297 research outputs found
Experimental Investigation of the DLR-F6 Transport Configuration in the National Transonic Facility
An experimental aerodynamic investigation of the DLR (German Aerospace Center) F6 generic transport configuration has been conducted in the NASA NTF (National Transonic Facility) for CFD validation within the framework of the AIAA Drag Prediction Workshop. Force and moment, surface pressure, model deformation, and surface flow visualization data have been obtained at Reynolds numbers of both 3 million and 5 million. Flow-through nacelles and a side-of-body fairing were also investigated on this wing-body configuration. Reynolds number effects on trailing edge separation have been assessed, and the effectiveness of the side-of-body fairing in eliminating a known region of separated flow has been determined. Data obtained at a Reynolds number of 3 million are presented together for comparison with data from a previous wind tunnel investigation in the ONERA S2MA facility. New surface flow visualization capabilities have also been successfully explored and demonstrated in the NTF for the high pressure and moderately low temperature conditions required in this investigation. Images detailing wing surface flow characteristics are presented
Approaching human 3D shape perception with neurally mappable models
Humans effortlessly infer the 3D shape of objects. What computations underlie
this ability? Although various computational models have been proposed, none of
them capture the human ability to match object shape across viewpoints. Here,
we ask whether and how this gap might be closed. We begin with a relatively
novel class of computational models, 3D neural fields, which encapsulate the
basic principles of classic analysis-by-synthesis in a deep neural network
(DNN). First, we find that a 3D Light Field Network (3D-LFN) supports 3D
matching judgments well aligned to humans for within-category comparisons,
adversarially-defined comparisons that accentuate the 3D failure cases of
standard DNN models, and adversarially-defined comparisons for algorithmically
generated shapes with no category structure. We then investigate the source of
the 3D-LFN's ability to achieve human-aligned performance through a series of
computational experiments. Exposure to multiple viewpoints of objects during
training and a multi-view learning objective are the primary factors behind
model-human alignment; even conventional DNN architectures come much closer to
human behavior when trained with multi-view objectives. Finally, we find that
while the models trained with multi-view learning objectives are able to
partially generalize to new object categories, they fall short of human
alignment. This work provides a foundation for understanding human shape
inferences within neurally mappable computational architectures and highlights
important questions for future work
Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision
Denoising diffusion models are a powerful type of generative models used to
capture complex distributions of real-world signals. However, their
applicability is limited to scenarios where training samples are readily
available, which is not always the case in real-world applications. For
example, in inverse graphics, the goal is to generate samples from a
distribution of 3D scenes that align with a given image, but ground-truth 3D
scenes are unavailable and only 2D images are accessible. To address this
limitation, we propose a novel class of denoising diffusion probabilistic
models that learn to sample from distributions of signals that are never
directly observed. Instead, these signals are measured indirectly through a
known differentiable forward model, which produces partial observations of the
unknown signal. Our approach involves integrating the forward model directly
into the denoising process. This integration effectively connects the
generative modeling of observations with the generative modeling of the
underlying signals, allowing for end-to-end training of a conditional
generative model over signals. During inference, our approach enables sampling
from the distribution of underlying signals that are consistent with a given
partial observation. We demonstrate the effectiveness of our method on three
challenging computer vision tasks. For instance, in the context of inverse
graphics, our model enables direct sampling from the distribution of 3D scenes
that align with a single 2D input image.Comment: Project page: https://diffusion-with-forward-models.github.io
Seeing 3D Objects in a Single Image via Self-Supervised Static-Dynamic Disentanglement
Human perception reliably identifies movable and immovable parts of 3D
scenes, and completes the 3D structure of objects and background from
incomplete observations. We learn this skill not via labeled examples, but
simply by observing objects move. In this work, we propose an approach that
observes unlabeled multi-view videos at training time and learns to map a
single image observation of a complex scene, such as a street with cars, to a
3D neural scene representation that is disentangled into movable and immovable
parts while plausibly completing its 3D structure. We separately parameterize
movable and immovable scene parts via 2D neural ground plans. These ground
plans are 2D grids of features aligned with the ground plane that can be
locally decoded into 3D neural radiance fields. Our model is trained
self-supervised via neural rendering. We demonstrate that the structure
inherent to our disentangled 3D representation enables a variety of downstream
tasks in street-scale 3D scenes using simple heuristics, such as extraction of
object-centric 3D representations, novel view synthesis, instance segmentation,
and 3D bounding box prediction, highlighting its value as a backbone for
data-efficient 3D scene understanding models. This disentanglement further
enables scene editing via object manipulation such as deletion, insertion, and
rigid-body motion.Comment: Project page: https://prafullsharma.net/see3d
Lessons Learned from Creating a Mobile Version of an Educational Board Game to Increase Situational Awareness
This paper reports on an iterative design process for a serious game, which aims to raise situational awareness among different stakeholders in a logistics value chain by introducing multi-user role-playing games. It does so in several phases: After introducing the field of logistics as a problem domain for an educational challenge, it firstly describes the design of an educational board game for the field of disruption handling in logistics processes. Secondly, it de-scribes how the board game can be realized in an open-source mobile serious games platform and identifies lessons learned based on advantages and issues found. Thirdly, it derives requirements for a re-design of the mobile game and finally draws conclusions.SALOM
Promises and Prospects of Educational Technology, Evidence from Systematic Reviews and Meta-analyses
Challenges in QCD matter physics - The Compressed Baryonic Matter experiment at FAIR
Substantial experimental and theoretical efforts worldwide are devoted to
explore the phase diagram of strongly interacting matter. At LHC and top RHIC
energies, QCD matter is studied at very high temperatures and nearly vanishing
net-baryon densities. There is evidence that a Quark-Gluon-Plasma (QGP) was
created at experiments at RHIC and LHC. The transition from the QGP back to the
hadron gas is found to be a smooth cross over. For larger net-baryon densities
and lower temperatures, it is expected that the QCD phase diagram exhibits a
rich structure, such as a first-order phase transition between hadronic and
partonic matter which terminates in a critical point, or exotic phases like
quarkyonic matter. The discovery of these landmarks would be a breakthrough in
our understanding of the strong interaction and is therefore in the focus of
various high-energy heavy-ion research programs. The Compressed Baryonic Matter
(CBM) experiment at FAIR will play a unique role in the exploration of the QCD
phase diagram in the region of high net-baryon densities, because it is
designed to run at unprecedented interaction rates. High-rate operation is the
key prerequisite for high-precision measurements of multi-differential
observables and of rare diagnostic probes which are sensitive to the dense
phase of the nuclear fireball. The goal of the CBM experiment at SIS100
(sqrt(s_NN) = 2.7 - 4.9 GeV) is to discover fundamental properties of QCD
matter: the phase structure at large baryon-chemical potentials (mu_B > 500
MeV), effects of chiral symmetry, and the equation-of-state at high density as
it is expected to occur in the core of neutron stars. In this article, we
review the motivation for and the physics programme of CBM, including
activities before the start of data taking in 2022, in the context of the
worldwide efforts to explore high-density QCD matter.Comment: 15 pages, 11 figures. Published in European Physical Journal
The Comparison between Circadian Oscillators in Mouse Liver and Pituitary Gland Reveals Different Integration of Feeding and Light Schedules
The mammalian circadian system is composed of multiple peripheral clocks that are synchronized by a central pacemaker in the suprachiasmatic nuclei of the hypothalamus. This system keeps track of the external world rhythms through entrainment by various time cues, such as the light-dark cycle and the feeding schedule. Alterations of photoperiod and meal time modulate the phase coupling between central and peripheral oscillators. In this study, we used real-time quantitative PCR to assess circadian clock gene expression in the liver and pituitary gland from mice raised under various photoperiods, or under a temporal restricted feeding protocol. Our results revealed unexpected differences between both organs. Whereas the liver oscillator always tracked meal time, the pituitary circadian clockwork showed an intermediate response, in between entrainment by the light regimen and the feeding-fasting rhythm. The same composite response was also observed in the pituitary gland from adrenalectomized mice under daytime restricted feeding, suggesting that circulating glucocorticoids do not inhibit full entrainment of the pituitary clockwork by meal time. Altogether our results reveal further aspects in the complexity of phase entrainment in the circadian system, and suggest that the pituitary may host oscillators able to integrate multiple time cues
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