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arXiv 2025年

作者： Burrage, Kevin Burrage, Pamela Kreikemeyer, Justin N. Uhrmacher, Adelinde M. Weerasinghe, Hasitha N. School of Mathematical Sciences Queensland University of Technology BrisbaneQLD Australia Department of Computer Science University of Oxford United Kingdom Modelling and Simulation Institute for Visual and Analytic Computing Faculty of Informatics and Electrical Engineering University of Rostock Rostock Germany

In this paper, we adapt a two species agent-based cancer model that describes the interaction between cancer cells and healthy cells on a uniform grid to include the interaction with a third species – namely immune cells. We run six different scenarios to explore the competition between cancer and immune cells and the initial concentration of the immune cells on cancer dynamics. We then use coupled equation learning to construct a population-based reaction model for each scenario. We show how they can be unified into a single surrogate population-based reaction model, whose underlying three coupled ordinary differential equations are much easier to analyse than the original agent-based model. As an example, by finding the single steady state of the cancer concentration, we are able to find a linear relationship between this concentration and the initial concentration of the immune cells. This then enables us to estimate suitable values for the competition and initial concentration to reduce the cancer substantially without performing additional complex and expensive simulations from an agent-based stochastic model. The work shows the importance of performing equation learning from agent-based stochastic data for gaining key insights about the behaviour of complex cellular dynamics. © 2025, CC BY.

关键词： Lung cancer

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The Odyssey Journey: Top-Tier Medical Resource Seeking for Specialized Disorder in China 25

The Odyssey Journey: Top-Tier Medical Resource Seeking for S...

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Proceedings of the 2025 CHI Conference on Human Factors in computing Systems

作者： Ka I Chan Siying Hu Yuntao Wang Xuhai Xu Zhicong Lu Yuanchun Shi Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Global Innovation Exchange (GIX) Institute Tsinghua University Beijing China Department of Computer Science City University of Hong Kong Hong Kong China Key Laboratory of Pervasive Computing Ministry of Education Department of Computer Science and Technology Tsinghua University Beijing China and School of Computer Science and Technology Qinghai University Xining China Google New York City New York USA Department of Computer Science George Mason University Fairfax Virginia USA Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China and Qinghai University Xining China

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Efficient IoT Intrusion Detection with an Improved Attention-Based CNN-BiLSTM Architecture

arXiv

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arXiv 2025年

作者： Naeem, Amna Khan, Muazzam A. Alasbali, Nada Ahinad, Jawad Khattak, Aizaz Ahmad Khan, Muhammad Shahbaz Department of Computer Science Quaid-i-azam University Islamabad Pakistan Department of Computer Sciences Quaid-e-Azam University Islamabad Pakistan Department ofInformatics and Computing Systems College of Computer Science King Khalid University Abha Saudi Arabia Cyber Security Center Prince Mohammad Bin Fahd University Alkhobar Saudi Arabia School of Computing Engineering and Built Environment Edinburgh Napier University Edinburgh United Kingdom

The ever-increasing security vulnerabilities in the Internet-of-Things (loT) systems require improved threat detection approaches. This paper presents a compact and efficient approach to detect botnet attacks by employing an integrated approach that consists of traffic pattern analysis, temporal support learning, and focused feature extraction. The proposed attention-based model benefits from a hybrid CNN-BiLSTM architecture and achieves 99% classification accuracy in detecting botnet attacks utilizing the N-BaloT dataset, while maintaining high precision and recall across various scenarios. The proposed model's performance is further validated by key parameters, such as Mathews Correlation Coefficient and Cohen's kappa Correlation Coefficient. The close-to-ideal results for these parameters demonstrate the proposed model's ability to detect botnet attacks accurately and efficiently in practical settings and on unseen data. The proposed model proved to be a powerful defense mechanism for IoT networks to face emerging security challenges. © 2025, CC BY-NC-ND.

关键词： Bot (Internet)

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Streaming quantum state purification for general mixed states

arXiv

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arXiv 2025年

作者： Grier, Daniel Leung, Debbie Li, Zhi Pashayan, Hakop Schaeffer, Luke Department of Mathematics Department of Computer Science and Engineering University of California San Diego United States Institute for Quantum Computing University of Waterloo Canada Perimeter Institute for Theoretical Physics Canada National Research Council of Canada Canada Dahlem Center for Complex Quantum Systems Freie Universität Berlin Germany Research Institute Taiwan Department of Computer Science Institute for Advanced Computer Studies Joint Center for Quantum Information and Computer Science University of Maryland United States

Given multiple copies of a mixed quantum state with an unknown, nondegenerate principal eigenspace, quantum state purification is the task of recovering a quantum state that is closer to the principal eigenstate. A streaming protocol relying on recursive swap tests has been proposed and analysed for noisy depolarized states with arbitrary dimension and noise level. Here, we show that the same algorithm applies much more broadly, enabling the purification of arbitrary mixed states with a nondegenerate principal eigenvalue. We demonstrate this through two approaches. In the first approach, we show that, given the largest two eigenvalues, the depolarized noise is the most difficult noise to purify for the recursive swap tests, thus the desirable bounds on performance and cost follow from prior work. In the second approach, we provide a new and direct analysis for the performance of purification using recursive swap tests for the more general noise. We also derive simple lower bounds on the sample complexity, showing that the recursive swap test algorithm attains optimal sample complexity (up to a constant factor) in the low-noise regime. © 2025, CC BY.

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Risk for ocular hypertension progression to early glaucoma: A predictive model and key predictors

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Photodiagnosis and Photodynamic Therapy 2025年 53卷 104614-104614页

作者： Huang, Shaofen Luo, Man Ye, Guitong Yang, Zhenlan Liao, Lingling Wu, Nina Zhu, Yingting Liu, Xing Zhuo, Yehong Xiao, Hui State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangdong Provincial Clinical Research Center for Ocular Diseases Guangzhou China Center on Frontiers of Computing Studies School of Computer Science Peking University Beijing China

Background: Ocular hypertension (OHT) is the most significant risk factor for glaucoma. We aimed to develop a model for predicting OHT progression to early glaucoma and to identify key predictors. Methods: Patients with OHT with at least two follow-up visits within 3 years were categorized into non-progressive and progressive groups based on optic nerve morphology and/or functional changes during follow-up. Data were split into training and testing sets (8:2 ratio). Least absolute shrinkage and selection operator regression and logistic regression were used to select predictors. Machine learning models were constructed using the selected predictors as input and evaluated using area under the receiver operating characteristic (AUC) and precision-recall (AP) curve values. The optimal model was further evaluated using 10-fold cross-validation and a validation set. The Shapley additive explanations method was applied to interpret the predictors. Results: Overall, 395 eyes from 395 patients were included (non-progressive: n = 295;progressive: n = 100). The random forest model outperformed all others, achieving AUC values of 0.881 (95 % confidence interval [CI]: 0.835–0.926) in the training set and 0.937 (95 % CI: 0.884–0.991) in the testing set. In the independent validation set (n = 82), the AUC and AP values were 0.865 (95 % CI: 0.782–0.947) and 0.707, respectively. Key predictors were baseline intraocular pressure, rim area, ganglion cell-inner plexiform layer (GCIPL) inferior-temporal thickness difference, and GCIPL superior-temporal thickness difference. Family history and male sex also contributed. Conclusions: The developed model could effectively predict the risk for OHT progression to early glaucoma and may aid devising individualized treatment plans and follow-up protocols. © 2025 The Authors

关键词： Early glaucoma OCT Ocular hypertension Predictive model Risk stratification

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Classification of Cacao Pods Ripeness Using Convolutional Neural Networks

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Procedia computer science 2025年 257卷 1197-1204页

作者： Karl O. Borromeo Melbert R. Bonotan Archie P. Llagas Joana Marie O. Juan Philip Donald C. Sanchez Nathan Kit A. Berro Department of Computer Science College of Computing and Information Sciences Caraga State University Butuan City Philippines Department of Information Technology College of Computing and Information Sciences Caraga State University Butuan City Philippines Center for Resource Assessment Analytics and Emerging Technologies College of Engineering and Geo-Sciences Caraga State University Butuan City Philippines Department of Information Systems College of Computing and Information Sciences Caraga State University Butuan City Philippines

This paper presents methods for the development of Ripeness Identification of Cacao Pods using Convolutional Neural Network (CNN) and precision agriculture employed in a Mobile Application. This research explores if there is a correlation between the color dynamics of cacao pods and its physiological characteristics, and the possibility of using this correlation to develop Artificial Intelligence (AI) model for ripeness and variety identification, which conventional eye estimation resulted to unripe and overripe harvesting of Cacao Pods. Data analysis and correlation was developed, integrating detection and classification models based on the Single Shot Detection (SSD) MobileNet and visual Geometry Group (VGG) 16 or VGG16 layers network architectures, respectively. The mobile application utilizes the TensorFlow template for object detection and incorporates image preprocessing techniques for enhanced accuracy. The successful integration of the model into a mobile application demonstrates its potential for practical applications in the cacao industry.

关键词： Cacao Physiological Properties Convolutional Neural Network CNN Deep Learning Ripeness Identification Precision Agriculture

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Eye Drop Instillation Success and Hand Function in Adults with Glaucoma: A Pilot Study

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Ophthalmology Glaucoma 2025年第3期8卷 275-284页

作者： Weber, Madeline K. Kabil, Gül G. Niziol, Leslie M. Urrego, Daniel Duque Haire, Cameron Cain, Stephen M. Sample, Alanson P. Burke, David T. Brown, Susan H. Newman-Casey, Paula Anne University of Michigan Medical School Ann Arbor MI United States University of Michigan School of Kinesiology Ann Arbor MI United States Department of Ophthalmology and Visual Sciences University of Michigan W.K. Kellogg Eye Center Ann Arbor MI United States Department of Chemical and Biomedical Engineering West Virginia University Morgantown WV United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI United States Department of Human Genetics University of Michigan Ann Arbor MI United States

Purpose: To investigate hand function and eye drop instillation success in adults with and without glaucoma. Design: Cross-sectional pilot study. Subjects: Adults aged ≥ 65 years with glaucoma who use eye drops daily and adults aged 65+ without glaucoma who do not regularly use eye drops. Methods: Hand function was evaluated using the Saehan Hydraulic Pinch Gauge, Jamar Hand Dynamometer, Grooved Pegboard Test (GPT), Arthritis Hand Function Tests (AHFT), Semmes-Weinstein Monofilament Test, and a tactile pattern recognition device. Eye drop instillation success was analyzed through videography. Main Outcome Measures: Proportion of successful eye drop instillation trials assessed by (1) overall success in getting at least one drop in the eye;(2) average number of drops dispensed;and (3) bottle tip contact with the eye, eyelashes, or skin. Hand function measures: mean pinch and grip strength, GPT and AHFT completion times, smallest detected monofilament, tactile pattern identification time, and accuracy. Results: A total of 25 participants with glaucoma and 79 participants without glaucoma were included. There was no difference in the proportion of trials where at least one drop was successfully instilled, disregarding bottle tip contact (glaucoma: 0.95, nonglaucoma: 0.91;P = 0.88). Participants with glaucoma were more likely to make contact between the bottle tip and the eye, eyelashes, or skin compared with those without (glaucoma: 0.49, nonglaucoma: 0.28;P = 0.01). Participants with glaucoma dispensed a similar number of drops as those without (glaucoma: 1.37, nonglaucoma: 1.46;P = 0.47). Participants with glaucoma had significantly reduced pinch strength (glaucoma: 4.8 kg, nonglaucoma 6.1 kg, P = 0.01), grip strength (glaucoma: 23.4 kg, nonglaucoma: 27.7 kg, P = 0.02), longer completion times for the GPT (glaucoma: 113.5 seconds, nonglaucoma: 85.5 seconds, P = 0.02), and specific AHFT tasks of fastening/unfastening buttons (glaucoma: 36.6 seconds, nonglaucoma: 27.7

关键词： Eye drop instillation success Glaucoma Hand function Sensorimotor deficits

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A Performance Analysis of Task Scheduling for UQ Workflows on HPC Systems

A Performance Analysis of Task Scheduling for UQ Workflows o...

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

作者： Chung Ming Loi Anne Reinarz Linus Seelinger William Hornsby James Buchanan Mikkel Lykkegaard Department of Computer Science Durham University Durham UK Scientific Computing Center Karlsruhe Institute of Technology Karlsruhe Germany UKAEA-CCFE Abingdon UK Danish Technological Institute Aarhus Denmark

ISBN: (数字)9783982633619

Uncertainty Quantification (UQ) workloads are becoming increasingly common in science and engineering. They involve the submission of thousands or even millions of similar tasks with potentially unpredictable runtimes, where the total number is usually not known a priori. A static one-size-fits-all batch script would likely lead to suboptimal scheduling, and native schedulers installed on High-Performance computing (HPC) systems such as SLURM often struggle to efficiently handle such workloads. In this paper, we introduce a new load balancing approach suitable for UQ workflows. To demonstrate its efficiency in a real-world setting, we focus on the GS2 gyrokinetic plasma turbulence simulator. Individual simulations can be computationally demanding, with runtimes varying significantly—from minutes to hours—depending on the high-dimensional input parameters. Our approach uses the UQ and Modelling Bridge, which offers a language-agnostic interface to a simulation model, combined with HyperQueue which works alongside the native scheduler. In particular, deploying this framework on HPC systems does not require system-level changes. We benchmark our proposed framework against a standalone SLURM approach using GS2 and a Gaussian Process surrogate thereof. Our results demonstrate a reduction in scheduling overhead by up to three orders of magnitude and a maximum reduction of 38% in CPU time for long-running simulations compared to the naïve SLURM approach, while making no assumptions about the job submission patterns inherent to UQ workflows.

关键词： Adaptation models Runtime Uncertainty Processor scheduling Computational modeling High performance computing Gaussian processes Load management Servers Load modeling

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Securing Consumer IoT Swarms Using Graph Transformers and SRAM for Firmware Attestation

Securing Consumer IoT Swarms Using Graph Transformers and SR...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Varun Kohli Bhavya Kohli Muhammad Naveed Aman Biplab Sikdar Department of Electrical and Computer Engineering National University of Singapore Singapore Center for Machine Intelligence and Data Science Indian Institute of Technology Bombay India School of Computing University of Nebraska-Lincoln USA

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Consumer Internet of Things (IoT) networks have gained widespread popularity due to their convenience, automation, and security provisions in personal and home environments. Ubiquitous resource-constrained devices, however, are plagued with security issues that often arise from firmware-related issues and their propagated effects. While various studies on firmware attestation are available, they require firmware copies, specific hardware, and complex computation on the IoT device. This paper presents a study on the application of Graph Transformer Networks (GTN) in verifying the firmware integrity of consumer IoT swarms using SRAM as an attestation feature. The proposed method achieves an overall 0.99 accuracy on authentic samples from development and physical twin networks, 0.99 on malware, and 0.97 on propagated misbehavior at a $\sim 10^{-4}$ second inference latency on a laptop CPU.

关键词： Portable computers Accuracy Random access memory Transformers Malware Graph neural networks Hardware Internet of Things Security Microprogramming

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Q-GEAR: Improving quantum simulation framework

arXiv

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arXiv 2025年

作者： Guo, Ziqing Pan, Ziwen Balewski, Jan National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA United States Department of Computer Science Texas Tech University LubbockTX United States

Fast execution of complex quantum circuit simulations are crucial for verification of theoretical algorithms paving the way for their successful execution on the quantum hardware. However, the main stream CPU-based platforms for circuit simulation are well-established but slower. Despite this, adoption of GPU platforms remains limited because different hardware architectures require specialized quantum simulation frameworks, each with distinct implementations and optimization strategies. Therefore, we introduce Q-Gear, a software framework that transforms Qiskit quantum circuits into Cuda-Q kernels. By leveraging Cuda-Q seamless execution on GPUs, Q-Gear accelerates both CPU and GPU based simulations by respectively two orders of magnitude and ten times with minimal coding effort. Furthermore, Q-Gear leverages Cuda-Q configuration to interconnect GPUs memory allowing the execution of much larger circuits, beyond the memory limit set by a single GPU or CPU node. Additionally, we created and deployed a Podman container and a Shifter image at Perlmutter (NERSC/LBNL), both derived from NVIDIA public image. These public NERSC containers were optimized for the Slurm job scheduler allowing for close to 100% GPU utilization. We present various benchmarks of the Q-Gear to prove the efficiency of our computation paradigm. © 2025, CC BY.

关键词： Program processors

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