Brain-in-the-Loop Learning for Intelligent Vehicle Decision-Making by Xiaofei Zhang (16483224)

“…In this paper, we utilize functional near-infrared spectroscopy (fNIRS) signals as real-time human risk-perception feedback to establish a brain-in-the-loop (BiTL) trained artificial intelligence algorithm for decision-making. …”

CSPP instance by peixiang wang (19499344)

“…</b></p><p dir="ltr">Its primary function is to create structured datasets that simulate container terminal operations, which can then be used for developing, testing, and benchmarking optimization algorithms (e.g., for yard stacking strategies, vessel stowage planning).…”

NanoDB: Research Activity Data Management System by Lorenci Gjurgjaj (19702207)

“…Cross-Platform Compatibility: Works on Windows, macOS, and Linux. In a Python environment or as an executable. Ease of Implementation: Using the flexibility of the Python framework all the data setup and algorithm can me modified and new functions can be easily added. …”

Code and Data for 'Fabrication and testing of lensed fiber optic probes for distance sensing using common path low coherence interferometry' by Radu Stancu (21165068)

“…Distance Sensing</p><p dir="ltr">Code and data to demonstrate extracting distance sensing data from A-scans and to generate Fig. 8 using the algorithm described in Fig. 7. Functions to generate distance measurements are in 'distance_sensing_utilities.py' and an example of how to use this on data in the 'data' folder is in 'distance_sensing_example.py', which generates Fig 8. …”

Communication-Efficient Distributed Sparse Learning with Oracle Property and Geometric Convergence* by Weidong Liu (444731)

“…In terms of computational efficacy, our algorithm exhibits a geometric convergence rate within each middle loop. …”

Evaluation of Binary Classifiers for Asymptotically Dependent and Independent Extremes by Juliette Legrand (21771740)

“…<p>Machine learning classification methods usually assume that all possible classes are sufficiently present within the training set. Due to their inherent rarities, extreme events are always under-represented and classifiers tailored for predicting extremes need to be carefully designed to handle this under-representation. …”

Mechanomics Code - JVT by Carlo Vittorio Cannistraci (5854046)

“…The functions were tested respectively in: MATLAB 2018a or youger, Python 3.9.4, R 4.0.3.…”

Table 1_CytoLNCpred-a computational method for predicting cytoplasm associated long non-coding RNAs in 15 cell-lines.xlsx by Shubham Choudhury (9192026)

“…<p>The function of long non-coding RNA (lncRNA) is largely determined by its specific location within a cell. …”

Finding the most diverse subset of proteins - Genome Informatics 2024 by Amanda Clare (98717)

“…We implemented a grey-box local search algorithm using the structure of the optimised function to guide the search. …”

Data Sheet 1_Prefrontal meta-control incorporating mental simulation enhances the adaptivity of reinforcement learning agents in dynamic environments.pdf by JiHun Kim (20945054)

“…In addition, hippocampal function, particularly mental simulation capacity, proves essential in this adaptive process. …”

Table 1_Kefir and healthy aging: revealing thematic gaps through AI-assisted screening and semantic evidence mapping.docx by Francesco Chiani (2661328)

“…To overcome this fragmentation, we applied an integrative approach that combines a cutting-edge AI-assisted algorithm for evidence screening with a Python-based semantic clustering pipeline. …”

Table 2_Kefir and healthy aging: revealing thematic gaps through AI-assisted screening and semantic evidence mapping.docx by Francesco Chiani (2661328)

“…To overcome this fragmentation, we applied an integrative approach that combines a cutting-edge AI-assisted algorithm for evidence screening with a Python-based semantic clustering pipeline. …”

Table 3_Kefir and healthy aging: revealing thematic gaps through AI-assisted screening and semantic evidence mapping.xlsx by Francesco Chiani (2661328)

“…To overcome this fragmentation, we applied an integrative approach that combines a cutting-edge AI-assisted algorithm for evidence screening with a Python-based semantic clustering pipeline. …”

Table 4_Kefir and healthy aging: revealing thematic gaps through AI-assisted screening and semantic evidence mapping.docx by Francesco Chiani (2661328)

“…To overcome this fragmentation, we applied an integrative approach that combines a cutting-edge AI-assisted algorithm for evidence screening with a Python-based semantic clustering pipeline. …”

Table 3_Comprehensive analysis of adverse events associated with onasemnogene abeparvovec (Zolgensma) in spinal muscular atrophy patients: insights from FAERS database.xlsx by Wenwen Zhang (331647)

“…<p>Onasemnogene Abeparvovec (Zolgensma) is a gene therapy for the treatment of Spinal Muscular Atrophy (SMA) with improved motor neuron function and the potential for a singular treatment. …”

Table 2_Comprehensive analysis of adverse events associated with onasemnogene abeparvovec (Zolgensma) in spinal muscular atrophy patients: insights from FAERS database.xlsx by Wenwen Zhang (331647)

“…<p>Onasemnogene Abeparvovec (Zolgensma) is a gene therapy for the treatment of Spinal Muscular Atrophy (SMA) with improved motor neuron function and the potential for a singular treatment. …”

Data Sheet 1_Comprehensive analysis of adverse events associated with onasemnogene abeparvovec (Zolgensma) in spinal muscular atrophy patients: insights from FAERS database.docx by Wenwen Zhang (331647)

“…<p>Onasemnogene Abeparvovec (Zolgensma) is a gene therapy for the treatment of Spinal Muscular Atrophy (SMA) with improved motor neuron function and the potential for a singular treatment. …”

Table 1_Comprehensive analysis of adverse events associated with onasemnogene abeparvovec (Zolgensma) in spinal muscular atrophy patients: insights from FAERS database.docx by Wenwen Zhang (331647)

“…<p>Onasemnogene Abeparvovec (Zolgensma) is a gene therapy for the treatment of Spinal Muscular Atrophy (SMA) with improved motor neuron function and the potential for a singular treatment. …”

Uncertainty and Novelty in Machine Learning by Derek Scott Prijatelj (20364288)

“…This demonstrates identifying information in finite steps to asymptotic statistics and PAC-learning, where we recover identification within finite observations at the cost of uncertainty and error.…”

Seamless integration of legacy robotic systems into a self-driving laboratory via NIMO: a case study on liquid handler automation by Ryo Tamura (1957942)

“…We developed NIMO (formerly NIMS-OS, NIMS Orchestration System), an OS explicitly designed to integrate multiple artificial intelligence (AI) algorithms with diverse exploratory objectives. NIMO provides a framework for integrating AI into robotic experimental systems that are controlled by other OS platforms based on both Python and non-Python languages. …”