The flowchart of the large model for font style transfer. by Weijia Zhu (65481)

Subjects:

Deep learning-based locomotive syndrome prediction method. by Junichi Kushioka (12236447)

Subjects:

Deep reinforcement learning process. by Sen Cao (6017846)

“…This study introduces an improved adaptive signal control approach using an enhanced dual-layer deep Q-network (EXP-DDQN), specifically tailored for intelligent connected environments. …”

Accuracy of multiple classifiers with a deep-learning-based feature set by Afira Aslam (21433666)

Subjects:

Inherently interpretable deep learning framework highlights clinically relevant image regions. by Kerol Djoumessi (21349992)

Few-Shot Symbol Detection in Engineering Drawings by Laura Jamieson (19759235)

Subjects:

Regression analyses of the number of all ultrasonic calls identified by the experienced researcher (A.P.; “Human”) and by the DeepSqueak machine learning tool. by Piotr Popik (292215)

Subjects:

Penalty conversions yield large practical decreases, but robustness and generalizability of result are limited by data quality and sample selection. by Ryan J. Treves (13527735)

Data Sheet 1_Grammar-constrained decoding for structured information extraction with fine-tuned generative models applied to clinical trial abstracts.pdf by David M. Schmidt (20517821)

“…In contrast, the evaluated pointer generator models decreased the performance drastically in some cases. …”

Decrease in loss during the training process. by Muhammad Idrees (92773)

Data from: Colony losses of stingless bees increase in agricultural areas, but decrease in forested areas by Malena Sibaja Leyton (18400983)

Dynamic Analysis of Membrane Emulsification: An In Situ Observation and Calculation with Deep Learning Model by Ke Zhou (131917)

“…In this study, a novel in situ observation device was developed, which couples a visible membrane module with a high-speed microscope system. Additionally, a deep learning model was embedded to realize the first visualization of the dynamic droplet formation process under demanding conditions. …”

<b>Supporting data for manuscript</b> "<b>Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins</b>" by Kira Shaw (18796168)

“…<p dir="ltr">The CSV file 'Eyreetal_DrainingVein_SourceData' contains the averaged time series traces and extracted metrics from individual experiments used across Figures 1-5 in the manuscript "Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins". The following acronyms included in the CSV file are defined as follows: Hbt is total hemoglobin, Art is artery region, DV is draining vein region, WV is whisker vein region, SEM is standard error mean, TS is time series, max peak is maximum peak, min peak is minima, AUC is area under the curve, WT is wild-type, AD is Alzheimer's disease, ATH is atherosclerosis and MIX is mixed AD/atherosclerosis. …”

Innovation and night light in large and small cities. by Saul Estrin (8629173)

Subjects:

Geographical distribution of large cities and small cities. by Saul Estrin (8629173)

Subjects:

Architecture of deep neural networks. by Ahmed Muqdad Alnasrallah (21647492)

“…This study proposes an IDS model for the IoMT that integrates advanced feature selection techniques and deep learning to enhance detection performance. The proposed model employs Information Gain (IG) and Recursive Feature Elimination (RFE) in parallel to select the top 50% of features, from which intersection and union subsets are created, followed by a deep autoencoder (DAE) to reduce dimensionality without losing important data. …”

ECoG timescales decrease during spatial attention. by Isabel Raposo (21615517)

Expression of lipid biosynthetic genes is decreased with polyamine depletion. by Yazmin E. Cruz-Pulido (20285238)

Subjects:

Comparison Of Performance at Different Optimizers and Learning Rates. by Mudhafar Jalil Jassim Ghrabat (22177655)

Subjects:

Overcoming limitations to customize DeepVariant for domesticated animals with TrioTrain by Robert Schnabel (236298)

“…While exclusively training with human genomes deters transferring deep-learning-based variant calling to new species, we use the diverse ancestry within bovids to illustrate the need for advanced tools designed for comparative genomics.…”