On the scalar nonet in the extended Nambu Jona-Lasinio model

We discuss the lightest scalar resonances, $f_0(600)$, $\kappa(800)$, $a_0(980)$ and $f_0(980)$ in the extended Nambu Jona-Lasinio model. We find that the model parameters can be tuned, but unnaturally, to accommodate for those scalars except the $f_0(980)$. We also discuss problems encountered in the K Matrix unitarization approximation by using $N_c$ counting technique.Comment: 23 pages 3 eps figures, To appear in Nucl. Phys.

Estimator Meets Equilibrium Perspective: A Rectified Straight Through Estimator for Binary Neural Networks Training

Binarization of neural networks is a dominant paradigm in neural networks compression. The pioneering work BinaryConnect uses Straight Through Estimator (STE) to mimic the gradients of the sign function, but it also causes the crucial inconsistency problem. Most of the previous methods design different estimators instead of STE to mitigate it. However, they ignore the fact that when reducing the estimating error, the gradient stability will decrease concomitantly. These highly divergent gradients will harm the model training and increase the risk of gradient vanishing and gradient exploding. To fully take the gradient stability into consideration, we present a new perspective to the BNNs training, regarding it as the equilibrium between the estimating error and the gradient stability. In this view, we firstly design two indicators to quantitatively demonstrate the equilibrium phenomenon. In addition, in order to balance the estimating error and the gradient stability well, we revise the original straight through estimator and propose a power function based estimator, Rectified Straight Through Estimator (ReSTE for short). Comparing to other estimators, ReSTE is rational and capable of flexibly balancing the estimating error with the gradient stability. Extensive experiments on CIFAR-10 and ImageNet datasets show that ReSTE has excellent performance and surpasses the state-of-the-art methods without any auxiliary modules or losses.Comment: 10 pages, 6 figures. Accepted in ICCV 202

Constructing the general partial waves and renormalization in EFT

We construct the general partial wave amplitude basis for the $N\to M$ scattering, which consists of Poincar\'e Clebsch-Gordan coefficients, with Lorentz invariant forms given in terms of spinor-helicity variables. The inner product of the Clebsch-Gordan coefficients is defined, which converts on-shell phase space integration into an algebraic problem. We also develop the technique of partial wave expansions of arbitrary amplitudes, including those with infrared divergence. These are applied to the computation of anomalous dimension matrix for general effective operators, where unitarity cuts for the loop amplitudes, with an arbitrary number of external particles, are obtained via partial wave expansion.Comment: 6 pages, 1 figure, 1 tabl

A Systematic Review of the Application of Machine Learning in CpG island (CGI) Detection and Methylation Prediction

Background: CpG island (CGI) detection and methylation prediction play important roles in studying the complex mechanisms of CGIs involved in genome regulation. In recent years, machine learning (ML) has been gradually applied to CGI detection and CGI methylation prediction algorithms in order to improve the accuracy of traditional methods. However, there are a few systematic reviews on the application of ML in CGI detection and CGI methylation prediction. Therefore, this systematic review aims to provide an overview of the application of ML in CGI detection and methylation prediction. Method: The review was carried out using the PRISMA guideline. The search strategy was applied to articles published on PubMed from 2000 to July 10, 2022. Two independent researchers screened the articles based on the retrieval strategies and identified a total of 54 articles. After that, we developed quality assessment questions to assess study quality and obtained 46 articles that met the eligibility criteria. Based on these articles, we first summarized the applications of ML methods in CGI detection and methylation prediction, and then identified the strengths and limitations of these studies. Result and Discussion: Finally, we have discussed the challenges and future research directions. Conclusion: This systematic review will contribute to the selection of algorithms and the future development of more efficient algorithms for CGI detection and methylation prediction

(E)-Ethyl 2-cyano-3-(3,4-dihydr­oxy-5-nitro­phen­yl)acrylate

The title compound, C12H10N2O6, was synthesized via a Knoevenagel condensation and crystallized from ethanol. In the crystal, strong classical inter­molecular O—H⋯O hydrogen bonds and weak C—H⋯N contacts link the mol­ecules into ribbons extending along [010]. Intra­molecular O—H⋯O and C—H⋯N contacts support the planar conformation of the mol­ecules (mean deivation 0.0270 Å)