Left: the algorithm’s best estimate of where to put breakpoints for each number of breakpoints considered. by Zhengtian Li (5076704)

Autocorrelation functions <i>C</i>(<i>r</i>, <i>t</i>) as functions of the rescaled distance <i>r</i>/<i>R</i>(<i>t</i>) for several time points, where <i>R</i>(<i>t</i>) is determ... by Marc Durand (556746)

WGCNA and machine learning algorithms. by Shushu Wang (6870191)

The GOR4 and SOPMA algorithm findings. by Ahmad Abdullah Mahdeen (19704401)

Function <i>χ</i>_<i>opt</i> superimposed with the phase-field function indicating the channel geometry. by Martina Bukač (20161460)

Brief sketch of the quasi-attraction/alignment algorithm. by Takayuki Niizato (162226)

“…(D) A brief sketch of the avoidance algorithm. Upper: Each direction is extended to the repulsion area = {<b><i>r</i></b>||<b><i>r</i></b>| = <i>R</i>}, where is the minimal sphere cap that covers all points on . …”

Ms.FPOP: A Fast Exact Segmentation Algorithm with a Multiscale Penalty by Arnaud Liehrmann (10970682)

“…This penalty was proposed by Verzelen et al. and achieves optimal rates for changepoint detection and changepoint localization in a non-asymptotic scenario. Our proposed algorithm, Multiscale Functional Pruning Optimal Partitioning (Ms.FPOP), extends functional pruning ideas presented in Rigaill and Maidstone et al. to multiscale penalties. …”

Algorithms for Sparse Support Vector Machines by Alfonso Landeros (798926)

“…The proximal distance principle takes a loss function <math><mrow><mi>L</mi><mo>(</mo><mi>β</mi><mo>)</mo></mrow></math> and adds the penalty <math><mrow><mi>ρ</mi><mn>2</mn>dist<mrow><mrow><mo>(</mo><mi>β</mi><mo>,</mo><msub><mrow><mi>S</mi></mrow><mi>k</mi></msub><mo>)</mo></mrow></mrow><mn>2</mn></mrow></math> capturing the squared Euclidean distance of the parameter vector <math><mi>β</mi></math> to the sparsity set <i>S_k</i> where at most <i>k</i> components of <math><mi>β</mi></math> are nonzero. …”

Distinct responses of thorny and a-thorny cells to clustered dendritic inputs. by Daniele Linaro (227790)

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Percentage of transcript ineligibility as a function of context size. by Tariq Daouda (8300553)

On the improvement of reinforcement active learning with the involvement of cross entropy to address one-shot learning problem by Honglan Huang (341951)

Subjects: “…Cell Biology…”

Task structure diagram. by Honglan Huang (341951)

Subjects: “…Cell Biology…”

Boxplots for the maximising function of pattern matching technique. by Pratik Mullick (12851011)

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Table_2_Visuospatial, oculomotor, and executive reading skills evolve in elementary school, and errors are significant: a topological RAN study.pdf by Mario Lecce (18777553)

Table_1_Visuospatial, oculomotor, and executive reading skills evolve in elementary school, and errors are significant: a topological RAN study.pdf by Mario Lecce (18777553)

Mean crossing time as a function of <i>α</i>. by Pratik Mullick (12851011)

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A Functional Bayesian Model for Hydrogen–Deuterium Exchange Mass Spectrometry by Oliver M. Crook (6013703)

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A Functional Bayesian Model for Hydrogen–Deuterium Exchange Mass Spectrometry by Oliver M. Crook (6013703)

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A synthetic biology approach for the design of genetic algorithms with bacterial agents by A. Gargantilla Becerra (8734841)

“…<div>Bacteria have been a source of inspiration for the design of evolutionary algorithms. We introduce the designing of evolutionary algorithms where all the steps are conducted by synthetic bacteria. …”

(a) The volcano plot of GSE89408, where the red scatter represents the upregulated genes and the blue scatter represents the downregulated genes. (b) The heatmap of GSE89408. by Devi Soorya Narayana Sasikumar (22331743)

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