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Magnetoresistance Oscillations in Vertical Junctions of 2D Antiferromagnetic Semiconductor CrPS4

Physical Review X 2024年第4期14卷 041065-041065页

作者： Pengyuan Shi Xiaoyu Wang Lihao Zhang Wenqin Song Kunlin Yang Shuxi Wang Ruisheng Zhang Liangliang Zhang Takashi Taniguchi MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter Shaanxi Province Key Laboratory of Advanced Materials and Mesoscopic Physics School of Physics Xi’an Jiaotong University Xi’an 710049 China State Key Laboratory of Surface Physics and Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai 200433 China State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong University Xi’an 710049 China Research Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan

Magnetoresistance (MR) oscillations serve as a hallmark of intrinsic quantum behavior, traditionally observed only in conducting systems. Here we report the discovery of MR oscillations in an insulating system, the vertical junctions of CrPS4 which is a two-dimensional A-type antiferromagnetic semiconductor. Systematic investigations of MR peaks under varying conditions, including electrode materials, magnetic field direction, temperature, voltage bias, and layer number, elucidate a correlation between MR oscillations and spin-canted states in CrPS4. Experimental data and analysis point out the important role of the in-gap electronic states in generating MR oscillations, and we propose that spin selected interlayer hopping of localized defect states may be responsible for it. Our findings not only illuminate the unusual electronic transport in CrPS4 but also underscore the potential of van der Waals magnets for exploring interesting phenomena.

关键词： Van der Waals forces

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A Short-term Net Load Forecasting Method Based on Two-stage Feature Selection and LightGBM with Hyperparameter Auto-Tuning

A Short-term Net Load Forecasting Method Based on Two-stage ...

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Annual Meeting Industrial and Commercial Power Systems Technical Conference

作者： Shuya Lei Xiao Liang Xiaohui Wang Jingyang Ding Xinxin Ge Fei Wang Jia Feng State Grid Laboratory of Grid Advanced Computing and Applications (State Grid Smart Grid Research Institute Co. Ltd) Beijing China Department of Electrical Engineering North China Electric Power University Baoding China State Grid Zhejiang Electric Power Company Ltd Hangzhou China

Reliable short-term net load forecasting (STNLF) is important for the stable operation of power systems and assists utilities in unit commitment and demand side management. With the increasing penetration of invisible behind-the-meter photovoltaic systems (BTM-PVs) installed close to customers, accurate STNLF is becoming more challenging. The intermittent of PV output is carried into the load due to the coupling of PV output and actual load, which makes the net load complex and difficult to forecast. In addition, the related influencing factors of BTM-PV also have a great impact on load characteristics based on the original features of load demand. To eliminate the redundant features is also one of the difficulties. This paper proposes a machine learning method to improve the accuracy of 24-hourahead net load forecasting, including PV-Load decoupling, feature selection and light gradient boosting machine (LightGBM) with Bayesian optimization. Firstly, the net load is decomposed into PV output and actual load using the data of small amount of observable PV customers. Then a two-step feature selection method is used to select key features for load demand and PV output, respectively, based on the forecasting accuracy of different feature combinations in the validation dataset. The selected features are used to train LightGBM model, and in order to fully maximize the performance of the forecasting model, the parameters of the model are optimized by Bayesian. Compared with other benchmark models, the proposed model has the best forecasting accuracy. The proposed framework is conducted on smart meter dataset in Ausgrid.

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Data-Driven Control Based on Function Approximation with Binomial Coefficients

Data-Driven Control Based on Function Approximation with Bin...

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第33届中国控制与决策会议

作者： Guoliang Zhu Lei Chen Kexin Liu Jinhu Lü School of Automation Science and Electrical Engineering Beihang Uniersity Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Advanced Innovation Center for Big Data and Brain Computing Beihang University

This paper investigates data-driven control methods based on function approximation with binomial coefficients and sampling *** propose two approximate models and corresponding controllers formed from sampled *** shows that the information truncated in the first approximate model is saved in the latter with parameter estimation *** results validate the effectiveness of proposed approaches.

关键词： Sampled-data driven systems Function approximation Approximate theory Adaptive control Binomial coefficients

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Synergizing Generative Adversarial Networks and Pseudo-Labeling for Improved Knee Osteoarthritis Detection

Synergizing Generative Adversarial Networks and Pseudo-Label...

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Sustainable computing and Smart Systems (ICSCSS), International Conference on

作者： Sajeev Ram Arumugam Sheela Gowr P Geetha Ponnaian Maharajan K Sankar Ganesh Karuppasamy Divya Muralitharan Department of AI&DS Sri Krishna Collefe of Engineering and Technology Coimbatore India Department of CSE Vels Institute of Science Technology and Advanced Studies Chennai India Department of CSE Saveetha Institute of Medical and Technical Sciences Chennai India Department of CSE School of computing Kalasalingam Academy of research and education India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Progressive cartilage breakdown and joint inflammation are hallmarks of the common joint disease known as osteoarthritis (OA). The timely intervention and management of osteoarthritis (OA) depend on early detection. However, this can be difficult to achieve because labelled medical imaging data is scarce, and the disease's presentations are complex. A notable skew between the proportion of healthy and OA knee images can be seen in knee OA datasets, a phenomenon known as class imbalance. As a result, the model may underperform on the minority class (OA) and prioritize the majority class during training. The goal of the research is to further the development of more accurate and reliable techniques for knee OA detection. A novel method for knee osteoarthritis detection that first uses ResNet, DenseNet, VGG16, and VGG19 convolutional neural network (CNN) architectures for classification after generative adversarial networks (GANs) for data augmentation and pseudo-labeling is predicted. The aim of the work is to use labeled and unlabeled data to develop the robustness and accuracy of osteoarthritis detection. It is shown via thorough experimentation that the strategy is beneficial in enhancing classification performance, with ResNet obtaining the greatest accuracy and F1 score among the networks we studied. According to the research, pseudo-labeling and GAN-based data augmentation strategies can greatly improve osteoarthritis diagnosis accuracy and clinical significance. This work advances the field of medical image analysis and has potential benefits for bettering osteoarthritis patient treatment. The proposed approach performs well with an accuracy of 96.23%, precision of 0.963, recall of 0.959, and F1 score of 0.9246.

关键词： Training Accuracy Neural networks Medical treatment Generative adversarial networks Data augmentation Robustness

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Electricity demand monitoring in Japan by using time-series DMSP stable lights images and its application to long-term damage assessment of natural disasters 11

Electricity demand monitoring in Japan by using time-series ...

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11th International Symposium on digital Earth, ISDE 2019

作者： Man Duc, Chuc Hiromichi, Fukui Graduate School of Engineering Chubu University Japan International Digital Earth Applied Science Research Center Chubu Institute for Advanced Studies Chubu University Japan

This paper demonstrates the potential of the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) stable light imagery for electricity power consumption (EPC) estimation in Japan and long-term damage assessment of natural disasters in term of yearly EPC fluctuation, taking the Japan Earthquake & Tsunami of 2011 as a case study. DMSP stable light data is proved useful for estimation of the economic variables. However, there are several well-known problems with the DMSP including lack of on-board calibration, geometric error, sensor saturation. Many researches have been conducted to develop methods for inter-calibration and saturation correction of DMSP stable light data, but much less effort on geometric correction which should be the first in the pre-processing chain. In term of disaster management, the 2011 Great Earthquake & Tsunami in Japan had severe instant and long-term damages, and DMSP data is potential for long-term damage assessment for the region. In this study, we have: (1) developed a new geometric correction method for DMSP stable light images;(2) developed an EPC estimation method from corrected DMSP data which is then used to derive electricity demand maps for Japan from 1992-2013;(3) performed analysis of correlations between DMSP night light data and GDP and population in Japan;(4) conducted a long-term damage assessment of the Japan Earthquake & Tsunami of 2011 by using the EPC maps in (2). It is found that the proposed geometric correction method gave better correlations between DMSP stable light images of the same year as compared to those of original images and most recent shift-based geometric correction method. Damage assessment reveals that an area of 286.208 km-squared has suffered from the disasters. It caused the EPC of the area drop significantly in 2011 (-42%) and to its lowest level in 2012 (-78%) before starting its recover in 2013. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Electric power utilization

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CMUNEXT: An Efficient Medical Image Segmentation Network Based on Large Kernel and Skip Fusion

CMUNEXT: An Efficient Medical Image Segmentation Network Bas...

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IEEE International Symposium on Biomedical Imaging

作者： Fenghe Tang Jianrui Ding Quan Quan Lingtao Wang Chunping Ning S. Kevin Zhou School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Hefei P.R. China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou P.R. China School of Computer Science and Technology Harbin Institute of Technology Harbin China Institute of Computing Technology Chinese Academy of Sciences (CAS) Beijing China Ultrasound Department The Affiliated Hospital of Qingdao University Qingdao China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

The u-shaped architecture has emerged as a crucial paradigm in the design of medical image segmentation networks. However, due to the inherent local limitations of convolution, a fully convolutional segmentation network with u-shaped architecture struggles to effectively extract global context information, which is vital for the precise localization of lesions. While hybrid architectures combining CNN and Transformer can address these issues, their applications are limited due to the computational resource. In addition, the inductive bias of convolution in lightweight networks adeptly fits the scarce medical data, which is lacking in the Transformer based network. To extract global context information while taking advantage of the inductive bias, we propose CMUNeXt, an efficient lightweight segmentation network, which leverages large kernel and inverted bottleneck design to thoroughly mix distant spatial and location information, efficiently extracting global context information. We also introduce the Skip-fusion block, designed to enable smooth skip-connections and ensure ample feature fusion. Experimental results demonstrate that CMUNeXt outperforms existing heavyweight and lightweight medical image segmentation networks, while offering a faster inference speed, lighter weights, and a reduced computational cost. The code is available at https://***/FengheTan9/CMUNeXt.

关键词： Location awareness Image segmentation Convolution Computer architecture Transformers Computational efficiency Data mining

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Applications of artificial intelligence for chemical analysis and monitoring of pharmaceutical and personal care products in water and wastewater: A review

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Chemosphere 2024年 368卷 143692页

作者： Kavianpour, Babak Piadeh, Farzad Gheibi, Mohammad Ardakanian, Atiyeh Behzadian, Kourosh Campos, Luiza C. School of Computing and Engineering University of West London St Mary's Rd LondonW5 5RF United Kingdom Centre for Engineering Research School of Physics Engineering and Computer Science University of Hertfordshire HatfieldAL10 9AB United Kingdom Institute for Nanomaterials Advanced Technologies and Innovation Technical University of Liberec Liberec46117 Czech Republic Centre for Urban Sustainability and Resilience Department of Civil Environmental and Geomatic Engineering University College London LondonWC1E6BT United Kingdom

Specifying and interpreting the occurrence of emerging pollutants is essential for assessing treatment processes and plants, conducting wastewater-based epidemiology, and advancing environmental toxicology research. In recent years, artificial intelligence (AI) has been increasingly applied to enhance chemical analysis and monitoring of contaminants in environmental water and wastewater. However, their specific roles targeting pharmaceuticals and personal care products (PPCPs) have not been reviewed sufficiently. This review aims to narrow the gap by highlighting, scoping, and discussing the incorporation of AI during the detection and quantification of PPCPs when utilising chemical analysis equipment and interpreting their monitoring data for the first time. In the chemical analysis of PPCPs, AI-assisted prediction of chromatographic retention times and collision cross-sections (CCS) in suspect and non-target screenings using high-resolution mass spectrometry (HRMS) enhances detection confidence, reduces analysis time, and lowers costs. AI also aids in interpreting spectroscopic analysis results. However, this approach still cannot be applied in all matrices, as it offers lower sensitivity than liquid chromatography coupled with tandem or HRMS. For the interpretation of monitoring of PPCPs, unsupervised AI methods have recently presented the capacity to survey regional or national community health and socioeconomic factors. Nevertheless, as a challenge, long-term monitoring data sources are not given in the literature, and more comparative AI studies are needed for both chemical analysis and monitoring. Finally, AI assistance anticipates more frequent applications of CCS prediction to enhance detection confidence and the use of AI methods in data processing for wastewater-based epidemiology and community health surveillance. © 2024 The Authors

关键词： Liquid chromatography

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Distribution Network digital Twin: a Basic Machine Learning based Voltage Estimator

Distribution Network Digital Twin: a Basic Machine Learning ...

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IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Mohamad El Iaali Reza Razi Antoine Bruyere Bruno Francois Joao Soares Univ. Lille Arts et Metiers Institute of Technology Centrale Lille Junia ULR 2697 - L2EP Lille France GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate LAboratory Polytechnic of Porto Porto Portugal

The increasing integration of renewable energy sources, and the widespread use of electric vehicles have introduced greater complexity and dynamics in power flows in distribution systems. With bidirectional power flows enhancing the observability of the grid becomes essential to identify critical feeders. In this context, data-driven methods offer a promising approach to enhance situational awareness for power system operators and improve their understanding of the system behavior under variabilities. This paper proposes a voltage estimation method based on a Machine Learning algorithm to address this challenge. By leveraging data-driven techniques, the proposed method aims to provide accurate and efficient voltage estimations in distribution networks, contributing to the effective management and control of modern power systems.

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Partial Equivalence Checking of Quantum Circuits

arXiv

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arXiv 2022年

作者： Chen, Tian-Fu Jiang, Jie-Hong R. Hsieh, Min-Hsiu Department of Electrical Engineering National Taiwan University Taipei Taiwan Graduate School of Advanced Technology National Taiwan University Taipei Taiwan Center for Quantum Science and Engineering National Taiwan University Taipei Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan Quantum Computing Research Center Hon Hai Research Institute Taipei Taiwan

Equivalence checking of quantum circuits is an essential element in quantum program compilation, in which a quantum program can be synthesized into different quantum circuits that may vary in the number of qubits, initialization requirements, and output states. Verifying the equivalences among the implementation variants requires proper generality. Although different notions of quantum circuit equivalence have been defined, prior methods cannot check observational equivalence between two quantum circuits whose qubits are partially initialized, which is referred to as partial equivalence. In this work, we prove a necessary and sufficient condition for two circuits to be partially equivalent. Based on the condition, we devise algorithms for checking quantum circuits whose partial equivalence cannot be verified by prior approaches. Experiment results confirm the generality and demonstrate the efficiency and effectiveness of our method. Our result may unleash the optimization power of quantum program compilation to take more aggressive steps. © 2022, CC BY-NC-ND.

关键词： Qubits

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Rethinking Exemplars for Continual Semantic Segmentation in Endoscopy Scenes: Entropy-based Mini-Batch Pseudo-Replay

arXiv

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arXiv 2023年

作者： Wang, Guankun Bai, Long Wu, Yanan Chen, Tong Ren, Hongliang Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China School of Electrical and Information Engineering The University of Sydney Sydney Australia Department of Biomedical Engineering National University of Singapore Singapore Suzhou Research Institute National University of Singapore Suzhou China Shun Hing Institute of Advanced Engineering The Chinese University of Hong Kong Hong Kong Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen China

Endoscopy is a widely used technique for the early detection of diseases or robotic-assisted minimally invasive surgery (RMIS). Numerous deep learning (DL)-based research works have been developed for automated diagnosis or processing of endoscopic view. However, existing DL models may suffer from catastrophic forgetting. When new target classes are introduced over time or cross institutions, the performance of old classes may suffer severe degradation. More seriously, data privacy and storage issues may lead to the unavailability of old data when updating the model. Therefore, it is necessary to develop a continual learning (CL) methodology to solve the problem of catastrophic forgetting in endoscopic image segmentation. To tackle this, we propose a Endoscopy Continual Semantic Segmentation (EndoCSS) framework that does not involve the storage and privacy issues of exemplar data. The framework includes a mini-batch pseudo-replay (MB-PR) mechanism and a self-adaptive noisy cross-entropy (SAN-CE) loss. The MB-PR strategy circumvents privacy and storage issues by generating pseudo-replay images through a generative model. Meanwhile, the MB-PR strategy can also correct the model deviation to the replay data and current training data, which is aroused by the significant difference in the amount of current and replay images. Therefore, the model can perform effective representation learning on both new and old tasks. SAN-CE loss can help model fitting by adjusting the model’s output logits, and also improve the robustness of training. Extensive continual semantic segmentation (CSS) experiments on public datasets demonstrate that our method can robustly and effectively address the catastrophic forgetting brought by class increment in endoscopy scenes. The results show that our framework holds excellent potential for real-world deployment in a streaming learning manner. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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