Cavity-Assisted Dynamical Spin-Orbit Coupling in Cold Atoms

We consider ultracold atoms subjected to a cavity-assisted two-photon Raman transition. The Raman coupling gives rise to effective spin-orbit interaction which couples atom's center-of-mass motion to its pseudospin degrees of freedom. Meanwhile, the cavity photon is dynamically affected by the atom. This feedback between atom and photon leads to a dramatic modification of the atomic dispersion relation, and further leads to dynamical instability of the system. We propose to detect the change of cavity photon number as a direct way to demonstrate dynamical instability.Comment: 5 pages, 5 figure

Monomial Hopf Algebras

Let $K$ be a field of characteristic 0 containing all roots of unity. We classify all the Hopf structures on monomial $K$-coalgebras, or, in dual version, on monomial $K$-algebras.Comment: 24 page

Spatial-Temporal Knowledge-Embedded Transformer for Video Scene Graph Generation

Video scene graph generation (VidSGG) aims to identify objects in visual scenes and infer their relationships for a given video. It requires not only a comprehensive understanding of each object scattered on the whole scene but also a deep dive into their temporal motions and interactions. Inherently, object pairs and their relationships enjoy spatial co-occurrence correlations within each image and temporal consistency/transition correlations across different images, which can serve as prior knowledge to facilitate VidSGG model learning and inference. In this work, we propose a spatial-temporal knowledge-embedded transformer (STKET) that incorporates the prior spatial-temporal knowledge into the multi-head cross-attention mechanism to learn more representative relationship representations. Specifically, we first learn spatial co-occurrence and temporal transition correlations in a statistical manner. Then, we design spatial and temporal knowledge-embedded layers that introduce the multi-head cross-attention mechanism to fully explore the interaction between visual representation and the knowledge to generate spatial- and temporal-embedded representations, respectively. Finally, we aggregate these representations for each subject-object pair to predict the final semantic labels and their relationships. Extensive experiments show that STKET outperforms current competing algorithms by a large margin, e.g., improving the mR@50 by 8.1%, 4.7%, and 2.1% on different settings over current algorithms.Comment: Technical Repor

Dual-Perspective Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels

Despite achieving impressive progress, current multi-label image recognition (MLR) algorithms heavily depend on large-scale datasets with complete labels, making collecting large-scale datasets extremely time-consuming and labor-intensive. Training the multi-label image recognition models with partial labels (MLR-PL) is an alternative way, in which merely some labels are known while others are unknown for each image. However, current MLP-PL algorithms rely on pre-trained image similarity models or iteratively updating the image classification models to generate pseudo labels for the unknown labels. Thus, they depend on a certain amount of annotations and inevitably suffer from obvious performance drops, especially when the known label proportion is low. To address this dilemma, we propose a dual-perspective semantic-aware representation blending (DSRB) that blends multi-granularity category-specific semantic representation across different images, from instance and prototype perspective respectively, to transfer information of known labels to complement unknown labels. Specifically, an instance-perspective representation blending (IPRB) module is designed to blend the representations of the known labels in an image with the representations of the corresponding unknown labels in another image to complement these unknown labels. Meanwhile, a prototype-perspective representation blending (PPRB) module is introduced to learn more stable representation prototypes for each category and blends the representation of unknown labels with the prototypes of corresponding labels, in a location-sensitive manner, to complement these unknown labels. Extensive experiments on the MS-COCO, Visual Genome, and Pascal VOC 2007 datasets show that the proposed DSRB consistently outperforms current state-of-the-art algorithms on all known label proportion settings.Comment: Technical Report. arXiv admin note: text overlap with arXiv:2203.0217

Semantic Representation and Dependency Learning for Multi-Label Image Recognition

Recently many multi-label image recognition (MLR) works have made significant progress by introducing pre-trained object detection models to generate lots of proposals or utilizing statistical label co-occurrence enhance the correlation among different categories. However, these works have some limitations: (1) the effectiveness of the network significantly depends on pre-trained object detection models that bring expensive and unaffordable computation; (2) the network performance degrades when there exist occasional co-occurrence objects in images, especially for the rare categories. To address these problems, we propose a novel and effective semantic representation and dependency learning (SRDL) framework to learn category-specific semantic representation for each category and capture semantic dependency among all categories. Specifically, we design a category-specific attentional regions (CAR) module to generate channel/spatial-wise attention matrices to guide model to focus on semantic-aware regions. We also design an object erasing (OE) module to implicitly learn semantic dependency among categories by erasing semantic-aware regions to regularize the network training. Extensive experiments and comparisons on two popular MLR benchmark datasets (i.e., MS-COCO and Pascal VOC 2007) demonstrate the effectiveness of the proposed framework over current state-of-the-art algorithms.Comment: 25 pages, 7 figure

Qualitative Evaluation of a Service Leadership Subject in a Chinese Context

Adopting a qualitative methodology, views of the students on a subject on service leadership were examined. Students taking the subject (n=153) were invited to use descriptors and metaphors to describe their experiences about the subject. Regarding the descriptors given by the students, most of them were positive in nature. Similar positive findings were obtained for the metaphors. The findings are generally consistent with those studies using the same methodology reported in the literature. In conjunction with other evaluation findings, the present findings suggest that students had positive experiences about taking the subject and regarded the subject to be able to promote their service leadership qualities

Associations between the platelet/high-density lipoprotein cholesterol ratio and likelihood of nephrolithiasis: a cross-sectional analysis in United States adults

AimsThe primary objective of this study was to investigate the relationship between the platelet/high-density lipoprotein cholesterol ratio (PHR) and the prevalence of nephrolithiasis within the adult population of the United States.MethodsThe data used in this study were obtained from the National Health and Nutrition Examination Survey (NHANES) conducted between 2007 and 2018. The analysis included a non-pregnant population aged 20 years or older, providing proper PHR index and nephrolithiasis data. The research utilized subgroup analyses and weighted univariate and multivariable logistic regression to evaluate the independent association between the PHR and the susceptibility to nephrolithiasis.ResultsThe study comprised 30,899 participants with an average PHR value of 19.30 ± 0.11. The overall prevalence rate of nephrolithiasis was estimated at 9.98% with an increase in the higher PHR tertiles (T1, 8.49%; T2, 10.11%; T3, 11.38%, P < 0.0001). An elevated PHR level was closely linked with a higher susceptibility to nephrolithiasis. Compared with patients in T1, and after adjusting for potential confounders in model 2, the corresponding odds ratio for nephrolithiasis in T3 was 1.48 (95% CI: 1.06 to 2.08), with a P-value = 0.02. The results of the interaction tests revealed a significant impact of chronic kidney disease on the relationship between PHR and nephrolithiasis. Furthermore, the restricted cubic spline analyses exhibited a positive, non-linear correlation between PHR and the risk of nephrolithiasis.ConclusionA convenient biomarker, the PHR, was independently associated with nephrolithiasis and could be a novel biomarker in predicting occurrence in clinical decision