Unconventional Rashba spin splitting, double SU(2) spin symmetry, and pure Dirac fermion system in PtSe2_2 nanoribbons

2D materials can host interesting physics and have important applications in various fields. Recent experiment shows that monolayer PtSe$_2$ nanoflakes with neutral zigzag edges are stable. Here, we study semiconducting PtSe$_2$ nanoribbons with the stable edges through first-principles investigation, and find relativistic energy dispersion and large unconventional Rashba spin splitting in the low-energy bands (even $N$) which originates from the nanoribbon edges. Furthermore, there exists SU(2) spin symmetry in both of the conduction and valence bands for each edge, which implies spin-conserving carrier transport or persistent spin helix (in the spin xy plane). When the inter-edge interaction becomes weak, a nearly-perfect Dirac fermion system can be achieved through combining the valence and conduction bands. Thus we realize unconventional Rashba splitting with double SU(2) spin symmetry and a pure Dirac fermion system in condensed matter physics.Comment: 6 pages, 5 figure

UNGDOM og de unges liv

Det er ikke svært i daglig tale at definere ungdom – alle ved, at det er en tidsperiode mellem barndom og voksenliv. Det er imidlertid mere vanskeligt at formulere en præcis videnskabelig definition af ungdom, idet ungdomsbegrebet varierer fra tid til anden og fra kultur til kultur. John Gillis har i Youth and History (1974) beskrevet ungdom som en specifik socialgruppe med sin egen funktion og position i samfundet, og det er netop i bestemmelsen af dette, at vi kommer i vanskeligheder. I stort set alle kulturer er voksenlivet defineret ved, at man kan stifte egen familie, samt principielt via sin arbejdskraft at kunne brødføde denne. I visse kulturer bliver børn imidlertid tidligt sat i arbejde og derfor er et af de primære kriterier for voksen status muligheden for familiedannelse. At der eksisterer undtagelser fra denne definition er tydeligt, når man ser på teenagegraviditeter – ”de unge mødre” – og på det faktum, at mennesker i den vestlige verden får bør senere og senere, når de er ude over den ”første ungdom”. Samtidig med, at ungdommen varer længere og længere, når man ser på livsstil og voksnes mennesker selvfremstilling

DanceMeld: Unraveling Dance Phrases with Hierarchical Latent Codes for Music-to-Dance Synthesis

In the realm of 3D digital human applications, music-to-dance presents a challenging task. Given the one-to-many relationship between music and dance, previous methods have been limited in their approach, relying solely on matching and generating corresponding dance movements based on music rhythm. In the professional field of choreography, a dance phrase consists of several dance poses and dance movements. Dance poses composed of a series of basic meaningful body postures, while dance movements can reflect dynamic changes such as the rhythm, melody, and style of dance. Taking inspiration from these concepts, we introduce an innovative dance generation pipeline called DanceMeld, which comprising two stages, i.e., the dance decouple stage and the dance generation stage. In the decouple stage, a hierarchical VQ-VAE is used to disentangle dance poses and dance movements in different feature space levels, where the bottom code represents dance poses, and the top code represents dance movements. In the generation stage, we utilize a diffusion model as a prior to model the distribution and generate latent codes conditioned on music features. We have experimentally demonstrated the representational capabilities of top code and bottom code, enabling the explicit decoupling expression of dance poses and dance movements. This disentanglement not only provides control over motion details, styles, and rhythm but also facilitates applications such as dance style transfer and dance unit editing. Our approach has undergone qualitative and quantitative experiments on the AIST++ dataset, demonstrating its superiority over other methods.Comment: 10 pages, 8 figure

Neural-Symbolic Recommendation with Graph-Enhanced Information

The recommendation system is not only a problem of inductive statistics from data but also a cognitive task that requires reasoning ability. The most advanced graph neural networks have been widely used in recommendation systems because they can capture implicit structured information from graph-structured data. However, like most neural network algorithms, they only learn matching patterns from a perception perspective. Some researchers use user behavior for logic reasoning to achieve recommendation prediction from the perspective of cognitive reasoning, but this kind of reasoning is a local one and ignores implicit information on a global scale. In this work, we combine the advantages of graph neural networks and propositional logic operations to construct a neuro-symbolic recommendation model with both global implicit reasoning ability and local explicit logic reasoning ability. We first build an item-item graph based on the principle of adjacent interaction and use graph neural networks to capture implicit information in global data. Then we transform user behavior into propositional logic expressions to achieve recommendations from the perspective of cognitive reasoning. Extensive experiments on five public datasets show that our proposed model outperforms several state-of-the-art methods, source code is avaliable at [https://github.com/hanzo2020/GNNLR].Comment: 12 pages, 2 figures, conferenc