Coping with Conflict: A Study of Interpersonal Conflict Resolution Styles of Adult Children of Alcoholics and Nonalcoholics

Research on adult children of alcoholics has indicated that such children have difficulty with behavioral and communicative characteristics. Specifically they have difficulty with such behaviors as lying, intimacy, responsibility, and trust. Research also has indicated that adult children of alcoholics rely on coping mechanisms to escape from their chaotic environments and such mechanisms are manifested in behaviors of co-dependency and family roles. Although the literature on adult children of alcoholics suggests that these individuals may have trouble with problem solving in conflict, no apparent literature discusses the strategies of conflict resolution for such individuals. This study predicted that adult children of alcoholics would choose conflict resolution styles of avoidance and/or accommodation more often than would adult children of nonalcoholics, The Thomas Kilmann MODE Instrument was given to a sample of Spring 1990 Fundamentals of Public Speaking students at the University of North Dakota. Results indicated that differences in responses to conflict resolution styles between adult children of alcoholics and adult children of nonalcoholics were not significant at the .05 level. Implications of this study of conflict resolution suggest a need to incorporate a new methodology or improve the existing instrument for a higher level of reliability. Recommendations for further research include relying on a formalized adult children of alcoholics group for testing. Also incorporating rhetorical critical analyses of metaphorical analysis, content analysis, or fantasy theme analysis to better assess conflict resolution styles may be useful

Learning a self-supervised tone mapping operator via feature contrast masking loss

High Dynamic Range (HDR) content is becoming ubiquitous due to the rapid development of capture technologies. Nevertheless, the dynamic range of common display devices is still limited, therefore tone mapping (TM) remains a key challenge for image visualization. Recent work has demonstrated that neural networks can achieve remarkable performance in this task when compared to traditional methods, however, the quality of the results of these learning-based methods is limited by the training data. Most existing works use as training set a curated selection of best-performing results from existing traditional tone mapping operators (often guided by a quality metric), therefore, the quality of newly generated results is fundamentally limited by the performance of such operators. This quality might be even further limited by the pool of HDR content that is used for training. In this work we propose a learning-based self-supervised tone mapping operator that is trained at test time specifically for each HDR image and does not need any data labeling. The key novelty of our approach is a carefully designed loss function built upon fundamental knowledge on contrast perception that allows for directly comparing the content in the HDR and tone mapped images. We achieve this goal by reformulating classic VGG feature maps into feature contrast maps that normalize local feature differences by their average magnitude in a local neighborhood, allowing our loss to account for contrast masking effects. We perform extensive ablation studies and exploration of parameters and demonstrate that our solution outperforms existing approaches with a single set of fixed parameters, as confirmed by both objective and subjective metrics

Analize naprezanja i pomaka suvremenog postolja tokarilice metodom konačnih elemenata (MKE)

The Finite element method (FEM) was used in this study for the analysis of the strain and stress of a turning machine body. The final design decisions were made on the basis of stress and displacement field analysis of various design versions related to the structure of the considered machine tool. The results presented in this paper will be helpful for practical static and dynamic strength evaluation as well as for the appropriate design of machine tools using the FEM.Metoda konačnih elemenata (MKE) je primjenjena za analizu deformacija i naprezanja postolja stroja. Konačna odluka o oblikovanju je donešena na osnovi analize polja pomaka za različite varijante oblikovovanja elemenata razmatranih strojeva. Rezultati predstavljeni u ovom radu će biti korisni za procjenu statičke i dinamičke čvrstoće kao i za odgovarajuće oblikovanje strojnih alata korištenjem MKE

Gloss Management for Consistent Reproduction of Real and Virtual Objects

A good match of material appearance between real-world objects and their digital on-screen representations is critical for many applications such as fabrication, design, and e-commerce. However, faithful appearance reproduction is challenging, especially for complex phenomena, such as gloss. In most cases, the view-dependent nature of gloss and the range of luminance values required for reproducing glossy materials exceeds the current capabilities of display devices. As a result, appearance reproduction poses significant problems even with accurately rendered images. This paper studies the gap between the gloss perceived from real-world objects and their digital counterparts. Based on our psychophysical experiments on a wide range of 3D printed samples and their corresponding photographs, we derive insights on the influence of geometry, illumination, and the display’s brightness and measure the change in gloss appearance due to the display limitations. Our evaluation experiments demonstrate that using the prediction to correct material parameters in a rendering system improves the match of gloss appearance between real objects and their visualization on a display device

Selecting texture resolution using a task-specific visibility metric

In real-time rendering, the appearance of scenes is greatly affected by the quality and resolution of the textures used for image synthesis. At the same time, the size of textures determines the performance and the memory requirements of rendering. As a result, finding the optimal texture resolution is critical, but also a non-trivial task since the visibility of texture imperfections depends on underlying geometry, illumination, interactions between several texture maps, and viewing positions. Ideally, we would like to automate the task with a visibility metric, which could predict the optimal texture resolution. To maximize the performance of such a metric, it should be trained on a given task. This, however, requires sufficient user data which is often difficult to obtain. To address this problem, we develop a procedure for training an image visibility metric for a specific task while reducing the effort required to collect new data. The procedure involves generating a large dataset using an existing visibility metric followed by refining that dataset with the help of an efficient perceptual experiment. Then, such a refined dataset is used to retune the metric. This way, we augment sparse perceptual data to a large number of per-pixel annotated visibility maps which serve as the training data for application-specific visibility metrics. While our approach is general and can be potentially applied for different image distortions, we demonstrate an application in a game-engine where we optimize the resolution of various textures, such as albedo and normal maps

The Effect of Geometry and Illumination on Appearance Perception of Different Material Categories

The understanding of material appearance perception is a complex problem due to interactions between material reflectance, surface geometry, and illumination. Recently, Serrano et al. collected the largest dataset to date with subjective ratings of material appearance attributes, including glossiness, metallicness, sharpness and contrast of reflections. In this work, we make use of their dataset to investigate for the first time the impact of the interactions between illumination, geometry, and eight different material categories in perceived appearance attributes. After an initial analysis, we select for further analysis the four material categories that cover the largest range for all perceptual attributes: fabric, plastic, ceramic, and metal. Using a cumulative link mixed model (CLMM) for robust regression, we discover interactions between these material categories and four representative illuminations and object geometries. We believe that our findings contribute to expanding the knowledge on material appearance perception and can be useful for many applications, such as scene design, where any particular material in a given shape can be aligned with dominant classes of illumination, so that a desired strength of appearance attributes can be achieved

GlowGAN: Unsupervised Learning of HDR Images from LDR Images in the Wild

Most in-the-wild images are stored in Low Dynamic Range (LDR) form, servingas a partial observation of the High Dynamic Range (HDR) visual world. Despitelimited dynamic range, these LDR images are often captured with differentexposures, implicitly containing information about the underlying HDR imagedistribution. Inspired by this intuition, in this work we present, to the bestof our knowledge, the first method for learning a generative model of HDRimages from in-the-wild LDR image collections in a fully unsupervised manner.The key idea is to train a generative adversarial network (GAN) to generate HDRimages which, when projected to LDR under various exposures, areindistinguishable from real LDR images. The projection from HDR to LDR isachieved via a camera model that captures the stochasticity in exposure andcamera response function. Experiments show that our method GlowGAN cansynthesize photorealistic HDR images in many challenging cases such aslandscapes, lightning, or windows, where previous supervised generative modelsproduce overexposed images. We further demonstrate the new application ofunsupervised inverse tone mapping (ITM) enabled by GlowGAN. Our ITM method doesnot need HDR images or paired multi-exposure images for training, yet itreconstructs more plausible information for overexposed regions thanstate-of-the-art supervised learning models trained on such data.<br

Dataset and metrics for predicting local visible differences

A large number of imaging and computer graphics applications require localized information on the visibility of image distortions. Existing image quality metrics are not suitable for this task as they provide a single quality value per image. Existing visibility metrics produce visual difference maps, and are specifically designed for detecting just noticeable distortions but their predictions are often inaccurate. In this work, we argue that the key reason for this problem is the lack of large image collections with a good coverage of possible distortions that occur in different applications. To address the problem, we collect an extensive dataset of reference and distorted image pairs together with user markings indicating whether distortions are visible or not. We propose a statistical model that is designed for the meaningful interpretation of such data, which is affected by visual search and imprecision of manual marking. We use our dataset for training existing metrics and we demonstrate that their performance significantly improves. We show that our dataset with the proposed statistical model can be used to train a new CNN-based metric, which outperforms the existing solutions. We demonstrate the utility of such a metric in visually lossless JPEG compression, super-resolution and watermarking.</jats:p

Geometry-Aware Scattering Compensation for 3D Printing

Commercially available full-color 3D printing allows for detailed control of material deposition in a volume, but an exact reproduction of a target surface appearance is hampered by the strong subsurface scattering that causes nontrivial volumetric cross-talk at the print surface. Previous work showed how an iterative optimization scheme based on accumulating absorptive materials at the surface can be used to find a volumetric distribution of print materials that closely approximates a given target appearance. // In this work, we first revisit the assumption that pushing the absorptive materials to the surface results in minimal volumetric cross-talk. We design a full-fledged optimization on a small domain for this task and confirm this previously reported heuristic. Then, we extend the above approach that is critically limited to color reproduction on planar surfaces, to arbitrary 3D shapes. Our proposed method enables high-fidelity color texture reproduction on 3D prints by effectively compensating for internal light scattering within arbitrarily shaped objects. In addition, we propose a content-aware gamut mapping that significantly improves color reproduction for the pathological case of thin geometric features. Using a wide range of sample objects with complex textures and geometries, we demonstrate color reproduction whose fidelity is superior to state-of-the-art drivers for color 3D printers