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How distinct sources of nuisance variability in natural images and scenes limit human stereopsis

PLoS Computational Biology 2025年第4期21卷 e1012945页

作者： White, David N. Burge, Johannes Neuroscience Graduate Group University of Pennsylvania PhiladelphiaPA United States Department of Electrical Engineering & Computer Science York University TorontoON Canada Department of Psychology University of Pennsylvania PhiladelphiaPA United States Department of Bioengineering University of Pennsylvania PhiladelphiaPA United States

Stimulus variability—a form of nuisance variability—is a primary source of perceptual uncertainty in everyday natural tasks. How do different properties of natural images and scenes contribute to this uncertainty? Using binocular disparity as a model system, we report a systematic investigation of how various forms of natural stimulus variability impact performance in a stereo-depth discrimination task. With stimuli sampled from a stereo-image database of real-world scenes having pixel-by-pixel ground-truth distance data, three human observers completed two closely related double-pass psychophysical experiments. In the two experiments, each human observer responded twice to ten thousand unique trials, in which twenty thousand unique stimuli were presented. New analytical methods reveal, from this data, the specific and nearly dissociable effects of two distinct sources of natural stimulus variability—variation in luminance-contrast patterns and variation in local-depth structure—on discrimination performance, as well as the relative importance of stimulus-driven-variability and internal-noise in determining performance limits. Between-observer analyses show that both stimulus-driven sources of uncertainty are responsible for a large proportion of total variance, have strikingly similar effects on different people, and—surprisingly—make stimulus-by-stimulus responses more predictable (not less). The consistency across observers raises the intriguing prospect that image-computable models can make reasonably accurate performance predictions in natural viewing. Overall, the findings provide a rich picture of stimulus factors that contribute to human perceptual performance in natural scenes. The approach should have broad application to other animal models and other sensory-perceptual tasks with natural or naturalistic stimuli. © 2025 White, Burge. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrest

关键词： Pixels

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In-situ real-time monitoring of GeSn growth using UHV-CVD to achieve high-quality material with lasing at 2250 nm and 100 K [Invited]

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Optical Materials Express 2025年第6期15卷 1371-1381页

作者： Joshua Grant Sudip Acharya Alexer Golden Enbo Yang Hryhorii Stanchu Guo-En Chang Greg Sun Wei Du Shui-Qing Yu Department of Electrical Engineering and Computer Science University of Arkansas Fayetteville AR 72701 USA Material Science and Engineering Program University of Arkansas Fayetteville AR 72701 USA Institute for Nanoscience and Engineering University of Arkansas Fayetteville AR 72701 USA Department of Mechanical Engineering National Chung Cheng University Chiayi 62102 Taiwan Department of Engineering University of Massachusetts Boston MA 02125 USA

GeSn alloys hold great promise for the development of Si photonics due to its direct bandgap with sufficient high Sn composition, tunable bandgap energies covering broad infrared wavelength range, and compatibility with the complementary metal-oxide-semiconductor (CMOS) process. For the past decades, tremendous efforts have been made to develop material growth recipes to obtain device-level material quality using chemical vapor deposition (CVD) reactors. A common issue in material growth is the formation of Sn droplets (i.e., Sn segregation) that seriously degrades the material quality. However, although industrial CVD has delivered high-quality materials for device demonstration, the dynamic process of material growth remains unavailable, and only ex-situ material characterizations could provide feedback. In this work, a specially designed ultra-high-vacuum CVD (UHV-CVD) was used to study the growth dynamic by employing an in-situ optical system, which enables us to monitor the formation of Sn droplets in real-time. The Sn segregation occurring at the cooling stage after growth completion rather than during material growth was discovered, indicating that a post-growth treatment is needed to minimize the Sn droplet formation. By extending the GeSn growth time and controlling the cooling cycle, the number of Sn droplets dramatically reduced, leading to a high-quality strain-relaxed GeSn layer with 10.2% Sn composition and over 730 nm thickness. Material quality is comparable with those grown using commercial RPCVD in the early stage, which was further confirmed by the demonstration of an optically pumped laser. A threshold of ∼600 kW/cm 2 at 77 K and a maximum operating temperature of 100 K were obtained, with the lasing peak at ∼2250 nm.

关键词： Avalanche photodiodes Fourier transform infrared spectroscopy Laser sources Materials processing Physical vapor deposition Thin films

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Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse

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IEEE Transactions on Mobile Computing 2025年

作者： Liu, Guangyuan Du, Hongyang Wang, Jiacheng Niyato, Dusit Kim, Dong In Nanyang Technological University College of Computing and Data Science Energy Research Institute @ NTU Interdisciplinary Graduate Program Singapore University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Sungkyunkwan University Department of Electrical and Computer Engineering Korea Republic of

The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic images to enhance user experience. However, generating these images, especially through Generative Diffusion Models (GDMs), in mobile edge computing environments presents significant challenges due to the limited computing resources of edge devices and the dynamic nature of wireless networks. This paper proposes a novel framework that integrates contract-inspired contest theory, Deep Reinforcement Learning (DRL), and GDMs to optimize image generation in these resource-constrained environments. The framework addresses the critical challenges of resource allocation and semantic data transmission quality by incentivizing edge devices to efficiently transmit high-quality semantic data, which is essential for creating realistic and immersive images. The use of contest and contract theory ensures that edge devices are motivated to allocate resources effectively, while DRL dynamically adjusts to network conditions, optimizing the overall image generation process. Experimental results demonstrate that the proposed approach not only improves the quality of generated images but also achieves superior convergence speed and stability compared to traditional methods. This makes the framework particularly effective for optimizing complex resource allocation tasks in mobile edge Metaverse applications, offering enhanced performance and efficiency in creating immersive virtual environments. © 2002-2012 IEEE.

关键词： Resource allocation

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Self-Assembly of Nanogold Triplets on Trimeric Viral Proteins for Infectious Disease Diagnosis

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ACS Nano 2025年第18期19卷 17514-17524页

作者： Liu, Wenpeng Soliman, Ahmed Emam, Hagar E. Zhang, Jun Bonventre, Joseph V. Lee, Luke P. Nasr, Mahmoud L. Renal Division Division of Engineering in Medicine Department of Medicine Brigham Women’s Hospital Harvard Medical School BostonMA02115 United States Department of Neurobiology Harvard Medical School BostonMA02115 United States Department of Bioengineering University of California Berkeley BerkeleyCA94720 United States Department of Electrical Engineering and Computer Science University of California Berkeley BerkeleyCA94720 United States Department of Biophysics Institute of Quantum Biophysics Sungkyunkwan University Suwon16419 Korea Republic of Department of Chemistry and Nanoscience Ewha Womans University Seoul03760 Korea Republic of College of Medicine Mohammed Bin Rashid University of Medicine and Health Sciences Dubai505055 United Arab Emirates Broad Institute MIT and Harvard CambridgeMA02142 United States

Timely and accurate diagnostics for infectious diseases are essential in preventing their worldwide spread. Though rapid diagnostic tests are favored for their speed, cost-effectiveness, and ease of use, most tests compromise sensitivity, which risks false-negative results. Here, we present the self-assembly of nanogold triplets on trimeric viral surface proteins for a sensitive colorimetric assay to identify viruses. Gold triplets were self-assembled on the viral trimeric surface proteins using ultrasmall gold nanoparticles. We observed a significant wavelength shift of 70 nm, enabling straightforward naked-eye detection through gold triplets that act as catalysts for producing nanoplasmonic viruses. We established the detection limit of 3 × 105 copies/ml using an effective colorimetric assay for detecting SARS-CoV-2. The self-assembly of gold triplets on trimeric viral surface proteins provides a reliable approach to the accurate and sensitive detection of viruses. © 2025 American Chemical Society.

关键词： Coronavirus

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MR Spatiospectral Reconstruction Integrating Subspace Modeling and Self-Supervised Spatiotemporal Denoising

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Zhao, Ruiyang Wang, Zepeng Anderson, Aaron Huesmann, Graham Lam, Fan University of Illinois Urbana-Champaign Department of Electrical and Computer Engineering Beckman Institute for Advanced Science and Technology United States University of Illinois Urbana-Champaign Department of Bioengineering Beckman Institute for Advanced Science and Technology United States University of Illinois Urbana-Champaign Beckman Institute for Advanced Science and Technology United States Carle Foundation Hospital United States University of Illinois Urbana-Champaign Department of Bioengineering Beckman Institute for Advanced Science and Technology IL61801 United States

We present a new method that integrates subspace modeling and a pre-learned spatiotemporal denoiser for reconstruction from highly noisy magnetic resonance spectroscopic imaging (MRSI) data. The subspace model imposes an explicit low-dimensional representation of the high-dimensional spatiospectral functions of interest for noise reduction, while the denoiser serves as a complementary spatiotemporal prior to constrain the subspace reconstruction. A self-supervised learning strategy was proposed to train a denoiser that can distinguish the spatio-temporally correlated signals from uncorrelated noise. An iterative reconstruction formalism was developed based on the Plug-and-Play (PnP)-ADMM framework to synergize the subspace constraint, plug-in denoiser and spatiospectral encoding model. We evaluated the proposed method using numerical simulations and in vivo data, demonstrating improved performance over state-of-the-art subspacebased methods. We also provided theoretical analysis on the utility of combining subspace projection and iterative denoising in terms of both algorithm convergence and performance. Our work demonstrated the potential of integrating self-supervised denoising priors and low-dimensional representations for high-dimensional imaging problems. © 2025 IEEE.

关键词： Magnetic resonance imaging

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A CT-based Prediction System for Determining Respiratory Support Level in COVID-19 Patients

A CT-based Prediction System for Determining Respiratory Sup...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ahmed Sharafeldeen Hossam Magdy Balaha Ibrahim Shawky Farahat Mohammed Ghazal James Connelly Eric van Bogaert Ayman El-Baz Bioengineering Department J.B. Speed School of Engineering University of Louisville Louisville KY USA Department of Computer Science Mansoura University Mansoura Egypt Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi UAE Department of Radiology Faculty of Medicine University of Louisville Louisville KY USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

A novel diagnostic system is introduced for assessing the required level of respiratory support for COVID-19 patients. It bases its assessments on the correlation between detected COVID-19 lesions and the respiratory support levels administered to the patients. The correlation with computed tomography (CT) scans will focus on three respiratory support levels, including minimal support (Level 0), non-invasive support (Level 1, such as soft oxygen), and invasive support (Level 3, e.g., mechanical ventilation). The system initially outlines the pulmonary regions from CT scans, then identifies COVID-19 lesions within these segmented lung regions. Subsequently, three second-order texture features are extracted from the identified COVID-19 lesion regions to detect differences and abnormalities across varying severity levels in COVID-19 cases. To obtain the suitable level of respiratory support for each patient, a fusion mechanism based on backpropagation neural network is utilized to integrate the diagnosis of these three features, individually generated by the support vector machine (SVM) classifier. The system’s performance is evaluated on 307 COVID-19 patients using a hold-out validation approach. This evaluation included various metrics, such as sensitivity, specificity, F1-score, Cohen’s kappa, and accuracy, demonstrating impressive results. Specifi-cally, it achieved a score of 97.25%, 98.56%, 97.26%, 95.79%, and 97.25%, respectively. The results demonstrate the effectiveness of the integrated system, which uses various second-order features, in predicting respiratory support needs for COVID-19 patients, outperforming both its individual components and other machine learning-based classification systems.

关键词： COVID-19 Support vector machines Correlation Computed tomography Lungs Signal processing Feature extraction Ventilation Lesions Speech processing

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Email Spam Detection Using Logistic Regression and Explainable AI

Email Spam Detection Using Logistic Regression and Explainab...

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International Conference on electrical, computer and Communication engineering (ECCE)

作者： Sumit Kanti Sarker Ratul Bhattacharjee Md Abu Sufian Md Solaiman Ahamed Md Abu Talha Faiza Tasnim K.M. Nazmul Islam Sharia Tasnim Adrita Department of Computer Science & Engineering (CSE) Faculty of Science & Technology (FST) American International University Bangladesh Dhaka Bangladesh Bachelor of Science in Information Technology (BSIT) Cybersecurity Program Concentrations Washington University of Science and Technology (WUST) VA USA Department of Electrical and Electronics Engineering (EEE) Faculty of Engineering (FE) American International University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering (CSE) Faculty of Science & Engineering Northern University Bangladesh-(NUB) Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Email spam detection is crucial for ensuring a positive user experience and maintaining communication security. This study presents a novel spam detection approach leveraging Logistic Regression, optimized through hyperparameter tuning and enhanced with Explainable Artificial Intelligence. The proposed method is evaluated on a large-scale dataset of emails collected from a real-world spam detection system. Term Frequency-Inverse Document Frequency is utilized for feature extraction, converting email text into numerical representations. XAI introduces interpretability by highlighting critical features such as "pill," "PHP," and "Businessweek," which indicate spam classification patterns. Metrics such as precision, recall, and the confusion matrix are employed to assess the model’s performance, ensuring a balanced evaluation. Hyperparameter optimization using Grid Search CV achieves an optimal regularization parameter (C = 100) and maximum iterations (maxiter=100), resulting in an impressive F1 score of 98.76%, accuracy of 98.82%, precision of 98.62%, and recall of 98.90%.

关键词： Logistic regression Explainable AI Filtering Unsolicited e-mail Computational modeling Predictive models Transformers Frequency conversion Feature extraction Tuning

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Observation of Photonic Chiral Flatbands

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Physical Review Letters 2025年第10期134卷 103801-103801页

作者： Minho Choi Andrea Alù Arka Majumdar Department of Electrical and Computer Engineering University of Washington Seattle Washington 98195 USA Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA and Physics Program Graduate Center City University of New York New York New York 10031 USA Department of Electrical and Computer Engineering University of Washington Seattle Washington 98195 USA and Department of Physics University of Washington Seattle Washington 98195 USA

Distinct selectivity to the spin angular momenta of photons has garnered significant attention in recent years, for its relevance in basic science and for imaging and sensing applications. While nonlocal metasurfaces with strong chiral responses to the incident light have been reported, these responses are typically limited to a narrow range of incident angles. In this study, we demonstrate a nonlocal metasurface that showcases strong chirality, characterized by circular dichroism (∼0.6), over a wide range of incident angles ±5°. Its quality factor, circular dichroism and resonant frequency can be optimized by design. These findings pave the way to further advance the development of valley-selective optical cavities and augmented reality applications.

关键词： Natural frequencies

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NExpR: Neural Explicit Representation for fast arbitrary-scale medical image super-resolution

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computers in Biology and Medicine 2025年 184卷 109354-109354页

作者： Pang, Kaifeng Zhao, Kai Hung, Alex Ling Yu Zheng, Haoxin Yan, Ran Sung, Kyunghyun Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States Department of Radiological Sciences University of California Los AngelesCA90095 United States Department of Computer Science University of California Los AngelesCA90095 United States Department of Bioengineering University of California Los AngelesCA90095 United States

Medical images often require rescaling to various spatial resolutions to ensure interpretations at different levels. Conventional deep learning-based image super-resolution (SR) enhances the fixed-scale resolution. Implicit neural representation (INR) is a promising way of achieving arbitrary-scale image SR. However, existing INR-based methods require the repeated execution of the neural network (NN), which is slow and inefficient. In this paper, we present Neural Explicit Representation (NExpR) for fast arbitrary-scale medical image SR. Our algorithm represents an image with an explicit analytical function, whose input is the low-resolution image and output is the parameterization of the analytical function. After obtaining the analytical representation through a single NN inference, SR images of arbitrary scales can be derived by evaluating the explicit functions at desired coordinates. Because of the analytical explicit representation, NExpR is significantly faster than INR-based methods. In addition to speed, our method achieves on-par or better image quality than other strong competitors. Extensive experiments on Magnetic Resonance Imaging (MRI) datasets, including ProstateX, fastMRI, and our in-house clinical prostate dataset, as well as the computerized Tomography (CT) dataset, specifically the Medical Segmentation Decathlon (MSD) liver dataset, demonstrate the superiority of our method. Our method reduces the rescaling time from the order of 1 ms to the order of 0.01 ms, achieving an over 100× speedup without losing the image quality. Code is available at https://***/Calvin-Pang/NExpR. © 2024

关键词： Magnetic resonance imaging

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Correction: Materials laboratories of the future for alloys, amorphous, and composite materials (MRS Bulletin, (2025), 50, 2, (190-207), 10.1557/s43577-024-00846-y)

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MRS Bulletin 2025年 1-2页

作者： Banerjee, Sarbajit Meng, Y. Shirley Minor, Andrew M. Zhang, Minghao Zaluzec, Nestor J. Chan, Maria K.Y. Seidler, Gerald McComb, David W. Agar, Joshua Mukherjee, Partha P. Melot, Brent Chapman, Karena Guiton, Beth S. Klie, Robert F. McCue, Ian D. Voyles, Paul M. Robertson, Ian Li, Ling Chi, Miaofang Destino, Joel F. Devaraj, Arun Marquis, Emmanuelle A. Segre, Carlo U. Liu, Huinan H. Yang, Judith C. Momeni, Kasra Misra, Amit Abdolrahim, Niaz Medvedeva, Julia E. Cai, Wenjun Sehirlioglu, Alp Dizbay-Onat, Melike Mehta, Apurva Graham-Brady, Lori Maruyama, Benji Rajan, Krishna Warner, Jamie H. Taheri, Mitra L. Kalinin, Sergei V. Reeja-Jayan, B. Schwarz, Udo D. Simon, Sindee L. Brown, Craig M. Department of Chemistry and Department of Materials Science & Engineering Texas A & M University College Station United States Pritzker School of Molecular Engineering The University of Chicago Chicago United States Department of Materials Science & Engineering University of California Berkeley Berkeley United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley United States Center for Nanoscale Materials Argonne National Laboratory Lemont United States Department of Physics University of Washington Seattle United States Department of Materials Science & Engineering The Ohio State University Columbus United States Department of Mechanical Engineering and Mechanics Drexel University Philadelphia United States School of Mechanical Engineering Purdue University West Lafayette United States Department of Chemistry University of Southern California Los Angeles United States Department of Chemistry Stony Brook University The State University of New York Stony Brook United States Department of Chemistry University of Kentucky Lexington United States Department of Physics University of Illinois at Chicago Chicago United States Department of Materials Science & Engineering Northwestern University Evanston Bangladesh Department of Materials Science & Engineering University of Wisconsin–Madison Madison United States Department of Materials Science and Engineering University of Pennsylvania Philadelphia United States Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Chemistry Creighton University Omaha United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Richland United States Department of Materials Science & Engineering University of Michigan–Ann Arbor Ann Arbor United States Department of Physics Illinois Institute of Technology Chicago United States Department of Bioengineering and the Mater

This article was updated to correct Minghao Zhang’s affiliation from "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA" to "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA;Argonne National Laboratory, Lemont, USA." Argonne National Laboratory was left off the original publication. © The Author(s) 2025.

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