MCCN Case Study 2 - Spatial projection via modelled data by Donald Hobern (21435904)

“…This study demonstrates: 1) Description of spatial assets using STAC, 2) Loading heterogeneous data sources into a cube, 3) Spatial projection in xarray using different algorithms offered by the <a href="https://pypi.org/project/PyKrige/" rel="nofollow" target="_blank">pykrige</a> and <a href="https://pypi.org/project/rioxarray/" rel="nofollow" target="_blank">rioxarray</a> packages.…”

Data Sheet 1_Machine learning models integrating intracranial artery calcification to predict outcomes of mechanical thrombectomy.pdf by Guangzong Li (16696443)

“…Eleven ML algorithms were trained and validated using Python, and external validation and performance evaluations were conducted. …”

Supplementary file 3_Optimising the selection of welfare indicators in farm animals.docx by Jon Day (19128586)

“…The enhanced algorithm identified globally optimal combinations within 0.2 s for all species, regardless of problem size. …”

Supplementary file 2_Optimising the selection of welfare indicators in farm animals.docx by Jon Day (19128586)

“…The enhanced algorithm identified globally optimal combinations within 0.2 s for all species, regardless of problem size. …”

Supplementary file 6_Optimising the selection of welfare indicators in farm animals.docx by Jon Day (19128586)

“…The enhanced algorithm identified globally optimal combinations within 0.2 s for all species, regardless of problem size. …”

Supplementary file 1_Optimising the selection of welfare indicators in farm animals.docx by Jon Day (19128586)

“…The enhanced algorithm identified globally optimal combinations within 0.2 s for all species, regardless of problem size. …”

Supplementary file 4_Optimising the selection of welfare indicators in farm animals.docx by Jon Day (19128586)

“…The enhanced algorithm identified globally optimal combinations within 0.2 s for all species, regardless of problem size. …”

Supplementary file 5_Optimising the selection of welfare indicators in farm animals.docx by Jon Day (19128586)

“…The enhanced algorithm identified globally optimal combinations within 0.2 s for all species, regardless of problem size. …”

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. …”

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. …”

Echo Peak by Rocco De Marco (14146593)

“…</p><p dir="ltr">For classification, the algorithm iteratively processes the audio in overlapping time windows. …”

Examples of model selection methods for the PHMM. by Tianshu Li (1527088)

“…<i>Left panel:</i> LL on validation sets as a function of <i>m</i>. The black curves show the average log-likelihoods (across initial guesses) of all models whose algorithm converged to a solution during training (vertical bars display one standard deviation across 100 different initial guesses for the parameter values). …”

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

“…This function is particularly relevant in manufacturing environments where these machines are present, as meeting due dates is crucial on these bottleneck machines. …”

Finding the most diverse subset of proteins - Genome Informatics 2024 by Amanda Clare (98717)

“…We implemented a grey-box local search algorithm using the structure of the optimised function to guide the search. …”

<b>Fig. 6 |</b> <b>Autonomous microrobot navigation upstream in a flow environment.</b> by Mahmoud Medany (20766911)

“…</b> Schematic of the reward function adjustment to promote microrobot navigation close to the wall, minimizing drag. …”

Tree-Enhanced Latent Space Models for Two-Mode Networks by Dan Pu (3714616)

“…In this framework, each node is characterized by a latent embedding vector, reparameterized as the aggregate of intermediate vectors corresponding to nodes within the tree structure. By optimizing the log-likelihood function augmented with a tree-based regularization term, the proposed model facilitates the simultaneous estimation of embedding vectors and the derivation of interpretable community structures. …”

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. …”

LDD microtubule-based nucleation naturally results in a local microtubule density-dependent fraction of microtubule-based nucleation. by Marco Saltini (22209094)

“…<p>Fraction of microtubule-based nucleation as a function of (A) local and (B) global microtubule density (<i>ρ</i>) during a single simulation run. …”

PhD Proposal Defense: Threshold-based Association Analysis Frameworks for Continuous Variables by Md Mahin (21434966)

“…Norma associates point-wise functions with each variable-e.g., a function that returns poverty rates for each location in New Mexico. …”

PhD Defense: Threshold-based Association Analysis Frameworks for Continuous Variables by Md Mahin (21434966)

“…Norma associates point-wise functions with each variable-e.g., a function that returns poverty rates for each location in New Mexico. …”