Speed limits and gradient from SJZ to XHM. by Yunhu Huang (21402795)

“…On the basis of EITO_E, we propose EITO_P algorithm using the PPO algorithm to optimize multiple objectives by designing reinforcement learning strategies, rewards, and value functions. …”

The profile log-likelihood of data simulated from four distinct MA(1) models, demonstrating a few examples of multimodal likelihood surfaces. by Jesse Wheeler (18462457)

Distribution of the adjusted Rand index, above, and Rand index, below, for the 50 simulations from a model with <i>K</i> = 3 clusters, i.i.d. random error term. by Susana Conde (5495675)

Machine learning analysis of 10 hub genes related to SCI was performed using eight algorithms applied to the dataset. by Tianbao Feng (21722233)

Distribution of the True Positive Rate, above, and True Negative Rate, below, for the 50 simulations from a model with <i>K</i> = 3 clusters, i.i.d. random error term. by Susana Conde (5495675)

Comparison of the set found when solving IM for <i>k</i> = 19 by the greedy algorithm (left) and the one found by (1 + 1)-WEA for a network with <i>n</i> = 50 vertices. by Alexander Andreev (16822333)

Metapopulation model notation. by Jeffrey Keithley (14626551)

“…We provide a theoretical explanation for this effectiveness by showing that the approximation factor (a measure of how well the algorithmic output for a problem instance compares to its theoretical optimum) of these algorithms depends on the <i>submodularity ratio</i> of the objective function <i>g</i>. …”

Estimates of for each problem instance. by Jeffrey Keithley (14626551)

“…We provide a theoretical explanation for this effectiveness by showing that the approximation factor (a measure of how well the algorithmic output for a problem instance compares to its theoretical optimum) of these algorithms depends on the <i>submodularity ratio</i> of the objective function <i>g</i>. …”

Approximation factors for each problem instance. by Jeffrey Keithley (14626551)

“…We provide a theoretical explanation for this effectiveness by showing that the approximation factor (a measure of how well the algorithmic output for a problem instance compares to its theoretical optimum) of these algorithms depends on the <i>submodularity ratio</i> of the objective function <i>g</i>. …”

Pseudo live-stream of EEG data is generated, and a simple pick-and-place flowchart is designed for channel selection and filtering. by Gursimran Singh (575288)

Subjects:

Functions in nhppp. by Thomas A. Trikalinos (5785877)

“…We developed it to facilitate the sampling of event times in discrete event and statistical simulations. The package’s functions are based on three algorithms that provably sample from a target NHPPP: the time-transformation of a homogeneous Poisson process (of intensity one) via the inverse of the integrated intensity function; the generation of a Poisson number of order statistics from a fixed density function; and the thinning of a majorizing NHPPP via an acceptance-rejection scheme. …”

A Chemoproteomic Approach for System-Wide and Site-Specific Uncovering of Functional Protein N‑Glycosylation by Guoli Wang (59357)

“…Technological advances in proteomics, including sample separation, mass spectrometry, and searching algorithms, have empowered in-depth discovery of protein post-translational modifications from biological samples. …”

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)

(a) Radar chart of these algorithms (23 Benchmark functions), (b) The sorting diagram of these algorithms (23 Benchmark functions). by Yu Liu (6938)

“…<p>(a) Radar chart of these algorithms (23 Benchmark functions), (b) The sorting diagram of these algorithms (23 Benchmark functions).…”

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

The run time for each algorithm in seconds. by Edward Antonian (21453161)

“…<div><p>In this paper, we study a class of non-parametric regression models for predicting graph signals as a function of explanatory variables . …”

Flowchart of simple ant colony algorithm. by Yang Yu (4292)

“…This paper introduces an Intelligent Tuning Method for Service Scheduling in Electric Power Communication Networks Based on Operational Risk and Quality of Service (QoS) Guarantee. Based on a comprehensive assessment of service transmission reliability and time costs, a route satisfaction evaluation function model has been developed. …”

Physics-Informed Bayesian Optimization for Conformational Ensemble Augmentation by Ivan A. Bespalov (21491561)

“…In this paper, we introduce a Bayesian optimization algorithm for conformational ensemble augmentation, that is, locating missing conformers in an existing ensemble, which employs Bayesian optimization with physics-informed torsion-potential-based kernel function and a novel acquisition function that prioritizes potential energy surface exploration for increased conformer diversity. …”

Physics-Informed Bayesian Optimization for Conformational Ensemble Augmentation by Ivan A. Bespalov (21491561)

“…In this paper, we introduce a Bayesian optimization algorithm for conformational ensemble augmentation, that is, locating missing conformers in an existing ensemble, which employs Bayesian optimization with physics-informed torsion-potential-based kernel function and a novel acquisition function that prioritizes potential energy surface exploration for increased conformer diversity. …”

Physics-Informed Bayesian Optimization for Conformational Ensemble Augmentation by Ivan A. Bespalov (21491561)

“…In this paper, we introduce a Bayesian optimization algorithm for conformational ensemble augmentation, that is, locating missing conformers in an existing ensemble, which employs Bayesian optimization with physics-informed torsion-potential-based kernel function and a novel acquisition function that prioritizes potential energy surface exploration for increased conformer diversity. …”