Higher molecular PψsΛ/ΣP_{\psi s}^{\Lambda/\Sigma} pentaquarks arising from the Ξc(′,∗)Dˉ1/Ξc(′,∗)Dˉ2∗\Xi_c^{(\prime,*)}\bar D_1/\Xi_c^{(\prime,*)}\bar D_2^* interactions

The discoveries of the $P_{\psi s}^\Lambda(4459)$ and $P_{\psi s}^\Lambda(4338)$ as the potential $\Xi_c\bar D^{(*)}$ molecules have sparked our curiosity in exploring a novel class of molecular $P_{\psi s}^{\Lambda/\Sigma}$ pentaquarks. In this study, we carry out an investigation into the higher molecular pentaquarks, specifically focusing on the $P_{\psi s}^{\Lambda/\Sigma}$ states arising from the $\Xi_c^{(\prime,*)}\bar D_1/\Xi_c^{(\prime,*)}\bar D_2^*$ interactions. Our approach employs the one-boson-exchange model, incorporating both the $S$-$D$ wave mixing effect and the coupled channel effect. Our numerical results suggest that the $\Xi_c\bar D_1$ states with $I(J^P)=0({1}/{2}^+,\,{3}/{2}^+)$, the $\Xi_c\bar D_2^*$ states with $I(J^P)=0({3}/{2}^+,\,{5}/{2}^+)$, the $\Xi_c^{\prime}\bar D_1$ states with $I(J^P)=0({1}/{2}^+,\,{3}/{2}^+)$, the $\Xi_c^{\prime}\bar D_2^*$ states with $I(J^P)=0({3}/{2}^+,\,{5}/{2}^+)$, the $\Xi_c^{*}\bar D_1$ states with $I(J^P)=0({1}/{2}^+,\,{3}/{2}^+,\,{5}/{2}^+)$, and the $\Xi_c^{*}\bar D_2^*$ states with $I(J^P)=0({1}/{2}^+,\,{3}/{2}^+,\,{5}/{2}^+,\,{7}/{2}^+)$ can be recommended as the most promising molecular $P_{\psi s}^\Lambda$ pentaquark candidates, and there may exist the potential molecular $P_{\psi s}^\Sigma$ pentaquark candidates for several isovector $\Xi_c^{(\prime,*)}\bar D_1/\Xi_c^{(\prime,*)}\bar D_2^*$ states. With the higher statistical data accumulation at the LHCb's Run II and Run III status, there is the possibility that our predicted $P_{\psi s}^{\Lambda/\Sigma}$ states can be detected through the weak decay of the $\Xi_b$ baryon, especially in hunting for the predicted $P_{\psi s}^\Lambda$ states.Comment: 22 pages, 6 figures, and 10 table

New type of doubly charmed molecular pentaquarks containing most strange quarks: Mass spectra, radiative decays, and magnetic moments

In this work, we first predict the mass spectra of the $\Omega_{c}^{(*)}{D}_s^{(*)}$-type doubly charmed molecular pentaquark candidates, where the one-boson-exchange model is adopted by considering both the $S$-$D$ wave mixing effect and the coupled channel effect. Our findings indicate that the $\Omega_{c}{D}_s^*$ state with $J^P={1}/{2}^{-}$, the $\Omega_{c}^*{D}_s^*$ state with $J^P={1}/{2}^{-}$, and the $\Omega_{c}^*{D}_s^*$ state with $J^P={3}/{2}^{-}$ can be considered as the most promising doubly charmed molecular pentaquark candidates, and the $\Omega_{c}{D}_s$ state with $J^P={1}/{2}^{-}$, the $\Omega_{c}^*{D}_s$ state with $J^P={3}/{2}^{-}$, and the $\Omega_{c}{D}_s^*$ state with $J^P={3}/{2}^{-}$ are the possible doubly charmed molecular pentaquark candidates. Furthermore, we further explore the radiative decays and the magnetic moments of the most promising doubly charmed molecular pentaquark candidates in the constituent quark model. As a crucial aspect of spectroscopy, the information of the radiative decays and the magnetic moments can provide the valuable clues to reflect their inner structures. With the accumulation of higher statistical data at the Large Hadron Collider, we propose that the LHCb Collaboration should focus on the problem of searching for these predicted doubly charmed molecular pentaquark candidates containing most strange quarks in the coming years.Comment: 16 pages, 1 figure, and 7 table

Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive Feature Learning

Domain adaptation problems arise in a variety of applications, where a training dataset from the \textit{source} domain and a test dataset from the \textit{target} domain typically follow different distributions. The primary difficulty in designing effective learning models to solve such problems lies in how to bridge the gap between the source and target distributions. In this paper, we provide comprehensive analysis of feature learning algorithms used in conjunction with linear classifiers for domain adaptation. Our analysis shows that in order to achieve good adaptation performance, the second moments of the source domain distribution and target domain distribution should be similar. Based on our new analysis, a novel extremely easy feature learning algorithm for domain adaptation is proposed. Furthermore, our algorithm is extended by leveraging multiple layers, leading to a deep linear model. We evaluate the effectiveness of the proposed algorithms in terms of domain adaptation tasks on the Amazon review dataset and the spam dataset from the ECML/PKDD 2006 discovery challenge.Comment: ijca

Radiative decays and magnetic moments of the predicted BcB_c-like molecules

In this work, we first perform a systematic study of the transition magnetic moments and the corresponding radiative decay behaviors of the $B_c$-like molecular states associated with their mass spectra, where the constituent quark model is adopted by considering the $S$-$D$ wave mixing effect. Our numerical results show that the radiative decay properties can be considered as the effective physical observable to reflect the inner structures of these $B_c$-like molecular states. Meanwhile, we also discuss the magnetic moments of the $B_c$-like molecular states, and we find that the magnetic moment properties can be used to distinguish the $B_c$-like molecular states from the conventional $B_c$ mesonic states, which have the same quantum numbers and similar masses. We expect that the present study can inspire the interest of experimentalist in exploring the electromagnetic properties of the $B_c$-like molecular states, especially the radiative decay properties.Comment: 13 pages, 8 tables, 1 figur

Timestamps as Prompts for Geography-Aware Location Recommendation

Location recommendation plays a vital role in improving users' travel experience. The timestamp of the POI to be predicted is of great significance, since a user will go to different places at different times. However, most existing methods either do not use this kind of temporal information, or just implicitly fuse it with other contextual information. In this paper, we revisit the problem of location recommendation and point out that explicitly modeling temporal information is a great help when the model needs to predict not only the next location but also further locations. In addition, state-of-the-art methods do not make effective use of geographic information and suffer from the hard boundary problem when encoding geographic information by gridding. To this end, a Temporal Prompt-based and Geography-aware (TPG) framework is proposed. The temporal prompt is firstly designed to incorporate temporal information of any further check-in. A shifted window mechanism is then devised to augment geographic data for addressing the hard boundary problem. Via extensive comparisons with existing methods and ablation studies on five real-world datasets, we demonstrate the effectiveness and superiority of the proposed method under various settings. Most importantly, our proposed model has the superior ability of interval prediction. In particular, the model can predict the location that a user wants to go to at a certain time while the most recent check-in behavioral data is masked, or it can predict specific future check-in (not just the next one) at a given timestamp