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Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence

作者： Zhiqiang Zhang Yongqiang Jiang Qian Gao Zhipeng Wang Key Laboratory of Computing Power Network and information Security Ministry of Eaucation Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China and Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China and Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China China Telecom Digital Intelligence Techonology Co Jinan China

Existing multimodal recommendation research still faces some challenges, such as not being able to fully mine the implicit relevance information of neighbor nodes, and the unreasonable weight allocation to imbalanced nodes. To address the aforementioned challenges, this paper introduces a new multimodal recommendation model called NS-MAR+. Specifically, the model firstly constructs a neighbor similarity graph convolutional network to capture the implicit relevance information and reasonably assigns the attention weights through the graph attention mechanism. Secondly, the model introduces a modal alignment and fusion mechanism by using a multilayer perceptron (MLP) to map image and text features into a shared space for comparison and fusion. In addition, the model constructs a user co-interaction graph and an item semantic graph based on the original information and performs graph convolution operations to enhance the preference information of users and items, and to better capture the interactions and internal features between users and items. Finally, MLP is employed to aggregate user and item representations and predict personalized recommendation rankings. To validate the experiment's efficacy, this paper compares with several leading multimodal recommendation models on public datasets with a performance improvement of 1% to 3%. The experimental outcomes indicate that the model in this paper has good superiority and accuracy in multimodal recommendation tasks.

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Predicting Response to Patients with Gastric Cancer Via a Dynamic-Aware Model with Longitudinal Liquid Biopsy data

SSRN

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SSRN 2024年

作者： Chen, Zifan Zhao, Jie Li, Yanyan Li, Yilin Liu, Huimin Feng, Xujiao Nan, Xinyu Dong, Bin Shen, Lin Chen, Yang Zhang, Li Center for Data Science Peking University Beijing China Department of Gastrointestinal Oncology Key Laboratory of Carcinogenesis and Translational Research Ministry of Education Peking University Cancer Hospital and Institute Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Guangzhou Medical University Guangzhou China Peking University Beijing China Center for Machine Learning Research Peking University Beijing China Peking University Changsha Institute for Computing and Digital Economy Changsha China

Gastric cancer (GC) presents challenges in predicting treatment responses due to its patient-specific heterogeneity. Recently, liquid biopsies have become recognized as a valuable data modality, offering essential cellular and molecular insights and enabling the capture of time-sensitive information. This study aimed to harness artificial intelligence (AI) technology to analyze longitudinal liquid biopsy data. We collected a dataset from longitudinal liquid biopsies of 91 patients at Peking Cancer Hospital, spanning from July 2019 to April 2022, which included 1,895 tumor-related cellular images and 1,698 tumor marker indices. Subsequently, we introduced the Dynamic-Aware Model (DAM) to predict GC treatment responses. DAM incorporates the dynamic data through AI-engineered components, enabling an in-depth longitudinal analysis. Utilizing three-fold cross-validation, DAM exhibited superior performance over traditional cell-counting-based methods, achieving an AUC of 0.807 in predicting GC treatment responses. In the test set, DAM maintained stable efficacy with an AUC of 0.802. Besides, DAM showed the capability to accurately predict treatment responses from early treatment data. Moreover, DAM's visual analysis of attention mechanisms identified six dynamic focus-area-related visual features and their strong association with treatment response. These findings represent a pioneering effort in applying AI technology for interpreting longitudinal liquid biopsy data and employ visual analytics in GC, offering a promising avenue toward precise response prediction and tailored treatment strategies for patients with ***: This work was supported by the National Natural science Foundation of China (81801778 to Li Zhang., 82203881 to Yang Chen, U22A20327 to Lin Shen, 12090022 to Bin Dong), Beijing Natural science Foundation (7222021 to Yang Chen), Beijing Hospitals Authority Youth Programme (QML20231115 to Yang Chen), Clinical Medicine Plus X-Young Scholars Project of P

关键词： Diseases

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Exploring data Geometry for Continual Learning

arXiv

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arXiv 2023年

作者： Gao, Zhi Xu, Chen Li, Feng Jia, Yunde Harandi, Mehrtash Wu, Yuwei Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Department of Electrical and Computer Systems Eng. Monash University Data61 Australia

Continual learning aims to efficiently learn from a non-stationary stream of data while avoiding forgetting the knowledge of old data. In many practical applications, data complies with non-Euclidean geometry. As such, the commonly used Euclidean space cannot gracefully capture non-Euclidean geometric structures of data, leading to inferior results. In this paper, we study continual learning from a novel perspective by exploring data geometry for the non-stationary stream of data. Our method dynamically expands the geometry of the underlying space to match growing geometric structures induced by new data, and prevents forgetting by keeping geometric structures of old data into account. In doing so, making use of the mixed curvature space, we propose an incremental search scheme, through which the growing geometric structures are encoded. Then, we introduce an angular-regularization loss and a neighbor-robustness loss to train the model, capable of penalizing the change of global geometric structures and local geometric structures. Experiments show that our method achieves better performance than baseline methods designed in Euclidean space. Copyright © 2023, The Authors. All rights reserved.

关键词： Geometry

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Deconfined classical criticality in the anisotropic quantum spin-12 XY model on the square lattice

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Physical Review B 2025年第10期111卷 104402-104402页

作者： Christopher Mudry Ömer M. Aksoy Claudio Chamon Akira Furusaki Condensed Matter Theory Group PSI Center for Scientific Computing Theory and Data 5232 Villigen PSI Switzerland Institut de Physique EPF Lausanne CH-1015 Lausanne Switzerland Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics Boston University Boston Massachusetts 02215 USA RIKEN Center for Emergent Matter Science Wako Saitama 351-0198 Japan Condensed Matter Theory Laboratory RIKEN Wako Saitama 351-0198 Japan

The anisotropic quantum spin-12 XY model on a linear chain was solved by Lieb, Schultz, and Mattis [Ann. Phys. 16, 407 (1961)] and shown to display a continuous quantum phase transition at the O(2) symmetric point separating two gapped phases with competing Ising long-range order. For the square lattice, the following is known. The two competing Ising ordered phases extend to finite temperatures, up to a boundary where a transition to the paramagnetic phase occurs, and meet at the O(2) symmetric critical line along the temperature axis that ends at a tricritical point at the Berezinskii-Kosterlitz-Thouless transition temperature where the two competing phases meet the paramagnetic phase. We show that the first-order zero-temperature (quantum) phase transition that separates the competing phases as a function of the anisotropy parameter is smoothed by thermal fluctuations into deconfined classical criticality.

关键词： Paramagnetism

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High-Fidelity Coherent-One-Way QKD Simulation Framework for 6G Networks: Bridging Theory and Reality

arXiv

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arXiv 2025年

作者： Brazaola-Vicario, Aitor Kouvakis, Vasileios Trevlakis, Stylianos E. Ruiz, Alejandra Boulogeorgos, Alexandros-Apostolos A. Tsiftsis, Theodoros Niyato, Dusit Spain The Department of Research and Development InnoCube P.C. 79 17is Noemvriou Str. Thessaloniki55534 Greece The Department of Electrical and Computer Engineering University of Western Macedonia ZEP Area Kozani50100 Greece The Department of Informatics & Telecommunications University of Thessaly Lamia35100 Greece The College of Computing and Data Science Nanyang Technological University 639798 Singapore

Quantum key distribution (QKD) has been emerged as a promising solution for guaranteeing information-theoretic security. Inspired by this, a great amount of research effort has been recently put on designing and testing QKD systems as well as articulating preliminary application scenarios. However, due to the considerable high-cost of QKD equipment, a lack of QKD communication system design tools, wide deployment of such systems and networks is challenging. Motivated by this, this paper introduces a QKD communication system design tool. First we articulate key operation elements of the QKD, and explain the feasibility and applicability of coherent-one-way (COW) QKD solutions. Next, we focus on documenting the corresponding simulation framework as well as defining the key performance metrics, i.e., quantum bit error rate (QBER), and secrecy key rate. To verify the accuracy of the simulation framework, we design and deploy a real-world QKD setup. We perform extensive experiments for three deployments of diverse transmission distance in the presence or absence of a QKD eavesdropper. The results reveal an acceptable match between simulations and experiments rendering the simulation framework a suitable tool for QKD communication system design. © 2025, CC BY.

关键词： Bit error rate

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data Assimilation in the Latent Space of a Convolutional Autoencoder 21st

Data Assimilation in the Latent Space of a Convolutional Aut...

引用

21st International Conference on Computational science, ICCS 2021

作者： Amendola, Maddalena Arcucci, Rossella Mottet, Laetitia Casas, César Quilodrán Fan, Shiwei Pain, Christopher Linden, Paul Guo, Yi-Ke Data Science Institute Department of Computing Imperial College London London United Kingdom Department of Earth Science and Engineering Imperial College London London United Kingdom Department of Chemistry University of Cambridge Cambridge United Kingdom Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Department of Computer Science Hong Kong Baptist University Kowloon Hong Kong

ISBN: (纸本)9783030779764

data Assimilation (DA) is a Bayesian inference that combines the state of a dynamical system with real data collected by instruments at a given time. The goal of DA is to improve the accuracy of the dynamic system making its result as real as possible. One of the most popular technique for DA is the Kalman Filter (KF). When the dynamic system refers to a real world application, the representation of the state of a physical system usually leads to a big data problem. For these problems, KF results computationally too expensive and mandates to use of reduced order modeling techniques. In this paper we proposed a new methodology we called Latent Assimilation (LA). It consists in performing the KF in the latent space obtained by an Autoencoder with non-linear encoder functions and non-linear decoder functions. In the latent space, the dynamic system is represented by a surrogate model built by a Recurrent Neural Network. In particular, an Long Short Term Memory (LSTM) network is used to train a function which emulates the dynamic system in the latent space. The data from the dynamic model and the real data coming from the instruments are both processed through the Autoencoder. We apply the methodology to a real test case and we show that the LA has a good performance both in accuracy and in efficiency. © 2021, Springer Nature Switzerland AG.

关键词： Long short-term memory

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Precompactness in bivariate metric semigroup-valued bounded variation spaces

arXiv

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arXiv 2025年

作者： Niu, Yinglian Xu, Jingshi School of Mathematics and Computing Science Guilin University of Electronic Technology 1 Jinjilu Guangxi Guilin541004 China Center for Applied Mathematics of Guangxi Guilin University of Electronic Technology 1 Jinjilu Guangxi Guilin541004 China Guangxi Colleges Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology 1 Jinjilu Guangxi Guilin541004 China

In this paper, we show that if a set in bivariate metric semigroups-valued bounded variation spaces is pointwise totally bounded and joint equivariated then it is precompact. These spaces include bounded Jordan variation spaces, bounded Wiener variation spaces, bounded Waterman variation spaces, bounded Riesz variation spaces and bounded Korenblum variation spaces. To do so, we introduce the concept of equimetric *** Codes 26A45, 54E50 Copyright © 2025, The Authors. All rights reserved.

关键词： Vector spaces

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X-ray Classification of Tuberculosis Based on Convolutional Networks

X-ray Classification of Tuberculosis Based on Convolutional ...

引用

2021 IEEE International Conference on Artificial Intelligence and Industrial Design, AIID 2021

作者： Cao, Kai Zhang, Jingyi Huang, Mengge Deng, Tao Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University Gansu Lanzhou730000 China Key Laboratory of Streaming Data Computing Technologies and Application School of Mathematics and Computer Science Northwest Minzu University Gansu Lanzhou730000 China

ISBN: (纸本)9780738110837

Tuberculosis is a chronic infectious disease caused by Mycobacterium tuberculosis, which can invade many organs, and pulmonary tuberculosis is the most common infection. It is the key to treat tuberculosis to detect and diagnose the disease in the early stage. The existing computer-aided detection system has made preliminary progress in the diagnosis of pulmonary tuberculosis based on chest X-ray, but there is still a lack of further research on the classification of image signs of tuberculosis. In recent years, with the in-depth research and development in the field of deep learning, convolutional networks have emerged. Convolutional networks have achieved the best current results in image recognition, image classification, image segmentation, and other fields. Therefore, this paper applies the convolutional network to tuberculosis CT images and uses different convolutional network models to study the classification of tuberculosis CT images. Experiments show that the DenseNet121 model has higher performance than VGGNet16, VGGNet19, and ResNet152 models. As a result of classification, the accuracy rate is over 90%. © 2021 IEEE.

关键词： Image classification

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Design Paradigms for Workplace-Integrated Learning in the Smart Factory

Design Paradigms for Workplace-Integrated Learning in the Sm...

引用

International Conference on Emerging eLearning Technologies and Applications (ICETA)

作者： Bernhard Göschlberger Christian Thomay Fabian Dopler Research Studio Data Science Research Studios Austria FG Research Studio Pervasive Computing Applications Research Studios Austria FG

ISBN: (纸本)9781665421034

The vision of smart factories as a modern self-organizing production plant redefines the role of production workers. Knowledge, know-how, experience and competencies of production workers become ever more important, making knowledge management and knowledge transfer key objectives for production companies. In this paper we present five different use cases from five different companies, motivating a set of design paradigms for the creation of technical systems for workplace-integrated learning in the smart factory. These design paradigms seek to generalize the solution design for each of the five use cases. We find them to be in line with literature and previous research findings, and valuable guidelines for the design and implementation of said systems.

关键词： Solution design Electronic learning Production Companies Knowledge transfer Smart manufacturing Guidelines

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Streamlining Eye-Tracking and Observational data for Field Study Visual Analysis 25

Streamlining Eye-Tracking and Observational Data for Field S...

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Proceedings of the 2025 Symposium on Eye Tracking Research and Applications

作者： Yidan Zhang Nethara Athukorala Ziying Liang Yidan Qiao Simran - Yu Xuan Yio Lee Lawrence Benjamin Tag Mor Vered Michael Wybrow Sarah Goodwin Department of Human-Centred Computing Monash University Melbourne Australia Monash University Melbourne Victoria Australia Faculty of Information Technology Monash University Melbourne Victoria Australia Monash University Melboure Victoria Australia Faculty of Information Technology Monash University Melboure Victoria Australia Monash Business School Monash University Melbourne Australia School of Computer Science and Engineering University of New South Wales Sydney New South Wales Australia Dept of Data Science & AI Monash University Clayton VIC Australia Department of Human-Centred Computing Monash University Melbourne VIC Australia Monash University Melbourne Australia

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