Different judgments about visual textures invoke different eye movement patterns

Top-down influences on the guidance of the eyes are generally modeled as modulating influences on bottom-up salience maps. Interested in task-driven influences on how, rather than where, the eyes are guided, we expected differences in eye movement parameters accompanying beauty and roughness judgments about visual textures. Participants judged textures for beauty and roughness, while their gaze-behavior was recorded. Eye movement parameters differed between the judgments, showing task effects on how people look at images. Similarity in the spatial distribution of attention suggests that differences in the guidance of attention are non-spatial, possibly feature-based. During the beauty judgment, participants fixated on patches that were richer in color information, further supporting the idea that differences in the guidance of attention are feature-based. A finding of shorter fixation durations during beauty judgments may indicate that extraction of the relevant features is easier during this judgment. This finding is consistent with a more ambient scanning mode during this judgment. The differences in eye movement parameters during different judgments about highly repetitive stimuli highlight the need for models of eye guidance to go beyond salience maps, to include the temporal dynamics of eye guidance

Aesthetics by Numbers: Links between Perceived Texture Qualities and Computed Visual Texture Properties.

Our world is filled with texture. For the human visual system, this is an important source of information for assessing environmental and material properties. Indeed-and presumably for this reason-the human visual system has regions dedicated to processing textures. Despite their abundance and apparent relevance, only recently the relationships between texture features and high-level judgments have captured the interest of mainstream science, despite long-standing indications for such relationships. In this study, we explore such relationships, as these might be used to predict perceived texture qualities. This is relevant, not only from a psychological/neuroscience perspective, but also for more applied fields such as design, architecture, and the visual arts. In two separate experiments, observers judged various qualities of visual textures such as beauty, roughness, naturalness, elegance, and complexity. Based on factor analysis, we find that in both experiments, ~75% of the variability in the judgments could be explained by a two-dimensional space, with axes that are closely aligned to the beauty and roughness judgments. That a two-dimensional judgment space suffices to capture most of the variability in the perceived texture qualities suggests that observers use a relatively limited set of internal scales on which to base various judgments, including aesthetic ones. Finally, for both of these judgments, we determined the relationship with a large number of texture features computed for each of the texture stimuli. We find that the presence of lower spatial frequencies, oblique orientations, higher intensity variation, higher saturation, and redness correlates with higher beauty ratings. Features that captured image intensity and uniformity correlated with roughness ratings. Therefore, a number of computational texture features are predictive of these judgments. This suggests that perceived texture qualities-including the aesthetic appreciation-are sufficiently universal to be predicted-with reasonable accuracy-based on the computed feature content of the textures

Effects of Visual Priming on Taste-Odor Interaction

Little is known about the influence of visual characteristics other than colour on flavor perception, and the complex interactions between more than two sensory modalities. This study focused on the effects of recognizability of visual (texture) information on flavor perception of odorized sweet beverages

Der Leib in Nietzsches Zarathustra

Der Leib in Nietzsches Zarathustra : zur Überwindung des Nihilismus in seiner Radikalisierung. - Frankfurt am Main u.a. : Lang, 1995. - 355 S. - (Europäische Hochschulschriften : 20, Philosophie ; 448). - Zugl.: Augsburg, Univ., Diss., 199

Der Leib in Nietzsches Zarathustra

Der Leib in Nietzsches Zarathustra : zur Überwindung des Nihilismus in seiner Radikalisierung. - Frankfurt am Main u.a. : Lang, 1995. - 355 S. - (Europäische Hochschulschriften : 20, Philosophie ; 448). - Zugl.: Augsburg, Univ., Diss., 199

Modeling of 3D Blood Flows with Physics-Informed Neural Networks: Comparison of Network Architectures

Machine learning-based modeling of physical systems has attracted significant interest in recent years. Based solely on the underlying physical equations and initial and boundary conditions, these new approaches allow to approximate, for example, the complex flow of blood in the case of fluid dynamics. Physics-informed neural networks offer certain advantages compared to conventional computational fluid dynamics methods as they avoid the need for discretized meshes and allow to readily solve inverse problems and integrate additional data into the algorithms. Today, the majority of published reports on learning-based flow modeling relies on fully-connected neural networks. However, many different network architectures are introduced into deep learning each year, each with specific benefits for certain applications. In this paper, we present the first comprehensive comparison of various state-of-the-art networks and evaluate their performance in terms of computational cost and accuracy relative to numerical references. We found that while fully-connected networks offer an attractive balance between training time and accuracy, more elaborate architectures (e.g., Deep Galerkin Method) generated superior results. Moreover, we observed high accuracy in simple cylindrical geometries, but slightly poorer estimates in complex aneurysms. This paper provides quantitative guidance for practitioners interested in complex flow modeling using physics-based deep learning

Modeling of 3D Blood Flows with Physics-Informed Neural Networks: Comparison of Network Architectures

Machine learning-based modeling of physical systems has attracted significant interest in recent years. Based solely on the underlying physical equations and initial and boundary conditions, these new approaches allow to approximate, for example, the complex flow of blood in the case of fluid dynamics. Physics-informed neural networks offer certain advantages compared to conventional computational fluid dynamics methods as they avoid the need for discretized meshes and allow to readily solve inverse problems and integrate additional data into the algorithms. Today, the majority of published reports on learning-based flow modeling relies on fully-connected neural networks. However, many different network architectures are introduced into deep learning each year, each with specific benefits for certain applications. In this paper, we present the first comprehensive comparison of various state-of-the-art networks and evaluate their performance in terms of computational cost and accuracy relative to numerical references. We found that while fully-connected networks offer an attractive balance between training time and accuracy, more elaborate architectures (e.g., Deep Galerkin Method) generated superior results. Moreover, we observed high accuracy in simple cylindrical geometries, but slightly poorer estimates in complex aneurysms. This paper provides quantitative guidance for practitioners interested in complex flow modeling using physics-based deep learning

Traffic Sign Detection and Classification on the Austrian Highway Traffic Sign Data Set

Advanced Driver Assistance Systems rely on automated traffic sign recognition. Today, Deep Learning methods outperform other approaches in terms of accuracy and processing time; however, they require vast and well-curated data sets for training. In this paper, we present the Austrian Highway Traffic Sign Data Set (ATSD), a comprehensive annotated data set of images of almost all traffic signs on Austrian highways in 2014, and corresponding images of full traffic scenes they are contained in. Altogether, the data set consists of almost 7500 scene images with more than 28,000 detailed annotations of more than 100 distinct traffic sign classes. It covers diverse environments, ranging from urban to rural and mountainous areas, and includes many images recorded in tunnels. We further evaluate state-of-the-art traffic sign detectors and classifiers on ATSD to establish baselines for future experiments. The data set and our baseline models are freely available online

Lifting Hospital Electronic Health Record Data Treasures: Challenges and Opportunities

Electronic health records (EHRs) have been successfully used in data science and machine learning projects. However, most of these data are collected for clinical use rather than for retrospective analysis. This means that researchers typically face many different issues when attempting to access and prepare the data for secondary use. We aimed to investigate how raw EHRs can be accessed and prepared in retrospective data science projects in a disciplined, effective, and efficient way. We report our experience and findings from a large-scale data science project analyzing routinely acquired retrospective data from the Kepler University Hospital in Linz, Austria. The project involved data collection from more than 150,000 patients over a period of 10 years. It included diverse data modalities, such as static demographic data, irregularly acquired laboratory test results, regularly sampled vital signs, and high-frequency physiological waveform signals. Raw medical data can be corrupted in many unexpected ways that demand thorough manual inspection and highly individualized data cleaning solutions. We present a general data preparation workflow, which was shaped in the course of our project and consists of the following 7 steps: obtain a rough overview of the available EHR data, define clinically meaningful labels for supervised learning, extract relevant data from the hospital’s data warehouses, match data extracted from different sources, deidentify them, detect errors and inconsistencies therein through a careful exploratory analysis, and implement a suitable data processing pipeline in actual code. Only few of the data preparation issues encountered in our project were addressed by generic medical data preprocessing tools that have been proposed recently. Instead, highly individualized solutions for the specific data used in one’s own research seem inevitable. We believe that the proposed workflow can serve as a guidance for practitioners, helping them to identify and address potential problems early and avoid some common pitfalls

Modeling Human Aesthetic Perception of Visual Textures

Texture is extensively used in areas such as product design and architecture to convey specific aesthetic information. Using the results of a psychological experiment, we model the relationship between computational texture features and aesthetic properties of visual textures. Contrary to previous approaches, we build a layered model, which provides insights into hierarchical relationships involved in human aesthetic texture perception. This model uses a set of intermediate judgements to link computational texture features with aesthetic texture properties. We pursue two different approaches for modeling. (1) Supervised machine-learning methods are used to generate linear and nonlinear models from the experimental data automatically. The quality of these models is discussed, mainly focusing on interpretability and accuracy. (2) We apply a psychological-based approach that models the processing pathways in human perception of naturalness, introducing judgement dimensions (principal components) mediating the relationship between texture features and naturalness judgements. This multiple mediator model serves as a verification of the machine-learning approach. We conclude with a comparison of these two approaches, highlighting the similarities and discrepancies in terms of identified relationships between computational texture features and aesthetic properties of visual textures