Functional Projection <i>K</i>-means by Roberto Rocci (16022201)

“…In contrast to existing literature, which largely considers the smoothing as a pre-processing step, in our proposal regularization is integrated with the identification of both subspace and cluster partition. An alternating least squares algorithm is introduced to compute model parameter estimates. …”

Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional by Ryan Stocks (16867476)

“…Kohn–Sham density functional theory (KS-DFT) has become a cornerstone for studying the electronic structure of molecules and materials. …”

Decoding results. by Sam Hall-McMaster (10343795)

Behavioral results. by Sam Hall-McMaster (10343795)

Pictorial representation of isomorphically scaling down spots within a fixed field of view and corresponding imaging configuration. by Nicholas H. Vitale (20469289)

A detailed process of iterative simulation coupled with bone density algorithm; (a) a function of stimulus and related bone density changes, and (b) iterative calculations of finit... by Hassan Mehboob (8960273)

The pseudocode for the NAFPSO algorithm. by Huichao Guo (14515171)

“…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”

PSO algorithm flowchart. by Huichao Guo (14515171)

“…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”

The traffic flow distribution diagram when traffic lights in both entrance and exit of the short-distance road section are red. by Yulin Tian (1457986)

The traffic flow distribution diagram when traffic lights in both entrance and exit of the short-distance road section are green. by Yulin Tian (1457986)

Algorithm comparison experiment on the NWPU VHR-10 dataset. by Pingping Yan (462509)

Subjects:

Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information. by Yuanchen Zhao (12905580)

“…Whiskers show the lowest and highest values within 1.5 × <i>IQR</i> from the first and third quartiles, respectively. …”

Classification algorithm implemented in SPARTA. by Baptiste Ruiz (20285217)

Geometric interpretation and data visualisation equivalent describing the DI algorithmic pipeline. by Borjan Milinkovic (21349908)

Code snippets to add user-specific custom functionality in PyNoetic. by Gursimran Singh (575288)

Subjects:

The benchmark functions. by Shuhui Yu (1535500)

Gillespie algorithm simulation parameters. by Nicholas H. Vitale (20469289)

“…Both the ensemble and stochastic models presented in this work have been verified using Monte Carlo molecular dynamic simulations that utilize the Gillespie algorithm. …”

Scheduling time of five algorithms. by Huichao Guo (14515171)

“…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”

Convergence speed of five algorithms. by Huichao Guo (14515171)

“…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”

Elementary effects (EE) results for the generation of villous vessels within a placentone employing the input parameter ranges from Table 1. by Diana C. de Oliveira (9220729)