A high-performance and highly reusable fast multipole method library and its application to solvation energy calculations at virus-scale حسب Tingyu Wang (12342757)

"…It empowers researchers to compute solvation energy at the scale of viruses interactively via easy-to-use Python interfaces. The 1/N convergence rate observed in mesh-refinement studies confirms code correctness, and result comparison with other trusted PB software shows agreement using a wide range of proteins.Performance results report timings and breakdowns with between 8,000 and 2 million boundary elements and confirm the linear complexity in both time and space. …"

Examples of Tissue Forge modeling features. حسب T. J. Sego (9904909)

Datasets To EVAL. حسب Jin Lu (428513)

"…We evaluated our proposed system on five educational datasets—AI2_ARC, OpenBookQA, E-EVAL, TQA, and ScienceQA—which represent diverse question types and domains. Compared to vanilla Large Language Models (LLMs), our approach combining Retrieval-Augmented Generation (RAG) with Code Interpreters achieved an average accuracy improvement of 10−15 percentage points. …"

Statistical significance test results. حسب Jin Lu (428513)

"…We evaluated our proposed system on five educational datasets—AI2_ARC, OpenBookQA, E-EVAL, TQA, and ScienceQA—which represent diverse question types and domains. Compared to vanilla Large Language Models (LLMs), our approach combining Retrieval-Augmented Generation (RAG) with Code Interpreters achieved an average accuracy improvement of 10−15 percentage points. …"

How RAG work. حسب Jin Lu (428513)

"…We evaluated our proposed system on five educational datasets—AI2_ARC, OpenBookQA, E-EVAL, TQA, and ScienceQA—which represent diverse question types and domains. Compared to vanilla Large Language Models (LLMs), our approach combining Retrieval-Augmented Generation (RAG) with Code Interpreters achieved an average accuracy improvement of 10−15 percentage points. …"

OpenBookQA experimental results. حسب Jin Lu (428513)

"…We evaluated our proposed system on five educational datasets—AI2_ARC, OpenBookQA, E-EVAL, TQA, and ScienceQA—which represent diverse question types and domains. Compared to vanilla Large Language Models (LLMs), our approach combining Retrieval-Augmented Generation (RAG) with Code Interpreters achieved an average accuracy improvement of 10−15 percentage points. …"

AI2_ARC experimental results. حسب Jin Lu (428513)

"…We evaluated our proposed system on five educational datasets—AI2_ARC, OpenBookQA, E-EVAL, TQA, and ScienceQA—which represent diverse question types and domains. Compared to vanilla Large Language Models (LLMs), our approach combining Retrieval-Augmented Generation (RAG) with Code Interpreters achieved an average accuracy improvement of 10−15 percentage points. …"

TQA experimental results. حسب Jin Lu (428513)

"…We evaluated our proposed system on five educational datasets—AI2_ARC, OpenBookQA, E-EVAL, TQA, and ScienceQA—which represent diverse question types and domains. Compared to vanilla Large Language Models (LLMs), our approach combining Retrieval-Augmented Generation (RAG) with Code Interpreters achieved an average accuracy improvement of 10−15 percentage points. …"

E-EVAL experimental results. حسب Jin Lu (428513)

"…We evaluated our proposed system on five educational datasets—AI2_ARC, OpenBookQA, E-EVAL, TQA, and ScienceQA—which represent diverse question types and domains. Compared to vanilla Large Language Models (LLMs), our approach combining Retrieval-Augmented Generation (RAG) with Code Interpreters achieved an average accuracy improvement of 10−15 percentage points. …"

TQA Accuracy Comparison Chart on different LLM. حسب Jin Lu (428513)

"…We evaluated our proposed system on five educational datasets—AI2_ARC, OpenBookQA, E-EVAL, TQA, and ScienceQA—which represent diverse question types and domains. Compared to vanilla Large Language Models (LLMs), our approach combining Retrieval-Augmented Generation (RAG) with Code Interpreters achieved an average accuracy improvement of 10−15 percentage points. …"

ScienceQA experimental results. حسب Jin Lu (428513)

"…We evaluated our proposed system on five educational datasets—AI2_ARC, OpenBookQA, E-EVAL, TQA, and ScienceQA—which represent diverse question types and domains. Compared to vanilla Large Language Models (LLMs), our approach combining Retrieval-Augmented Generation (RAG) with Code Interpreters achieved an average accuracy improvement of 10−15 percentage points. …"

Example of model modularity. حسب Adam Husar (18437351)

الموضوعات:

Recombination and mutation estimation times using the fastest (dynamic-numba) likelihood implementation depending on the number of markers. حسب Serena Aneli (17002149)

Example of the angular spectrum approach to numerically focus a hologram using a circular spatial filter tool. حسب Raul Castañeda (13932971)

Identification of metabolic end goals by BOFdat Step 3. حسب Jean-Christophe Lachance (6619307)

BOFdat Step 1: Calculating the biomass objective function stoichiometric coefficients (BOFsc) for the 4 principal macromolecular categories of the cell. حسب Jean-Christophe Lachance (6619307)

Comparison of phenotypic predictions and metabolite composition between the three steps of BOFdat, the original <i>i</i>ML1515 BOF, SEED, and BOSS. حسب Jean-Christophe Lachance (6619307)

BOFdat Step 3: Identifying species-specific metabolic end goals. حسب Jean-Christophe Lachance (6619307)

Local Python Code Protector Script: A Tool for Source Code Protection and Secure Code Sharing حسب Pavel Izosimov (20096259)

"…<p dir="ltr">Local Python Code Protector Script: Advanced Tool for Python Code Protection and Secure Sharing</p><h2>Introduction</h2><p dir="ltr">The <b>Local Python Code Protector Script</b> is a powerful command-line tool designed to provide <a href="https://xn--mxac.net/local-python-code-protector.html" target="_blank"><b>source code protection</b></a> and <a href="https://xn--mxac.net/local-python-code-protector.html" target="_blank"><b>secure code sharing</b></a> for Python scripts. …"

Validation of model of the SNARE complex. حسب Adam Husar (18437351)

الموضوعات: