An Efficient Algorithm for Minimizing Multi Non-Smooth Component Functions by Minh Pham (2458399)

“…<p>Many problems in statistics and machine learning can be formulated as an optimization problem of a finite sum of nonsmooth convex functions. We propose an algorithm to minimize this type of objective functions based on the idea of alternating linearization. …”

Auscultation locations for the CirCor DigiScope dataset [6], which was used for the Challenge: Pulmonary valve (PV), aortic valve (AV), mitral valve (MV), and tricuspid valve (TV). by Matthew A. Reyna (16965038)

Average accuracy (across all 12 subject) as a function of the number of eigenvectors obtained with different dimensionality reduction techniques for the best classifier—k-NN (see a... by Elnaz Lashgari (11278808)

Retinal thickness analysis algorithm in which mean thicknesses (in micrometers) are provided for each of 9 areas defined by an Early Treatment Diabetic Retinopathy Study (ETDRS). by Adriano Magli (5782706)

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

“…Introduction<p>Intracranial electrodes are implanted in patients with drug-resistant epilepsy as part of their pre-surgical evaluation. This allows the investigation of normal and pathological brain functions with excellent spatial and temporal resolution. …”

Three examples for which the match between experimental data and simulations is challenging. by Robin Dirk (17296179)

Function convergence curve. by Guilin Yang (583364)

“…To evaluate the performance of the proposed algorithm, 27 well-known test reference functions were selected for experimentation, which showed significant advantages compared to other algorithms. …”

As for Fig 2, we present failure rates as a function of the cohort size (vertical axis) versus the number of distractors (horizontal axis), for the Smyth and McClave baseline algor... by Daniela Huppenkothen (9174507)

Continuous Probability Distributions generated by the PIPE Algorithm by LUIS G.B. PINHO (14073372)

“…The PIPE algorithm can generate several candidate functions to fit the empirical distribution of data. …”

Pyrho Validation - Check re-gridded periodic data by Jimmy Shen (12664675)

23 benchmark functions. by Yu Liu (6938)

“…<div><p>The competition of tribes and cooperation of members algorithm (CTCM) is a novel swarm intelligence algorithm, which increases the diversity of the population to a certain extent through tribal competition and member cooperation mechanisms. …”

Theoretical models used in this study and the aspects in which they were applied. by Jennifer Hornung Garvin (20765727)

Pipeline of the interactor algorithm. by Jose Cleydson F. Silva (8685594)

Automatic cropping algorithm. by Thomas W Georgi (12427189)

Metabolic tasks can be inferred from omics data to determine which tasks should be protected during the model extraction process. by Anne Richelle (6589178)

Predictions from a Universal Value Function Approximator (UVFA) algorithm. by Sam Hall-McMaster (10343795)

Data pre-processing and identification of DEGs. by Ziyang Liu (242055)

The SSIM for the different algorithms. by Bingbing Li (461702)

“…The mean square error decreased by 0.10dB. When using the algorithm for denoising, the research method had a minimum denoising time of only 13ms, which saved 9ms and 3ms compared to the hard threshold algorithm (Hard TA) and soft threshold algorithm (Soft TA), respectively. …”

Algorithm operation steps. by Junyan Wang (4738518)

“…To address these issues, this paper proposes an improved object detection algorithm named SCI-YOLO11, which optimizes the YOLO11 framework from three aspects: feature extraction, attention mechanism, and loss function. …”

Comparison of algorithm search curves. by Bowen Li (200859)

“…At the same time, the characteristics of Gaussian function are introduced to ensure the activity of hidden layer neurons. …”