Beyond the Shadow of Z: Non-Linear Reading and Experimental Approaches to Comics

The aim of this study is to broaden the field of investigation of empirical research on the reading of comics by including aspects related to narrative rhythm and non-linear paths, while associating them with a narrative genre, in this case, the Franco-Belgian humorous comic strip. On the basis of this corpus, on one hand we are interested in the retrograde reading operations induced by visual gags in one page, and on the other hand, we investigate the impact of sequences that we call “chronophotographic”, the latter having received little attention until our study

Au-delà de l’ombre du Z : lecture non linéaire et approches expérimentales de la bande dessinée

L’objectif de cette étude consiste à élargir de champ d’investigation des recherches empiriques portant sur la lecture des bandes dessinées en incluant des aspects liés au rythme narratif et aux cheminements non-linéaires, tout en les associant à un genre narratif spécifique, en l’occurrence la bande dessinée humoristique franco-belge. Sur la base de ce corpus, on s’intéressera d’une part aux opérations de lectures rétrogrades induites par les gags visuels en une page, d’autre part, on se penchera sur les effets de séquences que nous qualifierons de « chronophotographiques », ces dernières n’ayant, jusqu’à ce jour, reçu que peu d’attention

Virtual reality to assess visual attraction and perceived interest to daylit scene variations

Façades and light pattern composition have been shown to influence the spatial experience and physiological responses of humans [1,2]. The present study examines the effect of sunlight penetration and window size on fixations to the floor of the scene, and the relation between visual interest and fixations in an experiment using 360° scenes displayed in Virtual Reality. One hundred participants were shown the same daylit interior space with varying presence of sun patches (based on sky type and time-of-day variations) and window size in a mixed experimental design. Participants' head movements were recorded during the first 25 seconds of silent free-viewing exposure to each scene, after which they rated the visual interest of the scene. Fixation areas were derived from head movement data and were used to extract the percentage of fixations towards different areas in the scene. Linear Mixed Model (LMM) analyses showed that sun patch presence influenced the percentage of fixations towards both the front part of the floor (near the façade) and the whole floor. Pairwise comparisons showed that participants spent more time fixating towards the floor in the presence of small sun patch compared to no sun patch. Adding visual interest as a fixed factor in the LMM did not show a statistically significant relation between fixations towards the floor and visual interest ratings. Although limited to Virtual Reality and thus to its relatively small luminance range, these findings show that the presence of a sun patch in one's field of view elicits visual attraction

Data Augmentation via Latent Diffusion for Saliency Prediction

Saliency prediction models are constrained by the limited diversity and quantity of labeled data. Standard data augmentation techniques such as rotating and cropping alter scene composition, affecting saliency. We propose a novel data augmentation method for deep saliency prediction that edits natural images while preserving the complexity and variability of real-world scenes. Since saliency depends on high-level and low-level features, our approach involves learning both by incorporating photometric and semantic attributes such as color, contrast, brightness, and class. To that end, we introduce a saliency-guided cross-attention mechanism that enables targeted edits on the photometric properties, thereby enhancing saliency within specific image regions. Experimental results show that our data augmentation method consistently improves the performance of various saliency models. Moreover, leveraging the augmentation features for saliency prediction yields superior performance on publicly available saliency benchmarks. Our predictions align closely with human visual attention patterns in the edited images, as validated by a user study

Data Augmentation via Latent Diffusion for Saliency Prediction

Saliency prediction models are constrained by the limited diversity and quantity of labeled data. Standard data augmentation techniques such as rotating and cropping alter scene composition, affecting saliency. We propose a novel data augmentation method for deep saliency prediction that edits natural images while preserving the complexity and variability of real-world scenes. Since saliency depends on high-level and low-level features, our approach involves learning both by incorporating photometric and semantic attributes such as color, contrast, brightness, and class. To that end, we introduce a saliency-guided cross-attention mechanism that enables targeted edits on the photometric properties, thereby enhancing saliency within specific image regions. Experimental results show that our data augmentation method consistently improves the performance of various saliency models. Moreover, leveraging the augmentation features for saliency prediction yields superior performance on publicly available saliency benchmarks. Our predictions align closely with human visual attention patterns in the edited images, as validated by a user study

Saliency prediction in 360° architectural scenes: Performance and impact of daylight variations

Saliency models are image-based prediction models that estimate human visual attention. Such models, when applied to architectural spaces, could pave the way for design decisions where visual attention is taken into account. In this study, we tested the performance of eleven commonly used saliency models that combine traditional and deep learning methods on 126 rendered interior scenes with associated head tracking data. The data was extracted from three experiments conducted in virtual reality between 2016 and 2018. Two of these datasets pertain to the perceptual effects of daylight and include variations of daylighting conditions for a limited set of interior spaces, thereby allowing to test the influence of light conditions on human head movement. Ground truth maps were extracted from the collected head tracking logs, and the prediction accuracy of the models was tested via the correlation coefficient between ground truth and prediction maps. To address the possible inflation of results due to the equator bias, we conducted complementary analyses by restricting the area of investigation to the equatorial image regions. Although limited to immersive virtual environments, the promising performance of some traditional models such as GBVS360eq and BMS360eq for colored and textured architectural rendered spaces offers us the prospect of their possible integration into design tools. We also observed a strong correlation in head movements for the same space lit by different types of sky, a finding whose generalization requires further investigations based on datasets more specifically developed to address this question

Saliency prediction in 360° architectural scenes:Performance and impact of daylight variations

Saliency models are image-based prediction models that estimate human visual attention. Such models, when applied to architectural spaces, could pave the way for design decisions where visual attention is taken into account. In this study, we tested the performance of eleven commonly used saliency models that combine traditional and deep learning methods on 126 rendered interior scenes with associated head tracking data. The data was extracted from three experiments conducted in virtual reality between 2016 and 2018. Two of these datasets pertain to the perceptual effects of daylight and include variations of daylighting conditions for a limited set of interior spaces, thereby allowing to test the influence of light conditions on human head movement. Ground truth maps were extracted from the collected head tracking logs, and the prediction accuracy of the models was tested via the correlation coefficient between ground truth and prediction maps. To address the possible inflation of results due to the equator bias, we conducted complementary analyses by restricting the area of investigation to the equatorial image regions. Although limited to immersive virtual environments, the promising performance of some traditional models such as GBVS360eq and BMS360eq for colored and textured architectural rendered spaces offers us the prospect of their possible integration into design tools. We also observed a strong correlation in head movements for the same space lit by different types of sky, a finding whose generalization requires further investigations based on datasets more specifically developed to address this question.</p

Saliency prediction in 360° architectural scenes:Performance and impact of daylight variations

Saliency models are image-based prediction models that estimate human visual attention. Such models, when applied to architectural spaces, could pave the way for design decisions where visual attention is taken into account. In this study, we tested the performance of eleven commonly used saliency models that combine traditional and deep learning methods on 126 rendered interior scenes with associated head tracking data. The data was extracted from three experiments conducted in virtual reality between 2016 and 2018. Two of these datasets pertain to the perceptual effects of daylight and include variations of daylighting conditions for a limited set of interior spaces, thereby allowing to test the influence of light conditions on human head movement. Ground truth maps were extracted from the collected head tracking logs, and the prediction accuracy of the models was tested via the correlation coefficient between ground truth and prediction maps. To address the possible inflation of results due to the equator bias, we conducted complementary analyses by restricting the area of investigation to the equatorial image regions. Although limited to immersive virtual environments, the promising performance of some traditional models such as GBVS360eq and BMS360eq for colored and textured architectural rendered spaces offers us the prospect of their possible integration into design tools. We also observed a strong correlation in head movements for the same space lit by different types of sky, a finding whose generalization requires further investigations based on datasets more specifically developed to address this question.</p

Evaluation of patients with fibrotic interstitial lung disease: Preliminary results from the Turk-UIP study

OBJECTIVE: Differential diagnosis of idiopathic pulmonary fibrosis (IPF) is important among fibrotic interstitial lung diseases (ILD). This study aimed to evaluate the rate of IPF in patients with fibrotic ILD and to determine the clinical-laboratory features of patients with and without IPF that would provide the differential diagnosis of IPF. MATERIAL AND METHODS: The study included the patients with the usual interstitial pneumonia (UIP) pattern or possible UIP pattern on thorax high-resolution computed tomography, and/or UIP pattern, probable UIP or possible UIP pattern at lung biopsy according to the 2011 ATS/ERSARS/ALAT guidelines. Demographics and clinical and radiological data of the patients were recorded. All data recorded by researchers was evaluated by radiology and the clinical decision board. RESULTS: A total of 336 patients (253 men, 83 women, age 65.8 +/- 9.0 years) were evaluated. Of the patients with sufficient data for diag-nosis (n=300), the diagnosis was IPF in 121 (40.3%), unclassified idiopathic interstitial pneumonia in 50 (16.7%), combined pulmonary fibrosis and emphysema (CPFE) in 40 (13.3%), and lung involvement of connective tissue disease (CTD) in 16 (5.3%). When 29 patients with definite IPF features were added to the patients with CPFE, the total number of IPF patients reached 150 (50%). Rate of male sex (p<0.001), smoking history (p<0.001), and the presence of clubbing (p=0.001) were significantly high in patients with IPE None of the women <50 years and none of the men <50 years of age without a smoking history were diagnosed with IPE Presence of at least 1 of the symptoms suggestive of CTD, erythrocyte sedimentation rate (ESR), and antinuclear antibody (FANA) positivity rates were significantly higher in the non-IPF group (p<0.001, p=0.029, p=0.009, respectively). CONCLUSION: The rate of IPF among patients with fibrotic ILD was 50%. In the differential diagnosis of IPF, sex, smoking habits, and the presence of clubbing are important. The presence of symptoms related to CTD, ESR elevation, and EANA positivity reduce the likelihood of IPF