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High-performance polarization management devices based on thin-film lithium niobate

Light(Science & Applications) 2022年第5期11卷 868-876页

作者： Zhongjin Lin Yanmei Lin Hao Li Mengyue Xu Mingbo He Wei Ke Heyun Tan Ya Han Zhaohui Li Dawei Wang X.Steve Yao Songnian Fu Siyuan Yu Xinlun Cai State Key Laboratory of Optoelectronic Materials and Technologies School of Electronics and Information TechnologySun Yat-sen University510275 GuangzhouChina Department of Electrical and Computer Engineering The University of British ColumbiaVancouverBC V6T 1Z4Canada Photonics Information Innovation Center and Hebei Provincial Center for Optical Sensing Innovations College of Physics Science and TechnologyHebei University071002 BaodingChina Institute of Advanced Photonics Technology School of Information EngineeringGuangdong University of Technology510006 GuangzhouChina

High-speed polarization management is highly desirable for many applications,such as remote sensing,telecommunication,and medical ***,most of the approaches for polarization management rely on bulky optical components that are slow to respond,cumbersome to use,and sometimes with high drive ***,we overcome these limitations by harnessing photonic integrated circuits based on thin-film lithium niobate *** successfully realize a portfolio of thin-film lithium niobate devices for essential polarization management functionalities,including arbitrary polarization generation,fast polarization measurement,polarization scrambling,and automatic polarization *** present devices feature ultra-fast control speeds,low drive voltages,low optical losses and compact *** these devices,we achieve high fidelity polarization generation with a polarization extinction ratio up to 41.9 dB and fast polarization scrambling with a scrambling rate up to 65 Mrad s−1,both of which are best results in integrated *** also demonstrate the endless polarization state tracking operation in our *** demonstrated devices unlock a drastically new level of performance and scales in polarization management devices,leading to a paradigm shift in polarization management.

关键词： polarization lithium film

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Inference-Aware State Reconstruction for Industrial Metaverse under Synchronous/Asynchronous Short-Packet Transmission

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IEEE Transactions on Communications 2025年

作者： Xiong, Qinqin Cao, Jie Zhu, Xu Jiang, Yufei Pappas, Nikolaos Harbin Institute of Technology School of Electronic and Information Engineering Shenzhen518055 China The Shenzhen Key Laboratory of Wireless AloT Communications Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen Guangdong China Linkoping University Department of Computer and Information Science Linköping58183 Sweden

We consider a real-time state reconstruction system for industrial metaverse. The time-varying physical process states in real space are captured by multiple sensors via wireless links, and then reconstructed in virtual space. In this paper, we use the spatial-temporal correlation of the sensor data of interest to infer the real-time data of the target sensor to reduce the mean squared error (MSE) of reconstruction for industrial metaverse under short-packet transmission (SPT). Both synchronous and asynchronous transmission modes for multiple sensors are considered. It is proved that the average reconstruction MSE and average block error probability (BLEP) have a positive correlation under inference with synchronous transmission scheme, whereas they have a negative correlation under inference with asynchronous transmission scheme in certain conditions. Additionally, the average reconstruction MSE decreases monotonically with the mean squared spatial correlation (MSSC), which characterizes the global spatial correlation level. With a high BLEP or long transmission period, even under weak MSSC, the inference scheme still significantly reduces the average reconstruction MSE compared to the no inference case. Moreover, closed-form MSSC thresholds are derived for the superiority regions of the inference with synchronous transmission and inference with asynchronous transmission schemes, respectively. Adaptations of blocklength and time shift of asynchronous transmission are conducted to minimize the average reconstruction MSE. Simulation results show that the two inference schemes outperform the no inference case, with an average MSE reduction of more than 50%. © 1972-2012 IEEE.

关键词： Packet networks

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Exploring Rationale Learning for Continual Graph Learning 39

Exploring Rationale Learning for Continual Graph Learning

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Song, Lei Li, Jiaxing Si, Qinghua Guan, Shihan Kong, Youyong Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University Ministry of Education China

ISBN: (纸本)157735897X

Catastrophic forgetting poses a significant challenge for graph neural networks in continuously updating their knowledge base with data streams. To address this issue, much of the research has focused on node-level continual learning using parameter regularization or rehearsal-based strategies, while little attention given to graph-level tasks. Furthermore, current paradigms for continual graph learning may inadvertently capture spurious correlations for specific tasks through shortcuts, thereby exacerbating the forgetting of previous knowledge when new tasks are introduced. To tackle these challenges, we propose a novel paradigm, Rationale Learning GNN (RL-GNN), for graph-level continual graph learning. Specifically, we harness the invariant learning principle to incorporate environmental interventions into both the current and historical distributions, aiming to uncover rationales by minimizing empirical risk across all environments. The rationale serves as the sole factor guiding the learning process. Therefore, continual graph learning is redefined as capturing these invariant rationales within task sequences, alleviating catastrophic forgetting caused by spurious features. Extensive experiments on real-world datasets with varying task lengths demonstrate the effectiveness of our RL-GNN in continuous knowledge assimilation and reduction of catastrophic forgetting. Copyright © 2025, Association for the Advancement of Artificia Intelligence (***). All rights reserved.

关键词： Knowledge engineering

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High-Fidelity Editable Portrait Synthesis with 3D GAN Inversion

High-Fidelity Editable Portrait Synthesis with 3D GAN Invers...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jindong Xie Jiachen Liu Yupei Lin Jinbao Wang Xianxu Hou Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of Information Engineering Guangdong University of Technology Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Information Processing School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The 3D generative adversarial network (GAN) inversion converts an image into 3D representation to attain high-fidelity reconstruction and facilitate realistic image manipulation within the 3D latent space. However, previous approaches face challenges regarding the trade-off between the reconstruction ability and editability. That is, reversing a real-world image to a low-dimensional latent code would inevitably lead to information loss, and achieving a near-perfect reconstruction using high-rate triplane representation often limits the ability to manipulate the image freely in the latent space. To address these issues, we propose a novel latent conditioning encoder-based framework with the alignment between the low-dimensional latent and high-dimensional triplane. A non-semantic guided editing strategy bridges the intrinsic relation between the latent condition and triplane generation, making it possible to edit the high-dimensional representation by latent manipulation. As a result, our method can achieve high-fidelity reconstruction and editing simultaneously by directly controlling the latent code. Experimental results demonstrate that our approach excels in reconstruction and editing quality compared to previous 3D inversion methods. Furthermore, our method can also edit even real faces with large poses and out-of-domain cases.

关键词： Bridges Three-dimensional displays Codes Signal processing Generative adversarial networks Vectors Acoustics Speech processing Image reconstruction Faces

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HePa: Heterogeneous Graph Prompting For All-level Classification Tasks 39

HePa: Heterogeneous Graph Prompting For All-level Classifica...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Jinghong, Jia Song, Lei Li, Jiaxing Kong, Youyong Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) Ministry of Education China

ISBN: (纸本)157735897X

Heterogeneous graphs, which are common in real-world downstream tasks, have recently sparked a wave of research interest. The performance of end-to-end heterogeneous graph neural networks (HGNNs) greatly relies on supervised training for specific tasks. To reduce the labeling cost, the”pretrain-finetune” paradigm has been widely adopted, but it leads to a knowledge gap between the pre-trained model and downstream tasks. In an effort to address this gap, the”pretrain-prompt” paradigm has emerged as a promising approach. This involves fine-tuning randomly initialized learnable vectors in downstream tasks. However, this approach may result in an insufficient representation of downstream task features. Existing techniques for heterogeneous graph prompting restructure the heterogeneous graph to align with the homogeneous graph prompting scheme. This can potentially introduce the same limitations as homogeneous graph prompt learning. In this paper, we propose HePa, short for Heterogeneous Graph Prompting for all-level classification tasks. It not only includes a unified prompt template-graph adapted for heterogeneous graphs but also introduces a novel pre-prompt token optimized during the pre-training phase to convey task information downstream. With these designs, HePa can complete all levels of classification tasks toward few-shot scenarios while activating in-context learning. Finally, we conducted a comprehensive experimental analysis of HePa on three benchmark datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Supervised learning

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FindINT: Detect and Locate the Lost in-Band Network Telemetry Packet

IEEE Networking Letters

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IEEE Networking Letters 2022年第1期4卷 20-24页

作者： Tan, Lizhuang Su, Wei Miao, Jingying Zhang, Wei School of Electronics and Information Engineering Beijing Jiaotong University Beijing100044 China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center National Supercomputer Center in ji'Nan Qilu University of Technology Shandong Academy of Sciences Ji'nan250000 China

In-band network telemetry (INT) is a new network measurement technology that uses business packet to collect network information hop by hop. Since business packets may be lost due to various reasons, INT telemetry information will inevitably be lost. Unfortunately, telemetry system is not aware of this loss. In this letter, we present FindINT, an INT packet coding scheme with two marking strategies, to measure per-flow packet loss rate and location. FindINT is inspired by alternative marking method, which is triggered by loss event instead of *** results show that FindINT has extremely low overhead and high detection accuracy. © 2019 IEEE.

关键词： Packet networks

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Exploring Prompt-based Multi-task Learning for Multimodal Dialog State Tracking and Immersive Multimodal Conversation 11

Exploring Prompt-based Multi-task Learning for Multimodal Di...

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11th Dialog System technology Challenge, DSTC 2023

作者： Chen, Yirong Li, Ya Wang, Tao Xing, Xiaofen Xu, Xiangmin Liu, Quan Liu, Cong Hu, Guoping Guangdong Provincial Key Laboratory of Human Digital Twin School of EE South China University of Technology Guangzhou China iFLYTEK Research Hefei China Pazhou Lab. Guangzhou China School of Future Technology South China University of Technology Guangzhou China State Key Laboratory of Cognitive Intelligence Hefei China National Engineering Research Center of Speech and Language Information Processing Hefei China

With the rise of the metaverse, immersive multimodal conversation has attracted more and more researchers’ attention. Multimodal contexts will become more important for human-computer interaction in the metaverse, especially in shopping domain. Unlike traditional conversation tasks, immersive multimodal conversation has challenges such as multimodal ambiguous candidate identification and multimodal coreference resolution, which makes it more difficult to dialog state tracking and response generation, as described in SIMMC 2.1 challenge, a part of DSTC11. In particular, as the number of objects in the scene increases, the difficulty will increase dramatically. We proposed PMTLED (Prompt-based Multi-Task Learning Encoder-Decoder), in which different subtasks use different prompts to make the model tend to focus on the current subtask. We achieve the winner in ambiguous candidates indentification and runner-up in multimodal coreference resolution (MM-Coref), multimodal dialog state tracking (MM-DST) and assistant response generation. Our code and model are made publicly available at https://***/scutcyr/dstc11-simmc2.1-scut-bds-lab. © 2023 Association for Computational Linguistics.

关键词： Human computer interaction

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Positive-Guided Knowledge Distillation for Document-Level Relation Extraction with Noisy Labeled Data 12th

Positive-Guided Knowledge Distillation for Document-Level R...

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12th National CCF Conference on Natural Language processing and Chinese Computing, NLPCC 2023

作者： Zeng, Daojian Zhu, Jianling Jiang, Lincheng Dai, Jianhua Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Institute of AI and Targeted International Communication Hunan Normal University Changsha China College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha China

ISBN: (纸本)9783031446924

Since one entity may have multiple mentions and relations between entities may stretch across multiple sentences in a document, the annotation of document-level relation extraction datasets becomes a challenging task. Many studies have identified that these datasets contain a large number of noisy labels, hindering performance improvement for the document-level relation extraction task. The previous and most straightforward method is denoising noisy labeled data from a data annotation perspective. However, this time-consuming approach is not suitable for large-scale datasets. In this paper, we propose a novel Positive-Guided Knowledge Distillation (PGKD) model to address the noisy labeled data problem for document-level relation extraction. We design a new teacher-student architecture. The teacher model trained with only positive samples can partially supervise the student model. The positive-guided knowledge distillation algorithm transfers the clean positive-class patterns from the teacher model to the student model. In this way, the student model trained with all samples can efficiently prevent the interference of false negative samples. Extensive experiments on Mix-DocRED demonstrate that PGKD achieves state-of-the-art effectiveness for document-level relation extraction with noisy labeled data. Moreover, PGKD also surpasses other baselines even on the well-annotated Re-DocRED. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Distillation

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Parallel Optimization of Computational Fluid Dynamics Application code_saturne Based on Next Generation Sunway Supercomputer 14

Parallel Optimization of Computational Fluid Dynamics Applic...

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14th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2023

作者： Liu, Anjun Liao, Yin Xu, Xihao Chen, YanYan Zhong, Hongjun Wu, Jianming Kong, Bo Raikov, Alexander Guo, Meng Jinan Institute of Supercomputing Technology Jinan Key Laboratory of High Performance Industrial Software Jinan China Zhejiang Yuansuan Technology Co. Ltd Jinan Supercomputing-Zhejiang Yuansuan Industrial Simulation System Joint Laboratory Hangzhou China Qilu University of Technology Shandong Academy of Sciences Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Key Laboratory of Computing Power Network and Information Security China Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Shandong Jinan China

ISBN: (纸本)9798350371024

code_saturne is an open-source general large-scale computational fluid dynamics software independently developed by Electricite de France since 1997. Due to its high computing performance, code_saturne has been widely used in the nuclear industry, heavy fuel and biomass combustion, particle tracking and other computational fluid dynamics fields. In this work, we implemented code_saturne on the new generation Sunway supercomputer (SW26010pro) at the first time. By applying the data value-taking technology of double cache, combined with Sunway shared memory technology, we have adapted the data structure and Modular programming of code_saturne conducively to the new generation Sunway supercomputer (SW26010pro) and accelerated the software hotspots the software hotspots by 1-2 times. After optimizing and porting, this software has successfully served in the numerical simulation of the lateral flow of fuel assemblies in the nuclear industry. © 2023 IEEE.

关键词： Supercomputers

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Enhancing NR-IQA Model Robustness through Simple Image Compression Techniques

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Liu, Yujia Yang, Chenxi Yu, Zhaofei Huang, Tiejun Peking University National Engineering Research Center of Visual Technology State Key Laboratory of Multimedia Information Processing School of Computer Science Beijing China Peking University School of Mathematical Sciences Beijing China China Institution for Artificial Intelligence Peking University National Engineering Research Center of Visual Technology State Key Laboratory of Multimedia Information Processing China

No-Reference Image Quality Assessment (NR-IQA) plays a crucial role in various real-world applications by predicting image quality scores without the need for reference images. Despite the impressive performance of deep learning-based NR-IQA models, they remain vulnerable to adversarial attacks, which introduce imperceptible perturbations to input images, causing significant changes in predicted scores. In this study, we explore the use of simple JPEG compression techniques, as well as their combination with norm regularization training, to defend against these adversarial attacks. Our results demonstrate that image compression is an effective method to enhance model robustness, and it can further improve the robustness of NR-IQA models when combined with appropriate training strategies. Since excessive compression may reduce performance on clean images, it is essential to strike a balance. This work provides valuable insights into designing effective image compression methods for NR-IQA models. © 1991-2012 IEEE.

关键词： Image compression

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