Notations and definitions used in the algorithm. by Nazym Alimbekova (22766158)

Federated averaging algorithm pseudocode. by Hadi Mohammadian KhalafAnsar (22783784)

Archive containing manufacturing files. by Julian Bigge (22331861)

“…<div><p>In this paper we propose the Hatching-Box, a novel in situ imaging and analysis system to automatically monitor and quantify the developmental behavior of <i>Drosophila melanogaster</i> in standard rearing vials and during regular rearing routines, reducing the need for explicit experiments.This is achieved by combining custom tailored imaging hardware with dedicated detection and tracking algorithms, enabling the quantification of larvae, filled/empty pupae and flies over multiple days. …”

Comparison of periodicity of selected clock mutants. by Julian Bigge (22331861)

MD5 signatures from specific regions from a set of files by Paul A. Gagniuc (1818325)

Table 1_Early detection of retinal and choroidal microvascular impairments in diabetic patients with myopia.docx by Yufei Wu (3625967)

Predicting AMI in PTBXL. by Axel Nyström (15220943)

“…There is a long history of applying machine learning algorithms to ECGs, but such algorithms are quite data hungry, and correctly labeled high-quality ECGs are difficult to obtain. …”

Patient characteristics. by Axel Nyström (15220943)

“…There is a long history of applying machine learning algorithms to ECGs, but such algorithms are quite data hungry, and correctly labeled high-quality ECGs are difficult to obtain. …”

Main results. by Axel Nyström (15220943)

“…There is a long history of applying machine learning algorithms to ECGs, but such algorithms are quite data hungry, and correctly labeled high-quality ECGs are difficult to obtain. …”

Model summary. by Axel Nyström (15220943)

“…There is a long history of applying machine learning algorithms to ECGs, but such algorithms are quite data hungry, and correctly labeled high-quality ECGs are difficult to obtain. …”

AMI incidence. by Axel Nyström (15220943)

“…There is a long history of applying machine learning algorithms to ECGs, but such algorithms are quite data hungry, and correctly labeled high-quality ECGs are difficult to obtain. …”

Pre-training results. by Axel Nyström (15220943)

“…There is a long history of applying machine learning algorithms to ECGs, but such algorithms are quite data hungry, and correctly labeled high-quality ECGs are difficult to obtain. …”

Bioinspired Gradient-Modulus Iontronic Sensors with Drift-Suppressed Stability for Biomechanical Monitoring by Yong Zhang (5893)

“…In biomechanical sensing, achieving flexible sensors with a broad detection range, ultrahigh sensitivity, and long-term stability remains a major challenge. …”

Bioinspired Gradient-Modulus Iontronic Sensors with Drift-Suppressed Stability for Biomechanical Monitoring by Yong Zhang (5893)

“…In biomechanical sensing, achieving flexible sensors with a broad detection range, ultrahigh sensitivity, and long-term stability remains a major challenge. …”

Bioinspired Gradient-Modulus Iontronic Sensors with Drift-Suppressed Stability for Biomechanical Monitoring by Yong Zhang (5893)

“…In biomechanical sensing, achieving flexible sensors with a broad detection range, ultrahigh sensitivity, and long-term stability remains a major challenge. …”

S1 File - by Xihao Shen (20347942)

“…A cluster analysis of rejection samples was conducted using the consensus clustering algorithm. Subsequently, we utilized multiple machine learning methods (RF, SVM, XGB, GLM and LASSO) based on pSRGs to develop the optimal Acute Rejection (AR) diagnostic model and long-term graft survival predictive signatures. …”

6 data sets included in this study. by Xihao Shen (20347942)

“…A cluster analysis of rejection samples was conducted using the consensus clustering algorithm. Subsequently, we utilized multiple machine learning methods (RF, SVM, XGB, GLM and LASSO) based on pSRGs to develop the optimal Acute Rejection (AR) diagnostic model and long-term graft survival predictive signatures. …”

Ultralong-Range Sensing of Non-Contact Triboelectric Nanogenerator via Synergistic Design of Porous Microspheres and High Dielectric Properties by Shenzhuo Zhang (20960274)

“…In addition, with deep learning algorithms optimizing recognition, the system can accurately distinguish multiple human dynamic behaviors, such as walking, running, and jumping, within the aforementioned distances. …”

Ultralong-Range Sensing of Non-Contact Triboelectric Nanogenerator via Synergistic Design of Porous Microspheres and High Dielectric Properties by Shenzhuo Zhang (20960274)

“…In addition, with deep learning algorithms optimizing recognition, the system can accurately distinguish multiple human dynamic behaviors, such as walking, running, and jumping, within the aforementioned distances. …”

Image 5_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.jpeg by Tim Unger (20457145)

“…This study investigates whether a single webcam, combined with human pose estimation and deep learning algorithms, can automatically detect compensatory movements in persons with stroke performing a drinking task. …”