5,070 research outputs found
Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants
Causal discovery with latent variables is a crucial but challenging task.
Despite the emergence of numerous methods aimed at addressing this challenge,
they are not fully identified to the structure that two observed variables are
influenced by one latent variable and there might be a directed edge in
between. Interestingly, we notice that this structure can be identified through
the utilization of higher-order cumulants. By leveraging the higher-order
cumulants of non-Gaussian data, we provide an analytical solution for
estimating the causal coefficients or their ratios. With the estimated (ratios
of) causal coefficients, we propose a novel approach to identify the existence
of a causal edge between two observed variables subject to latent variable
influence. In case when such a causal edge exits, we introduce an asymmetry
criterion to determine the causal direction. The experimental results
demonstrate the effectiveness of our proposed method.Comment: Accepted by AAAI 202
A Survey on Causal Reinforcement Learning
While Reinforcement Learning (RL) achieves tremendous success in sequential
decision-making problems of many domains, it still faces key challenges of data
inefficiency and the lack of interpretability. Interestingly, many researchers
have leveraged insights from the causality literature recently, bringing forth
flourishing works to unify the merits of causality and address well the
challenges from RL. As such, it is of great necessity and significance to
collate these Causal Reinforcement Learning (CRL) works, offer a review of CRL
methods, and investigate the potential functionality from causality toward RL.
In particular, we divide existing CRL approaches into two categories according
to whether their causality-based information is given in advance or not. We
further analyze each category in terms of the formalization of different
models, ranging from the Markov Decision Process (MDP), Partially Observed
Markov Decision Process (POMDP), Multi-Arm Bandits (MAB), and Dynamic Treatment
Regime (DTR). Moreover, we summarize the evaluation matrices and open sources
while we discuss emerging applications, along with promising prospects for the
future development of CRL.Comment: 29 pages, 20 figure
Hamiltonian formalism of the DNLS equation with nonvanished boundary value
Hamiltonian formalism of the DNLS equation with nonvanishing boundary value
is developed by the standard procedure.Comment: 11 page
Interleaving One-Class and Weakly-Supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly Detection
Without human annotations, a typical Unsupervised Video Anomaly Detection
(UVAD) method needs to train two models that generate pseudo labels for each
other. In previous work, the two models are closely entangled with each other,
and it is not known how to upgrade their method without modifying their
training framework significantly. Second, previous work usually adopts fixed
thresholding to obtain pseudo labels, however the user-specified threshold is
not reliable which inevitably introduces errors into the training process. To
alleviate these two problems, we propose a novel interleaved framework that
alternately trains a One-Class Classification (OCC) model and a
Weakly-Supervised (WS) model for UVAD. The OCC or WS models in our method can
be easily replaced with other OCC or WS models, which facilitates our method to
upgrade with the most recent developments in both fields. For handling the
fixed thresholding problem, we break through the conventional cognitive
boundary and propose a weighted OCC model that can be trained on both normal
and abnormal data. We also propose an adaptive mechanism for automatically
finding the optimal threshold for the WS model in a loose to strict manner.
Experiments demonstrate that the proposed UVAD method outperforms previous
approaches
Diagnosing the possible chiral superconductivity in SrRuO and beyond using supercurrent
One approach to probe the still controversial superconductivity in
SrRuO is to apply external perturbations that break the underlying
tetragonal crystalline symmetry. Chiral and states
respond to such perturbations in ways that may help to distinguish them from
other superconducting pairings. However, past experimental efforts along this
line, using uniaxial strains and magnetic fields parallel to the RuO plane,
have not been able to reach unambiguous conclusion. In this study, we propose
to diagnose the possible chiral superconducting order in SrRuO using an
alternative tetragonal-symmetry-breaking perturbation -- in-plane supercurrent.
We study the superconducting phase diagram as a function of both temperature
and the applied supercurrent. Supercurrent generically splits the transition of
the two chiral order parameter components, and we show that the splitting can
give rise to visible specific heat anomalies. Furthermore, supercurrent
parallel and anti-parallel to the unidirectional propagation of the chiral edge
modes impact the edge states in different manner. This difference manifests in
tunneling spectrum, thereby providing an additional means to probe the
chirality even when the related spontaneous edge current is vanishingly small.
Finally, we discuss how supercurrent may help to identity other time-reversal
symmetry breaking superconducting states. Our proposal applies to other
candidate chiral superconductors.Comment: 7 pages, 7 figure
Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation
In this paper, we present new data pre-processing and augmentation techniques
for DNN-based raw image denoising. Compared with traditional RGB image
denoising, performing this task on direct camera sensor readings presents new
challenges such as how to effectively handle various Bayer patterns from
different data sources, and subsequently how to perform valid data augmentation
with raw images. To address the first problem, we propose a Bayer pattern
unification (BayerUnify) method to unify different Bayer patterns. This allows
us to fully utilize a heterogeneous dataset to train a single denoising model
instead of training one model for each pattern. Furthermore, while it is
essential to augment the dataset to improve model generalization and
performance, we discovered that it is error-prone to modify raw images by
adapting augmentation methods designed for RGB images. Towards this end, we
present a Bayer preserving augmentation (BayerAug) method as an effective
approach for raw image augmentation. Combining these data processing technqiues
with a modified U-Net, our method achieves a PSNR of 52.11 and a SSIM of 0.9969
in NTIRE 2019 Real Image Denoising Challenge, demonstrating the
state-of-the-art performance. Our code is available at
https://github.com/Jiaming-Liu/BayerUnifyAug.Comment: Accepted by CVPRW 201
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