Video 4_Characterize neuronal responses to natural movies in the mouse superior colliculus.avi by Ya-tang Li (20858759)

“…An unsupervised learning algorithm grouped recorded neurons into 16 clusters based on their response patterns. …”

Efficient Distributed Learning over Decentralized Networks with Convoluted Support Vector Machine by Canyi Chen (13532043)

“…To address this issue, we consider a convolution-based smoothing technique for the nonsmooth hinge loss function. This results in a loss function that is both convex and smooth. …”

Spatiotemporal Soil Erosion Dataset for the Yarlung Tsangpo River Basin (1990–2100) by peng xin (21382394)

“…Additionally, a Genetic Algorithm (GA) was applied in each iteration to optimize the hyperparameters of the XGBoost model, which is crucial for enhancing both the efficiency and robustness of the model (Zhong and Liu, 2024; Zou et al., 2024). …”

Seamless integration of legacy robotic systems into a self-driving laboratory via NIMO: a case study on liquid handler automation by Ryo Tamura (1957942)

“…We developed NIMO (formerly NIMS-OS, NIMS Orchestration System), an OS explicitly designed to integrate multiple artificial intelligence (AI) algorithms with diverse exploratory objectives. NIMO provides a framework for integrating AI into robotic experimental systems that are controlled by other OS platforms based on both Python and non-Python languages. …”

Table 1_Exploring the role of TikTok for intersectionality marginalized groups: the case of Muslim female content creators in Germany.docx by Fatima El Sayed (20146977)

“…The hijab emerges as a unique issue, framed within both political and fashion discourses. Overall, TikTok functions as a “third space” where Muslim women challenge mainstream stereotypes and offer alternative interpretations of their identity. …”

Oryza australiensis species-specific gene and protein candidates by Sabrina Morrison (19846869)

“…</p><p dir="ltr">The protein sequence data for both <i>O. australiensis</i> and <i>O. sativa</i> (Osativa323v7 protein file Phytozome). were filtered for the longest isomer and then analysed for orthologous and unique protein clusters within the O. australiensis genome using OrthoVenn3 (parameters: OrthoFinder algorithm, E-value: 1e-2, Inflation value:1.50) (Sun et al., 2023, Emms and Kelly, 2019). …”

CSPP instance by peixiang wang (19499344)

“…</b></p><p dir="ltr">Its primary function is to create structured datasets that simulate container terminal operations, which can then be used for developing, testing, and benchmarking optimization algorithms (e.g., for yard stacking strategies, vessel stowage planning).…”

Uncertainty and Novelty in Machine Learning by Derek Scott Prijatelj (20364288)

“…We introduce an abstraction of novelty that is then further developed in terms of information theory and algorithms.</p> <p>This formalizes the concept of identifiable information that arises from the language used to express the relationship between distinct states. …”

Active Control of Laminar and Turbulent Flows Using Adjoint-Based Machine Learning by Xuemin Liu (20372739)

“…This dissertation extends and applies an adjoint-based machine learning method, the deep learning PDE augmentation method (DPM), for closed-loop active control on both laminar and turbulent flows. The end-to-end sensitivities for optimization are computed using adjoints of the governing equations without restriction on the terms that may appear in the objective function, which we construct using algorithmic differentiation applied to the flow solver. …”

Code by Baoqiang Chen (21099509)

“…We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”

Core data by Baoqiang Chen (21099509)

“…We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”

<b>Assessing human and environmental impacts on forest coverage in historical relic sites using XGBoost</b> by Jinlong Chen (20554607)

“…It iteratively adds decision trees to optimize an objective function comprising a loss function for measuring prediction errors and a regularization term to control model complexity. …”

Source_Code_With_Help.rar by Zisong Wang (19838553)

“…<p dir="ltr">In the proposed framework, the Elite Preservation Strategy Chimp Optimization Algorithm (EPSCHOA) is embedded for the improving function and hyperparameter tuning of a Long Short-Term Memory (LSTM) model. …”

Data for revision version by Santanu Saha (18277927)

“…This study enhances the well-established min-max method based interactive fuzzy bi-objective optimization algorithm by incorporating the absolute difference function along with the trade-off ratio based autonomized optimization approach. …”

Conditional probability tensor decompositions for multivariate categorical response regression by Aaron J. Molstad (5243561)

“…We demonstrate the encouraging performance of our method through both simulation studies and an application to modeling the functional classes of genes.…”

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. …”

Table 1_Identification and validation of immune and diagnostic biomarkers for interstitial cystitis/painful bladder syndrome by integrating bioinformatics and machine-learning.docx by Tao Zhou (117050)

“…Hub genes in IC/BPS patients were identified through the application of three distinct machine-learning algorithms. Additionally, the inflammatory status and immune landscape of IC/BPS patients were evaluated using the ssGSEA algorithm. …”

Supporting data for "Machine learning based prognosis prediction of intracerebral hemorrhage outcome". by Eunhye Yang (17853260)

“…Surgical treatments such as clot evacuation (CE) and external ventricular drainage (EVD) are most effective in reducing mortality and morbidity when performed within 4 to 8 hours after the ictus. To support timely clinical decision-making, numerous prognosis scoring systems have been established, yet no scores have been found to be accurate in predicting both mortality and morbidity. …”

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. …”

A. Explanation of the data points used by EpiFusion; B. the key parameters of the EpiFusion particle filter. by Ciara Judge (20161514)

“…Beta must vary over time and can either be fit using (i) a random walk within the particle filter, (ii) linear splines within the particle filter, (iii) MCMC fitting in epochs by fixing or fitting change times and interval values, or (iv) MCMC fitting the parameters of a logistic function which defines beta over time; C. …”