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IEEE Workshop on Applications of computer Vision (WACV)

作者： Shuhan Xiao Lukas Klein Jens Petersen Philipp Vollmuth Paul F. Jaeger Klaus H. Maier-Hein German Cancer Research Center (DKFZ) Heidelberg Division of Medical Image Computing Germany Faculty of Mathematics and Computer Science Heidelberg University Germany DKFZ Heidelberg Interactive Machine Learning Group Germany Institute for Machine Learning ETH Zürich Switzerland DKFZ Heidelberg Helmholtz Imaging Germany Division for Computational Radiology Clinical AI (CCIBonn.ai) Clinic for Neuroradiology University Hospital Bonn Germany Medical Faculty Bonn University of Bonn Germany Department of Radiation Oncology Pattern Analysis and Learning Group Heidelberg University Hospital Germany

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Identifying predictive covariates, which forecast individual treatment effectiveness, is crucial for decision-making across different disciplines such as personalized medicine. These covariates, referred to as biomarkers, are extracted from pretreatment data, often within randomized controlled trials, and should be distinguished from prognostic biomarkers, which are independent of treatment assignment. Our study focuses on discovering predictive imaging biomarkers, specific image features, by leveraging pretreatment images to uncover new causal relationships. Unlike laborintensive approaches relying on handcrafted features prone to bias, we present a novel task of directly learning predictive features from images. We propose an evaluation protocol to assess a model's ability to identify predictive imaging biomarkers and differentiate them from purely prognostic ones by employing statistical testing and a comprehensive analysis of image feature attribution. We explore the suitability of deep learning models originally developed for estimating the conditional average treatment effect (CATE) for this task, which have been assessed primarily for their precision of CATE estimation while overlooking the evaluation of imaging biomarker discovery. Our proof-of-concept analysis demonstrates the feasibility and potential of our approach in discovering and validating predictive imaging biomarkers from synthetic outcomes and real-world image datasets. Our code is available at https://***/MIC-DKFZ/predictive_image_biomarker_analysis.

关键词： Analytical models Statistical analysis Biological system modeling Precision medicine Decision making Imaging Estimation Biomarkers Predictive models Feature extraction

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MatterChat: A Multi-Modal LLM for Material science

arXiv

引用

arXiv 2025年

作者： Tang, Yingheng Xu, Wenbin Cao, Jie Ma, Jianzhu Gao, Weilu Farrell, Steve Erichson, Benjamin Mahoney, Michael W. Nonaka, Andy Yao, Zhi Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA United States NSF National AI Institute for Student-AI Teaming University of Colorado at Boulder Boulder United States Institute for AI Industry Research Tsinghua University Beijing China Department of Electrical and Computer Engineering The University of Utah Salt Lake CityUT United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA United States International Computer Science Institute BerkeleyCA United States Department of Statistics University of California at Berkeley BerkeleyCA United States

Understanding and predicting the properties of inorganic materials is crucial for accelerating advancements in materials science and driving applications in energy, electronics, and beyond. Integrating material structure data with language-based information through multi-modal large language models (LLMs) offers great potential to support these efforts by enhancing human–AI interaction. However, a key challenge lies in integrating atomic structures at full resolution into LLMs. In this work, we introduce MatterChat, a versatile structure-aware multi-modal LLM that unifies material structural data and textual inputs into a single cohesive model. MatterChat employs a bridging module to effectively align a pretrained machine learning interatomic potential with a pretrained LLM, reducing training costs and enhancing flexibility. Our results demonstrate that MatterChat significantly improves performance in material property prediction and human-AI interaction, surpassing general-purpose LLMs such as GPT-4. We also demonstrate its usefulness in applications such as more advanced scientific reasoning and step-by-step material synthesis. © 2025, CC BY.

关键词： Materials properties

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computing TIME-DELAY AND ITS EFFECTS ON REAL-TIME CONTROL-SYSTEMS

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 1995年第2期3卷 218-224页

作者： SHIN, KG CUI, XZ Real-Time Computing Laboratory Division of Comuuter Science and Engineering. Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA

The reliability of a real-time digital control computer depends not only on the reliability of the hardware and software used, but also on the time delay in computing the control output, because of the negative effects of computing time delay on control system performance. For a given fixed sampling interval, the effects of computing time delay are classified into the delay and loss problems, The delay problem, occurs when the computing time delay is nonzero but smaller than the sampling interval, while the loss problem occurs when the computing time delay is greater than, or equal to, the sampling interval, i,e., loss of the control output, These two problems are analyzed as a means of evaluating real-time control systems, First, a generic analysis of the effects of computing time delay is presented along with necessary conditions for system stability, Then, we present both qualitative and quantitative analyses of the computing time delay effects on a robot control system, deriving upper bounds of the computing time delay with respect to system stability and system performance.

关键词： Delay effects Real time systems Control systems Sampling methods System performance Stability analysis Digital control Hardware Software performance Performance analysis

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nnLandmark: A Self-Configuring Method for 3D Medical Landmark Detection

arXiv

引用

arXiv 2025年

作者： Ertl, Alexandra Xiao, Shuhan Denner, Stefan Peretzke, Robin Zimmerer, David Neher, Peter Isensee, Fabian Maier-Hein, Klaus H. Heidelberg Division of Medical Image Computing Heidelberg Germany Medical Faculty Heidelberg Heidelberg University Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany DKFZ core center Heidelberg Germany Helmholtz Imaging DKFZ Heidelberg Germany NCT Heidelberg DKFZ The University Medical Center Heidelberg Heidelberg Germany Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany HIDSS4Health Heidelberg Germany

Landmark detection plays a crucial role in medical imaging tasks that rely on precise spatial localization, including specific applications in diagnosis, treatment planning, image registration, and surgical navigation. However, manual annotation is labor-intensive and requires expert knowledge. While deep learning shows promise in automating this task, progress is hindered by limited public datasets, inconsistent benchmarks, and non-standardized baselines, restricting reproducibility, fair comparisons, and model generalizability. This work introduces nnLandmark, a self-configuring deep learning framework for 3D medical landmark detection, adapting nnU-Net to perform heatmap-based regression. By leveraging nnU-Net’s automated configuration, nnLandmark eliminates the need for manual parameter tuning, offering out-of-the-box usability. It achieves state-of-the-art accuracy across two public datasets, with a mean radial error (MRE) of 1.5 mm on the Mandibular Molar Landmark (MML) dental CT dataset and 1.2 mm for anatomical fiducials on a brain MRI dataset (AFIDs), where nnLandmark aligns with the inter-rater variability of 1.5 mm. With its strong generalization, reproducibility, and ease of deployment, nnLandmark establishes a reliable baseline for 3D landmark detection, supporting research in anatomical localization and clinical workflows that depend on precise landmark identification. The code will be available soon. © 2025, CC BY.

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Advantages of two quantum programming platforms in quantum computing and quantum chemistry

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Journal of cheminformatics 2025年第1期17卷 77页

作者： Pei-Hua Wang Wei-Yeh Wu Che-Yu Lee Jia-Cheng Hong Yufeng Jane Tseng Undergraduate Program in Intelligent Computing and Big Data Chung Yuan Christian University No. 200 Zhongbei Road Taoyuan 320314 Taiwan. Quantum Information Center Chung Yuan Christian University No. 200 Zhongbei Road Taoyuan 320314 Taiwan. Department of Computer Science and Information Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. Department of Computer Science and Information Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. yjtseng@csie.ntu.edu.tw. Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. yjtseng@csie.ntu.edu.tw. Physics Division National Center for Theoretical Sciences No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. yjtseng@csie.ntu.edu.tw. Center for Quantum Science and Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. yjtseng@csie.ntu.edu.tw.

Quantum computing is at the forefront of technological advancement and has the potential to revolutionize various fields, including quantum chemistry. Choosing an appropriate quantum programming language becomes critical as quantum education and research increase. In this paper, we comprehensively compare two leading quantum programming languages, Qiskit and PennyLane, focusing on their suitability for teaching and research. We delve into their basic and advanced usage, examine their learning curves, and evaluate their capabilities in quantum computing experiments. We also demonstrate using a quantum programming language to build a half adder and a machine learning model. Our study reveals that each language has distinct advantages. While PennyLane excels in research applications due to its flexibility to adjust parameters in detail and access multiple sources of real quantum devices, Qiskit stands out in education because of its web-based graphical user interface and smaller code size. The codes and the dataset used in the studies are available at https://***/wangpeihua1231/quantum-programming-platform .

关键词： PennyLane Qiskit Quantum chemistry Quantum computing Quantum programming language

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Server-Based Data Push Architecture for Multi-Processor Environments

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Journal of computer science & Technology 2007年第5期22卷 641-652页

作者：孙贤和 Surendra Byna 陈勇 Department of Computer Science Illinois Institute of Technology ChicagoIllinois 60616U.S.A.Computing DivisionFermi National Accelerator Laboratory.BataviaIL 60510-0500U.S.A. Department of Computer Science Illinois Institute of Technology ChicagoIllinois 60616U.S.A.

Data access delay is a major bottleneck in utilizing current high-end computing （HEC） machines. Prefetching, where data is fetched before CPU demands for it, has been considered as an effective solution to masking data access delay. However, current client-initiated prefetching strategies, where a computing processor initiates prefetching instructions, have many limitations. They do not work well for applications with complex, non-contiguous data access patterns. While technology advances continue to increase the gap between computing and data access performance, trading computing power for reducing data access delay has become a natural choice. In this paper, we present a serverbased data-push approach and discuss its associated implementation mechanisms. In the server-push architecture, a dedicated server called Data Push Server （DPS） initiates and proactively pushes data closer to the client in time. Issues, such as what data to fetch, when to fetch, and how to push are studied. The SimpleScalar simulator is modified with a dedicated prefetching engine that pushes data for another processor to test DPS based prefetching. Simulation results show that L1 Cache miss rate can be reduced by up to 97% （71% on average） over a superscalar processor for SPEC CPU2000 benchmarks that have high cache miss rates.

关键词： performance measurement evaluation modeling simulation of multiple-processor system cache memory

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Large Language Model Enhanced Particle Swarm Optimization for Hyperparameter Tuning for Deep Learning Models

arXiv

引用

arXiv 2025年

作者： Hameed, Saad Qolomany, Basheer Belhaouari, Samir Brahim Abdallah, Mohamed Qadir, Junaid Al-Fuqaha, Ala Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Doha5825 Qatar Department of Medicine College of Medicine Howard University WashingtonDC20059 United States Department of Computer Science and Engineering Qatar University Doha5825 Qatar

Determining the ideal architecture for deep learning models, such as the number of layers and neurons, is a difficult and resource-intensive process that frequently relies on human tuning or computationally costly optimization approaches. While Particle Swarm Optimization (PSO) and Large Language Models (LLMs) have been individually applied in optimization and deep learning, their combined use for enhancing convergence in numerical optimization tasks remains underexplored. Our work addresses this gap by integrating LLMs into PSO to reduce model evaluations and improve convergence for deep learning hyperparameter tuning. The proposed LLM-enhanced PSO method addresses the difficulties of efficiency and convergence by using LLMs (particularly ChatGPT-3.5 and Llama3) to improve PSO performance, allowing for faster achievement of target objectives. Our method speeds up search space exploration by substituting underperforming particle placements with best suggestions offered by LLMs. Comprehensive experiments across three scenarios—(1) optimizing the Rastrigin function, (2) using Long Short-Term Memory (LSTM) networks for time series regression, and (3) using Convolutional Neural Networks (CNNs) for material classification—show that the method significantly improves convergence rates and lowers computational costs. Depending on the application, computational complexity is lowered by 20% to 60% compared to traditional PSO methods. Llama3 achieved a 20% to 40% reduction in model calls for regression tasks, whereas ChatGPT-3.5 reduced model calls by 60% for both regression and classification tasks, all while preserving accuracy and error rates. This groundbreaking methodology offers a very efficient and effective solution for optimizing deep learning models, leading to substantial computational performance improvements across a wide range of applications. © 2025, CC BY.

关键词： Particle swarm optimization (PSO)

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A Moving-Mesh Finite Element Method and its Application to the Numerical Solution of Phase-Change Problems

引用

Communications in Computational Physics 2009年第8期6卷 595-624页

作者： M.J.Baines M.E.Hubbard P.K.Jimack R.Mahmood Department of Mathematics The University of ReadingUK School of Computing University of LeedsUK Computer Division Pakistan Institute of Nuclear Science and Technology(PINSTECH)IslamabadPakistan

A distributed Lagrangian moving-mesh finite element method is applied to problems involving changes of *** algorithm uses a distributed conservation principle to determine nodal mesh velocities,which are then used to move the *** nodal values are obtained from an ALE(Arbitrary Lagrangian-Eulerian)equation,which represents a generalization of the original algorithm presented in Applied Numerical Mathematics,54:450–469(2005).Having described the details of the generalized algorithm it is validated on two test cases from the original paper and is then applied to one-phase and,for the first time,two-phase Stefan problems in one and two space dimensions,paying particular attention to the implementation of the interface boundary *** are presented to demonstrate the accuracy and the effectiveness of the method,including comparisons against analytical solutions where available.

关键词： Moving mesh method finite elements multiphase flows interface tracking

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Prediction of Cloud Ranking in a Hyperconverged Cloud Ecosystem Using Machine Learning

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computers, Materials & Continua 2021年第6期67卷 3129-3141页

作者： Nadia Tabassum Allah Ditta Tahir Alyas Sagheer Abbas Hani Alquhayz Natash Ali Mian Muhammad Adnan Khan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Information Sciences Division of Science&TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan School of Computer Science NCBA&ELahore54000Pakistan Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia School of Computer and Information Technology Beaconhouse National UniversityTarogilLahore53700Pakistan Faculty of Computing Riphah School of Computing&InnovationRiphah International University Lahore CampusLahore54000Pakistan

Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the *** design of reliable and high-quality cloud applications requires a strong Quality of Service QoS parameter *** a hyperconverged cloud ecosystem environment,building high-reliability cloud applications is a challenging *** selection of cloud services is based on the QoS parameters that play essential roles in optimizing and improving cloud *** emergence of cloud computing is significantly reshaping the digital ecosystem,and the numerous services offered by cloud service providers are playing a vital role in this *** software-based unified utilities combine storage virtualization,compute virtualization,and network *** availability of the latter has also raised the demand for *** to the diversity of services,the respective quality parameters are also in abundance and need a carefully designed mechanism to compare and identify the critical,common,and impactful *** is also necessary to reconsider the market needs in terms of service requirements and the QoS provided by various *** research provides a machine learning-based mechanism to monitor the QoS in a hyperconverged environment with three core service parameters:service quality,downtime of servers,and outage of cloud services.

关键词： Cloud computing hyperconverged neural network QoS parameter cloud service providers ranking prediction

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MORAL: A Multimodal Reinforcement Learning Framework for Decision Making in Autonomous Laboratories

arXiv

引用

arXiv 2025年

作者： Tirabassi, Natalie Kumar, Sathish A.P. Jha, Sumit Ramanathan, Arvind Department of Computer Science Cleveland State University ClevelandOH United States School of Computing and Information Sciences Florida International University MiamiFL United States Data Science and Learning Division Argonne National Laboratory LemontIL United States

We propose MORAL (a multimodal reinforcement learning framework for decision making in autonomous laboratories) that enhances sequential decision-making in autonomous robotic laboratories through the integration of visual and textual inputs. Using the BridgeData V2 dataset, we generate fine-tuned image captions with a pretrained BLIP-2 vision-language model and combine them with visual features through an early fusion strategy. The fused representations are processed using Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) agents. Experimental results demonstrate that multimodal agents achieve a 20% improvement in task completion rates and significantly outperform visual-only and textual-only baselines after sufficient training. Compared to transformer-based and recurrent multimodal RL models, our approach achieves superior performance in cumulative reward and caption quality metrics (BLEU, METEOR, ROUGE-L). These results highlight the impact of semantically aligned language cues in enhancing agent learning efficiency and generalization. The proposed framework contributes to the advancement of multimodal reinforcement learning and embodied AI systems in dynamic, real-world environments. © 2025, CC BY.

关键词： Reinforcement learning

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