Model for the effective bandwidth of element-wise operations (lines = model prediction by Eq 11; symbols = measured maximal effective bandwidth; filled symbols = matrix operations;... by Xuzhen He (14676114)

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Information of the tested GPU platforms. by Xuzhen He (14676114)

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Optimal implementation of matrix operations on the CPU platform (double precision; solid black line = benchmarked bandwidth; lines + symbols = effective bandwidth; black = COPY2D;... by Xuzhen He (14676114)

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Optimal implementation of HEAT1D on the GPU platform (double precision; solid black line = benchmarked bandwidth; lines + symbols = effective bandwidth; red = VEC COPY; black = HEA... by Xuzhen He (14676114)

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S1 Data - by Xuzhen He (14676114)

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Relative efficiency of XLA for numerical models (hollow = f64; filled = f32; circles = HEAT1D; triangles = HEAT2D; squares = NS2D; black = PC CPU; red = PC GPU; blue = HPC CPU; mag... by Xuzhen He (14676114)

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The optimal solution set of NYN by using different algorithms. by Yi Tao (178829)

The optimal solution set of HN by using different algorithms. by Yi Tao (178829)

<b>Fortran program to generate Hilbert trajectories for N=2 fish (+generated trajectories and python code to generate Fig. 3)</b> by Clement Sire (4673599)

“…</p><p><br></p><p dir="ltr"><b>The trajectories will be saved in 2_fish-MEM_3-WALL</b> (create the directory and adapt the value of the Memory and the presence of the wall in the program; no wall adds the extension "-NO_WALL" to the directory name)<br>The current submission includes a zip file of the 2_fish-MEM_3-WALL with the trajectories used to produce Fig. 3 after executing the python code <b>Plot_Hilbert_Fish.py</b>.…”

<b>Fortran program to generate Hilbert trajectories for N=5 fish (+generated trajectories and python code to generate Fig. 4)</b> by Clement Sire (4673599)

“…<br><br><b>T</b><b>h</b><b>e trajectories will be saved in 5_fish-MEM_3-WAL</b><b>L</b> (create the directory and adapt the value of the Memory and the presence of the wall in the program; no wall adds the extension "-NO_WALL" to the directory name)<br>The current submission includes a zip file of the 5_fish-MEM_3-WALL with the trajectories used to produce Fig. 4 after executing the python code <b>Plot_Hilbert_Fish.py</b> (CAUTION: if the option Plot_Cor=1 to plot the correlation functions is activated, the execution of this python code takes around 45 minutes as it exploits all Hilbert and experimental data)</p><p><br></p>…”

Example of use of the slightly off-axis strategies. by Raul Castañeda (13932971)

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Distribution of the 494 TTS hits identified in 123 unique transcripts between the MFE and SO structures, which were predicted using minimization algorithms (<i>mfold</i>, <i>RNAstr... by Atara Neugroschl (16884406)

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The use of optimal parameters for HDDRs improves 3D modeling quality. by Giacomo Janson (8138517)

Formalise2023 by samia Alblwi (14497517)

Enhancing usability and utility of USGS 3D Elevation Program (3DEP) lidar data and web services with Jupyter Notebooks by Cole Speed (13123511)

“…OpenTopography, supported by the USGS Community for Data Integration (CDI) program, is developing well-documented and customizable Jupyter Notebook-based Python workflows for programmatically accessing, processing, and visualizing 3DEP data products for a variety of use-cases geared toward USGS applications and for users of point cloud data across the geospatial community. …”

Table 1_Entropy-adaptive differential privacy federated learning for student performance prediction and privacy protection: a case study in Python programming.docx by Shanwei Chen (12031760)

“…Based on online test records from Python programming courses for Electronic Engineering students (grade 2021–2023) at the School of Physics and Optoelectronic Technology, Baoji University of Arts and Sciences, China, the study uses a Multilayer Perceptron (MLP) model and 10 distributed clients for training. …”

Awkward Arrays: Accelerating scientific data analysis on irregularly shaped data by Jim Pivarski (17087893)

Awkward Arrays: Accelerating scientific data analysis on irregularly shaped data by Jim Pivarski (17087893)

Quanfima: An open source Python package for automated fiber analysis of biomaterials by Roman Shkarin (6580823)

“…Due to the demand for reproducible science with Jupyter notebooks and the broad use of the Python programming language, we have developed the new Python package quanfima offering a complete analysis of hybrid biomaterials, that include the determination of fiber orientation, fiber and/or particle diameter and porosity. …”

The R code that was used to analyze the data in S2 File. by Amrutaa Vibho (19688247)