DeepVoxels: Learning Persistent 3D Feature Embeddings

In this work, we address the lack of 3D understanding of generative neural networks by introducing a persistent 3D feature embedding for view synthesis. To this end, we propose DeepVoxels, a learned representation that encodes the view-dependent appearance of a 3D scene without having to explicitly model its geometry. At its core, our approach is based on a Cartesian 3D grid of persistent embedded features that learn to make use of the underlying 3D scene structure. Our approach combines insights from 3D geometric computer vision with recent advances in learning image-to-image mappings based on adversarial loss functions. DeepVoxels is supervised, without requiring a 3D reconstruction of the scene, using a 2D re-rendering loss and enforces perspective and multi-view geometry in a principled manner. We apply our persistent 3D scene representation to the problem of novel view synthesis demonstrating high-quality results for a variety of challenging scenes.Comment: Video: https://www.youtube.com/watch?v=HM_WsZhoGXw Supplemental material: https://drive.google.com/file/d/1BnZRyNcVUty6-LxAstN83H79ktUq8Cjp/view?usp=sharing Code: https://github.com/vsitzmann/deepvoxels Project page: https://vsitzmann.github.io/deepvoxels

Detailed Numerical Simulations on the Formation of Pillars around HII-regions

We study the structural evolution of turbulent molecular clouds under the influence of ionizing radiation emitted from a nearby massive star by performing a high resolution parameter study with the iVINE code. The temperature is taken to be 10K or 100K, the mean number density is either 100cm^3 or 300cm^3. Besides, the turbulence is varied between Mach 1.5 and Mach 12.5 and the main driving scale between 1pc and 8pc. We vary the ionizing flux by an order of magnitude. In our simulations the ionizing radiation enhances the initial turbulent density distribution and thus leads to the formation of pillar-like structures observed adjacent to HII regions in a natural way. Gravitational collapse occurs regularly at the tips of the structures. We find a clear correlation between the initial state of the turbulent cold cloud and the final morphology and physical properties of the structures formed. The most favorable regime for the formation of pillars is Mach 4-10. Structures and therefore stars only form if the initial density contrast between the high density unionized gas and the gas that is going to be ionized is lower than the temperature contrast between the hot and the cold gas. The density of the resulting pillars is determined by a pressure equilibrium between the hot and the cold gas. A thorough analysis of the simulations shows that the complex kinematical and geometrical structure of the formed elongated filaments reflects that of observed pillars to an impressive level of detail. In addition, we find that the observed line-of sight velocities allow for a distinct determination of different formation mechanisms. Comparing the current simulations to previous results and recent observations we conclude that e.g. the pillars of creation in M16 formed by the mechanism proposed here and not by the radiation driven implosion of pre-existing clumps.Comment: 15 pages, 12 figures, accepted for publication in Ap

Driving Turbulence and Triggering Star Formation by Ionizing Radiation

We present high resolution simulations on the impact of ionizing radiation of massive O-stars on the surrounding turbulent interstellar medium (ISM). The simulations are performed with the newly developed software iVINE which combines ionization with smoothed particle hydrodynamics (SPH) and gravitational forces. We show that radiation from hot stars penetrates the ISM, efficiently heats cold low density gas and amplifies over-densities seeded by the initial turbulence. The formation of observed pillar-like structures in star forming regions (e.g. in M16) can be explained by this scenario. At the tip of the pillars gravitational collapse can be induced, eventually leading to the formation of low mass stars. Detailed analysis of the evolution of the turbulent spectra shows that UV-radiation of O-stars indeed provides an excellent mechanism to sustain and even drive turbulence in the parental molecular cloud.Comment: 5 pages, 2 figures, accepted by ApJ Letter

Day care study showed no differences in long-term symptoms in children who were and were not infected during COVID-19 outbreaks

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Operationalizing multimorbidity and autonomy for health services research in aging populations - the OMAHA study

<p>Abstract</p> <p>Background</p> <p>As part of a Berlin-based research consortium on health in old age, the OMAHA (Operationalizing Multimorbidity and Autonomy for Health Services Research in Aging Populations) study aims to develop a conceptual framework and a set of standardized instruments and indicators for continuous monitoring of multimorbidity and associated health care needs in the population 65 years and older.</p> <p>Methods/Design</p> <p>OMAHA is a longitudinal epidemiological study including a comprehensive assessment at baseline and at 12-month follow-up as well as brief intermediate telephone interviews at 6 and 18 months. In order to evaluate different sampling procedures and modes of data collection, the study is conducted in two different population-based samples of men and women aged 65 years and older. A geographically defined sample was recruited from an age and sex stratified random sample from the register of residents in Berlin-Mitte (Berlin OMAHA study cohort, n = 299) for assessment by face-to-face interview and examination. A larger nationwide sample (German OMAHA study cohort, n = 730) was recruited for assessment by telephone interview among participants in previous German Telephone Health Surveys. In both cohorts, we successfully applied a multi-dimensional set of instruments to assess multimorbidity, functional disability in daily life, autonomy, quality of life (QoL), health care services utilization, personal and social resources as well as socio-demographic and biographical context variables. Response rates considerably varied between the Berlin and German OMAHA study cohorts (22.8% vs. 59.7%), whereas completeness of follow-up at month 12 was comparably high in both cohorts (82.9% vs. 81.2%).</p> <p>Discussion</p> <p>The OMAHA study offers a wide spectrum of data concerning health, functioning, social involvement, psychological well-being, and cognitive capacity in community-dwelling older people in Germany. Results from the study will add to methodological and content-specific discourses on human resources for maintaining quality of life and autonomy throughout old age, even in the face of multiple health complaints.</p

State of the Art on Diffusion Models for Visual Computing

The field of visual computing is rapidly advancing due to the emergence of generative artificial intelligence (AI), which unlocks unprecedented capabilities for the generation, editing, and reconstruction of images, videos, and 3D scenes. In these domains, diffusion models are the generative AI architecture of choice. Within the last year alone, the literature on diffusion-based tools and applications has seen exponential growth and relevant papers are published across the computer graphics, computer vision, and AI communities with new works appearing daily on arXiv. This rapid growth of the field makes it difficult to keep up with all recent developments. The goal of this state-of-the-art report (STAR) is to introduce the basic mathematical concepts of diffusion models, implementation details and design choices of the popular Stable Diffusion model, as well as overview important aspects of these generative AI tools, including personalization, conditioning, inversion, among others. Moreover, we give a comprehensive overview of the rapidly growing literature on diffusion-based generation and editing, categorized by the type of generated medium, including 2D images, videos, 3D objects, locomotion, and 4D scenes. Finally, we discuss available datasets, metrics, open challenges, and social implications. This STAR provides an intuitive starting point to explore this exciting topic for researchers, artists, and practitioners alike

Ionisation feedback in star formation simulations: The role of diffuse fields

We compare the three-dimensional gas temperature distributions obtained by a dedicated radiative transfer and photoionisation code, MOCASSIN, against those obtained by the recently-developed Smooth Particle Hydrodynamics (SPH) plus ionisation code iVINE for snapshots of an hydrodynamical simulation of a turbulent interstellar medium (ISM) irradiated by a nearby O star. Our tests demonstrate that the global ionisation properties of the region are correctly reproduced by iVINE, hence validating further application of this code to the study of feedback in star forming regions. However we highlight potentially important discrepancies in the detailed temperature distribution. In particular we show that in the case of highly inhomogenous density distributions the commonly employed on-the-spot (OTS) approximation yields unrealistically sharp shadow regions which can affect the dynamical evolution of the system. We implement a simple strategy to include the effects of the diffuse field in future calculations, which makes use of physically motivated temperature calibrations of the diffuse-field dominated regions and can be readily applied to similar codes. We find that while the global qualitative behaviour of the system is captured by simulations with the OTS approximation, the inclusion of the diffuse field in iVINE (called DiVINE) results in a stronger confinement of the cold gas, leading to denser and less coherent structures. This in turn leads to earlier triggering of star formation. We confirm that turbulence is being driven in simulations that include the diffuse field, but the efficiency is slightly lower than in simulations that use the OTS approximation.Comment: 11 pages, 10 Figures, Accepted for publication in MNRA

SARS-CoV-2 Transmissibility Within Day Care Centers—Study Protocol of a Prospective Analysis of Outbreaks in Germany

Introduction: Until today, the role of children in the transmission dynamics of SARS-CoV-2 and the development of the COVID-19 pandemic seems to be dynamic and is not finally resolved. The primary aim of this study is to investigate the transmission dynamics of SARS-CoV-2 in child day care centers and connected households as well as transmission-related indicators and clinical symptoms among children and adults. Methods and Analysis: COALA (“Corona outbreak-related examinations in day care centers”) is a day care center- and household-based study with a case-ascertained study design. Based on day care centers with at least one reported case of SARS-CoV-2, we include one- to six-year-old children and staff of the affected group in the day care center as well as their respective households. We visit each child's and adult's household. During the home visit we take from each household member a combined mouth and nose swab as well as a saliva sample for analysis of SARS-CoV-2-RNA by real-time reverse transcription polymerase chain reaction (real-time RT-PCR) and a capillary blood sample for a retrospective assessment of an earlier SARS-CoV-2 infection. Furthermore, information on health status, socio-demographics and COVID-19 protective measures are collected via a short telephone interview in the subsequent days. In the following 12 days, household members (or parents for their children) self-collect the same respiratory samples as described above every 3 days and a stool sample for children once. COVID-19 symptoms are documented daily in a symptom diary. Approximately 35 days after testing the index case, every participant who tested positive for SARS-CoV-2 during the study is re-visited at home for another capillary blood sample and a standardized interview. The analysis includes secondary attack rates, by age of primary case, both in the day care center and in households, as well as viral shedding dynamics, including the beginning of shedding relative to symptom onset and viral clearance. Discussion: The results contribute to a better understanding of the epidemiological and virological transmission-related indicators of SARS-CoV-2 among young children, as compared to adults and the interplay between day care and households.Peer Reviewe