Theoretical analysis of direct CPCP violation and differential decay width in D±→π±π+π−D^\pm\to \pi^\pm \pi^+\pi^- in phase space around the resonances ρ0(770)\rho^0(770) and f0(500)f_0(500)

We perform a theoretical study on direct $CP$ violation in $D^\pm\to \pi^\pm \pi^+\pi^-$ in phase space around the intermediate states $\rho^0(770)$ and $f_0(500)$. The possible interference between the amplitudes corresponding to the two resonances is taken into account, and the relative strong phase of the two amplitudes is treated as a free parameter. Our analysis shows that by properly chosen the strong phase, both the $CP$ violation strength and differential decay width accommodate to the experimental results.Comment: 15 pages, 5 figure

Methyl 2-[(4-chloro-2-meth­oxy-5-oxo-2,5-dihydro­furan-3-yl)amino]­acetate

The title compound, C8H10ClNO5, was obtained via a tandem Michael addition–elimination reaction of 3,4-dichloro-5-meth­oxy­furan-2(5H)-one and glycine methyl ester in the presence of triethyl­amine. The mol­ecular structure contains an approximately planar [maximum atomic deviation = 0.010 (2) Å] five-membered furan­one ring. The crystal packing is stabilized by inter­molecular N—H⋯O and weak C—H⋯O hydrogen bonding

A Lite Fireworks Algorithm with Fractal Dimension Constraint for Feature Selection

As the use of robotics becomes more widespread, the huge amount of vision data leads to a dramatic increase in data dimensionality. Although deep learning methods can effectively process these high-dimensional vision data. Due to the limitation of computational resources, some special scenarios still rely on traditional machine learning methods. However, these high-dimensional visual data lead to great challenges for traditional machine learning methods. Therefore, we propose a Lite Fireworks Algorithm with Fractal Dimension constraint for feature selection (LFWA+FD) and use it to solve the feature selection problem driven by robot vision. The "LFWA+FD" focuses on searching the ideal feature subset by simplifying the fireworks algorithm and constraining the dimensionality of selected features by fractal dimensionality, which in turn reduces the approximate features and reduces the noise in the original data to improve the accuracy of the model. The comparative experimental results of two publicly available datasets from UCI show that the proposed method can effectively select a subset of features useful for model inference and remove a large amount of noise noise present in the original data to improve the performance.Comment: International Conference on Pharmaceutical Sciences 202