Vortex patterns and the critical rotational frequency in rotating dipolar Bose-Einstein condensates

Based on the two-dimensional mean-field equations for pancake-shaped dipolar Bose-Einstein condensates in a rotating frame with both attractive and repulsive dipole-dipole interaction (DDI) as well as arbitrary polarization angle, we study the profiles of the single vortex state and show how the critical rotational frequency change with the s-wave contact interaction strengths, DDI strengths and the polarization angles. In addition, we find numerically that at the `magic angle' $\vartheta=\arccos(\sqrt{3}/3)$, the critical rotational frequency is almost independent of the DDI strength. By numerically solving the dipolar GPE at high rotational speed, we identify different patterns of vortex lattices which strongly depend on the polarization direction. As a result, we undergo a study of vortex lattice structures for the whole regime of polarization direction and find evidence that the vortex lattice orientation tends to be aligned with the direction of the dipoles

The interaction between copper species and pyrite surfaces in copper cyanide solutions

The adsorption of copper ions and the formation of a copper sulfide phase on pyrite surfaces are of vital importance to alter the surface property of pyrite and determine its fate either to be rejected in the flotation of polymetallic sulfide ores or to be recovered in the flotation of pyritic gold ores. Cyanide and copper may co-exist in the process water with complicated speciation. The objective of this study is to understand the interaction between copper cyanide species and pyrite and clarify the possible adsorption of copper on pyrite surfaces from cyanide-bearing solutions. Surface-enhanced Raman spectroscopy and electrochemical measurements were used to determine the reaction products formed on pyrite surfaces. It was found that Cu(I)-bearing species were incorporated into pyrite, forming a CuS-like sulfide from copper cyanide solutions at a more oxidizing potential, while a Cu2S-like sulfide formed at a more reducing potential. The amount of copper deposited on pyrite was significantly improved at a more reducing potential at which the pyrite surface tended to be FeS-like. In addition, these Cu(I)-sulfides on pyrite surfaces were dissolved by cyanide-bearing species at a high CN/Cu ratio, compromising the total amount of copper uptake

Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete Labels

Multi-label recognition (MLR) with incomplete labels is very challenging. Recent works strive to explore the image-to-label correspondence in the vision-language model, \ie, CLIP, to compensate for insufficient annotations. In spite of promising performance, they generally overlook the valuable prior about the label-to-label correspondence. In this paper, we advocate remedying the deficiency of label supervision for the MLR with incomplete labels by deriving a structured semantic prior about the label-to-label correspondence via a semantic prior prompter. We then present a novel Semantic Correspondence Prompt Network (SCPNet), which can thoroughly explore the structured semantic prior. A Prior-Enhanced Self-Supervised Learning method is further introduced to enhance the use of the prior. Comprehensive experiments and analyses on several widely used benchmark datasets show that our method significantly outperforms existing methods on all datasets, well demonstrating the effectiveness and the superiority of our method. Our code will be available at https://github.com/jameslahm/SCPNet.Comment: Accepted by IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 202

Probing Product Description Generation via Posterior Distillation

In product description generation (PDG), the user-cared aspect is critical for the recommendation system, which can not only improve user's experiences but also obtain more clicks. High-quality customer reviews can be considered as an ideal source to mine user-cared aspects. However, in reality, a large number of new products (known as long-tailed commodities) cannot gather sufficient amount of customer reviews, which brings a big challenge in the product description generation task. Existing works tend to generate the product description solely based on item information, i.e., product attributes or title words, which leads to tedious contents and cannot attract customers effectively. To tackle this problem, we propose an adaptive posterior network based on Transformer architecture that can utilize user-cared information from customer reviews. Specifically, we first extend the self-attentive Transformer encoder to encode product titles and attributes. Then, we apply an adaptive posterior distillation module to utilize useful review information, which integrates user-cared aspects to the generation process. Finally, we apply a Transformer-based decoding phase with copy mechanism to automatically generate the product description. Besides, we also collect a large-scare Chinese product description dataset to support our work and further research in this field. Experimental results show that our model is superior to traditional generative models in both automatic indicators and human evaluation