检索结果-内蒙古大学图书馆

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yulun Wu Mingrui Lao Yanming Guo Dongmei Chen Tianyuan Yua Laboratory of Big Data and Decision National University of Defense Technology Changsha China Department of Computer Science and Artificial Intelligence University of Granada Granada Spain

Adversarial Robust Distillation (ARD) has emerged as a potent defense mechanism tailored to small models against adversarial threats. However, mainstream ARD methods typically exploit teachers’ response as the transferred knowledge, while neglecting the analysis of involved target-related knowledge to mitigate adversarial attacks. Furthermore, these methods primarily focus on logits-level distillation, which overlook the features-level knowledge in teacher models. In this paper, we introduce a novel Hybrid Decomposed Distillation (HDD) approach, which attempts to identify the vital knowledge against adversarial threats through dual-level distillation. Specifically, we first seek to separate the predictions of teacher model into target-related and target-unrelated knowledge for flexible yet efficient logits-level distillation. Besides, to further boost the distillation efficacy, HDD leverages the channel correlations to decompose intermediate features into highly and less relevant components. Extensive experiments on two benchmarks demonstrate that our HDD achieves superior performance in both clean accuracy and robustness, in contrast to current state-of-the-art methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A cognitive Solver with Autonomously Knowledge Learning for Reasoning Mathematical Answers

A Cognitive Solver with Autonomously Knowledge Learning for ...

引用

IEEE International Conference on data Mining (ICDM)

作者： Jiayu Liu Zhenya Huang Xin Lin Qi Liu Jianhui Ma Enhong Chen Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science & School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence

Reasoning answers to mathematical problems requires machines to think and operate like a human to learn knowledge from mathematical data, which is one of the fundamental tasks for exploring general artificial intelligence. Most solutions focus on mimicking how humans understand problems, which generate the necessary expressions for answers. However, they are still far from enough since they ignore the core ability of humans to acquire knowledge from experience. In this paper, we propose a cognitive Solver (CogSolver) that is capable of autonomously learning knowledge from scratch to solve mathematical problems, inspired by two cognitive science theories. Specifically, we draw one insight from the dual process theory to establish an intelligent BRAIN-ARM framework, and refer to another information processing theory to summarize the knowledge learning process into Store-Apply-Update steps. In CogSolver, the BRAIN system stores three types of mathematical knowledge, including semantics knowledge, relation knowledge, and mathematic rule knowledge. Then, the ARM system applies the knowledge in BRAIN to answer the problems. Specifically, we design a knowledge-aware module and a commutative module in ARM to improve its reasoning ability, where the knowledge is organically integrated into answer reasoning process. After solving the problems, BRAIN updates the stored knowledge according to the feedback of ARM, where we develop knowledge filters to eliminate the redundant ones and further form a more reasonable knowledge base. Our CogSolver carries out the above three steps iteratively, which behaves more like a human. We conduct extensive experiments on real-world math word problem datasets. The experimental results demonstrate the improvement in answer reasoning and clearly show how CogSolver gains knowledge from the problems, leading to superior interpretability. Our codes are available at https://***/bigdata-ustc/CogSolver.

关键词： Commutation Semantics Knowledge based systems Information processing Cognition Mathematics Filtering theory

来源：评论

学校读者我要写书评

暂无评论

An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

引用

IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1772-1774页

作者： Zhihao Hao Guancheng Wang Bob Zhang Leyuan Fang Haisheng Li the Department of Computer and Information Science University of MacaoMacao 999078China the School of Data Science the Chinese University of Hong KongShenzhen 518172 Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000 China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the Peng Cheng Laboratory Shenzhen 518000China the Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048 the School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing 100048China

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

关键词： ism thereby letter

来源：评论

学校读者我要写书评

暂无评论

PlantEMS:A comprehensive database of epigenetic modification sites across multiple plant species

引用

Plant Communications 2025年第4期6卷 8-11页

作者： Fuying Dao Xueqin Xie Hongqi Zhang Zhengxing Guan Changchun Wu Wei Su Yijie Wei Feitong Hong Xinwei Luo Sijia Xie Hongyan Lai Dong Gao Yuhe Yang Yang Zhang Lin Ning Shihao Li Yuduo Hao Benjamin Lebeau Crystal Chia Yin Ling Jian Huang Melissa Jane Fullwood Hao Lin Hao Lv The Clinical Hospital of Chengdu Brain Science Institute School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengdu 610054P.R.China School of Biological Sciences Nanyang Technological UniversitySingapore 639798Singapore Chongqing Key Laboratory of Big Data for Bio Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Innovative Institute of Chinese Medicine and Pharmacy Chengdu University of Traditional Chinese MedicineChengdu 611137 China School of Healthcare Technology Chengdu Neusoft UniversityChengdu 611844China

Dear Editor,Epigenetic modifications are regulatory codes that control gene expression and can be stably inherited without alterations in the genomic sequence(Allis and Jenuwein,2016).Numerous studies have demonstrated that epigenetic modifications regulate critical biological processes in plants,such as growth,development,response to environmental stressors,and reproduction(Zhang et al.,2023;Bulgakov,2024).

关键词： regulatory codes genomic sequence allis epigenetic modifications growth plant species biological processes gene expression

来源：评论

学校读者我要写书评

暂无评论

DFGET: Displacement-Field Assisted Graph Energy Transmitter for Gland Instance Segmentation

DFGET: Displacement-Field Assisted Graph Energy Transmitter ...

引用

International Conference on Signal Processing Proceedings (ICSP)

作者： Caiqing Jian Yongbin Qin Lihui Wang Chen Ye Xinyu Cheng GState Key Laboratory of Public Big Data College of Computer Science and Technology Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Guizhou University Guiyang China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Gland instance segmentation is an essential but challenging task in the diagnosis of adenocarcinoma. The existing models usually achieve gland instance segmentation through multi-task learning and boundary loss constraint. However, how to deal with the problems of gland adhesion and inaccurate boundary in segmenting the complex samples remains a challenge. In this work, we propose a novel geometric feature named displacement field (DF), which can represent the geometric information such as shape, size, and orientation of the glands. Based on the explicit constraint of DF, a displacement-field assisted graph energy transmitter (DFGET) framework is proposed for solving the above problems. Using such graph framework, the gland semantic segmentation map and the displacement field of the graph nodes are estimated with two graph network branches. With the constraint of DF, a graph cluster module is presented to improve the intra-class feature consistency and inter-class feature discrepancy, as well as to separate the adherent glands. Extensive comparison and ablation experiments on the GlaS dataset demonstrate the effectiveness of the displacement field and the superiority of DFGET. Compared to the best comparative model, DFGET increases the object-Dice and object-F1 score by 2.27% and 3.11% respectively, while decreases the object-HD by 15.58, achieving state-of-the-art performance, which indicates that DFGET can serve as an auxiliary tool for the diagnosis of adenocarcinoma.

关键词： Instance segmentation Measurement Visualization Adhesives Shape Semantic segmentation Glands Signal processing algorithms Signal processing Market research

来源：评论

学校读者我要写书评

暂无评论

Swarm Robots Decentralized Control using Reinforcement Learning Stepwise Training Method in the Encircling Task

Swarm Robots Decentralized Control using Reinforcement Learn...

引用

2022 Global Conference on Robotics, Artificial intelligence and Information technology, GCRAIT 2022

作者： Liu, Yang Wang, Xu Nie, Yiming Dai, Bin Zhu, Qi Shang, Erke College of Intelligence Science and Technology National University of Defence Technology Changsha China Xi'An Key Laboratory of Big Data and Intelligent Vision Xidian University Xi'an China National Innovation Institute of Defense Technology Beijing China

ISBN: (数字)9781665481922

ISBN: (纸本)9781665481922

We consider a special multiple-robot task, pursuit-evasion (PE) game, in which a swarm of hunting robots (Pursuers) attempts to encircle a target robot (Evader) in a two-dimensional plane with some fixed obstacles on it. Instead of the central control method, we suggest a fully decentralized pursuit strategy generated by the deep reinforcement learning (DRL) which has greatly advanced the research in decision-making field in recent years. While the DRL method in this encircling task faces a sparse reward and a nonconvex space challenge which makes convergence result unacceptable. In this article, we have clarified the definition of a successful encirclement by escaping angel. In order to overcome the inefficient exploration and premature convergence caused by the above challenges during the training process, we use a stepwise training method of an adaptive various reward function. All prosecutors use the same policy given by stepwise training method and the multi-agent deep deterministic policy gradient (MADDPG) algorithms. Simulations and comparisons are provided to validate the effectiveness of this work. © 2022 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Hyperspectral Image Classification Based on Unsupervised Heterogeneous Domain Adaptation CycleGan

引用

Chinese Journal of Electronics 2020年第4期29卷 608-614页

作者： WANG Xuesong LI Yiran CHENG Yuhu School of Information and Control Engineering China University of Mining and Technology Xuzhou Key Laboratory of Artificial Intelligence and Big Data

Aiming at the difficulty of obtaining sufficient labeled Hyperspectral image(HSI) data and the inconsistent feature distribution of different HSIs, a novel Unsupervised heterogeneous domain adaptation CycleGan(UHDAC) is proposed by using CycleGan to capture the transferable features in the absence of similar *** the one hand, the two-way mapping is used to find the internal relationship between the source and target domain data,while the two-way adversary is used to constrain the source and target domain features, realizing the alignment of feature *** the other hand, the CORAL loss function is introduced to minimize the distance between the second-order statistical difference between the source and target domain features, so as to solve the insufficient constraint of mapping relationship caused by the low consistency of HSI data structure in different *** on three real HSI datasets show that UHDAC can effectively realize the unsupervised classification of target domain HSI with high classification accuracy by using the labeled HSI data in the source domain.

关键词： Unsupervised Heterogeneous Domain adaptation CycleGan Hyperspectral image Classification

来源：评论

学校读者我要写书评

暂无评论

LRAE-UNet: Lightweight MRI Brain Tumor Automatic Segmentation Network

SSRN

引用

SSRN 2023年

作者： Yu, Wang Jiahao, Lin Minhua, Liu Beijing Key Laboratory of Big Data Technology for Food Safety School of Artificial Intelligence Beijing Technology and Business University Beijing China

The brain is the important part of central nervous system of the human being where brain tumor seriously threatens human health. For effective treatment, fully automatic brain tumor segmentation is a very significant and challenging work. The currently mainstream segmentation model U-Net makes the three-dimensional medical image discontinuous between the layers, which is unable to balance the information between sparse layers and the one in the dense layer. In this paper, a lightweight residual attention enhanced U-Net, called LRAE-UNet, for fully automatic brain tumor segmentation is proposed which main includes the lightweight residual module, the lightweight attention module, and the enhanced average pooling module. The lightweight residual module is used to solve the problem of gradient disappearance and network degradation when the number of network layers increases. The lightweight attention module can suppress the irrelevant areas and highlight the significant features of specific local areas. The enhanced average pooling module has a larger field of perception which can save computing resources. The experimental results on BraTsXXX database show that the mean accuracy of Dice scores reach 91.24%, 88.64%, and 88.32% respectively on the whole tumor, tumor core, and enhancing tumor segmentation, which proves that the proposed LRAE-UNet is feasible and effective for fully automatic segmentation of brain © 2023, The Authors. All rights reserved.

关键词： Brain

来源：评论

学校读者我要写书评

暂无评论

The application of artificial intelligence in upper gastrointestinal cancers

Journal of the National Cancer Center

引用

Journal of the National Cancer Center 2025年第2期5卷 113-131页

作者： Xiaoying Huang Minghao Qin Mengjie Fang Zipei Wang Chaoen Hu Tongyu Zhao Zhuyuan Qin Haishan Zhu Ling Wu Guowei Yu Francesco De Cobelli Xuebin Xie Diego Palumbo Jie Tian Di Dong CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijingChina School of Artificial Intelligence University of Chinese Academy of SciencesBeijingChina University of Science and Technology Beijing BeijingChina Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang UniversityBeijingChina Key Laboratory of Big Data-Based Precision Medicine Beihang UniversityMinistry of Industry and Information TechnologyBeijingChina University of Science and Technology of China HefeiChina Beijing Institute of Genomics Chinese Academy of SciencesBeijingChina Beijing University of Chinese Medicine BeijingChina KiangWu Hospital MacaoChina Department of Radiology IRCCS San Raffaele Scientific InstituteMilanItaly

Upper gastrointestinal cancers,mainly comprising esophageal and gastric cancers,are among the most prevalent cancers *** are many new cases of upper gastrointestinal cancers annually,and the survival rate tends to be ***,timely screening,precise diagnosis,appropriate treatment strategies,and effective prognosis are crucial for patients with upper gastrointestinal *** recent years,an increasing number of studies suggest that artificial intelligence(AI)technology can effectively address clinical tasks related to upper gastrointestinal *** studies mainly focus on four aspects:screening,diagnosis,treatment,and *** this review,we focus on the application of AI technology in clinical tasks related to upper gastrointestinal ***,the basic application pipelines of radiomics and deep learning in medical image analysis were ***,we separately reviewed the application of AI technology in the aforementioned aspects for both esophageal and gastric ***,the current limitations and challenges faced in the field of upper gastrointestinal cancers were summarized,and explorations were conducted on the selection of AI algorithms in various scenarios,the popularization of early screening,the clinical applications of AI,and large multimodal models.

关键词： Upper gastrointestinal cancers Artificial intelligence Radiomics Esophageal cancer Gastric cancer

来源：评论

学校读者我要写书评

暂无评论

Adaptive normalization for non-stationary time series forecasting: a temporal slice perspective 23

Adaptive normalization for non-stationary time series foreca...

引用

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Zhiding Liu Mingyue Cheng Zhi Li Zhenya Huang Qi Liu Yanhu Xie Enhong Chen Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence Shenzhen International Graduate School Tsinghua University The First Affiliated Hospital of University of Science and Technology of China

Deep learning models have progressively advanced time series forecasting due to their powerful capacity in capturing sequence dependence. Nevertheless, it is still challenging to make accurate predictions due to the existence of non-stationarity in real-world data, denoting the data distribution rapidly changes over time. To mitigate such a dilemma, several efforts have been conducted by reducing the non-stationarity with normalization operation. However, these methods typically overlook the distribution discrepancy between the input series and the horizon series, and assume that all time points within the same instance share the same statistical properties, which is too ideal and may lead to suboptimal relative improvements. To this end, we propose a novel slice-level adaptive normalization, referred to SAN, which is a novel scheme for empowering time series forecasting with more flexible normalization and denormalization. SAN includes two crucial designs. First, SAN tries to eliminate the non-stationarity of time series in units of a local temporal slice (i.e., sub-series) rather than a global instance. Second, SAN employs a slight network module to independently model the evolving trends of statistical properties of raw time series. Consequently, SAN could serve as a general model-agnostic plugin and better alleviate the impact of the non-stationary nature of time series data. We instantiate the proposed SAN on four widely used forecasting models and test their prediction results on benchmark datasets to evaluate its effectiveness. Also, we report some insightful findings to deeply analyze and understand our proposed SAN. We make our codes publicly available https://***/icantnamemyself/SAN.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：