Hardness characteristics and statistical significance analysis of different ingredients across IDDSI levels^b'*'. by Muxi Chen (9294270)

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Methylation detection in the rDNA 28S gene body by MSP is robust and sensitive to methylation state of specific sites. by Stephen Flint Smith (22614954)

MXene/Bi₂O₃ Nanocomposites as Supercapacitors for Portable Electronic Devices by Nanasaheb M. Shinde (8560572)

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S1 File - by Ahmed Akib Jawad Karim (20427740)

“…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”

Confusion matrix for ClinicalBERT model. by Ahmed Akib Jawad Karim (20427740)

“…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”

Confusion matrix for LastBERT model. by Ahmed Akib Jawad Karim (20427740)

“…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”

Student model architecture. by Ahmed Akib Jawad Karim (20427740)

“…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”

Configuration of the LastBERT model. by Ahmed Akib Jawad Karim (20427740)

“…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”

Confusion matrix for DistilBERT model. by Ahmed Akib Jawad Karim (20427740)

“…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”

ROC curve for LastBERT model. by Ahmed Akib Jawad Karim (20427740)

“…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”

Sample Posts from the ADHD dataset. by Ahmed Akib Jawad Karim (20427740)

“…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”

Top-level overview for ADHD classification study. by Ahmed Akib Jawad Karim (20427740)

“…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”

Expression analysis of color-related genes in the hepatopancreas and shell tissues of cherry shrimp (<i>N</i>. <i>davidi</i> var. red) fed with vehicle, astaxanthin alone and in co... by Wei-Wei Hou (3405947)

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Evolution of the four pre-stimuli psychological parameters (A: Valence, B: Arousal, C: Tension, D: Energy). by Felix Alexandre Schoeller (20988295)

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Expression level (2^-ΔCq) of microRNA in the cerebellum of control, pinealectomized (PINX) and melatonin-treated (PINX-MEL) rats. by Mísia Helena da Silva Ferro (20623666)

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Expression level (2^-ΔCq) of microRNA in the sperm of control, pinealectomized (PINX) and melatonin-treated (PINX-MEL) rats. by Mísia Helena da Silva Ferro (20623666)

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Bivariate analysis results. by Oejin Shin (20855617)

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Raw data. by Mísia Helena da Silva Ferro (20623666)

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Target miRNA information. by Mísia Helena da Silva Ferro (20623666)

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Multivariable mixed effect logistic regression for chills, with individual ID and stimulus as a random effect. Individual intercept variance = 1.02 and stimuli intercept variance =... by Felix Alexandre Schoeller (20988295)

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