1,268 research outputs found
AUT817716_Lay_Abstract – Supplemental material for Impairments in cognitive empathy and alexithymia occur independently of executive functioning in college students with autism
No full textSupplemental material, AUT817716_Lay_Abstract for Impairments in cognitive empathy and alexithymia occur independently of executive functioning in college students with autism by Tim Ziermans, Ymke de Bruijn, Renee Dijkhuis, Wouter Staal and Hanna Swaab in Autism</p
Digging through the dirt: a general method for abstract discrete state estimation with limited prior knowledge
No full textAutonomous robots are often successfully deployed in controlled environments. Operation in uncontrolled situations remains challenging; it is hypothesized that the detection of abstract discrete states (ADS) can improve operation in these circumstances. ADS are high-level system states that are not directly detectable and influence system dynamics. An example of a typical ADS problem that is used in this thesis is that of a wheeled robot driving through puddles of mud that, when entered, alters the velocity of the robot. When the robot is in such a puddle, it is in an ADS 'mud', and when it is not, it is in an ADS 'free'. ADS can be indirectly inferred through the analysis of lower-level data such as the velocity of the robot. The goal of this thesis is to design a general abstract discrete state estimator (ADSE) operating with limited prior knowledge. An ADSE is a hierarchical system for detecting changes in ADS. The ADSE should be general; applicable to multiple ADSE problems. The ADSE should further operate under limited prior knowledge: only assuming that the amount of ADS and the ADS that describes the regular operation are known. The basis for the ADSE designed in this thesis is a Gaussian hidden Markov model (GHMM), a hidden Markov model enhanced with Gaussian emissions. Randomly generated experiments are done on a simple but general ADSE problem. Two unsupervised learning methods derived from Expectation Maximization are evaluated, namely Baum-Welch (BW) and forward extraction (FWE). FWE is introduced in this thesis and is a simpler implementation of Viterbi extraction, leveraging assumptions of ADSE to in theory gain computational efficiency. We found that both BW and FWE exhibit superior performance compared to a likelihood-based baseline estimator when the maximum score of the learning curve is considered. When the final score is considered, in some cases, FWE displays a deteriorating learning curve, resulting in worse final scores compared to the baseline. Furthermore, it was found that the lower the overlap coefficient (therefore the less similar the ADS), the higher the maximum reached score. It was further shown that BW exhibits better convergence than FWE to the true model parameters. Besides this, FWE obtained comparable or in some cases even superior scores compared to BW. In general, from the results, the diversity of the experiments conducted, and the assumptions made we can conclude that the GHMM can be a general method for an ADSE with limited prior knowledge. To quantify the suitability of the GHMM for ADSE, further research should include the evaluation of different ADSE methods on the same problem. There exists a tradeoff between the lower computational cost FWE and the more stable but more computationally intensive BW learning. Therefore, future research can include a combination of these methods. Other extensions include extending the GHMM to a Gaussian mixture hidden Markov model to allow for the modeling of more complex distributions, or the application to multiple states or a changing environment.https://github.com/Wouter-deBoer/adseMechanical Engineering | Vehicle Engineering | Cognitive Robotic
embalming and reperfusion of porcine kidneys
No full text<p>These are the data of the following article:</p>
<p>Understanding Thiel embalming in pig kidneys to develop a new circulation model</p>
<p>First author: Wouter Willaert</p
Nederland op een kantelpunt: Interview met Wouter Veldhuis over het Stedelijk Netwerk Nederland en het sociaal netwerk van woonwijken
No full textDe stedenbouwkundige en architect Wouter Veldhuis en landschapsarchitect Jannemarie de Jonge zijn per 1 december 2020 Rijksadviseur voor de fysieke leefomgeving. Later in september 2021 komt daar de architect Francesco Veenstra bij als Rijksbouwmeester en dan is het nieuwe trio College van Rijksadviseurs weer compleet. De uitdagingen voor het college zijn groot. De ruimteclaims die er liggen in stad en land, de hooggestemde ambities om klimaatneutraal en circulair te zijn in 2050, de roep om een minister voor de fysieke leefomgeving en of wonen en weer een echt ministerie met budget. Het enorme probleem op de woningmarkt en de druk om één miljoen woningen ergens bij te bouwen. Op 24 april sprak het team van 1M Homes initiative van de TU Delft met de nieuw benoemde rijksadviseur voor de fysieke leefomgeving Wouter Veldhuis over de aanstaande veranderingen
Reduced Connectivity is confined to Amygdala Input and not Output Areas in Functional ASD Networks during Rest
No full textAutism is a behaviorally defined disorder with limited social skills, communicative difficulties, reduced attention switching, restricted interests and repetitive stereotyped behavior as well as limited imagination and mentalizing. Not only people with autism experience difficulties in daily life, their families often suffer a financial and psychological burden as well (Bägenholm and Gillberg 1991, Roeyers and Mycke 1995). Despite its burden and high prevalence (1 percent of the general population) (Kim, Leventhal et al. 2011 ), little is known about the causes and underlying pathophysiological mechanisms of autism spectrum disorders (ASD). Yet, a basic understanding of ASD as a disorder of the brain is needed to develop effective biological treatments.
Prior research revealed white and grey matter abnormalities in the limbic system including cingulate cortex, hippocampus, amygdala (Schumann, Hamstra et al. 2004, Groen, Teluij et al. 2010), thalamus (Hardan, Girgis et al. 2006) and cortical areas such as frontal lobe (Damasio and Maurer 1978, MunDy 2013), superior temporal area (Boddaert, Chabane et al. 2004) and sensorimotor cortex (Perry, Minassian et al. 2007). Many of these structures have been linked to specific autistic behaviors during task based neuroimaging experiments (for a review see Amaral, Schumann et al. (2008). Especially abnormalities in structures comprising emotion regulating circuits were found affected in autism, with the amygdala as a key component in ASD (Baron-Cohen, Ring et al. 2000).
The amygdala is situated within the temporal lobe. It is associated with social cognition, emotion recognition, emotional valence and regulation of personal space in the context of anxiety and fear invoking stimuli (Phelps and LeDoux 2005). Still, structural and functional studies investigating the amygdala in ASD have yielded inconclusive results. One reason for the mixed results may be the fact that the amygdala is usually treated as a single structure, while in fact it is comprised of at least 13 functionally and structurally distinct nuclei that can be classified into three main amygdalar subdivisions: the superfiscial, centromedial and laterobasal group. The centromedial part (CM) is the output area, which regulates cardiovacular control via projections to brainstem, cerebellum and hypothalamus (Davis, 1997; LeDoux, 2003). The laterobasal nucleus (LB) is linked to multisensory input and facilitates emotional learning (LeDoux, 2003; Phelps and LeDoux, 2005), whereas the superficial compartments (SF) maintain olfactory and striatal (Price 2003, Heimer and Van Hoesen 2006) projections. LB and SF are therefore mainly identified with amygdala input functionality. Functional neuroimaging studies confirm these distinctions in the human amygdala (Ball, Rahm et al. 2007, Bickart, Wright et al. 2010).
With last decade's paradigm shift in neuro-imaging from activity assessment within isolated structures to connectivity within large-scale networks, researchers have increasingly found evidence for abnormal neural connectivity in ASD. The term connectivity refers to the degree of synchrony of neural firing pattems, usually assessed with resting state fMRI. Some authors (Courchesne and Pierce 2005) hypothesized that the brain in ASD is characterized by long distance underconnectivity and local overconnectivity in autism. Others (Happé 1999, Wass)
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