Boundary Green functions of topological insulators and superconductors

Topological insulators and superconductors are characterized by their gapless boundary modes. In this paper, we develop a recursive approach to the boundary Green function which encodes this nontrivial boundary physics. Our approach describes the various topologically trivial and nontrivial phases as fixed points of a recursion and provides direct access to the phase diagram, the localization properties of the edge modes, as well as topological indices. We illustrate our approach in the context of various familiar models such as the Su-Schrieffer-Heeger model, the Kitaev chain, and a model for a Chern insulator. We also show that the method provides an intuitive approach to understand recently introduced topological phases which exhibit gapless corner states.Comment: 18 pages, 3 figures (a new Fig. 3 is added), Accepted by Phys. Rev.

Detection, Activity Measurement and Phylogeny of Ureolytic Bacteria Isolated from Elasmobranch Tissue

Free-ranging marine elasmobranch tissue-associated micro-organisms were cultured from free-ranging Atlantic stingray (Dasyatis sabina) and Atlantic sharpnose sharks (Rhizoprionodon terraenovae). 16S rRNA gene phylogeny indicated bacteria community structure in both elasmobranchs were under phylum Proteobacteria, Firmicutes and Actinobacteria. By conducting split-plot ANOVA, we found the microbial richness is significantly different (P=0.0814) between two superorders of elasmobranch, which may largely due to their preferred habitats and feeding habits. Urease presentence and activity was detected in phylogenetically diverse bacterial strains. Species with high urea-hydrolyzing ability, such as Micrococcus luteus (shark blood isolate: 46.84 mU/mg protein; stingray blood isolate: 24.36 mU/mg protein) and Staphylococcus saprophyticus (could also be xylosus) (66.46 mU/mg protein) were both isolated from blood samples. This study suggests the examination of urease activity to promote the better profile of the virulence of some novel bacteria species. The phylogeny of bacterial 16S rRNA genes and urease-coding ureC genes were analyzed and compared,combined with the examination of urease activity of ureolytic bacteria, we found ureC gene as a potential functional marker. The study of enzymatic (urease) activity and ureC gene-based phylogeny provides a better understanding of ureolytic bacteria for their urea-utilizing potential, enables the further study of urease-positive strains on bioengineering and bioremediating of marine urea eutrophication in a larger scale. To our knowledge, this should be the first study to unveil the urea-hydrolyzing ability of marine elasmobranch tissue-associated ureolytic microbes, and the potential of the ureC gene to be a functional marker

Monitoring of the central blood pressure waveform via a conformal ultrasonic device.

Continuous monitoring of the central-blood-pressure waveform from deeply embedded vessels, such as the carotid artery and jugular vein, has clinical value for the prediction of all-cause cardiovascular mortality. However, existing non-invasive approaches, including photoplethysmography and tonometry, only enable access to the superficial peripheral vasculature. Although current ultrasonic technologies allow non-invasive deep-tissue observation, unstable coupling with the tissue surface resulting from the bulkiness and rigidity of conventional ultrasound probes introduces usability constraints. Here, we describe the design and operation of an ultrasonic device that is conformal to the skin and capable of capturing blood-pressure waveforms at deeply embedded arterial and venous sites. The wearable device is ultrathin (240 μm) and stretchable (with strains up to 60%), and enables the non-invasive, continuous and accurate monitoring of cardiovascular events from multiple body locations, which should facilitate its use in a variety of clinical environments

Multimodal Federated Learning via Contrastive Representation Ensemble

With the increasing amount of multimedia data on modern mobile systems and IoT infrastructures, harnessing these rich multimodal data without breaching user privacy becomes a critical issue. Federated learning (FL) serves as a privacy-conscious alternative to centralized machine learning. However, existing FL methods extended to multimodal data all rely on model aggregation on single modality level, which restrains the server and clients to have identical model architecture for each modality. This limits the global model in terms of both model complexity and data capacity, not to mention task diversity. In this work, we propose Contrastive Representation Ensemble and Aggregation for Multimodal FL (CreamFL), a multimodal federated learning framework that enables training larger server models from clients with heterogeneous model architectures and data modalities, while only communicating knowledge on public dataset. To achieve better multimodal representation fusion, we design a global-local cross-modal ensemble strategy to aggregate client representations. To mitigate local model drift caused by two unprecedented heterogeneous factors stemming from multimodal discrepancy (modality gap and task gap), we further propose two inter-modal and intra-modal contrasts to regularize local training, which complements information of the absent modality for uni-modal clients and regularizes local clients to head towards global consensus. Thorough evaluations and ablation studies on image-text retrieval and visual question answering tasks showcase the superiority of CreamFL over state-of-the-art FL methods and its practical value.Comment: ICLR 2023. Code is available at https://github.com/FLAIR-THU/CreamF

Domain-generalization human activity recognition model based on CSI instance normalization

To achieve Wi-Fi cross-domain human activity perception that was not dependent on target domain data, a domain-generalization human activity recognition model based on CSI instance normalization called INDG-Fi was proposed. The instance normalization standardization was utilized to remove domain information from the representation of CSI features by INDG-Fi. Then action classifiers and domain classifiers were constructed for shared feature extraction. By employing activity bias learning and adversarial domain learning, the model biased the features extracted from the encoding layer towards signal variations caused by human actions while moving away from domain signals. To enhance the model’s focus on subcarrier signals that were more significantly influenced by human actions, a subcarrier attention module was incorporated into the encoding layer. The implemented results demonstrate that the proposed INDG-Fi achieves perceptual accuracies of 97.99% and 92.73% for unseen users and locations, respectively, thus enabling robust cross-domain perception

Fibroblast Growth Factor Signaling Mediates Pulmonary Endothelial Glycocalyx Reconstitution

The endothelial glycocalyx is a heparan sulfate (HS)-rich endovascular structure critical to endothelial function. Accordingly, endothelial glycocalyx degradation during sepsis contributes to tissue edema and organ injury. We determined the endogenous mechanisms governing pulmonary endothelial glycocalyx reconstitution, and if these reparative mechanisms are impaired during sepsis. We performed intravital microscopy of wild-type and transgenic mice to determine the rapidity of pulmonary endothelial glycocalyx reconstitution after nonseptic (heparinase-III mediated) or septic (cecal ligation and puncture mediated) endothelial glycocalyx degradation. We used mass spectrometry, surface plasmon resonance, and in vitro studies of human and mouse samples to determine the structure of HS fragments released during glycocalyx degradation and their impact on fibroblast growth factor receptor (FGFR) 1 signaling, a mediator of endothelial repair. Homeostatic pulmonary endothelial glycocalyx reconstitution occurred rapidly after nonseptic degradation and was associated with induction of the HS biosynthetic enzyme, exostosin (EXT)-1. In contrast, sepsis was characterized by loss of pulmonary EXT1 expression and delayed glycocalyx reconstitution. Rapid glycocalyx recovery after nonseptic degradation was dependent upon induction of FGFR1 expression and was augmented by FGF-promoting effects of circulating HS fragments released during glycocalyx degradation. Although sepsis-released HS fragments maintained this ability to activate FGFR1, sepsis was associated with the downstream absence of reparative pulmonary endothelial FGFR1 induction. Sepsis may cause vascular injury not only via glycocalyx degradation, but also by impairing FGFR1/EXT1-mediated glycocalyx reconstitution

NICE 2023 Zero-shot Image Captioning Challenge

In this report, we introduce NICE project\footnote{\url{https://nice.lgresearch.ai/}} and share the results and outcomes of NICE challenge 2023. This project is designed to challenge the computer vision community to develop robust image captioning models that advance the state-of-the-art both in terms of accuracy and fairness. Through the challenge, the image captioning models were tested using a new evaluation dataset that includes a large variety of visual concepts from many domains. There was no specific training data provided for the challenge, and therefore the challenge entries were required to adapt to new types of image descriptions that had not been seen during training. This report includes information on the newly proposed NICE dataset, evaluation methods, challenge results, and technical details of top-ranking entries. We expect that the outcomes of the challenge will contribute to the improvement of AI models on various vision-language tasks.Comment: Tech report, project page https://nice.lgresearch.ai