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Skin-like cryogel electronics from suppressed-freezing tuned polymer amorphization

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Nature Communications 2023年第1期14卷 1-10页

作者： Zhang, Xiansheng Yan, Hongwei Xu, Chongzhi Dong, Xia Wang, Yu Fu, Aiping Li, Hao Lee, Jin Yong Zhang, Sheng Ni, Jiahua Gao, Min Wang, Jing Yu, Jinpeng Ge, Shuzhi Sam Jin, Ming Liang Wang, Lili Xia, Yanzhi State Key Laboratory of Bio-Fibers and Eco-Textiles College of Textiles and Clothing Qingdao University Qingdao 266071 China Institute for Future Shandong Key Laboratory of Industrial Control Technology School of Automation Qingdao University Qingdao 266071 China College of Materials Science and Engineering Qingdao University Qingdao 266071 China CAS Key Laboratory of Engineering Plastics Institute of Chemistry Chinese Academy of Sciences Beijing 100190 China Department of Chemistry SungKyunKwan University Suwon 16419 South Korea State Key Laboratory of Fluid Power and Mechatronic Systems College of Mechanical Engineering Zhejiang University Hangzhou 310027 China College of Biological Science and Medical Engineering Donghua University Shanghai 201620 China Institute of Environmental Engineering ETH Zürich Zürich 8093 Switzerland Department of Electrical and Computer Engineering National University of Singapore Singapore 117576 Singapore

The sole situation of semi-crystalline structure induced single performance remarkably limits the green cryogels in the application of soft devices due to uncontrolled freezing field. Here, a facile strategy for achieving multifunctionality of cryogels is proposed using total amorphization of polymer. Through precisely lowering the freezing point of precursor solutions with an antifreezing salt, the suppressed growth of ice is achieved, creating an unusually weak and homogenous aggregation of polymer chains upon freezing, thereby realizing the tunable amorphization of polymer and the coexistence of free and hydrogen bonding hydroxyl groups. Such multi-scale microstructures trigger the integrated properties of tissue-like ultrasoftness (Young’s modulus <10 kPa) yet stretchability, high transparency (~92%), self-adhesion, and instantaneous self-healing (<0.3 s) for cryogels, along with superior ionic-conductivity, antifreezing (−58 °C) and water-retention abilities, pushing the development of skin-like cryogel electronics. These concepts open an attractive branch for cryogels that adopt regulated crystallization behavior for on-demand functionalities. © 2023, Springer Nature Limited.

关键词： Gels and hydrogels Polymers

PARAMETERIZED PROXIMAL-GRADIENT ALGORITHMS FOR L1/L2 SPARSE SIGNAL RECOVERY

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arXiv 2023年

作者： Zhang, Na Liu, Xinrui Li, Qia Department of Applied Mathematics College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China School of Mathematics and Statistics Shandong Normal University Jinan250358 China Guangdong Province Key Laboratory of Computational Science School of Computer Science and Engineering Sun Yat-sen University Guangzhou510275 China

The ratio of 1 and 2 norms (1/2), serving as a sparse promoting function, receives considerable attentions recently due to its effectiveness for sparse signal recovery. In this paper, we propose an 1/2 based penalty model for recovering sparse signals from noiseless or noisy observations. It is proven that stationary points of the proposed problem tend to those of the elliptically constrained 1/2 minimization problem as the smoothing parameter goes to zero. Moreover, inspired by the parametric approach for the fractional programming, we design a parameterized proximal-gradient algorithm (PPGA) as well as its line search counterpart (PPGA_L) for solving the proposed model. The closed-form solution of the involved proximity operator is derived, which enable the efficiency of the proposed algorithms. We establish the global convergence of the entire sequences generated by PPGA and PPGA_L with monotone objective values by taking advantage of the fact that the objective of the proposed model is a KL function. Numerical experiments show the efficiency of the proposed algorithms over the state-of-the-art methods in both noiseless and noisy sparse signal recovery problems. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

Distributional bias compromises leave-one-out cross-validation

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arXiv 2024年

作者： Austin, George I. Pe’er, Itsik Korem, Tal Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY United States Program for Mathematical Genomics Department of Systems Biology Columbia University Irving Medical Center New YorkNY United States Department of Computer Science Columbia University New YorkNY United States Department of Obstetrics and Gynecology Columbia University Irving Medical Center New YorkNY United States

Cross-validation is a common method for estimating the predictive performance of machine learning models. In a data-scarce regime, where one typically wishes to maximize the number of instances used for training the model, an approach called ‘leave-one-out cross-validation’ is often used. In this design, a separate model is built for predicting each data instance after training on all other instances. Since this results in a single test data point available per model trained, predictions are aggregated across the entire dataset to calculate common rank-based performance metrics such as the area under the receiver operating characteristic or precision-recall curves. In this work, we demonstrate that this approach creates a negative correlation between the average label of each training fold and the label of its corresponding test instance, a phenomenon that we term distributional bias. As machine learning models tend to regress to the mean of their training data, this distributional bias tends to negatively impact performance evaluation and hyperparameter optimization. We show that this effect generalizes to leave-P-out cross-validation and persists across a wide range of modeling and evaluation approaches, and that it can lead to a bias against stronger regularization. To address this, we propose a generalizable rebalanced cross-validation approach that corrects for distributional bias. We demonstrate that our approach improves cross-validation performance evaluation in synthetic simulations and in several published leave-one-out analyses. © 2024, CC BY-SA.

关键词： Statistical tests

Reconfigurable intelligent surfaces for wireless communications:Overview of hardware designs,channel models,and estimation techniques

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Intelligent and Converged Networks 2022年第1期3卷 1-32页

作者： Mengnan Jian George C.Alexandropoulos Ertugrul Basar Chongwen Huang Ruiqi Liu Yuanwei Liu Chau Yuen the Wireless Research Institute ZTE CorporationBeijing 100029China the State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen 518055China Department of Informatics and Telecommunications National and Kapodistrian University of AthensAthens 15784Greece Communications Research and Innovation Laboratory(CoreLab) Department of Electrical and Electronics EngineeringKoçUniversityIstanbul 34450Turkey College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310027China School of Electronic Engineering and Computer Science Queen Mary University of LondonLondonE14NSUK. the Engineering Product Development(EPD)Pillar Singapore University of Technology and DesignSingapore 487372Singapore

The demanding objectives for the future sixth generation(6G)of wireless communication networks have spurred recent research efforts on novel materials and radio-frequency front-end architectures for wireless connectivity,as well as revolutionary communication and computing *** the pioneering candidate technologies for 6G belong the reconfigurable intelligent surfaces(RISs),which are artificial planar structures with integrated electronic circuits that can be programmed to manipulate the incoming electromagnetic field in a wide variety of *** RISs in wireless networks have been recently advocated as a revolutionary means to transform any wireless signal propagation environment to a dynamically programmable one,intended for various networking objectives,such as coverage extension and capacity boosting,spatiotemporal focusing with benefits in energy efficiency and secrecy,and low electromagnetic field *** by the recent increasing interests in the field of RISs and the consequent pioneering concept of the RIS-enabled smart wireless environments,in this paper,we overview and taxonomize the latest advances in RIS hardware architectures as well as the most recent developments in the modeling of RIS unit elements and RIS-empowered wireless signal *** also present a thorough overview of the channel estimation approaches for RIS-empowered communications systems,which constitute a prerequisite step for the optimized incorporation of RISs in future wireless ***,we discuss the relevance of the RIS technology in the latest wireless communication standards,and highlight the current and future standardization activities for the RIS technology and the consequent RIS-empowered wireless networking approaches.

关键词： reconfigurable intelligent surfaces beyond 5G 6G channel modeling hardware architecture channel estimation smart wireless environment standardization

Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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Automatic Kidney Stone Detection System using Guided Bilateral Feature Detector for CT Images

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Open Public Health Journal 2024年 17卷

作者： Manoranjitham, R. Punitha, S. Ravi, Vinayakumar Stephan, Thompson Al Mazroa, Alanoud Singh, Prabhishek Diwakar, Manoj Gupta, Indrajeet Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Graphic Era (Deemed to be University) Uttarakhand Dehradun 248002 India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of CSE Graphic Era Deemed to be University Uttarakhand Dehradun India School of Computer Science & AI SR University Telangana Warangal 506371 India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University (PNU) P.O. Box 84428 Riyadh 11671 Saudi Arabia

Background: Kidney stones, common urological diseases worldwide, are formed from hard urine minerals in the kidneys. Early detection is essential to prevent kidney damage and manage recurring stones. CT imaging has made significant progress in providing detailed information for disease diagnosis. Aim: This study aimed to enhance kidney stone detection through advanced imaging and machine learning techniques. Objective: The Guided Bilateral Feature Detector was proposed to identify and extract features for kidney stone detection in CT images. Unlike traditional filters like Gaussian and Bilateral filters, the Guided Bilateral Filter Detector prevented halo artifacts and preserved image edges by employing a guide weight. The extracted features were combined with the SVM algorithm to accurately detect kidney stones in CT images. Methods: The proposed detector used the Guided Bilateral Filter to reduce the halo artifacts in the images and enhance the feature detection process. The detector operated in four stages to extract important features from CT images, and a 128-feature point generator provided a more detailed representation in aiding kidney stone detection and classification. The proposed detector combined with the Support Vector Machine algorithm to improve reliability and reduce computational requirements. Results: Experimental results showed that the proposed Guided Bilateral Feature Detector with SVM outperformed existing models, including SIFT+SVM, SURF+SVM, PCA+KNN, EANet, Inception v3, VGG16, and Resnet50. The key performance metrics achieved included an accuracy of 98.56%, precision of 98.9%, recall of 99.2%, and an F1 score of 99%. Conclusion: The findings indicate that the Guided Bilateral Feature Detector with SVM significantly enhances the accuracy and reliability of kidney stone detection, providing valuable implications for clinical practice and future research in medical imaging. © 2024 The Author(s).

关键词： CT imaging Guided bilateral feature detector Kidney stone detection Machine learning Support vector machine Urological diseases

MetaCompass: Reference-guided Assembly of Metagenomes

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arXiv 2024年

作者： Luan, Tu Cepeda, Victoria Liu, Bo Bowen, Zac Ayyangar, Ujjwal Almeida, Mathieu Hill, Christopher M. Koren, Sergey Treangen, Todd J. Porter, Adam Pop, Mihai Department of Computer Science University of Maryland College ParkMD United States Center for Bioinformatics and Computational Biology University of Maryland College ParkMD United States Fraunhofer USA Center Mid-Atlantic RiverdaleMD United States Genome Informatics Section Computational and Statistical Genomics Branch National Human Genome Research Institute BethesdaMD United States

Metagenomic studies have primarily relied on de novo assembly for reconstructing genes and genomes from microbial mixtures. While reference-guided approaches have been employed in the assembly of single organisms, they have not been used in a metagenomic context. Here we describe the first effective approach for reference-guided metagenomic assembly that can complement and improve upon de novo metagenomic assembly methods for certain organisms. Such approaches will be increasingly useful as more genomes are sequenced and made publicly available. © 2024, CC BY.

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Stability of SGD: Tightness analysis and improved bounds

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arXiv 2021年

作者： Zhang, Yikai Zhang, Wenjia Bald, Sammy Pingali, Vamsi Chen, Chao Goswami, Mayank Department of Computer Science Rutgers University United States Department of Computer Science Queens College of CUNY United States Department of Mathematics Indian Institute of Science United States Department of Biomedical Informatics Stony Brook University United States

Stochastic Gradient Descent (SGD) based methods have been widely used for training large-scale machine learning models that also generalize well in practice. Several explanations have been offered for this generalization performance, a prominent one being algorithmic stability [18]. However, there are no known examples of smooth loss functions for which the analysis can be shown to be tight. Furthermore, apart from properties of the loss function, data distribution has also been shown to be an important factor in generalization performance. This raises the question: is the stability analysis of [18] tight for smooth functions, and if not, for what kind of loss functions and data distributions can the stability analysis be improved? In this paper we first settle open questions regarding tightness of bounds in the data-independent setting: we show that for general datasets, the existing analysis for convex and strongly-convex loss functions is tight, but it can be improved for non-convex loss functions. Next, we give novel and improved data-dependent bounds: we show stability upper bounds for a large class of convex regularized loss functions, with negligible regularization parameters, and improve existing data-dependent bounds in the non-convex setting. We hope that our results will initiate further efforts to better understand the data-dependent setting under non-convex loss functions, leading to an improved understanding of generalization abilities of deep networks. © 2021, CC0.

关键词： Stability

Chiral Central Charge from a Single Bulk Wave Function

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Physical Review Letters 2022年第17期128卷 176402-176402页

作者： Isaac H. Kim Bowen Shi Kohtaro Kato Victor V. Albert Department of Computer Science University of California Davis Davis 95616 California USA Centre for Engineered Quantum Systems School of Physics University of Sydney Sydney 2043 NSW Australia Department of Physics University of California at San Diego La Jolla 92093 California USA Center for Quantum Information and Quantum Biology Osaka University Osaka 560-0043 Japan Graduate School of Informatics Nagoya University Nagoya 464-0814 Japan Joint Center for Quantum Information and Computer Science National Institute of Standards and Technology and University of Maryland College Park 20899 Maryland USA

A (2+1)-dimensional gapped quantum many-body system can have a topologically protected energy current at its edge. The magnitude of this current is determined entirely by the temperature and the chiral central charge, a quantity associated with the effective field theory of the edge. We derive a formula for the chiral central charge that, akin to the topological entanglement entropy, is completely determined by the many-body ground state wave function in the bulk. According to our formula, nonzero chiral central charge gives rise to a topological obstruction that prevents the ground state wave function from being real valued in any local product basis.

关键词： Entanglement entropy Entanglement in field theory Entanglement measures Fractional quantum Hall effect Integer quantum Hall effect Quantum entanglement Topological materials