287 research outputs found
Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network
Efficient and high-fidelity prior sampling and inversion for complex
geological media is still a largely unsolved challenge. Here, we use a deep
neural network of the variational autoencoder type to construct a parametric
low-dimensional base model parameterization of complex binary geological media.
For inversion purposes, it has the attractive feature that random draws from an
uncorrelated standard normal distribution yield model realizations with spatial
characteristics that are in agreement with the training set. In comparison with
the most commonly used parametric representations in probabilistic inversion,
we find that our dimensionality reduction (DR) approach outperforms principle
component analysis (PCA), optimization-PCA (OPCA) and discrete cosine transform
(DCT) DR techniques for unconditional geostatistical simulation of a
channelized prior model. For the considered examples, important compression
ratios (200 - 500) are achieved. Given that the construction of our
parameterization requires a training set of several tens of thousands of prior
model realizations, our DR approach is more suited for probabilistic (or
deterministic) inversion than for unconditional (or point-conditioned)
geostatistical simulation. Probabilistic inversions of 2D steady-state and 3D
transient hydraulic tomography data are used to demonstrate the DR-based
inversion. For the 2D case study, the performance is superior compared to
current state-of-the-art multiple-point statistics inversion by sequential
geostatistical resampling (SGR). Inversion results for the 3D application are
also encouraging
MOODv2: Masked Image Modeling for Out-of-Distribution Detection
The crux of effective out-of-distribution (OOD) detection lies in acquiring a
robust in-distribution (ID) representation, distinct from OOD samples. While
previous methods predominantly leaned on recognition-based techniques for this
purpose, they often resulted in shortcut learning, lacking comprehensive
representations. In our study, we conducted a comprehensive analysis, exploring
distinct pretraining tasks and employing various OOD score functions. The
results highlight that the feature representations pre-trained through
reconstruction yield a notable enhancement and narrow the performance gap among
various score functions. This suggests that even simple score functions can
rival complex ones when leveraging reconstruction-based pretext tasks.
Reconstruction-based pretext tasks adapt well to various score functions. As
such, it holds promising potential for further expansion. Our OOD detection
framework, MOODv2, employs the masked image modeling pretext task. Without
bells and whistles, MOODv2 impressively enhances 14.30% AUROC to 95.68% on
ImageNet and achieves 99.98% on CIFAR-10
Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Need
The core of out-of-distribution (OOD) detection is to learn the
in-distribution (ID) representation, which is distinguishable from OOD samples.
Previous work applied recognition-based methods to learn the ID features, which
tend to learn shortcuts instead of comprehensive representations. In this work,
we find surprisingly that simply using reconstruction-based methods could boost
the performance of OOD detection significantly. We deeply explore the main
contributors of OOD detection and find that reconstruction-based pretext tasks
have the potential to provide a generally applicable and efficacious prior,
which benefits the model in learning intrinsic data distributions of the ID
dataset. Specifically, we take Masked Image Modeling as a pretext task for our
OOD detection framework (MOOD). Without bells and whistles, MOOD outperforms
previous SOTA of one-class OOD detection by 5.7%, multi-class OOD detection by
3.0%, and near-distribution OOD detection by 2.1%. It even defeats the
10-shot-per-class outlier exposure OOD detection, although we do not include
any OOD samples for our detectionComment: This paper is accepted by CVPR2023 and our codes are released here:
https://github.com/JulietLJY/MOO
OA-Bug: An Olfactory-Auditory Augmented Bug Algorithm for Swarm Robots in a Denied Environment
Searching in a denied environment is challenging for swarm robots as no
assistance from GNSS, mapping, data sharing, and central processing is allowed.
However, using olfactory and auditory to cooperate like animals could be an
important way to improve the collaboration of swarm robots. In this paper, an
Olfactory-Auditory augmented Bug algorithm (OA-Bug) is proposed for a swarm of
autonomous robots to explore a denied environment. A simulation environment is
built to measure the performance of OA-Bug. The coverage of the search task
using OA-Bug can reach 96.93%, with the most significant improvement of 40.55%
compared with a similar algorithm, SGBA. Furthermore, experiments are conducted
on real swarm robots to prove the validity of OA-Bug. Results show that OA-Bug
can improve the performance of swarm robots in a denied environment.Comment: 7 pages, 5 figure
Methane Medicine: A Rising Star Gas with Powerful Anti-Inflammation, Antioxidant, and Antiapoptosis Properties
Methane, the simplest organic compound, was deemed to have little physiological action for decades. However, recently, many basic studies have discovered that methane has several important biological effects that can protect cells and organs from inflammation, oxidant, and apoptosis. Heretofore, there are two delivery methods that have been applied to researches and have been proved to be feasible, including the inhalation of methane gas and injection with the methane-rich saline. This review studies on the clinical development of methane and discusses about the mechanism behind these protective effects. As a new field in gas medicine, this study also comes up with some problems and prospects on methane and further studies
- …