5,727 research outputs found
An improved quasi-reversibility method for a terminal-boundary value multi-species model with white Gaussian noise
No full textUpon the recent development of the quasi-reversibility method for terminal value parabolic problems in Nguyen et al. (2019), it is imperative to investigate the convergence analysis of this regularization method in the stochastic setting. In this paper, we positively unravel this open question by focusing on a coupled system of Dirichlet reaction-diffusion equations with additive white Gaussian noise on the terminal data. In this regard, the approximate problem is designed by adding the so-called perturbing operator to the original problem and by exploiting the Fourier reconstructed terminal data. By this way, Gevrey-type source conditions are included, while we successfully maintain the logarithmic stability estimate of the corresponding stabilized operator, which is necessary for the error analysis. As the main theme of this work, we prove the error bounds for the concentrations and for the concentration gradients, driven by a large amount of weighted energy-like controls involving the expectation operator. Compared to the classical error bounds in L-2 and H-1 that we obtained in the previous studies, our analysis here needs a higher smoothness of the true terminal data to ensure their reconstructions from the stochastic fashion. Two numerical examples are provided to corroborate the theoretical results. Published by Elsevier B.V.V.A. Khoa was funded by US Army Research Laboratory and US Army Research Office grant W911NF-19-1-0044. V.A. Khoa's work was also partly supported by the Research Foundation-Flanders (FWO), Belgium under the project named "Approximations for forward and inverse reaction-diffusion problems related to cancer models''. N.H. Tuan and V.V. Au are funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under Grant No. B2020-18-03.Khoa, VA (corresponding author), Univ North Carolina Charlotte, Dept Math & Stat, Charlotte, NC 28223 USA.
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Standardized Hudup dataset based on Film Trust data
No full textStandardized Hudup dataset receives information from raw data, which is composed of ten units such as “hdp_config”, “hdp_account”, “hdp_attribute_map”, “hdp_nominal”, “hdp_user”, “hdp_item”, “hdp_rating”, “hdp_context_template”, “hdp_context”, and “hdp_sample”. Each unit has particular functions, which is described in the section of data description. Hudup dataset is meta-data which models any raw data with abstract level. The raw data which is source of Hudup dataset here is Film Trust data. It is possible to consider that Hudup dataset is secondary data whereas Film Trust is primary data. The raw rating data Film Trust has 35,497 ratings from 1,508 users on 2,071 films (items), which is available at https://guoguibing.github.io/librec/datasets/filmtrust.zip
Standardized Hudup dataset based on Movielens 100k
No full textStandardized Hudup dataset receives information from raw data, which is composed of ten units such as “hdp_config”, “hdp_account”, “hdp_attribute_map”, “hdp_nominal”, “hdp_user”, “hdp_item”, “hdp_rating”, “hdp_context_template”, “hdp_context”, and “hdp_sample”. Each unit has particular functions, which is described in the section of data description. Hudup dataset is meta-data which models any raw data with abstract level. The default raw data which is source of Hudup dataset here is Movielens dataset (GroupLens, 1998) 100K has 100,000 ratings from 943 users on 1682 movies (items), which is available at https://files.grouplens.org/datasets/movielens/ml-100k.zip
Standardized Hudup dataset based on Movielens 1m
No full textStandardized Hudup dataset receives information from raw data, which is composed of ten units such as “hdp_config”, “hdp_account”, “hdp_attribute_map”, “hdp_nominal”, “hdp_user”, “hdp_item”, “hdp_rating”, “hdp_context_template”, “hdp_context”, and “hdp_sample”. Each unit has particular functions, which is described in the section of data description. Hudup dataset is meta-data which models any raw data with abstract level. The default raw data which is source of Hudup dataset here is Movielens 1M. It is possible to consider that Hudup dataset is secondary data whereas Movielens is primary data. The raw rating data Movielens (GroupLens, 1998) 1M has 1,000,209 ratings from 6,040 users on 3,900 movies (items), which is available at https://files.grouplens.org/datasets/movielens/ml-1m.zip
Raw rating data Movielens (GroupLens, 2003) 1M
No full textThe raw rating data Movielens (GroupLens, 2003) 1M has 1,000,209 ratings from 6,040 users on 3,900 movies (items), which is available at https://files.grouplens.org/datasets/movielens/ml-1m.zip
Raw rating data Film Trust
No full textThe raw rating data Film Trust has 35,497 ratings from 1,508 users on 2,071 films (items), which is available at https://guoguibing.github.io/librec/datasets/filmtrust.zip
Lịch sử hình thành và phát triển của khoa học công nghệ
No full textNền văn minh hiện tại tiếp nối văn minh phương Tây từ Địa Trung Hải vụt lên phong trào Phục Hưng ngất ngưỡng như đỉnh Olympia trong thần thoại Hy La cổ đại, chủ yếu tập trung vào khoa học mà qua gần 2000 năm mới dựng được uy thế và ngưỡng mộ trong lòng người. Bài viết này kể lại lịch sử của khoa học và công nghệ chiếu qua cuộc thăng trầm của những cường quốc vươn lên từ Phục Hưng, một lần nữa khúc xạ đến đấu chí và nghị lực của những anh hùng hào kiệt vì bức bối mà tận lực làm nên đại nghiệp
Amynthas erroneous Nguyen & Nguyen 2015
No full textAmynthas erroneous Nguyen & Nguyen, 2015 Amynthas erroneous Nguyen D. A. & Nguyen, 2015: 129, Fig. 1. Pheretima multitheca multitheca— Nguyen 1994: 53; Pham 1995c: 68; Thai 2000a: 309; Huynh 2005a: 89; Huynh 2005b: 20; Nguyen V.T. & Tran 2008: 185; Pham 2010: 63. Type locality. Vietnam (Quang Ngai: Duc Pho). Type material. CTU, Vietnam. Examined material. 8 C (SORC-V.153.01), and 13 C (CTU-EW.071.02) garden, Duc Pho town, Pho Minh, Quang Ngai, 15/4/1995, coll. Huynh Thi Kim Hoi. Records from Vietnam. Quang Tri (Quang Tri town); Thua Thien Hue (Huong Tra; Hue; Nam Dong; Phu Loc); Da Nang; Quang Nam (Que Son); Quang Ngai (Quang Ngai city; Duc Pho); Binh Dinh; Dak Nong (Ta Dung Mts) (Nguyen 1994; Pham 1995c, 2010; Huynh 2005a, b; Nguyen & Tran 2008). Distribution. Only known from Vietnam. Remarks. The species had been erroneously identified as Pheretima multitheca multitheca Chen, 1938. It was corrected by Nguyen D. A. & Nguyen (2015).Published as part of Nguyen, Tung T., Nguyen, Anh D., Tran, Binh T. T. & Blakemore, Robert J., 2016, A comprehensive checklist of earthworm species and subspecies from Vietnam (Annelida: Clitellata: Oligochaeta: Almidae, Eudrilidae, Glossoscolecidae, Lumbricidae, Megascolecidae, Moniligastridae, Ocnerodrilidae, Octochaetidae), pp. 1-92 in Zootaxa 4140 (1) on page 27, DOI: 10.11646/zootaxa.4140.1.1, http://zenodo.org/record/25650
Application of molecular marker: Start Codon Target (SCoT) in individual identification of Cat Hoa Loc mango (Mangifera indica L): faculty of biotechnology graduation thesis.
No full text48 tr.Application of molecular marker: Start Codon Target (SCoT) in individual identification of Cat Hoa Loc mango (Mangifera indica L A total of 15 Cat Hoa Loc mango cultivars included in the study were performed DNA extraction, polymorphic selection, and amplification of SCoT markers, and genetic diversity analysis using NTSYSpc software, the polymorphism of 27 primers on 15 individuals of Hoa Loc mango was evaluated, the study resulted in the successful identification of specific DNA bands for 15 individuals
Convolutional Autoencoder for the Spatiotemporal Latent Representation of Turbulence
No full textTurbulence is characterised by chaotic dynamics and a high-dimensional state space, which make this phenomenon challenging to predict. However, turbulent flows are often characterised by coherent spatiotemporal structures, such as vortices or large-scale modes, which can help obtain a latent description of turbulent flows. However, current approaches are often limited by either the need to use some form of thresholding on quantities defining the isosurfaces to which the flow structures are associated or the linearity of traditional modal flow decomposition approaches, such as those based on proper orthogonal decomposition. This problem is exacerbated in flows that exhibit extreme events, which are rare and sudden changes in a turbulent state. The goal of this paper is to obtain an efficient and accurate reduced-order latent representation of a turbulent flow that exhibits extreme events. Specifically, we employ a three-dimensional multiscale convolutional autoencoder (CAE) to obtain such latent representation. We apply it to a three-dimensional turbulent flow. We show that the Multiscale CAE is efficient, requiring less than 10% degrees of freedom than proper orthogonal decomposition for compressing the data and is able to accurately reconstruct flow states related to extreme events. The proposed deep learning architecture opens opportunities for nonlinear reduced-order modeling of turbulent flows from data.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Aerodynamic
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