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arXiv 2024年

作者： Björnson, Emil Chae, Chan-Byoung Heath, Robert W. Marzetta, Thomas L. Mezghani, Amine Sanguinetti, Luca Rusek, Fredrik Castellanos, Miguel R. Jun, Dongsoo Demir, Özlem Tuğfe The School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden The School of Integrated Technology Yonsei University Seoul03722 Korea Republic of The Department of Electrical and Computer Engineering University of California San Diego La Jolla CA92161 United States The Department of Electrical and Computer Engineering New York University BrooklynNY11201 United States The Department of Electrical and Computer Engineering University of Manitoba WinnipegMBR3T 5V6 Canada The Dipartimento di Ingegneria dell’Informazione University of Pisa Pisa Italy Sony Europe B.V. Lund Sweden The Department of Electrical and Computer Engineering North Carolina State University RaleighNC27695 United States The TOBB University of Economics and Technology Ankara Turkey

The increasing demand for wireless data transfer has been the driving force behind the widespread adoption of Massive MIMO (multiple-input multiple-output) technology in 5G. The next-generation MIMO technology is now being developed to cater to the new data traffic and performance expectations generated by new user devices and services in the next decade. The evolution towards "ultra-massive MIMO (UM-MIMO)" is not only about adding more antennas but will also uncover new propagation and hardware phenomena that can only be treated by jointly utilizing insights from the communication, electromagnetic (EM), and circuit theory areas. This article offers a comprehensive overview of the key benefits of the UM-MIMO technology and the associated challenges. It explores massive multiplexing facilitated by radiative near-field effects, characterizes the spatial degrees-of-freedom, and practical channel estimation schemes tailored for massive arrays. Moreover, we provide a tutorial on EM theory and circuit theory, and how it is used to obtain physically consistent antenna and channel models. Subsequently, the article describes different ways to implement massive and dense antenna arrays, and how to codesign antennas with signal processing. The main open research challenges are identified at the end. Copyright © 2024, The Authors. All rights reserved.

关键词： Degrees of freedom (mechanics)

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Electro-Polymerization Process with Double-Sided Electrodes for Supercapacitors

Electro-Polymerization Process with Double-Sided Electrodes ...

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International Flexible Electronics Technology Conference (IFETC)

作者： Nandu Koripally Lulu Yao Naresh Eedugurala Jason D. Azoulay Tse Nga Ng Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA Materials Science Engineering Program University of California San Diego La Jolla CA USA Chemisty Department Georgia Institute of Technology Atlanta GA USA

Supercapacitors are known for longer cycle life and faster charging rate compared to batteries. However, the energy density of supercapacitors requires improvement to expand their application space. To raise the energy density of redox supercapacitors, this work demonstrates electro-polymerization on both sides of electrodes to increase material loading, reduce overall device mass, and consequently increase the capacitance, power, and energy density of supercapacitors. We achieved an energy density of 1823 mWh kg -1 and a power density of 16.5 W kg -1 with a wide potential window of 3 V.

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TRANSCENDENCE AND NORMALITY OF COMPLEX NUMBERS VIA HURWITZ CONTINUED FRACTIONS

arXiv

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arXiv 2023年

作者： García-Ramos, Felipe Robert, Gerardo González Hussain, Mumtaz Physics Institute Universidad Autónoma de San luis Potosí Mexico Faculty of Mathematics and Computer Science Uniwersytet Jagielloński Poland Department of Mathematical and Physical Sciences La Trobe University Bendigo3552 Australia

We study the topological, dynamical, and descriptive set-theoretic properties of Hurwitz continued fractions. Hurwitz continued fractions associate an infinite sequence of Gaussian integers to every complex number which is not a Gaussian rational. The resulting space of sequences of Gaussian integers Ω is not closed. Using an iterative procedure, we show that Ω contains a natural subset whose closure R encodes continued fraction expansions of complex numbers which are not Gaussian rationals. We prove that (R, σ) is a subshift with a feeble specification property. As an application, we determine the rank in the Borel hierarchy of the set of Hurwitz normal numbers with respect to the complex Gauss measure. We also construct a family of complex transcendental numbers with bounded partial *** Codes 11J70 (Primary) 03E15, 11J81 (Secondary) © 2023, CC BY.

关键词： Topology

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Deep learning assisted high resolution microscopy image processing for phase segmentation in functional composite materials

arXiv

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arXiv 2024年

作者： Raghavendran, Ganesh Han, Bing Adekogbe, Fortune Bai, Shuang Lu, Bingyu Wu, William Zhang, Minghao Meng, Ying Shirley Department of NanoEngineering University of California San Diego La Jolla CA92093 United States Pritzker School of Molecular Engineering University of Chicago ChicagoIL60637 United States Department of Chemical and Petroleum Engineering University of Lagos Lagos101017 Nigeria Department of Computer Science University of California San Diego La Jolla CA92093 United States

In the domain of battery research, the processing of high-resolution microscopy images is a challenging task, as it involves dealing with complex images and requires a prior understanding of the components involved. The utilization of deep learning methodologies for image analysis has attracted considerable interest in recent years, with multiple investigations employing such techniques for image segmentation and analysis within the realm of battery research. However, the automated analysis of high-resolution microscopy images for detecting phases and components in composite materials is still an underexplored area. This work proposes a novel workflow for FFT-based segmentation, periodic component detection and phase segmentation from raw high resolution Transmission Electron Microscopy (TEM) images using a trained U-Net segmentation model. The developed model can expedite the detection of components and their phase segmentation, diminishing the temporal and cognitive demands associated with scrutinizing an extensive array of TEM images, thereby mitigating the potential for human errors. This approach presents a novel and efficient image analysis approach with broad applicability beyond the battery field and holds potential for application in other related domains characterized by phase and composition distribution, such as alloy production. © 2024, CC BY.

关键词： Image segmentation

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Physics-Based ML and Polarimetric SAR for Soil Moisture Retrieval

Physics-Based ML and Polarimetric SAR for Soil Moisture Retr...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Lorenzo G. Papale Fabio Del Frate Leila Guerriero Giovanni Schiavon Jean Bouchat Department of Civil Engineering and Computer Science Engineering (DICII) Tor Vergata University of Rome Rome Italy Earth and Life Institute Universitè Catholique de Louvain Louvain-la-Nueve Belgium

Soil moisture represents a significant guiding factor for agricultural activities, especially for smart irrigation and crop yield estimation. In this context, SAR data is one of the most valuable sources of information for accurate and continuative estimation of soil moisture in agricultural areas. However, SAR-derived soil moisture retrievals are affected by several factors, including the vegetation cover, which is responsible for additional signal attenuation and scattering mechanisms. Concerning the algorithms for soil moisture estimation, Machine Learning (ML) has proved to be a valuable instrument for finding relations between SAR data and the soil dielectric properties. For this purpose, this study aims to synergically adopt electromagnetic data modelling and a ML algorithm to estimate the scattering contributions associated with the ground and demonstrate that they are more sensitive to soil moisture than the total received signal. To apply such approach to real SAR data, airborne acquisitions at L-band will be considered.

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Evaluation of large language models for discovery of gene set function

arXiv

引用

arXiv 2023年

作者： Hu, Mengzhou Alkhairy, Sahar Lee, Ingoo Pillich, Rudolf T. Fong, Dylan Smith, Kevin Bachelder, Robin Ideker, Trey Pratt, Dexter Department of Medicine University of California La Jolla San DiegoCA United States Department of Computer Science and Engineering University of California La Jolla San DiegoCA United States Department of Physics University of California La Jolla San DiegoCA United States

Gene set analysis is a mainstay of functional genomics, but it relies on curated databases of gene functions that are incomplete. Here we evaluate five large language Models (LLMs) for their ability to discover the common biological functions represented by a gene set, substantiated by supporting rationale, citations and a confidence assessment. Benchmarking against canonical gene sets from the Gene Ontology, GPT-4 confidently recovered the curated name or a more general concept (73% of cases), while benchmarking against random gene sets correctly yielded zero confidence. Gemini-Pro and Mixtral-Instruct showed ability in naming but were falsely confident for random sets, whereas Llama2-70b had poor performance overall. In gene sets derived from ‘omics data, GPT-4 identified novel functions not reported by classical functional enrichment (32% of cases), which independent review indicated were largely verifiable and not hallucinations. The ability to rapidly synthesize common gene functions positions LLMs as valuable ‘omics assistants. © 2023, CC BY.

关键词： Benchmarking

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Machine Learning Models to Classify Normal and Fibrotic Mouse Liver Model using Dielectric Properties

Machine Learning Models to Classify Normal and Fibrotic Mous...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Samaddar, Poulami Samadder, Poushali Baraskar, Bhavana Anvekar, Priyanka Khanal, Shalil Gaddam, Sunil Roy, Sayan Mitra, Dipankar Kostallari, Enis Arunachalam, Shivaram P. Division of Gastroenterology and Hepatology Mayo Clinic RochesterMN United States Department of Radiology Mayo Clinic RochesterMN United States Department of Medicine Mayo Clinic RochesterMN United States Gastroenterology Research Division of Gastroenterology and Hepatology Mayo Clinic RochesterMN United States Department of Electrical Engineering and Computer Science South Dakota Mines Rapid CitySD United States Department of Computer Science University of Wisconsin La CrosseWI United States

ISBN: (纸本)9781665468190

In this work dielectric permittivity was used to classify between normal and fibrotic mouse liver model. Data set was binary classified using six machine learning models i.e., Logistic Regression, Support vector classification, K-Nearest Neighbors Classifier, Decision Tree Classifier, Random Forest Classifier and Naive Bayes. Based on the accuracy and AUC score, K-Nearest Neighbors Classifier shows the best performance. This is a preliminary study to understand the feasibility of using electrical property such as complex permittivity as a biomarker to classify normal and fibrotic liver of diseased mouse model using machine learning models. The result of this work shows that the machine learning models can be used to distinguish between healthy and diseased liver with more than 80% accuracy. This technology shows immense possibility to expand in future to quantify disease severity and develop non-invasive diagnostic tools. © 2022 IEEE.

关键词： Nearest neighbor search

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Exploiting CNNs for Semantic Segmentation with Pascal VOC

arXiv

引用

arXiv 2023年

作者： Prakash, Sourabh Shah, Priyanshi Agrawal, Ashrya Halıcıoglu Data Science Institute University of California La Jolla San DiegoCA92092 United States Department of Computer Science and Engineering University of California La Jolla San DiegoCA92092 United States

In this paper, we present a comprehensive study on semantic segmentation with the Pascal VOC dataset. Here, we have to label each pixel with a class which in turn segments the entire image based on the objects/entities present. To tackle this, we firstly use a Fully Convolution Network (FCN) baseline which gave 71.31% pixel accuracy and 0.0527 mean IoU. We analyze its performance and working and subsequently address the issues in the baseline with three improvements - a) cosine annealing learning rate scheduler(pixel accuracy: 72.86%, IoU: 0.0529), b) data augmentation(pixel accuracy: 69.88%, IoU: 0.0585) c) class imbalance weights(pixel accuracy: 68.98%, IoU: 0.0596). Apart from these changes in training pipeline, we also explore three different architectures - a) Our proposed model - Advanced FCN (pixel accuracy: 67.20%, IoU: 0.0602) b) Transfer Learning with ResNet (Best performance) (pixel accuracy: 71.33%, IoU: 0.0926) c) UNet(pixel accuracy: 72.15%, IoU: 0.0649). We observe that the improvements help in greatly improving the performance, as reflected both, in metrics and segmentation maps. Interestingly, we observe that among the improvements, dataset augmentation has the greatest contribution. Also, note that transfer learning model performs the best on the pascal dataset. We analyse the performance of these using loss, accuracy and IoU plots along with segmentation maps, which help us draw valuable insights about the working of the models. © 2023, CC BY.

关键词： Pixels

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A Conceptual Exploration of Generative AI-Induced Cognitive Dissonance and its Emergence in university-Level Academic Writing

arXiv

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arXiv 2025年

作者： Seran, Carl Errol Tan, Myles Joshua Toledo Karim, Hezerul Abdul AlDahoul, Nouar Biology Program College of Arts and Sciences University of St. La Salle Bacolod Philippines Department of Natural Sciences College of Arts and Sciences University of St. La Salle Bacolod Philippines Faculty of Education University of the Philippines Open University Los Baños Philippines Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL United States Department of Epidemiology College of Public Health and Health Professions College of Medicine University of Florida GainesvilleFL United States Department of Chemical Engineering College of Engineering and Technology University of St. La Salle Bacolod Philippines Department of Electronics Engineering College of Engineering and Technology University of St. La Salle Bacolod Philippines Yo-Vivo Corporation Bacolod Philippines Faculty of Engineering Multimedia University Persiaran Multimedia Cyberjaya Malaysia Department of Computer Science Division of Science New York University Abu Dhabi Abu Dhabi United Arab Emirates

The integration of Generative Artificial Intelligence (GenAI) intouniversity-level academic writing presents both opportunities and challenges,particularly in relation to cognitive dissonance (CD). This work explores howGenAI serves as both a trigger and amplifier of CD, as students navigateethical concerns, academic integrity, and self-efficacy in their writingpractices. By synthesizing empirical evidence and theoretical insights, weintroduce a hypothetical construct of GenAI-induced CD, illustrating thepsychological tension between AI-driven efficiency and the principles oforiginality, effort, and intellectual ownership. We further discuss strategiesto mitigate this dissonance, including reflective pedagogy, AI literacyprograms, transparency in GenAI use, and discipline-specific task *** approaches reinforce critical engagement with AI, fostering a balancedperspective that integrates technological advancements while safeguarding humancreativity and learning. Our findings contribute to ongoing discussions on AIin education, self-regulated learning, and ethical AI use, offering aconceptual framework for institutions to develop guidelines that align AIadoption with academic values. © 2025, CC BY.

关键词： Generative adversarial networks

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COSIME: FeFET based Associative Memory for In-Memory Cosine Similarity Search

COSIME: FeFET based Associative Memory for In-Memory Cosine ...

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IEEE International Conference on computer-Aided Design

作者： Che-Kai Liu Haobang Chen Mohsen Imani Kai Ni Arman Kazemi Ann Franchesca laguna Michael Niemier Xiaobo Sharon Hu Liang Zhao Cheng Zhuo Xunzhao Yin College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Computer Science and Engineering University of Notre Dame IN USA Department of Computer Science University of California Irvine CA USA Department of Electrical and Microelectronic Engineering Rochester Institute of Technology NY USA Department of Computer Technology De La Salle University Manilla Philippines Zhejiang Lab China

In a number of machine learning models, an input query is searched across the trained class vectors to find the closest feature class vector in cosine similarity metric. However, performing the cosine similarities between the vectors in Von-Neumann machines involves a large number of multiplications, Euclidean normalizations and division operations, thus incurring heavy hardware energy and latency overheads. Moreover, due to the memory wall problem that presents in the conventional architecture, frequent cosine similarity-based searches (CSSs) over the class vectors requires a lot of data movements, limiting the throughput and efficiency of the system. To overcome the aforementioned challenges, this paper introduces COSIME, a general in-memory associative memory (AM) engine based on the ferroelectric FET (FeFET) device for efficient CSS. By leveraging the one-transistor AND gate function of FeFET devices, current-based translinear analog circuit and winner-take-all (WTA) circuitry, COSIME can realize parallel in-memory CSS across all the entries in a memory block, and output the closest word to the input query in cosine similarity metric. Evaluation results at the array level suggest that the proposed COSIME design achieves 333× and 90.5× latency and energy improvements, respectively, and realizes better classification accuracy when compared with an AM design implementing approximated CSS. The proposed in-memory computing fabric is evaluated for an HDC problem, showcasing that COSIME can achieve on average 47.1× and 98.5× speedup and energy efficiency improvements compared with an GPU implementation.

关键词： Measurement Support vector machines Associative memory Nonvolatile memory Graphics processing units Throughput Energy efficiency

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