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science Bulletin 2024年第11期69卷 1738-1747页

作者：李远宁杨虎铮顾实 School of Biomedical Engineering&State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech UniversityShanghai 201210China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Department of Computer and Information Science University of PennsylvaniaPhiladelphiaPA 19104USA Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen 518110China

Cognitive neuroscience aims to develop computational models that can accurately predict and explain neural responses to sensory inputs in the cortex. Recent studies attempt to leverage the representation power of deep neural networks(DNNs) to predict the brain response and suggest a correspondence between artificial and biological neural networks in their feature representations. However, typical voxel-wise encoding models tend to rely on specific networks designed for computer vision tasks, leading to suboptimal brain-wide correspondence during cognitive tasks. To address this challenge, this work proposes a novel approach that upgrades voxel-wise encoding models through multi-level integration of features from DNNs and information from brain networks. Our approach combines DNN feature-level ensemble learning and brain atlas-level model integration, resulting in significant improvements in predicting whole-brain neural activity during naturalistic video perception. Furthermore, this multi-level integration framework enables a deeper understanding of the brain's neural representation mechanism, accurately predicting the neural response to complex visual concepts. We demonstrate that neural encoding models can be optimized by leveraging a framework that integrates both data-driven approaches and theoretical insights into the functional structure of the cortical networks.

关键词： Neural encoding Visual perception Artificial neural networks Functional neuroimaging Naturalistic stimuli

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Fault Diagnosis for Limited Similar Batch Process using Two-dimensional Adversarial Domain Adaptation-based Graph Convolutional Network 43

Fault Diagnosis for Limited Similar Batch Process using Two-...

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43rd Chinese Control Conference, CCC 2024

作者： Gao, Xingke Zhang, Zheng Zhu, Jinlin Jiangnan University School of Artificial Intelligence and Computer Science Wuxi214122 China Hong Kong University of Science and Technology Department of Chemical and Biological Engineering Clear Water Bay Kowloon999077 Hong Kong Jiangnan University State Key Laboratory of Food Science and Resources Wuxi214122 China Jiangnan University School of Food Science and Technology Wuxi214122 China

ISBN: (纸本)9789887581581

Batch process fault diagnosis is critical for ensuring the quality and safety of batch processes production. However, recognizing fault types in low-volume and similar batch processes poses a significant challenge. This paper proposes a novel domain-adaptive transfer learning model named two-dimensional adversarial domain adaptation-based graph convolutional network (2D-ADGCN) for limited similar batch process fault diagnosis. Firstly, the method creates enhanced samples by integrating statistical information within a two-dimensional sliding window, significantly expanding the scale of the analyzed data by sliding the window in both time and batch dimensions. Secondly, by constructing an adversarial domain adaptation graph convolutional network, the data from the source domain (original batches) and the target domain (similar batches) are mapped into a shared feature space, reducing the distribution discrepancy between the two domains. Then, leveraging the labeled samples from the source domain, the model learns discriminative representations for the samples and performs classification on the target domain samples, enabling fault diagnosis for similar batches. Finally, the effectiveness of 2D-ADGCN is validated through industrial case studies of two Lactobacillus plantarum fed-batch fermentation processes, demonstrating its significant potential for fault diagnosis in low-volume and similar batch processes. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Convolution

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Functional Polyolefins and Composites

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Angewandte Chemie - International Edition 2025年第12期64卷 e202424529页

作者： Tan, Chen Si, Guifu Zou, Chen Chen, Changle Institutes of Physical Science and Information Technology Key Laboratory of Structure and Functional Regulation of Hybrid Materials of Ministry of Education Key Laboratory of Environment-Friendly Polymeric Materials of Anhui Province Anhui University Anhui Hefei230601 China State Key Laboratory of Precision and Intelligent Chemistry Department of Polymer Science and Engineering University of Science and Technology of China Hefei230026 China

Polyolefins are simple hydrocarbons that require additional chemical modifications or functional additives to give them custom functions. Recent research in the development of functional polyolefins has surpassed the traditional approach of simply improving surface properties by incorporating polar moieties. Creating custom functionalized polyolefins by using specific functional units has attracted increasing attention. This review summarizes advances in preparing custom functionalized polyolefin materials using functional units such as comonomers, chain-transfer agents, post-polymerization modification reagents, and functional fillers. Exploring new functional units and innovative synthetic strategies will further enhance the performance and expand the applications of functional polyolefins. © 2025 Wiley-VCH GmbH.

关键词： Polyolefin Sustainability Comonomer Functionalization Composite

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DMNER: Biomedical Named Entity Recognition by Detection and Matching

DMNER: Biomedical Named Entity Recognition by Detection and ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Bian, Junyi Jiang, Rongze Zhai, Weiqi Huang, Tianyang Huang, Xiaodi Zhou, Hong Zhu, Shanfeng Fudan University School of Computer Science Shanghai China Fudan University Institute of Science and Technology for Brain-Inspired Intelligence Shanghai China Charles Sturt University School of Computing Mathematics and Engineering Nsw Australia Atypon Systems Llc United Kingdom Zhangjiang Fudan International Innovation Center Fudan University Institute of Science and Technology for Brain-Inspired Intelligence Moe Frontiers Center for Brain Science Shanghai Key Lab of Intelligent Information Processing Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Shanghai China

ISBN: (纸本)9798350386226

Biomedical Named Entity Recognition (NER) is a crucial task in extracting information from biomedical texts. However, the diversity of professional terminology, semantic complexity, and the widespread presence of synonyms pose significant challenges. Traditional methods that rely on sequence labeling training datasets often struggle to handle these complexities. To address this, we introduce a novel framework for BioNER, termed DMNER, which leverages external knowledge and operates in two steps: entity boundary detection and entity category identification through matching. The core of DMNER is its second step, which determines the entity category by retrieving similar entities and their categories from a knowledge dictionary using semantic similarity matching. Our experiments on 10 biomedical datasets demonstrate that DMNER outperforms baselines across these tasks, proving its effectiveness and adaptability. DMNER is versatile and can be applied to various NER tasks, including supervised NER, distantly supervised NER, and NER on multiple datasets with disjoint label sets. The DMNER code is publicly available. © 2024 IEEE.

关键词： Semantics

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SE-COTR: A Novel Fruit Segmentation Model for Green Apples Application in Complex Orchard

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Plant Phenomics 2022年第1期4卷 12-25页

作者： Zhifen Wang Zhonghua Zhang Yuqi Lu Rong Luo Yi Niu Xinbo Yang Shaoxue Jing Chengzhi Ruan Yuanjie Zheng Weikuan Jia School of Information Science and Engineering Shandong Normal UniversityJinan 250358China State Key Laboratory of Biobased Materials and Green Papermaking Qilu University of Technology(Shandong Academy of Science)Jinan 25035China School of Informatics University of LeicesterLeicester LE17RHUK Department of Engineering Design and Mathematics University of the West of EnglandBristol BS161QYUK Fujian Key Laboratory of Intelligent Control and Manufacturing of Agricultural Machinery Wuyishan 354300China Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry Jiangsu UniversityZhenjiang 212013China

Because of the unstructured characteristics of natural orchards,the efficient detection and segmentation applications of green fruits remain an essential challenge for intelligent ***,an innovative fruit segmentation method based on deep learning,termed SE-COTR(segmentation based on coordinate transformer),is proposed to achieve accurate and real-time segmentation of green apples.

关键词： Application segmentation Complex

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Isotropic3D: Image-to-3D Generation Based on a Single CLIP Embedding

arXiv

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arXiv 2024年

作者： Liu, Pengkun Wang, Yikai Sun, Fuchun Li, Jiafang Xiao, Hang Xue, Hongxiang Wang, Xinzhou Academy for Engineering and Technology Fudan University Shanghai China State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing China Tongji University Shanghai China ShengShu Beijing China

Encouraged by the growing availability of pre-trained 2D diffusion models, image-to-3D generation by leveraging Score Distillation Sampling (SDS) is making remarkable progress. Most existing methods combine novel-view lifting from 2D diffusion models which usually take the reference image as a condition while applying hard L2 image supervision at the reference view. Yet heavily adhering to the image is prone to corrupting the inductive knowledge of the 2D diffusion model leading to flat or distorted 3D generation frequently. In this work, we reexamine image-to-3D in a novel perspective and present Isotropic3D, an image-to-3D generation pipeline that takes only an image CLIP embedding as input. Isotropic3D allows the optimization to be isotropic w.r.t. the azimuth angle by solely resting on the SDS loss. The core of our framework lies in a two-stage diffusion model fine-tuning. Firstly, we fine-tune a text-to-3D diffusion model by substituting its text encoder with an image encoder, by which the model preliminarily acquires image-to-image capabilities. Secondly, we perform fine-tuning using our Explicit Multi-view Attention (EMA) which combines noisy multi-view images with the noise-free reference image as an explicit condition. CLIP embedding is sent to the diffusion model throughout the whole process while reference images are discarded once after fine-tuning. As a result, with a single image CLIP embedding, Isotropic3D is capable of generating multi-view mutually consistent images and also a 3D model with more symmetrical and neat content, well-proportioned geometry, rich colored texture, and less distortion compared with existing image-to-3D methods while still preserving the similarity to the reference image to a large extent. The project page is available at https://***/. The code and models are available at https://***/pkunliu/Isotropic3D. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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Small Scale Data-Free Knowledge Distillation

arXiv

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arXiv 2024年

作者： Liu, He Wang, Yikai Liu, Huaping Sun, Fuchun Yao, Anbang China Telecom Cloud Technology Co. Ltd. China BNRist Center State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China Intel Labs China China

Data-free knowledge distillation is able to utilize the knowledge learned by a large teacher network to augment the training of a smaller student network without accessing the original training data, avoiding privacy, security, and proprietary risks in real applications. In this line of research, existing methods typically follow an inversion-and-distillation paradigm in which a generative adversarial network on-the-fly trained with the guidance of the pre-trained teacher network is used to synthesize a large-scale sample set for knowledge distillation. In this paper, we reexamine this common data-free knowledge distillation paradigm, showing that there is considerable room to improve the overall training efficiency through a lens of "small-scale inverted data for knowledge distillation". In light of three empirical observations indicating the importance of how to balance class distributions in terms of synthetic sample diversity and difficulty during both data inversion and distillation processes, we propose Small Scale Data-free Knowledge Distillation (SSD-KD). In formulation, SSD-KD introduces a modulating function to balance synthetic samples and a priority sampling function to select proper samples, facilitated by a dynamic replay buffer and a reinforcement learning strategy. As a result, SSD-KD can perform distillation training conditioned on an extremely small scale of synthetic samples (e.g., 10× less than the original training data scale), making the overall training efficiency one or two orders of magnitude faster than many mainstream methods while retaining superior or competitive model performance, as demonstrated on popular image classification and semantic segmentation benchmarks. The code is available at https://***/OSVAI/SSD-KD. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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Robust Beamforming for Downlink Multi-Cell systems: A Bilevel Optimization Perspective

arXiv

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arXiv 2024年

作者： Chen, Xingdi Xiong, Yu Yang, Kai Department of Computer Science and Technology Tongji University China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China Shanghai Research Institute for Intelligent Autonomous Systems China

Utilization of inter-base station cooperation for information processing has shown great potential in enhancing the overall quality of communication services (QoS) in wireless communication networks. Nevertheless, such cooperations require the knowledge of channel state information (CSI) at base stations (BSs), which is assumed to be perfectly known. However, CSI errors are inevitable in practice which necessitates beamforming techniques that can achieve robust performance in the presence of channel estimation errors. Existing approaches relax the robust beamforming design problems into semidefinite programming (SDP), which can only achieve a solution that is far from being optimal. To this end, this paper views robust beamforming design problems from a bilevel optimization perspective. In particular, we focus on maximizing the worst-case weighted sum-rate (WSR) in the downlink multi-cell multi-user multiple-input single-output (MISO) system considering bounded CSI errors. We first reformulate this problem into a bilevel optimization problem and then develop an efficient algorithm based on the cutting plane method. A distributed optimization algorithm has also been developed to facilitate the parallel processing in practical settings. Numerical results are provided to confirm the effectiveness of the proposed algorithm in terms of performance and complexity, particularly in the presence of CSI uncertainties. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel state information

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D2NT: A High-Performing Depth-to-Normal Translator

arXiv

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arXiv 2023年

作者： Feng, Yi Xue, Bohuan Liu, Ming Chen, Qijun Fan, Rui The College of Electronic & Information Engineering The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The Department of Computer Science & Engineering The Hong Kong University of Science and Technology Hong Kong Nansha Guangzhou511400 China

Surface normal holds significant importance in visual environmental perception, serving as a source of rich geometric information. However, the state-of-the-art (SoTA) surface normal estimators (SNEs) generally suffer from an unsatisfactory trade-off between efficiency and accuracy. To resolve this dilemma, this paper first presents a superfast depth-to-normal translator (D2NT), which can directly translate depth images into surface normal maps without calculating 3D coordinates. We then propose a discontinuity-aware gradient (DAG) filter, which adaptively generates gradient convolution kernels to improve depth gradient estimation. Finally, we propose a surface normal refinement module that can easily be integrated into any depth-to-normal SNEs, substantially improving the surface normal estimation accuracy. Our proposed algorithm demonstrates the best accuracy among all other existing real-time SNEs and achieves the SoTA trade-off between efficiency and accuracy. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

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TGNN: A GNN-Based Method with Multi-entity Node for Personal Banking Time Prediction 5th

TGNN: A GNN-Based Method with Multi-entity Node for Person...

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5th China Conference on intelligent Networked Things, CINT 2022

作者： Mo, Zhikang Shao, Qixiang Wu, Likang Yu, Runlong Liu, Qi Xu, Jiexin Song, Hongmei Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application State Key Laboratory of Cognitive Intelligence School of Computer Science and Techonology University of Science and Technology of China Hefei China Department of Information Technology China Merchant Bank Shenzhen China

ISBN: (纸本)9789811989148

The offline business of banks is deeply involved in bank clients’ financial lives and directly affects the satisfaction of clients. For offline banking scenarios, accurate prediction of clients’ banking time is essential for the service system of the banks, which can be used for multiple downstream tasks, such as queuing time prediction, real-time queuing planning, etc. However, the problem of banking time prediction remains open due to many technical and domain challenges, such as Anonymous Client Problem (ACP) and Sparse Interaction Problem (SIP). The existing works mainly utilize the traditional machine learning method and are relatively plain in modeling this practical scenario thus leading to an unpromising performance. Different from existing works, we predict the personal banking time by modeling the clients and banking services in the banking process through mining a large number of banking records. In this paper, we propose a Tuple-node-based Graph Neural Network (TGNN) framework to learn representations of clients and services of banks and estimate the personal banking time based on the representations. Empirical studies on two real-world datasets collected from a fintech bank demonstrate the effectiveness of TGNN. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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