Viewpoints: A high-performance high-dimensional exploratory data analysis tool

Scientific data sets continue to increase in both size and complexity. In the past, dedicated graphics systems at supercomputing centers were required to visualize large data sets, but as the price of commodity graphics hardware has dropped and its capability has increased, it is now possible, in principle, to view large complex data sets on a single workstation. To do this in practice, an investigator will need software that is written to take advantage of the relevant graphics hardware. The Viewpoints visualization package described herein is an example of such software. Viewpoints is an interactive tool for exploratory visual analysis of large, high-dimensional (multivariate) data. It leverages the capabilities of modern graphics boards (GPUs) to run on a single workstation or laptop. Viewpoints is minimalist: it attempts to do a small set of useful things very well (or at least very quickly) in comparison with similar packages today. Its basic feature set includes linked scatter plots with brushing, dynamic histograms, normalization and outlier detection/removal. Viewpoints was originally designed for astrophysicists, but it has since been used in a variety of fields that range from astronomy, quantum chemistry, fluid dynamics, machine learning, bioinformatics, and finance to information technology server log mining. In this article, we describe the Viewpoints package and show examples of its usage.Comment: 18 pages, 3 figures, PASP in press, this version corresponds more closely to that to be publishe

Shape: A 3D Modeling Tool for Astrophysics

We present a flexible interactive 3D morpho-kinematical modeling application for astrophysics. Compared to other systems, our application reduces the restrictions on the physical assumptions, data type and amount that is required for a reconstruction of an object's morphology. It is one of the first publicly available tools to apply interactive graphics to astrophysical modeling. The tool allows astrophysicists to provide a-priori knowledge about the object by interactively defining 3D structural elements. By direct comparison of model prediction with observational data, model parameters can then be automatically optimized to fit the observation. The tool has already been successfully used in a number of astrophysical research projects.Comment: 13 pages, 11 figures, accepted for publication in the "IEEE Transactions on Visualization and Computer Graphics

DoF-NeRF: Depth-of-Field Meets Neural Radiance Fields

Neural Radiance Field (NeRF) and its variants have exhibited great success on representing 3D scenes and synthesizing photo-realistic novel views. However, they are generally based on the pinhole camera model and assume all-in-focus inputs. This limits their applicability as images captured from the real world often have finite depth-of-field (DoF). To mitigate this issue, we introduce DoF-NeRF, a novel neural rendering approach that can deal with shallow DoF inputs and can simulate DoF effect. In particular, it extends NeRF to simulate the aperture of lens following the principles of geometric optics. Such a physical guarantee allows DoF-NeRF to operate views with different focus configurations. Benefiting from explicit aperture modeling, DoF-NeRF also enables direct manipulation of DoF effect by adjusting virtual aperture and focus parameters. It is plug-and-play and can be inserted into NeRF-based frameworks. Experiments on synthetic and real-world datasets show that, DoF-NeRF not only performs comparably with NeRF in the all-in-focus setting, but also can synthesize all-in-focus novel views conditioned on shallow DoF inputs. An interesting application of DoF-NeRF to DoF rendering is also demonstrated. The source code will be made available at https://github.com/zijinwuzijin/DoF-NeRF.Comment: Accepted by ACMMM 202

Design and operation of the wide angular-range chopper spectrometer ARCS at the Spallation Neutron Source

The wide angular-range chopper spectrometer ARCS at the Spallation Neutron Source (SNS) is optimized to provide a high neutron flux at the sample position with a large solid angle of detector coverage. The instrument incorporates modern neutron instrumentation, such as an elliptically focused neutron guide, high speed magnetic bearing choppers, and a massive array of ^3He linear position sensitive detectors. Novel features of the spectrometer include the use of a large gate valve between the sample and detector vacuum chambers and the placement of the detectors within the vacuum, both of which provide a window-free final flight path to minimize background scattering while allowing rapid changing of the sample and sample environment equipment. ARCS views the SNS decoupled ambient temperature water moderator, using neutrons with incident energy typically in the range from 15 to 1500 meV. This range, coupled with the large detector coverage, allows a wide variety of studies of excitations in condensed matter, such as lattice dynamics and magnetism, in both powder and single-crystal samples. Comparisons of early results to both analytical and Monte Carlo simulation of the instrument performance demonstrate that the instrument is operating as expected and its neutronic performance is understood. ARCS is currently in the SNS user program and continues to improve its scientific productivity by incorporating new instrumentation to increase the range of science covered and improve its effectiveness in data collection

System integration report

Several areas that arise from the system integration issue were examined. Intersystem analysis is discussed as it relates to software development, shared data bases and interfaces between TEMPUS and PLAID, shaded graphics rendering systems, object design (BUILD), the TEMPUS animation system, anthropometric lab integration, ongoing TEMPUS support and maintenance, and the impact of UNIX and local workstations on the OSDS environment

Adjustable Visual Appearance for Generalizable Novel View Synthesis

We present a generalizable novel view synthesis method where it is possible to modify the visual appearance of rendered views to match a target weather or lighting condition. Our method is based on a generalizable transformer architecture, trained on synthetically generated scenes under different appearance conditions. This allows for rendering novel views in a consistent manner of 3D scenes that were not included in the training set, along with the ability to (i) modify their appearance to match the target condition and (ii) smoothly interpolate between different conditions. Experiments on both real and synthetic scenes are provided including both qualitative and quantitative evaluations. Please refer to our project page for video results: https://ava-nvs.github.io