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Differentiating structure of in situ and ex situ formation of laser-induced graphene hybrids

Rare Metals 2022年第9期41卷 3035-3044页

作者： Li-Bei Huang Wei-Hua Guo Le Cheng Jian-Jun Su Yun Song Fei-Jun Hu Ying Lo Law Zheng Yan Jian Lin Ru-Quan Ye State Key Laboratory of Marine Pollution Department of ChemistryCity University of Hong KongHong Kong999077China Department of Mechanical and Aerospace Engineering University of MissouriColumbiaMO65211USA Department of Biomedical Biological&Chemical EngineeringUniversity of MissouriColumbiaMO65211USA Department of Electrical Engineering and Computer Science University of MissouriColumbiaMO65211USA City University of Hong Kong Shenzhen Research Institute Shenzhen518057China

Laser-induced graphene(LIG)has been exploited in various fields,such as batteries,water treatment,and flexible actuators and sensors,with the advantages of ease in patternable fabrication and graphene/metal hybrid *** in situ method which includes a single lase step and the ex situ method which uses two lase steps are both widely implemented to synthesize graphene/metal ***,the differences in the structures and properties of the resulting hybrids are not fully ***,we investigate the advantages of ex situ and in situ methods for preparing LIG hybrids using NiFe/LIG as an *** morphology,elemental composition,resistance to strong acid,and application in electrocatalysis and dye adsorption of ex situ and in situ NiFe/LIG hybrids were systematically studied and ***-dispersive X-ray spectrometry(EDS)and vibrating sample magnetometry(VSM)results confirmed the structural differences between the NiFe/LIG *** nanoparticles are mostly wrapped by carbon layers in the in situ NiFe/LIG hybrid,while the ex situ NiFe/LIG hybrid largely exposes the metal *** structural differences are significant for tailoring performance in applications,such as wastewater treatment and *** work provides insights into the synthesis and properties of LIGs and has important implications for future applications.

关键词： Ex situ In situ Laser-induced graphene Metal-containing hybrids Oxygen evolution reaction Dye adsorption

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A Simple But Effective Vision Transformer Framework for Visible-Infrared Person Re-Identification

SSRN

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SSRN 2024年

作者： Li, Yudong Zhao, Sanyuan Shen, Jianbing School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy Beijing Institute of Technology Jiaxing314019 China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China

In the context of visible-infrared person re-identification (VI-ReID), the acquisition of a robust visual representation is paramount. Existing approaches predominantly rely on convolutional neural networks (CNNs), which are guided by intricately designed loss functions to extract features. In contrast, the vision transformer (ViT), a potent visual backbone, has often yielded subpar results in VI-ReID. We contend that the prevailing training methodologies and insights derived from CNNs do not seamlessly apply to ViT, leading to underutilization of its potential in VI-ReID. One notable limitation is ViT’s appetite for extensive data, exemplified by the JFT-300M dataset, to surpass CNNs. Consequently, ViT struggles to transfer its knowledge from visible to infrared images due to inadequate training data. Even the largest available dataset, SYSU-MM01, proves insufficient for ViT to glean a robust representation of infrared images. This predicament exacerbates when ViT is trained on the smaller RegDB dataset, where slight data flow modifications drastically affect performance—a stark contrast to CNN behavior. These observations lead us to conjecture that the CNN-inspired paradigm impedes ViT’s progress in VI-ReID. In light of these challenges, we undertake comprehensive ablation studies to shed new light on ViT’s applicability in VI-ReID. We propose a straightforward yet effective framework, named "Idformer", to train a high-performing ViT for VI-ReID. Idformer serves as a robust baseline that can be further enhanced w ith carefully designed techniques akin to those used for CNNs. Remarkably, our method attains competitive results even in the absence of auxiliary information, achieving 78.58%/76.99% Rank-1/mAP on the SYSU-MM01 dataset, as well as 96.82%/91.83% Rank-1/mAP on the RegDB dataset. © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Slow electron-phonon relaxation controls the dynamics of the superconducting resistive transition

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Physical Review B 2024年第10期110卷 104519页

作者： E. M. Baeva A. I. Kolbatova N. A. Titova S. Saha A. Boltasseva S. Bogdanov V. M. Shalaev A. V. Semenov A. Levchenko G. N. Goltsman V. S. Khrapai Moscow 119435 Russian Federation 20 Myasnitskaya Street Moscow 101000 Russian Federation Laboratory of Photonic Gas Sensors University of Science and Technology MISIS Moscow 119049 Russian Federation Birck Nanotechnology Center and Elmore Family School of Electrical and Computer Engineering Department of Electrical and Computer Engineering Holonyak Micro and Nanotechnology Laboratory Illinois Quantum Information Science and Technology Center Moscow Institute of Physics and Technology Dolgoprudny 141701 Russian Federation Department of Physics Moscow 121205 Russian Federation Osipyan

We investigate the temporal and spatial scales of resistance fluctuations (R fluctuations) at the superconducting resistive transition accessed through voltage fluctuation measurements in thin epitaxial TiN films. This material is characterized by slow electron-phonon relaxation, which puts it far beyond the applicability range of the textbook scenario of superconducting fluctuations. The measured Lorentzian spectrum of the R fluctuations identifies their correlation time, which is nearly constant across the transition region and has no relation to the conventional Ginzburg-Landau timescale. Instead, the correlation time coincides with the energy relaxation time determined by a combination of the electron-phonon relaxation and the relaxation via diffusion into reservoirs. Our data are quantitatively consistent with the model of spontaneous temperature fluctuations and highlight the lack of understanding of the resistive transition in materials with slow electron-phonon relaxation.

关键词： Critical phenomena Fluctuations & noise Phonons Superconducting fluctuations Superconducting phase transition Low-temperature superconductors Thin films Resistivity measurements

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Optimized Transaction Processing in Lightweight Distributed Ledger Networks for Internet of Things 5th

Optimized Transaction Processing in Lightweight Distributed...

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5th International Conference on Blockchain, ICBC 2022, held as part of the Services Conference Federation, SCF 2022

作者： Tekeoglu, Ali Chiang, Chen-Fu Sengupta, Saumendra Ahmed, Norman Noor Stein, Michael Kusukuntla, Dilip Applied Physics Laboratory Johns Hopkins University LaurelMD United States Computer Science Department SUNY Polytechnic Institute UticaNY United States Air Force Research Laboratory/RIS RomeNY United States

ISBN: (纸本)9783031234941

The last decade has witnessed proliferation of Internet of Things (IoT) devices ranging from smart-watches to smart-cities. Concurrently, first distributed ledger technology was introduced and gained significant traction. Blockchain, integrated into the IoT world presents variety of opportunities such as, machine-to-machine automated micro-transactions, trusted dissemination of information in trust-less network environments. There are many potential applications of blockchain technology in Smart City context, such as smart-homes, and smart-grid. However, the obstacles encountered when merging these two emerging technologies are many-fold. IoT devices are ubiquitous, but they have limited resources, while most popular blockchain technologies require a significant amount of computational resources to participate. Recently, there have been novel proposals which adopt lightweight distributed ledger technologies, that would let IoT devices to participate. One such ledger, IOTA’s Tangle, uses a different underlying ledger data-structure, which is a directed-acyclic graph instead of a linked-list as in traditional blockchains. In this work, we have set-up a private, permissioned Tangle network, comprising of Raspberry-Pi devices as full-nodes and as transacting clients. Our previously proposed transaction optimization algorithm is implemented as an Nginx module. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Virtual Reality Support of Cognitive Processes in Firefighter Skills Training 1

Virtual Reality Support of Cognitive Processes in Firefighte...

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1st IEEE International Conference on Cognitive Aspects of Virtual Reality, CVR 2022

作者： Wijkmark, Cecilia Hammar Heldal, Ilona Metallinou, Maria Monika Western Norway University of Applied Sciences Chemistry and Biomedical Laboratory Sciences Department of Safety Haugesund Norway Western Norway University of Applied Sciences Electrical Engineering and Mathematical Sciences Department of Computer Science Bergen Norway

ISBN: (数字)9781665485166

ISBN: (纸本)9781665485166

Practice-based training in realistic settings is necessary to acquire and retain competence as a firefighter. Hot-Fire Live Simulation (HF-LS) training in real fire and smoke, using training buildings, vehicles, and equipment, has been the only available practice-based training format available, and therefore accepted despite its limitations. Virtual Reality (VR) development has provided possibilities for supplementing HF-LS training. However, hesitation exists, often because of doubt regarding the represented realism of virtual fire situations. This paper investigates how immersive VR supports cognitive processes and affects fidelity in firefighter training, compared with HF-LS. Data were collected from firefighter recruits, experienced firefighters, and instructors at a Fire and Rescue Service in Sweden, 33 persons in total, during VR and HF-LS training. VR experiences were compared with previous experiences of HF-LS. The results emphasize the current benefits and limitations of VR versus HF-LS training in relation to the support of cognitive processes and the representations' fidelity. © 2022 IEEE.

关键词： Fires

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CSCL-DTI: predicting drug-target interaction through cross-view and self-supervised contrastive learning

CSCL-DTI: predicting drug-target interaction through cross-v...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xuan Lin Xi Zhang Zu-Guo Yu Yahui Long Xiangxiang Zeng Philip S. Yu College of Computer Science Xiangtan University China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University China Singapore Immunology Network (SIgN) Agency for Science Technology and Research (A*STAR) Singapore College of Information Science and Engineering Hunan University China Department of Computer Science University of Illinois Chicago USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Accurately predicting drug-target interactions (DTI) is a critical step in drug discovery. Existing methods of DTI prediction primarily employ Simplified Molecular-Input Line-Entry System (SMILES) sequences or molecular graphs to learn drug representations. However, the features learned by such single-view approach is prone to incomplete. While some multiview methods that consider the views of both SMILES sequences and molecular graphs have been developed, these methods often fall in short in capturing potential interactions between views. In this work, we propose a novel dual contrastive learning framework CSCL-DTI for DTI prediction. First, we design a contrastive-enhanced cross-view representation learning (CVRL) to learn representations for drugs. In this module, Transformer-based and graph convolutional network (GCN)-based encoders are separately adopted to learn view-specific representations, followed by contrastive learning to enrich the representations by accounting for the potential interplay between local chemical context and topological structure. Second, we combine Transformer with self-supervised contrastive learning (SSCL) to learn representations for targets by modelling protein amino acids sequences. The scheme allows to effectively preserve the intrinsic characteristics of the sequences. Finally, we introduce a bilinear attention network to obtain an integrated representation by adaptively incorporating drug and target representations. Benchmarking experiments on two datasets demonstrated that CSCL-DTI 1 outperforms six state-of-the-art methods.

关键词： Drugs Proteins Representation learning Protein engineering Adaptation models Contrastive learning Predictive models Transformers Amino acids Diffusion tensor imaging

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Deep-OCTA: Ensemble Deep Learning Approaches for Diabetic Retinopathy Analysis on OCTA Images 25th

Deep-OCTA: Ensemble Deep Learning Approaches for Diabetic R...

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25th International Conference on Medical Image Computing and computer-Assisted Intervention , MICCAI 2022

作者： Hou, Junlin Xiao, Fan Xu, Jilan Zhang, Yuejie Zou, Haidong Feng, Rui School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Department of Ophthalmology Shanghai General Hospital School of Medicine Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783031336577

The ultra-wide optical coherence tomography angiography (OCTA) has become an important imaging modality in diabetic retinopathy (DR) diagnosis. However, there are few researches focusing on automatic DR analysis using ultra-wide OCTA. In this paper, we present novel and practical deep-learning solutions based on ultra-wide OCTA for the Diabetic Retinopathy Analysis Challenge (DRAC). In the first task of segmentation of DR lesions, we utilize UNet and UNet++ to segment three lesions with strong data augmentation and model ensemble. In the second task of image quality assessment, we create an ensemble of Inception-V3, SE-ResNeXt, and Vision Transformer models. Pre-training on the large dataset as well as the hybrid MixUp and CutMix strategy are both adopted to boost the generalization ability of our models. In the third task of DR grading, we build a Vision Transformer and find that the model pre-trained on color fundus images serves as a useful substrate for OCTA images. Extensive ablation studies demonstrate the effectiveness of each designed component in our solutions. The proposed methods rank 4th, 3rd, and 5th on the three leaderboards of DRAC, respectively. Our code is publicly available at https://***/FDU-VTS/DRAC. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Optical tomography

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Machine Learning for Cataract Classification/Grading on Ophthalmic Imaging Modalities:A Survey

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Machine Intelligence Research 2022年第3期19卷 184-208页

作者： Xiao-Qing Zhang Yan Hu Zun-Jie Xiao Jian-Sheng Fang Risa Higashita Jiang Liu Research Institute of Trustworthy Autonomous Systems Southern University of Science and TechnologyShenzhen 518055China Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Tomey Corporation Nagoya 4510051Japan Cixi Institute of Biomedical Engineering Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 315300China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and TechnologyShenzhen 518055China

Cataracts are the leading cause of visual impairment and blindness *** the years,researchers have achieved significant progress in developing state-of-the-art machine learning techniques for automatic cataract classification and grading,aiming to prevent cataracts early and improve clinicians′diagnosis *** survey provides a comprehensive survey of recent advances in machine learning techniques for cataract classification/grading based on ophthalmic *** summarize existing literature from two research directions:conventional machine learning methods and deep learning *** survey also provides insights into existing works of both merits and *** addition,we discuss several challenges of automatic cataract classification/grading based on machine learning techniques and present possible solutions to these challenges for future research.

关键词： Cataract classification and grading ophthalmic image machine learning deep learning

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An Efficient Energy Consumption Prediction Framework for High Performance Computing Cluster Jobs

An Efficient Energy Consumption Prediction Framework for Hig...

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International Conference on computer Engineering and Applications (ICCEA)

作者： Yantao Lou Jibin Wang Shoupeng Feng Xian Yu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Department of Public Security of Shandong Province Shandong Intelligent Public Security Technology Innovation Center Jinan China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

Energy consumption prediction is a crucial approach to enhance the operational efficiency of HPC (High Performance Computing) clusters. Existing predicting methods for the energy consumption mainly rely on time series mode, focusing on the instantaneous power during job execution. However, in practical applications, due to the large scale of HPC clusters, the prediction models need to process a vast amount of data, often resulting in poor performance. Moreover, it's challenging to provide effective predictions for jobs with significant power fluctuations. Thus we propose a more efficient energy consumption prediction framework for HPC cluster jobs, achieving two main objectives: On one hand, it uses a data encoding module to pre-encode the sampled data of energy consumption related indicators, simplifying the data that the time series prediction model needs to process and improving prediction efficiency. On the other hand, the prediction target is set to the power distribution range in a future period of time, rather than the instantaneous power at some specific time points, which improves the usability of the prediction results. Test results show that the proposed prediction framework can achieve more markable performance improvement while ensuring the effectiveness of the predictions.

关键词： Energy consumption Computational modeling High performance computing Time series analysis Power distribution Predictive models Feature extraction

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Towards a personalized pregnancy experience: Forecasting symptoms using graph neural networks and digital health technologies 4

Towards a personalized pregnancy experience: Forecasting sym...

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4th Machine Learning for Health Symposium, ML4H 2024

作者： Zhu, Rui Yu, Jennifer Friend, Stephen H. Goodday, Sarah M. Wang, Bo Goldenberg, Anna Department of Computer Science University of Toronto Toronto Canada Hospital for Sick Children Toronto Canada Vector Institute Toronto Canada 4YouandMe SeattleWA United States Department of Psychiatry University of Oxford Oxford United Kingdom Department of Medical Biophysics University of Toronto Toronto Canada University Health Network Toronto Canada Department of Laboratory Medicine and Pathobiology University of Toronto Toronto Canada

Pregnancy is an intricate process involving substantial physiological changes that impact both maternal and fetal health. In this study, we demonstrate the ability to predict two common symptoms during the third trimester of pregnancy-edema and fatigue-using physiological measures from wearable devices and self-reported daily surveys. Our approach employs a Graph Neural Network (GNN) framework, enhanced with a modified weighted Cross-Entropy loss to improve prediction performance. The model achieved AUC scores of 77.27% for edema and 69.70% for fatigue. Additionally, we aligned data from self-reported pregnancy symptoms with clinical examinations to carefully select participant cohorts for our experiments. We also explored how various features identified by the GNN are linked to these symptoms, gaining deeper insights into the relationship between physiological measures and pregnancy symptoms. Our findings indicate that heart rate variability plays a significant role in predicting symptoms of edema and fatigue, and features related to low-intensity activity also have a notable impact. Some of our findings closely align with previous studies on pregnancy. Our research serves as a proof of concept that symptoms can be predicted using wearable data, which may enhance the immediate well-being of expectant mothers and potentially personalize the overall pregnancy experience. © 2024 R. Zhu, J. Yu, S.H. Friend, S.M. Goodday, B. Wang & A. Goldenberg.

关键词： Graph neural networks

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