Remote sensing is an essential tool in efficiently gathering information about the Earth's surface on a global scale. This information can be used in various areas ranging from agriculture and forest management to battling the effects of heat islands in urban environments. There are many satellite missions with various types of sensors measuring different metrics. All come with their advantages and disadvantages. One of the most common trade-offs in remote sensing is spatial vs. temporal resolution due to physical constraints. This thesis explores the existing ways of enhancing the spatial resolution of land surface temperature (LST) images using environmental variables obtained at finer resolution. Additionally, we form and evaluate a hypothesis that chosen predictors that model anthropogenic heat flux will aid in improving the accuracy of the overall sharpening model.

With the recent improvements of the Large Language Models (LLMs), their usage became relevant for most daily tasks. Yet, many of those areas are underutilizing the full potential of LLMs. One such area is the learning domain, which our project focuses on. Our main goal is to create a platform to help students by integrating LLMs and speeding up their learning process. However, there are obstacles in this area; for example, study materials are provided in different formats - videos, PDFs, and slides, which we want to touch on. In the following presentation, we will analyze available tools for learning enhancement, propose a possible solution, and evaluate its results.

Previous works on natural language inference have examined how well transformer models can reason with text. But what was still lacking was addressing whether they could understand the logical semantics in natural language. The reason is mainly because the previously studied logical problems can be, depending on their structure, more or less computationally complex. Therefore, it is unclear whether the reason for lower performance is due to the difference in computational complexity or the inability to comprehend the logical semantics of natural language. The authors chose the model-checking problem, as their computational complexity is always PTIME. The results suggested that the form and type of language used significantly affect how well the transformer models perform. They can grasp some logical meanings in natural language but still fall short when learning the underlying algorithm of model-checking problems.

This research centers on automated invoice processing, entailing an analysis of existing methods and systems to construct a comprehensive overview. The objective is to develop a pipeline based on established software and to construct a corresponding testing framework. Leveraging this foundation, the aim is to refine the pipeline and evaluate its efficacy within the established testing framework. The subsequent findings shed light on the performance and potential enhancements of the automated invoice processing pipeline.

Feature construction (FC) is a crucial step in the machine learning pipeline, as the quality of features can significantly impact the model's performance. This presentation aims to acquaint listeners with feature construction and briefly overview the state-of-the-art FC methods. The primary focus of the presentation will be an experiment that was conducted using two FC frameworks based on genetic programming (GP) – Evolutionary Forest and M3GP.

Messenger RNA (mRNA) decay is a crucial process in regulating of gene expression, influencing cellular functions and organismal phenotypes. Precise identification of mRNA decay sites will help to understand the post-transcriptional control mechanisms that affect mRNA stability. In this presentation, we will discuss the prediction of mRNA decay sites using a DeBERTa transformer model. Our model is trained on sequences derived from direct RNA sequencing of polyadenylated RNA from HeLa cells, focusing on subsequences within mRNA coding regions to determine the presence of decay sites. Based on our model, we also analyze the role of single nucleotide variants in mRNA decay.

ICLR is among the most impactful ML conferences (A* in CORE ranking). In this presentation, we will report on the acceptance of our presented paper in the main track. We will comment on the main research direction in empirical NLP and show you the highlights of the research that struck our attention during the event.