Reconsideration of the QCD corrections to the ηc\eta_c decays into light hadrons using the principle of maximum conformality

In the paper, we analyze the $\eta_c$ decays into light hadrons at the next-to-leading order QCD corrections by applying the principle of maximum conformality (PMC). The relativistic correction at the ${\cal{O}}(\alpha_s v^2)$-order level has been included in the discussion, which gives about $10\%$ contribution to the ratio $R$. The PMC, which satisfies the renormalization group invariance, is designed to obtain a scale-fixed and scheme-independent prediction at any fixed order. To avoid the confusion of treating $n_f$-terms, we transform the usual $\overline{\rm MS}$ pQCD series into the one under the minimal momentum space subtraction scheme. To compare with the prediction under conventional scale setting, $R_{\rm{Conv,mMOM}-r}= \left(4.12^{+0.30}_{-0.28}\right)\times10^3$, after applying the PMC, we obtain $R_{\rm PMC,mMOM-r}=\left(6.09^{+0.62}_{-0.55}\right) \times10^3$, where the errors are squared averages of the ones caused by $m_c$ and $\Lambda_{\rm mMOM}$. The PMC prediction agrees with the recent PDG value within errors, i.e. $R^{\rm exp}=\left(6.3\pm0.5\right)\times10^3$. Thus we think the mismatching of the prediction under conventional scale-setting with the data is due to improper choice of scale, which however can be solved by using the PMC.Comment: 5 pages, 2 figure

When Prompt-based Incremental Learning Does Not Meet Strong Pretraining

Incremental learning aims to overcome catastrophic forgetting when learning deep networks from sequential tasks. With impressive learning efficiency and performance, prompt-based methods adopt a fixed backbone to sequential tasks by learning task-specific prompts. However, existing prompt-based methods heavily rely on strong pretraining (typically trained on ImageNet-21k), and we find that their models could be trapped if the potential gap between the pretraining task and unknown future tasks is large. In this work, we develop a learnable Adaptive Prompt Generator (APG). The key is to unify the prompt retrieval and prompt learning processes into a learnable prompt generator. Hence, the whole prompting process can be optimized to reduce the negative effects of the gap between tasks effectively. To make our APG avoid learning ineffective knowledge, we maintain a knowledge pool to regularize APG with the feature distribution of each class. Extensive experiments show that our method significantly outperforms advanced methods in exemplar-free incremental learning without (strong) pretraining. Besides, under strong retraining, our method also has comparable performance to existing prompt-based models, showing that our method can still benefit from pretraining. Codes can be found at https://github.com/TOM-tym/APGComment: Accepted to ICCV 202

Butane-1,2,3,4-tetra­carboxylic acid–4,4′-bipyridine (1/2)

The hydro­thermal reaction of butane-1,2,3,4-tetra­carboxylic acid (H4butca), 4,4′-bipyridine (bipy) and Mn(SO4)2·H2O afforded a new co-crystal, C8H10O8·2C10H8N2 or H4butca·2(bipy), in which strong O—H⋯N hydrogen-bonding and weak π–π stacking [centroid–centroid distance = 3.8459 (19) Å] inter­actions assemble the organic mol­ecules into a three-dimensional supra­molecular framework. C—H⋯O inter­actions are also present. The whole mol­ecule has inversion symmetry

Structure-driven intercalated architecture of septuple-atomic-layer MA2Z4MA_2Z_4 family with diverse properties from semiconductor to topological insulator to Ising superconductor

Motivated by the fact that septuple-atomic-layer MnBi$_2$Te$_4$ can be structurally viewed as the combination of double-atomic-layer MnTe intercalating into quintuple-atomic-layer Bi$_2$Te$_3$, we present a general approach of constructing twelve septuple-atomic-layer $\alpha_i$- and $\beta_i$-$MA_2Z_4$ monolayer family (\emph{i} = 1 to 6) by intercalating MoS$_2$-type $MZ$$_2$ monolayer into InSe-type A$_2$Z$_2$ monolayer. Besides reproducing the experimentally synthesized $\alpha_1$-MoSi$_2$N$_4$, $\alpha_1$-WSi$_2$N$_4$ and $\beta_5$-MnBi$_2$Te$_4$ monolayer materials, another 66 thermodynamically and dynamically stable $MA_2Z_4$ were predicted, which span a wide range of properties upon the number of valence electrons (VEC). $MA_2Z_4$ with the rules of 32 or 34 VEC are mostly semiconductors with direct or indirect band gap and, however, with 33 VEC are generally metal, half-metal ferromagnetism, or spin-gapless semiconductor upon whether or not an unpaired electron is spin polarized. Moreover, we propose $\alpha_2$-WSi$_2$P$_4$ for the spin-valley polarization, $\alpha_1$-TaSi$_2$N$_4$ for Ising superconductor and $\beta_2$-SrGa$_2$Se$_4$ for topological insulator.Comment: Maintext 9 pages; 5 figures; Supplementary Materials 8 figures and 4 table

Identifying risk factors for cesarean scar pregnancy: a retrospective study of 79 cases

 Objectives: To explore the possible risk factors for cesarean scar pregnancy (CSP), the incidence of which is increasing rapidly in China. Material and methods: 79 patients with CSP and 69 non-CSP expectant mothers with at least 1 previous cesarean section were employed in the study. The obstetric histories of the participants were collected and analyzed using Chi square test. Results: We found that 77.2% CSP patients had ≥ 3 pregnancies and only 36.2% women had ≥ 3 pregnacies in non-CSP group. During the previous cesarean delivery, 21.5% of CSP patients had entered the first stage of labor, which was 43.5% in non-CSP group (P < 0.05). Cephalopelvic disproportion occurred in 51.9% of CSP patients, which was significantly higher than that (23.2%) in non-CSP group (P < 0.01). 11.4% of CSP patients had undergone cesarean section due to breech and shoulder presentation in the past, which was only 1.4% in non-CSP group. However, no significance was noted (P > 0.05). We did not find significant differences between the CSP and non-CSP patients in maternal age, multiple cesarean sections, gestational age, emergency or elective caesarean section. Conclusions: Multiple pregnancies, absence of the first stage of labor, and cephalopelvic disproportion might be the risk factors for the occurrence of CSP.