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Holography, Diffractive Optics, and Applications XIII 2023

作者： Liu, H.P. Feng, J.J. Chen, J. Zhou, W.J. Bi, Q.Y. Zeng, H.P. School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Key Lab of Advanced Optical Manufacturing Technologies of Jiangsu Province Soochow University Suzhou215006 China Chongqing Key Laboratory of Precision Optics Chongqing Institute of East China Normal University Chongqing401120 China State Key Laboratory of Precision Spectroscopy East China Normal University Shanghai200241 China

ISBN: (纸本)9781510667853

A low-loss and wideband silicon polarizing beam splitter is demonstrated with the assistance of a nano-bridge waveguide. Transverse magnetic light can be coupled to the cross port through the nano-bridge waveguide, while the transverse electric light comes out mainly from the through port. The designed device has a coupling length of 19.6 µm, which can realize an extinction ratio of 32.43 dB for TM mode or 34.23 dB for TE mode at a 1565-nm wavelength. Compared with the conventional three-waveguide coupler structure, the proposed device is based on a resonant tunneling principle, which can help to effectively improve the fabrication tolerance. The device is fabricated with a commercial CMOS processing facility, which can achieve an extinction ratio of 23.07 dB for TM or 23.46 dB for TE mode with a low excess loss, and the extinction ratio of more than 10 dB can be realized in the wavelength range from 1525 to 1610 nm for both modes. The device performance can be further improved, which would facilitate its practical applications in commercial integrated optical circuits. © 2023 SPIE.

关键词： Silicon photonics

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Fusing Local and Global Mobility Patterns for Trajectory Recovery 28th

Fusing Local and Global Mobility Patterns for Trajectory R...

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28th International Conference on Database Systems for advanced Applications, DASFAA 2023

作者： Deng, Liwei Zhao, Yan Sun, Hao Yang, Changjie Xie, Jiandong Zheng, Kai University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chengdu China Aalborg University Aalborg Denmark Peking University Beijing China Cloud Database Innovation Lab of Cloud BU Huawei Technologies Co. Ltd. Chengdu China

ISBN: (纸本)9783031306365

The prevalence of various mobile devices translates into a proliferation of trajectory data that enables a broad range of applications such as POI recommendation, tour recommendation, urban transportation planning, and the next location prediction. However, trajectory data in real-world is often sparse, noisy and incomplete. To make the subsequent analysis more reliable, trajectory recovery is introduced as a pre-processing step and attracts increasing attention recently. In this paper, we propose a neural attention model based on graph convolutional networks, called TRILL, to enhance the accuracy of trajectory recovery. In particular, to capture global mobility patterns that reflect inherent spatio-temporal regularity in human mobilities, we construct a directed global location transition graph and model the mobility patterns of all the trajectories at point level using graph convolutional networks. Then, a self-attention layer and a window-based cross-attention layer are sequentially adopted to refine the representations of missing locations by considering intra-trajectory and inter-trajectory information, respectively. Meanwhile, an information aggregation layer is designed to leverage all the historical information. We conduct extensive experiments using real trajectory data, which verifies the superior performance of the proposed model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Trajectories

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Explore the Future Earth with Wander 2.0: AI Chatbot Driven By Knowledge-base Story Generation and Text-to-image Model

Explore the Future Earth with Wander 2.0: AI Chatbot Driven ...

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Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023

作者： Sun, Yuqian Xu, Ying Cheng, Chenhang Li, Yihua Lee, Chang Hee Asadipour, Ali Computer Science Research Centre Royal College of Art London United Kingdom Department of Industrial Design School of Art and Design Wuhan University of Technology Wuhan China Shanghai China Department of Product Design College of Fashion and Design Donghua University Shanghai China Affective Systems and Cognition Lab Industrial Design Department College of Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of

ISBN: (纸本)9781450394222

People always envision the future of earth through science fiction (Sci-fi), so can we create a unique experience of "visiting the future earth"through the lens of artificial intelligence (AI)? We introduce Wander 2.0, an AI chatbot that co-creates sci-fi stories through knowledge-based story generation on daily communication platforms like WeChat and Discord. Using location information from Google Maps, Wander generates narrative travelogues about specific locations (e.g. Paris) through a large-scale language model (LLM). Additionally, using the large-scale text-to-image model (LTGM) Stable Diffusion, Wander transfers future scenes that match both the text description and location photo, facilitating future imagination. The project also includes a real-time visualization of the human-AI collaborations on a future map. Through journeys with visitors from all over the world, Wander demonstrates how AI can serve as a subjective interface linking fiction and reality. Our research shows that multi-modal AI systems have the potential to extend the artistic experience and creative world-building through adaptive and unique content generation for different people. Wander 2.0 is available at http://***/ © 2023 Owner/Author.

关键词： Knowledge based systems

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Enabling Real World Scale Structural Superlubricity All-Atom Simulation on the Next-Generation Sunway Supercomputer 23

Enabling Real World Scale Structural Superlubricity All-Atom...

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2023 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023

作者： Duan, Xiaohui Wang, Jin Gao, Ping Ma, Ming Gan, Lin Liu, Xin Fu, Haohuan Xue, Wei Chen, Dexun Yang, Guangwen Liu, Weiguo School of Software Shandong University China International School for Advanced Studies Trieste Italy Tsinghua University Department of Computer Science and Technology Beijing China Department of Engineering Mechanics Center for Nano and Micro Mechanics Tsinghua University Beijing China National Supercomputing Center in Wuxi Wuxi China Ministry of Education Key Lab for Earth System Modeling Department of Earth System Science Tsinghua University Beijing China

ISBN: (纸本)9798400701092

Molecular dynamics (MD) simulation can provide an affordable way for inspecting microscopic phenomena, which is a powerful complement to real-world experiments. But the spatial scale of MD simulations is usually magnitudes smaller than experiment systems. In this paper, we present our work, redesigning the widely used inter-layer potential in structural superlubricity. By carrying out a specialized neighbor list for inter-layer potential computation, the total memory access amount is reduced significantly. Besides, a simple but efficient vectorization strategy is implemented based on the new neighbor list. In the extreme case, our work can scale to 38 million cores to achieve a sustainable performance of 61 PFLOPS, enabling a simulation of a superlubricity system of 32 μm2 with 7.2 billion atoms at 4.75 ns/day, which is 11,834 times of reported largest scale simulation in superlubricity systems in contact area and almost ten times faster in time-to-solution. Furthermore, we have done a simulation at 9 μm2 which results in consistency with real-world experiments and verified some theoretical predictions in the mesoscopic scale. © 2023 ACM.

关键词： Molecular dynamics

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WEAKLY SUPERVISED LEARNING FOR CELL RECOGNITION IN IMMUNOHISTOCHEMICAL CYTOPLASM STAINING IMAGES

arXiv

引用

arXiv 2022年

作者： Zhang, Shichuan Zhu, Chenglu Li, Honglin Cai, Jiatong Yang, Lin Artificial Intelligence and Biomedical Image Analysis Lab School of Engineering Westlake University China Institute of Advanced Technology Westlake Institute for Advanced Study China College of Computer Science & Technology Zhejiang University China

Cell classification and counting in immunohistochemical cytoplasm staining images play a pivotal role in cancer diagnosis. Weakly supervised learning is a potential method to deal with labor-intensive labeling. However, the inconstant cell morphology and subtle differences between classes also bring challenges. To this end, we present a novel cell recognition framework based on multi-task learning, which utilizes two additional auxiliary tasks to guide robust representation learning of the main task. To deal with misclassification, the tissue prior learning branch is introduced to capture the spatial representation of tumor cells without additional tissue annotation. Moreover, dynamic masks and consistency learning are adopted to learn the invariance of cell scale and shape. We have evaluated our framework on immunohistochemical cytoplasm staining images, and the results demonstrate that our method outperforms recent cell recognition approaches. Besides, we have also done some ablation studies to show significant improvements after adding the auxiliary branches. Copyright © 2022, The Authors. All rights reserved.

关键词： Cytology

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ChrSNet: Chromosome Straightening using Self-attention Guided Networks

arXiv

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arXiv 2022年

作者： Zheng, Sunyi Li, Jingxiong Shui, Zhongyi Zhu, Chenglu Zhang, Yunlong Chen, Pingyi Yang, Lin Artificial Intelligence and Biomedical Image Analysis Lab School of Engineering Westlake University China Institute of Advanced Technology Westlake Institute for Advanced Study China College of Computer Science and Technology Zhejiang University China

Karyotyping is an important procedure to assess the possible existence of chromosomal abnormalities. However, because of the non-rigid nature, chromosomes are usually heavily curved in microscopic images and such deformed shapes hinder the chromosome analysis for cytogeneticists. In this paper, we present a self-attention guided framework to erase the curvature of chromosomes. The proposed framework extracts spatial information and local textures to preserve banding patterns in a regression module. With complementary information from the bent chromosome, a refinement module is designed to further improve fine details. In addition, we propose two dedicated geometric constraints to maintain the length and restore the distortion of chromosomes. To train our framework, we create a synthetic dataset where curved chromosomes are generated from the real-world straight chromosomes by grid-deformation. Quantitative and qualitative experiments are conducted on synthetic and real-world data. Experimental results show that our proposed method can effectively straighten bent chromosomes while keeping banding details and length. Copyright © 2022, The Authors. All rights reserved.

关键词： Chromosomes

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Analysis of Metallization Effects of Pressure-Assisted Cu Nanoparticle Sintering on DBC by Experiment and Nanoscale Simulation

Analysis of Metallization Effects of Pressure-Assisted Cu Na...

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International Conference on (ICEPT) Electronic Packaging Technology

作者： Shizhen Li Jing Jiang Xu Liu Shaogang Wang Zhonghua Zhang Huaiyu Ye Joint Lab for Advanced Semiconductor Packaging Technologies Joint Lab for Advanced Packaging and Testing Technology of Integrated Circuits School of Microelectronics Southern University of Science and Technology Shenzhen China Academy for Engineering&Technology Fudan University Shanghai China Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands School of Medical Informatics Daqing Campus Harbin Medical University Daqing China

Substrate metallization is a crucial factor affecting the mechanical properties of sintered nanoparticles in microelectronics applications, as it is essential for ensuring good adhesion between the substrate and the sintered material. In this study, we investigated the influence of metallization on pressure-assisted nanocopper sintering and analyzed the related mechanism of interaction using experiments and molecular dynamics simulation. In the first session, we bonded dummy dies on various substrates, including bare Cu, and substrates with Ag or Au metallization by nanocopper pressure-assisted sintering. The mechanical properties of the bonding layers were estimated using shear strength and SEM image analysis of fracture and cross-section morphologies under different sintering conditions. We found that the group of Cu-bare Cu have better bonding strength as the sintering temperature or assisted pressure is not high enough. However, as more energy input to the bonding layer, such as higher temperature or larger sintering pressure, the mechanical performance showed a significant increase. In the second session, a sintering model, which contained a single nanoparticle and substrate, was built to illustrate the effects of metallization from the perspective of solid-state wetting. The contact angle was estimated using a creative method, and the crystallization structure evolutions under different sintering conditions were analyzed. We found that the lattice boundary generated as the Cu nanoparticle coalescence with Ag or Au substrate, which may decrease the bonding strength. However, for Ag and Au metallization, limited interface diffusion can be observed at the neck region, where a few numbers of substrate atoms transmitted toward Cu nanoparticle, and the contact area was larger than that of bare Cu substrate. Finally, a simple uniaxial stretching simulation was conducted to prove the results of sintering simulation. This study provides valuable insights into the effec

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Robust Wireless Power Transfer by Self-Oscillating Controlled Inverter and Circular Pads

Robust Wireless Power Transfer by Self-Oscillating Controlle...

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Electrical Machines and Drives (ICEMD), International Conference on

作者： Alireza Eikani Mohammad Amirkhani Hossein Jafari Sadegh Vaez-Zadeh Mojtaba Mirsalim Davood Arab Khaburi Advanced Motion Systems Research Lab School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Electrical Engineering Electrical Machines and Transformers Research Laboratory (EMTRL) Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran Department of Electrical Engineering Iran University of Science and Technology Tehran Iran

Inductive wireless power transfer (WPT) faces significant challenges in achieving high efficiency and stable output power for charging mobile devices like EVs. Using circular pads (CP) as the transmitter (Tx) and receiver (Rx) and a self-oscillating controlled inverter, this paper demonstrates a robust WPT system that maintains high constant transfer efficiency and output power in a defined region regardless of coupling coefficient variations without communication between Tx and Rx or control circuits for the receiver side. In contrast to couplers with non-identical Tx and Rx pads and multiple decoupled coils in the Rx pad, the compact CP-CP magnetic coupler with a single identical coil on the Tx and Rx is more robust at a wider transfer distance (air gap) and lateral and rotational misalignment between the Tx and Rx. The proposed 1-kW robust WPT system with up to a 20 cm air gap between the Tx and Rx and up to 12 cm lateral misalignment in all directions at a 15 cm air gap and all degrees of rotational misalignment can tolerate coupling variations and achieve constant output power with 93% constant transfer efficiency.

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Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization

arXiv

引用

arXiv 2023年

作者： Yuan, Haonan Sun, Qingyun Fu, Xingcheng Zhang, Ziwei Ji, Cheng Peng, Hao Li, Jianxin Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China School of Computer Science and Engineering Beihang University China Key Lab of Education Blockchain and Intelligent Technology Guangxi Normal University China Department of Computer Science and Technology Tsinghua University China

Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-temporal patterns on dynamic graphs. However, existing works fail to generalize under distribution shifts, which are common in real-world scenarios. As the generation of dynamic graphs is heavily influenced by latent environments, investigating their impacts on the out-of-distribution (OOD) generalization is critical. However, it remains unexplored with the following two major challenges: (1) How to properly model and infer the complex environments on dynamic graphs with distribution shifts? (2) How to discover invariant patterns given inferred spatio-temporal environments? To solve these challenges, we propose a novel Environment-Aware dynamic Graph LEarning (EAGLE) framework for OOD generalization by modeling complex coupled environments and exploiting spatiotemporal invariant patterns. Specifically, we first design the environment-aware EA-DGNN to model environments by multi-channel environments disentangling. Then, we propose an environment instantiation mechanism for environment diversification with inferred distributions. Finally, we discriminate spatio-temporal invariant patterns for out-of-distribution prediction by the invariant pattern recognition mechanism and perform fine-grained causal interventions node-wisely with a mixture of instantiated environment samples. Experiments on real-world and synthetic dynamic graph datasets demonstrate the superiority of our method against state-of-the-art baselines under distribution shifts. To the best of our knowledge, we are the first to study OOD generalization on dynamic graphs from the environment learning perspective. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Sensitivity Study of Wafer-Level Packaging Lifetime Prediction Using Different Creep Constitutive Equations

Sensitivity Study of Wafer-Level Packaging Lifetime Predicti...

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Thermal, Mechanical and Multiphysics Simulation and Experiments in Micro-Electronics and Micro-Systems EuroSimE

作者： C. E. Lee Cadmus Yuan K. N. Chiang Department of Power Mechanical Engineering National Tsing Hua University Hsinchu Taiwan R.O.C Department of Mechanical and Computer-aided Engineering Feng Chia University Taichung Taiwan R.O.C Advanced Microsystem Packaging and Nano-Mechanics Research Lab Taiwan R.O.C

Reliability is essential in electronic packaging development. Since low-cycle fatigue is a common WLP (Wafer-Level Packaging) solder joint failure mechanism, industries often apply the accelerated thermal cycle test to estimate the risk experimentally. Due to the cost and execution time of the accelerated thermal cycling test (ATCT) being high and long, so the finite element simulation is critically needed to replace the ATCT experiment. To numerically estimate the solder fatigue risk, equivalent plastic strain-based Coffin-Manson and energy density equations are often applied. However, only the energy density equation works well while time-dependent material nonlinearity is *** this research, we build five finite element models corresponding to five validated test vehicles. These test vehicles have a lifetime range from 313 to 1013 cycles. For reliability estimation, we use the energy density-based method. The modified energy density method is applied to establish the correlation between the strain energy and the lifetime. We also employed two creep models, Garofalo Hyperbolic Sine model and Anand model, to analyze five test vehicles, subsequently comparing the reliability predictions calculated using the modified energy method with the increment of strain energy density. After calculation, both the Garofalo and Anand models demonstrated the ability to provide accurate results, with errors measuring less than 20%. Notably, the Anand model outperformed the Garofalo model in terms of accuracy.

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