Reinforcement Learning for Extended Reality: Designing Self-Play Scenarios

A common problem for deep reinforcement learning networks is a lack of training data to learn specific tasks through generalization. In this review, we look at extended reality, a promising but often overlooked field, for training agents using reinforcement learning. We review several techniques from the literature and then synthesize the information in order to propose a recommended design. Meta learning offers an important way forward, but the agents ability to perform self-play is considered crucial for achieving successful AI. Therefore, we focus on improving self-play scenarios for teaching self-learning agents, by providing a supportive environment for improved agent-environment interaction

Predicting the helpfulness score of videogames of the STEAM platform

[EN] Online reviews comprise a flood of user-generated content, so to identify the most useful reviews is a vital task. As such, many computational models have been made to automatically analyze the helpfulness of online reviews. In this work, we aim to predict the helpfulness score of videogames reviews using an available online dataset of more than 1M rows. We trained three different machine learning algorithms by implementing two strategies, predicting the helpfulness as a regression problem or as a binary classification problem. Our findings show that binary classification is the best method, and the achieved ROC-AUC of the best model is 0.7 with only a selected set of features. In addition, we found that using the feature vectors from a pretrained NLP model does not improve the performance of the models.The work has been performed under the Project HPC-EUROPA3 (INFRAIA-2016-1-730897), with the support of the EC Research Innovation Action under the H2020 ProgrammeEspinosa-Leal, L.; Olmedilla, M.; Li, Z. (2023). Predicting the helpfulness score of videogames of the STEAM platform. Editorial Universitat Politècnica de València. 337-338. http://hdl.handle.net/10251/20176733733

The Role of Hydrogen Bonds in the Stabilization of Silver-Mediated Cytosine Tetramers

Peer reviewe

Assessing Trustworthy AI in times of COVID-19. Deep Learning for predicting a multi-regional score conveying the degree of lung compromise in COVID-19 patients

Abstract—The paper's main contributions are twofold: to demonstrate how to apply the general European Union’s High-Level Expert Group’s (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare; and to investigate the research question of what does “trustworthy AI” mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multi-regional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient’s lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia (Italy) since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses socio-technical scenarios to identify ethical, technical and domain-specific issues in the use of the AI system in the context of the pandemic.</p

Elective cancer surgery in COVID-19-free surgical pathways during the SARS-CoV-2 pandemic: An international, multicenter, comparative cohort study

PURPOSE As cancer surgery restarts after the first COVID-19 wave, health care providers urgently require data to determine where elective surgery is best performed. This study aimed to determine whether COVID-19–free surgical pathways were associated with lower postoperative pulmonary complication rates compared with hospitals with no defined pathway. PATIENTS AND METHODS This international, multicenter cohort study included patients who underwent elective surgery for 10 solid cancer types without preoperative suspicion of SARS-CoV-2. Participating hospitals included patients from local emergence of SARS-CoV-2 until April 19, 2020. At the time of surgery, hospitals were defined as having a COVID-19–free surgical pathway (complete segregation of the operating theater, critical care, and inpatient ward areas) or no defined pathway (incomplete or no segregation, areas shared with patients with COVID-19). The primary outcome was 30-day postoperative pulmonary complications (pneumonia, acute respiratory distress syndrome, unexpected ventilation). RESULTS Of 9,171 patients from 447 hospitals in 55 countries, 2,481 were operated on in COVID-19–free surgical pathways. Patients who underwent surgery within COVID-19–free surgical pathways were younger with fewer comorbidities than those in hospitals with no defined pathway but with similar proportions of major surgery. After adjustment, pulmonary complication rates were lower with COVID-19–free surgical pathways (2.2% v 4.9%; adjusted odds ratio [aOR], 0.62; 95% CI, 0.44 to 0.86). This was consistent in sensitivity analyses for low-risk patients (American Society of Anesthesiologists grade 1/2), propensity score–matched models, and patients with negative SARS-CoV-2 preoperative tests. The postoperative SARS-CoV-2 infection rate was also lower in COVID-19–free surgical pathways (2.1% v 3.6%; aOR, 0.53; 95% CI, 0.36 to 0.76). CONCLUSION Within available resources, dedicated COVID-19–free surgical pathways should be established to provide safe elective cancer surgery during current and before future SARS-CoV-2 outbreaks