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Temperature dependence of commensurate magnetic resonance in cuprate superconductors 5th

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Temperature dependence of commensurate magnetic resonance in...

5th Conference on New Advances in Condensed Matter Physics, NACMP 2018

作者： Lan, Yu Kuang, Lülin Hunan Provincial Key Laboratory of Intelligent Information Processing and Application College of Physics and Electronic Engineering Hengyang Normal University Hengyang421002 China National Research Center for High-Performance Computing Engineering Technology Sugon Beijing100193 China

ISBN: (纸本)9783035713275

Within the kinetic energy driven superconducting mechanism, we have studied the temperature dependence of commensurate magnetic resonance in cuprate superconductors. It is shown that the commensurate magnetic resonance peak at the antiferromagnetic wave vector point persists in the superconducting state until the temperature rises to the superconducting transition temperature Tc. The intensity of the resonance peak decreases with increasing temperature which is just like the temperature dependence of the superconducting gap parameter. Our results are in qualitative agreement with the inelastic neutron scattering experimental data and reflect that the commensurate magnetic resonance is closely related to the creation of the charge carrier pairs and thus the superconducting mechanism of cuprate superconductors. © 2018 Trans Tech Publications, Switzerland

关键词： Temperature distribution

UWB SAR image segmentation algorithm based on polynomial analysis of statistical distribution 6

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UWB SAR image segmentation algorithm based on polynomial ana...

6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Yuming, Wang Tian, Jin Chaopeng, Luo College of Information Science and Engineering Hunan Provincial Key Laboratory of Intelligent Computhing and Language Information Processing Science and Technology on Near-Surface Detection Laboratory Hunan Normal University Changsha China College of Electronic Science and Technology Science and Technology on Near-Surface Detection Laboratory National University of Defense Technology Changsha China Science and Technology on Near-Surface Detection Laboratory Wuxi214035 China

ISBN: (纸本)9781728129129

Aimed at the segmentation problem of ultrawideband synthetic aperture radar (UWB SAR) image, a novel algorithm based on polynomial analysis of statistical distribution is proposed in this letter. Firstly, we estimate the probability density function (PDF) for each pixel via sparse decomposition of which the dictionary consists of orthogonal polynomials (OP). Secondly, the weighting coefficients of corresponding OP are used to segment image using their maximum value. The experimental results validate the efficiency of this algorithm. © 2019 IEEE.

关键词： Synthetic aperture radar

Y-Mol: A Multiscale Biomedical Knowledge-Guided Large language Model for Drug Development

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arXiv 2024年

作者： Ma, Tengfei Lin, Xuan Li, Tianle Li, Chaoyi Chen, Long Zhou, Peng Cai, Xibao Yang, Xinyu Zeng, Daojian Cao, Dongsheng Zeng, Xiangxiang College of Computer Science and Electronic Engineering Hunan University China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiangtan University China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University China Xiangya School of Pharmaceutical Sciences Central South University China

Large language Models (LLMs) have recently demonstrated remarkable performance in general tasks across various fields. However, their effectiveness within specific domains such as drug development remains challenges. To solve these challenges, we introduce Y-Mol, forming a well-established LLM paradigm for the flow of drug development. Y-Mol is a multiscale biomedical knowledge-guided LLM designed to accomplish tasks across lead compound discovery, pre-clinic, and clinic prediction. By integrating millions of multiscale biomedical knowledge and using LLaMA2 as the base LLM, Y-Mol augments the reasoning capability in the biomedical domain by learning from a corpus of publications, knowledge graphs, and expert-designed synthetic data. The capability is further enriched with three types of drug-oriented instructions: description-based prompts from processed publications, semantic-based prompts for extracting associations from knowledge graphs, and template-based prompts for understanding expert knowledge from biomedical tools. Besides, Y-Mol offers a set of LLM paradigms that can autonomously execute the downstream tasks across the entire process of drug development, including virtual screening, drug design, pharmacological properties prediction, and drug-related interaction prediction. Our extensive evaluations of various biomedical sources demonstrate that Y-Mol significantly outperforms general-purpose LLMs in discovering lead compounds, predicting molecular properties, and identifying drug interaction events. The source code is available at https://***/r/Y-Mol. © 2024, CC BY.

关键词： Semantics

BGRA-Net: Boundary-Guided and Region-Aware Convolutional Neural Network for the Segmentation of Breast Ultrasound Images

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BGRA-Net: Boundary-Guided and Region-Aware Convolutional Neu...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiang Zhang Xuanya Li Kai Hu Xieping Gao Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China Baidu Inc. Beijing China Key Laboratory of Medical Imaging and Artifical Intelligence of Hunan Province Xiangnan University Chenzhou China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China

ISBN: (纸本)9781665429825

In this paper, we propose a novel convolutional neural network based on boundary-guided and region-aware (BGRA-Net) for breast tumor segmentation in ultrasound images. In particular, in the encoding stage, we propose a boundary-guided module (BGM) to guide the learning of boundary features in the decoding stage by explicitly strengthening the extracted boundary information. Meanwhile, in the decoding stage, we propose a region-aware module (RAM) to integrate different levels of detailed and semantic features to improve the comprehensive representation of tumor regional features. Besides, a scale-adaptive module (SAM) is further proposed to capture the characteristics of tumors with different sizes between the encoding and decoding stages. To evaluate the effectiveness of our BGRA-Net, we conduct extensive experiments on the UDIAT dataset and compare it with eight state-of-the-art methods. The experimental results show that our BGRA-Net outperforms the state-of-the-art methods and can achieve accurate segmentation of breast tumors with ambiguous boundaries.

关键词： Deep learning Image segmentation Breast tumors Ultrasonic imaging Semantics Feature extraction Visual effects

Semi-Supervised Feature Selection Based on Fuzzy Related Family

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SSRN

SSRN 2023年

作者： Yang, Tian Guo, Zhijun Li, Yuan-Jiang Deng, Yanfang Qian, Yuhua Hunan Provincial Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Hunan Changsha410081 China Institute of Big Data Science and Industry Shanxi University Shanxi Taiyuan030006 China

Existing machine learning algorithms face the problems of label-missing and high dimensionality. Feature selection is an effective dimensionality reduction method that can improve the efficiency and accuracy of subsequent machine learning tasks by removing irrelevant and redundant features. Related family is an efficient and rough set-based feature selection method, but is difficult to be used in simisupervised learning tasks. However, due to the difficulty of obtaining all labeled data, partially labeled data has become an important object that machine learning models must deal with. Therefore, this paper proposes a semi-supervised fuzzy related family-based feature selection method for partially labeled data. This method first calculates the fuzzy label values of unlabeled samples through fuzzy similarity relationships to establish a new fuzzy covering system. Then, based on the fuzzy covering system, it constructs a fuzzy related family based on consistent fuzzy set. Finally, based on the constructed feature significance measurement, a semi-supervised feature selection algorithm based on fuzzy related family (SFRF) is established. Compared with existing semi-supervised feature selection algorithms, SFRF can significantly improve the efficiency of feature selection while maintaining classification accuracy, with the average reduction efficiency of nine datasets increased by up to 201 times. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

Reachable Distance Function for KNN Classification

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arXiv 2021年

作者： Zhang, Shichao Li, Jiaye Li, Yangding School of Computer Science and Engineering Central South University Changsha410083 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China

Distance function is a main metrics of measuring the affinity between two data points in machine learning. Extant distance functions often provide unreachable distance values in real applications. This can lead to incorrect measure of the affinity between data points. This paper proposes a reachable distance function for KNN classification. The reachable distance function is not a geometric direct-line distance between two data points. It gives a consideration to the class attribute of a training dataset when measuring the affinity between data points. Concretely speaking, the reachable distance between data points includes their class center distance and real distance. Its shape looks like "Z," and we also call it a Z distance function. In this way, the affinity between data points in the same class is always stronger than that in different classes. Or, the intraclass data points are always closer than those interclass data points. We evaluated the reachable distance with experiments, and demonstrated that the proposed distance function achieved better performance in KNN classification. © 2021, CC BY-NC-ND.

关键词： Machine learning

Joint Optimization of Deployment and Trajectory in UAV and IRS-Assisted IoT Data Collection System

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arXiv 2022年

作者： Dong, Li Liu, Zhibin Jiang, Feibo Wang, Kezhi Changsha Social Laboratory of Artificial Intelligence Hunan University of Technology and Business Changsha China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China The Department of Computer and Information Sciences Northumbria University United Kingdom

Unmanned aerial vehicles (UAV) can be applied in many Internet of Things (IoT) systems, e.g., smart farms, as a data collection platform. However, the UAV-IoT wireless channels may be occasionally blocked by trees or high-rise buildings. intelligent reflecting surface (IRS) can be applied to improve the wireless channel quality by smartly reflecting the signal via a large number of low-cost passive reflective elements. This paper aims to minimize the energy consumption of the system by jointly optimizing the deployment and trajectory of the UAV. The problem can be formulated as a mixed-integer- and-nonlinear-programming (MINLP), which is difficult to be addressed by the traditional solution, which may be easily fall into the local optimal. To address this issue, we propose a Joint Optimization framework of depLoyment and Trajectory (JOLT), where an adaptive whale optimization algorithm (AWOA) is applied to optimize the deployment of the UAV, and an elastic ring self-organizing map (ERSOM) is introduced to optimize the trajectory of the UAV. Specifically, in AWOA, a variable-length population strategy is applied to find the optimal number of stop points, and a nonlinear parameter a and a partial mutation rule are introduced to balance the exploration and exploitation. In ERSOM, a competitive neural network is also introduced to learn the trajectory of the UAV by competitive learning, and a ring structure is presented to avoid the trajectory intersection. Extensive experiments are carried out to show the effectiveness of the proposed JOLT framework. Copyright © 2022, The Authors. All rights reserved.

关键词： Trajectories

Deep progressive reinforcement learning-based flexible resource scheduling framework for IRS and UAV-assisted MEC system

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arXiv 2024年

作者： Dong, Li Jiang, Feibo Wang, Minjie Peng, Yubo Li, Xiaolong School of Computer Science Hunan University of Technology and Business Changsha China Xiangjiang Laboratory Changsha China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China College of Information Science and Engineering Hunan Normal University Changsha China

The intelligent reflection surface (IRS) and unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is widely used in temporary and emergency scenarios. Our goal is to minimize the energy consumption of the MEC system by jointly optimizing UAV locations, IRS phase shift, task offloading, and resource allocation with a variable number of UAVs. To this end, we propose a Flexible REsource Scheduling (FRES) framework by employing a novel deep progressive reinforcement learning which includes the following innovations: Firstly, a novel multi-task agent is presented to deal with the mixed integer nonlinear programming (MINLP) problem. The multi-task agent has two output heads designed for different tasks, in which a classified head is employed to make offloading decisions with integer variables while a fitting head is applied to solve resource allocation with continuous variables. Secondly, a progressive scheduler is introduced to adapt the agent to the varying number of UAVs by progressively adjusting a part of neurons in the agent. This structure can naturally accumulate experiences and be immune to catastrophic forgetting. Finally, a light taboo search (LTS) is introduced to enhance the global search of the FRES. The numerical results demonstrate the superiority of the FRES framework which can make real-time and optimal resource scheduling even in dynamic MEC systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

Learning to Learn Transferable Generative Attack for Person Re-Identification

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arXiv 2024年

作者： Bian, Yuan Liu, Min Wang, Xueping Ma, Yunfeng Wang, Yaonan The College of Electrical and Information Engineering Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology Hunan Changsha China The College of Information Science and Engineering Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Changsha China

Deep learning-based person re-identification (re-id) models are widely employed in surveillance systems and inevitably inherit the vulnerability of deep networks to adversarial attacks. Existing attacks merely consider cross-dataset and cross-model transferability, ignoring the cross-test capability to perturb models trained in different domains. To powerfully examine the robustness of real-world re-id models, the Meta Transferable Generative Attack (MTGA) method is proposed, which adopts meta-learning optimization to promote the generative attacker producing highly transferable adversarial examples by learning comprehensively simulated transfer-based cross-model&dataset&test black-box meta attack tasks. Specifically, cross-model&dataset black-box attack tasks are first mimicked by selecting different re-id models and datasets for meta-train and meta-test attack processes. As different models may focus on different feature regions, the Perturbation Random Erasing module is further devised to prevent the attacker from learning to only corrupt model-specific features. To boost the attacker learning to possess cross-test transferability, the Normalization Mix strategy is introduced to imitate diverse feature embedding spaces by mixing multi-domain statistics of target models. Extensive experiments show the superiority of MTGA, especially in cross-model&dataset and cross-model&dataset&test attacks, our MTGA outperforms the SOTA methods by 21.5% and 11.3% on mean mAP drop rate, respectively. The code of MTGA will be released after the paper is accepted. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks