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programmable threshold sensing in wireless devices using Ising dynamics

Nature Electronics 2025年 1-8页

作者： Nicolas Casilli Hussein M. E. Hussein Ryan Tetro Luca Colombo Matteo Rinaldi Cristian Cassella Seunghwi Kim Andrea Alù Philip X.-L. Feng Institute for Nano Systems Innovation (NanoSI) Department of Electrical and Computer Engineering Northeastern University Boston MA USA Photonics Initiative Advanced Science Research Center City University of New York New York City NY USA Physics Program Graduate Center City University of New York New York City NY USA Department of Electrical and Computer Engineering University of Florida Gainesville FL USA

Ising machines—comprising dissipatively coupled nodes capable of emulating the behaviour of ferromagnetic spins—can form analogue computing engines that surpass the sequential processing constraints of von Neumann architectures. However, the incorporation of Ising dynamics into radio-frequency wireless technologies remains limited, especially in terms of their potential to enhance wireless sensing capabilities. Here we report a passive wireless sensor that uses Ising dynamics to accurately implement threshold sensing. The device correlates the occurrence of violations in a sensed parameter with transitions in the coupling state of two parametric oscillators acting as Ising spins. As a result, the accuracy of the device is unaffected by distortions in its input and output signals due to multipath and is less prone to clutter caused by co-site interference. We illustrate the potential of the approach in temperature threshold sensing using a microfabricated lithium niobate microelectromechanical temperature sensor to couple two radio-frequency parametric oscillators, and show that such a system allows the sensor threshold to be wirelessly reprogrammed.

关键词： Ising model

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Beyond Bounds on Light Scattering with Complex Frequency Excitations

arXiv

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arXiv 2022年

作者： Kim, Seunghwi Lepeshov, Sergey Krasnok, Alex Alù, Andrea Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States DTU Electro Department of Electrical and Photonics Engineering Technical University of Denmark Ørsteds Plads 343 Kgs. LyngbyDK-2800 Denmark Department of Electrical and Computer Engineering Florida International University MiamiFL33174 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States

Light scattering is one of the most established wave phenomena in optics, lying at the heart of light-matter interactions and of crucial importance for nanophotonic applications. Passivity, causality and energy conservation imply strict bounds on the degree of control over scattering from small particles, with implications on the performance of many optical devices. Here, we demonstrate that these bounds can be surpassed by considering excitations at complex frequencies, yielding extreme scattering responses as tailored nanoparticles reach a quasi-steady-state regime. These mechanisms can be used to engineer light scattering of nanostructures beyond conventional limits for noninvasive sensing, imaging, and nanoscale light manipulation. Copyright © 2022, The Authors. All rights reserved.

关键词： Light scattering

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Application of expert system for oil palm smallholder-owned plantations

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AIP Conference Proceedings 2023年第1期2594卷

作者： A. Umami H. G. Mawandha D. D. Puruhito Purwadi T. Suparyanto D. Sudigyo B. Pardamean Department of Agrotechnology Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Departement of Agricultural Economics Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Departement of Agriculture Engineering Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Most of the oil palm smallholders operate independently. As a result of this lack of support and assistance, oil palm owned by independent smallholders has the lowest productivity compared to large private (corporation) plantations and large state plantations. In addition, smallholder-owned plantations often operate without paying attention to sustainability aspects. There is a need for an effective, efficient, and user-friendly mentoring tool for oil palm farmers that are accessed independently. The Android-based platform was developed by applying an expert system to support increased production of oil palm cultivation for smallholders. Several stages of the expert system implementation, including the identification of planters and land profiles, were carried out using the direct interview method. The expert system includes land preparation management, planting material selection, seeding, weeds control, pests and diseases, fertilization, harvesting and transport, plantation administration, chat platforms, and data scrapping from data providers. The type of expert system is a data-driven Decision Support System (DSS).

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Drum-aware ensemble architecture for improved joint musical beat and downbeat tracking

arXiv

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arXiv 2021年

作者： Chiu, Ching-Yu Su, Alvin Wen-Yu Yang, Yi-Hsuan The Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Academia Sinica Taiwan The Department of Computer Science and Information Engineering National Cheng Kung University Taiwan Yating Music Team Taiwan AI Labs Taiwan Research Center for IT Innovation Academia Sinica Taiwan

This paper presents a novel system architecture that integrates blind source separation with joint beat and downbeat tracking in musical audio signals. The source separation module segregates the percussive and non-percussive components of the input signal, over which beat and downbeat tracking are performed separately and then the results are aggregated with a learnable fusion mechanism. This way, the system can adaptively determine how much the tracking result for an input signal should depend on the input's percussive or non-percussive components. Evaluation on four testing sets that feature different levels of presence of drum sounds shows that the new architecture consistently outperforms the widely-adopted baseline architecture that does not employ source separation. Copyright © 2021, The Authors. All rights reserved.

关键词： Blind source separation

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Learning management system for oil palm smallholder-owned plantations

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AIP Conference Proceedings 2023年第1期2594卷

作者： E. Firmansyah D. Nurjannah S. I. Dinarti D. Sudigyo T. Suparyanto B. Pardamean Department of Agrotechnology Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Departement of Agricultural Economics Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Departement of Agriculture Engineering Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

The production gap between oil palm smallholder-owned plantations and corporations shows a real problem in the national palm oil industry. To reduce the gap, user-friendly assistance for smallholder-owned plantations is needed for the cultivation of sustainable palm oil production. Android-based software can be a platform for bridging the transfer of knowledge and technology, transfer of problems, and transfer of solutions between planters and experts. The platform development was formulated in five stages which began with Learning Management System (LMS) application development, identification of Good Agricultural Practices (GAP) and Best Management Practices (BMP) materials, development of digital contents, uploading the digital content to the LMS server, and dissemination. The curriculum in the application platform was prepared based on the needs of the growers according to the identification results of the farmers' profiles. The curriculum consists of GAP and BMP material blocks. Each block was divided into discussion topics. The digital contents related to GAP and BMP were set in the LMS, which was built using Moodle platform.

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Fast, accurate, and versatile data analysis platform for the quantification of molecular spatiotemporal signals

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Cell 2025年第10期188卷 2794-2809.e21页

作者： Mi, Xuelong Chen, Alex Bo-Yuan Duarte, Daniela Carey, Erin Taylor, Charlotte R. Braaker, Philipp N. Bright, Mark Almeida, Rafael G. Lim, Jing-Xuan Ruetten, Virginia M.S. Wang, Yizhi Wang, Mengfan Zhang, Weizhan Zheng, Wei Reitman, Michael E. Huang, Yongkang Wang, Xiaoyu Li, Lei Deng, HanFei Shi, Song-Hai Poskanzer, Kira E. Lyons, David A. Nimmerjahn, Axel Ahrens, Misha B. Yu, Guoqiang Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Arlington 22203 VA United States Janelia Research Campus Howard Hughes Medical Institute Ashburn 20147 VA United States Department of Molecular and Cellular Biology Harvard University Cambridge 02138 MA United States Graduate Program in Neuroscience Harvard Medical School Boston 02115 MA United States Waitt Advanced Biophotonics Center The Salk Institute for Biological Studies La Jolla 92037 CA United States Department of Biochemistry & Biophysics University of California San Francisco San Francisco CA United States Neuroscience Graduate Program University of California San Francisco San Francisco CA United States Centre for Discovery Brain Sciences University of Edinburgh Edinburgh BioQuarter Edinburgh EH16 4SB United Kingdom Gatsby Computational Neuroscience Unit UCL London W1T 4JG United Kingdom Kavli Institute for Fundamental Neuroscience San Francisco CA United States Department of Automation Tsinghua University Beijing 100084 China IDG/McGovern Institute for Brain Research Tsinghua University Beijing 100084 China New Cornerstone Science Laboratory Tsinghua-Peking Center for Life Sciences Beijing Frontier Research Center for Biological Structure School of Life Sciences Tsinghua University Beijing 100084 China State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University Shanghai 200032 China Beijing National Research Center for Information Science and Technology Beijing 100084 China

Optical recording of intricate molecular dynamics is becoming an indispensable technique for biological studies, accelerated by the development of new or improved biosensors and microscopy technology. This creates major computational challenges to extract and quantify biologically meaningful spatiotemporal patterns embedded within complex and rich data sources, many of which cannot be captured with existing methods. Here, we introduce activity quantification and analysis (AQuA2), a fast, accurate, and versatile data analysis platform built upon advanced machine-learning techniques. It decomposes complex live-imaging-based datasets into elementary signaling events, allowing accurate and unbiased quantification of molecular activities and identification of consensus functional units. We demonstrate applications across a wide range of biosensors, cell types, organs, animal models, microscopy techniques, and imaging approaches. As exemplar findings, we show how AQuA2 identified drug-dependent interactions between neurons and astroglia, as well as distinct sensorimotor signal propagation patterns in the mouse spinal cord. © 2025 The Author(s)

关键词： AQuA2 astrocytes cell interaction analysis functional units glial cells image analysis machine learning molecular spatiotemporal signals neurons time-lapse imaging

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CNN-based cardiac motion extraction to generate deformable geometric left ventricle myocardial models from Cine MRI

arXiv

引用

arXiv 2021年

作者： Upendra, Roshan Reddy Wentz, Brian Jamison Simon, Richard Shontz, Suzanne M. Linte, Cristian A. Center for Imaging Science Rochester Institute of Technology RochesterNY United States Biomedical Engineering Rochester Institute of Technology RochesterNY United States Bioengineering Graduate Program University of Kansas LawrenceKS United States Electrical Engineering and Computer Science University of Kansas LawrenceKS United States Information and Telecommunication Technology Center University of Kansas LawrenceKS United States

Patient-specific left ventricle (LV) myocardial models have the potential to be used in a variety of clinical scenarios for improved diagnosis and treatment plans. Cine cardiac magnetic resonance (MR) imaging provides high resolution images to reconstruct patient-specific geometric models of the LV myocardium. With the advent of deep learning, accurate segmentation of cardiac chambers from cine cardiac MR images and unsupervised learning for image registration for cardiac motion estimation on a large number of image datasets is attainable. Here, we propose a deep leaning-based framework for the development of patient-specific geometric models of LV myocardium from cine cardiac MR images, using the Automated Cardiac Diagnosis Challenge (ACDC) dataset. We use the deformation field estimated from the VoxelMorph-based convolutional neural network (CNN) to propagate the isosurface mesh and volume mesh of the end-diastole (ED) frame to the subsequent frames of the cardiac cycle. We assess the CNN-based propagated models against segmented models at each cardiac phase, as well as models propagated using another traditional nonrigid image registration technique. © 2021, CC BY.

关键词： Image registration

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Missing Value Recovery for Single Cell RNA Sequencing Data

Missing Value Recovery for Single Cell RNA Sequencing Data

引用

International Conference on Computational science and Computational Intelligence (CSCI)

作者： Wenjuan Zhang William Yang John Talburt Sherman Weissman Mary Qu Yang MidSouth Bioinformatics Center and Bioinformatics Graduate Program University of Arkansas Little Rock and Univ. of Arkansas for Medical Sciences Little Rock USA Computer Science Department Princeton University Princeton USA Information Science Department University of Arkansas Little Rock Little Rock USA Department of Genetics Yale University New Haven USA

ISBN: (纸本)9781665458429

The emergence of single-cell sequencing technologies has enabled the production of high-resolution data at the individual cell level, providing unprecedented opportunities to capture cell population diversity and dissect the cellular heterogeneity of complex diseases. At the same time, relatively high biological and technical noise poses new challenges for single-cell data analysis. The single-cell RNA sequencing (scRNA-seq) data often contains substantial missing values due to gene dropout events. Here, we developed a convolutional neural network based model to recover missing values for scRNA-seq data. We first calculated the probability of dropout employing gamma-normal expectation maximum algorithm. Unlike most existing approaches, our model only recovered the expression values that have a dropout probability larger than a threshold. The mean square error and Pearson correlation coefficient were used to assess the accuracy of predicted expression values. The purity and entropy were computed to measure the homogeneity of cell clusters using imputed gene expression profiles. Across various scRNAseq datasets, our model demonstrated robust performance and achieved comparable or better results compared to the other imputation methods.

关键词： Sequential analysis Scientific computing Computational modeling RNA Sociology Production Mean square error methods

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Ultrastrong Spin-Photon Coupling in Gadolinium Gallium Garnet

Ultrastrong Spin-Photon Coupling in Gadolinium Gallium Garne...

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： T. Elijah Kritzell Junzhe Bao Jacques Doumani Jae Joon Lee Hongjing Xu Fuyang Tay Hiroyuki Nojiri Motoaki Bamba Andrey Baydin Junichiro Kono Department of Electrical and Computer Engineering Rice University Houston Texas USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston Texas USA Department of Physics and Astronomy Rice University Houston Texas USA Institute for Materials Research Tohoku University Senai Japan The Hakubi Center for Advanced Research Kyoto University Kyoto Japan Department of Physics I Kyoto University Kyoto Japan Smalley-Curl Institute Rice University Houston Texas USA Department of Material Science and NanoEngineering Rice University Houston Texas USA

We examine ultrastrong coupling of an ensemble of paramagnetic spins in Gd 3 Ga 5 O 12 with terahertz photons in the presence of a high external magnetic field both in the bulk and thin-film limits.

ISBN: (纸本)9781957171258

We examine ultrastrong coupling of an ensemble of paramagnetic spins in Gd 3 Ga 5 O 12 with terahertz photons in the presence of a high external magnetic field both in the bulk and thin-film limits.

关键词： CCD cameras Condensed matter Magnetic fields Photons Quantum optics Terahertz spectroscopy

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Ten-Year Compilation of #SaveKPK Twitter Dataset

Ten-Year Compilation of #SaveKPK Twitter Dataset

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International Conference on Information Management and Technology (ICIMTech)

作者： Reza Rahutomo Arif Budiarto Kartika Purwandari Anzaludin Samsinga Perbangsa Tjeng Wawan Cenggoro Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia School of Computer Science Bina Nusantara University Jakarta Indonesia School of Information Systems Bina Nusantara University Jakarta Indonesia BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728170718

ISBN: (纸本)9781728170725

Politic is one of the most favorite topics to discuss in social media for people in Indonesia. It was proven when a people movement to support the Commission of Corruption Eradication (KPK) named #SaveKPK has been enlived for ten years and become a trending topic on Twitter for several times. In this research, all tweets contain `#SaveKPK' are crawled and compiled using an alternative algorithm to get twitter historical data instead of using Twitter API. The result described the characteristic of the dataset statistically, from the most frequently used words to the most active users. A clustering algorithm named Latent Dirichlet Allocation (LDA) was run over the gathered text dataset to discover most relevant keywords using unsupervised learning approach.

关键词： Twitter computer science Resource management Natural language processing Information management Databases

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