The discovery of topological quantum states marks a new chapter in both condensed matter physics and materials sciences. By analogy to spin electronic system, topological concepts have been extended into phonons, boosting the birth of topological phononics (TPs). Here, we present a high-throughput screening and data-driven approach to compute and evaluate TPs among over 10,000 real materials. We have discovered 5014 TP materials and grouped them into two main classes of Weyl and nodal-line (ring) TPs. We have clarified the physical mechanism for the occurrence of single Weyl, high degenerate Weyl, individual nodal-line (ring), nodal-link, nodal-chain, and nodal-net TPs in various materials and their mutual correlations. Among the phononic systems, we have predicted the hourglass nodal net TPs in TeO3, as well as the clean and single type-I Weyl TPs between the acoustic and optical branches in half-Heusler LiCaAs. In addition, we found that different types of TPs can coexist in many materials (such as ScZn). Their potential applications and experimental detections have been discussed. This work substantially increases the amount of TP materials, which enables an in-depth investigation of their structure-property relations and opens new avenues for future device design related to TPs.
By introducing a small set of additional parameters, a probe learns to solve specific linguistic tasks (e.g., dependency parsing) in a supervised manner using feature representations (e.g., contextualized embeddings). The effectiveness of such probing tasks is taken as evidence that the pre-trained model encodes linguistic knowledge. However, this approach of evaluating a language model is undermined by the uncertainty of the amount of knowledge that is learned by the probe itself. Complementary to those works, we propose a parameter-free probing technique for analyzing pre-trained language models (e.g., BERT). Our method does not require direct supervision from the probing tasks, nor do we introduce additional parameters to the probing process. Our experiments on BERT show that syntactic trees recovered from BERT using our method are significantly better than linguistically-uninformed baselines. We further feed the empirically induced dependency structures into a downstream sentiment classification task and find its improvement compatible with or even superior to a human-designed dependency schema.
Neuroinflammation is involved in the pathogenesis of Parkinson’s disease (PD) and other neurodegenerative disorders. We show that lack of PINK1- a mitochondrial kinase linked to recessive familial PD – leads to glia type-specific abnormalities of innate immunity. PINK1 loss enhances LPS/IFN-γ stimulated pro-inflammatory phenotypes of mixed astrocytes/microglia (increased iNOS, nitric oxide and COX-2, reduced IL-10) and pure astrocytes (increased iNOS, nitric oxide, TNF-α and IL-1β), while attenuating expression of both pro-inflammatory (TNF-α, IL-1β) and anti-inflammatory (IL-10) cytokines in microglia. These abnormalities are associated with increased inflammation-induced NF-κB signaling in astrocytes, and cause enhanced death of neurons co-cultured with inflamed PINK1 −/− mixed glia and neuroblastoma cells exposed to conditioned medium from LPS/IFN-γ treated PINK1 −/− mixed glia. Neuroblastoma cell death is prevented with an iNOS inhibitor, implicating increased nitric oxide production as the cause for enhanced death. Finally, we show for the first time that lack of a recessive PD gene (PINK1) increases α-Synuclein-induced nitric oxide production in all glia types (mixed glia, astrocytes and microglia). Our results describe a novel pathogenic mechanism in recessive PD, where PINK1 deficiency may increase neuron death via exacerbation of inflammatory stimuli-induced nitric oxide production and abnormal innate immune responses in glia cells.
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