TV-BayesOpt algorithm performance for tracking a superimposed (gradual and periodic) drift in the optimal stimulation phase for phase-locked stimulation, <i>ψ</i>*. by John E. Fleming (8533956)

“…For each estimated GPR the confidence bounds observed at the predicted optimal phase value are small and become larger for values further away from this value due to the algorithm’s acquisition function prioritizing exploitation of the parameter space during the optimization process.…”

The results of the ablation experiments in different modules were enhanced with a randomly selected image from the Color-Check7 [34], OceanDark [33] datasets. by Kaibo Liao (18056473)

ROI setting diagram for AI calculation and optimization verification results. by Tsutomu Gomi (7382183)

Illustration of a trial of crossing flow from our experiments. by Pratik Mullick (12851011)

Online estimation of the coefficients <i>g</i>_<i>i</i>(<i>k</i>) of the non-stationary moving average process based on the colored noise data from recording <i>2–81731-A... by Martin Dodek (20623832)

Efficient Parameterization of Density Functional Tight-Binding for 5<i>f</i>‑Elements: A Th–O Case Study by Chang Liu (35901)

“…The optimized parameter set, <i>LANL-ThO</i>, displays good agreement with DFT-calculated properties such as energies, forces, and structures for both clusters and bulk ThO₂. Benefiting from the fewer number of parameters and lower computational costs for objective function evaluations, this new approach shows its potential applications in DFTB parametrization for elements with high angular momentum, which present a challenge to conventional methods.…”

Efficient Parameterization of Density Functional Tight-Binding for 5<i>f</i>‑Elements: A Th–O Case Study by Chang Liu (35901)

“…The optimized parameter set, <i>LANL-ThO</i>, displays good agreement with DFT-calculated properties such as energies, forces, and structures for both clusters and bulk ThO₂. Benefiting from the fewer number of parameters and lower computational costs for objective function evaluations, this new approach shows its potential applications in DFTB parametrization for elements with high angular momentum, which present a challenge to conventional methods.…”

Elementary effects (EE) results for chorionic vessel generation employing the input parameter ranges from Table 1. by Diana C. de Oliveira (9220729)

A hybrid algorithm based on improved threshold function and wavelet transform. by Bingbing Li (461702)

“…<p>A hybrid algorithm based on improved threshold function and wavelet transform.…”

The PS of ten comparative algorithms on the MMF14a Test Function. by Wenxing Wu (16853002)

“…<p>The PS of ten comparative algorithms on the MMF14a Test Function.</p>…”

a-c: (a) The nomogram model developed for the prognostic prediction of hub genes from the study. by Devi Soorya Narayana Sasikumar (22331743)

DataSheet1_An evolutionary variational autoencoder for perovskite discovery.PDF by Ericsson Tetteh Chenebuah (17024601)

“…The genetic algorithm performs adaptive metaheuristic search operations for finding the most theoretically stable candidates emerging from a target-learnable latent space of the generative SS-VAE model. …”

Parallel Implementation of Density Functional Theory Methods in the Quantum Interaction Computational Kernel Program by Madushanka Manathunga (1494862)

“…The acceleration gained in density functional theory calculations from a single V100 GPU significantly exceeds that of a modern CPU with 40 cores running in parallel.…”

Data_Sheet_1_Modeling intracranial electrodes. A simulation platform for the evaluation of localization algorithms.pdf by Alejandro O. Blenkmann (13914651)

“…This allows the investigation of normal and pathological brain functions with excellent spatial and temporal resolution. …”

Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation by Kyriacos Yiannacou (10292766)

“…In this work, we demonstrate a programmable microfluidic chip for the two-dimensional manipulation of droplets, based on ultrasonic bulk acoustic waves and a closed-loop machine-learning-based control algorithm. …”

Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation by Kyriacos Yiannacou (10292766)

“…In this work, we demonstrate a programmable microfluidic chip for the two-dimensional manipulation of droplets, based on ultrasonic bulk acoustic waves and a closed-loop machine-learning-based control algorithm. …”

Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation by Kyriacos Yiannacou (10292766)

“…In this work, we demonstrate a programmable microfluidic chip for the two-dimensional manipulation of droplets, based on ultrasonic bulk acoustic waves and a closed-loop machine-learning-based control algorithm. …”

Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation by Kyriacos Yiannacou (10292766)

“…In this work, we demonstrate a programmable microfluidic chip for the two-dimensional manipulation of droplets, based on ultrasonic bulk acoustic waves and a closed-loop machine-learning-based control algorithm. …”

Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation by Kyriacos Yiannacou (10292766)

“…In this work, we demonstrate a programmable microfluidic chip for the two-dimensional manipulation of droplets, based on ultrasonic bulk acoustic waves and a closed-loop machine-learning-based control algorithm. …”

Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation by Kyriacos Yiannacou (10292766)

“…In this work, we demonstrate a programmable microfluidic chip for the two-dimensional manipulation of droplets, based on ultrasonic bulk acoustic waves and a closed-loop machine-learning-based control algorithm. …”