Generation of Anisotropic Massless Dirac Fermions and Asymmetric Klein Tunneling in Few-Layer Black Phosphorus Superlattices

Artificial lattices have been employed in many two-dimensional systems, including those of electrons, atoms and photons, in a quest for massless Dirac particles with flexibility and controllability. Periodically patterned molecule assembly and electrostatic gating as well as moir\'e pattern induced by substrate, have produced electronic states with linear dispersions from isotropic two-dimensional electron gas (2DEG). Here we demonstrate that massless Dirac fermions with tunable anisotropic characteristics can, in general, be generated in highly anisotropic 2DEG under slowly varying external periodic potentials. For patterned few-layer black phosphorus superlattices, the new chiral quasiparticles exist exclusively in an isolated energy window and inherit the strong anisotropic properties of pristine black phosphorus. These states exhibit asymmetric Klein tunneling with the direction of incidence for wave packet with perfect transmission deviating from normal incidence by more than 50{\deg} under an appropriate barrier orientation

MiR-539-5p alleviates sepsis-induced acute lung injury by targeting ROCK1

Introduction. Sepsis-induced acute lung injury (ALI) is an inflammatory process involved with simultaneous production of inflammatory cytokines and chemokines. In this study, we investigated the regulatory role of miR-539-5p in sepsis-induced ALI using a mouse model of cecal ligation puncture (CLP) and an in vitro model of primary murine pulmonary microvascular endothelial cells (MPVECs). Material and methods. Adult male C57BL/6 mice were intravenously injected with or without miR-539-5p agomir or scrambled control one week before CLP operation. MPVECs were transfected with miR-539-5p mimics or control mimics, followed by lipopolysaccharide (LPS) stimulation. ROCK1 was predicted and confirmed as a direct target of miR-539-5p using dual-luciferase reporter assay. In rescue experiment, MPVECs were co-transfected with lentiviral vector expressing ROCK1 (or empty vector) and miR-539-5p mimics 24 h before LPS treatment. The transcriptional activity of caspase-3, the apoptosis ratio, the levels of miR-539-5p, interleukin-1b (IL-1b), interleukin-6 (IL-6), and ROCK1 were assessed. Results. Compared to sham group, mice following CLP showed pulmonary morphological abnormalities, elevated production of IL-1b and IL-6, and increased caspase-3 activity and apoptosis ratio in the lung. In MPVECs, LPS stimulation resulted in a significant induction of inflammatory cytokine levels and apoptosis compared to untreated cells. The overexpression of miR-539-5p in septic mice alleviated sepsis-induced pulmonary injury, apoptosis, and inflammation. MiR-539-5p also demonstrated anti-apoptotic and anti-inflammatory effect in LPS-treated MPVECs. The upregulation of ROCK1 in MPVECs recovered miR-539-5p-suppressed caspase-3 activity and proinflammatory cytokine production. Conclusion. In conclusion, miR-539-5p alleviated sepsis-induced ALI via suppressing its downstream target ROCK1, suggesting a therapeutic potential of miR-539-5p for the management of sepsis-induced ALI

Class-level Structural Relation Modelling and Smoothing for Visual Representation Learning

Representation learning for images has been advanced by recent progress in more complex neural models such as the Vision Transformers and new learning theories such as the structural causal models. However, these models mainly rely on the classification loss to implicitly regularize the class-level data distributions, and they may face difficulties when handling classes with diverse visual patterns. We argue that the incorporation of the structural information between data samples may improve this situation. To achieve this goal, this paper presents a framework termed \textbf{C}lass-level Structural Relation Modeling and Smoothing for Visual Representation Learning (CSRMS), which includes the Class-level Relation Modelling, Class-aware Graph Sampling, and Relational Graph-Guided Representation Learning modules to model a relational graph of the entire dataset and perform class-aware smoothing and regularization operations to alleviate the issue of intra-class visual diversity and inter-class similarity. Specifically, the Class-level Relation Modelling module uses a clustering algorithm to learn the data distributions in the feature space and identify three types of class-level sample relations for the training set; Class-aware Graph Sampling module extends typical training batch construction process with three strategies to sample dataset-level sub-graphs; and Relational Graph-Guided Representation Learning module employs a graph convolution network with knowledge-guided smoothing operations to ease the projection from different visual patterns to the same class. Experiments demonstrate the effectiveness of structured knowledge modelling for enhanced representation learning and show that CSRMS can be incorporated with any state-of-the-art visual representation learning models for performance gains. The source codes and demos have been released at https://github.com/czt117/CSRMS

Deep learning forecasts of cosmic acceleration parameters from DECi-hertz Interferometer Gravitational-wave Observatory

Validating the accelerating expansion of the universe is an important issue for understanding the evolution of the universe. By constraining the cosmic acceleration parameter $X_H$, we can discriminate between the $\Lambda \mathrm{CDM}$ (cosmological constant plus cold dark matter) model and LTB (the Lema\^itre-Tolman-Bondi) model. In this paper, we explore the possibility of constraining the cosmic acceleration parameter with the inspiral gravitational waveform of neutron star binaries (NSBs) in the frequency range of 0.1Hz-10Hz, which can be detected by the second-generation space-based gravitational wave detector DECIGO. We use a convolutional neural network (CNN), a long short-term memory (LSTM) network combined with a gated recurrent unit (GRU), and Fisher information matrix to derive constraints on the cosmic acceleration parameter $X_H$. Based on the simulated gravitational wave data with a time duration of 1 month, we conclude that CNN can limit the relative error to 14.09%, while LSTM network combined with GRU can limit the relative error to 13.53%. Additionally, using Fisher information matrix for gravitational wave data with a 5-year observation can limit the relative error to 32.94%. Compared with the Fisher information matrix method, deep learning techniques will significantly improve the constraints on the cosmic acceleration parameters at different redshifts. Therefore, DECIGO is expected to provide direct measurements of the acceleration of the universe, by observing the chirp signals of coalescing binary neutron stars

Exploiting Prompt Caption for Video Grounding

Video grounding aims to locate a moment of interest matching the given query sentence from an untrimmed video. Previous works ignore the \emph{sparsity dilemma} in video annotations, which fails to provide the context information between potential events and query sentences in the dataset. In this paper, we contend that exploiting easily available captions which describe general actions \ie, prompt captions (PC) defined in our paper, will significantly boost the performance. To this end, we propose a Prompt Caption Network (PCNet) for video grounding. Specifically, we first introduce dense video captioning to generate dense captions and then obtain prompt captions by Non-Prompt Caption Suppression (NPCS). To capture the potential information in prompt captions, we propose Caption Guided Attention (CGA) project the semantic relations between prompt captions and query sentences into temporal space and fuse them into visual representations. Considering the gap between prompt captions and ground truth, we propose Asymmetric Cross-modal Contrastive Learning (ACCL) for constructing more negative pairs to maximize cross-modal mutual information. Without bells and whistles, extensive experiments on three public datasets (\ie, ActivityNet Captions, TACoS and ActivityNet-CG) demonstrate that our method significantly outperforms state-of-the-art methods

G2L: Semantically Aligned and Uniform Video Grounding via Geodesic and Game Theory

The recent video grounding works attempt to introduce vanilla contrastive learning into video grounding. However, we claim that this naive solution is suboptimal. Contrastive learning requires two key properties: (1) \emph{alignment} of features of similar samples, and (2) \emph{uniformity} of the induced distribution of the normalized features on the hypersphere. Due to two annoying issues in video grounding: (1) the co-existence of some visual entities in both ground truth and other moments, \ie semantic overlapping; (2) only a few moments in the video are annotated, \ie sparse annotation dilemma, vanilla contrastive learning is unable to model the correlations between temporally distant moments and learned inconsistent video representations. Both characteristics lead to vanilla contrastive learning being unsuitable for video grounding. In this paper, we introduce Geodesic and Game Localization (G2L), a semantically aligned and uniform video grounding framework via geodesic and game theory. We quantify the correlations among moments leveraging the geodesic distance that guides the model to learn the correct cross-modal representations. Furthermore, from the novel perspective of game theory, we propose semantic Shapley interaction based on geodesic distance sampling to learn fine-grained semantic alignment in similar moments. Experiments on three benchmarks demonstrate the effectiveness of our method.Comment: ICCV202

Safety and efficacy of etomidate and propofol anesthesia in elderly patients undergoing gastroscopy: A double-blind randomized clinical study

The aim of the present study is to compare the safety, efficacy and cost effectiveness of anesthetic regimens by compound, using etomidate and propofol in elderly patients undergoing gastroscopy. A total of 200 volunteers (65–79 years of age) scheduled for gastroscopy under anesthesia were randomly divided into the following groups: P, propofol (1.5–2.0 mg/kg); E, etomidate (0.15-0.2 mg/kg); P+E, propofol (0.75–1 mg/kg) followed by etomidate (0.075-0.1 mg/kg); and E+P, etomidate (0.075-0.01 mg/kg) followed by propofol (0.75–1 mg/kg). Vital signs and bispectral index were monitored at different time points. Complications, induction and examination time, anesthesia duration, and recovery and discharge time were recorded. At the end of the procedure, the satisfaction of patients, endoscopists and the anesthetist were evaluated. The recovery (6.1±1.2 h) and discharge times (24.8±2.8 h) in group E were significantly longer compared with groups P, P+E and E+P (P<0.05). The occurrence of injection pain in group P+E was significantly higher compared with the other three groups (P<0.05). In addition, the incidence of myoclonus and post-operative nausea and vomiting were significantly higher in group P+E compared with the other three groups (P<0.05). There was no statistical difference among the four groups with regards to the patients' immediate, post-procedure satisfaction (P>0.05). Furthermore, there was no difference in the satisfaction of anesthesia, as evaluated by the anesthetist and endoscopist, among the four groups (P>0.05). The present study demonstrates that anesthesia for gastroscopy in elderly patients can be safely and effectively accomplished using a drug regimen that combines propofol with etomidate. The combined use of propofol and etomidate has unique characteristics which improve hemodynamic stability, cause minimal respiratory depression and less side effects, provide rapid return to full activity and result in high levels of satisfaction