Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data by Changhun Kim (682542)

“…Therefore, the resampling algorithm employed should vary depending on the data distribution to achieve optimal classification performance. …”

Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment by Jianfang Cao (1881379)

“…<div><p>An image classification algorithm based on adaptive feature weight updating is proposed to address the low classification accuracy of the current single-feature classification algorithms and simple multifeature fusion algorithms. …”

Data_Sheet_1_Fast Simulation of a Multi-Area Spiking Network Model of Macaque Cortex on an MPI-GPU Cluster.PDF by Gianmarco Tiddia (10824118)

“…NEST GPU is a GPU library written in CUDA-C/C++ for large-scale simulations of spiking neural networks, which was recently extended with a novel algorithm for remote spike communication through MPI on a GPU cluster. …”

Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. by Enrico Bertozzi (22461709)

“…Multiple SVM models were trained and evaluated, including configurations with linear and RBF (Radial Basis Function) kernels. </p><p dir="ltr">Additionally, an exhaustive hyperparameter search was performed using GridSearchCV to optimize the C, gamma, and kernel parameters (testing 'linear,' 'rbf,' 'poly,' and 'sigmoid'), aiming to find the highest-performing configuration. …”

Data_Sheet_1_Prediction of Mental Health in Medical Workers During COVID-19 Based on Machine Learning.ZIP by Xiaofeng Wang (119575)

“…In this study, we propose a novel prediction model based on optimization algorithm and neural network, which can select and rank the most important factors that affect mental health of medical workers. …”

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

“…Technical Architecture Overview Computational Environment Specifications Our experimental infrastructure leverages a heterogeneous multi-node computational topology encompassing three distinct hardware abstraction layers: Node Configuration Alpha (Intel-NVIDIA Heterogeneous Architecture) Processor: Intel Core i7-12700K (Alder Lake microarchitecture) - 12-core hybrid architecture (8 P-cores + 4 E-cores) - Base frequency: 3.6 GHz, Max turbo: 5.0 GHz - Cache hierarchy: 32KB L1I + 48KB L1D per P-core, 12MB L3 shared - Instruction set extensions: AVX2, AVX-512, SSE4.2 - Thermal design power: 125W (PL1), 190W (PL2) Memory Subsystem: 32GB DDR4-3200 JEDEC-compliant DIMM - Dual-channel configuration, ECC-disabled - Memory controller integrated within CPU die - Peak theoretical bandwidth: 51.2 GB/s GPU Accelerator: NVIDIA GeForce RTX 3070 (GA104 silicon) - CUDA compute capability: 8.6 - RT cores: 46 (2nd gen), Tensor cores: 184 (3rd gen) - Memory: 8GB GDDR6 @ 448 GB/s bandwidth - PCIe 4.0 x16 interface with GPU Direct RDMA support Node Configuration Beta (AMD Zen3+ Architecture) Processor: AMD Ryzen 7 5800X (Zen 3 microarchitecture) - 8-core monolithic design, simultaneous multithreading enabled - Base frequency: 3.8 GHz, Max boost: 4.7 GHz - Cache hierarchy: 32KB L1I + 32KB L1D per core, 32MB L3 shared - Infinity Fabric interconnect @ 1800 MHz - Thermal design power: 105W Memory Subsystem: 16GB DDR4-3600 overclocked configuration - Dual-channel with optimized subtimings (CL16-19-19-39) - Memory controller frequency: 1800 MHz (1:1 FCLK ratio) GPU Accelerator: NVIDIA GeForce GTX 1660 (TU116 silicon) - CUDA compute capability: 7.5 - Memory: 6GB GDDR5 @ 192 GB/s bandwidth - Turing shader architecture without RT/Tensor cores Node Configuration Gamma (Intel Raptor Lake High-Performance) Processor: Intel Core i9-13900K (Raptor Lake microarchitecture) - 24-core hybrid topology (8 P-cores + 16 E-cores) - P-core frequency: 3.0 GHz base, 5.8 GHz max turbo - E-core frequency: 2.2 GHz base, 4.3 GHz max turbo - Cache hierarchy: 36MB L3 shared, Intel Smart Cache technology - Thermal velocity boost with thermal monitoring Memory Subsystem: 64GB DDR5-5600 high-bandwidth configuration - Quad-channel topology with advanced error correction - Peak theoretical bandwidth: 89.6 GB/s GPU Accelerator: NVIDIA GeForce RTX 4080 (AD103 silicon) - Ada Lovelace architecture, CUDA compute capability: 8.9 - RT cores: 76 (3rd gen), Tensor cores: 304 (4th gen) - Memory: 16GB GDDR6X @ 716.8 GB/s bandwidth - PCIe 4.0 x16 with NVLink-ready topology Instrumentation and Telemetry Framework Power Consumption Monitoring Infrastructure Our energy profiling subsystem employs a multi-layered approach to capture granular power consumption metrics across the entire computational stack: Hardware Performance Counters (HPC): Intel RAPL (Running Average Power Limit) interface for CPU package power measurement with sub-millisecond resolution GPU Telemetry: NVIDIA Management Library (NVML) API for real-time GPU power draw monitoring via PCIe sideband signaling System-level PMU: Performance Monitoring Unit instrumentation leveraging MSR (Model Specific Register) access for architectural event sampling Network Interface Telemetry: SNMP-based monitoring of NIC power consumption during cloud upload/download phases Temporal Synchronization Protocol All measurement vectors utilize high-resolution performance counters (HPET) with nanosecond precision timestamps, synchronized via Network Time Protocol (NTP) to ensure temporal coherence across distributed measurement points. …”

Supplementary Material 8 by Nishitha R Kumar (19750617)

“…</li><li><b>XGboost: </b>An optimized gradient boosting algorithm that efficiently handles large genomic datasets, commonly used for high-accuracy predictions in <i>E. coli</i> classification.…”

Aluminum alloy industrial materials defect by Ying Han (20349093)

“…</p><p dir="ltr">Install PyTorch based on your system:</p><p dir="ltr">For Windows/Linux users with a CUDA GPU: bash conda install pytorch==1.10.0 torchvision==0.11.0 torchaudio==0.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge</p><p dir="ltr">Install some necessary libraries:</p><p dir="ltr">Install scikit-learn with the command: conda install anaconda scikit-learn=0.24.1</p><p dir="ltr">Install astropy with: conda install astropy=4.2.1</p><p dir="ltr">Install pandas using: conda install anaconda pandas=1.2.4</p><p dir="ltr">Install Matplotlib with: conda install conda-forge matplotlib=3.5.3</p><p dir="ltr">Install scipy by entering: conda install scipy=1.10.1</p><h4><b>Repeatability</b></h4><p dir="ltr">For PyTorch, it's a well-known fact:</p><p dir="ltr">There is no guarantee of fully reproducible results between PyTorch versions, individual commits, or different platforms. …”

LinearSolve.jl: because A\b is not good enough by Christopher Rackauckas (9197216)

“…Short list: LU, QR, SVD, RecursiveFactorization.jl (pure Julia, and the fastest?), GPU-offload LU, UMFPACK, KLU, CG, GMRES, Pardiso, ...…”