Time step optimization can greatly improve the efficiency of training and inference. by Weijia Zhu (65481)

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The introduction of mutualisms into assembled communities increases their connectance and complexity while decreasing their richness. by Gui Araujo (22170819)

“…When they stop being introduced in further assembly events (i.e. introduced species do not carry any mutualistic interactions), their proportion slowly decreases with successive invasions. (B) Even though higher proportions of mutualism promote higher richness, introducing this type of interaction into already assembled large communities promotes a sudden drop in richness, while stopping mutualism promotes a slight boost in richness increase. …”

The impact of different diffusion model time steps on the model. by Weijia Zhu (65481)

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Participant demographics. * P-values were determined using Fisher-Freeman-Halton test when contingency tables are larger than 2 × 2. by Elie Mulhem (22537863)

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Prediction (a-d) and infusion deviation (e-f) results under different training sets and test sets. by Leijie Wang (21485157)

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Accuracy and loss of LSTM model training and testing. by Leijie Wang (21485157)

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<b>Supporting data for manuscript</b> "<b>Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins</b>" by Kira Shaw (18796168)

“…<p dir="ltr">The CSV file 'Eyreetal_DrainingVein_SourceData' contains the averaged time series traces and extracted metrics from individual experiments used across Figures 1-5 in the manuscript "Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins". The following acronyms included in the CSV file are defined as follows: Hbt is total hemoglobin, Art is artery region, DV is draining vein region, WV is whisker vein region, SEM is standard error mean, TS is time series, max peak is maximum peak, min peak is minima, AUC is area under the curve, WT is wild-type, AD is Alzheimer's disease, ATH is atherosclerosis and MIX is mixed AD/atherosclerosis. …”

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

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Self-confidence for LIO-ROP after each step of training (score 0–4). by Narisa Rattanalert (21445983)

Larger example of stimuli with cue relevant for Task 3. by Marcel F. Hinss (14675457)

Includes a file with the results on step parameters. by Sanne Ettema (18786312)

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RMSE versus training parameters. by Zhe Hu (787283)

“…However, prediction accuracy diminishes for longer time steps as the step length increases. Following model updates with measured data, the accumulated prediction error rapidly decreases. …”

The design of the brushless direct current motor (BLDCM) closed-loop speed regulating system. by Leijie Wang (21485157)

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Comparison of the transmission structure of the insulin pump. by Leijie Wang (21485157)

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LSTM parameter combination by using the grid search method. by Leijie Wang (21485157)

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Comparison of single infusion accuracy between single-stage and two-stage pumps. by Leijie Wang (21485157)

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Design of insulin infusion regimen for experimental verification. by Leijie Wang (21485157)

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Boxplot of theoretical and actual injection doses. by Leijie Wang (21485157)

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Simulation performance and control effect of BP-PID. by Leijie Wang (21485157)

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Workflow and structure of the insulin pump. by Leijie Wang (21485157)

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