Family Impact and Infant Emotional Outcomes when an Infant Has Serious Liver Disease: A Longitudinal Mixed Methods Study

Background Serious liver disease in infancy causes significant morbidity. Up to 80% of children will eventually require transplantation. This study aims to investigate parent and family responses to the diagnosis of serious liver disease in infancy and to identify family factors that are predictive of the infants’ emotional and behavioural outcomes. Methods The study uses quantitative and qualitative methods. Parents of infants recently diagnosed with serious liver disease completed validated measures of parent stress, family function, impact of the illness on the family, and father engagement, as well as an interview about their experience of the infants’ illness. The measures were repeated after one year, with the addition of the Child Behavior Checklist (CBCL). Results Parents of 42 infants enrolled, and parents of 37 infants completed the study. Illness severity, liver diagnosis other than Biliary Atresia and parent perceptions of greater impact of the infants’ illness on the family predicted poorer infant outcomes. For mothers, the final best-fit model explained 32% of the variation in CBCL (P = .001). Fathers’ best-fit model explained 44% of the variation in CBCL (P < .001). Thematic analysis of the parent interviews revealed six major themes: uncertainty; awareness of the infant’s vulnerability; feelings of isolation; dealing with other aspects of life; the importance of shared experience; and adjustment. The integrated data analysis demonstrated that lack of extended family support, poor family adjustment to the illness, and financial stress are related to greater impact of the illness on the family. Conclusions The study identifies early risk factors for poor emotional and behavioural outcomes for infants with serious liver disease, providing an opportunity for early intervention. Parents who lack support from extended family, who have financial stress, or who report a high impact of the illness on the family, should be referred for psychosocial assessment

Novel View Synthesis of Humans using Differentiable Rendering

We present a new approach for synthesizing novel views of people in new poses. Our novel differentiable renderer enables the synthesis of highly realistic images from any viewpoint. Rather than operating over mesh-based structures, our renderer makes use of diffuse Gaussian primitives that directly represent the underlying skeletal structure of a human. Rendering these primitives gives results in a high-dimensional latent image, which is then transformed into an RGB image by a decoder network. The formulation gives rise to a fully differentiable framework that can be trained end-to-end. We demonstrate the effectiveness of our approach to image reconstruction on both the Human3.6M and Panoptic Studio datasets. We show how our approach can be used for motion transfer between individuals; novel view synthesis of individuals captured from just a single camera; to synthesize individuals from any virtual viewpoint; and to re-render people in novel poses. Code and video results are available at https://github.com/GuillaumeRochette/HumanViewSynthesis.Comment: Accepted at IEEE transactions on Biometrics, Behavior, and Identity Science, 10 pages, 11 figures. arXiv admin note: substantial text overlap with arXiv:2111.1273

Learning Adaptive Neighborhoods for Graph Neural Networks

Graph convolutional networks (GCNs) enable end-to-end learning on graph structured data. However, many works assume a given graph structure. When the input graph is noisy or unavailable, one approach is to construct or learn a latent graph structure. These methods typically fix the choice of node degree for the entire graph, which is suboptimal. Instead, we propose a novel end-to-end differentiable graph generator which builds graph topologies where each node selects both its neighborhood and its size. Our module can be readily integrated into existing pipelines involving graph convolution operations, replacing the predetermined or existing adjacency matrix with one that is learned, and optimized, as part of the general objective. As such it is applicable to any GCN. We integrate our module into trajectory prediction, point cloud classification and node classification pipelines resulting in improved accuracy over other structure-learning methods across a wide range of datasets and GCN backbones.Comment: ICCV 202

Kick back & relax: learning to reconstruct the world by watching SlowTV

Self-supervised monocular depth estimation (SS-MDE) has the potential to scale to vast quantities of data. Unfortunately, existing approaches limit themselves to the automotive domain, resulting in models incapable of generalizing to complex environments such as natural or indoor settings. To address this, we propose a large-scale SlowTV dataset curated from YouTube, containing an order of magnitude more data than existing automotive datasets. SlowTV contains 1.7M images from a rich diversity of environments, such as worldwide seasonal hiking, scenic driving and scuba diving. Using this dataset, we train an SS-MDE model that provides zero-shot generalization to a large collection of indoor/outdoor datasets. The resulting model outperforms all existing SSL approaches and closes the gap on supervised SoTA, despite using a more efficient architecture. We additionally introduce a collection of best-practices to further maximize performance and zero-shot generalization. This includes 1) aspect ratio augmentation, 2) camera intrinsic estimation, 3) support frame randomization and 4) flexible motion estimation

Learning adaptive neighborhoods for graph neural networks

Graph convolutional networks (GCNs) enable end-to-end learning on graph structured data. However, many works assume a given graph structure. When the input graph is noisy or unavailable, one approach is to construct or learn a latent graph structure. These methods typically fix the choice of node degree for the entire graph, which is suboptimal. Instead, we propose a novel end-to-end differentiable graph generator which builds graph topologies where each node selects both its neighborhood and its size. Our module can be readily integrated into existing pipelines involving graph convolution operations, replacing the predetermined or existing adjacency matrix with one that is learned, and optimized, as part of the general objective. As such it is applicable to any GCN. We integrate our module into trajectory prediction, point cloud classification and node classification pipelines resulting in improved accuracy over other structure-learning methods across a wide range of datasets and GCN backbones. We will release the code

Coastal sands of Northeastern Tasmania: geomorphology and groundwater hydrology

A regional study of the Quaternary geomorphology of coastal northeastern Tasmania defined landforms and deposits which offer good groundwater development potential, and also pointed to geomorphic problems worthy of more detailed research. Marine transgression and regression appear to have been a main feature of landform development in coastal northeastern Tasmania since Late Tertiary times. The present landscape is dominated by low, sandy plains created during the Last Interglacial marine transgression and by aeolian landforms which were formed during the succeeding glacial stage. The immediate coastal areas are backed by marine and aeolian landforms deposited during and since the marine transgression. The regional study revealed that deposits of possible marine origin and interglacial age, occur to an elevation of approximately 32 m. This is — 10 m above the upper limits of similar deposits elsewhere in Tasmania and is — 26 in higher than equivalent features in stable areas of mainland Australia. These relationships indicated that tectonic uplift in Tasmania may have occurred during the late Quaternary. Further research indicated that the sea level in northeastern Tasmania most likely attained an elevation of — 32 in during the Last Interglacial Stage, and that the area has experienced a moderate uplift rate of approximately 0.2 m/ka. The stratigraphic relationships between Quaternary marine deposits also indicate that older, probably of Oxygen Isotope Stages 7 and 9 age, marine deposits occur to 49 and 71 in respectively, thus indicating that uplift in Tasmania has been occurring over at least 300,000 years. Mapping and examination of the extensive longitudinal dunes and lunettes during initial stages of the programme indicated that they are products of environmental conditions substantially different from those of today. Dune morphology and grainsize characteristics suggest that zonal westerly air flows appear to have been stronger and from a slightly more northerly direction during the Last Glacial Stage than air flows which occur today. Stratigraphic studies infer that the temperature was markedly lower during formation of the longitudinal dunes. Evidence from fossil groundwater podzols indicates that precipitation during the Last Glacial Stage may have been only approximately one half of the present rainfall. Lunette stratigraphy and morphology reveal shifts in the relative importance of key components to the hydrologic cycle, such as precipitation, evapotranspiration and surface run-off, both during and since the late Last Glacial Stage. The coastal plains of interglacial marine sand form extensive unconfined aquifers and contain Abundant and accessible groundwater supplies. Computer and graphical simulations are applied to pumping test and drilling results, water table maps and continuous water level records to assess the groundwater system. Groundwater dynamics are controlled principally by precipitation and evapotranspiration. The system is renewable and moderate rates of groundwater withdrawal may even be beneficial

Translating Images into Maps

We approach instantaneous mapping, converting images to a top-down view of the world, as a translation problem. We show how a novel form of transformer network can be used to map from images and video directly to an overhead map or bird's-eye-view (BEV) of the world, in a single end-to-end network. We assume a 1-1 correspondence between a vertical scanline in the image, and rays passing through the camera location in an overhead map. This lets us formulate map generation from an image as a set of sequence-to-sequence translations. Posing the problem as translation allows the network to use the context of the image when interpreting the role of each pixel. This constrained formulation, based upon a strong physical grounding of the problem, leads to a restricted transformer network that is convolutional in the horizontal direction only. The structure allows us to make efficient use of data when training, and obtains state-of-the-art results for instantaneous mapping of three large-scale datasets, including a 15% and 30% relative gain against existing best performing methods on the nuScenes and Argoverse datasets, respectively. We make our code available on https://github.com/avishkarsaha/translating-images-into-maps.Comment: Accepted to ICRA 202

Inhibitory cognitive control allows automated advice to improve accuracy while minimizing misuse

Humans increasingly use automated decision aids. However, environmental uncertainty means that automated advice can be incorrect, creating the potential for humans to action incorrect advice or to disregard correct advice. We present a quantitative model of the cognitive process by which humans use automation when deciding whether aircraft would violate minimum separation. The model closely fitted the performance of twenty-four participants, whom each made 2400 conflict detection decisions (conflict vs non-conflict), either manually (with no assistance) or with the assistance of 90% reliable automation. When the decision aid was correct, conflict detection accuracy improved, but when the decision aid was incorrect, accuracy and response time were impaired. The model indicated that participants integrated advice into their decision process by inhibiting evidence accumulation toward the task response incongruent with that advice, thereby ensuring that decisions could not be made solely on automated advice without first sampling information from the task environment

Telling partners about chlamydia: how acceptable are the new technologies?

BACKGROUND Partner notification is accepted as a vital component in the control of chlamydia. However, in reality, many sexual partners of individuals diagnosed with chlamydia are never informed of their risk. The newer technologies of email and SMS have been used as a means of improving partner notification rates. This study explored the use and acceptability of different partner notification methods to help inform the development of strategies and resources to increase the number of partners notified. METHODS Semi-structured telephone interviews were conducted with 40 people who were recently diagnosed with chlamydia from three sexual health centres and two general practices across three Australian jurisdictions. RESULTS Most participants chose to contact their partners either in person (56%) or by phone (44%). Only 17% chose email or SMS. Participants viewed face-to-face as the "gold standard" in partner notification because it demonstrated caring, respect and courage. Telephone contact, while considered insensitive by some, was often valued because it was quick, convenient and less confronting. Email was often seen as less personal while SMS was generally considered the least acceptable method for telling partners. There was also concern that emails and SMS could be misunderstood, not taken seriously or shown to others. Despite these, email and SMS were seen to be appropriate and useful in some circumstances. Letters, both from the patients or from their doctor, were viewed more favourably but were seldom used. CONCLUSION These findings suggest that many people diagnosed with chlamydia are reluctant to use the new technologies for partner notification, except in specific circumstances, and our efforts in developing partner notification resources may best be focused on giving patients the skills and confidence for personal interaction.The study was funded by the Australian Federal Government Department of Health and Ageing Chlamydia Pilot Program of Targeted Grants