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arXiv 2021年

作者： Ma, Yu-Jen Shuai, Hong-Han Cheng, Wen-Huang Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu Taiwan Institute of Electronics National Chiao Tung Univresity Hsinchu300 Taiwan Artificial Intelligence and Data Science Program National Chung Hsing University Taichung400 Taiwan

In this paper, we propose a novel SpatioTemporal convolutional Dense Network (STDNet) to address the video-based crowd counting problem, which contains the decomposition of 3D convolution and the 3D spatiotemporal dilated dense convolution to alleviate the rapid growth of the model size caused by the Conv3D layer. Moreover, since the dilated convolution extracts the multiscale features, we combine the dilated convolution with the channel attention block to enhance the feature representations. Due to the error that occurs from the difficulty of labeling crowds, especially for videos, imprecise or standard-inconsistent labels may lead to poor convergence for the model. To address this issue, we further propose a new patch-wise regression loss (PRL) to improve the original pixel-wise loss. Experimental results on three video-based benchmarks, i.e., the UCSD, Mall and WorldExpo’10 datasets, show that STDNet outperforms both image- and video-based state-of-the-art methods. The source codes are released at https://***/STDNet/STDNet. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

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Deep convolutional neural network model for short-term electricity price forecasting

arXiv

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arXiv 2020年

作者： Cheng, Hsu-Yung Kuo, Ping-Huan Shen, Yamin Huang, Chiou-Jye Department of Computer Science and Information Engineering National Central University Computer and Intelligent Robot Program for Bachelor Degree National Pingtung University School of Electrical Engineering and Automation Jiangxi University of Science and Technology

In the modern power market, electricity trading is an extremely competitive industry. More accurate price forecast is crucial to help electricity producers and traders make better decisions. In this paper, a novel method of convolutional neural network (CNN) is proposed to rapidly provide hourly forecasting in the energy market. To improve prediction accuracy, we divide the annual electricity price data into four categories by seasons and conduct training and forecasting for each category respectively. By comparing the proposed method with other existing methods, we find that the proposed model has achieved outstanding results, the mean absolute percentage error (MAPE) and root mean square error (RMSE) for each category are about 5.5% and 3, respectively. Copyright © 2020, The Authors. All rights reserved.

关键词： Power markets

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Longitudinal and Geographic Trends in Perceived Racial Discrimination Among Adolescents in the United States: The Adolescent Brain Cognitive Development Study

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Journal of Adolescent Health 2025年第1期77卷 118-127页

作者： Fields, Christopher T. Black, Carmen Calhoun, Amanda J. Rosenblatt, Matthew Rodriguez, Raimundo Aina, Joseph Thind, Jannat K. Grayson, Jalen Khalifa, Fahmi Assari, Shervin Zhou, Xin Nagata, Jason M. Gee, Dylan G. Program for Recovery and Community Health Department of Psychiatry Yale School of Medicine New Haven Connecticut United States Department of Radiology Magnetic Resonance Research Center Yale School of Medicine New Haven Connecticut United States Institute of Living at Hartford Healthcare Hartford Connecticut United States Department of Psychiatry Yale School of Medicine New Haven Connecticut United States Department of Psychiatry University of Connecticut Health Center Farmington Connecticut United States Department of Electrical and Computer Engineering Morgan State University Detroit MI United States Semel Institute for Neuroscience and Human Behavior University of California-Los Angeles Los Angeles California United States Department of Family Medicine Charles R Drew University of Medicine and Science Los Angeles California United States Department of Biostatistics Yale School of Medicine New Haven Connecticut United States Division of Adolescent and Young Adult Medicine Department of Pediatrics University of California San Francisco San Francisco California United States Department of Psychology Yale University New Haven Connecticut United States

Purpose: To assess longitudinal and geographic variation in perceived discrimination from ages 10–11 to 13–14 years in the Adolescent Brain Cognitive Development cohort, and to examine how these experiences are shaped by contextual factors such as neighborhood segregation and state-level racial bias. Methods: Data were drawn from the longitudinal Adolescent Brain Cognitive Development Study (release 5.1), analyzing years 1, 2, and 4, corresponding to approximate ages 10–11, 11–12, and 13–14 years. Perceived discrimination was assessed using items adapted from the Perceived Discrimination Scale. Mixed-effects logistic regression models examined how perceived discrimination varied across time, demographic factors, and contextual variables, with models weighted using American Community Survey raked propensity scores to ensure national representativeness. Results: Black, Asian American and Pacific Islander, and Other/Multiracial non-Hispanic youth showed increasing trajectories of perceived discrimination over time, while Native American and White non-Hispanic youth exhibited decreasing trends. Significant geographic variation emerged, with Black youth reporting elevated discrimination across all regions, particularly in the West and South. Youth living in areas with concentrated poor Black households and in states with high anti-Black bias reported higher discrimination. Youth with immigrant backgrounds generally reported higher levels of perceived discrimination across most racial/ethnic groups. Discussion: Perceived discrimination follows distinct developmental trajectories during early adolescence that vary significantly by race, ethnicity, geography, and structural context. These findings highlight the critical need for targeted interventions during this developmental period, particularly for Black, Asian American and Pacific Islander, and Other/Multiracial youth. Context-specific approaches to addressing racism are essential for mitigating its harmful effects on a

关键词： Adolescent health Health disparities Intergroup relations Interpersonal racism Public health Racial discrimination Racism Racism-related stress Social determinants of health

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Superformula-Inspired Split-Ring Resonators with Applications to Compact Bandpass Filters

Superformula-Inspired Split-Ring Resonators with Application...

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2019 International Applied Computational Electromagnetics Society Symposium in Miami, ACES-Miami 2019

作者： Hill, Zephaniah McShane, Jack Zapata, Roman Shamaileh, Khair Al Abushamleh, Said Electrical and Computer Engineering Department Purdue University Northwest Hammond United States Physics Department-Engineering Program University of Nebraska Kearney Kearney United States

ISBN: (纸本)9780996007887

We introduce an effective technique for miniaturizing a split-ring resonator (SRR) structure by replacing its conventional circular rings with shapes obtained from the superformula. By maintaining the resonator's effective electrical lengths and the gaps between the rings, our experiments have found it possible to achieve a comparable electrical performance at a reduced physical size. To validate the design concept, cross-like SRR bandpass filter (BPF) layouts with a 1.2 GHz center frequency are simulated and measured. A 21.3% reduction in the physical area is achieved with acceptable ports matching and transmission parameters. © 2019 ACES.

关键词： Bandpass filters

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Electrochemical measurement of serotonin by Au-CNT electrodes fabricated on microporous cell culture membranes

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Microsystems & Nanoengineering 2020年第1期6卷 494-506页

作者： Ashley A.Chapin Pradeep R.Rajasekaran David N.Quan Liangbing Hu Jens Herberholz William E.Bentley Reza Ghodssi Fischell Department of Bioengineering College ParkMD 20742USA Institute for Systems Research College ParkMD 20740USA Department of Materials Science and Engineering College ParkMD 20740USA Department of Psychology and Neuroscience and Cognitive Science Program College ParkMD 20740USA Institute for Bioscience and Biotechnology Research RockvilleMD 20850USA Robert E.Fischell Institute for Biomedical Devices RockvilleMD 20850USA Department of Electrical and Computer Engineering College ParkMD 20742USA

Gut–brain axis(GBA)communication relies on serotonin(5-HT)signaling between the gut epithelium and the peripheral nervous system,where 5-HT release patterns from the basolateral(i.e.,bottom)side of the epithelium activate nerve *** have been few quantitative studies of this gut-neuron signaling due to a lack of real-time measurement tools that can access the basolateral gut *** vitro platforms allow quantitative studies of cultured gut tissue,but they mainly employ offline and endpoint assays that cannot resolve dynamic molecular-release ***,we present the modification of a microporous cell culture membrane with carbon nanotube-coated gold(Au-CNT)electrodes capable of continuous,label-free,and direct detection of 5-HT at physiological *** characterization of single-walled carbon nanotube(SWCNT)-coated Au electrodes shows increased electroactive surface area,5-HT specificity,sensitivity,and saturation time,which are correlated with the CNT film drop-cast *** microliters of CNT films,with a 10-min saturation time,0.6μA/μM 5-HT sensitivity,and reliable detection within a linear range of 500 nM–10μM 5-HT,can be targeted for high-concentration,high-time-resolution 5-HT *** films(12.5μL)with a 2-h saturation time,4.5μA/μM 5-HT sensitivity,and quantitative detection in the linear range of 100 nM–1μM can target low concentrations with low time *** electrodes achieved continuous detection of dynamic diffusion across the porous membrane,mimicking basolateral 5-HT release from cells,and detection of cell-released 5-HT from separately cultured RIN14B cell ***-integrated cell culture systems such as this can improve in vitro molecular detection mechanisms and aid in quantitative GBA signaling studies.

关键词： release saturation dynamic

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Prediction of lithium-ion battery capacity in UAVs 6

Prediction of lithium-ion battery capacity in UAVs

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6th International Conference on Control, Decision and Information Technologies, CoDIT 2019

作者： Costa, Erick F. Souza, Darielson A. Pinto, Vandilberto P. Araujo, Miqueias S. Peixoto, Artur M. Da Costa, Erivaldo P. Post-Graduate Program in Electrical and Computer Engineering Federal University of Ceará Brazil Federal University of Rio Grande Do Norte Brazil

ISBN: (纸本)9781728105215

This article proposes a research on degradation of lithium-ion batteries. The life of these batteries can be measured by monitoring capacity degradation. A dataset made available by NASA for the present study was used. Also, 3 methods were used to predict the failure. The methods were Non-recursive Least Squares, a Kalman Filter and an ELM neural network with a very simple architecture, but the results achieved good results and can be compared to other more robust algorithms. © 2019 IEEE.

关键词： Kalman filters

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MorphoITH: A Framework for Deconvolving Intra-Tumor Heterogeneity Using Tissue Morphology

arXiv

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arXiv 2025年

作者： Nielsen, Aleksandra Weronika Manoochehri, Hafez Eslami Zhong, Hua Panwar, Vandana Jarmale, Vipul Jasti, Jay Nourani, Mehrdad Rakheja, Dinesh Brugarolas, James Kapur, Payal Rajaram, Satwik Lyda Hill Department of Bioinformatics University of Texas Southwestern Medical Center DallasTX United States Department of Electrical and Computer Engineering The University of Texas at Dallas RichardsonTX United States Department of Pathology University of Texas Southwestern Medical Center DallasTX United States Kidney Cancer Program Simmons Comprehensive Cancer Center University of Texas Southwestern Medical Center DallasTX United States University of Texas Southwestern Medical Center DallasTX United States Department of Urology University of Texas Southwestern Medical Center at Dallas DallasTX United States

The ability of tumors to evolve and adapt by developing subclones in different genetic and epigenetic states is a major challenge in oncology. Traditional tools like multi-regional sequencing used to study tumor evolution and the resultant intra-tumor heterogeneity (ITH) are often impractical because of their resource-intensiveness and limited scalability. Here, we present MorphoITH, a novel framework that leverages histopathology slides to deconvolve molecular ITH through tissue morphology. MorphoITH integrates a self-supervised deep learning similarity measure to capture phenotypic variation across multiple dimensions (cytology, architecture, and microenvironment) with rigorous methods to eliminate spurious sources of variation. Using a prototype of ITH, clear cell renal cell carcinoma (ccRCC), we show that MorphoITH captures clinically-significant biological features, such as vascular architecture and nuclear grades. Furthermore, we find that MorphoITH recognizes differential biological states corresponding to subclonal changes in key driver genes (BAP1/PBRM1/SETD2). Finally, by applying MorphoITH to a multi-regional sequencing experiment, we postulate evolutionary trajectories that largely recapitulate genetic evolution. In summary, MorphoITH provides a scalable phenotypic lens that bridges the gap between histopathology and genomics, advancing precision oncology. © 2025, CC BY-NC-ND.

关键词： Tumors

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Winter Cabin Water Tap Monitor and Release System to Reduce Risk of Freezing 10

Winter Cabin Water Tap Monitor and Release System to Reduce ...

引用

10th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2019

作者： Kaprielian, Paul L. Pham, Thach Young, Alex Dow, Douglas E. Grenquist, Scott A. Decker, Bruce M. Program in Electronic Eng. Technology Wentworth Institute of Technology Dept. of Electrical and Computer Engineering BostonMA United States

ISBN: (纸本)9781728138855

Frozen water pipes lead to leaks that result in property damage and repair costs. Frozen water pipes not only occur in northern climates, but also in more moderate climates that have occasional periods of temperatures below the freezing point. Water pipes at high risk for ice formation include ones outside of buildings, in poorly insulated structures or in seasonal buildings that are not heated during the winter months. Methods of protection include heating the building, insulation and heat tape on exposed water pipes, turning off the water supply and draining all pipes of water, or turning on a water valve to allow a continual flow of water through the pipes during the entire period having risk of low temperatures. The flow would hinder ice formation in the pipes. These protective measures have associated costs for equipment, heating fuel, and water loss. If the low temperature drops further or is sustained for longer periods than expected, there would still be a risk of frozen pipes. The purpose of this project was to develop and test a prototype system to monitor temperature, and initiate intermittent bursts of water flow only when temperatures are low enough to have risk of ice formation in the water pipes. The prototype utilized a temperature sensor, Arduino microcontroller, power supply, relay switch, and solenoid valve. The solenoid valve was incorporated into the water facet of a sink. When the sensor detected temperatures below the threshold of 25°C, intermittent bursts of flow were initiated, with 20 seconds of water flow followed by 2 minutes of no flow. Each flow event was recorded in a web service. A HTTP Get request was transmitted to a Google App Script program that stored the date and time in a Google Sheet. The owner or manager could remotely monitor the intermittent flow events. © 2019 IEEE.

关键词： Temperature

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Artificial Intelligence for Digital and Computational Pathology

arXiv

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arXiv 2023年

作者： Song, Andrew H. Jaume, Guillaume Williamson, Drew F.K. Lu, Ming Y. Vaidya, Anurag Miller, Tiffany R. Mahmood, Faisal Department of Pathology Brigham and Women's Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Harvard Medical School BostonMA United States

Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence, including deep learning, have boosted the field of computational pathology. This field holds tremendous potential to automate clinical diagnosis, predict patient prognosis and response to therapy, and discover new morphological biomarkers from tissue images. Some of these artificial intelligence-based systems are now getting approved to assist clinical diagnosis;however, technical barriers remain for their widespread clinical adoption and integration as a research tool. This Review consolidates recent methodological advances in computational pathology for predicting clinical end points in whole-slide images and highlights how these developments enable the automation of clinical practice and the discovery of new biomarkers. We then provide future perspectives as the field expands into a broader range of clinical and research tasks with increasingly diverse modalities of clinical data. © 2023, CC BY.

关键词： Pathology

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Two-step electronic response to magnetic ordering in a van der Waals ferromagnet

引用

Physical Review B 2024年第4期109卷 045416-045416页

作者： Han Wu Jian-Xin Zhu Lebing Chen Matthew W. Butcher Ziqin Yue Dongsheng Yuan Yu He Ji Seop Oh Bin Gao Jianwei Huang Shan Wu Cheng Gong Yucheng Guo Sung-Kwan Mo Jonathan Denlinger Donghui Lu Makoto Hashimoto Matthew B. Stone Alexander I. Kolesnikov Songxue Chi Junichiro Kono Andriy H. Nevidomskyy Robert J. Birgeneau Pengcheng Dai Ming Yi Department of Physics and Astronomy and Rice Center for Quantum Materials Rice University Houston Texas 77005 USA Theoretical Division Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Center for Integrated Nanotechnologies Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Department of Physics University of California at Berkeley Berkeley California 94720 USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston Texas 77005 USA Materials Sciences Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA National Institute for Materials Science 1-1 Namiki Tsukuba Ibaraki 305-0044 Japan Department of Applied Physics Yale University New Haven Connecticut 06511 USA Department of Electrical and Computer Engineering and Quantum Technology Center University of Maryland College Park Maryland 20742 USA Advanced Light Source Lawrence Berkeley National Laboratory Berkeley California 94720 USA Stanford Synchrotron Radiation Lightsource SLAC National Accelerator Laboratory Menlo Park California 94025 USA Neutron Scattering Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Department of Electrical and Computer Engineering Rice University Houston Texas 77005 USA Smalley-Curl Institute Rice University Houston Texas 77005 USA Department of Material Science and Nanoengineering Rice University Houston Texas 77005 USA Department of Materials Science and Engineering University of California Berkeley California 94720 USA

The two-dimensional material Cr2Ge2Te6 is a member of the class of insulating van der Waals (vdW) magnets. Here, using high resolution angle-resolved photoemission spectroscopy in a detailed temperature dependence study, we identify a clear response of the electronic structure to a dimensional crossover in the form of two distinct temperature scales marking onsets of modifications in the electronic structure. Specifically, we observe Te p-orbital-dominated bands to undergo changes at the Curie transition temperature TC while the Cr d-orbital-dominated bands begin evolving at a higher temperature scale. Combined with neutron scattering, density functional theory calculations, and Monte Carlo simulations, we find that the electronic system can be consistently understood to respond sequentially to the distinct temperatures at which in-plane and out-of-plane spin correlations exceed a characteristic length scale. Our findings reveal the sensitivity of the orbital-selective electronic structure for probing the dynamical evolution of local moment correlations in vdW insulating magnets.

关键词： Electronic structure Magnetic order Magnetic phase transitions Van der Waals systems Angle-resolved photoemission spectroscopy

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