Academic literature on the topic 'SGD asynchrone'

Abstract Influence of genetic dissimilarity among teak (Tectona grandis Linn. f.) clones on flowering synchrony was studied in a Clonal Seed Orchard (CSO) of teak in Karnataka, Southern India. Flowering phenology was monitored for all the 24 teak clones of the CSO and flowering synchrony between clones was assessed adopting a novel ‘overlap index’. Genetic dissimilarity among these clones was assessed adopting DNA based ISSR (Inter Simple Sequence Repeats) analysis. Large variation in the time of ‘flower initiation’ and of ‘peak flowering initiation’ was observed among the clones belonging to diverse sources, suggesting large asynchronous flowering. Cluster analysis based on ISSR marker indicated that the clones originating from a same source clustered together and there was a clear segregation based on their origin. Correlation analysis revealed a significant negative association between the average Jaccards’s dissimilarity index between pairs of clones and average peak flowering overlap index. Clones from geographically diverse regions had high genetic dissimilarity and also showed high flowering asynchrony within them.

Facing the challenge of the degradation of global water quality, it is urgent to realize the Sustainable Development Goal 6.3.2 (SDG 6.3.2), which focuses on improving global water quality. Currently, remote sensing technology is widely used for water quality monitoring. Existing water quality-monitoring studies have been conducted based on quantitative water quality inversion. It requires a high degree of the synchronization of the time and location of the collection of station monitoring data and remote sensing data (air–ground spatiotemporal synchronization), which can be resource intensive and time consuming. However, policymakers and the public are more interested in the quality of water (good or poor) than in the specific values of the water quality parameters, as evidenced by the emergence of SDG 6.3.2. In this study, we change the traditional idea of quantitative water quality research, focus on water quality qualitative research combined with the characteristics of water pollution, propose a remote sensing water quality sample enhancement method under the condition of “air–ground spatiotemporal asynchrony”, and construct a remote sensing water quality sample library. On the basis of this sample library, a random forest water quality classification model was constructed to classify water quality qualitatively. We obtained the distribution of good water bodies in Deqing County, China, for example, from 2013 to 2022. The results show that the model has high accuracy (Kappa = 0.6004, OA = 0.8387), and we found that the water quality in Deqing County improved in the order of “major rivers, lakes, and tributaries” during the period from 2013 to 2015. This also verifies the feasibility of using this sample enhancement method to conduct qualitative research on water quality. Based on this water quality classification model, a set of spatial-type evaluation processes of SDG 6.3.2 based on image elements was designed. The evaluation results show that the water quality situation in Deqing County can be divided into two stages: there is a trend of substantial improvement from 2013 (evaluated value of SDG 6.3.2 = 63.25) to 2015 (evaluated value of SDG 6.3.2 = 83.16); and it has remained stable and fluctuating after reaching the good environmental water quality since 2015. This study proposes a simple method for rapidly evaluating SDG 6.3.2 via utilizing easily accessible Landsat 8 and water quality-monitoring data to classify water quality. The method can directly obtain water quality category information without the need for additional sampling, thus saving costs. It is a very simple process that is easy to implement, while also providing a high level of accuracy. This significantly reduces the barriers to evaluating SDG 6.3.2, supports the realization of the sustainable management of water resources globally, and is highly generalizable.

Abstract Mitral valve prolapse (MVP) is the common valvular heart disease, with a prevalence about 1–3% in the population. MVP is characterized on echocardiography by an abnormal systolic displacement of mitral valve leaflets into left atrium (LA) ≥ 2 mm above the mitral annulus. We described a clinical case of a 15 years–old boy with a first clinical presentation of out–of–hospital cardiac arrest related to ventricular fibrillation (VF) with a successful resuscitation after 2 DC shocks. Retrospectively, an episode of loss of awareness reported as post–minational. No familiar history of sudden cardiac death (SCD) or MVP was noticed. In the emergency room, the patient was hemodynamically unstable and was sedated and intubated. The surface electrocardiogram (ECG) showed sinus rhythm, QRS with fragmentation, and frequent monomorphic isolated premature ventricular contractions (PVCs) with RBBB/LAFB configuration.Frequent monomorphic PVCs with bigeminy was observed during continuous ECG monitoring, so the patient was treated with magnesium sulphate and metoprolol. The patient was subsequently weaned from ventilation support, presenting good gas exchange and without neurological sequalae. The 24h Holter monitoring showed an average of 53 bpm, extremely rare supraventricular extrasystoles, high incidence of monomorphic PVCs (23% of the total beats) with 4 couplets. Transthoracic echocardiography documented normal size and thickness with systolic function (GLS –21%, mild asynchrony of posterior basal segments), bi–leaflet prolapse associated to mild–moderate mitral regurgitation, and mitral annular disjunction (MAD) of 6.2 mm. The late gadolinium enhancement cardiac magnetic resonance showed bi–leaflet prolapse with mild insufficiency and maximum MAD 9 mm, mild LA dilatation, and slightly dilated both ventricles, without regional wall motion abnormalities; no scar or fibrosis at the late gadolinium enhancement was observed. According to guidelines, the patient received a subcutaneous implantable cardiac defibrillator for the secondary prevention of SCD and maintained under betablockers in order to control the arrhythmic burden. The genetic sampling did not show any pathogenetic mutation potentially related to cardiomyopathies or channelopathies. This case show an arrhythmic MVP complex with probably posterior papillary muscle source. The early PVCs with R/T constituted a possible trigger of VF. The betablocker therapy exhibited a full suppression of extrasystolic burden.

To the present day, Covid19 has become the star of phenomena. Many of the gaps in education, colourful stories, have emerged as a consequence of the pandemic education; research and innovation as the eyes of the educational body; on the one side, modern countries have been successful in pandemic education, but sad stories come from developing countries, and underdevelopment countries have been terrified by pandemic education until I have written this article. Thousands of papers on science and innovation have had a significant effect on scientific results, but this is just a snapshot of a narrative on the distorted trajectory of pandemic education. However, in-depth analyses of whether and how to continue education in the midst of the pandemic also had an impact on the pandemic education. In addition; digital, internet, synchronized, Asynchronize, distance learning, remote learning has been used as an approach that underpins the policies of each country's current global circumstances. Technology, social and humaniora are in the process of research and have shown a measurable path for pandemic education. The primary focus of pandemic education research was meaningful visionary education; what, when, where, or which measurable approach [is and would be] to solve local and global challenges. So, through this article, I conducted narratively to leverage educational society insight in order to achieve a visionary education system. I concluded that visionary education would establish significant education from a local and global viewpoint.

This paper 1 studies how to schedule hyperparameters to improve generalization of both centralized single-machine stochastic gradient descent (SGD) and distributed asynchronous SGD (ASGD). SGD augmented with momentum variants (e.g., heavy ball momentum (SHB) and Nesterov’s accelerated gradient (NAG)) has been the default optimizer for many tasks, in both centralized and distributed environments. However, many advanced momentum variants, despite empirical advantage over classical SHB/NAG, introduce extra hyperparameters to tune. The error-prone tuning is the main barrier for AutoML. Centralized SGD : We first focus on centralized single-machine SGD and show how to efficiently schedule the hyperparameters of a large class of momentum variants to improve generalization. We propose a unified framework called multistage quasi-hyperbolic momentum (Multistage QHM), which covers a large family of momentum variants as its special cases (e.g. vanilla SGD/SHB/NAG). Existing works mainly focus on only scheduling learning rate α ’s decay, while multistage QHM allows additional varying hyperparameters (e.g., momentum factor), and demonstrates better generalization than only tuning α . We show the convergence of multistage QHM for general nonconvex objectives. Distributed SGD : We then extend our theory to distributed asynchronous SGD (ASGD), in which a parameter server distributes data batches to several worker machines and updates parameters via aggregating batch gradients from workers. We quantify the asynchrony between different workers (i.e., gradient staleness), model the dynamics of asynchronous iterations via a stochastic differential equation (SDE), and then derive a PAC-Bayesian generalization bound for ASGD. As a byproduct, we show how a moderately large learning rate helps ASGD to generalize better. Our tuning strategies have rigorous justifications rather than a blind trial-and-error as we theoretically prove why our tuning strategies could decrease our derived generalization errors in both cases. Our strategies simplify the tuning process and beat competitive optimizers in test accuracy empirically. Our codes are publicly available https://github.com/jsycsjh/centralized-asynchronous-tuning.

Dans le secteur aérien, la prévision des prix joue un rôle important tant pour les clients que pour les agences de voyage. Dans cette thèse, nous présentons quelques approches pratiques pour aider les voyageurs à faire face à l'incertitude dans l'évolution du prix des billets et nous proposons un cadre basé sur des données pour surveiller les performances des modèles de prévision de séries chronologiques. La descente de gradient stochastique (SGD) représente la méthode d'optimisation plus utilisée dans le domaine de l'apprentissage automatique et cela est également vrai pour les systèmes distribués, qui ces dernières années sont de plus en plus utilisés pour des modèles complexes formés sur des ensembles de données massifs. Dans les systèmes asynchrones, les travailleurs peuvent utiliser des versions obsolètes des paramètres, ce qui ralentit la convergence SGD. Dans cette thèse, nous fournissons une analyse concise du taux de convergence lorsque les effets conjoints de la sparsification et de l'asynchronie sont pris en compte, et montrons que le SGD clairsemé converge au même taux que le SGD standard. Récemment, SGD a également joué un rôle important en tant que moyen d'effectuer une inférence bayésienne approximative. Les algorithmes MCMC à gradient stochastique utilisent SGD avec un taux d'apprentissage constant pour obtenir des échantillons à partir de la distribution postérieure. Dans cette thèse, nous introduisons une approche pratique de l'échantillonnage postérieur, qui nécessite des hypothèses plus faibles que les algorithmes existants
In the airline industry, price prediction plays a significant role both for customers and travel companies. The former are interested in knowing the price evolution to get the cheapest ticket, the latter want to offer attractive tour packages and maximize their revenue margin. In this work we introduce some practical approaches to help travelers in dealing with uncertainty in ticket price evolution and we propose a data-driven framework to monitor time-series forecasting models' performance. Stochastic Gradient Descent (SGD) represents the workhorse optimization method in the field of machine learning and this is true also for distributed systems, which in last years are increasingly used for complex models trained on massive datasets. In asynchronous systems workers can use stale versions of the parameters, which slows SGD convergence. In this thesis we fill the gap in the literature and study sparsification methods in asynchronous settings. We provide a concise convergence rate analysis when the joint effects of sparsification and asynchrony are taken into account, and show that sparsified SGD converges at the same rate of standard SGD. Recently, SGD has played an important role also as a way to perform approximate Bayesian Inference. Stochastic gradient MCMC algorithms use indeed SGD with constant learning rate to obtain samples from the posterior distribution. Despite some promising results restricted to simple models, most of the existing works fall short in easily dealing with the complexity of the loss landscape of deep models. In this thesis we introduce a practical approach to posterior sampling, which requires weaker assumptions than existing algorithms

Thèse (M.Ing) -- École de technologie supérieure, Montréal, 2005.
"Mémoire présenté à l'École de technologie supérieure comme exigence partielle à l'obtention de la maîtrise en génie électrique". CaQMUQET Bibliogr. : f. [97]. Également disponible en version électronique. CaQMUQET