Abstract
Over these past five years, significant breakthroughs, led by Transformers and large language models, have been made in understanding natural language text. However, the ability to capture contextual nuances in longer texts is still an elusive goal, let alone the understanding of consistent fine-grained narrative structures in text. These unsolved challenges and the interest in the community are at the basis of the sixth edition of Text2Story workshop to be held in Dublin on April 2nd, 2023 in conjunction with the 45th European Conference on Information Retrieval (ECIR'23). In its sixth edition, we aim to bring to the forefront the challenges involved in understanding the structure of narratives and in incorporating their representation in well-established models, as well as in modern architectures (e.g., transformers) which are now common and form the backbone of almost every IR and NLP application. It is hoped that the workshop will provide a common forum to consolidate the multi-disciplinary efforts and foster discussions to identify the wide-ranging issues related to the narrative extraction and generation task. Text2Story includes sessions devoted to full research papers, work-in-progress, demos and dissemination papers, keynote talks and space for an informal discussion of the methods, of the challenges and of the future of this research area.

Abstract
IntroductionHereditary transthyretin amyloidosis (ATTRv amyloidosis) is a rare neurological hereditary disease clinically characterized as severe, progressive, and life-threatening while the age of onset represents the moment in time when the first symptoms are felt. In this study, we present and discuss our results on the study, development, and evaluation of an approach that allows for time-to-event prediction of the age of onset, while focusing on genealogical feature construction. Materials and methodsThis research was triggered by the need to answer the medical problem of when will an asymptomatic ATTRv patient show symptoms of the disease. To do so, we defined and studied the impact of 77 features (ranging from demographic and genealogical to familial disease history) we studied and compared a pool of prediction algorithms, namely, linear regression (LR), elastic net (EN), lasso (LA), ridge (RI), support vector machines (SV), decision tree (DT), random forest (RF), and XGboost (XG), both in a classification as well as a regression setting; we assembled a baseline (BL) which corresponds to the current medical knowledge of the disease; we studied the problem of predicting the age of onset of ATTRv patients; we assessed the viability of predicting age of onset on short term horizons, with a classification framing, on localized sets of patients (currently symptomatic and asymptomatic carriers, with and without genealogical information); and we compared the results with an out-of-bag evaluation set and assembled in a different time-frame than the original data in order to account for data leakage. ResultsCurrently, we observe that our approach outperforms the BL model, which follows a set of clinical heuristics and represents current medical practice. Overall, our results show the supremacy of SV and XG for both the prediction tasks although impacted by data characteristics, namely, the existence of missing values, complex data, and small-sized available inputs. DiscussionWith this study, we defined a predictive model approach capable to be well-understood by medical professionals, compared with the current practice, namely, the baseline approach (BL), and successfully showed the improvement achieved to the current medical knowledge.

Abstract

Abstract
Repetitive controllers (RCs) are linear control structures based on the internal model principle. This control strategy is known for its ability to control periodic reference signals, even if these signals have many harmonic components. Despite being a solution that results in a good performance, several parameters of the repetitive controller need to be correctly tuned to guarantee its stability. Among these parameters, one that has high impact on the system performance and stability is the finite impulse response (FIR) filter, which is usually used to increase the stability domain of RC-based controllers. In this context, this paper presents a complete tutorial for designing the zero-phase FIR filter, which is often used to stabilize control systems that use RC-based controllers. In addition, this paper presents a Matlab® application developed for performing the stability analysis of RC systems and designing its FIR filter. Simulation and experimental results of a shunt active power filter are used to validate the algorithm and the Matlab® application.

Abstract
Artificial Intelligence (AI) is one of the big hopes for the future of a positive revolution in the use of medical data to improve clinical routine and personalized medicine [...]

Abstract
Predicting taxi demands in large cities can help in better traffic management as well as ensure better commuter satisfaction for an intelligent transportation system. However, the traffic demands across different locations have varying spatio-temporal correlations that are difficult to model. Despite the ability of the existing Deep Neural Network (DNN) models to capture the non-linearity in spatial and temporal characteristics of the demand time-series, capturing spatio-temporal characteristics in different real-world scenarios like varying historic and prediction time frame, spatio-temporal variations due to noise or missing data, etc. still remain a big challenge for the state-of-the-art models. In this paper, we introduce Encoder-ApproXimator (EnAppX), an encoder-decoder DNN-based model that uses Chebyshev function approximation in the decoding stage for taxi demand times-series prediction and can better estimate the time-series in the presence of large spatio-temporal variations. Opposed to any existing state-of-the-art model, the proposed model approximates complete spatiotemporal characteristics in the frequency domain which in turn enables the model to make a robust and improved prediction in different scenarios. Validation over two real-world taxi datasets from different cities shows a considerable improvement of around 23% in RMSE scores compared to the state-of-the-art baseline model. Unlike several existing state-of-the-art models, EnAppX also produces improved prediction accuracy across two regions for both to and fro demands.