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Strong nonlinear optical processes with extraordinary polarization anisotropy in inversionsymmetry broken two-dimensional PdPSe

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Light(Science & Applications) 2024年第8期13卷 1524-1533页

作者： Song Zhu Ruihuan Duan Xiaodong Xu Fangyuan Sun Wenduo Chen Fakun Wang Siyuan Li Ming Ye Xin Zhou Jinluo Cheng Yao Wu Houkun Liang Junichiro Kono Xingji Li Zheng Liu Qi Jie Wang School of Electrical and Electronic Engineering Nanyang Technological University639798 SingaporeSingapore School of Material Science and Engineering Nanyang Technological University639798 SingaporeSingapore CINTRA CNRS/NTU/THALES UMI 3288Research Techno PlazaNanyang Technological University637371 SingaporeSingapore School of Materials Science and Engineering Harbin Institute of Technology150001 HarbinChina Department of Chemistry National University of Singapore117543 SingaporeSingapore GPL Photonics Lab State Key Laboratory of Applied OpticsChangchun Institute of OpticsFine Mechanics and PhysicsChinese Academy of Sciences130033 ChangchunChina School of Electronics and Information Engineering Sichuan University610064 ChengduSichuanChina School of Physical and Mathematical Sciences Nanyang Technological University637371 SingaporeSingapore Departments of Electrical and Computer Engineering Physics and Astronomyand Materials Science and NanoEngineeringRice UniversityHoustonTXUSA

Nonlinear optical activities,especially second harmonic generation(SHG),are key phenomena in inversion-symmetrybroken two-dimensional(2D)transition metal dichalcogenides(TMDCs).On the other hand,anisotropic nonlinear optical processes are important for unique applications in nano-nonlinear photonic devices with polarization functions,having become one of focused research topics in the field of nonlinear ***,the strong nonlinearity and strong optical anisotropy do not exist simultaneously in common 2D ***,we demonstrate strong secondorder and third-order susceptibilities of 64 pm/V and 6.2×10^(-19)m^(2)/V^(2),respectively,in the even-layer PdPSe,which has not been discovered in other common TMDCs(e.g.,MoS2).Strikingly,it also simultaneously exhibited strong SHG anisotropy with an anisotropic ratio of~45,which is the largest reported among all 2D materials to date,to the best of our *** addition,the SHG anisotropy ratio can be harnessed from 0.12 to 45(375 times)by varying the excitation wavelength due to the dispersion ofχ^(2)*** an illustrative example,we further demonstrate polarized SHG imaging for potential applications in crystal orientation identification and polarization-dependent spatial *** findings in 2D PdPSe are promising for nonlinear nanophotonic and optoelectronic applications.

关键词： materials anisotropy nonlinear

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Long- and Short- Term Sequential Recommendation with Temporal Interval 19

Long- and Short- Term Sequential Recommendation with Tempora...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： He, Kun Liu, Qiyan Li, Qianmu Meng, Shunmei Nanjing University of Science and Technology Nanjing China University of Toronto Computer Engineering Canada Nanjing University State Key Lab. for Novel Software Technology Nanjing China

ISBN: (纸本)9798350346558

Sequential recommendation aims to learn the changes of users' interests according to their historical behaviors and predict the most likely next item. Since user's historical behavior are sequential actions, user's interest in different time periods has different emphases. For predicting a user's next item, not only the recent behavior is important, but also long-term preference with all historical behaviors could not be ignored. Recently, self-attention based user preference modeling has drawn much attention for its advantages of fewer parameters and parallelism. However, most of the existing self-attention based model do not make a good distinction between the long-term and short-term preferences of users. Based on the observations, we design a sequential recommendation model based on a combination of long-term and short-term preference. On the one hand, considering the changes of users' interests in different periods, we divide the sequence of users' behaviors into different temporal windows. Then, we use GRU to capture users' interests in different temporal window. On the other hand, to better combine the global and local information of user's, we adopt a locally constrained multi-head attention mechanism based on Transformer encoder. On three real-world public datasets, we finally validate the efficacy of our method. © 2022 IEEE.

关键词： User profile

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2S-UDF: A Novel Two-Stage UDF Learning Method for Robust Non-Watertight Model Reconstruction from Multi-View Images

2S-UDF: A Novel Two-Stage UDF Learning Method for Robust Non...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Junkai Deng Fei Hou Xuhui Chen Wencheng Wang Ying He State Key Laboratory of Computer Science Chinese Academy of Sciences Institute of Software University of Chinese Academy of Sciences School of Computer Science and Engineering Nanyang Technological University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Recently, building on the foundation of neural radiance field, various techniques have emerged to learn unsigned distance fields (UDF) to reconstruct 3D non-watertight models from multi-view images. Yet, a central challenge in UDF-based volume rendering is formulating a proper way to convert unsigned distance values into volume density, ensuring that the resulting weight function remains unbiased and sensitive to occlusions. Falling short on these requirements often results in incorrect topology or large reconstruction errors in resulting models. This paper addresses this challenge by presenting a novel two-stage algorithm, 2S-UDF, for learning a high-quality UDF from multi-view images. Initially, the method applies an easily trainable density function that, while slightly biased and transparent, aids in coarse reconstruction. The subsequent stage then refines the geometry and appearance of the object to achieve a high-quality reconstruction by directly adjusting the weight function used in volume rendering to ensure that it is unbiased and occlusion-aware. Decoupling density and weight in two stages makes our training stable and robust, distinguishing our technique from existing UDF learning approaches. Evaluations on the DeepFashion3D, DTU, and BlendedMVS datasets validate the robustness and effectiveness of our proposed approach. In both quantitative metrics and visual quality, the results indicate our superior performance over other UDF learning techniques in reconstructing 3D non-watertight models from multi-view images. Our code is available at https://***/jkdeng/2sudf/.

关键词： Training Solid modeling Visualization Three-dimensional displays Accuracy Rendering (computer graphics) Density functional theory

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A Novel Meta-Hierarchical Active Inference Reinforcement Learning Approach for QoS-Driven Resource Allocation in Dynamic Clouds

A Novel Meta-Hierarchical Active Inference Reinforcement Lea...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Xian, Peijie He, Ying Yu, F. Richard Du, Jianbo Shenzhen Univ. College of Computer Science and Software Engineering Shenzhen China Xi'an Univ. of Posts & Telecomm. Shaanxi Key Lab. of Information Comm. Network and Security Shaanxi Xi'an China Carleton Univ. School of Information Technology OttawaON Canada

ISBN: (纸本)9798350351255

The cloud computing environment is highly dynamic due to a variety of external factors such as seasonal changes, market trends, and social events. In this case, tenant behavior patterns exhibit significant variability. Therefore, the cloud computing resource allocation model must continuously adapt to these changes. To this end, we propose an adaptive algorithm for dynamic cloud resource allocation based on meta-hierarchical active inference reinforcement learning (MHAIRL). The algorithm combines active inference with meta-hierarchical reinforcement learning. It improves the overall performance of the algorithm, as well as quickly adapts to environmental changes, and improves the generalization performance. In addition, we design a novel polling scheduling framework combined with long short-term memory (LSTM) network. The framework ensures scheduling fairness and flexibility while greatly reducing the state and action space dimensions of the agent. Extensive simulation results show that our method outperforms baseline algorithms in quality of service (QoS) metrics and significantly improves system performance in highly dynamic cloud resource allocation. © 2024 IEEE.

关键词： Active learning

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Pooling Under the Sun: A Mining Pool Centralized Revisit and Solution

Pooling Under the Sun: A Mining Pool Centralized Revisit and...

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

作者： Kundu Chen Jie Luo State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Mining pools are fundamental infrastructure in most public blockchain networks that utilize Proof-of-Work consensus, ensuring that miners receive stable rewards and contribute to the computational power and storage of the network. However, mining pools also introduce centralization to the network, which increases the risk of security issues such as bribe attacks and censorship. To address the issue, this paper proposes a decentralized mining pool schema SSMP. Firstly, we distinguish between two aspects of centralization in mining pools, which are data determination, and block ownership, and suggest solutions to mitigate these issues. The proposed method utilizes anonymous broadcasting for a random leader selection schema to ensure each participating miner has an equal possibility to generate the block data. Additionally, a special threshold signature design prevents the leader miner from controlling the valid block solely of the mining pool through bribery. To validate our approach, we theoretically verify the security of the proposed method and compare it to traditional centralized mining pools. In summary, SSMP withholds the essential properties of the decentralized mining pools that maintain efficiency.

关键词： Privacy Voting Proof of Work Censorship Blockchains Safety Encryption

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RefScale: Multi-temporal Assisted Image Rescaling in Repetitive Observation Scenarios 24

RefScale: Multi-temporal Assisted Image Rescaling in Repetit...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Zhen Xiao, Jing Liao, Liang Wang, Mi School of Computer Science Wuhan University Wuhan China S-Lab for Advanced Intelligence Nanyang Technological University Singapore City Singapore State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China

ISBN: (纸本)9798400706868

With the continuous development of imaging technology and the gradual expansion of the amount of image data, how to achieve high compression efficiency of high-resolution images is a challenge problem for storage and transmission. Image rescaling aims to reduce the original data amount through downscaling to facilitate data transmission and storage before encoding, and reconstruct the quality through upscaling after decoding, which is a key technology to assist in high-ratio image compression. However, existing rescaling approaches are more focused on reconstruction quality rather than image compressibility. In repetitive observation scenarios, multi-temporal images brought by periodic observations provide an opportunity to alleviate the conflict between reconstruction quality and compressibility, that is, the historical images as reference indicates what information can be dropped at downscaling to reduce the information content in downscaled image and provides the dropped information to improve the image restoration quality at upscaling. Based on this consideration, we propose a novel multi-temporal assisted reference-based image rescaling framework (RefScale). Specifically, a referencing network is proposed to calculate the similarity map to provide the referencing condition, which is then injected into the conditional invertible neural network to guide the information drop at the downscaling stage and information fusion at the upscaling stage. Additionally, a low-resolution guidance loss is proposed to further constrain the data amount of the downscaled image. Experiments conducted on both satellite imaging and autonomous driving show the superior performance of our approach over the state-of-the-art methods. © 2024 ACM.

关键词： Image compression

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UniSleepPos: Sleep Posture Identification System Utilizing Millimeter-wave Radar

UniSleepPos: Sleep Posture Identification System Utilizing M...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kun Liu Min Li Chu He Xu Tian Junbin Mao Min Zeng Jin Liu Xinjiang Engineering Research Center of Big Data and Intelligent Software School of Software Xinjiang University Urumqi China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Sleep posture identification is crucial for accurately assessing sleep quality and diagnosing related diseases. In the realm of non-intrusive sleep monitoring, non-contact technologies are becoming increasingly mainstream. Millimeter-wave radar is frequently utilized in sleep posture identification due to its high resolution, strong penetration, and excellent sensitivity. However, traditional radar-based methods for sleep posture identification often struggle with reliability when dealing with diverse individuals and complex sleep environments. To address these challenges, we propose UniSleepPos, which designs a novel dual-view fusion mechanism to integrate depression and elevation angle signals obtained from radar, thus accurately capturing the posture information of the monitored subject in three-dimensional space. Furthermore, we combine sleep posture identification with individual characteristics, utilizing existing individual labels as prior knowledge to assist in sleep posture identification. The integration of prior knowledge provides a valuable information source for the model, helping to enhance its understanding of the data and improve its performance. We collected sleep posture data from eight volunteers using millimeter-wave radar devices under various environmental conditions. Leave-one-subject-out experiments were conducted to validate the effectiveness of UniSleepPos. The results indicated that UniSleepPos significantly outperforms existing methods, demonstrating its potential for practical applications.

关键词： Performance evaluation Sensitivity Spaceborne radar Millimeter wave radar Depression Data models Reliability Monitoring Signal resolution Diseases

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Explainable data transformation recommendation for automatic visualization

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Frontiers of Information Technology & Electronic engineering 2023年第7期24卷 1007-1027页

作者： Ziliang WU Wei CHEN Yuxin MA Tong XU Fan YAN Lei LV Zhonghao QIAN Jiazhi XIA State Key Lab of CAD&CG Zhejiang UniversityHangzhou 310058China Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China School of Computer Science and Engineering Central South UniversityChangsha 410083China

Automatic visualization generates meaningful visualizations to support data analysis and pattern finding for novice or casual users who are not familiar with visualization *** automatic visualization approaches adopt mainly aggregation and filtering to extract patterns from the original ***,these limited data transformations fail to capture complex patterns such as clusters and *** recent advances in feature engineering provide the potential for more kinds of automatic data transformations,the auto-generated transformations lack explainability concerning how patterns are connected with the original *** tackle these challenges,we propose a novel explainable recommendation approach for extended kinds of data transformations in automatic *** summarize the space of feasible data transformations and measures on explainability of transformation operations with a literature review and a pilot study,respectively.A recommendation algorithm is designed to compute optimal transformations,which can reveal specified types of patterns and maintain *** demonstrate the effectiveness of our approach through two cases and a user study.

关键词： Data transformation Data transformation recommendation Automatic visualization Explainability

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Enhancing Topic Interpretability for Neural Topic Modeling Through Topic-Wise Contrastive Learning

Enhancing Topic Interpretability for Neural Topic Modeling T...

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International Conference on Data engineering

作者： Xin Gao Yang Lin Ruiqing Li Yasha Wang Xu Chu Xinyu Ma Hailong Yu Software Engineering Institution School of Computer Science Peking University Beijing China Key Lab of High Confidence Software Technologies Ministry of Education Beijing China National Engineering Research Center of Software Engineering Peking University Beijing China Center on Frontiers of Computing Studies School of Computer Science Peking University

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Data mining and knowledge discovery are essential aspects of extracting valuable insights from vast datasets. Neural topic models (NTMs) have emerged as a valuable unsupervised tool in this field. However, the predominant objective in NTMs, which aims to discover topics maximizing data likelihood, often lacks alignment with the central goals of data mining and knowledge discovery which is to reveal interpretable insights from large data repositories. Overemphasizing likelihood maximization without incorporating topic regularization can lead to an overly expansive latent space for topic modeling. In this paper, we present an innovative approach to NTMs that addresses this misalignment by introducing contrastive learning measures to assess topic interpretability. We propose a novel NTM framework, named ContraTopic, that integrates a differentiable regularizer capable of evaluating multiple facets of topic interpretability throughout the training process. Our regularizer adopts a unique topic-wise contrastive methodology, fostering both internal coherence within topics and clear external distinctions among them. Comprehensive experiments conducted on three diverse datasets demonstrate that our approach consistently produces topics with superior interpretability compared to state-of-the-art NTMs.

关键词： Training Measurement Contrastive learning Coherence Knowledge discovery Data engineering Data models

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Anonym-Recognizer: Relationship-preserving Face Anonymization and Recognition 3

Anonym-Recognizer: Relationship-preserving Face Anonymizatio...

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3rd International Workshop on Human-Centric Multimedia Analysis, HCMA 2022, held in conjunction with the ACM Multimedia 2022

作者： Peng, Chunlei Wan, Shuang Miao, Zimin Liu, Decheng Zheng, Yu Wang, Nannan School of Cyber Engineering Xidian University Xi'an China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China State Key Laboratory of Integrated Services Networks Xidian University Xi'an China

ISBN: (纸本)9781450394925

With the widespread application of big data technology, we are exposed to more and more video monitoring. To prevent serious social problems caused by face data leakage, face anonymization has become an important kind of method to protect face privacy. The face anonymization mentioned in this paper refers to the anonymization generation of the visual appearance in face images. Existing face anonymization methods mainly focus on removing identity information. However, in the scenario of face recognition technology that needs to protect privacy, existing face anonymization technology makes anonymized faces that can no longer be used for face recognition, limiting the application scope of face anonymization. Therefore, when using face anonymization, it is equally important to ensure that the anonymized face images can still be used for downstream tasks such as face recognition. To this end, we propose Anonym-Recognizer, a relationship-preserving face anonymization and recognition method. Our method uses relationship cyphertext which can be any binary identity number representing the identity of the image owner and designs a generative adversarial network to perform face anonymization and relationship cyphertexts embedding. In our framework, we first use Visual Anonymizer to manipulate the visual appearance of the input image, then use Cyphertext Embedder to get the anonymized image with the identity information embedded. With the help of Anonym Recognizer, the face recognition system can extract the relationship cyphertexts from the anonymized image as the credentials to match the identity information. The proposed Anonym-Recognizer provides a new perspective for the recognition and application of anonymized face images. Experiments on the Megaface dataset show that our method can encourage a 100% recognition accuracy on anonymized faces while finishing the task of face anonymization with high qualitative and quantitative quality. © 2022 ACM.

关键词： Face recognition

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