741 research outputs found
SATVSR: Scenario Adaptive Transformer for Cross Scenarios Video Super-Resolution
Video Super-Resolution (VSR) aims to recover sequences of high-resolution
(HR) frames from low-resolution (LR) frames. Previous methods mainly utilize
temporally adjacent frames to assist the reconstruction of target frames.
However, in the real world, there is a lot of irrelevant information in
adjacent frames of videos with fast scene switching, these VSR methods cannot
adaptively distinguish and select useful information. In contrast, with a
transformer structure suitable for temporal tasks, we devise a novel adaptive
scenario video super-resolution method. Specifically, we use optical flow to
label the patches in each video frame, only calculate the attention of patches
with the same label. Then select the most relevant label among them to
supplement the spatial-temporal information of the target frame. This design
can directly make the supplementary information come from the same scene as
much as possible. We further propose a cross-scale feature aggregation module
to better handle the scale variation problem. Compared with other video
super-resolution methods, our method not only achieves significant performance
gains on single-scene videos but also has better robustness on cross-scene
datasets
A Bimodel Algorithm with Data-Divider to Predict Stock Index
There is not yet reliable software for stock prediction, because most experts of this area have been trying to predict an exact stock index. Considering that the fluctuation of a stock index usually is no more than 1% in a day, the error between the forecasted and the actual values should be no more than 0.5%. It is too difficult to realize. However, forecasting whether a stock index will rise or fall does not need to be so exact a numerical value. A few scholars noted the fact, but their systems do not yet work very well because different periods of a stock have different inherent laws. So, we should not depend on a single model or a set of parameters to solve the problem. In this paper, we developed a data-divider to divide a set of historical stock data into two parts according to rising period and falling period, training, respectively, two neural networks optimized by a GA. Above all, the data-divider enables us to avoid the most difficult problem, the effect of unexpected news, which could hardly be predicted. Experiments show that the accuracy of our method increases 20% compared to those of traditional methods
Conditional Local Convolution for Spatio-temporal Meteorological Forecasting
Spatio-temporal forecasting is challenging attributing to the high
nonlinearity in temporal dynamics as well as complex location-characterized
patterns in spatial domains, especially in fields like weather forecasting.
Graph convolutions are usually used for modeling the spatial dependency in
meteorology to handle the irregular distribution of sensors' spatial location.
In this work, a novel graph-based convolution for imitating the meteorological
flows is proposed to capture the local spatial patterns. Based on the
assumption of smoothness of location-characterized patterns, we propose
conditional local convolution whose shared kernel on nodes' local space is
approximated by feedforward networks, with local representations of coordinate
obtained by horizon maps into cylindrical-tangent space as its input. The
established united standard of local coordinate system preserves the
orientation on geography. We further propose the distance and orientation
scaling terms to reduce the impacts of irregular spatial distribution. The
convolution is embedded in a Recurrent Neural Network architecture to model the
temporal dynamics, leading to the Conditional Local Convolution Recurrent
Network (CLCRN). Our model is evaluated on real-world weather benchmark
datasets, achieving state-of-the-art performance with obvious improvements. We
conduct further analysis on local pattern visualization, model's framework
choice, advantages of horizon maps and etc.Comment: 14 page
Non-equispaced Fourier Neural Solvers for PDEs
Solving partial differential equations is difficult. Recently proposed neural
resolution-invariant models, despite their effectiveness and efficiency,
usually require equispaced spatial points of data. However, sampling in spatial
domain is sometimes inevitably non-equispaced in real-world systems, limiting
their applicability. In this paper, we propose a Non-equispaced Fourier PDE
Solver (\textsc{NFS}) with adaptive interpolation on resampled equispaced
points and a variant of Fourier Neural Operators as its components.
Experimental results on complex PDEs demonstrate its advantages in accuracy and
efficiency. Compared with the spatially-equispaced benchmark methods, it
achieves superior performance with improvements on MAE, and is able
to handle non-equispaced data with a tiny loss of accuracy. Besides, to our
best knowledge, \textsc{NFS} is the first ML-based method with mesh invariant
inference ability to successfully model turbulent flows in non-equispaced
scenarios, with a minor deviation of the error on unseen spatial points.Comment: 27 page
- …