Author: ailab

AILAB-Udine is proud to be one of the organizers of the European Summer School on Quantum AI, (EQAI), which has been held yearly since 2022. Find the latest updated information on the official website:http://eqai.eu Or subscribe to the official google group to be notified of any major update!https://groups.google.com/g/eqai The summer school will take place in Lignano Sabbiadoro (Italy) on August 31 – September 04, 2026.All the speakers will be there in person to make the experience more immersive and interactive! Find out more about the past editions at: https://eqai.eu/past-editions/ EQAI 2025 – Details The summer school will take place in Lignano Sabbiadoro (Italy) on September 01 – 05, 2025.All the speakers will be there in person to make the experience more immersive and interactive! EQAI 2024 – Details The summer school will take place in Lignano Sabbiadoro (Italy) on September 02 – 06, 2024. EQAI 2023 – Details The summer school will take place in Udine (Italy) on May 29 […]

Quantum machine learning (QML) is an emerging field that combines the principles of quantum computing with classical machine learning techniques. It aims to exploit the unique properties of quantum systems, characterised by their ability to perform computations exponentially faster than classical devices, to improve the processing and analysis of complex data. This advance makes QML a very promising frontier. However, in the current Noisy Intermediate-Scale Quantum (NISQ) era, characterised by the presence of noise in quantum devices that limits their scalability, it is crucial to develop specific techniques to fully exploit quantum capabilities. This research focuses on the development and optimisation of QML models suitable for NISQ environments. A key aspect of our work is to adapt established optimisation methods from classical machine learning, such as batch normalisation and regularisation, for use in Quantum Neural Networks (QNNs). The project also explores the potential of Quanvolutional Neural Networks and Quantum Kernel methods. These efforts are aimed at improving the functionality and […]

In recent years, the Metaverse has sparked an increasing interest across the globe and is projected to reach a market size of more than $1000B by 2030. This is due to its many potential applications in highly heterogeneous fields, such as entertainment and multimedia consumption, training, and industry. This new technology raises many research challenges since, as opposed to the more traditional scene understanding, metaverse scenarios contain additional multimedia content, such as movies in virtual cinemas and operas in digital theaters, which greatly influence the relevance of the metaverse to a user query. For instance, if a user is looking for Impressionist exhibitions in a virtual museum, only the museums that showcase exhibitions featuring various Impressionist painters should be considered relevant. We introduce the novel problem of text-to-metaverse retrieval, to support the users in finding the most suitable metaverse according to a given textual query. It is a challenging task, since the multimedia content present in the metaverse greatly influences […]

This research project focuses on the application of machine and deep learning methods for forestry applications. In particular, the main focus is forest growing stock prediction in the Friuli Venezia Giulia region (Italy), but the developed methods can be applied to produce estimations of biophysical forest attributes on any large territory. This study will take into account different sources of data such as Forest inventory data from Nationla surveys, multispectral satellite images, climatic data and various environmental features collected through different services. Several methods will be applied to produce a forest-growing stock volume map, which will be useful to create management plans for forestry areas in the region. Traditionally, the growing stock is considered an important indicator of forest health and productivity. The growing stock is estimated through forest inventory under which both qualitative and quantitative parameters are recorded to know the overall health of growing forests. So, we will produce the results that can be considered as a basis […]

Inscriptions are a testimony to the past but their poor condition, caused by the deterioration of the material on which they are engraved upon, often makes them partially or completely illegible. The process of restoring these inscriptions is time-consuming and requires the involvement of an expert epigraphist. It is possible to speed-up this process by adopting a semi-automatic assisting tool based on deep neural networks. This project aims to develop a complete methodology, from the acquisition of the inscriptions, to the description of four possible approaches to predict the missing text in a Latin inscription, that our research team plans to implement in the near future as part of a interdisciplinary research project. Related publications: Alessandro Locaputo, Beatrice Portelli, Emanuela Colombi, Giuseppe Serra. Filling the Lacunae in ancient Latin inscriptions. 19th The Conference on Information and Research science Connecting to Digital and Library science (IRCDL), 2023. [pdf] Andrea Brunello, Emanuela Colombi, Alessandro Locaputo, Stefano Magnani, Nicola Saccomanno, Giuseppe Serra. Usage […]

The text-to-video retrieval task requires to rank all the videos in a database based on how semantically close they are to an input query. To do so, both the visual and the textual contents need to be carefully analyzed and understood, meaning that a wide range of Computer Vision and Natural Language Processing techniques are required. Despite the intrinsic difficulty of such a problem, it is a fundamental one: in fact, nowadays several hundreds of hours of video content are uploaded to the Internet every minute, therefore solutions to this important problem are fundamental to perform searches effectively and retrieve all the videos which the user is looking for. Moreover, considering the need for multi-modal content understanding, advancements in this field may lead to improvements in many other problems, including Captioning and Question Answering. EPIC-Kitchens-100 Multi-Instance Retrieval Challenge tech reports: Alex Falcon, Giuseppe Serra. UniUD Submission to the EPIC-Kitchens-100 Multi-Instance Retrieval Challenge 2023. We ranked 3rd using only 25% of […]

Video Question Answering (VideoQA) is a task that requires to analyze and jointly reason on both the given video data and a visual content-related question, to produce a meaningful and coherent answer to it. Solving this task would approach human-level capability of the model to deal with both complex video data and the visual contents-related textual data, since it would require to learn to isolate and pinpoint objects of interest in video, to identify and reason about their interactions in both spatial and temporal domains, while finding the essential bindings with the given question. Thus, VideoQA represents a challenging task at the interface between Computer Vision and Natural Language Processing (NLP). Modern approaches to this task involve a wide selection of different techniques, such as: temporal attention and spatial attention, in order to learn which frames and which regions in each frame are more important to solve the task; given the multimodal nature of the data, cross-modality fusion mechanisms, question-answer-aware […]

Regulators, such as the Food and Drug Administration (FDA) and the European Medicine Agency (EMA), approve every year dozens of drugs, after verifying their safety and therapeutic effectiveness in clinical trials. Sometimes, however, clinical trials are not sufficient to discover all potential Adverse Drug Events (ADE). Pharmacovigilance, therefore, monitors the drugs in the market to ensure that unexpected effects are immediately identified and actions are taken to minimize their harm. This process relies on formal reporting methods, such as physician notes. However, a constantly growing number of patients prefers to describe the side effects on social media platforms, health forums and similar outlets. Patients have started reporting Adverse Drug Event (ADE) on social media, health forums and similar outlets, often utilizing informal language. Given the need to monitor these sources for pharmacovigilance purposes, systems for the automatic extraction of ADE are becoming an important research topic in the NLP community. Recent shared tasks on the topic of ADE extraction have […]

The remaining useful life (RUL) estimation of a component is an interesting problem within the Prognostics and Health Management (PHM) field, which consists in estimating the number of time steps occurring between the current time step and the end of the component life. Being able to reliably estimate this value can lead to an improvement of the maintenance scheduling and a reduction of the costs associated with it. Data driven approaches are often used in the literature and they are the preferred choice over model-based approaches: in fact, not only they are easier to build, but the data over which they are built can be gathered easily in many industrial applications. During the last years, neural networks like Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) have found many applications in this area, this because of their suitability to uncover hidden patterns within the sensor data. In recent years a greater availability of high quality sensors and easiness of data […]