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Article Synopsis
  • The study assesses the return to performance of MLB pitchers after undergoing ulnar collateral ligament (UCL) surgery using advanced analytics and pitch-tracking metrics.
  • At 1, 2, and 3 years post-surgery, the data showed that only 1.6% of pitchers returned to play within the first year, but the rates increased significantly to 71.9% at 2 years and 82.0% at 3 years.
  • The research highlights the importance of modern metrics, examining factors like expected fielding independent pitching and velocity, to evaluate pitchers' recovery and performance levels after surgery.
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Panoptic Part Segmentation (PPS) unifies panoptic and part segmentation into one task. Previous works utilize separate approaches to handle things, stuff, and part predictions without shared computation and task association. We aim to unify these tasks at the architectural level, designing the first end-to-end unified framework, Panoptic-PartFormer.

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Herein, we have manufactured a newly designed bifunctional impedimetric and amperometric immunosensor for rapidly detecting erythromycin (ERY) in complicated environments and food stuffs. For this, bimetallic cobalt/cerium-layered double hydroxide nanosheets (CoCe-LDH NSs), which was derived from Co-based zeolite imidazole framework via the structure conversion, was simultaneously utilized as the bioplatform for anchoring the ERY-targeted antibody and for modifying the gold and screen printed electrode. Basic characterizations revealed that CoCe-LDH NSs was composed of mixed metal valences, enrich redox, and abundant oxygen vacancies, facilitating the adhesion on the electrode, the antibody adsorption, and the electron transfers.

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Panoptic Scene Graph (PSG) is a challenging task in Scene Graph Generation (SGG) that aims to create a more comprehensive scene graph representation using panoptic segmentation instead of boxes. Compared to SGG, PSG has several challenging problems: pixel-level segment outputs and full relationship exploration (It also considers thing and stuff relation). Thus, current PSG methods have limited performance, which hinders downstream tasks or applications.

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DECNet: Dense embedding contrast for unsupervised semantic segmentation.

Neural Netw

November 2024

The College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, Zhejiang, China; Hangzhou Xsuan Technology Co., Ltd, Hangzhou, 310052, Zhejiang, China. Electronic address:

Unsupervised semantic segmentation is important for understanding that each pixel belongs to known categories without annotation. Recent studies have demonstrated promising outcomes by employing a vision transformer backbone pre-trained on an image-level dataset in a self-supervised manner. However, those methods always depend on complex architectures or meticulously designed inputs.

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