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Machine-learning-assisted intelligent synthesis of UiO-66(Ce):Balancing the trade-off between structural defects and thermal stability for e hydrogenation of Dicyclopentadiene

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Materials Genome Engineering Advances 2024年第3期2卷 96-106页

作者： Jing Lin Tao Ban Tian Li Ye Sun Shenglan Zhou Rushuo Li Yanjing Su Jitti Kasemchainan Hongyi Gao Lei Shi Ge Wang Beijing Key Laboratory of Function Materials for Molecule&Structure Construction Beijing Advanced Innovation Center for Materials Genome EngineeringSchool of Materials Science and EngineeringUniversity of Science and Technology BeijingBeijingChina Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and EngineeringBeihang UniversityBeijingChina Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologySchool of Materials Science and EngineeringUniversity of Science and Technology BeijingBeijingChina Department of Chemical Technology Chulalongkorn UniversityBangkokThailand

Metal-organic frameworks(MOFs),renowned for structural diversity and design flexibility,exhibit potential in ***,the pursuit of higher catalytic activity through defects often compromises stability,requiring a delicate *** trial-and-error method for optimizing synthesis parameters within the complex chemical space is ***,taking the typical MOF UiO-66(Ce)as an illustrative example,a closed loop workflow is built,which integrates ma-chine learning(ML)-assissted prediction,multi-objective optimization(MOO)and experimental preparation to synergistically optimize the defect content and thermal stability of UiO-66(Ce)for efficient hydrogenation of dicyclopentadiene(DCPD).An automatic data extraction program ensures data accuracy,establishing a high-quality *** is employed to explore the intricate synthesis-structure-property correlations,enabling precise delineation of pure-phase subspace and accurate predictions of *** two iterations,MOO model identifies optimal protocols for high defect content(>40%)and thermal stability(>300℃).The optimized UiO-66(Ce)exhibits superior catalytic performance in hydroge-nation of DCPD,validating the precision and reliability of our *** ML-assisted approach offers a valuable paradigm for solving the trade-off riddle in materials field.

关键词： defect content machine learning metal-organic frameworks multi-objective optimization thermal stability

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Transformer for Object Re-Identification: A Survey

arXiv

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arXiv 2024年

作者： Ye, Mang Chen, Shuoyi Li, Chenyue Zheng, Wei-Shi Crandall, David Du, Bo National Engineering Research Center for Multimedia Software School of Computer Science Hubei Luojia Laboratory Wuhan University Wuhan China School of Data and Computer Science Sun Yat-sen University Guangzhou China Luddy School of Informatics Computing and Engineering Indiana University United States

Object Re-identification (Re-ID) aims to identify specific objects across different times and scenes, which is a widely researched task in computer vision. For a prolonged period, this field has been predominantly driven by deep learning technology based on convolutional neural networks. In recent years, the emergence of Vision Transformers has spurred a growing number of studies delving deeper into Transformer-based Re-ID, continuously breaking performance records and witnessing significant progress in the Re-ID field. Offering a powerful, flexible, and unified solution, Transformers cater to a wide array of Re-ID tasks with unparalleled efficacy. This paper provides a comprehensive review and in-depth analysis of the Transformer-based Re-ID. In categorizing existing works into Image/Video-Based Re-ID, Re-ID with limited data/annotations, Cross-Modal Re-ID, and Special Re-ID Scenarios, we thoroughly elucidate the advantages demonstrated by the Transformer in addressing a multitude of challenges across these domains. Considering the trending unsupervised Re-ID, we propose a new Transformer baseline, UntransReID, achieving state-of-the-art performance on both single/cross modal tasks. For the under-explored animal Re-ID, we devise a standardized experimental benchmark and conduct extensive experiments to explore the applicability of Transformer for this task and facilitate future research. Finally, we discuss some important yet under-investigated open issues in the large foundation model era, we believe it will serve as a new handbook for researchers in this field. A periodically updated website will be available at https://***/mangye16/ ReID-Survey. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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FaceCaps for facial expression recognition

FaceCaps for facial expression recognition

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作者： Wu, Fangyu Pang, Chaoyi Zhang, Bailing College of Computer Science and Technology Zhejiang University HangzhouZhejiang China School of Computing and Data Engineering NingboTech University NingboZhejiang China

Facial expression recognition (FER) is a significant research task in the computer vision field. In this paper, we present a novel network FaceCaps for facial expression recognition with the following novel characteristics: an embedding structure based on a Capsule network which encodes relative spatial relationships between features;incorporates the feature polymerization property of FaceNet, thus offering a more efficient approach to discriminate complex facial expressions;a target reconstruction loss as a better regularization term for Capsule networks. Experimental results on both lab-controlled datasets (CK+) and real-world databases (RAF-DB and SFEW 2.0) demonstrate that the method significantly outperforms the state-of-the-art. © 2021 John Wiley & Sons, Ltd.

关键词： Face recognition

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SimSIMS: Simulation-based Supernova Ia Model Selection with thousands of latent variables

arXiv

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arXiv 2023年

作者： Karchev, Konstantin Trotta, Roberto Weniger, Christoph Theoretical and Scientific Data Science SISSA Trieste Italy Department of Physics Imperial College London United Kingdom Italian Research Center on High Performance Computing Big Data and Quantum Computing Italy National Institute for Nuclear Physics Trieste Italy GRAPPA Institute University of Amsterdam Netherlands

We present principled Bayesian model comparison through simulation-based neural classification applied to SN Ia analysis. We validate our approach on realistically simulated SN Ia light curve data, demonstrating its ability to recover posterior model probabilities while marginalizing over > 4000 latent variables. The amortized nature of our technique allows us to explore the dependence of Bayes factors on the true parameters of simulated data, demonstrating Occam’s razor for nested models. When applied to a sample of 86 low-redshift SNæ Ia from the Carnegie Supernova Project, our method prefers a model with a single dust law and no magnitude step with host mass, disfavouring different dust laws for low- and high-mass hosts with odds in excess of 100:1. Copyright © 2023, The Authors. All rights reserved.

关键词： Supernovae

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From Relevance to Utility: Evidence Retrieval with Feedback for Fact Verification

arXiv

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arXiv 2023年

作者： Zhang, Hengran Zhang, Ruqing Guo, Jiafeng de Rijke, Maarten Fan, Yixing Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China University of Amsterdam Netherlands The University of Amsterdam Netherlands

Retrieval-enhanced methods have become a primary approach in fact verification (FV);it requires reasoning over multiple retrieved pieces of evidence to verify the integrity of a claim. To retrieve evidence, existing work often employs off-the-shelf retrieval models whose design is based on the probability ranking principle. We argue that, rather than relevance, for FV we need to focus on the utility that a claim verifier derives from the retrieved evidence. We introduce the feedback-based evidence retriever (FER) that optimizes the evidence retrieval process by incorporating feedback from the claim verifier. As a feedback signal we use the divergence in utility between how effectively the verifier utilizes the retrieved evidence and the ground-truth evidence to produce the final claim label. Empirical studies demonstrate the superiority of FER over prevailing baselines.1 Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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AI-based Framework for Discriminating Human-authored and AI-generated Text

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年

作者： Wani, Mudasir Ahmad Abd El-Latif, Ahmed A. Elaffendi, Mohammad Hussain, Amir Prince Sultan University EIAS Data Science Lab College of Computer and Information Sciences Center of Excellence in Quantum and Intelligent Computing Riyadh11586 Saudi Arabia Menoufia University Department of Mathematics and Computer Science Faculty of Science Shebin El-Koom32511 Egypt Edinburgh Napier University School of Computing Merchiston Campus EdinburghEH10 5DT United Kingdom

Deep learning techniques are increasingly adept at distinguishing between human-written and AI-generated text. This study presents a deep learning-based text classification system to discern the source of text - whether it's generated by AI or authored by a human. We compiled a comprehensive and diverse dataset incorporating existing datasets, real-time tweets, and synthetic data generated by cutting-edge AI models such as Google Bard and OpenAI's ChatGPT. Seven deep learning techniques including Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (BiLSTM), and Bidirectional RNN (BiRNN) and four text embedding techniques namely One-hot, Word2Vec, TF-IDF, and BERT, are employed to perform 28 experiments (4∗7). Our findings exhibit outstanding performance across all models. BERT-based models notably outshine the others, with BERT-CNN achieving the highest accuracy of 98.87%, an MCC of 0.9742, and an F1 measure of 0.9916. Following closely, Bert-BiLSTM attained an accuracy, MCC, and F1 score of 98.77%, 0.9720, and 0.9909, respectively. This paper demonstrates the effectiveness of deep learning approaches in distinguishing between human and AI-generated text, with potential applications in automated content moderation, detection of AI-generated spam, and safeguarding the authenticity of user-generated content. © 2020 IEEE.

关键词： Convolutional neural networks

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Integrating transcriptomics,metabolomics,and network pharmacology to investigate multi-target effects of sporoderm-broken spores of Ganoderma lucidum on improving HFD-induced diabetic nephropathy rats

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Journal of Pharmaceutical Analysis 2024年第12期14卷 1906-1920页

作者： Lidan Hu Lili Yu Zhongkai Cao Yue Wang Caifeng Zhu Yayu Li Jiazhen Yin Zhichao Ma Xuelin He Ying Zhang Wunan Huang Yuelin Guan Yue Chen Xue Li Xiangjun Chen Department of Nephrology The Children's HospitalZhejiang University School of MedicineNational Clinical Research Center for Child HealthHangzhou310003China Centre for Global Health Usher InstituteThe University of EdinburghEdinburghEH164UXUK Department of Big Data in Health Science School of Public Health Zhejiang University School of MedicineHangzhou310058China Hubei Normal University HuangshiHubei435002China Department of Nephrology Hangzhou TCM HospitalHangzhou310007China Kidney Disease Center The First Affiliated HospitalZhejiang University School of MedicineHangzhou310003China Institute of Translational Medicine Zhejiang University School of MedicineHangzhou310020China Department of Urology The Second Hospital of Tianjin Medical UniversityTianjin Institute of UrologyTianjin300211China

Diabetes mellitus(DM)is a major metabolic disease endangering global health,with diabetic nephropathy(DN)as a primary complication lacking curative ***-broken spores of Ganoderma lucidum(GLP),an herbal medicine,has been used for the treatment of metabolic *** this study,DN was induced in Sprague-Dawley rats using streptozotocin(STZ)and a high-fat diet(HFD),and the protective mechanisms of GLP were investigated through transcriptomic,metabolomic,and network pharmacology(NP)*** results demonstrated that GLP intervention ameliorated renal damage and inflammation levels in DN *** metabolomic and transcriptomic analysis revealed that GLP treatment modulated glucose and cellular energy metabolisms by regulating relevant *** significantly suppressed the inflammations by impacting glucose and energy metabolism-related gene expression(Igfbp1 and Angptl4)and enhanced metabolic biomarkers of *** addition,NP analysis further indicated that GLP may efficiently alleviate DN via immune-related *** conclusion,this study provides supportive evidence of the anti-inflammatory effects of GLP supplements,highlighting their potential for promising clinical applications in treating DN.

关键词： Diabetic nephropathy Metabolome Network pharmacology Sporoderm-broken spores of Ganoderma lucidum Transcriptome

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A multi-dimensional index for privacy-preserving queries in cloud computing

A multi-dimensional index for privacy-preserving queries in ...

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作者： Xu, Hui Ding, Xiaofeng Jin, Hai Yu, Qing National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Although cloud computing saves the cost of enterprises and individuals to manage data in various applications on public cloud servers, it also causes the problem of the leakage of critical personal information and sensitive data. Therefore, the issues about data privacy of the published personal information have become a challenging problem. Most of the existing methods focus on cryptography-based techniques by encrypting the sensitive data before publishing. However, these techniques are either too computation expensive or only some authorized users can get access to the encrypted cloud data. The consequence is that the search response time is not satisfactory and the sharing extent of the cloud data is becoming limited. Motivated by this, we propose a perturbation-based method to preserve the privacy of data in cloud. To accelerate the query processing without privacy breaches, a high-efficiency multi-dimensional index is built for answering range counting queries under differential privacy, which embeds privacy-preserving R-tree index in Content Addressable Network. Experimental results demonstrate that our methods not only protect data privacy in different degree, but also accelerate the query speed efficiently. Compared with the existing method Quad-opt, our method provides about 20% better data utility under the same differential privacy principles. © 2019 John Wiley & Sons, Ltd.

关键词： Sensitive data

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The Entry-Extensible Cuckoo Filter 17th

The Entry-Extensible Cuckoo Filter

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17th IFIP WG 10.3 International Conference on Network and Parallel computing, NPC 2020

作者： Yu, Shuiying Wu, Sijie Chen, Hanhua Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783030794774

The emergence of large-scale dynamic sets in real applications brings severe challenges in approximate set representation structures. A dynamic set with changing cardinality requires an elastic capacity of the approximate set representation structure, while traditional static structures, e.g., bloom filter, cuckoo filter, and their variants cannot satisfy the requirements. Existing dynamic approximate set representation structures only provide filter-level extensions, which require a single membership query to probe all discrete filters one by one. The large number of small discrete memory accesses takes up the vast majority of query time and results in unsatisfied query performance. To address the problem, in this work we propose the entry-extensible cuckoo filter (E2CF) to reduce memory access overhead for dynamic set representation and accelerate the membership query. E2CF utilizes adjacent buckets with continuous physical addresses in a cuckoo filter to extend bucket entries, which avoids many discrete memory accesses in a query. To further make E2CF space and time efficient, we adopt asynchronous extension and fine-grained splitting methods. Experiment results show that compared to state-of-the-art designs, E2CF reduces the query and insertion time by 82% and 28%, respectively. © 2021, IFIP International Federation for Information Processing.

关键词： data structures

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Finding Simplex Items in data Streams 39

Finding Simplex Items in Data Streams

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Fan, Zhuochen Guo, Jiarui Li, Xiaodong Yang, Tong Zhao, Yikai Wu, Yuhan Cui, Bin Xu, Yanwei Uhlig, Steve Zhang, Gong Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China Central Research Institute 2012 Labs Huawei Technologies Co. Ltd. Theory Lab Hong Kong Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom

ISBN: (纸本)9798350322279

In this paper, we propose a new type of item in data streams, called simplex items. Simplex items have frequencies in consecutive p windows that can be approximated by a polynomial of degree at most k, where k = 0, 1, 2. These low-order representable simplex items have a wide range of potential applications. For example, when k = 1, we can leverage these items whose frequency has obvious linear increase or decrease to speed up the running time of a class of machine learning models and detect network attacks such as distributed denial-of-service (DDoS), etc. To find k-degree simplex items in real time, we propose a novel sketch, namely X-Sketch, to accurately record simplex items in a compact space. The key idea of X-Sketch is to effectively filter out non-simplex items with less memory overhead, and then monitor the remaining potential simplex items and keep those items with more consecutive windows. We conduct extensive experiments, and the experimental results show that the F1 Score of X-Sketch is on average 68.6%, 57.9%, and 42.2% higher than the baseline solution for k = 0, 1, 2, respectively. Finally, we also provide a case study that applies X-Sketch to "accelerate"the two machine learning models through end-to-end experiments. We have released our source code at GitHub. © 2023 IEEE.

关键词： Denial-of-service attack

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