Identification of cell cycle re-engaging postmitotic excitatory neurons in single-nucleus transcriptomic profiles. by Deng Wu (805710)

miRNA profiling in renal carcinoma suggest the existence of a group of pro-angionenic tumors in localized clear cell renal carcinoma - Fig 3 by Lucía Trilla-Fuertes (4096813)

Image_1_MRI-Based Classification of Neuropsychiatric Systemic Lupus Erythematosus Patients With Self-Supervised Contrastive Learning.tif by Francesca Inglese (12093458)

Table_1_MRI-Based Classification of Neuropsychiatric Systemic Lupus Erythematosus Patients With Self-Supervised Contrastive Learning.pdf by Francesca Inglese (12093458)

Video_1_ES-ImageNet: A Million Event-Stream Classification Dataset for Spiking Neural Networks.MP4 by Yihan Lin (115246)

“…<p>With event-driven algorithms, especially spiking neural networks (SNNs), achieving continuous improvement in neuromorphic vision processing, a more challenging event-stream dataset is urgently needed. …”

Supporting data for "Structural Identification and Model Updating Based on Active Learning Kriging Approach and Bayesian Inference" by Ye Yuan (9071153)

“…The Kriging model generated by this data-driven process with a limited sample size has satisfactory local accuracy, high efficiency and robust performance for model updating. …”

An overview of the Nanopore, tri-color protein identification method. by Shilo Ohayon (6239066)

<b>A study on an efficient citrus Huanglong disease detection algorithm based on three-channel aggregated attention</b> by yizong wang (20387247)

“…Additionally, the inference speed increased by 14.6%, fully meeting the real-time requirements for detecting diseases in citrus fields and illustrating the effectiveness and advanced nature of the improved algorithm, thereby providing robust support for the rapid identification of diseases in the citrus cultivation process.…”

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows by Pierre-Alexis DELAROCHE (22092572)

“…Performance Profiling Algorithms Energy Measurement Methodology # Pseudo-algorithmic representation of measurement protocol def capture_energy_metrics(workflow_type: WorkflowEnum, asset_vector: List[PhotoAsset]) -> EnergyProfile: baseline_power = sample_idle_power_draw(duration=30) with PowerMonitoringContext() as pmc: start_timestamp = rdtsc() # Read time-stamp counter if workflow_type == WorkflowEnum.LOCAL: result = execute_local_pipeline(asset_vector) elif workflow_type == WorkflowEnum.CLOUD: result = execute_cloud_pipeline(asset_vector) end_timestamp = rdtsc() energy_profile = EnergyProfile( duration=cycles_to_seconds(end_timestamp - start_timestamp), peak_power=pmc.get_peak_consumption(), average_power=pmc.get_mean_consumption(), total_energy=integrate_power_curve(pmc.get_power_trace()) ) return energy_profile Statistical Analysis Framework Our analytical pipeline employs advanced statistical methodologies including: Variance Decomposition: ANOVA with nested factors for hardware configuration effects Regression Analysis: Generalized Linear Models (GLM) with log-link functions for energy modeling Temporal Analysis: Fourier transform-based frequency domain analysis of power consumption patterns Cluster Analysis: K-means clustering with Euclidean distance metrics for workflow classification Data Validation and Quality Assurance Measurement Uncertainty Quantification All energy measurements incorporate systematic and random error propagation analysis: Instrument Precision: ±0.1W for CPU power, ±0.5W for GPU power Temporal Resolution: 1ms sampling with Nyquist frequency considerations Calibration Protocol: NIST-traceable power standards with periodic recalibration Environmental Controls: Temperature-compensated measurements in climate-controlled facility Outlier Detection Algorithms Statistical outliers are identified using the Interquartile Range (IQR) method with Tukey's fence criteria (Q₁ - 1.5×IQR, Q₃ + 1.5×IQR). …”

The SUSTech-SYSU dataset for automated exudate detection and diabetic retinopathy grading by Li Lin (9031415)

Speed of the CeNDeR pipeline. by Yuxiang Wu (748543)

The CeNDeR pipeline. by Yuxiang Wu (748543)

Image_2_Differential diagnosis of frontotemporal dementia subtypes with explainable deep learning on structural MRI.jpg by Da Ma (344152)

Image_3_Differential diagnosis of frontotemporal dementia subtypes with explainable deep learning on structural MRI.jpg by Da Ma (344152)

Image_1_Differential diagnosis of frontotemporal dementia subtypes with explainable deep learning on structural MRI.jpg by Da Ma (344152)

3-D CNN decoder. by Xueliang Guo (4797057)

“…The development of the 3D CNN model utilizes the ADAM optimization algorithm to facilitate the training process. The Bra2020 dataset is utilized to assess the accuracy of the proposed deep learning neural network. …”

Learning of the transformer layer. by Xueliang Guo (4797057)

“…The development of the 3D CNN model utilizes the ADAM optimization algorithm to facilitate the training process. The Bra2020 dataset is utilized to assess the accuracy of the proposed deep learning neural network. …”

Original datasets for providing ROC-AUC curves. by Xueliang Guo (4797057)

“…The development of the 3D CNN model utilizes the ADAM optimization algorithm to facilitate the training process. The Bra2020 dataset is utilized to assess the accuracy of the proposed deep learning neural network. …”

ROC-AUC plot comparison of seven ML models. by Xueliang Guo (4797057)

“…The development of the 3D CNN model utilizes the ADAM optimization algorithm to facilitate the training process. The Bra2020 dataset is utilized to assess the accuracy of the proposed deep learning neural network. …”

Statistical performance of ML models. by Xueliang Guo (4797057)

“…The development of the 3D CNN model utilizes the ADAM optimization algorithm to facilitate the training process. The Bra2020 dataset is utilized to assess the accuracy of the proposed deep learning neural network. …”