Local topological scales of hierarchical community organization in structural and functional brain graphs. by Arian Ashourvan (6685232)

Comparison between different PSO algorithms on function 4–6. by Lai Xu (1828831)

Comparison between different PSO algorithms on function 1–3. by Lai Xu (1828831)

Comparison between different PSO algorithms on function 7–8. by Lai Xu (1828831)

Similarity between the allegiance matrices of the multiplex SC-FC graph, the FC graph alone, and the SC graph alone, as a function of the topological scale for the group. by Arian Ashourvan (6685232)

RMSE values of different algorithms for different bands in the "Kherson" region. by YueSheng Jiang (19267984)

RMSE values of different algorithms for different bands in the "PY" region. by YueSheng Jiang (19267984)

How fries chooses which sequences to keep and which sequences to prune. by Anna U. Lowegard (8051597)

DataSheet1_The evolution of flexibility and function in the Fc domains of IgM, IgY, and IgE.pdf by Rosaleen A. Calvert (10039787)

“…This permits additional flexibility within the Fc region, which has been exploited by nature to modulate antibody effector functions. …”

Mutations of the gene expression fold change (FC) over wildtype. by Fangzhou Shen (5140994)

In this example the MPC algorithm perfectly tracks the noisy signal , which results in many false spikes. by Nicholas A. Rondoni (21573261)

Data of PV, WT, FC, and utilized coefficients. by Obaid Aldosari (19797428)

“…A day-ahead scheduling model is proposed for optimal energy management (EM) of the μG investigated, which comprises photovoltaics (PVs), fuel cells (FCs), wind turbines (WTs), BSSs, and EV charging stations, with shed light on the viability and benefits of connecting BSS with EV charging stations in the μG. …”

Details the DLKA structure, which includes the DLKA-Attention module and the FFN module. The DLKA-Attention module mainly consists of the DLKA module, which comprises deformable de... by Xunqian Xu (21261899)

RMSE results. by YueSheng Jiang (19267984)

“…To overcome these limitations, this paper developed a simple and fast adaptive remote sensing image Spatio-Temporal fusion method based on Fit-FC, called Adapt Lasso-Fit-FC (AL-FF). Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. …”

Results of the Kherson Area Visual Assessment. by YueSheng Jiang (19267984)

“…To overcome these limitations, this paper developed a simple and fast adaptive remote sensing image Spatio-Temporal fusion method based on Fit-FC, called Adapt Lasso-Fit-FC (AL-FF). Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. …”

Work flow chart. by YueSheng Jiang (19267984)

“…To overcome these limitations, this paper developed a simple and fast adaptive remote sensing image Spatio-Temporal fusion method based on Fit-FC, called Adapt Lasso-Fit-FC (AL-FF). Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. …”

Experimental data. by YueSheng Jiang (19267984)

“…To overcome these limitations, this paper developed a simple and fast adaptive remote sensing image Spatio-Temporal fusion method based on Fit-FC, called Adapt Lasso-Fit-FC (AL-FF). Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. …”

Results of the PY area visual assessment. by YueSheng Jiang (19267984)

“…To overcome these limitations, this paper developed a simple and fast adaptive remote sensing image Spatio-Temporal fusion method based on Fit-FC, called Adapt Lasso-Fit-FC (AL-FF). Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. …”

List of selected ranges of movements(ROM), from which features were then counted. by Jindřich Adolf (17334216)

Average quality scores of the models of the analysis set as a function of the <i>w</i>_<i>SP</i> with which the DOPE potential has been included in the objective function... by Giacomo Janson (8138517)