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Digital Twin for mobility: simulation integrated approach for demand matrices calibration with empirical data

Transportation Research Procedia 2025年 86卷 516-523页

作者： Anna Laura Pala Giovanni Felici Vincenza Torrisi Davide Donato Russo Alessio Salvatore Pierfrancesco Leonardi Giuseppe Stecca Matteo Ignaccolo Giuseppe Inturri National Research Council Institute of Systems Analysis and Computer Science ”Antonio Ruberti” Rome Italy DIAG Sapienza Rome Italy Department of Electric Electronic and Computer Engineering Catania Italy f Department of Bioscience and Territory University of Molise Department of Enterprise Engineering University of Rome ”Tor Vergata” Italy Department of Civil Engineering and Architecture Catania Italy

A Digital Twin for Mobility (DTmob) is based on the digital representation of the real-world transport system. A crucial element underlying the development of such systems is an accurate simulation of traffic flows within the road network, which is generally based on transport demand data in the form of Origin-Destination (O-D) matrices. Estimating demand matrices accurately is a complex challenge, as they are hindered by incomplete data, inaccuracies in transport models and temporal-spatial variability. In this paper we present an integrated methodological approach for the calibration of O-D matrices via the implementation of an iterative calibration procedure incorporating a traffic survey dataset, followed by the assignment procedure performed by a macrosimulation model. This study addresses this issue by employing Nielsen’s Single Path Matrix Estimation Method (SPME) jointly with the macrosimulation tool PTV Visum, developing a script in Python to achieve their integration. We tested it on the real-case study of Catania urban road network (Italy). Several scenarios have been analyzed and evaluated through the GEH statistic as KPI, also providing results with Ordinary Least Square (OLS) method as benchmark. Obtained results prove the validity of our integrated framework as the threshold in terms of the difference between estimated and counted flows was satisfied in all performed analyses. Research findings lay the basis for investigate further calibration algorithms and for exploring the use of real-time data in line with DTmob requirements.

关键词： Digital Twin transport modelling traffic simulation OD matrix calibration

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Relip: Reliable in-Band Parallel Communication for Magnetic MIMO Wireless Power Transfer System

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IEEE Transactions on Mobile Computing 2025年

作者： Wang, Xinyu Zhou, Wangqiu Zhou, Hao Jiang, Shenyao Liu, Zhi Wang, Xiaoyan Ji, Yusheng Song, Qi City University of Hong Kong Department of Computer Science Hong Kong University of Science and Technology of China School of Computer Science and Technology Hefei230027 China The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182-8585 Japan Ibaraki University Graduate School of Science and Engineering Ibaraki310-8512 Japan National Institute of Informatics Information Systems Architecture Science Research Division Tokyo101-0003 Japan

In magnetic resonant coupling (MRC) based wireless power transfer (WPT) systems, receiver (RX) feedback communication is promising to enhance the capability and efficiency of the system. Although some studies have explored in-band implementations with low overhead costs, it has not been comprehensively investigated. In this paper, we propose Relip, a Reliable layer-level in-band parallel feedback communication mechanism for MIMO MRC-WPT systems, which addresses the impact of RX-RX couplings (i.e., non-negligible interference from strong couplings and positive effects of relay phenomenon), and provides a theoretical analysis of communication reliability. Technically, we first devise an On-Off based two-phase modulation mechanism to achieve RX identification and dependency detection under relay phenomenon. Then, we utilize observed channel decomposability to collect group-level power transfer channel conditions for eliminating the interference caused by strong RX-RX couplings. Furthermore, we perform RX selection to optimize the trade-off between communication reliability and time overhead. We design and implement the Relip prototype and conduct extensive experiments. The results validate the effectiveness of our mechanism, i.e., Relip can provide ≥99% average decoding accuracy for concurrent feedback communication of 14 devices, achieving an 18.31% improvement compared to the state-of-the-art solution. © 2002-2012 IEEE.

关键词： Inductive power transmission

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MapSAM: adapting segment anything model for automated feature detection in historical maps

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GIscience and Remote Sensing 2025年第1期62卷

作者： Xia, Xue Zhang, Daiwei Song, Wenxuan Huang, Wei Hurni, Lorenz Institute of Cartography and Geoinformation ETH Zurich Zurich Switzerland Computer Science Department ETH Zurich Zurich Switzerland Chair of Data Science in Earth Observation Technical University of Munich Munich Germany

Automated feature detection in historical maps can significantly accelerate the reconstruction of the geospatial past. However, this process is often constrained by the time-consuming task of manually digitizing sufficient high-quality training data. The emergence of visual foundation models, such as the Segment Anything Model (SAM), offers a promising solution due to their remarkable generalization capabilities and rapid adaptation to new data distributions. Despite this, directly applying SAM in a zero-shot manner to historical map segmentation poses significant challenges, including poor recognition of certain geospatial features and a reliance on input prompts, which limits its ability to be fully automated. To address these challenges, we introduce MapSAM, a parameter-efficient fine-tuning strategy that adapts SAM into a prompt-free and versatile solution for various downstream historical map segmentation tasks. Specifically, we employ Weight-Decomposed Low-Rank Adaptation (DoRA) to integrate domain-specific knowledge into the image encoder. Additionally, we develop an automatic prompt generation process, eliminating the need for manual input. We further enhance the positional prompt in SAM, transforming it into a higher-level positional-semantic prompt, and modify the cross-attention mechanism in the mask decoder with masked attention for more effective feature aggregation. The proposed MapSAM framework demonstrates promising performance across three distinct historical map segmentation tasks: railway, vineyard, and building block detection. Experimental results show that it adapts well to various features, even when fine-tuned with extremely limited data (e.g. 10 shots). The code is available at https://***/Xue-Xia/MapSAM. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Historical map segmentation parameter-efficient fine-tuning segment anything model visual foundation models

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TopoMortar: A dataset to evaluate image segmentation methods focused on topology accuracy

arXiv

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arXiv 2025年

作者： Valverde, Juan Miguel Koga, Motoya Otsuka, Nijihiko Dahl, Anders Bjorholm Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark A.I. Virtanen Institute University of Eastern Finland Finland Department of Architecture Faculty of Engineering Sojo University Japan

We present TopoMortar, a brick wall dataset that is the first dataset specifically designed to evaluate topology-focused image segmentation methods, such as topology loss functions. TopoMortar enables to investigate in two ways whether methods incorporate prior topological knowledge. First, by eliminating challenges seen in real-world data, such as small training set, noisy labels, and out-of-distribution test-set images, that, as we show, impact the effectiveness of topology loss functions. Second, by allowing to assess in the same dataset (TopoMortar) topology accuracy across dataset challenges, isolating dataset-related effects from the effect of incorporating prior topological knowledge. In these two experiments, it is deliberately difficult to improve topology accuracy without actually leveraging topology information, thus, permitting to attribute an improvement in topology accuracy to the incorporation of prior topological knowledge. To this end, TopoMortar includes three types of labels (accurate, pseudo-labels, noisy labels), two fixed training sets (large and small), and in-distribution and out-of-distribution test-set images. We compared eight loss functions on TopoMortar, and we found that clDice generally achieved the most topologically accurate segmentations, Skeleton Recall loss performed best particularly with noisy labels, and the relative advantageousness of the other loss functions depended on the experimental setting. Additionally, we show that simple methods, such as data augmentation and self-distillation, can elevate Cross entropy Dice loss to surpass most topology loss functions, and that those simple methods can enhance topology loss functions as well. clDice and Skeleton Recall loss, both skeletonization-based loss functions, were also the fastest to train, making this type of loss function a promising research direction. TopoMortar and our code can be found at https://***/jmlipman/TopoMortar. © 2025, CC BY.

关键词： Musculoskeletal system

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DHAG-DTA: Dynamic Hierarchical Affinity Graph Model for Drug-Target Binding Affinity Prediction

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 697-709页

作者： Cheng Wang Yang Liu Shitao Song Kun Cao Xiaoyan Liu Gaurav Sharma Maozu Guo School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Electrical Engineering Liaoning University of Technology Liaoning China Department of Electrical and Computer Engineering University of Rochester Rochester NY USA School of Electrical and Information Engineering Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

Computational methods for predicting drug-target binding affinity (DTA) are critical for large-scale screening of prospective therapeutic compounds during drug discovery. Deep neural networks (DNNs) have recently shown significant promise for DTA prediction. By leveraging available data for training, DNNs can expand the use of DTA prediction to situations where only sequence information is available for potential drug molecules and their targets, and there is no prior knowledge regarding the molecular geometric conformations. We propose DHAG-DTA, a general dynamic hierarchical affinity graph DNN approach, for DTA prediction using molecular sequence information and already known drug-target interactions. DHAG-DTA introduces a two-level hierarchical graph structure: at the upper level, interactions between drug and target molecules are represented via an affinity graph and at the lower level, embedded molecular graphs represent interactions within the individual molecules. This allows for integration of information from both inter and intra molecular interactions for DTA prediction, which has also been addressed in other recent independent work. The fundamental innovations introduced by DHAG-DTA include: (a) a single overall hierarchical graph that allows better assimilation of information during the learning process compared with loosely-coupled individual graphs, (b) dynamic determination of the affinity graph structure via the introduction of unlabeled edges and a maximum entropy criterion for active edge selection, (c) skip connections in the DNN for fusing intra and inter molecular information, and (d) fusion of both model-based and similarity-based feature embeddings to get robust embeddings of unseen molecules. Experimental results on two common benchmark datasets demonstrate that DHAG-DTA outperforms other existing models on multiple evaluation metrics, achieving state-of-the-art performance.

关键词： Drugs Predictive models Graph neural networks Artificial neural networks Biological system modeling Proteins Atoms Computational modeling Bioinformatics Entropy

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Text-guided diverse-expression diffusion model for molecule generation

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Chinese Physics B 2025年第5期34卷

作者： Weng, Wenchao Jiang, Hanyu Kong, Xiangjie Pau, Giovanni College of Computer Science and Technology Zhejiang University of Technology Hangzhou310014 China Hangzhou Dianzi University ITMO Joint Institute Hangzhou Dianzi University Hangzhou310018 China Faculty of Engineering and Architecture Kore University of Enna Italy

The task of molecule generation guided by specific text descriptions has been proposed to generate molecules that match given text inputs. Mainstream methods typically use simplified molecular input line entry system (SMILES) to represent molecules and rely on diffusion models or autoregressive structures for modeling. However, the one-to-many mapping diversity when using SMILES to represent molecules causes existing methods to require complex model architectures and larger training datasets to improve performance, which affects the efficiency of model training and generation. In this paper, we propose a text-guided diverse-expression diffusion (TGDD) model for molecule generation. TGDD combines both SMILES and self-referencing embedded strings (SELFIES) into a novel diverse-expression molecular representation, enabling precise molecule mapping based on natural language. By leveraging this diverse-expression representation, TGDD simplifies the segmented diffusion generation process, achieving faster training and reduced memory consumption, while also exhibiting stronger alignment with natural language. TGDD outperforms both TGM-LDM and the autoregressive model MolT5-Base on most evaluation metrics. © 2025 Chinese Physical Society and IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.

关键词： AI for science diffusion model molecule generation

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Leveraging Hybrid Classical-Quantum Methods for Efficient Load Rebalancing in HPC 24

Leveraging Hybrid Classical-Quantum Methods for Efficient Lo...

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Proceedings of the SC '24 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis

作者： Justyna Zawalska Minh Chung Katarzyna Rycerz Laura Schulz Martin Schulz Dieter Kranzlmüller AGH University of Krakow Institute of Computer Science Krakow Poland and Academic Computer Center CYFRONET AGH Krakow Poland Leibniz Supercomputing Centre (LRZ) Garching bei München Germany and MNM-Team Ludwig-Maximilians-Universität München (LMU) Germany Leibniz Supercomputing Centre (LRZ) Garching bei München Germany Leibniz Supercomputing Centre (LRZ) Garching bei München Germany and Chair for Computer Architecture and Parallel Systems Technical University of Munich (TUM) Germany

ISBN: (纸本)9798350355543

Load imbalance is a challenge for parallel applications in High Performance Computing (HPC). It is caused by processes having different execution times or load values, leading to idle or wait times at synchronization points, where faster processes must wait for the slowest process to catch up. To mitigate this issue, applications can employ load balancing (LB) strategies, which migrate load between processes to even out load. This is often referred to as the Load Rebalancing Problem (LRP). While many approaches solving the LRP exist, they can only be heuristics and hence further optimization potential exists. In our work, we turn to a novel approach by using hybrid classical-quantum approaches and present two versions of the constrained quadratic model for solving the LRP; the two differ in how they balance the number of qubits required with the types of applied constraints. We compare the quantum-based methods with classical methods using heuristic algorithms Greedy, Karmarkar--Karp, and ProactLB. We evaluate our approaches using imbalance ratio and speedup as metrics, as well as the number of migrated tasks to indicate overhead caused by migrations. Our results show that the quantum-based methods outperform the classic methods. For example, we need only 1/4 of the number of migrated tasks in a realistic use case compared with classical methods, particularly Greedy and KK, to balance the load.

关键词： CQM

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Fairness-Aware Demodulator Allocation in LoRa Multi-Gateway Networks

IEEE Networking Letters

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IEEE Networking Letters 2025年

作者： Guitton, Alexandre Kaneko, Megumi Clermont-Auvergne-INP Universitè Clermont-Auvergne LIMOS CNRS Clermont-Ferrand63000 France National Institute of Informatics Information Systems Architecture Science Research Division Tokyo101-8430 Japan The University of Tokyo Department of Computer Science Tokyo113-8656 Japan

The literature has shown the drastic decrease of the achievable LoRa network throughput, due to the limited number of demodulators that are supported by LoRaWAN gateways. Unlike existing approaches, in this work, we design fairness-aware algorithms under this stringent constraint. By taking the efficient demodulation time ratio as a fairness metric, our algorithms enable to prioritize frames with larger spreading factors, while increasing the total demodulation time thanks to collaboration among gateways. Numerical results demonstrate that our proposed methods largely outperform LoRaWAN baselines, while closely approaching their performance upper bounds. © 2019 IEEE.

关键词： Gateways (computer networks)

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Telemetry for Quantum Systems in HPC Centers

Telemetry for Quantum Systems in HPC Centers

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

作者： Hossam Ahmed Burak Mete Helmut Heller Matthew Tovey Xiaolong Deng Asim Zulfiqar Muhammad Nufail Farooqi Mahmoud Abuzayed Martin Schulz Laura Schulz QCT Department Leibniz Supercomputing Centre of the Bavarian Academy of Science and Humanities (LRZ) Garching b. München Germany Chair for Computer Architecture and Parallel Systems (CAPS) Technical University of Munich (TUM) Garching b. München Germany

ISBN: (数字)9783982633619

Quantum systems have traditionally operated within highly controlled laboratory environments to minimize internal noise and maximize stability. However, when these systems are integrated into High Performance Computing (HPC) environments and setup for continuous operation, they encounter significantly higher levels of external noise and instability coupled with higher demands. This paper introduces a comprehensive framework to counter the challenges of maintaining the stability of operational quantum computers in such noisy HPC settings. We propose a novel system architecture that monitors and manages the environmental conditions of quantum computing systems using a broad set of sensors, enhancing their robustness and reliability. Our architecture is open and can be extended to other setups. It can serve as a blueprint for other sites as it spans quantum technologies and offers broad coverage. Ultimately, this data will be essential to facilitate advancements in machine learning to drive real-time error mitigation.

关键词： computers Quantum system Trapped ions Quantum computing Working environment noise Systems architecture Machine learning Stability analysis Telemetry Monitoring

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AEC Co-design workflow for cross-domain querying and reasoning using Semantic Web Technologies

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Automation in Construction 2025年 176卷

作者： Elshani, Diellza Lombardi, Alessio Hernandez, Daniel Staab, Steffen Fisher, Al Wortmann, Thomas Faculty of Architecture and Urban Planning University of Stuttgart Stuttgart Germany Buro Happold London United Kingdom Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany Electronics and Computer Science University of Southampton University Road Southampton United Kingdom

The architecture, Engineering, and Construction (AEC) industry faces data integration challenges due to fragmented silos and diverse data representations, hindering cross-domain queries and early detection of design constraints. Semantic Web Technologies (SWTs) address data integration challenges. This paper evaluates the impact of SWTs on co-design workflows by comparing them with alternative approaches to assess their effectiveness in supporting interdisciplinary collaboration and design constraint detection. Using Design science Research, a co-design methodology is developed that integrates SWTs with AEC tools for reasoning and federated querying. A component of this methodology is a bidirectional mapping strategy for translating object-oriented data models, demonstrated with the Building Habitat Object Model (BHoM), an AEC interoperability framework. Findings reveal that integrating SWTs enables reasoning and complex queries across federated datasets, improving co-design efficiency. These findings support AEC professionals in advancing co-design and data-driven decision-making, while also informing future research on integrating SWTs into AEC design workflows. © 2025

关键词： Mapping

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