Hybrid nodal surface and nodal line phonons in solids

Phonons have provided an ideal platform for a variety of intriguing physical states, such as non-abelian braiding and Haldane model. It is promising that phonons will realize the complicated nodal states accompanying with unusual quantum phenomena. Here, we propose the hybrid nodal surface and nodal line (NS+NL) phonons beyond the single genre nodal phonons. We categorize the NS+NL phonons into two-band and four-band situations based on symmetry analysis and compatibility relationships. Combing database screening with first-principles calculations, we identify the ideal candidate materials for realizing all categorized NS+NL phonons. Our calculations and tight-binding models further demonstrate that the interplay between NS and NL induces unique phenomena. In space group 113, the quadratic NL acts as a hub of the Berry curvature between two NSs, generating ribbon-like surface states. In space group 128, the NS serve as counterpart of Weyl NL that NS-NL mixed topological surface states are observed. Our findings extend the scope of hybrid nodal states and enrich the phononic states in realistic materials.Comment: 23+35 pages, 5+44 figures, 1+3 table

DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment

Recent research demonstrates the effectiveness of using pre-trained language models for legal case retrieval. Most of the existing works focus on improving the representation ability for the contextualized embedding of the [CLS] token and calculate relevance using textual semantic similarity. However, in the legal domain, textual semantic similarity does not always imply that the cases are relevant enough. Instead, relevance in legal cases primarily depends on the similarity of key facts that impact the final judgment. Without proper treatments, the discriminative ability of learned representations could be limited since legal cases are lengthy and contain numerous non-key facts. To this end, we introduce DELTA, a discriminative model designed for legal case retrieval. The basic idea involves pinpointing key facts in legal cases and pulling the contextualized embedding of the [CLS] token closer to the key facts while pushing away from the non-key facts, which can warm up the case embedding space in an unsupervised manner. To be specific, this study brings the word alignment mechanism to the contextual masked auto-encoder. First, we leverage shallow decoders to create information bottlenecks, aiming to enhance the representation ability. Second, we employ the deep decoder to enable translation between different structures, with the goal of pinpointing key facts to enhance discriminative ability. Comprehensive experiments conducted on publicly available legal benchmarks show that our approach can outperform existing state-of-the-art methods in legal case retrieval. It provides a new perspective on the in-depth understanding and processing of legal case documents.Comment: 11 page

Neurochemical characterization of pERK-expressing spinal neurons in histamine-induced itch

Date of Acceptance: 08/07/2015 Acknowledgements This work was supported by grants from the Ministry of Science and Technology of China (2012CB966904, 2011CB51005), National Natural Science Foundation of China (31271182, 81200692, 91232724, 81200933, 81101026), Shanghai Natural Science Foundation (12ZR1434300), Key Specialty Construction Project of Pudong Health Bureau of Shanghai (PWZz2013-17), Shenzhen Key Laboratory for Molecular Biology of Neural Development (ZDSY20120617112838879), Fundamental Research Funds for the Central Universities (1500219072) and Sino-UK Higher Education Research Partnership for PhD Studies.Peer reviewedPublisher PD