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Multi-Band Compatible Camouflage Enabled by Phase Transition Modulation of Flexible Gst Films

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SSRN

SSRN 2024年

作者： Wang, Jian Wu, Zuoxu Sun, Xiaoyu Tang, Zunqian Wang, Chong Yu, Fangyuan Zhao, Zirui Mao, Jun Zhang, Qian Cao, Feng School of Science Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Harbin Institute of Technology Shenzhen518055 China School of Materials Science and Engineering Institute of Materials Genome & Big Data Harbin Institute of Technology Shenzhen518055 China State Key Laboratory of Advanced Welding and Joining China Fuzhou University China

With the rapid development of detection technology, there is an urgent need for multi-band compatible camouflage technology, particularly in the visible-infrared band. However, the conflicting requirements between camouflage across different bands and surface thermal management pose significant challenges in achieving synchronized control for multiple bands in diverse application environments and targets. Herein, we propose a spectrally selective emitter leveraging phase-change modulation of Ge2Sb2Te5, capable of achieving effective camouflage in both the visible band and two atmospheric windows over a wide temperature range. The designed emitters exhibit colorful appearances, compatible with various background environments, thereby ensuring the visible camouflage. Meanwhile, the emitters demonstrate low emissivity values of 0.1 in the MWIR band and 0.08 in the LWIR band, effectively reducing the infrared emission through atmospheric *** radiative temperatures of the optimized emitters in the MWIR and LWIR bands are reduced by 105 °C and 140 °C relative to the target surface (200 °C), respectively, providing excellent radiative suppression compared to similar studies. Remarkably, the designed emitter still demonstrates robust flexibility, making it suitable for a variety of targets with complex curved surfaces. This work paves the way for designing the flexible visible-infrared compatible camouflage coatings with integrated thermal management functionality. © 2024, The Authors. All rights reserved.

关键词： Germanium alloys

Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generation

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arXiv 2024年

作者： Zhang, Odin Huang, Yufei Cheng, Shichen Yu, Mengyao Zhang, Xujun Lin, Haitao Zeng, Yundian Wang, Mingyang Wu, Zhenxing Zhao, Huifeng Zhang, Zaixi Hua, Chenqing Kang, Yu Cui, Sunliang Pan, Peichen Hsieh, Chang-Yu Hou, Tingjun College of Pharmaceutical Sciences Zhejiang University Zhejiang Hangzhou310058 China Zhejiang University Zhejiang Hangzhou310058 China Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China Anhui Hefei China Montreal Institute for Learning Algorithms McGill University MontrealQC Canada

3D structure-based molecular graph generation has attracted tremendous attention for its vast potential in designing potent drug candidates. Most earlier approaches follow an atom-wise paradigm, incrementally adding atoms to a partially built molecular fragment within protein pockets. These methods, while effective in designing tightly bound ligands, often overlook other essential properties such as synthesizability and drugability. The fragment-wise generation paradigm offers a promising solution by assembling chemically sensible fragments to reduce synthesis difficulty. However, a common challenge across both atom-wise and fragment-wise methods lies in their limited ability to co-design plausible chemical and geometrical structures, resulting in distorted conformations and inferior binding tendencies. In response to this challenge, we introduce the Deep Geometry Handling protocol, a more abstract design that decomposes the entire geometry into multiple sets of geometric variables, extending the design focus beyond the model architecture. Through a comprehensive review of existing geometry-related models, we identify and discuss six protocols, each with its own strengths and weaknesses. Building upon these insights, we propose a novel hybrid strategy, culminating in the development of FragGen - the first geometry-reliable, fragment-wise molecular generation method. FragGen marks a significant leap forward in the quality of generated geometry and the synthesis accessibility of molecules-addressing two major challenges in the application of molecular generation algorithms. The efficacy of FragGen is further validated by its successful application in designing type II kinase inhibitors at the nanomolar level, grounding its algorithmic advancements in real-world drug development. We believe that this concept-algorithm-application loop will not only inspire researchers working on other geometry-centric tasks to look beyond architecture design, but also offer a concrete

关键词： Geometry

BiKT: Unleashing the potential of GNNs via Bi-directional Knowledge Transfer

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arXiv 2023年

作者： Zheng, Shuai Liu, Zhizhe Zhu, Zhenfeng Zhang, Xingxing Li, Jianxin Zhao, Yao The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Qiyuan Lab Beijing China The Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing100083 China

Based on the message-passing paradigm, there has been an amount of research proposing diverse and impressive feature propagation mechanisms to improve the performance of GNNs. However, less focus has been put on feature transformation, another major operation of the message-passing framework. In this paper, we first empirically investigate the performance of the feature transformation operation in several typical GNNs. Unexpectedly, we notice that GNNs do not completely free up the power of the inherent feature transformation operation. By this observation, we propose the Bi-directional Knowledge Transfer (BiKT), a plug-and-play approach to unleash the potential of the feature transformation operations without modifying the original architecture. Taking the feature transformation operation as a derived representation learning model that shares parameters with the original GNN, the direct prediction by this model provides a topological-agnostic knowledge feedback that can further instruct the learning of GNN and the feature transformations therein. On this basis, BiKT not only allows us to acquire knowledge from both the GNN and its derived model but promotes each other by injecting the knowledge into the other. In addition, a theoretical analysis is further provided to demonstrate that BiKT improves the generalization bound of the GNNs from the perspective of domain adaption. An extensive group of experiments on up to 7 datasets with 5 typical GNNs demonstrates that BiKT brings up to 0.5% - 4% performance gain over the original GNN, which means a boosted GNN is obtained. Meanwhile, the derived model also shows a powerful performance to compete with or even surpass the original GNN, enabling us to flexibly apply it independently to some other specific downstream tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Message passing

Gene selection and classification combining information gain ratio with fruit fly optimisation algorithm for single-cell RNA-seq data

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Gene selection and classification combining information gain...

作者： Zhang, Jie Feng, Junhong Yang, Xiani Liu, Jianming School of Computer Science and Engineering Guangxi Colleges Universities Key Lab of Complex System Optimization and Big Data Processing Yulin Normal University Guangxi Yulin537000 China Business School Yulin Normal University Guangxi Yulin537000 China

There are a wide range of genes in single-cell data, but some are not beneficial to classification. In order to eliminate these redundant genes and select beneficial genes, this study first utilises the information gain (IG) to select some genes coarsely, then uses the modified fruit fly optimisation algorithm (FOA) to choose the relevant genes refinedly from the subsets after performing IG. The proposed algorithm makes full use of respective advantages of the IG and FOA, and is abbreviated as IGFOA. The proposed algorithm is implemented on multiple scRNA-seq datasets with various numbers of cells and genes, and the obtained results validate that the IGFOA can select effectively some superior genes and acquire good classification performance. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Genes

SIFN: A sentiment-aware interactive fusion network for review-based item recommendation

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arXiv 2021年

作者： Zhang, Kai Qian, Hao Liu, Qi Zhang, Zhiqiang Zhou, Jun Ma, Jianhui Chen, Enhong Anhui Province Key Lab of Big Data Analysis and Application School of Data Science University of Science and Technology of China Ant Group Hangzhou China School of Computer Science and Technology University of Science and Technology of China

Recent studies in recommender systems have managed to achieve significantly improved performance by leveraging reviews for rating prediction. However, despite being extensively studied, these methods still suffer from some limitations. First, previous studies either encode the document or extract latent sentiment via neural networks, which are difficult to interpret the sentiment of reviewers intuitively. Second, they neglect the personalized interaction of reviews with user/item, i.e., each review has different contributions when modeling the sentiment preference of user/item. To remedy these issues, we propose a Sentiment-aware Interactive Fusion Network (SIFN) for review-based item recommendation. Specifically, we first encode user/item reviews via BERT and propose a light-weighted sentiment learner to extract semantic features of each review. Then, we propose a sentiment prediction task that guides the sentiment learner to extract sentiment-aware features via explicit sentiment labels. Finally, we design a rating prediction task that contains a rating learner with an interactive and fusion module to fuse the identity (i.e., user and item ID) and each review representation so that various interactive features can synergistically influence the final rating score. Experimental results on five real-world datasets demonstrate that the proposed model is superior to state-of-the-art models. © 2021, CC BY.

关键词： Encoding (symbols)

BIM: Improving Graph Neural Networks with Balanced Influence Maximization

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BIM: Improving Graph Neural Networks with Balanced Influence...

International Conference on data Engineering

作者： Wentao Zhang Xinyi Gao Ling Yang Meng Cao Ping Huang Jiulong Shan Hongzhi Yin Bin Cui Center for Machine Learning Research Peking University Institute of Advanced Algorithms Research Shanghai National Engineering Labratory for Big Data Analytics and Applications The University of Queensland Australia Key Lab of High Confidence Software Technologies Peking University Apple Inc. Institute of Computational Social Science Peking University Qingdao

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The imbalanced data classification problem has aroused lots of concerns from both academia and industry since data imbalance is a widespread phenomenon in many real-world scenarios. Although this problem has been well researched from the view of imbalanced class samples, we further argue that graph neural networks (GNNs) expose a unique source of imbalance from the influenced nodes of different classes of labeled nodes, i.e., labeled nodes are imbalanced in terms of the number of nodes they influenced during the influence propagation in GNNs. To tackle this previously unexplored influence-imbalance issue, we connect social influence maximization with the imbalanced node classification problem and propose balanced influence maximization (BIM). Specifically, BIM greedily assigns the pseudo label to the node which can maximize the number of influenced nodes in GNN training while making the influence of each class more balance. Experimental results on five public datasets demonstrate the effectiveness of our method in relieving the influence-imbalance issue. For example, when training a GCN with an imbalance ratio of 0.1, BIM significantly outperforms the most competitive baseline by 0.6% -9.8% in five public datasets in terms of the F1 score.

关键词： Training Industries data engineering Graph neural networks

An Intermediate-level Attack Framework on The Basis of Linear Regression

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arXiv 2022年

作者： Guo, Yiwen Li, Qizhang Zuo, Wangmeng Chen, Hao ByteDance AI Lab China Tencent Security Big Data Lab The School of Computer Science and Technology Harbin Institute of Technology China The School of Computer Science and Technology Harbin Institute of Technology China The Department of Computer Science University of California Davis United States

This paper substantially extends our work published at ECCV [1], in which an intermediate-level attack was proposed to improve the transferability of some baseline adversarial examples. Specifically, we advocate a framework in which a direct linear mapping from the intermediate-level discrepancies (between adversarial features and benign features) to prediction loss of the adversarial example is established. By delving deep into the core components of such a framework, we show that 1) a variety of linear regression models can all be considered in order to establish the mapping, 2) the magnitude of the finally obtained intermediate-level adversarial discrepancy is correlated with the transferability, 3) further boost of the performance can be achieved by performing multiple runs of the baseline attack with random initialization. In addition, by leveraging these findings, we achieve new state-of-the-arts on transfer-based ∞ and 2 attacks. Our code is publicly available at https://***/qizhangli/ila-plus-plus-lr. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

StrokePEO: Construction of a Clinical Ontology for Physical Examination of Stroke 9

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StrokePEO: Construction of a Clinical Ontology for Physical ...

9th International Conference on Digital Home, ICDH 2022

作者： Gu, Zhanzhong Yang, Xiguang Jia, Wenjing Xu, Chengpei Yu, Ping He, Xiangjian Chen, Hongjie Lin, Yiguang University of Technology Sydney Global Big Data Technologies Centre Sydney Australia The University of New South Wales MIoT & IPIN Lab MERE Sydney Australia University of Wollongong School of Computing and IT Sydney Australia University of Nottingham Ningbo School of Computer Science Ningbo China Third Affiliated Hospital of Sun Yat-sen University Department of Traditional Chinese Medicine China University of Technology School of Life Science Sydney Australia

ISBN: (数字)9781665454780

ISBN: (纸本)9781665454780

Clinical ontology is a standardized medical knowledge representation model that facilitates the integration and analysis of a large amount of heterogeneous electronic health record (EHR) data. Using ontologies to represent clinical terms can improve data integration to build robust and interoperable medical information systems. To date, there is no ontology existing to represent the medical knowledge for physical examination of stroke, which has inhibited the stroke physicians to make full use of clinical information captured in EHR data to understand stroke patient's health status and plan effective medication and rehabilitation treatment. In this research, we co-design with two stroke clinical specialists a stroke clinical ontology "StrokePEO"using advanced natural language processing and deep learning techniques to extract terms and their relationships from real clinical case records provided by a tertiary hospital in China. We apply the W3C Resource Description Framework (RDF) data model to represent these clinical terms and relationships, and successfully store all case data in a graph database with StrokePEO. Our experiment results suggest that our methods and the output of StrokePEO can be applied in various medical contexts that require extraction of medical knowledge from free text for decision making. These include, but not limited to, physical assessment, drug and rehabilitation treatment outcome evaluation, medication effect analysis, and patient risk prediction. © 2022 IEEE.

关键词： Ontology

Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

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IEEE Transactions on Intelligent Transportation Systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting