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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Qin, Yifang Xie, Jiaxuan Xiao, Zhiping Zhang, Ming State Key Laboratory for Multimedia Information Processing School of Computer Science PKU-Anker LLM Lab Peking University China School of Earth and Space Sciences Peking University China Paul G. Allen School of Computer Science and Engineering University of Washington United States

ISBN: (纸本)157735897X

Point-of-Interest (POI) recommendation plays an important role in a wide range of location-based social network applications, aiming to accurately predicting users' next visits based on their historical check-in records. Previous efforts have primarily focused on the modifications of existing sequential models, neglecting the fact that POI visiting sequences typically involve continuous state transformation of geographical and intention signals. Additionally, the diverse time span between check-ins require the model to properly recognize user's multi-granular preference. While recent advances of state Space Model (SSM) have revealed their potential in handling intricate temporal signals, we propose a state-based model that is tailored for spatio-temporal POI sequences. On top of traditional SSMs that are typically limited to linear sequences like Mamba, we propose GeoMamba, which customizes the model states to accommodate the spatio-temporal sequences, especially fitting for POI recommendations. Specifically, while the approximation operator HiPPO sets the foundation of linear SSMs, we introduce a novel GaPPO operator that extends the model's state space into graph-represented geographical domains. This innovation allows us to construct locational SSM encoders that seamlessly integrate users' spatio-temporal characteristics. The sequence-aware outputs of GeoMamba are further processed to generate multi-scale behavior representations. Extensive experimental results illustrate the superiority of GeoMamba over several state-of-the-art baselines. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： state space methods

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A dual plasmonic core-shell Pt/[TiN@TiO_(2)]catalyst for enhanced photothermal synergistic catalytic activity of VOCs abatement

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Nano Research 2022年第8期15卷 7071-7080页

作者： Anqi Li Qianpeng Zhang Shuaiqi Zhao Yanan Chong Peng Wu Yifei Li Xiaojing Jin Guangxu Chen Yongcai Qiu Shihe Yang Daiqi Ye Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control School of Environment and EnergySouth China University of TechnologyGuangzhou 510006China Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina Guangzhou HKUST Fok Ying Tung Research Institute NanshaGuangzhou 511458China State Key Laboratory of Luminescent Materials and Devices South China University of TechnologyGuangzhou 510640China Guangdong Key Lab of Nano-Micro Material Research School of Chemical Biology and Biotechnology Shenzhen Graduate SchoolPeking UniversityShenzhen 518055China

Volatile organic compounds(VOCs)are ubiquitous organic pollutants affecting atmospheric environment and human *** development of new efficient and environmentally friendly materials utilizing photothermal synergistic catalysis for purification of VOCs is still ***,we design and prepare a core–shell TiN@TiO_(2)nanostructure integrating with nanoscaled Pt(Pt/[TiN@TiO_(2)])by an attractive quenching *** strong light-harvesting capability of Pt and TiN components improve light-to-heat utilization efficiency by their intrinsic surface plasmon resonance *** TiO_(2)component upon the surface and the coexisting coupling effect of Pt0 and Pt2+enhance the photocatalytic effect of the *** a result,the catalytic performance is significantly improved with toluene(120 ppm)conversion of 100%under the gas hourly space velocity of 72,000 mL·g^(−1)·h^(−1)and light illumination of 500 mW·cm^(−2).The desired catalyst thus achieves highly efficient coupling effect of photocatalysis and light-to-heat conversion for promoting VOCs abatement.

关键词： core-shell Pt/[TiN@TiO_(2)]catalyst volatile organic compounds(VOCs)abatement photothermal synergistic catalysis full spectrum sunlight utilization

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Directly wireless communication of human minds via non-invasive brain-computer-metasurface platform

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eLight 2022年第1期2卷 132-142页

作者： Qian Ma Wei Gao Qiang Xiao Lingsong Ding Tianyi Gao Yajun Zhou Xinxin Gao Tao Yan Che Liu Ze Gu Xianghong Kong Qammer HAbbasi Lianlin Li Cheng‑Wei Qiu Yuanqing Li Tie Jun Cui Institute of Electromagnetic Space Southeast UniversityNanjing 210096China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China State Key Laboratory of Millimeter Wave Southeast UniversityNanjing 210096China Center of Intelligent Metamaterials Pazhou Laboratory Guangzhou 510330China Research Center for Brain-Computer Interface Pazhou LabGuangzhou 510330China Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore University of Glasgow James Watt School of EngineeringGlasgow G128QQUK State Key Laboratory of Advanced Optical Communication Systems and Networks Department of ElectronicsPeking UniversityBeijing 100871China

Brain-computer interfaces(BCIs),invasive or non-invasive,have projected unparalleled vision and promise for assisting patients in need to better their interaction with the *** by the BCI-based rehabilitation technologies for nerve-system impairments and amputation,we propose an electromagnetic brain-computer-metasurface(EBCM)paradigm,regulated by human’s cognition by brain signals directly and *** experimentally show that our EBCM platform can translate human’s mind from evoked potentials of P300-based electroencephalography to digital coding information in the electromagnetic domain non-invasively,which can be further processed and transported by an information metasurface in automated and wireless *** wireless communications of the human minds are performed between two EBCM operators with accurate text ***,several other proof-of-concept mind-control schemes are presented using the same EBCM platform,exhibiting flexibly-customized capabilities of information processing and synthesis like visual-beam scanning,wave modulations,and pattern encoding.

关键词： communication computer surface

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Neural Machine Translation with Contrastive Translation Memories

Neural Machine Translation with Contrastive Translation Memo...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Cheng, Xin Gao, Shen Liu, Lemao Zhao, Dongyan Yan, Rui Wangxuan Institute of Computer Technology Peking University China Center for Data Science Peking University China Tencent AI Lab School of Computer Science and Technology Shandong University China State Key Laboratory of Media Convergence Production Technology and Systems China China Gaoling School of Artificial Intelligence Renmin University of China China

Retrieval-augmented Neural Machine Translation models have been successful in many translation scenarios. Different from previous works that make use of mutually similar but redundant translation memories (TMs), we propose a new retrieval-augmented NMT to model contrastively retrieved translation memories that are holistically similar to the source sentence while individually contrastive to each other providing maximal information gains in three phases. First, in TM retrieval phase, we adopt a contrastive retrieval algorithm to avoid redundancy and uninformativeness of similar translation pieces. Second, in memory encoding stage, given a set of TMs we propose a novel Hierarchical Group Attention module to gather both local context of each TM and global context of the whole TM set. Finally, in training phase, a Multi-TM contrastive learning objective is introduced to learn salient feature of each TM with respect to target sentence. Experimental results show that our framework obtains improvements over strong baselines on the benchmark datasets. © 2022 Association for Computational Linguistics.

关键词： Neural machine translation

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A biosensing system using a multiparameter nonlinear dynamic analysis of cardiomyocyte beating for drug-induced arrhythmia recognition

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Microsystems & Nanoengineering 2022年第3期8卷 277-290页

作者： Hao Wang Yue Wu Quchao Zou Wenjian Yang Zhongyuan Xu Hao Dong Zhjing Zhu Depeng Wang Tanxing Wang Ning Hu Diming Zhang State Key Laboratory of Optoelectronic Materials and Technologies Guangdong Province Key Laboratory of Display Material and TechnologySchool of Electronics and Information TechnologySun Yat-sen UniversityGuangzhou 510006China Research Center for Itelligent Sensing Systems Zhejiang LabHangzhou 311121China ZJU-Hangzhou Global Scientific and Technological Innovation Center Department of ChemistryThe Second Affiliated Hospital Zhejiang University School of MedicineDepartment of Clinical Medical EngineeringZhejiang UniversityHangzhou 310058China Key Laboratory of Novel Target and Drug Study for Neural Repair of Zhejang Province School of MedicineSchool of Computer&Computing ScienceZhejiang University City CollegeHangzhou 310015China School of Brain Science and Brain Medicine Zhejiang UniversityHangzhou 310058China College of Energy and Power Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China E-LinkCare Meditech Co LtdHangzhou 310011China State Key Laboratory of Transducer Technology Chinese Academy of SciencesShanghai 200050China

Cardiovascular disease is the number one cause of death in ***,cardiotoxicity is one of the most important adverse effects assessed by arrhythmia recognition in drug ***,cell-based techniques developed for arhythmia recognition primarily employ linear methods such as time-domain analysis that detect and compare individual waveforms and thus fall short in some applications that require automated and efficient arrhythmia recognition from large *** carried out the frst report to develop a biosensing system that integrated impedance measurement and multiparameter nonlinear dynamic algorithm(MNDA)analysis for druginduced arrhythmia recognition and *** biosensing system cultured cardiomyocytes as physiologically relevant models,used interdigitated electrodes to detect the mechanical beating of the cardiomyocytes,and employed MNDA analysis to recognize drug-induced arrhythmia from the cardiomyocyte beating *** best performing MNDA parameter,approximate entropy,enabled the system to recognize the appearance of sertindoleand norepinephrine-induced arrhythmia in the *** MNDA reconstruction in phase space enabled the system to classify the different arrhythmias and quantify the severity of *** new biosensing system utilizing MNDA provides a promising and alternative method for drug-induced arrhythmia recognition and classification in cardiological and pharmaceutical applications.

关键词： arrhythmia cardio analysis

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Locate Then Generate: Bridging Vision and Language with Bounding Box for Scene-Text VQA

arXiv

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arXiv 2023年

作者： Zhu, Yongxin Liu, Zhen Liang, Yukang Li, Xin Liu, Hao Bao, Changcun Xu, Linli School of Data Science University of Science and Technology of China China School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China Tencent YouTu Lab China

In this paper, we propose a novel multi-modal framework for Scene Text Visual Question Answering (STVQA), which requires models to read scene text in images for question answering. Apart from text or visual objects, which could exist independently, scene text naturally links text and visual modalities together by conveying linguistic semantics while being a visual object in an image simultaneously. Different to conventional STVQA models which take the linguistic semantics and visual semantics in scene text as two separate features, in this paper, we propose a paradigm of "Locate Then Generate" (LTG), which explicitly unifies this two semantics with the spatial bounding box as a bridge connecting them. Specifically, at first, LTG locates the region in an image that may contain the answer words with an answer location module (ALM) consisting of a region proposal network and a language refinement network, both of which can transform to each other with one-to-one mapping via the scene text bounding box. Next, given the answer words selected by ALM, LTG generates a readable answer sequence with an answer generation module (AGM) based on a pre-trained language model. As a benefit of the explicit alignment of the visual and linguistic semantics, even without any scene text based pre-training tasks, LTG can boost the absolute accuracy by +6.06% and +6.92% on the TextVQA dataset and the ST-VQA dataset respectively, compared with a non-pre-training baseline. We further demonstrate that LTG effectively unifies visual and text modalities through the spatial bounding box connection, which is underappreciated in previous methods. © 2023, CC BY.

关键词： Semantics

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A Secure Multiparty Quantum Homomorphic Encryption Scheme

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computers, Materials & Continua 2022年第11期73卷 2835-2848页

作者： Jing-Wen Zhang Xiu-Bo Chen Gang Xu Heng-Ji Li Ya-Lan Wang Li-Hua Miao Yi-Xian Yang Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China School of Information Science and Technology North China University of TechnologyBeijing100144China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Sichuan610025China Quantum Technology Lab&Applied Mechanics Group University of MilanMilan20133Italy Department of Computer Science Faculty of Engineering and Physical SciencesUniversity of SurreyGuildfordSurreyGU27XHUnited Kingdom Huawei Technologies Co.Ltd Shenzhen518129China

The significant advantage of the quantum homomorphic encryption scheme is to ensure the perfect security of quantum private *** this paper,a novel secure multiparty quantum homomorphic encryption scheme is proposed,which can complete arbitrary quantum computation on the private data of multiple clients without decryption by an almost dishonest ***,each client obtains a secure encryption key through the measurement device independent quantum key distribution protocol and encrypts the private data by using the encryption operator and ***,with the help of the almost dishonest server,the non-maximally entangled states are preshared between the client and the server to correct errors in the homomorphic evaluation of T gates,so as to realize universal quantum circuit evaluation on encrypted ***,from the perspective of the application scenario of secure multi-party computation,this work is based on the probabilistic quantum homomorphic encryption scheme,allowing multiple parties to delegate the server to perform the secure homomorphic *** operation and the permission to access the data performed by the client and the server are clearly pointed ***,a concrete security analysis shows that the proposed multiparty quantum homomorphic encryption scheme can securely resist outside and inside attacks.

关键词： Quantum homomorphic encryption secure multiparty computation almost dishonest server security

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Multi-Grained Multimodal Interaction Network for Entity Linking

arXiv

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arXiv 2023年

作者： Luo, Pengfei Xu, Tong Wu, Shiwei Zhu, Chen Xu, Linli Chen, Enhong School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Anhui Hefei China School of Data Science University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Anhui Hefei China Career Science Lab BOSS Zhipin School of Management University of Science and Technology of China Beijing China

Multimodal entity linking (MEL) task, which aims at resolving ambiguous mentions to a multimodal knowledge graph, has attracted wide attention in recent years. Though large efforts have been made to explore the complementary effect among multiple modalities, however, they may fail to fully absorb the comprehensive expression of abbreviated textual context and implicit visual indication. Even worse, the inevitable noisy data may cause inconsistency of different modalities during the learning process, which severely degenerates the performance. To address the above issues, in this paper, we propose a novel Multi-GraIned Multimodal InteraCtion Network (MIMIC) framework for solving the MEL task. Specifically, the unified inputs of mentions and entities are first encoded by textual/visual encoders separately, to extract global descriptive features and local detailed features. Then, to derive the similarity matching score for each mention-entity pair, we device three interaction units to comprehensively explore the intra-modal interaction and inter-modal fusion among features of entities and mentions. In particular, three modules, namely the Text-based Global-Local interaction Unit (TGLU), Vision-based DuaL interaction Unit (VDLU) and Cross-Modal Fusion-based interaction Unit (CMFU) are designed to capture and integrate the fine-grained representation lying in abbreviated text and implicit visual cues. Afterwards, we introduce a unit-consistency objective function via contrastive learning to avoid inconsistency and model degradation. Experimental results on three public benchmark datasets demonstrate that our solution outperforms various state-of-the-art baselines, and ablation studies verify the effectiveness of designed modules. © 2023, CC BY-NC-ND.

关键词： Learning systems

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EGODE: an event-attended graph ODE framework for modeling rigid dynamics 24

EGODE: an event-attended graph ODE framework for modeling ri...

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

作者： Jingyang Yuan Gongbo Sun Zhiping Xiao Hang Zhou Xiao Luo Junyu Luo Yusheng Zhao Wei Ju Ming Zhang School of Computer Science State Key Laboratory for Multimedia Information Processing PKU-Anker LLM Lab Peking University University of Wisconsin-Madison University of Washington University of California Davis University of California Los Angeles

ISBN: (纸本)9798331314385

This paper studies the problem of rigid dynamics modeling, which has a wide range of applications in robotics, graphics, and mechanical design. The problem is partly solved by graph neural network (GNN) simulators. However, these approaches cannot effectively handle the relationship between intrinsic continuity and instantaneous changes in rigid dynamics. Moreover, they usually neglect hierarchical structures across mesh nodes and objects in systems. In this paper, we propose a novel approach named Event-attend Graph ODE (EGODE) for effective rigid dynamics modeling. In particular, we describe the rigid system using both mesh node representations and object representations. To model continuous dynamics across hierarchical structures, we use a coupled graph ODE framework for the evolution of both types of representations over a long period. In addition, to capture instantaneous changes during the collision, we introduce an event module, which can effectively estimate the occurrence of the collision and update the states of both mesh node and object representations during evolution. Extensive experiments on a range of benchmark datasets validate the superiority of the proposed EGODE compared to various state-of-the-art baselines. The source code can be found at https://***/yuanjypku/EGODE.

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CoT-Drive: Efficient Motion Forecasting for Autonomous Driving with LLMs and Chain-of-Thought Prompting

arXiv

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arXiv 2025年

作者： Liao, Haicheng Kong, Hanlin Wang, Bonan Wang, Chengyue Ye, Wang He, Zhengbing Xu, Chengzhong Li, Zhenning State Key Laboratory of Internet of Things for Smart City University of Macau China Department of Computer and Information Science University of Macau China Senseable City Lab Massachusetts Institute of Technology CambridgeMA United States

Accurate motion forecasting is crucial for safe autonomous driving (AD). This study proposes CoT-Drive, a novel approach that enhances motion forecasting by leveraging large language models (LLMs) and a chain-of-thought (CoT) prompting method. We introduce a teacher-student knowledge distillation strategy to effectively transfer LLMs’ advanced scene understanding capabilities to lightweight language models (LMs), ensuring that CoT-Drive operates in real-time on edge devices while maintaining comprehensive scene understanding and generalization capabilities. By leveraging CoT prompting techniques for LLMs without additional training, CoT-Drive generates semantic annotations that significantly improve the understanding of complex traffic environments, thereby boosting the accuracy and robustness of predictions. Additionally, we present two new scene description datasets, Highway-Text and Urban-Text, designed for fine-tuning lightweight LMs to generate context-specific semantic annotations. Comprehensive evaluations of five real-world datasets demonstrate that CoT-Drive outperforms existing models, highlighting its effectiveness and efficiency in handling complex traffic scenarios. Overall, this study is the first to consider the practical application of LLMs in this field. It pioneers the training and use of a lightweight LLM surrogate for motion forecasting, setting a new benchmark and showcasing the potential of integrating LLMs into AD systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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