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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yunkai Tang Chengxuan Zhu Renjie Wan Chao Xu Boxin Shi National Engineering Research Center of Visual Technology School of Computer Science Peking University National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Key Lab of General AI School of Intelligence Science and Technology Peking University Department of Computer Science Hong Kong Baptist University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Among the numerous efforts towards digitally recovering the physical world, Neural Radiance Fields (NeRFs) have proved effective in most cases. However, underwater scene introduces unique challenges due to the absorbing water medium, the local change in lighting and the dynamic contents in the scene. We aim at developing a neural under-water scene representation for these challenges, modeling the complex process of attenuation, unstable in-scattering and moving objects during light transport. The proposed method can reconstruct the scenes from both established datasets and in-the-wild videos with outstanding fidelity.

关键词： Solid modeling Visualization Computational modeling Urban areas Lighting Virtual reality Neural radiance field

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MSDAC: A multi-source domain adversarial framework for motion prediction in intracortical brain-computer interfaces

MSDAC: A multi-source domain adversarial framework for motio...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Haozhou Liu Banghua Yang Shouliang Guan Fenqi Rong Mengao Guo Ying Fang Bingyu Liu Yan Gao Yong Gu School of Mechatronic Engineering and Automation Research Center of Brain-Computer Engineering Shanghai University Shanghai China Lingang Laboratory Shanghai China National Center for Nanoscience and Technology Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Institute of Neurosciences Shanghai China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Intracortical brain-computer interfaces (iBCIs) restore motor function in patients with paralysis by converting neural activity into control signals for external devices. However, the frequent recalibration required by current decoding methods due to turnover and loss of recording neurons poses a challenge for achieving stable online decoding. To address these issues, we propose a multi-source domain adversarial classification (MSDAC) framework for cross-day decoding that utilizes an out-of-distribution (OOD) generalization approach. This framework divides the historical data into source domains by date and employs adversarial networks to minimize the distribution discrepancies among multiple source domains, thereby achieving robust domain-invariant characteristics and superior performance on unseen test data. The MSDAC framework was evaluated using five months of monkey center-out neural activity data and demonstrated exceptional performance. Without relying on test day data for model calibration or parameter updating, the MSDAC achieved an average decoding accuracy of 84.38% (day-5 to day-150, 27968 trials). These results underscore that the MSDAC-based decoding framework can be an ideal choice for establishing stable iBCI systems.

关键词： Accuracy Recurrent neural networks Neural activity Time series analysis Neurons Brain-computer interfaces Stability analysis Decoding Recording Paralysis

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Hyperbolic metamaterials: fusing artificial structures to natural 2D materials

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eLight 2022年第1期2卷 1-23页

作者： Dasol Lee Sunae So Guangwei Hu Minkyung Kim Trevon Badloe Hanlyun Cho Jaekyung Kim Hongyoon Kim Cheng‑Wei Qiu Junsuk Rho Department of Mechanical Engineering Pohang University of Science and Technology(POSTECH)Pohang 37673Republic of Korea Department of Biomedical Engineering Yonsei UniversityWonju 26493Republic of Korea Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore Department of Chemical Engineering Pohang University of Science and Technology(POSTECH)Pohang 37673Republic of Korea POSCO-POSTECH-RIST Convergence Research Center for Flat Optics and Metaphotonics Pohang 37673Republic of Korea.

Optical metamaterials have presented an innovative method of manipulating *** metamaterials have an extremely high anisotropy with a hyperbolic dispersion *** are able to support high-k modes and exhibit a high density of states which produce distinctive properties that have been exploited in various applications,such as super-resolution imaging,negative refraction,and enhanced emission ***,state-of-the-art hyperbolic metamaterials are reviewed,starting from the fundamental principles to applications of artificially structured hyperbolic media to suggest ways to fuse natural two-dimensional hyperbolic *** review concludes by indicating the current challenges and our vision for future applications of hyperbolic metamaterials.

关键词： Hyperbolic metamaterials High resolution optical imaging Nanoscale lithography Light propagation and manipulation Spontaneous emission engineering Natural 2D materials

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Enhanced ferromagnetism in an artificially stretched lattice in quasi-two-dimensional Cr2Ge2Te6

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Physical Review B 2025年第2期111卷 L020402-L020402页

作者： Hiroshi Idzuchi Andres E. Llacsahuanga Allcca Anh Khoa Augustin Lu Mitsuhiro Saito Subhadip Das John F. Ribeiro Michel Houssa Ruishen Meng Kazutoshi Inoue WPI Advanced Institute for Materials Research (AIMR) Tohoku University Sendai 980-8577 Japan Center for Science and Innovation in Spintronics (CSIS) Tohoku University Sendai 980-8577 Japan Department of Physics and Astronomy Purdue University West Lafayette Indiana 47907 USA Department of Physics The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan Birck Nanotechnology Center and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana 47907 USA Mathematics for Advanced Materials Open Innovation Laboratory National Institute of Advanced Industrial Science and Technology Sendai 980-8577 Japan Research Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba 305-0044 Japan Institute of Engineering Innovation The University of Tokyo Tokyo 113-8656 Japan Institute of Physics and Astronomy and Villum Centers for Dirac Materials and for Hybrid Quantum Materials and Devices Aarhus University 8000 Aarhus-C Denmark School of Electrical and Computer Engineering Purdue University West Lafayette Indiana 47907 USA Department of Physics and Astronomy Katholieke Universiteit Leuven 3001 Leuven Belgium

Crystal lattice is key in understanding magnetic interactions between atoms in solids. As the effective control of the lattice via tensile stress is limited, there are only a few demonstrations of controlling magnetic properties by expanding the lattice. This study reveals a clear correlation between enhanced magnetism, where Curie temperature (TC) is doubled from ∼60 to ∼120 K, and lattice expansion in prototypical van der Waals magnet Cr2Ge2Te6 with sputtered dielectric overlayers NiO and MgO. We ascribe the mechanism of this TC increase to a change in exchange interactions induced by strain, studied by several experimental probes and computational approaches.

关键词： Van der Waals forces

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Differentially Private Deep Learning with Iterative Gradient Descent Optimization

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ACM/IMS Transactions on Data Science 2021年第4期2卷 1–27页

作者： Ding, Xiaofeng Chen, Lin Zhou, Pan Jiang, Wenbin Jin, Hai National Engineering Research Center for Big Data Technology and System Lab Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

Deep learning has achieved great success in various areas and its success is closely linked to the availability of massive data. But in general, a large dataset could include sensitive data and therefore the model should have the capability to avoid privacy leakage. To achieve this aim, many works apply the famous privacy framework named differential privacy into deep learning to preserve privacy. In this article, we propose a novel perturbed iterative gradient descent optimization (PIGDO) algorithm and prove that this algorithm satisfies the differential privacy. Besides, we propose a modified moments accountant (MMA) method to conduct the privacy analysis and obtain a tighter bound of privacy loss compared with the original moments accountant method. A number of experiments demonstrate that our optimization algorithm can not only improve the model accuracy and training speed, but also achieve better privacy guarantees over the state-of-the-art algorithm while reaching the equivalent accuracy. We provide codes for all of our experiments in https://***/CGCL-codes/***. © 2022 Association for Computing Machinery.

关键词： Large dataset

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Merging UAV-Derived Metrics with Crop Physiology to Estimate Sunflower Yield

Merging UAV-Derived Metrics with Crop Physiology to Estimate...

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Frontiers of Information technology (FIT)

作者： Usama Athar Muhammad Ali Zuhair Zafar Haris Khurshid Karsten Berns Muhammad Moazam Fraz School of Electrical Engineering and Computer Science National University of Sciences and Technology (NUST) Islamabad Pakistan Oilseeds Research Program National Agricultural Research Center (NARC) Islamabad Pakistan Department of Computer Science Rheinland-Pfälzische Technische Universität Kaiserslautern Germany

ISBN: (数字)9798331510503

ISBN: (纸本)9798331510510

Accurate and timely estimation of sunflower yield is crucial for agricultural researchers, farmers, and breeders. Use of Unmanned Aerial Vehicles (UAVs) with multi-spectral sensors has been adopted to meet the need for precise sunflower seed yield predictions. This study proposes a combined approach that integrates 12 remotely sensed vegetation indices (VIs) with 3 key physiological traits - fresh weight, dry weight, and moisture factor - to capture the complex dynamics affecting sunflower crop. Predictive performance of VIs alone and in combination with physiological traits is evaluated using Root Mean Squared Error (RMSE). Our results show that incorporating these two sets of features, along with their temporal, spectral, and statistical characteristics, leads to consistent improvements in sunflower seed yield estimation that can reduce the root mean squared error to as low as 0.4075 ( $\text{kg} / \text{plot}$ ).

关键词： Vegetation mapping Moisture Autonomous aerial vehicles Physiology Seeds (agriculture) Sensors Yield estimation Reliability Vehicle dynamics Remote sensing

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Predicting Mutation-Disease Associations Through Protein Interactions Via Deep Learning

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4512-4523页

作者： Xue Li Ben Cao Jianmin Wang Xiangyu Meng Shuang Wang Yu Huang Enrico Petretto Tao Song School of Computer Science and Technology China University of Petroleum (East China) Qingdao Shandong China Centre for Computational Biology Duke-NUS Medical School Singapore Singapore School of Computer Science and Technology Dalian University of Technology Dalian Liaoning China Department of Integrative Biotechnology Yonsei University Incheon South Korea National Engineering Research Center for Software Engineering Peking University Beijing China

Disease is one of the primary factors affecting life activities, with complex etiologies often influenced by gene expression and mutation. Currently, wet lab experiments have analyzed the mechanisms of mutations, but these are usually limited by the costs of wet experiments and constraints in sample types and scales. Therefore, this paper constructs a real-world mutation-induced disease dataset and proposes Capsule and Graph topology networks with Multi-head attention (CGM) to predict the mutation-disease associations. CGM can accurately predict protein mutation-disease associations, and to further elucidate the pathogenicity of protein mutations, we also verified that protein mutations lead to protein structural alterations by the model, which suggests that mutation-induced conformational changes may be an important pathogenic factor. Limited by the size of the mutated protein dataset, we also performed experiments on benchmark and imbalanced datasets, where CGM mined 22 unknown protein interaction pairs from the benchmark dataset, better illustrating the potential of CGM in predicting mutation-disease associations. In summary, this paper curates a real dataset. It proposes that CGM predicts protein mutations and disease associations, providing a novel tool for further understanding of biomolecular pathways and disease mechanisms.

关键词： Proteins Diseases Protein engineering Predictive models Cancer Electronic mail Heart Deep learning Bioinformatics Tumors

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NeRSP: Neural 3D Reconstruction for Reflective Objects with Sparse Polarized Images

NeRSP: Neural 3D Reconstruction for Reflective Objects with ...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yufei Han Heng Guo Koki Fukai Hiroaki Santo Boxin Shi Fumio Okura Zhanyu Ma Yunpeng Jia Beijing University of Posts and Telecommunications Graduate School of Information Science and Technology Osaka University National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We present NeRSP, a Neural 3D reconstruction technique for Reflective surfaces with Sparse Polarized images. Reflective surface reconstruction is extremely challenging as specular reflections are view-dependent and thus violate the multiview consistency for multiview stereo. On the other hand, sparse image inputs, as a practical capture setting, commonly cause incomplete or distorted results due to the lack of correspondence matching. This paper jointly han-dles the challenges from sparse inputs and reflective surfaces by leveraging polarized images. We derive photomet-ric and geometric cues from the polarimetric image formation model and multiview azimuth consistency, which jointly optimize the surface geometry modeled via implicit neural representation. Based on the experiments on our synthetic and real datasets, we achieve the state-of-the-art surface reconstruction results with only 6 views as input.

关键词： Reflectivity Geometry Surface reconstruction computer vision Three-dimensional displays Shape Focusing

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A Novel Approach for Trajectory Partition Privacy in Location-Based Services

A Novel Approach for Trajectory Partition Privacy in Locatio...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Chundong Wang Yongxin Zhao School of Cyber Security and Computer National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin University of Technology Tianjin China School of Computer Science and Engineering Engineering Research Center of Ministry of Education for Learning Intelligent Systems Tianjin University of Technology Tianjin China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Location-based service (LBS) devices have enhanced daily life convenience but raised privacy concerns regarding user location data. For this, this research presents a novel approach to safeguard trajectory privacy through Similarity-based Trajectory Partitioning (STSM). The process begins by dividing trajectory equivalence classes based on distinct timestamps. Trajectory similarity is then assessed within each class using metrics such as Frechet distance, trajectory direction, and speed. Subsequently, a trajectory graph is constructed for each class, and the Dijkstra algorithm is applied to partition the graph using the k-subgraph method, effectively converting trajectory partitioning into graph partitioning. To meet publication requirements, Laplace noise is added to sensitive data linked to nodes and edges within the subgraph. Comparative experiments on real-world data confirm the efficacy, rationality, data availability, and privacy protection capabilities of the proposed trajectory partitioning algorithm, underscoring its superiority over alternative methods.

关键词： Weight measurement Privacy Differential privacy Conferences Noise Trajectory Partitioning algorithms

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Distributed Stochastic Optimization with Random Communication and Computational Delays: Optimal Policies and Performance Analysis

Distributed Stochastic Optimization with Random Communicatio...

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IEEE International Conference on Communications (ICC)

作者： Siyuan Yu Wei Chen H. Vincent Poor Department of Electronic Engineering Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology (BNRist) Department of Electrical and Computer Engineering Princeton University Princeton NJ USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Distributed stochastic optimization has attracted considerable attention due to its potential of scaling the computational resources, reducing the training time, and helping protect user privacy in decentralized machine learning. However, the staggers and limited bandwidth may induce random computational and communication delays, thereby severely hindering the optimization or learning process. As a result, we are interested in the optimal policies and their performance analysis for latency-aware distributed Stochastic Gradient Descent (SGD). To understand the effect of staleness and error of gradients in distributed optimization, both of which may determine the convergence time, we present a unified framework based on the stochastic delay differential equation to characterize the random convergence time. It is interestingly found that the average convergence time is much more sensitive to the gradient staleness rather than its error. To provide further insights, we show that the time cost of fully asynchronous SGD is approximately determined by the product of the gradient staleness and the 2-norm of the Hessian matrix of the objective function. Moreover, small staleness may slightly accelerate the SGD, while large staleness will result in its divergence.

关键词： Training Privacy Noise Machine learning Linear programming Delays Performance analysis

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