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Towards next-generation diagnostic pathology:AIempowered label-free multiphoton microscopy

Light(science & Applications) 2024年第12期13卷 2887-2911页

作者： Shu Wang Junlin Pan Xiao Zhang Yueying Li Wenxi Liu Ruolan Lin Xingfu Wang Deyong Kang Zhijun Li Feng Huang Liangyi Chen Jianxin Chen School of Mechanical Engineering and Automation Fuzhou UniversityFuzhou 350108China Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education Fujian Provincial Key Laboratory of Photonics TechnologyFujian Normal UniversityFuzhou 350007China College of Computer and Data Science Fuzhou UniversityFuzhou 350108China Department of Radiology Fujian Medical University Union HospitalFuzhou 350001China Department of Pathology The First Affiliated Hospital of Fujian Medical UniversityFuzhou 350005China Department of Pathology Fujian Medical University Union HospitalFuzhou 350001China New Cornerstone Laboratory State Key Laboratory of Membrane BiologyBeijing Key Laboratory of Cardiometabolic Molecular MedicineInstitute of Molecular MedicineNational Biomedical Imaging CenterSchool of Future TechnologyPeking UniversityBeijing 100091China

Diagnostic pathology,historically dependent on visual scrutiny by experts,is essential for disease *** digital pathology and developments in computer vision technology have led to the application of artificialintelligence(AI)in this *** these advancements,the variability in pathologists’subjective interpretations ofdiagnostic criteria can lead to inconsistent *** meet the need for precision in cancer therapies,there is anincreasing demand for accurate pathological ***,traditional diagnostic pathology is evolvingtowards“next-generation diagnostic pathology”,prioritizing on the development of a multi-dimensional,intelligentdiagnostic *** nonlinear optical effects arising from the interaction of light with biological tissues,multiphoton microscopy(MPM)enables high-resolution label-free imaging of multiple intrinsic components acrossvarious human pathological ***-empowered MPM further improves the accuracy and efficiency of diagnosis,holding promise for providing auxiliary pathology diagnostic methods based on multiphoton diagnostic *** review,we systematically outline the applications of MPM in pathological diagnosis across various human diseases,and summarize common multiphoton diagnostic ***,we examine the significant role of AI inenhancing multiphoton pathological diagnosis,including aspects such as image preprocessing,refined differentialdiagnosis,and the prognostication of *** also discuss the challenges and perspectives faced by theintegration of MPM and AI,encompassing equipment,datasets,analytical models,and integration into the existingclinical ***,the review explores the synergy between AI and label-free MPM to forge novel diagnosticframeworks,aiming to accelerate the adoption and implementation of intelligent multiphoton pathology systems inclinical settings.

关键词： multiphoton refined diagnosis

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MOL-Mamba: Enhancing Molecular Representation with Structural & Electronic Insights 39

MOL-Mamba: Enhancing Molecular Representation with Structura...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Hu, Jingjing Guo, Dan Si, Zhan Liu, Deguang Diao, Yunfeng Zhang, Jing Zhou, Jinxing Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China Department of Chemistry Centre for Atomic Engineering of Advanced Materials Anhui University China Department of Applied Chemistry University of Science and Technology of China China

ISBN: (纸本)157735897X

Molecular representation learning plays a crucial role in various downstream tasks, such as molecular property prediction and drug design. To accurately represent molecules, Graph Neural Networks (GNNs) and Graph Transformers (GTs) have shown potential in the realm of self-supervised pretraining. However, existing approaches often overlook the relationship between molecular structure and electronic information, as well as the internal semantic reasoning within molecules. This omission of fundamental chemical knowledge in graph semantics leads to incomplete molecular representations, missing the integration of structural and electronic data. To address these issues, we introduce MOL-Mamba, a framework that enhances molecular representation by combining structural and electronic insights. MOL-Mamba consists of an Atom & Fragment Mamba-Graph (MG) for hierarchical structural reasoning and a Mamba-Transformer (MT) fuser for integrating molecular structure and electronic correlation learning. Additionally, we propose a Structural Distribution Collaborative Training and E-semantic Fusion Training framework to further enhance molecular representation learning. Extensive experiments demonstrate that MOL-Mamba outperforms state-of-the-art baselines across eleven chemical-biological molecular datasets. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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A Tamper-Proof Smart Contract Metamodel for Blockchain to Optimise Computational Latency 3rd

A Tamper-Proof Smart Contract Metamodel for Blockchain to Op...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Sengupta, Ratul Srivastava, Ruchika Mishra, Sushruta Abualigah, Laith Kalinga Institute of Industrial Technology Deemed to be University Bhubaneswar India Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al-Albayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Gelugor11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

ISBN: (纸本)9789819726707

In recent years, blockchain technology has garnered considerable interest on account of its peer-to-peer, distributed consensus, and anonymity functionalities. A smart contract functions as a verifiable, self-executing, and self-validating computing layer. This study seeks to identify the key concepts and associated future research goals pertaining to smart contracts based on the blockchain. Smart contracts enable the implementation of programmable assets, including money, and the automation of business logic processes that were previously executed manually. In response to this disclosure, a comprehensive examination of smart contracts employing blockchain technology is undertaken. In this article, we propose an innovative and practical metamodel for ensuring the integrity of protocols within smart contracts facilitated by blockchain technology. This article begins with an explanation of smart contracts’ operational models, functionalities, and applications. Analysis of the results reveals that the utilisation of the model alleviates the workload associated with numerous measurements. It was determined that the mean response latency, computation time, and overhead were each 1.47, 1.19, and 3.04 s. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart contract

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A TDOA localization method for complex environment localization 2

A TDOA localization method for complex environment localizat...

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2nd International Conference on Big Data Mining and Information Processes, BDMIP 2021

作者： Ma, Bole Tong, Chao Zou, Lipeng Tian, Ming School of Computer Science and Engineering Beihang University Beijing China Information Technology Innovation Service Center of Guizhou Province China Science and Technology Information Center of Guizhou Province China

Time Difference of Arrival (TDOA) algorithm is a widely used wireless network-based localization algorithm. However, with the increasing complexity of real-life localization environments, the disturbances of noise, perturbations and multi-path effect have limited the performance of the classical TDOA algorithm. In view of these, we design an Interaction-derived Sequence Selection (IDSS) method and an Interaction-derived Sequence Adjustment (IDSA) method to reduce localization error caused by Non Line of Sight (NLOS) environments. Furthermore, an Interacting Multiple Model (IMM) framework is proposed to deal with the localization problem in complex mixed environments. Experimental results demonstrate that the IDSS and IDSA methods can enhance TDOA localization performance in NLOS environments, and the IMM framework can maintain strong adaptability in complex mixed environments. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Time difference of arrival

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InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling

arXiv

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arXiv 2024年

作者： Miao, Yuchun Zhang, Sen Ding, Liang Bao, Rong Zhang, Lefei Tao, Dacheng National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China The University of Sydney Australia Fudan University China Nanyang Technological University Singapore

Despite the success of reinforcement learning from human feedback (RLHF) in aligning language models with human values, reward hacking, also termed reward overoptimization, remains a critical challenge. This issue primarily arises from reward misgeneralization, where reward models (RMs) compute reward using spurious features that are irrelevant to human preferences. In this work, we tackle this problem from an information-theoretic perspective and propose a framework for reward modeling, namely InfoRM, by introducing a variational information bottleneck objective to filter out irrelevant information. Notably, we further identify a correlation between overoptimization and outliers in the IB latent space of InfoRM, establishing it as a promising tool for detecting reward overoptimization. Inspired by this finding, we propose the Cluster Separation Index (CSI), which quantifies deviations in the IB latent space, as an indicator of reward overoptimization to facilitate the development of online mitigation strategies. Extensive experiments on a wide range of settings and RM scales (70M, 440M, 1.4B, and 7B) demonstrate the effectiveness of InfoRM. Further analyses reveal that InfoRM’s overoptimization detection mechanism is not only effective but also robust across a broad range of datasets, signifying a notable advancement in the field of RLHF. Code is available at: https://***/miaoyuchun/InfoRM. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Hierarchical Reverse Games for the Resource Ecosystem in Cloud-Edge Computing

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IEEE Transactions on Consumer Electronics 2024年

作者： Pei, Manlin Wang, Chenyu Wang, Xia Zheng, Zhigao Huang, Jianhui Wang, Shengling Guo, Yufei School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Computer Science Georgia State University United States Beijing University of Technology School of Statistics and Data Science Faculty of Science Beijing China Institute of Artificial Intelligence School of Computer Science National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Hubei Luojia Laboratory Wuhan University Wuhan China Chinese Academy of Sciences Institute of Computing Technology Beijing China Beijing Normal University School of Artificial Intelligence Beijing China George Washington University Department of Statistics United States

Edge computing has emerged as a killer technology for a hyper-connected world due to its distributed architecture and customer-proximity property. Combined edge nodes with the cloud data center, a cloud-edge computing paradigm is formed, whose resource ecosystem calls for competitive pricing and optimal resource allocation. However, it is not feasible to realize global information-based optimization since some information is kept private to their owners. To solve this challenge, this paper develops the user-independent hierarchical reverse game and the user-assisted hierarchical reverse game to depict the relationship among the resource provider, the resource tenant, and the resource user. The aim is to seek the optimal strategies for players without revealing any private information. The common trait of both games is to force the players to report the optimal strategies based on their actual private information. This salient trait can produce privacy-preservation optimization since there is no requirement for the players to release their individual information directly, and the optimal strategies of other players can be deduced naturally. The difference between the user-independent hierarchical reverse game and the user-assisted one is that the game rules are stipulated entirely by the provider and the tenants (edge nodes) in the former, while some game rules can be customized according to the advice of users in the latter. This leads the user-independent hierarchical reverse game is more concise and easy to guarantee the incentive compatibility constraint, while the user-assisted hierarchical reverse game can attract more users since the game rules can adapt to their economic status, requirements, and preferences. © 1975-2011 IEEE.

关键词： Cloud platforms

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Rlsnake: A hybrid reinforcement learning approach for road detection 24

Rlsnake: A hybrid reinforcement learning approach for road d...

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2021 24th ISPRS Congress Commission III: Imaging Today, Foreseeing Tomorrow

作者： Botteghi, N. Sirmacek, B. Ünsalan, C. Robotics and Mechatronics Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Smart Cities School of Creative Technology Saxion University of Applied Sciences Netherlands Electrical and Electronics Engineering Faculty of Engineering Marmara University Turkey

Road network detection from very high resolution satellite and aerial images is highly important for diverse domains. Although an expert can label road pixels in a given image, this operation is prone to error and quite time consuming remembering that road maps must be updated regularly. Therefore, various computer vision based automated algorithms have been proposed in the last two decades. Nevertheless, due to the diversity of scenes, the field is still open for robust methods which might detect roads on different resolution images of different type of environments. In this study, we picked an earlier proposed road detection method which works based on traditional computer vision and probability theory algorithms. We improved it by further steps using reinforcement learning theory. With the help of the novel hybrid technique (traditional computer vision method combined with reinforcement learning based artificial intelligence), we achieved a solution that we call RLSnake. This new method can learn new image scenes and resolutions rapidly and can work reliably. We believe that the proposed RLSnake will be a significant step in the remote sensing field in order to develop solutions which might increase performance by combining the power of traditional and new techniques. © 2021 International Archives of the Photogrammetry, Remote Sensing and Spatial Information sciences - ISPRS Archives. All rights reserved.

关键词： Reinforcement learning

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UniGenX: Unified Generation of Sequence and Structure with Autoregressive Diffusion

arXiv

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arXiv 2025年

作者： Zhang, Gongbo Li, Yanting Luo, Renqian Hu, Pipi Zhao, Zeru Li, Lingbo Liu, Guoqing Wang, Zun Bi, Ran Gao, Kaiyuan Guo, Liya Xie, Yu Liu, Chang Zhang, Jia Xie, Tian Pinsler, Robert Zeni, Claudio Lu, Ziheng Xia, Yingce Segler, Marwin Riechert, Maik Yuan, Li Chen, Lei Liu, Haiguang Qin, Tao Microsoft Research AI for Science United States School of Electronic and Computer Engineering Peking University China DSA The Hong Kong University of Science and Technology Guangzhou China School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Department of Automation Tsinghua University China School of Computer Science and Technology Huazhong University of Science and Technology China Yau Mathematical Sciences Center Department of Mathematical Sciences Tsinghua University China

Unified generation of sequence and structure for scientific data (e.g., materials, molecules, proteins) is a critical task. Existing approaches primarily rely on either autoregressive sequence models or diffusion models, each offering distinct advantages and facing notable limitations. Autoregressive models, such as GPT, Llama, and Phi-4, have demonstrated remarkable success in natural language generation and have been extended to multimodal tasks (e.g., image, video, and audio) using advanced encoders like VQ-VAE to represent complex modalities as discrete sequences. However, their direct application to scientific domains is challenging due to the high precision requirements and the diverse nature of scientific data. On the other hand, diffusion models excel at generating high-dimensional scientific data, such as protein, molecule, and material structures, with remarkable accuracy. Yet, their inability to effectively model sequences limits their potential as general-purpose multimodal foundation models. To address these challenges, we propose UniGenX, a unified framework that combines autoregressive next-token prediction with conditional diffusion models. This integration leverages the strengths of autoregressive models to ease the training of conditional diffusion models, while diffusion-based generative heads enhance the precision of autoregressive predictions. We validate the effectiveness of UniGenX on material and small molecule generation tasks, achieving a significant leap in state-of-the-art performance for material crystal structure prediction and establishing new state-of-the-art results for small molecule structure prediction, de novo design, and conditional generation. Notably, UniGenX demonstrates significant improvements, especially in handling long sequences for complex structures, showcasing its efficacy as a versatile tool for scientific data generation. © 2025, CC BY.

关键词： Prediction models

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Resource Allocation Driven by Large Models in Future Semantic-Aware Networks

arXiv

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arXiv 2025年

作者： Zhang, Haijun Ni, Jiaxin Wu, Zijun Liu, Xiangnan Leung, V.C.M. Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing10083 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden College of Computer Science and Software Engineering Shenzhen University Shenzhen51806 China VancouverBCV6T 1Z4 Canada

Large model has emerged as a key enabler for the popularity of future networked intelligent applications. However, the surge of data traffic brought by intelligent applications puts pressure on the resource utilization and energy consumption of the future networks. With efficient content understanding capabilities, semantic communication holds significant potential for reducing data transmission in intelligent applications. In this article, resource allocation driven by large models in semantic-aware networks is investigated. Specifically, a semantic-aware communication network architecture based on scene graph models and multimodal pre-trained models is designed to achieve efficient data transmission. On the basis of the proposed network architecture, an intelligent resource allocation scheme in semantic-aware network is proposed to further enhance resource utilization efficiency. In the resource allocation scheme, the semantic transmission quality is adopted as an evaluation metric and the impact of wireless channel fading on semantic transmission is analyzed. To maximize the semantic transmission quality for multiple users, a diffusion model-based decision-making scheme is designed to address the power allocation problem in semantic-aware networks. Simulation results demonstrate that the proposed large-model-driven network architecture and resource allocation scheme achieve high-quality semantic transmission. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Web

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Effective Synergies at Technical Universities to Actively Promote STEM at K-12 schools

Effective Synergies at Technical Universities to Actively Pr...

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IEEE Education engineering (EDUCON)

作者： Jorge Torres Gómez Nicolai Spicher Jan Haase School of Electrical Engineering and Computer Science TU Berlin Berlin Germany University Medical Center Göttingen Germany NORDAKADEMIE gAG Hochschule der Wirtschaft Elmshorn Germany

ISBN: (数字)9798350394023

ISBN: (纸本)9798350394030

Promoting science, technology, engineering and mathematics (STEM) education at K-12 level motivates pupils to pursue technical careers at universities, which is of high importance due to skilled labor shortage and other factors. In particular, in Germany the population with a tertiary education level is less than the average in the European Union (35–41 %. Motivating the study of technical careers, this paper features a model for promoting STEM at the K-12 education level by developing joint teaching platforms with students at technical universities. The model is derived from hands-on experience at TU Berlin targeting a climate change topic: As part of student semester projects, Bachelor students integrated a humidity sensor into a drone and conducted atmospheric measurements. The resulting hard and software components provided by the students allowed the preparation of a lesson plan to promote STEM at the K-12 level. In this way, we synergically connect the means from the university with hands-on activities as means to promote STEM topics with K-12 students. The resulting benefits are manifold; students at the university develop technical skills and become part of a social intention to promote STEM topics on the one hand. On the second hand, pre-university students might have first-hand experiences with hardware prototypes and their applicability to relevant topics like climate change surveillance. The developed model may illustrate straightforward means for educators to design lesson plans, including actual practices at university and pre-university levels.

关键词： Manifolds Climate change engineering profession Surveillance Sociology Prototypes Software

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