326 research outputs found
Client: Cross-variable Linear Integrated Enhanced Transformer for Multivariate Long-Term Time Series Forecasting
Long-term time series forecasting (LTSF) is a crucial aspect of modern
society, playing a pivotal role in facilitating long-term planning and
developing early warning systems. While many Transformer-based models have
recently been introduced for LTSF, a doubt have been raised regarding the
effectiveness of attention modules in capturing cross-time dependencies. In
this study, we design a mask-series experiment to validate this assumption and
subsequently propose the "Cross-variable Linear Integrated ENhanced Transformer
for Multivariate Long-Term Time Series Forecasting" (Client), an advanced model
that outperforms both traditional Transformer-based models and linear models.
Client employs linear modules to learn trend information and attention modules
to capture cross-variable dependencies. Meanwhile, it simplifies the embedding
and position encoding layers and replaces the decoder module with a projection
layer. Essentially, Client incorporates non-linearity and cross-variable
dependencies, which sets it apart from conventional linear models and
Transformer-based models. Extensive experiments with nine real-world datasets
have confirmed the SOTA performance of Client with the least computation time
and memory consumption compared with the previous Transformer-based models. Our
code is available at https://github.com/daxin007/Client
Learning with Constraint Learning: New Perspective, Solution Strategy and Various Applications
The complexity of learning problems, such as Generative Adversarial Network
(GAN) and its variants, multi-task and meta-learning, hyper-parameter learning,
and a variety of real-world vision applications, demands a deeper understanding
of their underlying coupling mechanisms. Existing approaches often address
these problems in isolation, lacking a unified perspective that can reveal
commonalities and enable effective solutions. Therefore, in this work, we
proposed a new framework, named Learning with Constraint Learning (LwCL), that
can holistically examine challenges and provide a unified methodology to tackle
all the above-mentioned complex learning and vision problems. Specifically,
LwCL is designed as a general hierarchical optimization model that captures the
essence of these diverse learning and vision problems. Furthermore, we develop
a gradient-response based fast solution strategy to overcome optimization
challenges of the LwCL framework. Our proposed framework efficiently addresses
a wide range of applications in learning and vision, encompassing three
categories and nine different problem types. Extensive experiments on synthetic
tasks and real-world applications verify the effectiveness of our approach. The
LwCL framework offers a comprehensive solution for tackling complex machine
learning and computer vision problems, bridging the gap between theory and
practice
Extending the unified subhalo model to warm dark matter
Using a set of high-resolution N-body simulations, we extend the unified
distribution model of cold dark matter (CDM) subhaloes to the warm dark
matter(WDM) case. The same model framework combining the unevolved mass
function, unevolved radial distribution, and tidal stripping can predict the
mass function and spatial distribution of subhaloes in both CDM and WDM
simulations. The dependence of the model on the DM particle property is
universally parameterized through the half-mode mass of the initial power
spectrum. Compared with the CDM model, the WDM model differs most notably in
two aspects. 1) In contrast to the power-law form in CDM, the unevolved subhalo
mass function for WDM is scale-dependent at the low mass end due to the cut-off
in the initial power spectrum. 2) WDM subhaloes are more vulnerable to tidal
stripping and disruption due to their lower concentrations at accretion time.
Their survival rate is also found to depend on the infall mass. Accounting for
these differences, the model predicts a final WDM subhalo mass function that is
also proportional to the unevolved subhalo mass function. The radial
distribution of WDM subhaloes is predicted to be mass-dependent. For low mass
subhaloes, the radial distribution is flatter in the inner halo and steeper in
the outer halo compared to the CDM counterpart, due to the scale-dependent
unevolved mass function and the enhanced tidal stripping. The code for sampling
subhaloes according to our generalized model is available at
https://github.com/fhtouma/subgen2 .Comment: 15 pages, 14 figure
Motion-Scenario Decoupling for Rat-Aware Video Position Prediction: Strategy and Benchmark
Recently significant progress has been made in human action recognition and
behavior prediction using deep learning techniques, leading to improved
vision-based semantic understanding. However, there is still a lack of
high-quality motion datasets for small bio-robotics, which presents more
challenging scenarios for long-term movement prediction and behavior control
based on third-person observation. In this study, we introduce RatPose, a
bio-robot motion prediction dataset constructed by considering the influence
factors of individuals and environments based on predefined annotation rules.
To enhance the robustness of motion prediction against these factors, we
propose a Dual-stream Motion-Scenario Decoupling (\textit{DMSD}) framework that
effectively separates scenario-oriented and motion-oriented features and
designs a scenario contrast loss and motion clustering loss for overall
training. With such distinctive architecture, the dual-branch feature flow
information is interacted and compensated in a decomposition-then-fusion
manner. Moreover, we demonstrate significant performance improvements of the
proposed \textit{DMSD} framework on different difficulty-level tasks. We also
implement long-term discretized trajectory prediction tasks to verify the
generalization ability of the proposed dataset.Comment: Rat, Video Position Predictio
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