Primers sequences of the genes examined by real-time PCR. by Takafumi Kabuto (14797727)

Subjects:

Details of the 11 fully trained dogs exposed to sweat samples of SARS-CoV-2 positive and negative humans. by Víctor M. Vidal-Martínez (18707335)

Subjects:

a-d. Difference in (a) probing depth; (b) clinical attachment level; (c) bleeding on probing, and (d) plaque index values between baseline and six months after treatment using syst... by Iva Milinkovic (22331710)

Subjects:

Information of QTL interval and lead SNP significantly associated with meat color in fast-growing white-feathered chickens. by Jiahong Sun (6497603)

Predictive Significance of Glycosyltransferase-Related lncRNAs in Endometrial Cancer: A Comprehensive Analysis and Experimental Validation by Xiaoyu Shen (4715430)

Subjects:

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

Subjects:

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

Subjects:

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

Subjects:

This figure shows the forest plot for the multivariable logistic regression found in Table 1. by Scott J. Halperin (17835402)

Effect of supplementation of oocyte maturation medium (OMM) with follicular fluid (FF) from small or large follicles on development of embryos to the blastocyst stage. by Verónica M. Negrón-Pérez (20691412)

Effect of follicular fluid (FF) supplementation and time of maturation on development of embryos to the blastocyst stage. by Verónica M. Negrón-Pérez (20691412)

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

Subjects:

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. …”