2,534 research outputs found
GDP growth incentives and earnings management: evidence from China
Lee Kong Chian Professorship at Singapore Management Universit
Properties of Steady Sub-Alfv\'enic Solar Wind in Comparison with Super-Alfv\'enic Wind from Measurements of Parker Solar Probe
We identify more than ten steady sub-Alfv\'enic solar wind intervals from the
measurements of the Parker Solar Probe (PSP) from encounter 8 to encounter 14.
An analysis of these sub-Alfv\'enic intervals reveals similar properties and
similar origins. In situ measurements show that these intervals feature a
decreased radial Alfv\'en Mach number resulting from a reduced density and a
relatively low velocity, and that switchbacks are suppressed in these
intervals. Magnetic source tracing indicates that these sub-Alfv\'enic streams
generally originate from the boundaries inside coronal holes, or narrow/small
regions of open magnetic fields. Such properties and origins suggest that these
streams are low Mach-number boundary layers (LMBLs), which is a special
component of the pristine solar wind proposed by Liu et al. We find that the
LMBL wind, the fast wind from deep inside coronal holes, and the slow streamer
wind constitute three typical components of the young solar wind near the Sun.
In these sub-Alfv\'enic intervals, the Alfv\'en radius varies between 15 and 25
solar radii, in contrast with a typical 12 radii for the Alfv\'en radius of the
super-Alfv\'enic wind. These results give a self-consistent picture
interpreting the PSP measurements in the vicinity of the Sun.Comment: Accepted for publication in Ap
Generating evidential BEV maps in continuous driving space
Safety is critical for autonomous driving, and one aspect of improving safety is to accurately capture the uncertainties of the perception system, especially knowing the unknown. Different from only providing deterministic or probabilistic results, e.g., probabilistic object detection, that only provide partial information for the perception scenario, we propose a complete probabilistic model named GevBEV. It interprets the 2D driving space as a probabilistic Bird's Eye View (BEV) map with point-based spatial Gaussian distributions, from which one can draw evidence as the parameters for the categorical Dirichlet distribution of any new sample point in the continuous driving space. The experimental results show that GevBEV not only provides more reliable uncertainty quantification but also outperforms the previous works on the benchmarks OPV2V and V2V4Real of BEV map interpretation for cooperative perception in simulated and real-world driving scenarios, respectively. A critical factor in cooperative perception is the data transmission size through the communication channels. GevBEV helps reduce communication overhead by selecting only the most important information to share from the learned uncertainty, reducing the average information communicated by 87% with only a slight performance drop. Our code is published at https://github.com/YuanYunshuang/GevBEV
Generating Evidential BEV Maps in Continuous Driving Space
Safety is critical for autonomous driving, and one aspect of improving safety
is to accurately capture the uncertainties of the perception system, especially
knowing the unknown. Different from only providing deterministic or
probabilistic results, e.g., probabilistic object detection, that only provide
partial information for the perception scenario, we propose a complete
probabilistic model named GevBEV. It interprets the 2D driving space as a
probabilistic Bird's Eye View (BEV) map with point-based spatial Gaussian
distributions, from which one can draw evidence as the parameters for the
categorical Dirichlet distribution of any new sample point in the continuous
driving space. The experimental results show that GevBEV not only provides more
reliable uncertainty quantification but also outperforms the previous works on
the benchmarks OPV2V and V2V4Real of BEV map interpretation for cooperative
perception in simulated and real-world driving scenarios, respectively. A
critical factor in cooperative perception is the data transmission size through
the communication channels. GevBEV helps reduce communication overhead by
selecting only the most important information to share from the learned
uncertainty, reducing the average information communicated by 87% with only a
slight performance drop. Our code is published at
https://github.com/YuanYunshuang/GevBEV
MCENET: Multi-Context Encoder Network for Homogeneous Agent Trajectory Prediction in Mixed Traffic
Trajectory prediction in urban mixed-traffic zones (a.k.a. shared spaces) is
critical for many intelligent transportation systems, such as intent detection
for autonomous driving. However, there are many challenges to predict the
trajectories of heterogeneous road agents (pedestrians, cyclists and vehicles)
at a microscopical level. For example, an agent might be able to choose
multiple plausible paths in complex interactions with other agents in varying
environments. To this end, we propose an approach named Multi-Context Encoder
Network (MCENET) that is trained by encoding both past and future scene
context, interaction context and motion information to capture the patterns and
variations of the future trajectories using a set of stochastic latent
variables. In inference time, we combine the past context and motion
information of the target agent with samplings of the latent variables to
predict multiple realistic trajectories in the future. Through experiments on
several datasets of varying scenes, our method outperforms some of the recent
state-of-the-art methods for mixed traffic trajectory prediction by a large
margin and more robust in a very challenging environment. The impact of each
context is justified via ablation studies.Comment: 8 pages, 5 figures, code is available on
https://github.com/haohao11/MCENE
AMENet: Attentive Maps Encoder Network for Trajectory Prediction
Trajectory prediction is critical for applications of planning safe future
movements and remains challenging even for the next few seconds in urban mixed
traffic. How an agent moves is affected by the various behaviors of its
neighboring agents in different environments. To predict movements, we propose
an end-to-end generative model named Attentive Maps Encoder Network (AMENet)
that encodes the agent's motion and interaction information for accurate and
realistic multi-path trajectory prediction. A conditional variational
auto-encoder module is trained to learn the latent space of possible future
paths based on attentive dynamic maps for interaction modeling and then is used
to predict multiple plausible future trajectories conditioned on the observed
past trajectories. The efficacy of AMENet is validated using two public
trajectory prediction benchmarks Trajnet and InD.Comment: Accepted by ISPRS Journal of Photogrammetry and Remote Sensin
Bi-level Physics-Informed Neural Networks for PDE Constrained Optimization using Broyden's Hypergradients
Deep learning based approaches like Physics-informed neural networks (PINNs)
and DeepONets have shown promise on solving PDE constrained optimization
(PDECO) problems. However, existing methods are insufficient to handle those
PDE constraints that have a complicated or nonlinear dependency on optimization
targets. In this paper, we present a novel bi-level optimization framework to
resolve the challenge by decoupling the optimization of the targets and
constraints. For the inner loop optimization, we adopt PINNs to solve the PDE
constraints only. For the outer loop, we design a novel method by using
Broyden's method based on the Implicit Function Theorem (IFT), which is
efficient and accurate for approximating hypergradients. We further present
theoretical explanations and error analysis of the hypergradients computation.
Extensive experiments on multiple large-scale and nonlinear PDE constrained
optimization problems demonstrate that our method achieves state-of-the-art
results compared with strong baselines
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