Younger adults, high task demand. by Niobe Haitas (10974716)

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Older adults, high task demands. by Niobe Haitas (10974716)

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Younger adults, low task demand. by Niobe Haitas (10974716)

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Data Sheet 1_Significance of multi-task deep learning neural networks for diagnosing clinically significant prostate cancer in plain abdominal CT.docx by Yujun Geng (21232145)

“…The predictive performance of this model was compared with a radiomics model and a single-task deep learning model using ResNet18. …”

The degree of the impact of variables on predictive outcomes. by Hoang Thanh Nhon (22311837)

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Comparative analysis of the Logistic model predictive regions: predictive regions obtained from a 10-point dataset subjected to noise. by Alberto Portela (21349980)

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Sketch of de-mixing task for time delay reservoir computing. by S. Kamyar Tavakoli (20746169)

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Comparative analysis of CGTNet and other models on the same task metrics. by Dianli Wang (22765892)

Makespan with fixed number of tasks. by Yousef Sanjalawe (22216626)

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Spatial information is significantly decreased in dCA1 and vCA1 in APP/PS1 mice. by Udaysankar Chockanathan (18510288)

“…The spatial information in dCA1 was significantly larger than circularly shuffled spike trains with similar mean firing rates for C57BL/6 mice (mean ± std: empirical = 0.132 ± 0.048, shuffled = 0.124 ± 0.035, p < 0.001, two-sided Wilcoxon rank-sum test, n_empirical = 305 units from 5 recording sessions, n_shuffled = 30500 simulated units from 5 recording sessions), but not for APP/PS1 mice (mean ± std: empirical = 0.128 ± 0.051, shuffled = 0.123 ± .047, p = 0.39, two-sided Wilcoxon rank-sum test, n_empirical = 180 units from 4 recording sessions, n_shuffled = 18000 simulated units from 4 recording sessions). …”

Combined dataset overview for segmentation and classification tasks. by İlhan Uzel (14838394)

Histogram of predictions. by Niccolo Pescetelli (7000043)

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Typical prediction analysis scenario. by Shaowei Sun (13392160)

“…<div><p>This paper presents a novel framework for detecting and predicting abnormal traffic events on highways. Current traffic monitoring systems often rely on single data sources, which limits their detection accuracy and robustness in complex environments. …”

Mean prediction errors of a BiLSTM trained on real, synthetic, and augmented data for a downstream prediction task. The numbers in parentheses represent the standard deviation by Raffaele Marchesi (21387979)

Sway values in the four stimulation conditions and the four tasks. by Andrea Kelemen (5480144)

Significant effects found in the reaction times. by Kamil K. Imbir (5906273)

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Performance of VAE models in downstream tasks. by Mostafa Eltager (17102176)

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RTs and task demands in younger and older adults. by Niobe Haitas (10974716)

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Accuracy and task demands in younger and older adults. by Niobe Haitas (10974716)

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Predictive margins and linear predictions by sex. by Cristina Castellanos-Serrano (13215037)