UMAHand: Hand Activity Dataset (Universidad de Málaga) by Eduardo Casilari (3333906)

“…</p><p dir="ltr">Finally, the SCRIPTS subfolder comprises two scripts written in Python and Matlab. These two programs (named Load_traces), which perform the same function, are designed to automate the downloading and processing of the data. …”

Data Sheet 1_Spatial prediction of ground substrate thickness in shallow mountain area based on machine learning model.pdf by Xiaosong Zhu (11721357)

“…Introduction<p>The thickness of ground substrate in shallow mountainous areas is a crucial indicator for substrate investigations and a key factor in evaluating substrate quality and function. Reliable data acquisition methods are essential for effective investigation.…”

Power and sensitivity analysis assessing the robustness of SINATRA Pro to different free parameter settings in controlled molecular dynamic (MD) simulations. by Wai Shing Tang (9541179)

“…The area under the curve (AUC) details the ability of SINATRA Pro to identify “true class defining” atoms located within the Ω-loop region as a function of changing the different free parameters used in the algorithm. …”

Parameter estimation for a simple two-state model. by Nathaniel J. Linden (11265707)

“…<i>Bottom row:</i> Trajectories of the input function <i>u</i>(<i>t</i>) and corresponding state trajectories. …”

Data_Sheet_1_Tomographic Imaging of the Agung-Batur Volcano Complex, Bali, Indonesia, From the Ambient Seismic Noise Field.PDF by Zulfakriza Zulfakriza (8470824)

“…Delineating the magma structure beneath ABVC is an important first step in understanding the physics of these eruptions. …”

Code by Baoqiang Chen (21099509)

“…We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”

Core data by Baoqiang Chen (21099509)

“…We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”

DataSheet1_On Consensus-Optimality Trade-offs in Collaborative Deep Learning.ZIP by Zhanhong Jiang (11432362)

“…We analytically establish convergence of the proposed algorithms for strongly convex and nonconvex objective functions; we also analyze the momentum variants of the algorithms for the strongly convex case. …”

DataSheet2_On Consensus-Optimality Trade-offs in Collaborative Deep Learning.PDF by Zhanhong Jiang (11432362)

“…We analytically establish convergence of the proposed algorithms for strongly convex and nonconvex objective functions; we also analyze the momentum variants of the algorithms for the strongly convex case. …”

Credit analysis using data mining: application in the case of a credit union by Marcos de Moraes Sousa (5716367)

“…The goal of this study is to develop models to analyze the capacity of a credit union's members to settle their commitments, using a decision tree—C4.5 algorithm and an artificial neural network—multilayer perceptron algorithm. …”

DataSheet_2_A New Thinking: Deciphering the Aberrance and Clinical Implication of IGF Axis Regulation Pattern in Clear Cell Renal Cell Carcinoma.csv by Aimin Jiang (6423755)

“…</p>Conclusion<p>Targeting IGF regulators may be functioned as a treatment approach among multi-cancers. …”

DataSheet_4_A New Thinking: Deciphering the Aberrance and Clinical Implication of IGF Axis Regulation Pattern in Clear Cell Renal Cell Carcinoma.csv by Aimin Jiang (6423755)

“…</p>Conclusion<p>Targeting IGF regulators may be functioned as a treatment approach among multi-cancers. …”

Image_2_A New Thinking: Deciphering the Aberrance and Clinical Implication of IGF Axis Regulation Pattern in Clear Cell Renal Cell Carcinoma.tif by Aimin Jiang (6423755)

“…</p>Conclusion<p>Targeting IGF regulators may be functioned as a treatment approach among multi-cancers. …”

DataSheet_6_A New Thinking: Deciphering the Aberrance and Clinical Implication of IGF Axis Regulation Pattern in Clear Cell Renal Cell Carcinoma.zip by Aimin Jiang (6423755)

“…</p>Conclusion<p>Targeting IGF regulators may be functioned as a treatment approach among multi-cancers. …”

Image_3_A New Thinking: Deciphering the Aberrance and Clinical Implication of IGF Axis Regulation Pattern in Clear Cell Renal Cell Carcinoma.tif by Aimin Jiang (6423755)

“…</p>Conclusion<p>Targeting IGF regulators may be functioned as a treatment approach among multi-cancers. …”

DataSheet_5_A New Thinking: Deciphering the Aberrance and Clinical Implication of IGF Axis Regulation Pattern in Clear Cell Renal Cell Carcinoma.csv by Aimin Jiang (6423755)

“…</p>Conclusion<p>Targeting IGF regulators may be functioned as a treatment approach among multi-cancers. …”

Image_5_A New Thinking: Deciphering the Aberrance and Clinical Implication of IGF Axis Regulation Pattern in Clear Cell Renal Cell Carcinoma.tif by Aimin Jiang (6423755)

“…</p>Conclusion<p>Targeting IGF regulators may be functioned as a treatment approach among multi-cancers. …”

Table_1_A New Thinking: Deciphering the Aberrance and Clinical Implication of IGF Axis Regulation Pattern in Clear Cell Renal Cell Carcinoma.docx by Aimin Jiang (6423755)

“…</p>Conclusion<p>Targeting IGF regulators may be functioned as a treatment approach among multi-cancers. …”

Image_4_A New Thinking: Deciphering the Aberrance and Clinical Implication of IGF Axis Regulation Pattern in Clear Cell Renal Cell Carcinoma.tif by Aimin Jiang (6423755)

“…</p>Conclusion<p>Targeting IGF regulators may be functioned as a treatment approach among multi-cancers. …”

Image_1_A New Thinking: Deciphering the Aberrance and Clinical Implication of IGF Axis Regulation Pattern in Clear Cell Renal Cell Carcinoma.tif by Aimin Jiang (6423755)

“…</p>Conclusion<p>Targeting IGF regulators may be functioned as a treatment approach among multi-cancers. …”