Software: Learning zero-cost portfolio selection with pattern matching by Tim Gebbie (8064947)

“…</p><p dir="ltr">Key function is a class: pattern.m (pattern/pattern)</p><p dir="ltr">PATTERN Pattern matching and learning class </p><p dir="ltr">The class implements both online and offline pattern matching and learning over k-tuples for M objects and N features as specified in an MxNxP data matrix X. …”

Variational Estimation for Multidimensional Graded Response Model by Qian-Zhen Zheng (22439680)

“…Simulation studies show that our GVEM and IW-GVEM run significantly faster than the MH-RM algorithm and are of competitiveness in both confirmatory and exploratory analysis. …”

Sample size determination for multidimensional parameters and the A-optimal subsampling in a big data linear regression model by Sheng Zhang (111330)

“…<p>To efficiently approximate the least squares estimator (LSE) in a Big Data linear regression model using a subsampling approach, optimal sampling distributions were derived by minimizing the trace norm of the covariance matrix of a smooth function of the subsampling LSE. An algorithm was developed that significantly reduces the computation time for the subsampling LSE compared to the full-sample LSE. …”

Table 2_In-depth immunochemical characterization of the serum antibody response using a dual-titration microspot assay.xlsx by Ágnes Kovács (3252267)

“…Both titration curves were simultaneously fitted using an algorithm based on the generalized logistic function and adapted for analyzing biochemical variables of binding. …”

Data Sheet 2_In-depth immunochemical characterization of the serum antibody response using a dual-titration microspot assay.pdf by Ágnes Kovács (3252267)

“…Both titration curves were simultaneously fitted using an algorithm based on the generalized logistic function and adapted for analyzing biochemical variables of binding. …”

Data Sheet 1_In-depth immunochemical characterization of the serum antibody response using a dual-titration microspot assay.pdf by Ágnes Kovács (3252267)

“…Both titration curves were simultaneously fitted using an algorithm based on the generalized logistic function and adapted for analyzing biochemical variables of binding. …”

Table 1_In-depth immunochemical characterization of the serum antibody response using a dual-titration microspot assay.xlsx by Ágnes Kovács (3252267)

“…Both titration curves were simultaneously fitted using an algorithm based on the generalized logistic function and adapted for analyzing biochemical variables of binding. …”

MHSC: A meta-heuristic method to optimize throughput and energy using sensitivity rate computing by Arash GhorbanniaDelavar (22563696)

“…By combining <b>Genetic and Bat algorithms</b>, MHSC leverages the strengths of both meta-heuristics while avoiding their limitations, allowing it to reach global optima faster and escape local traps.…”

Instances and detailed results of the paper <i>Stochastic scheduling on a restricted batching machine</i> by Yasmin Rios Solis (3909469)

“…We have developed mixed integer programming formulations and solved the stochastic problem using both a Benders decomposition approach and a biased random-key genetic algorithm. …”

A Subsampling Strategy for AIC-based Model Averaging with Generalized Linear Models by Jun Yu (5904)

“…A practically implementable algorithm is developed, and its performance is evaluated through numerical experiments on both real and simulated datasets.…”

Model-Free UAV Navigation in Unknown Complex Environments Using Vision-Based Reinforcement Learning by Satoshi Suzuki (20437556)

“…However, most existing approaches rely heavily on precise modeling of both the UAV and its operating environment, which tends to be unrealistic under real-world conditions. …”

Data Sheet 1_PAQR6 as a prognostic biomarker and potential therapeutic target in kidney renal clear cell carcinoma.zip by Tao Zou (662198)

“…However, its biological function in kidney renal clear cell carcinoma (KIRC) and its potential as a therapeutic target remain underexplored.…”

Wasserstein-Kaplan-Meier Survival Regression by Yidong Zhou (580573)

“…The proposed model is supported with a solid theoretical foundation including pointwise and uniform convergence rates, along with an efficient algorithm for model fitting. The proposed model effectively accommodates random variation that may exist in the probability measures across different subgroups, demonstrating superior performance in both simulations and two case studies compared to the Cox proportional hazards model and other alternative models. …”

Pipeline for mean shape based post-processing method. by Zhicheng Lin (218641)

“…To reduce substantial spatial disparities between the original prediction and the mean shape, a rigid registration based on an iterative closest point algorithm (ICP) was performed in MATLAB (fixed shape: original prediction; moving shape: mean shape; function: pcregistericp (moving, fixed)) (b-1). …”

An Extension of the Unified Skew-Normal Family of Distributions and its Application to Bayesian Binary Regression by Paolo Onorati (20461248)

“…For more general prior distributions, the proposed methodology is based on a simple Gibbs sampler algorithm. We also claim that, in the <math><mrow><mi>p</mi><mo>></mo><mi>n</mi></mrow></math> case, our proposal presents better performances—both in terms of mixing and accuracy—compared to the existing methods.…”

An extensible framework for the probabilistic search of stochastically-moving targets characterized by generalized Gaussian distributions or experimentally-defined regions of inter... by Benjamin L. Hanson (11173296)

“…<p>This article presents a continuous-time framework for modeling the evolution of a probability density function (PDF) summarizing the region of interest (ROI) during the search for a stochastically-moving, statistically stationary target. …”

PEG neurons encoded more complex features than A1 neurons. by Shoutik Mukherjee (18626028)

“…The magnitudes of STRFs were computed (second column) and approximated by a probability distribution function for a Gaussian mixture model (GMM) fit with a boosting algorithm with large- and small-covariance Gaussian weak learners (third and fourth columns, respectively) and by <i>k</i> components of its singular value decomposition (fifth column). …”

Semiparametric Estimation for Error-Prone Partially Linear Single-Index Models by Li-Pang Chen (9747423)

“…To implement the proposed method efficiently, we develop a boosting algorithm that enables us to select variables and estimate the parameters without handling non-differentiable penalty functions. …”

Mean squared Error on all unseen data. 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 . Recently, Kernel Graph Regression (KGR) and Gaussian Processes over Graph (GPoG) have emerged as promising techniques for this task. …”

The notational conventions used in this paper. 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 . Recently, Kernel Graph Regression (KGR) and Gaussian Processes over Graph (GPoG) have emerged as promising techniques for this task. …”