Enhancing off-policy optimisation in structured Markov decision processes via Thompson Sampling by Sourav Ganguly (293262)

Experiment 2D and 5D: Progressive Sample Scaling Algorithm To Solve Many-Affine BBOB Functions. by Boris Almonacid (4110337)

Model performance before and after optimisation. by Daniel Sanchez-Gomez (19065975)

Data_Sheet_1_How Risk Profiles of Investors Affect Robo-Advised Portfolios.pdf by Dmitri Boreiko (3937526)

Data_Sheet_1_A multi-class logistic regression algorithm to reliably infer network connectivity from cell membrane potentials.PDF by Thierry Nieus (165355)

Data_Sheet_1_A New Bayesian Methodology for Nonlinear Model Calibration in Computational Systems Biology.PDF by Fortunato Bianconi (6938102)

Data_Sheet_1_Investigating Bayesian optimization for expensive-to-evaluate black box functions: Application in fluid dynamics.pdf by Mike Diessner (14236922)

Supplementary file 2_Patient-specific and interpretable deep brain stimulation optimisation using MRI and clinical review data.xlsx by Apostolos Mikroulis (22473742)

“…Here, we present a geometry-based optimisation approach for DBS electrode contact and current selection, grounded in routinely collected MRI data, well-established tools (Lead-DBS) and optionally, clinical review records.…”

Supplementary file 3_Patient-specific and interpretable deep brain stimulation optimisation using MRI and clinical review data.zip by Apostolos Mikroulis (22473742)

“…Here, we present a geometry-based optimisation approach for DBS electrode contact and current selection, grounded in routinely collected MRI data, well-established tools (Lead-DBS) and optionally, clinical review records.…”

Supplementary file 1_Patient-specific and interpretable deep brain stimulation optimisation using MRI and clinical review data.docx by Apostolos Mikroulis (22473742)

“…Here, we present a geometry-based optimisation approach for DBS electrode contact and current selection, grounded in routinely collected MRI data, well-established tools (Lead-DBS) and optionally, clinical review records.…”

Data supporting figures and tables in Reimagining Electrical Diagrams in Construction: Automated Symbol Detection and Wiring Design and Generation with Deep Learning by IKENNA EKEKE (20996041)

Precision-Recall data for improved models. by Haibin Liu (403499)

Multi-zone simulation results of Europoint complex for self-sufficiency in energy consumption and food production in Rotterdam by Berk Ekici (9126502)

“…Latin Hypercube Sampling method is used with Honeybee and Ladybug plug-ins developed for environmental analysis in Grasshopper during the sampling process. …”

OpenACC pgfortran: substantial speedups and beyond for the O3 Condensation algorithm for determinants and estimation by Damien Mather (9636747)

“…We utilise intel MPI libraries and up to 4 of Mahuika’s P-100 GPUs per batch job and show (a) how substantial speedups can be had with four additional OpenACC compiler directives, and (b) how evolving the algorithm to optimise data locality and reduce process blocking can achieve further substantial speedup. …”

DataSheet1_High-resolution 3D mapping of cold-water coral reefs using machine learning.docx by Larissa Macedo Cruz de Oliveira (10338704)

“…The Piddington Mound area, southwest of Ireland, was selected for 3D reconstruction from high-definition video data acquired with an ROV. Six ML algorithms, namely: Support Vector Machines, Random Forests, Gradient Boosting Trees, k-Nearest Neighbours, Logistic Regression and Multilayer Perceptron, were trained in two datasets of different sizes (1,000 samples and 10,000 samples) in order to evaluate accuracy variation between approaches in relation to the number of samples. …”

Data_Sheet_1_scGPS: Determining Cell States and Global Fate Potential of Subpopulations.PDF by Michael Thompson (348965)

“…Here, we present Single Cell Global fate Potential of Subpopulations (scGPS) to characterise transcriptional relationship between cell states. scGPS decomposes mixed cell populations in one or more samples into clusters (SCORE algorithm) and estimates pairwise transitioning potential (scGPS algorithm) of any pair of clusters. …”

DataSheet_1_Accelerating Species Recognition and Labelling of Fish From Underwater Video With Machine-Assisted Deep Learning.pdf by Daniel Marrable (112335)

“…In both cases, substantial volumes of data are required and this is currently a manual, labour-intensive process, resulting in a paucity of the labelled data currently required for training object detection models for species detection. …”

DataSheet_2_Accelerating Species Recognition and Labelling of Fish From Underwater Video With Machine-Assisted Deep Learning.pdf by Daniel Marrable (112335)

“…In both cases, substantial volumes of data are required and this is currently a manual, labour-intensive process, resulting in a paucity of the labelled data currently required for training object detection models for species detection. …”

DataSheet_2_Accelerating Species Recognition and Labelling of Fish From Underwater Video With Machine-Assisted Deep Learning.pdf by Daniel Marrable (112335)

“…In both cases, substantial volumes of data are required and this is currently a manual, labour-intensive process, resulting in a paucity of the labelled data currently required for training object detection models for species detection. …”

DataSheet_1_Accelerating Species Recognition and Labelling of Fish From Underwater Video With Machine-Assisted Deep Learning.pdf by Daniel Marrable (112335)

“…In both cases, substantial volumes of data are required and this is currently a manual, labour-intensive process, resulting in a paucity of the labelled data currently required for training object detection models for species detection. …”