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Correction: Experimental Investigation and Thermodynamic Assessment of Phase Equilibria in the Al-Ta-V Ternary System

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Journal of Phase Equilibria and Diffusion 2025年第1期46卷 215-215页

作者： Wang, Cuiping Zheng, Debin Zheng, Zhangcan Wu, Lianzhang Le, Jianping Guo, Yihui Huang, Yixiong Zhang, Jinbin Lu, Yong Liu, Xingjun College of Materials and Fujian Key Laboratory of Surface and Interface Engineering for High Performance Materials Xiamen University Xiamen People’s Republic of China Xiamen Key Laboratory of High Performance Metals and Materials Xiamen University Xiamen People’s Republic of China School of Materials Science and Engineering Institute of Materials Genome & Big Data Harbin Institute of Technology Shenzhen People’s Republic of China

A generic advanced computing framework for executing windows-based dynamic contingency analysis tool in parallel on cluster machines

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A generic advanced computing framework for executing windows...

2020 IEEE Power and Energy Society General Meeting, PESGM 2020

作者： Chen, Yousu Glaesemann, Kurt Li, Xinya Palmer, Bruce Huang, Renke Vyakaranam, Bharat Pacific Northwest National Laboratory Electricity Infrastructure RichlandWA99354 United States Pacific Northwest National Laboratory Research Computing - HPC RichlandWA99354 United States Pacific Northwest National Laboratory Earth System Data Science RichlandWA99354 United States Pacific Northwest National Laboratory High Performance Computing RichlandWA99354 United States

ISBN: (纸本)9781728155081

Dynamic contingency analysis tool (DCAT) has been developed to assess the impact and likelihood of extreme contingencies and potential cascading events across their systems and interconnections. By including more customized protection models and corrective actions into the Windows-based commercial tools, DCAT can help operators understand the cascading behavior and find mitigation approaches to reduce the risk of cascading outages in a more realistic manner. In order to further enhance the capability of DCAT, this paper presents a design of an advanced computing framework that enables DCAT to run on a cluster machine to improve its computational performance. This framework is generic and can be applied to other Windows-based simulation tools to fill the technical gap of applying advanced computing technology to vendors' Windows-based tools. The preliminary tests using medium to large power systems have shown the effectiveness of this framework and its potential for accelerating the adoption of advanced computing in utilities. © 2020 IEEE.

关键词： Cluster computing

STRATLEARN-Z: IMPROVED PHOTO-Z ESTIMATION FROM SPECTROSCOPIC data SUBJECT TO SELECTION EFFECTS

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arXiv 2024年

作者： Moretti, Chiara Autenrieth, Maximilian Serra, Riccardo Trotta, Roberto van Dyk, David A. Mesinger, Andrei SISSA - International School for Advanced Studies Via Bonomea 265 Trieste34136 Italy Centro Nazionale "High Performance Computer Big Data and Quantum Computing" INAF – Osservatorio Astronomico di Trieste Via Tiepolo 11 TriesteI-34143 Italy INFN sezione di Trieste Italy Statistic Section Department of Mathematics Imperial College London 180 Queen’s Gate LondonSW7 2AZ United Kingdom Department of Physics Imperial College London Blackett Laboratory Prince Consort Rd LondonSW7 2AZ United Kingdom Piazza dei Cavalieri 7 PI Pisa56125 Italy

A precise measurement of photometric redshifts (photo-z) is crucial for the success of modern photometric galaxy surveys. Machine learning (ML) methods show great promise in this context, but suffer from covariate shift in training sets due to selection bias where interesting sources, e.g., high redshift objects, are underrepresented, and the corresponding ML models exhibit poor generalisation properties. We present an application of the StratLearn method to the estimation of photo-z (StratLearn-z), validating against simulations where we enforce the presence of covariate shift to different degrees. StratLearn is a statistically principled approach which relies on splitting the combined source and target datasets into strata, based on estimated propensity scores. The latter is the probability for an object in the dataset to be in the source set, given its observed covariates. After stratification, two conditional density estimators are fit separately within each stratum, and then combined via a weighted average. We benchmark our results against the GPz algorithm, quantifying the performance of the two algorithms with a set of metrics. Our results show that the StratLearn-z metrics are only marginally affected by the presence of covariate shift, while GPz shows a significant degradation of performance, specifically concerning the photo-z prediction for fainter objects for which there is little training data. In particular, for the strongest covariate shift scenario considered, StratLearn-z yields a reduced fraction of catastrophic errors, a factor of 2 improvement for the RMSE as well as one order of magnitude improvement on the bias. We also assess the quality of the predicted conditional redshift estimates using the probability integral transform (PIT). The PIT distribution obtained from StratLearn-z features far fewer outliers and is symmetric, i.e., the predictions appear to be well-centered around the true redshift value, despite showing a rather conservative es

关键词： Risk assessment

A Deep Concept-aware Model for predicting and explaining restaurant future status

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A Deep Concept-aware Model for predicting and explaining res...

IEEE International Conference on Web Services (ICWS)

作者： Hao Liao Xiaojie Zhang Xin Li Mingyang Zhou Alexandre Vidmer Rui Mao Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Guangdong Province Engineering Center of China-made High Performance Data Computing System Shenzhen City Key Laboratory of Service Computing and Application Shenzhen China Institute of Big Data Intelligent Management and Decision Shenzhen University Shenzhen China

ISBN: (数字)9781728187860

ISBN: (纸本)9781728187877

Nowadays, with the development of web services, the survival of online service has captured public attention significantly. Future prediction of business is crucial to the shopkeepers. Predicting how the business will develop and explaining what key factors are leading to it are two most important tasks. Existing literatures usually tackle only one of these two tasks, ignoring that they are two closely related tasks and complement each other. In this paper, we propose a review-based neural model, named Deep Concept-aware Model (DCA), to predict restaurants' future status and provide explainable sentences simultaneously in an end2end framework. Specifically, we use co-attention to select concepts and implement prediction and explanation tasks through Factorization Machine and Gated Recurrent Unit, respectively. We conduct extensive experiments on three Chinese cities' datasets. The proposed joint model outperforms the state-of-the-art baseline methods for both prediction (average 40.96% improvement in AUC) and explanation (average 9.72% improvement in BLEU and 86.05% in Precision metric of ROUGE).

关键词： Web services Urban areas Predictive models Prediction algorithms data models Task analysis Business

Multi-Modal Transformer for Accelerated MR Imaging

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arXiv 2021年

作者： Feng, Chun-Mei Yan, Yunlu Chen, Geng Xu, Yong Hu, Ying Shao, Ling Fu, Huazhu Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Terminus Group China Institute of High Performance Computing A*STAR Singapore138632 Singapore

Accelerated multi-modal magnetic resonance (MR) imaging is a new and effective solution for fast MR imaging, providing superior performance in restoring the target modality from its undersampled counterpart with guidance from an auxiliary modality. However, existing works simply combine the auxiliary modality as prior information, lacking in-depth investigations on the potential mechanisms for fusing different modalities. Further, they usually rely on the convolutional neural networks (CNNs), which is limited by the intrinsic locality in capturing the long-distance dependency. To this end, we propose a multi-modal transformer (MTrans), which is capable of transferring multi-scale features from the target modality to the auxiliary modality, for accelerated MR imaging. To capture deep multi-modal information, our MTrans utilizes an improved multi-head attention mechanism, named cross attention module, which absorbs features from the auxiliary modality that contribute to the target modality. Our framework provides three appealing benefits: (i) Our MTrans use an improved transformers for multi-modal MR imaging, affording more global information compared with existing CNN-based methods. (ii) A new cross attention module is proposed to exploit the useful information in each modality at different scales. The small patch in the target modality aims to keep more fine details, the large patch in the auxiliary modality aims to obtain high-level context features from the larger region and supplement the target modality effectively. (iii) We evaluate MTrans with various accelerated multi-modal MR imaging tasks, e.g., MR image reconstruction and super-resolution, where MTrans outperforms state-of-the-art methods on fastMRI and real-world clinical datasets. © 2021, CC BY.

关键词： Magnetic resonance imaging

RNC: Reliable network property classifier based on graph embedding 20

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RNC: Reliable network property classifier based on graph emb...

20th International Conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2019

作者： Liao, Hao Liu, Qixin Vidmer, Alexandre Zhou, Mingyang Mao, Rui National Engineering Laboratory for Big Data System Computing Technology Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126166

In the past two decades, analyzing the information network has been intensively studied from various disciplines. Small world property and scale-free property prevail in network science research. The comparison and classification of different kinds of graphs are extremely important. However, how to design a robust and accurate classification with deep learning techniques for network property still lack enough attention, which is a vital task in various application scenarios. In this paper, we proposed the reliable network property classifier based on graph embedding(RNC) to classify the network property (scale free or small world property). In order to process non-euclidean data, we embedded each network into an image and use dimensional reduction, rasterization, and convolutional neural networks to complete the classification problem. The method can effectively accomplish classification tasks in not only artificial networks but also real networks. Besides, RNC wins greatly in terms of robustness on real networks, showing the robustness of RNC against the incomplete structure of the network. © 2019 IEEE.

关键词： Deep learning

Neural Posterior Estimation with guaranteed exact coverage: the ringdown of GW150914

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arXiv 2023年

作者： Crisostomi, Marco Dey, Kallol Barausse, Enrico Trotta, Roberto SISSA Via Bonomea 265 Trieste34136 Italy INFN Sezione di Trieste Italy IFPU - Institute for Fundamental Physics of the Universe Via Beirut 2 Trieste34014 Italy School of Physics Indian Institute of Science Education and Research Thiruvananthapuram Maruthamala PO Vithura Kerala Thiruvananthapuram695551 India Physics Department Imperial College London Prince Consort Rd LondonSW7 2AZ United Kingdom Centro Nazionale "High Performance Computer Big Data and Quantum Computing" Italy

We analyze the ringdown phase of the first detected black-hole merger, GW150914, using a simulation-based inference pipeline based on masked autoregressive flows. We obtain approximate marginal posterior distributions for the ringdown parameters, namely the mass, spin, and the amplitude and phases of the dominant mode and its first overtone. Thanks to the locally amortized nature of our method, we are able to calibrate our posteriors with injected simulations, producing posterior regions with guaranteed (i.e. exact) frequentist coverage of the true values. For GW150914, our calibrated posteriors provide only mild evidence (∼ 2σ) for the presence of an overtone, even if the ringdown is assumed to start at the peak of the amplitude. Copyright © 2023, The Authors. All rights reserved.

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Cosmological constraints from abundance, weak-lensing and clustering of galaxy clusters: application to the SDSS

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arXiv 2023年

作者： Fumagalli, Alessandra Costanzi, Matteo Saro, Alexandro Castro, Tiago Borgani, Stefano Dipartimento di Fisica - Sezione di Astronomia Universitá di Trieste Via Tiepolo 11 Trieste34131 Italy INAF-Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 Trieste34143 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy University Observatory Faculty of Physics Ludwig-MaximiliansUniversität Scheinerstr. 1 Munich81679 Germany INFN Sezione di Trieste Via Valerio 2 TS Trieste34127 Italy ICSC - Italian Research Center on High-Performance Computing Big Data and Quantum Computing Italy

The clustering of galaxy clusters is a powerful cosmological tool, which can help to break degeneracies between parameters when combined with other cosmological observables. We aim to demonstrate its potential in constraining cosmological parameters and scaling relations when combined with cluster counts and weak lensing mass information, using as a case study the redMaPPer cluster catalog derived from the Sloan Digital Sky Survey (SDSS). We extend the analysis of number counts and weak lensing signal performed by Costanzi et al. 2019a, with the addition of the real-space 2-point correlation function. We derive cosmological and scaling relation posteriors for all the possible combinations of the three observables to assess their constraining power, parameter degeneracies, and possible internal tensions. We find no evidence for tensions between the three data set analyzed. We demonstrate that the inclusion of the cluster clustering statistic can greatly enhance the constraining power of the sample thanks to its capability of breaking the Ωm – σ8 degeneracy characteristic of cluster abundance studies. In particular, for a flat ΛCDM model with massive neutrinos, we obtain Ωm = 0.28 ± 0.03 and σ8 = 0.82 ± 0.05, a 33 % and 50 % improvement compared to the posteriors derived combining cluster abundance and weak lensing analyses. Our results are consistent with cosmological posteriors from other cluster surveys, as well as with Planck CMB results and DES-Y3 galaxy clustering and weak-lensing *** Codes 85A40 © 2023, CC BY-NC-ND.

关键词： Galaxies

Intertwined formation of H2, dust, and stars in cosmological simulations

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arXiv 2024年

作者： Ragone-Figueroa, Cinthia Granato, Gian Luigi Parente, Massimiliano Murante, Giuseppe Valentini, Milena Borgani, Stefano Maio, Umberto Universidad Nacional de Córdoba Laprida 854 CórdobaX5000BGR Argentina INAF Osservatorio Astronomico di Trieste via Tiepolo 11 TriesteI-34131 Italy IFPU Institute for Fundamental Physics of the Universe Via Beirut 2 Trieste34014 Italy SISSA Via Bonomea 265 TriesteI-34136 Italy Dipartimento di Fisica Università di Trieste Sez. di Astronomia via Tiepolo 11 TriesteI-34131 Italy INFN Instituto Nazionale di Fisica Nucleare Via Valerio 2 TriesteI-34127 Italy ICSC - Italian Research Center on High Performance Computing Big Data and Quantum Computing via Magnanelli 2 Casalecchio di Reno40033 Italy

Context. Molecular hydrogen (H2) plays a crucial role in the formation and evolution of galaxies, serving as the primary fuel reservoir for star formation. In a metal-enriched Universe, H2 forms mostly through catalysis on interstellar dust grain surfaces. However, due to the complexities of modelling this process, star formation in cosmological simulations often relies on empirical or theoretical frameworks that have only been validated in the local Universe to estimate the abundance of H2. Aims. The goal of this work is to model the connection between the processes of star, dust, and H2 formation in our cosmological simulations. Methods. Building upon our recent integration of a dust evolution model into the star formation and feedback model MUPPI, we included the formation of molecular hydrogen on the surfaces of dust grains. We also accounted for the destruction of molecules and their shielding from harmful radiation. Results. The model reproduces, reasonably well, the main statistical properties of the observed galaxy population for the stellar, dust, and H2 components. The evolution of the molecular hydrogen cosmic density (ρH2) in our simulated boxes peaks around redshift z = 1.5, consistent with observations. Following its peak, ρH2 decreases by a factor of two towards z = 0, which is a milder evolution than observed. Similarly, the evolution of the molecular hydrogen mass function since z = 2 displays a gentler evolution when compared to observations. Our model recovers satisfactorily the integrated molecular Kennicut-Schmidt (mKS) law between the surface star formation rate (ΣSFR) and surface H2 density (ΣH2) at z = 0. This relationship is already evident at z = 2, albeit with a higher normalization. We find hints of a broken power law with a steeper slope at higher ΣH2. We also study the H2-to-dust mass ratio in galaxies as a function of their gas metallicity and stellar mass, observing a decreasing trend with respect to both quantities. The H2-to-dust ma

关键词： Cosmology