Automatic social role recognition and its application in structuring multiparty interactions

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Biological motor control is versatile, efficient, and depends on proprioceptive feedback. Muscles are flexible and undergo continuous changes, requiring distributed adaptive control mechanisms that continuously account for the body's state. The canonical r ...

This training dataset includes a total of 34,913 manually transcribed text segments. It is dedicated to the handwritten text recognition (HTR) of historical sources, typically tabular records, such as censuses. This dataset is based on a sample of 83 pages ...

This study presents a self-supervised Bayesian Neural Network (BNN) framework using air-borne Acoustic Emission (AE) to identify different Laser Powder Bed Fusion (LPBF) process regimes such as Lack of Fusion, conduction mode, and keyhole without ground-tr ...

Blood pressure (BP) is a crucial indicator of cardiovascular health. Hypertension is a common life-threatening condition and a key factor of cardiovascular diseases (CVDs). Identifying abnormal BP fluctuations can allow for early detection and management o ...

Curiosity refers to the intrinsic desire of humans and animals to explore the unknown, even when there is no apparent reason to do so. Thus far, no single, widely accepted definition or framework for curiosity has emerged, but there is growing consensus th ...

Atypical aspects in speech concern speech that deviates from what is commonly considered normal or healthy. In this thesis, we propose novel methods for detection and analysis of these aspects, e.g. to monitor the temporary state of a speaker, diseases tha ...

In this paper we propose a novel virtual simulation-pilot engine for speeding up air traffic controller (ATCo) training by integrating different state-of-the-art artificial intelligence (AI)-based tools. The virtual simulation-pilot engine receives spoken ...

This thesis focuses on two selected learning problems: 1) statistical inference on graphs models, and, 2) gradient descent on neural networks, with the common objective of defining and analysing the measures that characterize the fundamental limits. In th ...

As an 'early alerting' sense, one of the primary tasks for the human visual system is to recognize distant objects. In the specific context of facial identification, this ecologically important task has received surprisingly little attention. Most studies ...

Natural language processing has experienced significant improvements with the development of Transformer-based models, which employ self-attention mechanism and pre-training strategies. However, these models still present several obstacles. A notable issue ...

Self-supervised learning (SSL) models use only the intrinsic structure of a given signal, independent of its acoustic domain, to extract essential information from the input to an embedding space. This implies that the utility of such representations is no ...

BackgroundInterstitial lung diseases (ILD), such as idiopathic pulmonary fibrosis (IPF) and non-specific interstitial pneumonia (NSIP), and chronic obstructive pulmonary disease (COPD) are severe, progressive pulmonary disorders with a poor prognosis. Prom ...

Automatic social role recognition and its application in structuring multiparty interactions

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Contrasting action and posture coding with hierarchical deep neural network models of proprioception

Lausanne Historical Censuses Dataset HTR 35k

Self-Supervised Bayesian representation learning of acoustic emissions from laser powder bed Fusion process for in-situ monitoring

Data-driven approaches for non-invasive cuffless blood pressure monitoring

Curiosity-driven exploration: foundations in neuroscience and computational modeling

Novel Methods For Detection And Analysis Of Atypical Aspects In Speech

A Virtual Simulation-Pilot Agent for Training of Air Traffic Controllers

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Recognizing distant faces

Modeling Structured Data in Attention-based Models

Can Self-Supervised Neural Representations Pre-Trained on Human Speech distinguish Animal Callers?

Deep learning diagnostic and severity-stratification for interstitial lung diseases and chronic obstructive pulmonary disease in digital lung auscultations and ultrasonography: clinical protocol for an observational case-control study