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. …”

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

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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)

Results obtained from the Bistable Gut Inflammation Detector model. by Fabricio Cravo (21387854)

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Segmentation module training details. by Khelina Fedorchuk (19130089)

PTPC-UHT bounce by David Parry (22169299)

“…<br>It contains the full Python implementation of the PTPC bounce model (<code>PTPC_UHT_bounce.py</code>) and representative outputs used to generate the figures in the paper. …”

Python scripts and notebooks by Ismail Belgacem (20845811)

(a) System dynamics: schematic representation of all the reactions in the Tree model, which are aging, reproduction, color cycle, and competition. by Fabricio Cravo (21387854)

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

“…</li><li><b>Commercial Distribution</b>: Safely share Python code with clients or customers, implementing advanced <b>Python code protection tools</b> for sales or rentals.…”

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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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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Correlation (a) and Mean-Absolute-Error (b) between frequencies at a reference cutoff, δ₀, and different frequencies, , at different cutoff-values, δ_scan ∊ [3... by Guillermo Pérez-Hernández (21156182)

Left schematic of the XOR gate implementation [11]. by Fabricio Cravo (21387854)

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Simulated dynamics of the Mutual Annihilation protocol [26]. by Fabricio Cravo (21387854)

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Heatmap showing the simulated output of the XOR circuit by Tamsir <i>et al</i>. [11]. by Fabricio Cravo (21387854)

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Simulation results for the phage communication circuit from Pathania <i>et al</i>. [5]. by Fabricio Cravo (21387854)

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Matching products with reactants for the example meta-reaction where S1 and S2 are distinct meta-species. by Fabricio Cravo (21387854)

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