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arXiv 2023年

作者： Bassignana, Elisa Ginter, Filip Pyysalo, Sampo van der Goot, Rob Plank, Barbara Department of Computer Science IT University of Copenhagen Denmark TurkuNLP Department of Computing University of Turku Finland MaiNLP Center for Information and Language Processing LMU Munich Germany

Most research in Relation Extraction (RE) involves the English language, mainly due to the lack of multi-lingual resources. We propose MULTI-CROSSRE, the broadest multi-lingual dataset for RE, including 26 languages in addition to English, and covering six text domains. MULTI-CROSSRE is a machine translated version of CrossRE (Bassignana and Plank, 2022a), with a sub-portion including more than 200 sentences in seven diverse languages checked by native speakers. We run a baseline model over the 26 new datasets and-as sanity check-over the 26 back-translations to English. Results on the back-translated data are consistent with the ones on the original English CrossRE, indicating high quality of the translation and the resulting dataset. © 2023, CC BY.

关键词： Extraction

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Quantum chi-squared tomography and mutual information testing

arXiv

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arXiv 2023年

作者： Flammia, Steven T. O’Donnell, Ryan AWS Center for Quantum Computing IQIM California Institute of Technology United States Computer Science Department Carnegie Mellon University United States

For quantum state tomography on rank-r dimension-dstates, we show that Õ(r.5d1.5/∊) ≤ Õ(d2/∊) copies suffice for accuracy ∊ with respect to (Bures) X2-divergence, and Õ(rd/∊) copies suffice for accuracy ∊ with respect to quantum relative entropy. The best previous bound was Õ(rd/∊) ≤ Õ(d2/∊) with respect to infidelity;our results are an improvement since infidelity is bounded above by both the relative entropy and the X2divergence. For algorithms that are required to use single-copy measurements, we show that Õ(r1.5d1.5/∊) ≤ Õ(d3/∊) copies suffice for X2-divergence, and Õ(r2d/∊) suffice for relative entropy.1 Using this tomography algorithm, we show that Õ(d2.5/∊) copies of a d×d-dimensional bipartite state suffice to test if it has quantum mutual information 0 or at least ∊. As a corollary, we also improve the best known sample complexity for the classical version of mutual information testing to Õ(d/∊). © 2023, CC BY-SA.

关键词： Tomography

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3D Human Mesh Estimation from Virtual Markers

arXiv

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arXiv 2023年

作者： Ma, Xiaoxuan Su, Jiajun Wang, Chunyu Zhu, Wentao Wang, Yizhou School of Computer Science Center on Frontiers of Computing Studies Peking University China Inst. for Artificial Intelligence Peking University China Microsoft Research Asia China Nat’l Eng. Research Center of Visual Technology China

Inspired by the success of volumetric 3D pose estimation, some recent human mesh estimators propose to estimate 3D skeletons as intermediate representations, from which, the dense 3D meshes are regressed by exploiting the mesh topology. However, body shape information is lost in extracting skeletons, leading to mediocre performance. The advanced motion capture systems solve the problem by placing dense physical markers on the body surface, which allows to extract realistic meshes from their non-rigid motions. However, they cannot be applied to wild images without markers. In this work, we present an intermediate representation, named virtual markers, which learns 64 landmark keypoints on the body surface based on the large-scale mocap data in a generative style, mimicking the effects of physical markers. The virtual markers can be accurately detected from wild images and can reconstruct the intact meshes with realistic shapes by simple interpolation. Our approach outperforms the state-of-the-art methods on three datasets. In particular, it surpasses the existing methods by a notable margin on the SURREAL dataset, which has diverse body shapes. Code is available at https://***/ShirleyMaxx/VirtualMarker. Copyright © 2023, The Authors. All rights reserved.

关键词： Mesh generation

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Transferability Estimation Based On Principal Gradient Expectation

arXiv

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arXiv 2022年

作者： Qi, Huiyan Cheng, Lechao Chen, Jingjing Yu, Yue Song, Xue Fengg, Zunlei Jiang, Yu-Gang School of Computer Science Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University China Zhejiang Lab China Zhejiang University China

Transfer learning aims to improve the performance of target tasks by transferring knowledge acquired in source tasks. The standard approach is pre-training followed by fine-tuning or linear probing. Especially, selecting a proper source domain for a specific target domain under predefined tasks is crucial for improving efficiency and effectiveness. It is conventional to solve this problem via estimating transferability. However, existing methods can not reach a trade-off between performance and cost. To comprehensively evaluate estimation methods, we summarize three properties: stability, reliability and efficiency. Building upon them, we propose Principal Gradient Expectation (PGE), a simple yet effective method for assessing transferability. Specifically, we calculate the gradient over each weight unit multiple times with a restart scheme, and then we compute the expectation of all gradients. Finally, the transferability between the source and target is estimated by computing the gap of normalized principal gradients. Extensive experiments show that the proposed metric is superior to state-of-the-art methods on all properties. © 2022, CC BY.

关键词： Economic and social effects

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Three-dimensional C-scan-based generation adversarial network with synthetic input to improve optical coherence tomography angiography

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Journal of Biomedical Optics 2025年第5期30卷 056006页

作者： Xu, Jingjiang Feng, Zhongwu Qiu, Haixia Tang, Peijun Gao, Kai Huang, Yanping Lan, Gongpu Qin, Jia An, Lin Jia, Gangyong Wu, Qing Foshan University Guangdong-Hong Kong-Macao Intelligent Micro-Nano Optoelectronic Technology Joint Laboratory Foshan China Guangdong Weiren Meditech Co. Ltd Innovation and Entrepreneurship Teams Project of Guangdong Pearl River Talents Program Foshan China Foshan University School of Mechatronic Engineering and Automation Foshan China Chinese PLA General Hospital The First Medical Centre Department of Laser Medicine Beijing China South China Normal University College of Biophotonics Guangzhou China Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science State Key Laboratory of Ophthalmology Guangdong Provincial Clinical Research Center for Ocular Diseases Zhongshan Ophthalmic Center Guangzhou China Hangzhou Dianzi University School of Computer Science Hangzhou China

Significance: Optical coherence tomography angiography (OCTA) usually suffers from the inherent random fluctuations of noise and speckles in the imaging system. Previous deep learning methods have mainly focused on improving the quality of B-scan blood flow images or en face projection images. We propose a deep learning method to reconstruct high-quality 3D vasculature, which fully utilizes the volumetric OCTA data and the topological features of the vascular network. Aim: We propose a deep learning method called the three-dimensional C-scanbased generation adversarial network (3DCS-GAN) to improve vascular visualization for volumetric OCTA data. Approach: To train the network, we superimposed the single-shot en face OCTA images on avascular noisy C-scan images to synthesize the input data and used the multiple averaged en face OCTA images as the reference labels. The deep learning algorithm is based on Pix2Pix architecture and consists of a generator model and a discriminator model. A perceptual loss function was utilized by combining content loss and adversarial loss. The proposed algorithm is applied to the C-scan images depth-by-depth to suppress the background noise and enhance vascular visualization in the 3D OCTA data. Results: The proposed method has improved the contrast-to-noise ratio of crosssectional OCTA images by ∼2 times. It greatly enhances the visualization of blood vessels in the deep layer and offers much clearer blood vessel topology in the 3D volume-rendering of OCTA. 3DCS-GAN has exhibited superior image enhancement compared with alternative methods. It has been used to enhance the OCTA images of port wine stain disease for clinical investigation. Conclusions: It demonstrates that the proposed 3DCS-GAN can greatly improve vascular visualization in the deep layer, provide better image quality than the multiple averaged OCTA images, and achieve superior image enhancement for volumetric OCTA data. © 2025 SPIE. All rights reserved.

关键词： Image enhancement

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A Privacy-Preserving Approach for Multi-Source Domain Adaptive Object Detection

A Privacy-Preserving Approach for Multi-Source Domain Adapti...

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IEEE International Conference on Image Processing

作者： Peggy Joy Lu Chia-Yung Jui Jen-Hui Chuang Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan National Center for High-Performance Computing Taichung Taiwan

A new research topic, multi-source domain adaptive object detection (MSDAOD) under privacy-preserving constraint is explored in this paper, where the clients can only access their own source data while the server can only access unlabeled target data. Accordingly, a novel MSDAOD framework is proposed wherein the clients employ a source-only Probabilistic Faster R-CNN (PFRCNN) to generate models with localization uncertainty, while a Multi-teacher Pseudo-label Ensemble Network (MPEN) is developed on the server side. In MPEN, FedMA-based algorithm aggregates the above models to a domain-invariant backbone while a novel pseudo-label ensemble (PLE) scheme is employed to reduce false positives arising from domain specific parts, and enhance the overall system performance using target domain information. Experiments demonstrate that our method outperforms other state-of-the-art MSDAOD and privacy-preserving methods by 10%~16% in average precision (AP).

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A Performance Analysis of Task Scheduling for UQ Workflows on HPC Systems

A Performance Analysis of Task Scheduling for UQ Workflows o...

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ISC High Performance 2025 Research Paper Proceedings (40th International Conference)

作者： Chung Ming Loi Anne Reinarz Linus Seelinger William Hornsby James Buchanan Mikkel Lykkegaard Department of Computer Science Durham University Durham UK Scientific Computing Center Karlsruhe Institute of Technology Karlsruhe Germany UKAEA-CCFE Abingdon UK Danish Technological Institute Aarhus Denmark

ISBN: (数字)9783982633619

Uncertainty Quantification (UQ) workloads are becoming increasingly common in science and engineering. They involve the submission of thousands or even millions of similar tasks with potentially unpredictable runtimes, where the total number is usually not known a priori. A static one-size-fits-all batch script would likely lead to suboptimal scheduling, and native schedulers installed on High-Performance computing (HPC) systems such as SLURM often struggle to efficiently handle such workloads. In this paper, we introduce a new load balancing approach suitable for UQ workflows. To demonstrate its efficiency in a real-world setting, we focus on the GS2 gyrokinetic plasma turbulence simulator. Individual simulations can be computationally demanding, with runtimes varying significantly—from minutes to hours—depending on the high-dimensional input parameters. Our approach uses the UQ and Modelling Bridge, which offers a language-agnostic interface to a simulation model, combined with HyperQueue which works alongside the native scheduler. In particular, deploying this framework on HPC systems does not require system-level changes. We benchmark our proposed framework against a standalone SLURM approach using GS2 and a Gaussian Process surrogate thereof. Our results demonstrate a reduction in scheduling overhead by up to three orders of magnitude and a maximum reduction of 38% in CPU time for long-running simulations compared to the naïve SLURM approach, while making no assumptions about the job submission patterns inherent to UQ workflows.

关键词： Adaptation models Runtime Uncertainty Processor scheduling Computational modeling High performance computing Gaussian processes Load management Servers Load modeling

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On authenticated skyline query processing over road networks

On authenticated skyline query processing over road networks

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作者： Zhu, Xiaoyu Wu, Jie Chang, Wei Zakirul Alam Bhuiyan, Md Raymond Choo, Kim-Kwang Qi, Fang Liu, Qin Wang, Guojun School of Computer Science and Engineering Central South University Changsha China Center for Networked Computing Temple University PhiladelphiaPA United States Department of Computer and Information Sciences Saint Joseph's University PhiladelphiaPA United States Department of Computer Science Fordham University New York CityNY United States Department of Information Systems and Cyber Security University of Texas at San Antonio San AntonioTX United States School of Computer Science and Electronic Engineering Hunan University Changsha China School of Computer Science Guangzhou University Guangzhou China

In recent times, many location-based service providers (LBSPs) choose to outsource data query services to third-party cloud service providers (CSPs). This allows users to easily search for points of interests (POIs), such as restaurants and parking lots in their vicinity, using their mobile devices and in-vehicle infotainment units. Skyline query is one potential technique to be deployed for road networks. However, the untrusted CSPs may forge or omit query results, intentionally or not. Therefore, in this article, we posit that by observing the unique properties of skyline query results in road networks, we can bind each POI with four nearby POIs with special properties using signature chain technology. Our proposed approach not only provides users with skyline query result authentication ability over the road network, but also have low communication overhead. Specifically, the overhead analysis and experimental results show that our proposed approach decreases the communication overhead. © 2020 John Wiley & Sons, Ltd.

关键词： Location based services

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Richelieu: self-evolving LLM-based agents for AI diplomacy 24

Richelieu: self-evolving LLM-based agents for AI diplomacy

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Zhenyu Guan Xiangyu Kong Fangwei Zhong Yizhou Wang Institute for Artificial Intelligence Peking University College of Computer Science Beijing Information Science and Technology University and State Key Laboratory of General Artificial Intelligence BIGAI School of Artificial Intelligence Beijing Normal University and State Key Laboratory of General Artificial Intelligence BIGAI Center on Frontiers of Computing Studies School of Computer Science Nat'l Eng. Research Center of Visual Technology Peking University and Institute for Artificial Intelligence Peking University

ISBN: (纸本)9798331314385

Diplomacy is one of the most sophisticated activities in human society, involving complex interactions among multiple parties that require skills in social reasoning, negotiation, and long-term strategic planning. Previous AI agents have demonstrated their ability to handle multi-step games and large action spaces in multi-agent tasks. However, diplomacy involves a staggering magnitude of decision spaces, especially considering the negotiation stage required. While recent agents based on large language models (LLMs) have shown potential in various applications, they still struggle with extended planning periods in complex multi-agent settings. Leveraging recent technologies for LLM-based agents, we aim to explore AI's potential to create a human-like agent capable of executing comprehensive multi-agent missions by integrating three fundamental capabilities: 1) strategic planning with memory and reflection; 2) goal-oriented negotiation with social reasoning; and 3) augmenting memory through self-play games for self-evolution without human in the loop. Project page: https://***/view/richelieu-diplomacy.

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Securing Consumer IoT Swarms Using Graph Transformers and SRAM for Firmware Attestation

Securing Consumer IoT Swarms Using Graph Transformers and SR...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Varun Kohli Bhavya Kohli Muhammad Naveed Aman Biplab Sikdar Department of Electrical and Computer Engineering National University of Singapore Singapore Center for Machine Intelligence and Data Science Indian Institute of Technology Bombay India School of Computing University of Nebraska-Lincoln USA

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Consumer Internet of Things (IoT) networks have gained widespread popularity due to their convenience, automation, and security provisions in personal and home environments. Ubiquitous resource-constrained devices, however, are plagued with security issues that often arise from firmware-related issues and their propagated effects. While various studies on firmware attestation are available, they require firmware copies, specific hardware, and complex computation on the IoT device. This paper presents a study on the application of Graph Transformer Networks (GTN) in verifying the firmware integrity of consumer IoT swarms using SRAM as an attestation feature. The proposed method achieves an overall 0.99 accuracy on authentic samples from development and physical twin networks, 0.99 on malware, and 0.97 on propagated misbehavior at a $\sim 10^{-4}$ second inference latency on a laptop CPU.

关键词： Portable computers Accuracy Random access memory Transformers Malware Graph neural networks Hardware Internet of Things Security Microprogramming

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