2D Rastrigin function. حسب Shikun Chen (14625352)

"…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…"

2D Levy function. حسب Shikun Chen (14625352)

"…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…"

2D Rosenbrock function. حسب Shikun Chen (14625352)

"…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…"

Optimization outcome for the Levy function. حسب Shikun Chen (14625352)

"…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…"

ClaritySpectra: Raman spectra analysis tool حسب Aaron Celestian (9395696)

"…</li><li>Pre-compiled database from RRUFF and SLOPP that includes Hey Index of classification, chemistry, locality, paragenesis, mineral evolution stage, etc.</li></ul><h3>PEAK FITTING </h3><ul><li>Automated background subtraction using asymmetric least squares fitting</li><li>A new suggested background feature that lets you preview the background that you like best</li><li>Interactive background fitting lets you further tune the background to perfection</li><li>Four choice of peaks: Gaussian, Lorentzian, Pseudo-Voigt, and the new Asymmetric Voigt functions</li><li>Overlapping view of how well the peaks fit with quality metrics</li><li>No need to define regions, the algorithm is smart enough to what a peak looks like.…"

Python code, input data, and outputs of Fourier analysis and Dynamic Time Warping from McGlasson et al. حسب Riley McGlasson (13876874)

"…</p> <h4><strong>Python Function Files:</strong></h4> <p>radarfuncs.py: Holds three functions used for FFT and DTW analysis.…"

Drug Release Nanoparticle System Design: Data Set Compilation and Machine Learning Modeling حسب Shan He (121220)

الموضوعات:

CMA-ES performance during the iterations of the normalization method according to the objective function with <i>t</i> = 0. حسب Sofya Feldman (19590649)

Table_1_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf حسب Zitong Lu (9913770)

Presentation_2_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf حسب Zitong Lu (9913770)

Table_2_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf حسب Zitong Lu (9913770)

Image_1_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf حسب Zitong Lu (9913770)

Presentation_3_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf حسب Zitong Lu (9913770)

Presentation_1_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf حسب Zitong Lu (9913770)

Table_3_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf حسب Zitong Lu (9913770)

Simulation results by genetic algorithms (GAs). حسب Yuji Omachi (15307241)

Algorithms for Sparse Support Vector Machines حسب Alfonso Landeros (798926)

"…The proximal distance principle takes a loss function <math><mrow><mi>L</mi><mo>(</mo><mi>β</mi><mo>)</mo></mrow></math> and adds the penalty <math><mrow><mi>ρ</mi><mn>2</mn>dist<mrow><mrow><mo>(</mo><mi>β</mi><mo>,</mo><msub><mrow><mi>S</mi></mrow><mi>k</mi></msub><mo>)</mo></mrow></mrow><mn>2</mn></mrow></math> capturing the squared Euclidean distance of the parameter vector <math><mi>β</mi></math> to the sparsity set <i>S_k</i> where at most <i>k</i> components of <math><mi>β</mi></math> are nonzero. …"

NLDock: a Fast Nucleic Acid–Ligand Docking Algorithm for Modeling RNA/DNA–Ligand Complexes حسب Yuyu Feng (11371729)

"…Nucleic acid–ligand interactions play an important role in numerous cellular processes such as gene function expression and regulation. Therefore, nucleic acids such as RNAs have become more and more important drug targets, where the structural determination of nucleic acid–ligand complexes is pivotal for understanding their functions and thus developing therapeutic interventions. …"

Attack and defense behaviors with the sensory–motor algorithm between multiple predators and prey. حسب Hiroyuki Ichijo (13891153)

Open Binding Pose Metadynamics: An Effective Approach for the Ranking of Protein–Ligand Binding Poses حسب Dominykas Lukauskis (14143149)

"…OpenBPMD is powered by the OpenMM simulation engine and uses a revised scoring function. The algorithm was validated by testing it on a wide range of targets and showing that it matches or exceeds the performance of the original BPMD. …"