153 research outputs found
Heavy Neutral Leptons at Muon Colliders
The future high-energy muon colliders, featuring both high energy and low
background, could play a critical role in our searches for new physics. The
smallness of neutrino mass is a puzzle of particle physics. Broad classes of
solutions to the neutrino puzzles can be best tested by seeking the partners of
SM light neutrinos, dubbed as heavy neutral leptons (HNLs), at muon colliders.
We can parametrize HNLs in terms of the mass and the mixing angle with
-flavor . In this work, we focus on the regime GeV
and study the projected sensitivities on the plane with the
full-reconstructable HNL decay into a hadronic and a charged lepton. The
projected reach in leads to the best sensitivities in the TeV
realm.Comment: 33 pages, 10 figure
FastLLVE: Real-Time Low-Light Video Enhancement with Intensity-Aware Lookup Table
Low-Light Video Enhancement (LLVE) has received considerable attention in
recent years. One of the critical requirements of LLVE is inter-frame
brightness consistency, which is essential for maintaining the temporal
coherence of the enhanced video. However, most existing single-image-based
methods fail to address this issue, resulting in flickering effect that
degrades the overall quality after enhancement. Moreover, 3D Convolution Neural
Network (CNN)-based methods, which are designed for video to maintain
inter-frame consistency, are computationally expensive, making them impractical
for real-time applications. To address these issues, we propose an efficient
pipeline named FastLLVE that leverages the Look-Up-Table (LUT) technique to
maintain inter-frame brightness consistency effectively. Specifically, we
design a learnable Intensity-Aware LUT (IA-LUT) module for adaptive
enhancement, which addresses the low-dynamic problem in low-light scenarios.
This enables FastLLVE to perform low-latency and low-complexity enhancement
operations while maintaining high-quality results. Experimental results on
benchmark datasets demonstrate that our method achieves the State-Of-The-Art
(SOTA) performance in terms of both image quality and inter-frame brightness
consistency. More importantly, our FastLLVE can process 1,080p videos at
Frames Per Second (FPS), which is faster
than SOTA CNN-based methods in inference time, making it a promising solution
for real-time applications. The code is available at
https://github.com/Wenhao-Li-777/FastLLVE.Comment: 11pages, 9 Figures, and 6 Tables. Accepted by ACMMM 202
DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders
Image colorization is a challenging problem due to multi-modal uncertainty
and high ill-posedness. Directly training a deep neural network usually leads
to incorrect semantic colors and low color richness. While transformer-based
methods can deliver better results, they often rely on manually designed
priors, suffer from poor generalization ability, and introduce color bleeding
effects. To address these issues, we propose DDColor, an end-to-end method with
dual decoders for image colorization. Our approach includes a pixel decoder and
a query-based color decoder. The former restores the spatial resolution of the
image, while the latter utilizes rich visual features to refine color queries,
thus avoiding hand-crafted priors. Our two decoders work together to establish
correlations between color and multi-scale semantic representations via
cross-attention, significantly alleviating the color bleeding effect.
Additionally, a simple yet effective colorfulness loss is introduced to enhance
the color richness. Extensive experiments demonstrate that DDColor achieves
superior performance to existing state-of-the-art works both quantitatively and
qualitatively. The codes and models are publicly available at
https://github.com/piddnad/DDColor.Comment: ICCV 2023; Code: https://github.com/piddnad/DDColo
A Transfer Learning Approach for Malignant Prostate Lesion Detection on Multiparametric MRI
Purpose: In prostate focal therapy, it is important to accurately localize malignant lesions in order to increase biological effect of the tumor region while achieving a reduction in dose to noncancerous tissue. In this work, we proposed a transfer learning–based deep learning approach, for classification of prostate lesions in multiparametric magnetic resonance imaging images. Methods: Magnetic resonance imaging images were preprocessed to remove bias artifact and normalize the data. Two state-of-the-art deep convolutional neural network models, InceptionV3 and VGG-16, were pretrained on ImageNet data set and retuned on the multiparametric magnetic resonance imaging data set. As lesion appearances differ by the prostate zone that it resides in, separate models were trained. Ensembling was performed on each prostate zone to improve area under the curve. In addition, the predictions from lesions on each prostate zone were scaled separately to increase the area under the curve for all lesions combined. Results: The models were tuned to produce the highest area under the curve on validation data set. When it was applied to the unseen test data set, the transferred InceptionV3 model achieved an area under the curve of 0.81 and the transferred VGG-16 model achieved an area under the curve of 0.83. This was the third best score among the 72 methods from 33 participating groups in ProstateX competition. Conclusion: The transfer learning approach is a promising method for prostate cancer detection on multiparametric magnetic resonance imaging images. Features learned from ImageNet data set can be useful for medical images
Analysis of the control strategy of range extender system on the vehicle NVH performance
With focus on NVH performance, this paper studies the range extender system control strategy such as the initial start speed, operating points, speed up and down control method between operating points of the range extender, etc. At the same time, the confirmation of the operating points of the range extender based on the full vehicle frequency distribution and vibration and noise level of key points (seat rail, driver’s inner ear) was performed. Finally, we conducted objective test and compared the test data with benchmark vehicles
Biomechanical microenvironment regulates fusogenicity of breast cancer cells
Fusion of cancer cells is thought to contribute to tumor development and drug resistance. The low frequency of cell fusion events and the instability of fused cells have hindered our ability to understand the molecular mechanisms that govern cell fusion. We have demonstrated that several breast cancer cell lines can fuse into multinucleated giant cells in vitro, and the initiation and longevity of fused cells can be regulated solely by biophysical factors. Dynamically tuning the adhesive area of the patterned substrates, reducing cytoskeletal tensions pharmacologically, altering matrix stiffness, and modulating pattern curvature all supported the spontaneous fusion and stability of these multinucleated giant cells. These observations highlight that the biomechanical microenvironment of cancer cells, including the matrix rigidity and interfacial curvature, can directly modulate their fusogenicity, an unexplored mechanism through which biophysical cues regulate tumor progression
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