Hexapod Coloron at the LHC

Instead of the usual dijet decay, the coloron may mainly decay into its own "Higgs bosons", which subsequently decay into many jets. This is a general feature of the renormalizable coloron model, where the corresponding "Higgs bosons" are a color-octet $\Theta$ and a color-singlet $\phi_I$. In this paper, we perform a detailed collider study for the signature of $pp \rightarrow G' \rightarrow (\Theta \rightarrow gg) (\phi_I \rightarrow gg q\bar{q})$ with the coloron $G'$ as a six-jet resonance. For a light $\phi_I$ below around 0.5 TeV, it may be boosted and behave as a four-prong fat jet. We also develop a jet-substructure-based search strategy to cover this boosted $\phi_I$ case. Independent of whether $\phi_I$ is boosted or not, the 13 TeV LHC with 100 fb$^{-1}$ has great discovery potential for a coloron with the mass sensitivity up to 5 TeV.Comment: 18 pages, 10 figure

Extremum selection method of random variable for nonlinear dynamic reliability analysis of turbine blade deformation

AbstractTo effectively select random variable in nonlinear dynamic reliability analysis, the extremum selection method (ESM) is proposed. Firstly, the basic idea was introduced and the mathematical model was established for the ESM. The nonlinear dynamic reliability analysis of turbine blade radial deformation was taken as an example to verify the ESM. The results show that the analysis precision of the ESM is 99.972%, which is almost kept consistent with that of the Monte Carlo method; moreover, the computing time of the ESM is shorter than that of the traditional method. Hence, it is demonstrated that the ESM is able to save calculation time and improve the computational efficiency while keeping the calculation precision for nonlinear dynamic reliability analysis. The present study provides a method to enhance the nonlinear dynamic reliability analysis in selecting the random variables and offers a way to design structure and machine in future work

Performance-Based Plastic Design Method for Steel Concentrically Braced Frames Using Target Drift and Yield Mechanism

Under severe earthquakes, steel concentrically braced frames (SCBFs) will experience large  inelastic deformations in an uncontrolled manner. According to the energy-work balance concept, a performance-based plastic design (PBPD) methodology for steel concentrically braced frames was presented here. This method uses pre-selected target drift and yield mechanism as key performance limit states. The designed base shear for selected hazard levels was derived based on work-energy balance equations. Plastic design was performed to design bracing members and connection nodes in order to achieve the expected yield mechanism and behavior. The method has been successively applied to design a six-storey steel concentrically braced frame. Results of inelastic dynamic analyses showed that the story drifts were well within the target values, thus to meet the desired performance requirements. The proposed method provided a basis for performance-based plastic design of steel concentrically braced frames

Uncertainty-Aware Learning Against Label Noise on Imbalanced Datasets

Learning against label noise is a vital topic to guarantee a reliable performance for deep neural networks. Recent research usually refers to dynamic noise modeling with model output probabilities and loss values, and then separates clean and noisy samples. These methods have gained notable success. However, unlike cherry-picked data, existing approaches often cannot perform well when facing imbalanced datasets, a common scenario in the real world. We thoroughly investigate this phenomenon and point out two major issues that hinder the performance, i.e., \emph{inter-class loss distribution discrepancy} and \emph{misleading predictions due to uncertainty}. The first issue is that existing methods often perform class-agnostic noise modeling. However, loss distributions show a significant discrepancy among classes under class imbalance, and class-agnostic noise modeling can easily get confused with noisy samples and samples in minority classes. The second issue refers to that models may output misleading predictions due to epistemic uncertainty and aleatoric uncertainty, thus existing methods that rely solely on the output probabilities may fail to distinguish confident samples. Inspired by our observations, we propose an Uncertainty-aware Label Correction framework~(ULC) to handle label noise on imbalanced datasets. First, we perform epistemic uncertainty-aware class-specific noise modeling to identify trustworthy clean samples and refine/discard highly confident true/corrupted labels. Then, we introduce aleatoric uncertainty in the subsequent learning process to prevent noise accumulation in the label noise modeling process. We conduct experiments on several synthetic and real-world datasets. The results demonstrate the effectiveness of the proposed method, especially on imbalanced datasets