作者: 矛盾心理 時(shí)間: 2025-3-21 20:58
Amadou Thierno Diallo,Ahmet Suayb Gundogdutral step of the Data Assimilation approach and, in general, in several data analysis procedures. In particular, we propose a parallel algorithm, based on the use of Recursive Filters to approximate the Gaussian convolution in a very fast way. Tests and experiments confirm the efficiency of the proposed implementation.作者: 違反 時(shí)間: 2025-3-22 03:20 作者: CRUMB 時(shí)間: 2025-3-22 07:22
Accelerated Gaussian Convolution in a Data Assimilation Scenariotral step of the Data Assimilation approach and, in general, in several data analysis procedures. In particular, we propose a parallel algorithm, based on the use of Recursive Filters to approximate the Gaussian convolution in a very fast way. Tests and experiments confirm the efficiency of the proposed implementation.作者: hyperuricemia 時(shí)間: 2025-3-22 10:11 作者: 松緊帶 時(shí)間: 2025-3-22 16:50 作者: 松緊帶 時(shí)間: 2025-3-22 17:17 作者: 裂口 時(shí)間: 2025-3-22 23:31 作者: Consensus 時(shí)間: 2025-3-23 04:17 作者: 不來(lái) 時(shí)間: 2025-3-23 09:32
https://doi.org/10.1007/978-1-4020-6598-9eriodic monitoring of the fire spread prediction error . estimated by the normalized symmetric difference for each simulation run. Our new strategy avoid wasting too much computing time running unfit individuals thanks to an early adaptive evaluation.作者: FOVEA 時(shí)間: 2025-3-23 12:09 作者: 解決 時(shí)間: 2025-3-23 17:29
Kosta Kostadinov,Jagadish Thakerextra cost of solving a small number of ordinary differential equations that contain physical information. This framework shows the potential of using machine learning techniques combined with prior physical knowledge to improve the prediction of time-averaged quantities in chaotic systems.作者: 或者發(fā)神韻 時(shí)間: 2025-3-23 19:09 作者: CHOKE 時(shí)間: 2025-3-23 23:22
Early Adaptive Evaluation Scheme for Data-Driven Calibration in Forest Fire Spread Predictioneriodic monitoring of the fire spread prediction error . estimated by the normalized symmetric difference for each simulation run. Our new strategy avoid wasting too much computing time running unfit individuals thanks to an early adaptive evaluation.作者: 反對(duì) 時(shí)間: 2025-3-24 05:38
Applications of Data Assimilation Methods on a Coupled Dual Porosity Stokes Modelt data assimilation methods on a finite element implementation of the coupled dual porosity Stokes system. We also study how observations on different variables of the system affect the data assimilation process.作者: pacific 時(shí)間: 2025-3-24 09:57
Learning Ergodic Averages in Chaotic Systemsextra cost of solving a small number of ordinary differential equations that contain physical information. This framework shows the potential of using machine learning techniques combined with prior physical knowledge to improve the prediction of time-averaged quantities in chaotic systems.作者: Anemia 時(shí)間: 2025-3-24 13:22 作者: Bother 時(shí)間: 2025-3-24 16:42 作者: 階層 時(shí)間: 2025-3-24 19:38
A Machine-Learning-Based Importance Sampling Method to Compute Rare Event Probabilitiesne-learning-based surrogates to solve the Bayesian inverse problems that give rise to the biasing distribution. This biasing distribution can then be used in an importance sampling procedure to estimate the extreme excursion probabilities.作者: forthy 時(shí)間: 2025-3-25 01:54
0302-9743 nce on Computational Science, ICCS 2020, held in Amsterdam, The Netherlands, in June 2020.*..The total of 101 papers and 248 workshop papers presented in this book set were carefully reviewed and selected from 719 submissions (230 submissions to the main track and 489 submissions to the workshops). 作者: 子女 時(shí)間: 2025-3-25 05:47
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/233084.jpg作者: Desert 時(shí)間: 2025-3-25 10:54 作者: defibrillator 時(shí)間: 2025-3-25 14:56
https://doi.org/10.1007/978-1-4020-6598-9ging technologies are used to help wildfire analysts determine fire behavior and spread aiming at a more accurate prediction results and efficient use of resources in fire fighting. Natural hazards simulations need to deal with data input uncertainty and their impact on prediction results, usually r作者: 改良 時(shí)間: 2025-3-25 17:54
https://doi.org/10.1007/978-1-4020-6598-9A) methods aim to best approximate model states with imperfect measurements, where particle Filters (PFs) are commonly used with discrete-event simulations. In this paper, we study three critical conditions of DA using PFs: (1) the time interval of iterations, (2) the number of particles and (3) the作者: 即席演說(shuō) 時(shí)間: 2025-3-25 23:23
Ivo Allegrini,Francesca Costabilelatforms such as Foursquare, Twitter, and Yelp has motivated a considerable amount of research focused on POI recommendations within the last decade. Inspired by the success of deep neural networks in many fields, researchers are increasingly interested in using neural networks such as Recurrent Neu作者: milligram 時(shí)間: 2025-3-26 04:06
Global Warming Affecting Mexican Firms, We then generated a dynamic graph with our visualization tool and performed malware attribution analysis. We found: 1) malware distribution networks form clusters rather than a single network; 2) those cluster sizes follow the Power Law; 3) there is a correlation between cluster size and the number作者: escalate 時(shí)間: 2025-3-26 07:59 作者: 去掉 時(shí)間: 2025-3-26 09:11
D. Prabhakar,P. K. Garg,Anoop Kumar Shuklaon various scales can help to improve quality of living, enhance urban management, and advance the development of smart cities. But it is widely known that the performance of algorithms for data mining and analysis heavily relies on the quality of input data. The main aim of this paper is helping LB作者: 共同時(shí)代 時(shí)間: 2025-3-26 14:48 作者: cumber 時(shí)間: 2025-3-26 20:14
Integrated Water Resource Management,ystem. The PI-ESN is trained by using (i) data, which contains no information on the unmeasured state, and (ii) the physical equations of a prototypical chaotic dynamical system. Non-noisy and noisy datasets are considered. First, it is shown that the PI-ESN can accurately reconstruct the unmeasured作者: galley 時(shí)間: 2025-3-27 00:35 作者: Notify 時(shí)間: 2025-3-27 01:59 作者: 胎兒 時(shí)間: 2025-3-27 05:24
https://doi.org/10.1057/9781137329417etworks is difficult and is mostly done with a static approach, neglecting time delayed interdependences. Tensors are objects that naturally represent multilayer networks and in this paper, we propose a new methodology based on Tucker tensor autoregression in order to build a multilayer network dire作者: 得罪 時(shí)間: 2025-3-27 11:29
Kosta Kostadinov,Jagadish Thakered combining time-distributed observations with a dynamic model in an optimal way. The typical assimilation scheme is made up of two major steps: a . and a . of the prediction by including information provided by observed data. This is the so called .-. cycle. Classical methods for DA include Kalman作者: 親愛 時(shí)間: 2025-3-27 17:30 作者: Hallmark 時(shí)間: 2025-3-27 17:51
https://doi.org/10.1057/9781137329417de classification, as well as community detection tasks, are still open research problems in SNA. Hence, SNA has become an interesting and appealing domain in Artificial Intelligence (AI) research. Immanent facts about social network structures can be effectively harnessed for training AI models in 作者: Muffle 時(shí)間: 2025-3-28 01:41
Amadou Thierno Diallo,Ahmet Suayb Gundogdu nature of real systems, it is very difficult to predict data: a small perturbation from initial state can generate serious errors. Data Assimilation is used to estimate the best initial state of a system in order to predict carefully the future states. Therefore, an accurate and fast Data Assimilat作者: 津貼 時(shí)間: 2025-3-28 02:43 作者: macular-edema 時(shí)間: 2025-3-28 09:56
https://doi.org/10.1007/978-3-030-50433-5artificial intelligence; computer networks; genetic algorithms; image processing; machine learning; mathe作者: 連詞 時(shí)間: 2025-3-28 11:27
978-3-030-50432-8Springer Nature Switzerland AG 2020作者: assail 時(shí)間: 2025-3-28 15:28
Computational Science – ICCS 2020978-3-030-50433-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 連累 時(shí)間: 2025-3-28 21:22 作者: 上腭 時(shí)間: 2025-3-29 02:44
Conference proceedings 2020omplex Social Systems through the Lens of Computational Science; Computational Health; Computational Methods for Emerging Problems in (Dis-)Information Analysis.Part V: Computational Optimization, Modelling and Simulation; Computational Science in IoT and Smart Systems; Computer Graphics, Image Proc作者: 車床 時(shí)間: 2025-3-29 06:53 作者: 尊重 時(shí)間: 2025-3-29 09:07
https://doi.org/10.1007/978-1-4020-6598-9ent errors has advantages over an under estimation. Moreover, a slight over estimation has better estimation accuracy and is more responsive to system changes than an accurate perceived level of measurement errors.作者: 易于 時(shí)間: 2025-3-29 14:36 作者: OREX 時(shí)間: 2025-3-29 16:25 作者: 調(diào)整校對(duì) 時(shí)間: 2025-3-29 23:25 作者: tenuous 時(shí)間: 2025-3-30 01:39 作者: 記成螞蟻 時(shí)間: 2025-3-30 08:01
Kosta Kostadinov,Jagadish Thakereural Assimilation (NA), a coupled neural network made of two Recurrent Neural Networks trained on forecasting data and observed data respectively. We prove that the solution of NA is the same of KF. As NA is trained on both forecasting and observed data, after the phase of training NA is used for t作者: Irrepressible 時(shí)間: 2025-3-30 08:31
https://doi.org/10.1057/9781137329417on Learning via Knowledge-Graph Embeddings and ConvNet (RLVECN), for studying and extracting meaningful facts from social network structures to aid in node classification as well as community detection tasks. Our proposition utilizes an edge sampling approach for exploiting features of the social gr作者: Expostulate 時(shí)間: 2025-3-30 15:16 作者: diathermy 時(shí)間: 2025-3-30 18:22
Strategic Use of Data Assimilation for Dynamic Data-Driven Simulationent errors has advantages over an under estimation. Moreover, a slight over estimation has better estimation accuracy and is more responsive to system changes than an accurate perceived level of measurement errors.作者: 自戀 時(shí)間: 2025-3-30 23:46
PDPNN: Modeling User Personal Dynamic Preference for Next Point-of-Interest Recommendationh can guide predictions in different temporal and spatial contexts. To this end, we propose a new deep neural network model called Personal Dynamic Preference Neural Network(PDPNN). The core of the PDPNN model includes two parts: one part learns the user’s personal long-term preferences from the his作者: 執(zhí)拗 時(shí)間: 2025-3-31 04:49
Spatiotemporal Filtering Pipeline for Efficient Social Networks Data Processing Algorithmsiciency of the pipeline is demonstrated on three practical applications using different LBSN: touristic itinerary generation using Facebook locations, sentiment analysis of an area with the help of Twitter and VK.com, and multiscale events detection from Instagram posts.作者: faucet 時(shí)間: 2025-3-31 07:40
Normal Grouping Density Separation (NGDS): A Novel Object-Driven Indoor Point Cloud Partition Method3.8pp), and SSP (by 10.3pp). The experiment carried out indicates superiority of the proposed method as a partition/segmentation algorithm - a process being usually a preprocessing stage of many object detection workflows.作者: amyloid 時(shí)間: 2025-3-31 09:22
