The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

IRBMO vs. meta-heuristic algorithms boxplot. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

<i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

“…From this dataset we can compute the MI between each interaction and the outcome and <b>(D)</b> obtain a ranked list (<i>I</i>_<i>δ</i>) based on this metric. <b>(E)</b> Starting from the interaction at the top of <i>I</i>_<i>δ</i>, <i>hi</i>PRS constructs <i>I</i>_<i>K</i>, selecting <i>K</i> (where <i>K</i> is user-specified) terms through the greedy optimization of the ratio between MI (<i>relevance</i>) and a suitable measure of similarity for interactions (<i>redundancy)</i> (cf. …”

Pseudo Code of RBMO. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

P-value on CEC-2017(Dim = 30). by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

Memory storage behavior. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

Elite search behavior. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

Description of the datasets. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

S and V shaped transfer functions. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

S- and V-Type transfer function diagrams. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

Collaborative hunting behavior. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

Friedman average rank sum test results. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

IRBMO vs. variant comparison adaptation data. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

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. …”

Optimal Timings Codebook.xlsx by Emma Spillane (11249121)

Optimal Time Intervals of Breech Births Dataset by Emma Spillane (11249121)

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

“…<br>The consistency of the results across different kernels demonstrates that the information contained in the habitat, by itself, leads to a very simple optimal decision rule (mostly the prediction of the most frequent class per habitat), which cannot be improved solely by model adjustments. …”

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. …”

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. …”