MSSM Anatomy of the Polarization Puzzle in B --> phi K* Decays

We analyze the $B \to \phi K^{*}$ polarization puzzle in the Minimal Supersymmetric Standard Model (MSSM) including the neutral Higgs boson (NHB) contributions. To calculate the non-factorizable contributions to hadronic matrix elements of operators, we have used the QCD factorization framework to the $\alpha_s$ order. It is shown that the recent experimental results of the polarization fractions in $B\to \phi K^{*}$ decays, which are difficult to be explained in SM, could be explained in MSSM if there are flavor non-diagonal squark mass matrix elements of 2nd and 3rd generations, which also satisfy all relevant constraints from known experiments ($B\to X_s\gamma, B_s\to \mu^+\mu^-, B\to X_s \mu^+\mu^-, B\to X_s g, \Delta M_s$, etc.). We have shown in details that the experimental results can be accommodated with the flavor non-diagonal mass insertion of chirality RL, RL+LR, RR, or LL+ RR when the NHB contributions as well as $\mathcal{O}(\alpha_s)$ corrections of hadronic matrix elements of operators are included. However the branching ratios for the decay are smaller than the experimental measurements.Comment: 15 pages, 5 figures, minor revision and references adde

Nanoparticles for post-infarct ventricular remodeling

YesIn recent years, tremendous progress has been made in the treatment of acute myocardial infarction (AMI), but pathological ventricular remodeling often causes survivors to suffer from fatal heart failure. Currently, there is no effective therapy to attenuate ventricular remodeling. Recently, nanoparticles-based drug delivery system is widely applied in biomedicine especially in cancer and liver fibrosis, owing to its excellent physical, chemical, and biological properties. Therefore, using nanoparticles as delivery vehicles of small molecules, polypeptides, etc to improve post-infarct ventricular remodeling are expected. In this review, we summarized the updated researches in this fast-growing area and suggested further works needed

Debiasing Made State-of-the-art: Revisiting the Simple Seed-based Weak Supervision for Text Classification

Recent advances in weakly supervised text classification mostly focus on designing sophisticated methods to turn high-level human heuristics into quality pseudo-labels. In this paper, we revisit the seed matching-based method, which is arguably the simplest way to generate pseudo-labels, and show that its power was greatly underestimated. We show that the limited performance of seed matching is largely due to the label bias injected by the simple seed-match rule, which prevents the classifier from learning reliable confidence for selecting high-quality pseudo-labels. Interestingly, simply deleting the seed words present in the matched input texts can mitigate the label bias and help learn better confidence. Subsequently, the performance achieved by seed matching can be improved significantly, making it on par with or even better than the state-of-the-art. Furthermore, to handle the case when the seed words are not made known, we propose to simply delete the word tokens in the input text randomly with a high deletion ratio. Remarkably, seed matching equipped with this random deletion method can often achieve even better performance than that with seed deletion