194 research outputs found
ECL: Class-Enhancement Contrastive Learning for Long-tailed Skin Lesion Classification
Skin image datasets often suffer from imbalanced data distribution,
exacerbating the difficulty of computer-aided skin disease diagnosis. Some
recent works exploit supervised contrastive learning (SCL) for this long-tailed
challenge. Despite achieving significant performance, these SCL-based methods
focus more on head classes, yet ignoring the utilization of information in tail
classes. In this paper, we propose class-Enhancement Contrastive Learning
(ECL), which enriches the information of minority classes and treats different
classes equally. For information enhancement, we design a hybrid-proxy model to
generate class-dependent proxies and propose a cycle update strategy for
parameters optimization. A balanced-hybrid-proxy loss is designed to exploit
relations between samples and proxies with different classes treated equally.
Taking both "imbalanced data" and "imbalanced diagnosis difficulty" into
account, we further present a balanced-weighted cross-entropy loss following
curriculum learning schedule. Experimental results on the classification of
imbalanced skin lesion data have demonstrated the superiority and effectiveness
of our method
A short-term hybrid wind speed prediction model based on decomposition and improved optimization algorithm
Introduction: In the field of wind power generation, short-term wind speed prediction plays an increasingly important role as the foundation for effective utilization of wind energy. However, accurately predicting wind speed is highly challenging due to its complexity and randomness in practical applications. Currently, single algorithms exhibit poor accuracy in short-term wind speed prediction, leading to the widespread adoption of hybrid wind speed prediction models based on deep learning techniques. To comprehensively enhance the predictive performance of short-term wind speed models, this study proposes a hybrid model, VMDAttention LSTM-ASSA, which consists of three stages: decomposition of the original wind speed sequence, prediction of each mode component, and weight optimization.Methods: To comprehensively enhance the predictive performance of short-term wind speed models, this study proposes a hybrid model, VMDAttention LSTM-ASSA, which consists of three stages: decomposition of the original wind speed sequence, prediction of each mode component, and weight optimization. Firstly, the model incorporates an attention mechanism into the LSTM model to extract important temporal slices from each mode component, effectively improving the slice prediction accuracy. Secondly, two different search operators are introduced to enhance the original Salp Swarm Algorithm, addressing the issue of getting trapped in local optima and achieving globally optimal short-term wind speed predictions.Result: Through comparative experiments using multiple-site short-term wind speed datasets, this study demonstrates that the proposed VMD-AtLSTM-ASSA model outperforms other hybrid prediction models (VMD-RNN, VMD-BPNN, VMD-GRU, VMD-LSTM) with a maximum reduction of 80.33% in MAPE values. The experimental results validate the high accuracy and stability of the VMD-AtLSTM-ASSA model.Discussion: Short-term wind speed prediction is of paramount importance for the effective utilization of wind power generation, and our research provides strong support for enhancing the efficiency and reliability of wind power generation systems. Future research directions may include further improvements in model performance and extension into other meteorological and environmental application domains
Photoactivatable nanogenerators of reactive species for cancer therapy
In recent years, reactive species-based cancer therapies have attracted tremendous attention due to their simplicity, controllability, and effectiveness. Herein, we overviewed the state-of-art advance for photo-controlled generation of highly reactive radical species with nanomaterials for cancer therapy. First, we summarized the most widely explored reactive species, such as singlet oxygen, superoxide radical anion (O2●), nitric oxide (●NO), carbon monoxide, alkyl radicals, and their corresponding secondary reactive species generated by interaction with other biological molecules. Then, we discussed the generating mechanisms of these highly reactive species stimulated by light irradiation, followed by their anticancer effect, and the synergetic principles with other therapeutic modalities. This review might unveil the advantages of reactive species-based therapeutic methodology and encourage the pre-clinical exploration of reactive species-mediated cancer treatments
HybridGait: A Benchmark for Spatial-Temporal Cloth-Changing Gait Recognition with Hybrid Explorations
Existing gait recognition benchmarks mostly include minor clothing variations
in the laboratory environments, but lack persistent changes in appearance over
time and space. In this paper, we propose the first in-the-wild benchmark
CCGait for cloth-changing gait recognition, which incorporates diverse clothing
changes, indoor and outdoor scenes, and multi-modal statistics over 92 days. To
further address the coupling effect of clothing and viewpoint variations, we
propose a hybrid approach HybridGait that exploits both temporal dynamics and
the projected 2D information of 3D human meshes. Specifically, we introduce a
Canonical Alignment Spatial-Temporal Transformer (CA-STT) module to encode
human joint position-aware features, and fully exploit 3D dense priors via a
Silhouette-guided Deformation with 3D-2D Appearance Projection (SilD) strategy.
Our contributions are twofold: we provide a challenging benchmark CCGait that
captures realistic appearance changes across an expanded and space, and we
propose a hybrid framework HybridGait that outperforms prior works on CCGait
and Gait3D benchmarks. Our project page is available at
https://github.com/HCVLab/HybridGait
Data-Driven Modeling of Landau Damping by Physics-Informed Neural Networks
Kinetic approaches are generally accurate in dealing with microscale plasma
physics problems but are computationally expensive for large-scale or
multiscale systems. One of the long-standing problems in plasma physics is the
integration of kinetic physics into fluid models, which is often achieved
through sophisticated analytical closure terms. In this study, we successfully
construct a multi-moment fluid model with an implicit fluid closure included in
the neural network using machine learning. The multi-moment fluid model is
trained with a small fraction of sparsely sampled data from kinetic simulations
of Landau damping, using the physics-informed neural network (PINN) and the
gradient-enhanced physics-informed neural network (gPINN). The multi-moment
fluid model constructed using either PINN or gPINN reproduces the time
evolution of the electric field energy, including its damping rate, and the
plasma dynamics from the kinetic simulations. For the first time, we introduce
a new variant of the gPINN architecture, namely, gPINN to capture the Landau
damping process. Instead of including the gradients of all the equation
residuals, gPINN only adds the gradient of the pressure equation residual as
one additional constraint. Among the three approaches, the gPINN-constructed
multi-moment fluid model offers the most accurate results. This work sheds new
light on the accurate and efficient modeling of large-scale systems, which can
be extended to complex multiscale laboratory, space, and astrophysical plasma
physics problems.Comment: 11 pages, 7 figure
Bio-oil based biorefinery strategy for the production of succinic acid
Background: Succinic acid is one of the key platform chemicals which can be produced via biotechnology process instead of petrochemical process. Biomass derived bio-oil have been investigated intensively as an alternative of diesel and gasoline fuels. Bio-oil could be fractionized into organic phase and aqueous phase parts. The organic phase bio-oil can be easily upgraded to transport fuel. The aqueous phase bio-oil (AP-bio-oil) is of low value. There is no report for its usage or upgrading via biological methods. In this paper, the use of AP-bio-oil for the production of succinic acid was investigated
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