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C. Lofi

87 records found

Grounding Large Language Models (LLMs) in chemical knowledge graphs (KGs) offers a promising way to support synthesis planning, but reliably retrieving information from these complex structures remains a challenge. Therefore, this work addresses that gap by constructing a biparti ...
Applying Large Language Models (LLMs) to high-stakes classification tasks like systematic review screening is challenged by prompt sensitivity and a lack of transparency. We introduce IMAPR (Iterative Multi-signal Adaptive Prompt Refinement), a novel framework where a single LLM ...
Apache Spark is a popular batch processing framework that is integrated into the ASML data analytics platform. However, having multiple users share the same resources creates fairness and efficiency problems in the scheduler, where some users may receive more resources than other ...

Heuristic Optimization of Amazon Redshift Table Configurations

Focusing on Distribution Style, Sort Keys and Column Encodings in Amazon Redshift

This thesis presents a comprehensive, heuristic cost-driven framework for optimizing database table configuration in Amazon Redshift focusing on distribution styles, sort keys and column encodings. Unlike existing approaches that treat optimization parameters independently, this ...

Learning to Learn from Microbiome Data

Benchmarking Meta-Learning for Disease Classification on Microbiome Abundance Data

The human gut microbiome has emerged as a key player in health and disease, yet machine learning on microbiome data remains challenging due to its high dimensionality, sparsity, compositionality, and inter-study heterogeneity. Although classical and deep learning methods have dem ...

Transformer-Based Synthetic Relational Data

Closing the Gap Between Diffusion-Based and Transformer-Based Synthetic Relational Data Generation

Data sharing for research and industrial applications faces significant challenges due to privacy constraints and regulatory requirements, driving the need for high-quality synthetic alternatives.
Recent advances in synthetic data generation have demonstrated considerable suc ...

Sample-Based t-SNE Embeddings

How different Sampling Strategies influence the Quality of Low-Dimensional Embeddings

Data visualisation is an important area of research: as the amount of data keeps increasing, we have to find ways of showcasing this data to provide an intuition for trends and patterns within it. This can be a particular challenge for high-dimensional data, since we cannot perce ...
Modern data analysis often involves working with large multidimensional datasets. Visualizing this kind of data helps leverage human intuition and pattern recognition to reveal hidden relationships. t-SNE is a widely used tool for creating such visualizations. Despite its popular ...

High-Dimensional Data Visualization via Sampling-Based Approaches

Measurement of structural similarity between different embeddings as a way of predicting a suitable perplexity

Dimensionality reduction techniques, such as t-SNE, are widely used to visualize high-dimensional data and have a crucial role in practical tasks such as biological data exploration, anomaly detection, or clustering large datasets. However, they are highly dependent on hyperparam ...

High-Dimensional Data Visualization via Sampling-Based Approaches

Effect of Perplexity at different levels of Sampling-Based Approach

Visualizing high-dimensional data is a key challenge in modern data analysis. T-distributed Stochastic Neighbor Embedding (t-SNE) is a popular nonlinear dimensionality reduction technique that maps such data into a low-dimensional embedding while preserving local relationships. A ...
T-SNE is widely used for visualising high-dimensional data in lower dimensions.
To reduce the costs of parameter optimisation, t-SNE is performed on a sample of the original data. After sampling the points, the distances between them need to be calculated, which is expensive ...

A Storytelling Robot for People with Dementia

Keeping people with dementia and family members involved in the storytelling process

Storytelling has many benefits for people with dementia (PwD), such as improved well-being, confidence and communication. However, there is not much research on robots conducting such activities in dementia care. Thus, the goal of this project was to implement a system that can f ...

A Storytelling Robot for People with Dementia

LLM-Based Persona Simulation to Support Testing of a Storytelling Robot for People with Dementia

Personas are a particularly useful testing tool for storytelling robots for people with dementia (PwD) because they offer an alternative to direct user involvement, which is often limited by recruitment, privacy, and consent-related challenges. The manual creation of realistic pe ...

A Storytelling Robot for People with Dementia

Designing a simple interface, suitable for People with Dementia

This project investigates the design process of a straightforward, user-friendly interface that enables cooperative storytelling between a person with dementia and a family member, through the mediation of a social robot. Due to ethical constraints, evaluation was conducted only ...

A Storytelling Robot for People with Dementia

Enhancing Collaborative Storytelling for People with Dementia through AI-Based Media Generation

Storytelling is a powerful non-pharmacological intervention in care for People with Dementia (PwD), offering the opportunity of self-expression, emotional connection and identity preservation. However, creation of multimedia material to accompany such stories usually requires tra ...

A Storytelling Robot for People with Dementia

Evaluating Data Bias and User Enjoyment in the Full System

This paper presents a unified evaluation framework for assessing multimodal storytelling robots used in dementia care. Dementia increasingly affects the quality of life of older adults, and co-creative storytelling with social robots has shown promise in supporting social engagem ...
Grocery delivery company Picnic has identified affordable meal planning, especially in the context of recipe-based shopping, as an ongoing challenge faced by its customers. While recipes enhance customer experience and operational efficiency, Picnic currently lacks an algorithmic ...
Normative modeling is a promising statistical framework in clinical neuroscience that characterizes individual deviations from population-based reference distributions. While traditional approaches focus on univariate modeling of individual brain measures, multivariate normative ...
Heart rate (HR) is a critical indicator of an individual’s health, serving as a key metric for detecting potential cardiac issues. This paper explores a method for real-time heart rate measurement using RGB camera footage, aimed at general health monitoring. The proposed method u ...
The measurement of the heart rate (HR) is of vitalimportance in modern medicine. Advancements in medical technology have resulted in a myriad of techniques to measure and analyze these bio-signals, and the advent of telemedicine and the post-COVID-19 world has placed greater emph ...