Datasets To EVAL. by 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. by 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. by 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. by 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. by 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. by 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. by 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. by 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. by 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. …”

Cost functions implemented in Neuroptimus. by Máté Mohácsi (20469514)

“…We used the common interface provided by Neuroptimus to conduct a detailed comparison of more than twenty different algorithms (and implementations) on six distinct benchmarks that represent typical scenarios in neuronal parameter search. …”

Example change statistic implementations. by Alex Stivala (8356257)

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Overview of the implemented simulation tool and training framework in Diffrax. by Paul van Lent (16876977)

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System Hardware ID Generator Script: A Cross-Platform Hardware Identification Tool by Pavel Izosimov (20096259)

“…This tool provides <a href="https://xn--mxac.net/local-python-code-protector.html" target="_blank">code obfuscation in Python</a> and <a href="https://xn--mxac.net/secure-python-code-manager.html" target="_blank">Python code encryption</a>, enabling developers to <a href="https://xn--mxac.net/local-python-code-protector.html" target="_blank">protect Python code</a> effectively.…”

Pseudo live-stream of EEG data is generated, and a simple pick-and-place flowchart is designed for channel selection and filtering. by Gursimran Singh (575288)

Python script for binary classification with RoBERTa by Maija Ozola-Schade (20938460)

Secure Python Code Manager: A Tool for Protected Python Code Distribution and Management by Pavel Izosimov (20096259)

Python sript. by Kan Jia (6438599)

Overview of the SBML models used in this study. The collection of models was retrieved from Biomodels [43] and a previously reported collection of SBML benchmark models [11]. The n... by Paul van Lent (16876977)

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Jaxkineticmodel allows for hybridizing kinetic models with neural networks. by Paul van Lent (16876977)

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Analysis of convergence properties for a collection of SBML models. by Paul van Lent (16876977)

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