The challenges of managing water resources in tidal rivers, exacerbated by climate change and anthropogenic impacts, require innovative approaches for accurate estimation of hydrological parameters. In tidal rivers and estuaries, water levels depend primarily on river discharge and tidal dynamics. Microtidal estuaries are particularly complex due to the strong stratification and two-layer structure, which also […]
Research Papers
Recursively Autoregressive Autoencoder for Pyramidal Text Representation
We introduce Pyramidal Recursive learning (PyRv), a novel method for text representationlearning. This approach constructs a pyramidal hierarchy by recursively building representations of phrases, starting from tokens (characters, subwords, or words). At each level, N representations are recursively combined, resulting in N-1 representations on the level above, abstracting the input text from characters or subwords […]
Estimating Water Levels and Discharges in Tidal Rivers and Estuaries: Review of Machine Learning Approaches
Understanding the dynamics of tidal rivers and estuaries is critical for reliable water management. Recently, the use of Machine Learning (ML) has increased in favor of hydrologic and hydraulic models. The advantages of ML over physically based models are most evident in modeling complex and nonlinear hydrologic processes and inverse problems. This study provides a […]
Building Individual Player Performance Profiles According to Pre-Game Expectations and Goal Difference in Soccer
Soccer player performance is influenced by multiple unpredictable factors. During a game, score changes and pre-game expectations affect the effort exerted by players. This study used GPS wearable sensors to track players’ energy expenditure in 5-min intervals, alongside recording the goal timings and the win and lose probabilities from betting sites. A mathematical model was […]
Aiming at Well-Being with Brain Implants: Any Risk of Implanting Unprecedented Vulnerabilities?
Many experimental brain-computer interfaces (BCIs) are currently being medically tested in paralyzed patients. While the new generations of implantable BCIs move rapidly ahead at trying to increase the patients’ well-being, ethical concerns about their potential effects on patients’ psychological dimensions (e.g. sense of agency and control) are growing. An important ethical concern to explore is […]
Retweet Prediction Based on Heterogeneous Data Sources: The Combination of Text and Multilayer Network Features
Retweet prediction is an important task in the context of various problems, such as information spreading analysis, automatic fake news detection, social media monitoring, etc. In this study, we explore retweet prediction based on heterogeneous data sources. In order to classify a tweet according to the number of retweets, we combine features extracted from the […]
Is enhancement with brain–computer interfaces ethical? Evidence in favor of symbiotic augmentation
This book chapter clarifies the unique status of BCI enhancement as symbiotic augmentation based on three distinct characteristics, which we derive from evidence pertaining to empirical cases of implanted patients…This chapter clarifies the concept of symbiotic augmentation and shows how the symbiotic ethical framework aids the users to coherently integrate the changes to personal self-understanding, […]
Method for Automatic Estimation of Instantaneous Frequency and Group Delay in Time–Frequency Distributions with Application in EEG Seizure Signals Analysis
Instantaneous frequency (IF) is commonly used in the analysis of electroencephalogram (EEG) signals to detect oscillatory-type seizures. However, IF cannot be used to analyze seizures that appear as spikes. In this paper, we present a novel method for the automatic estimation of IF and group delay (GD) in order to detect seizures with both spike […]
Sparse Time-Frequency Distribution Reconstruction Using the Adaptive Compressed Sensed Area Optimized with the Multi-Objective Approach
Compressive sensing (CS) of the signal ambiguity function (AF) and enforcing the sparsity constraint on the resulting signal time-frequency distribution (TFD) has been shown to be an efficient method for time-frequency signal processing. This paper proposes a method for adaptive CS-AF area selection, which extracts the magnitude-significant AF samples through a clustering approach using the […]
Robots as moral environments
In this philosophical exploration, we investigate the concept of robotic moral environment interaction. The common view understands moral interaction to occur between agents endowed with ethical and interactive capacities. However, recent developments in moral philosophy argue that moral interaction also occurs in relation to the environment. Here conditions and situations of the environment contribute to […]
