Collision-free motion planning for fiber positioner robots: discretization of velocity profiles

The next generation of large-scale spectroscopic survey experiments such as DESI, will use thousands of fiber positioner robots packed on a focal plate. In order to maximize the observing time with this robotic system we need to move in parallel the fiber-ends of all positioners from the previous to the next target coordinates. Direct trajectories are not feasible due to collision risks that could undeniably damage the robots and impact the survey operation and performance. We have previously developed a motion planning method based on a novel decentralized navigation function for collision-free coordination of fiber positioners. The navigation function takes into account the configuration of positioners as well as their envelope constraints. The motion planning scheme has linear complexity and short motion duration (~2.5 seconds with the maximum speed of 30 rpm for the positioner), which is independent of the number of positioners. These two key advantages of the decentralization designate the method as a promising solution for the collision-free motion-planning problem in the next-generation of fiber-fed spectrographs. In a framework where a centralized computer communicates with the positioner robots, communication overhead can be reduced significantly by using velocity profiles consisting of a few bits only. We present here the discretization of velocity profiles to ensure the feasibility of a real-time coordination for a large number of positioners. The modified motion planning method that generates piecewise linearized position profiles guarantees collision-free trajectories for all the robots. The velocity profiles fit few bits at the expense of higher computational costs.Comment: SPIE Astronomical Telescopes + Instrumentation 2014 in Montr\'eal, Quebec, Canada. arXiv admin note: substantial text overlap with arXiv:1312.164

Prediction of early death after atrial fibrillation diagnosis using a machine learning approach: A French nationwide cohort study.

AimsAtrial fibrillation is associated with important mortality but the usual clinical risk factor based scores only modestly predict mortality. This study aimed to develop machine learning models for the prediction of death occurrence within the year following atrial fibrillation diagnosis and compare predictive ability against usual clinical risk scores.Methods and resultsWe used a nationwide cohort of 2,435,541 newly diagnosed atrial fibrillation patients seen in French hospitals from 2011 to 2019. Three machine learning models were trained to predict mortality within the first year using a training set (70% of the cohort). The best model was selected to be evaluate and compared with previously published scores on the validation set (30% of the cohort). Discrimination of the best model was evaluated using the C index. Within the first year following atrial fibrillation diagnosis, 342,005 patients (14.4%) died after a period of 83 (SD 98) days (median 37 [10-129]). The best machine learning model selected was a deep neural network with a C index of 0.785 (95% CI, 0.781-0.789) on the validation set. Compared to clinical risk scores, the selected model was superior to the CHA2DS2-VASc and HAS-BLED risk scores and superior to dedicated scores such as Charlson Comorbidity Index and Hospital Frailty Risk Score to predict death within the year following atrial fibrillation diagnosis (C indexes: 0.597; 0.562; 0.643; 0.626 respectively. PConclusionMachine learning algorithms predict early death after atrial fibrillation diagnosis and may help clinicians to better risk stratify atrial fibrillation patients at high risk of mortality.Translational perspectiveAtrial fibrillation is responsible for a substantial proportion of short-term mortality making futile, complex and expensive, cardiovascular procedures/devices or therapies that will not change overall prognosis due to competing risk between cardiovascular and non-cardiovascular death. Machine learning algorithms predict early mortality in atrial fibrillation patients with a better ability than previously developed traditional clinical risk scores. A Machine learning approach may help clinicians to better stratify atrial fibrillation patients at high risk of mortality and may assist physicians in decision-making when managing atrial fibrillation patients in a holistic and integrated care manner

laurent bouri's Quick Files

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Evaluation des logiciels de correction de lectures longues

This report compares several read error correction software that use 3rd generation sequencing technology (long reads). The experimentations have been performed on several reference genomes and the results evaluated with QUAST. The long read error correctors that have been evaluated are: LSC-2, Proovread, Ectools, Lordec, Nanocorr, Nas, Jabba, Pacbiotoca, Lorma et MHAP. The first 8 software can merge long and short reads, while the last 3 software use only long reads.Ce rapport compare plusieurs programmes de correction d'erreurs de lectures (reads)issus de la technologie de séquençage de 3ème génération (longues lectures). Les expérimentations ont été menées sur plusieurs génomes de référence. Les logiciels de correction d'erreurs évalués sont : LSC-2, Proovread, Ectools, Lordec, Nanocorr, Nas , Jabba, Pacbiotoca, Lorma et MHAP. Les 8 premiers mixent longues et courtes lectures tandis que les 3 derniers n'utilisent que des longues lectures

Evaluation des logiciels d'assemblage utilisant des lectures longues

Ce rapport compare plusieurs programmes d'assemblage de génomes qui utilisent les technologies de séquençage de 3ème génération (longues lectures). Les expérimentations ont été faites sur 4 génomes de référence et les résultats évalués avec le logiciel QUAST.Les 11 logiciels d'assemblage évalués sont : Celera Assembler, Falcon, Miniasm, Newbler, SGA Assembler, Smart-denovo, Abruijn, RA, DBG2OLC, Spades et Cerulean. Les 8 premiers n'utilisent que des longues lectures tandis que les 3 derniers mixent longues et courtes lectures

Evaluation of genome assembly software based on long reads

During the last 30 years, Genomics has been revolutionized by the development of first- and second-generation sequencing (SGS) technologies, enabling the completion of many remarkable projects as the Human Genome Project, the 1000 Genomes Project and the Human Microbiome Project.In the last decade, SGS technologies based on massive parallel sequencing have dominated the market, thanks to their ability to produce enormous volumes of data cheaply. However, often genes and regions of interest are not completely or accurately assembled, complicating analyses or requiring additional cloning efforts for obtaining the correct sequences. The fundamental obstacle in SGS technologies for obtaining high quality genome assembly is the existence of repetitions in the sequences. A promising solution to this issue is the advent of Third-generation sequencing (TGS) technologies based on long read sequencing.TGS technologies have been used to produce highly accurate de novo assemblies of hundreds of microbial genomes and highly contiguous reconstructions of many dozens of plant and animal genomes, enabling new insights into evolution and sequence diversity. They have also been applied to resequencing analyses, to create detailed maps of structural variations in many species. Also, these new technologies have been used to fill in many of the gaps in the human reference genome. In this report, we compare and evaluate several genome assembly software based on TSG technology. The experimentation has been performed on 4 reference genomes and the results evaluated with the QUAST software

Prediction of incident atrial fibrillation in post-stroke patients using machine learning: a French nationwide study

BackgroundTargeting ischemic strokes patients at risk of incident atrial fibrillation (AF) for prolonged cardiac monitoring and oral anticoagulation remains a challenge. Clinical risk scores have been developed to predict post-stroke AF with suboptimal performances. Machine learning (ML) models are developing in the field of AF prediction and may be used to discriminate post-stroke patients at risk of new onset AF. This study aimed to evaluate ML models for the prediction of AF and to compare predictive ability to usual clinical scores.MethodsBased on a French nationwide cohort of 240,459 ischemic stroke patients without AF at baseline from 2009 to 2012, ML models were trained on a train set and the best model was selected to be evaluate on the test set. Discrimination of the best model was evaluated using the C index. We finally compared our best model with previously described clinical scores.ResultsDuring a mean follow-up of 7.9 ± 11.5 months, 14,095 patients (mean age 77.6 ± 10.6; 50.3% female) developed incident AF. After training, the best ML model selected was a deep neural network with a C index of 0.77 (95% CI 0.76-0.78) on the test set. Compared to traditional clinical scores, the selected model was statistically significantly superior to the CHA2DS2-VASc score, Framingham risk score, HAVOC score and C2HEST score (P ConclusionsML algorithms predict incident AF post-stroke with a better ability than previously developed clinical scores. AF: atrial fibrillation; DNN: deep neural network; IS: ischemic stroke; KNN: K-nearest neighbors; LR: logistic regression; RFC: random forest classifier; XGBoost: extreme gradient boosting

Collision avoidance in next-generation fiber positioner robotic systems for large survey spectrographs

Some of the next-generation massive spectroscopic survey projects plan to use thousands of fiber positioner robots packed at a focal plane to quickly move the fiber ends in parallel from the previous to the next target points. The most direct trajectories are prone to collision that could damage the robots and have an impact on the survey operation. We thus present here a motion planning method based on a novel decentralized navigation function for collision-free coordination of fiber positioners. The navigation function takes into account the configuration of positioners as well as the actuator constraints. We provide details of the proof of convergence and collision avoidance. Decentralization results in linear complexity for the motion planning as well as no dependence of motion duration on the number of positioners. Therefore, the coordination method is scalable for large-scale spectrograph robots. The short in-motion duration of positioner robots will thus allow the time dedicated for observation to be maximized

Clinical Phenotypes and Atrial Fibrillation Recurrences after Catheter Ablation: An Unsupervised Cluster Analysis.

Catheter ablation (CA) is a well-established treatment of atrial fibrillation (AF). Data-driven cluster analysis is able to better distinguish prognostically-relevant phenotype clusters among patients with AF. We performed a hierarchical cluster analysis in a cohort of AF patients undergoing a first CA and evaluate associations between identified clusters and recurrences of arrhythmia following ablation. The study included 209 AF patients treated with CA. 3 clusters with distinct characteristics were identified. Recurrences at one year occurred in 27.2% in Cluster 1, 43.2% in Cluster 2 and 60.9% in Cluster 3 (p2DS2-VASc score, left atrial volume, type of atrial fibrillation and ejection fraction. To concluded, cluster analysis identified three statistically-driven groups among AF patients treated with CA with different risks for arrhythmia recurrences