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Coherent photoproduction of low-pT charmonium in peripheral heavy ion collisions within the color dipole model

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arXiv 2021年

作者： Yu, Gongming Cai, Yanbing Fu, Yongping Yang, Haitao Gao, Quangui Hu, Qiang Hu, Liyuan Li, Wei Song, Yushou Fundamental Science on Nuclear Safety and Simulation Technology Laboratory Harbin Engineering University Harbin150000 China Guizhou Key Laboratory in Physics and Related Areas Guizhou Key Laboratory of Big Data Statistic Analysis Guizhou University of Finance and Economics Guiyang550025 China Department of Physics West Yunnan University Lincang677000 China Department of Physics Zhaotong University Zhaotong657000 China Department of Physics Yuxi Normal University Yuxi653100 China Institute of Modern Physics Chinese Academy of Sciences Lanzhou730000 China

We calculate the centrality dependence for coherent photoproduction of very low-pT J/ψ at Relativistic Heavy Ion Collider (RHIC) and Large Hadron Collider (LHC) energies within the impact parameter dependent saturated color dipole model. By using the large equivalent photon fluxes, we present the differential cross section of very low-pT J/ψ produced by coherent photonuclear in peripheral heavy-ion collisions. The numerical results demonstrate that our calculation are agree with J/ψ data in peripheral heavy ion collisions at Relativistic Heavy Ion Collider (RHIC) energies. © 2021, CC BY.

关键词： Colliding beam accelerators

ADVERSARIAL ROBUSTNESS OF DEEP CONVOLUTIONAL CANDLESTICK LEARNER

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arXiv 2020年

作者： Chen, Jun-Hao Chen, Samuel Yen-Chi Tsai, Yun-Cheng Shur, Chih-Shiang Department of Computer Science & Information Engineering National Taiwan University Taipei10617 Taiwan Computational Science Initiative Brookhaven National Laboratory UptonNY11973 United States School of Big Data Management Soochow University Taipei11102 Taiwan Department of Business Administration National Sun Yat-sen University Taipei10617 Taiwan

Deep learning (DL) has been applied extensively in a wide range of fields. However, it has been shown that DL models are susceptible to a certain kinds of perturbations called adversarial attacks. To fully unlock the power of DL in critical fields such as financial trading, it is necessary to address such issues. In this paper, we present a method of constructing perturbed examples and use these examples to boost the robustness of the model. Our algorithm increases the stability of DL models for candlestick classification with respect to perturbations in the input data. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

Seeing biodiversity: Perspectives in machine learning for wildlife conservation

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arXiv 2021年

作者： Tuia, Devis Kellenberger, Benjamin Beery, Sara Costelloe, Blair R. Zuffi, Silvia Risse, Benjamin Mathis, Alexander Mathis, Mackenzie Weygandt van Langevelde, Frank Burghardt, Tilo Kays, Roland Klinck, Holger Wikelski, Martin Couzin, Iain D. van Horn, Grant Crofoot, Margaret C. Stewart, Charles V. Berger-Wolf, Tanya Switzerland United States Max Planck Institute of Animal Behavior Germany Centre for the Advanced Study of Collective Behaviour University of Konstanz Germany Department of Biology University of Konstanz Germany Institute for Applied Mathematics and Information Technologies IMATI-CNR Italy Computer Science Department University of Münster Germany Switzerland Environmental Sciences Group Wageningen University Netherlands Computer Science Department University of Bristol United Kingdom Department of Forestry and Environmental Resources North Carolina State University United States North Carolina Museum of Natural Sciences United States Cornell Lab of Ornithology Cornell University United States Department of Computer Science Rensselaer Polytechnic Institute United States Translational Data Analytics Institute Ohio State University United States

data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices. These new technologies and the data they generate hold great potential for large-scale environmental monitoring and understanding, but are limited by current data processing approaches which are inefficient in how they ingest, digest, and distill data into relevant information. We argue that machine learning, and especially deep learning approaches, can meet this analytic challenge to enhance our understanding, monitoring capacity, and conservation of wildlife species. Incorporating machine learning into ecological workflows could improve inputs for population and behavior models and eventually lead to integrated hybrid modeling tools, with ecological models acting as constraints for machine learning models and the latter providing data-supported insights. In essence, by combining new machine learning approaches with ecological domain knowledge, animal ecologists can capitalize on the abundance of data generated by modern sensor technologies in order to reliably estimate population abundances, study animal behavior and mitigate human/wildlife conflicts. To succeed, this approach will require close collaboration and cross-disciplinary education between the computer science and animal ecology communities in order to ensure the quality of machine learning approaches and train a new generation of data scientists in ecology and conservation. © 2021, CC BY-NC-SA.

关键词： Ecology

Transcriptional and Neurochemical Signatures of Cerebral Blood Flow Alterations in Individuals With Schizophrenia or at Clinical High Risk for Psychosis

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Biological Psychiatry 2025年

作者： Knight, Samuel R. Abbasova, Leyla Zeighami, Yashar Hansen, Justine Y. Martins, Daniel Zelaya, Fernando Dipasquale, Ottavia Liu, Thomas Shin, David Bossong, Matthijs Azis, Matilda Antoniades, Mathilde Howes, Oliver D. Bonoldi, Ilaria Egerton, Alice Allen, Paul McGuire, Philip Modinos, Gemma Department of Psychological Medicine Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Centre for Developmental Neurobiology Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Medical Research Council Centre for Neurodevelopmental Disorders King's College London London United Kingdom Douglas Research Centre Department of Psychiatry McGill University Montreal QC Canada Montreal Neurological Institute McGill University Montreal QC Canada Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Olea Medical La Ciotat France Centre for Functional MRI University of California San Diego San Diego CA United States Global MR Applications and Workflow GE Healthcare Menlo Park CA United States Department of Psychosis Studies Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Department of Psychiatry Brain Center Rudoph Magnus University Medical Center Utrecht Utrecht Netherlands Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA United States Department of Psychiatry Oxford University Oxford United Kingdom

Background: The brain integrates multiple scales of description, from the level of cells and molecules to large-scale networks and behavior. Understanding relationships across these scales may be fundamental to advancing understanding of brain function in health and disease. Recent neuroimaging research has shown that functional brain alterations that are associated with schizophrenia spectrum disorders (SSDs) are already present in young adults at clinical high risk for psychosis (CHR-P), but the cellular and molecular determinants of these alterations remain unclear. Methods: Here, we used regional cerebral blood flow (rCBF) data from 425 individuals (122 with an SSD compared with 116 healthy control participants [HCs] and 129 individuals at CHR-P compared with 58 HCs) and applied a novel pipeline to integrate brainwide rCBF case-control maps with publicly available transcriptomic data (17,205 gene maps) and neurotransmitter atlases (19 maps) from 1074 healthy volunteers. Results: We identified significant correlations between astrocyte, oligodendrocyte, oligodendrocyte precursor cell, and vascular leptomeningeal cell gene modules for both SSD and CHR-P rCBF phenotypes. Additionally, endothelial cell genes were correlated in SSD, and microglia in CHR-P. Receptor distribution significantly predicted case-control rCBF differences, with dominance analysis highlighting dopamine (D₁, D₂, dopamine transporter), acetylcholine (VAChT, M₁), gamma-aminobutyric acid A (GABA_A), and glutamate (NMDA) receptors as key predictors for SSD (R²_adjusted = 0.58, false discovery rate [FDR]–corrected p <.05) and CHR-P (R²_adjusted = 0.6, p_FDR <.05) rCBF phenotypes. These associations were primarily localized in subcortical regions and implicate cell types involved in stress response and inflammation, alongside specific neuroreceptor systems, in shared and distinct rCBF phenotypes in psychos

关键词： Arterial spin labeling Biomarkers Gene expression Mental health Neuroimaging Neurotransmitters

WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image

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arXiv 2021年

作者： Luo, Xiangde Liao, Wenjun Xiao, Jianghong Chen, Jieneng Song, Tao Zhang, Xiaofan Li, Kang Metaxas, Dimitris N. Wang, Guotai Zhang, Shaoting School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Department of Radiation Oncology Sichuan Cancer Hospital and Institute Sichuan Cancer Center Chengdu China Department of Radiation Oncology Cancer Center West China Hospital Sichuan University Chengdu China Shanghai Artificial Intelligence Laboratory Shanghai China School of Medicine University of Electronic Science and Technology of China Chengdu China Department of Computer Science Johns Hopkins University Baltimore United States SenseTime Research Shanghai China West China Hospital-SenseTime Joint Lab West China Biomedical Big Data Center Sichuan University Chengdu China Department of Computer Science Rutgers University PiscatawayNJ United States

Whole abdominal organ segmentation is important in diagnosing abdomen lesions, radiotherapy, and follow-up. However, oncologists’ delineating all abdominal organs from 3D volumes is time-consuming and very expensive. Deep learning-based medical image segmentation has shown the potential to reduce manual delineation efforts, but it still requires a large-scale fine annotated dataset for training, and there is a lack of large-scale datasets covering the whole abdomen region with accurate and detailed annotations for the whole abdominal organ segmentation. In this work, we establish a new large-scale Whole abdominal ORgan dataset (WORD) for algorithm research and clinical application development. This dataset contains 150 abdominal CT volumes (30495 slices). Each volume has 16 organs with fine pixel-level annotations and scribble-based sparse annotations, which may be the largest dataset with whole abdominal organ annotation. Several state-of-the-art segmentation methods are evaluated on this dataset. And we also invited three experienced oncologists to revise the model predictions to measure the gap between the deep learning method and oncologists. Afterwards, we investigate the inference-efficient learning on the WORD, as the high-resolution image requires large GPU memory and a long inference time in the test stage. We further evaluate the scribble-based annotation-efficient learning on this dataset, as the pixel-wise manual annotation is time-consuming and expensive. The work provided a new benchmark for the abdominal multi-organ segmentation task, and these experiments can serve as the baseline for future research and clinical application development. © 2021, CC BY.

关键词： Large dataset

P18.04.B SINGLE CELL TRANSCRIPTOMIC ANALYSIS IDENTIFIES DISTINCT INTRATUMORAL SUBCLONES IN MENINGIOMA

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Neuro-Oncology 2023年第SUPPLEMENT_2期25卷 ii122–ii122页

作者： Blume, C Tang, Y Dogan, H Ritter, M Dentro, S von Deimling, A Gerstung, M Schlesner, M Sahm, F Maas, S L N University Hospital Heidelberg Dept. of Neuropathology Heidelberg Germany German Cancer Research Center (DKFZ) German Consortium for Translational Cancer Research (DKTK) Clinical Cooperation Unit Neuropathology Heidelberg Germany Division of AI in Oncology German Cancer Research Center (DKFZ) Heidelberg Germany European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Hinxton United Kingdom Biomedical Informatics Data Mining and Data Analytics Faculty of Applied Computer Science and Medical Faculty University of Augsburg Augsburg Germany Department of Pathology Leiden University Medical Center Leiden Netherlands Department of Pathology Erasmus Medical Center Rotterdam Netherlands

Determination factors of internet advertising affecting internet users’ behavior using canonical correlation analysis

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AIP Conference Proceedings 2024年第1期2895卷

作者： Farah Nabilah Zabidi Nurain Ibrahim Nur Izzah Atiqah Nurul Athirah Norzaidi Salsabilla Kamaludin Adina Najwa Kamarudin School of Mathematical Sciences College of Computing Informatics and Media Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Kompleks Al-Khawarizmi Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia Regional Department Suu Balm Malaysia Livemed Sdn Bhd Aurora Place Plaza No 1 Persiaran Bukit Jalil 57000 Kuala Lumpur Malaysia Federal Government Administrative Centre Block C6 Complex C Lebuh Perdana Selatan Presint 1 62514 Putrajaya Wilayah Persekutuan Putrajaya Malaysia Department of Mathematical Sciences Faculty of Science Universiti Teknologi Malaysia 81310 Johor Bahru Malaysia

One of the most important and fundamental parts of today’s business world is advertising. Advertising is necessary for both business and consumers. We can now observe that the advertising industry has experienced substantial changes and is now employed globally. An advertising company has found this platform to be quite useful for increasing revenue by influencing the online users’ behaviour, which may change in either a positive or bad way. Therefore, the primary topic of this study paper is on how internet advertising affects consumers’ online behaviour in UiTM Shah Alam. Through surveys, we gathered information about two sets of variables: Internet Users’ Behaviour (Purchase Intention, Effective Medium, Frequency of Browsing, and Demographic Variable), and sets of variables Internet Advertising (Webpage Content, Informativeness and Interactivity). To determine the aspects of internet advertising influencing internet users’ behaviour, Canonical Correlation Analysis was applied to the data that was gathered. Internet advertising and internet users’ behaviour are significantly correlated, according to the results analysis. As a result, Internet advertising plays a big role in influencing consumers’ behaviour because of its close connection to their purchasing decisions. Based on findings, it can be concluded that internet advertising plays significant roles in examining internet users’ behaviour, which may be advantageous for many industries globally, particularly in current era of digitalization. With information on consumer behaviour both now and in the future, these findings can assist marketers and advertisers in developing more effective online advertising strategies that better engage young adults’ attention and influence their behaviour, particularly with regard to purchase intention, leading to a misalignment. It will then be able to create more successful online advertising.

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Estimation of Matrix Variance-Covariance on Nonparametric Regression Spline Truncated for Longitudinal data

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Journal of Physics: Conference Series 2020年第1期1562卷

作者： Mustain Ramli Vita Ratnasari I Nyoman Budiantara Department of Statistics Faculty of Science and Data Analytics Sepuluh Nopember Institute of Technology Surabaya Indonesia

Regression analysis is a statistical analysis used to determine the pattern of relationships between predictor variables and response variables. There are two models estimation approaches in regression analysis, namely parametric regression and nonparametric regression. The parametric regression approach is used if the shape of the regression curve is known. In cases with unknown relationship patterns, the development is done using nonparametric regression. Nonparametric regression is a model estimation method which is based on an approach that is not bound by certain assumptions of the regression curve shape. One of the most commonly used nonparametric regression curve estimates is the spline approach. Spline is one of the nonparametric approaches that is often used and has a very good ability to handle data characters that are smooth as well as data whose behavior changes at certain sub-intervals. Theoretically, the spline approach is not only able to estimate nonparametric regression curves for cross section data, but can also be developed for longitudinal data. Longitudinal data is a combination of cross section data and time series data where the longitudinal data between subjects are mutually independent, but between observations are dependent. This study aims to find the estimation curves of spline truncated multivariable nonparametric regression for longitudinal data using the Weighted Least Square (WLS) method and to find the estimation of variance-covariance matrix using the Maximum Likelihood Estimate (MLE) method.

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Facilitating the Electrochemical Oxidation of ZnS through Iodide Catalysis for Aqueous Zinc-Sulfur Batteries

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Angewandte Chemie 2024年第9期136卷

作者： Peng Hei Ya Sai Chang Liu Wenjie Li Jing Wang Xiaoqi Sun Yu Song Xiao-Xia Liu Department of Chemistry Northeastern University 3-11 Wenhua Road Heping district Shenyang 110819 China State Key Laboratory of Metastable Materials Science and Technology Yanshan University Qinhuangdao 066004 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University 3-11 Wenhua Road Heping district Shenyang 110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education 3-11 Wenhua Road Heping district Shenyang 110819 China

Aqueous zinc-sulfur (Zn-S) batteries show great potential for unlocking high energy and safety aqueous batteries. Yet, the sluggish kinetic and poor redox reversibility of the sulfur conversion reaction in aqueous solution challenge the development of Zn-S batteries. Here, we fabricate a high-performance Zn-S battery using highly water-soluble ZnI 2 as an effective catalyst. In situ experimental characterizations and theoretical calculations reveal that the strong interaction between I − and the ZnS nanoparticles (discharge product) leads to the atomic rearrangement of ZnS, weakening the Zn-S bonding, and thus facilitating the electrochemical oxidation reaction of ZnS to S. The aqueous Zn-S battery exhibited a high energy density of 742 Wh kg (sulfur) −1 at the power density of 210.8 W kg (sulfur) −1 and good cycling stability over 550 cycles. Our findings provide new insights about the iodide catalytic effect for cathode conversion reaction in Zn-S batteries, which is conducive to promoting the future development of high-performance aqueous batteries.

关键词： Aqueous Battery Catalytic Effect High Performance I− Additive Mechanism Study