Python implementation of the Trajectory Adaptive Multilevel Sampling algorithm for rare events and improvements حسب Pascal Wang (10130612)

"…In `main.py`, the parameters for the TAMS algorithm are specified (trajectory time, time step, score function, number of particles, type of score threshold, maximum number of iterations, noise level etc.). …"

Simulation Results Plot of the SBW System Response: (a) Step response curve of the SBW system; (b) Sine response curve of the SBW system; (c) Steady state error curve of the SBW sy... حسب Honglei Pang (22693724)

Simulation Results Plot of the EMB System Response: (a) Step response curve of the EMB system; (b) Sine response curve of the EMB system; (c) Steady state error curve of the EMB sy... حسب Honglei Pang (22693724)

Data_Sheet_1_Processing Pipeline for Atlas-Based Imaging Data Analysis of Structural and Functional Mouse Brain MRI (AIDAmri).docx حسب Niklas Pallast (6796196)

"…Following a modular structure developed in Python scripting language, the pipeline integrates established and newly developed algorithms. …"

Data_Sheet_2_Processing Pipeline for Atlas-Based Imaging Data Analysis of Structural and Functional Mouse Brain MRI (AIDAmri).pdf حسب Niklas Pallast (6796196)

"…Following a modular structure developed in Python scripting language, the pipeline integrates established and newly developed algorithms. …"

Landscape17 حسب Vlad Carare (22092515)

"…For each of the selected six molecules (ethanol, malonaldehyde, paracetamol, salicylic acid, azobenzene, and aspirin) we provide all the minima and transition states, along with configurations from the two approximate steepest-descent paths connecting each transition state to the corresponding minima, computed using hybrid-level density functional theory. …"

Map Matching on Low Sampling Rate Trajectories Through Deep Inverse Reinforcement Learning and Multi Intention Modeling حسب Reza Safarzadeh (18072472)

"…It includes classes and functions related to representing the graph environment, nodes, edges, state space, and action space.…"

GameOfLife Prediction Dataset حسب David Towers (12857447)

"…This task is relatively simple for a human to do if a bit tedious, and should theoretically be simple for Machine Learning algorithms. Each cells's state is calculated based off the number of alive neighbour's in the previous step. …"

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows حسب 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). …"