Flowchart depicting the optimal control framework. by Antoine Falisse (6061601)

“…<p>We developed two approaches (AD-ADOLC and AD-Recorder) to make an OpenSim function <i>F</i> and its forward (<i>F fwd</i>) and reverse (<i>F rev</i>) directional derivatives available within the CasADi environment for use by the NLP solver during the optimization. In the AD-ADOLC approach (top), ADOL-C’s algorithms are used in a C++ code to provide <i>F fwd</i> and <i>F rev</i>. …”

Reverse Designing the Wavelength-Specific Thermally Activation Delayed Fluorescent Molecules Using a Genetic Algorithm Coupled with Cheap QM Methods by Xubin Wang (1861147)

“…Genetic algorithm (GA) optimization coupled with the semiempirical intermediate neglect of differential overlap (INDO)/CIS method is presented to inversely design the red thermally activation delayed fluorescent (TADF) molecules. …”

Code by Baoqiang Chen (21099509)

A high-performance and highly reusable fast multipole method library and its application to solvation energy calculations at virus-scale by Tingyu Wang (12342757)

“…<div>The fast multipole method (FMM), recognized as one of the top ten algorithms from the 20th century, can rapidly evaluate the ubiquitous N-body problems in scientific computations in linear time. …”

ECE6327_SmartGrid by Xingpeng Li (11825663)

tUbenet Foundation Model Weights by Natalie Holroyd (7099391)

bcp for divisional seru formation by shiming chen (21391865)

“…<p dir="ltr">Specific experimental results, data, and code</p><p dir="ltr"># Branch-and-Price Algorithm for Divisional Seru Scheduling Problem in C#</p><p dir="ltr">This repository contains the C# code for solving the Divisional Seru Scheduling Problem (DSCP) using the Branch-and-Price-and-Cut (BPC) algorithm with Gurobi. …”

RabbitSketch by tong zhang (20852432)

“…<p dir="ltr">RabbitSketch is a highly optimized sketching library that exploits the power of modern multi-core CPUs. …”

Aluminum alloy industrial materials defect by Ying Han (20349093)

“…<p dir="ltr">The dataset used in this study experiment was from the preliminary competition dataset of the 2018 Guangdong Industrial Intelligent Manufacturing Big Data Intelligent Algorithm Competition organized by Tianchi Feiyue Cloud (https://tianchi.aliyun.com/competition/entrance/231682/introduction). …”

Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles by Soham Savarkar (21811825)

“…</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…”

Otago's Network for Engagement and Research: Mapping Academic Expertise and Connections by Sander Zwanenburg (8552102)

“…He obtained Bachelor and Master of Science degrees from the University of Groningen, The Netherlands, and a PhD degree in Management Information Systems from The University of Hong Kong. …”

Table_3_G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction.xlsx by Qian Wang (32718)

“…Based on the evaluation outcome, G2P performs auto-ensemble algorithms that not only can automatically select the most precise models but also can integrate prediction results from multiple models. …”

Image_1_G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction.jpeg by Qian Wang (32718)

“…Based on the evaluation outcome, G2P performs auto-ensemble algorithms that not only can automatically select the most precise models but also can integrate prediction results from multiple models. …”

Image_2_G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction.jpeg by Qian Wang (32718)

“…Based on the evaluation outcome, G2P performs auto-ensemble algorithms that not only can automatically select the most precise models but also can integrate prediction results from multiple models. …”

DataSheet_1_G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction.docx by Qian Wang (32718)

“…Based on the evaluation outcome, G2P performs auto-ensemble algorithms that not only can automatically select the most precise models but also can integrate prediction results from multiple models. …”

Image_3_G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction.jpeg by Qian Wang (32718)

“…Based on the evaluation outcome, G2P performs auto-ensemble algorithms that not only can automatically select the most precise models but also can integrate prediction results from multiple models. …”

Table_4_G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction.xlsx by Qian Wang (32718)

“…Based on the evaluation outcome, G2P performs auto-ensemble algorithms that not only can automatically select the most precise models but also can integrate prediction results from multiple models. …”

Table_2_G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction.xlsx by Qian Wang (32718)

“…Based on the evaluation outcome, G2P performs auto-ensemble algorithms that not only can automatically select the most precise models but also can integrate prediction results from multiple models. …”

Table_1_G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction.xlsx by Qian Wang (32718)

“…Based on the evaluation outcome, G2P performs auto-ensemble algorithms that not only can automatically select the most precise models but also can integrate prediction results from multiple models. …”

Data_Sheet_1_Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning and HPC Workloads.PDF by Evangelos Georganas (12429885)

“…DL workloads leverage either highly-optimized, yet platform-specific and inflexible kernels from DL libraries, or in the case of novel operators, reference implementations are built via DL framework primitives with underwhelming performance. …”