. taken as soon as possible from partially observed data. However, the later a decision is made, the more its accuracy tends to improve, since the description of the problem to hand is enriched over time. Such a compromise between the earliness and the accuracy of decisions has been particularly studied in the field of Early Time Series Classification. This paper introduces a more general problem, called Machine Learning based Early Decision Making (ML-EDM), which consists in optimizing the decision times of models in a wide range of settings where data is collected over time. After defining the ML-EDM problem, ten challenges are identified and proposed to the scientific community to further research in this area. These challenges open important application perspectives, discussed in this paper

Virus preparations from the mixed-infected P70 Pinot Noir accession exhibit GLRaV-1/GVA ‘end-to-end’ particles

P70 is a Pinot Noir grapevine accession that displays strong leafroll disease symptoms. A high-throughput sequencing (HTS)-based analysis established that P70 was mixed-infected by two variants of grapevine leafroll-associated virus 1 (GLRaV-1, genus Ampelovirus) and one of grapevine virus A (GVA, genus Vitivirus) as well as by two viroids (hop stunt viroid [HSVd] and grapevine yellow speckle viroid 1 [GYSVd1]) and four variants of grapevine rupestris stem pitting-associated virus (GRSPaV). Immunogold labelling using gold particles of two different diameters revealed the existence of ‘hybrid’ particles labelled at one end as GLRaV-1, with the rest labelled as GVA. In this work, we suggest that immunogold labelling can provide information about the biology of the viruses, going deeper than just genomic information provided by HTS, from which no recombinant or ‘chimeric’ GLRaV-1/GVA sequences had been identified in the dataset. Our observations suggest an unknown interaction between members of two different viral species that are often encountered together in a single grapevine, highlighting potential consequences in the vector biology and epidemiology of leafroll and rugose-wood diseases