Algorithms for designing continuous curricula A: Decision tree showing the continuous version of ADP which includes actions that “grow” and “shrink” the increments between continuo... by William L. Tong (22238845)

“…Similar to the discrete setting, INC shows catastrophic extinction and never learns the task for sufficiently small <i>ε</i>. Continuous ADP first decreases increment size and smoothly increases the difficulty level while balancing reinforcement and extinction.…”

Exploring the relationship between graft dysfunction with serum metabolites and inflammatory proteins: integrating Mendelian randomization, single-cell analysis, machine learning,... by Jiyuan Li (4784196)

“…Second, we further intergrated the single-cell analysis, machine learning, and Shapley Additive exPlanations (SHAP) methods to validate the role of the inflammatory protein in rejected transplanted kidneys.…”

VMR task and subject behavior. by Corson N. Areshenkoff (20378635)

“…During learning trials, the cursor moved at a 45^<i>°</i> angle relative to the hand, so that subjects had to learn to adjust their movement direction in order to contact the target successfully. …”

Data_Sheet_1_Loneliness, online learning and student outcomes in college students living with disabilities: results from the National College Health Assessment Spring 2022.docx by William Bevens (9319157)

“…Interactions indicate a significant effect of in-person learning compared with online learning for deaf or hard of hearing students, and those with multiple disabilities. …”

Genus and species level abundance across the dataset. by Fathi A. Mubaraki (21736511)

Class and family relative abundance across the dataset. by Fathi A. Mubaraki (21736511)

Depth and unit sub-grouping improve performance and neural prediction. (A) depth was added by stacking RNNs while controlling the number of trainable parameters (the number of hidden units per layer is indicated in white). (B) Deeper models have lower word error rate. (C) Deeper models also achieved better predictive power for brain responses, when units in each layer were used to create separate predictors (solid lines). However, increasing the number of predictors derived from flat models by K-means clustering also increased predictive power (dotted lines), with the highest predictive power for the flat localist model at K = 32. (D) K-means clustering applied to deep models (ignoring the differentiation of units into different layers). While depth generally improved predictive power of GloVe models, it did not seem to have a clear effect on localist models. (E) Acoustic-phonetic structure in the learned output mappings is found even in deep localist models, but it decreases as a by Christian Brodbeck (211096)

“…While depth generally improved predictive power of GloVe models, it did not seem to have a clear effect on localist models. (E) Acoustic-phonetic structure in the learned output mappings is found even in deep localist models, but it decreases as a</p>…”

Partner selection GLMM results: pilot and main experiments. by Gota Morishita (22806463)

Data Sheet 1_Decoding the hypoxia-exosome-immune triad in OSA: PRCP/UCHL1/BTG2-driven metabolic dysregulation revealed by interpretable machine learning.zip by Weilong Ye (12984647)

“…OSA is associated with a reproducible transcriptional signature characterized by increased PRCP and UCHL1 expression and decreased BTG2 expression.…”

Comprehensive comparative analysis of the proposed model versus existing methods for cotton disease detection and other applications in the agricultural domain. Results are based o... by Afira Aslam (21433666)

Performance comparison of the proposed model on the cotton leaf disease dataset [76] against State-of-the-Art methods for robustness evaluation. by Afira Aslam (21433666)

Graphical abstract regarding program development. by Ana Beato (20489933)

“…The quantitative results from Phase 1 demonstrated a decreasing trend in all primary outcomes. In phase 2, participants acknowledged the activities’ relevance, reported overall satisfaction with the program, and showed great enthusiasm and willingness to learn more. …”

Phases of the intervention program. by Ana Beato (20489933)

“…The quantitative results from Phase 1 demonstrated a decreasing trend in all primary outcomes. In phase 2, participants acknowledged the activities’ relevance, reported overall satisfaction with the program, and showed great enthusiasm and willingness to learn more. …”

Consort diagram. by Ana Beato (20489933)

“…The quantitative results from Phase 1 demonstrated a decreasing trend in all primary outcomes. In phase 2, participants acknowledged the activities’ relevance, reported overall satisfaction with the program, and showed great enthusiasm and willingness to learn more. …”

Characteristics of the sample. by Ana Beato (20489933)

“…The quantitative results from Phase 1 demonstrated a decreasing trend in all primary outcomes. In phase 2, participants acknowledged the activities’ relevance, reported overall satisfaction with the program, and showed great enthusiasm and willingness to learn more. …”

The flowchart of the large model for font style transfer. by Weijia Zhu (65481)

Representative sample images from each category used in the multi-class classification task. by Afira Aslam (21433666)

Data Sheet 1_LoCS-Net: Localizing convolutional spiking neural network for fast visual place recognition.pdf by Ugur Akcal (20633144)

Empty classroom during the lockdown. by Mohammed B. A. Sarhan (19838131)

“…The third theme included beliefs and behaviours that either increased or decreased COVID-19 risk. The final theme addressed schools’ responses to COVID-19, including factors such as maintaining connections with schools, preventive measures and the transition to remote learning.…”

Service statistics dataset. by Gizachew Tadele Tiruneh (8407686)