7 research outputs found
Improving Model Drift for Robust Object Tracking
Discriminative correlation filters show excellent performance in object
tracking. However, in complex scenes, the apparent characteristics of the
tracked target are variable, which makes it easy to pollute the model and cause
the model drift. In this paper, considering that the secondary peak has a
greater impact on the model update, we propose a method for detecting the
primary and secondary peaks of the response map. Secondly, a novel confidence
function which uses the adaptive update discriminant mechanism is proposed,
which yield good robustness. Thirdly, we propose a robust tracker with
correlation filters, which uses hand-crafted features and can improve model
drift in complex scenes. Finally, in order to cope with the current trackers'
multi-feature response merge, we propose a simple exponential adaptive merge
approach. Extensive experiments are performed on OTB2013, OTB100 and TC128
datasets. Our approach performs superiorly against several state-of-the-art
trackers while runs at speed in real time.Comment: 7 pages, 6 figures, 4 table
Efficient refinements on YOLOv3 for real-time detection and assessment of diabetic foot Wagner grades
Currently, the screening of Wagner grades of diabetic feet (DF) still relies
on professional podiatrists. However, in less-developed countries, podiatrists
are scarce, which led to the majority of undiagnosed patients. In this study,
we proposed the real-time detection and location method for Wagner grades of DF
based on refinements on YOLOv3. We collected 2,688 data samples and implemented
several methods, such as a visual coherent image mixup, label smoothing, and
training scheduler revamping, based on the ablation study. The experimental
results suggested that the refinements on YOLOv3 achieved an accuracy of 91.95%
and the inference speed of a single picture reaches 31ms with the NVIDIA Tesla
V100. To test the performance of the model on a smartphone, we deployed the
refinements on YOLOv3 models on an Android 9 system smartphone. This work has
the potential to lead to a paradigm shift for clinical treatment of the DF in
the future, to provide an effective healthcare solution for DF tissue analysis
and healing status.Comment: 11 pages with 11 figure
LncRNA wires up Hippo and Hedgehog signaling to reprogramme glucose metabolism
The Hippo pathway plays essential roles in organ size control and cancer prevention via restricting its downstream effector, Yes-associated protein (YAP). Previous studies have revealed an oncogenic function of YAP in reprogramming glucose metabolism, while the underlying mechanism remains to be fully clarified. Accumulating evidence suggests long noncoding RNAs (lncRNAs) as attractive therapeutic targets, given their roles in modulating various cancer-related signaling pathways. In this study, we report that lncRNA breast cancer anti-estrogen resistance 4 (BCAR4) is required for YAP-dependent glycolysis. Mechanistically, YAP promotes the expression of BCAR4, which subsequently coordinates the Hedgehog signaling to enhance the transcription of glycolysis activators HK2 and PFKFB3. Therapeutic delivery of locked nucleic acids (LNAs) targeting BCAR4 attenuated YAP-dependent glycolysis and tumor growth. The expression levels of BCAR4 and YAP are positively correlated in tissue samples from breast cancer patients, where high expression of both BCAR4 and YAP is associated with poor patient survival outcome. Taken together, our study not only reveals the mechanism by which YAP reprograms glucose metabolism, but also highlights the therapeutic potential of targeting YAP-BCAR4-glycolysis axis for breast cancer treatment