Terahertz imaging with sub-wavelength resolution by femtosecond laser filament in air

Terahertz (THz) imaging provides cutting edge technique in biology, medical sciences and non-destructive evaluation. However, due to the long wavelength of the THz wave, the obtained resolution of THz imaging is normally a few hundred microns and is much lower than that of the traditional optical imaging. We introduce a sub-wavelength resolution THz imaging technique which uses the THz radiation generated by a femtosecond laser filament in air as the probe. This method is based on the fact that the femtosecond laser filament forms a waveguide for the THz wave in air. The diameter of the THz beam, which propagates inside the filament, varies from 20 {\mu}m to 50 {\mu}m, which is significantly smaller than the wavelength of the THz wave. Using this highly spatially confined THz beam as the probe, THz imaging with resolution as high as 20 {\mu}m (~{\lambda}/38) can be realized.Comment: 10 pages, 7 figure

Knockdown of TNFAIP1 mitigates sevoflurane-induced cognitive dysfunction by activating CREB/Nrf2 pathway

Purpose: To investigate the role of tumor necrosis factor-induced protein 1 (TNFAIP1) and cAMPresponsive element binding protein (CREB)/nuclear factor-erythroid factor 2-related factor 2 (Nrf2) pathway in sevoflurane (SEV)-induced cognitive dysfunction. Methods: A SEV-induced cognitive dysfunction rat model was developed. Bcl-2, Bax, heme oxygenase-1, Nrf2, p-CREB, and CREB protein levels in rat hippocampal tissue were assessed by western blot. Learning and long-term memory were evaluated using Morris water maze test. Glutathione peroxidase, malondialdehyde, and superoxide dismutase levels in hippocampal tissue were measured by enzyme-linked immunosorbent assay (ELISA). The 2,7-dichlorodihydro-fluorescein diacetate fluorescent assay was used to measure reactive oxygen species, while TUNEL staining was used to assess neuronal cell apoptosis. Results: Knockdown of TNFAIP1 attenuated SEV-induced learning and long-term memory dysfunction (p < 0.005), oxidative stress (p < 0.005), apoptosis (p < 0.005), and inhibition of the CREB/Nrf2 signaling pathway. Conclusion: This study demonstrates that knockdown of TNFAIP1 alleviates SEV-induced cognitive dysfunction by reversing inhibition of the CREB/Nrf2 signaling pathway. Keywords: TNFAIP1; Postoperative cognitive dysfunction; Sevoflurane; cAMP-responsive element binding protein (CREB); Nuclear factor-erythroid factor 2-related factor 2 (Nrf2

Terahertz Wave Guiding by Femtosecond Laser Filament in Air

Femtosecond laser filament generates strong terahertz (THz) pulse in air. In this paper, THz pulse waveform generated by femtosecond laser filament has been experimentally investigated as a function of the length of the filament. Superluminal propagation of THz pulse has been uncovered, indicating that the filament creates a THz waveguide in air. Numerical simulation has confirmed that the waveguide is formed because of the radially non-uniform refractive index distribution inside the filament. The underlying physical mechanisms and the control techniques of this type THz pulse generation method might be revisited based on our findings. It might also potentially open a new approach for long-distance propagation of THz wave in air.Comment: 5 pages, 6 figure

Interface Modes in Honeycomb Topological Photonic Structures with Broken Reflection Symmetry

In this work, we present a mathematical theory for Dirac points and interface modes in honeycomb topological photonic structures consisting of impenetrable obstacles. Starting from a honeycomb lattice of obstacles attaining $120^\circ$-rotation symmetry and horizontal reflection symmetry, we apply the boundary integral equation method to show the existence of Dirac points for the first two bands at the vertices of the Brillouin zone. We then study interface modes in a joint honeycomb photonic structure, which consists of two periodic lattices obtained by perturbing the honeycomb one with Dirac points differently. The perturbations break the reflection symmetry of the system, as a result, they annihilate the Dirac points and generate two structures with different topological phases, which mimics the quantum valley Hall effect in topological insulators. We investigate the interface modes that decay exponentially away from the interface of the joint structure in several configurations with different interface geometries, including the zigzag interface, the armchair interface, and the rational interfaces. Using the layer potential technique and asymptotic analysis, we first characterize the band-gap opening for the two perturbed periodic structures and derive the asymptotic expansions of the Bloch modes near the band gap surfaces. By formulating the eigenvalue problem for each joint honeycomb structure using boundary integral equations over the interface and analyzing the characteristic values of the associated boundary integral operators, we prove the existence of interface modes when the perturbation is small

SpeechColab Leaderboard: An Open-Source Platform for Automatic Speech Recognition Evaluation

In the wake of the surging tide of deep learning over the past decade, Automatic Speech Recognition (ASR) has garnered substantial attention, leading to the emergence of numerous publicly accessible ASR systems that are actively being integrated into our daily lives. Nonetheless, the impartial and replicable evaluation of these ASR systems encounters challenges due to various crucial subtleties. In this paper we introduce the SpeechColab Leaderboard, a general-purpose, open-source platform designed for ASR evaluation. With this platform: (i) We report a comprehensive benchmark, unveiling the current state-of-the-art panorama for ASR systems, covering both open-source models and industrial commercial services. (ii) We quantize how distinct nuances in the scoring pipeline influence the final benchmark outcomes. These include nuances related to capitalization, punctuation, interjection, contraction, synonym usage, compound words, etc. These issues have gained prominence in the context of the transition towards an End-to-End future. (iii) We propose a practical modification to the conventional Token-Error-Rate (TER) evaluation metric, with inspirations from Kolmogorov complexity and Normalized Information Distance (NID). This adaptation, called modified-TER (mTER), achieves proper normalization and symmetrical treatment of reference and hypothesis. By leveraging this platform as a large-scale testing ground, this study demonstrates the robustness and backward compatibility of mTER when compared to TER. The SpeechColab Leaderboard is accessible at https://github.com/SpeechColab/Leaderboar

Scalable Fair Influence Maximization

Given a graph $G$, a community structure $\mathcal{C}$, and a budget $k$, the fair influence maximization problem aims to select a seed set $S$ ($|S|\leq k$) that maximizes the influence spread while narrowing the influence gap between different communities. While various fairness notions exist, the welfare fairness notion, which balances fairness level and influence spread, has shown promising effectiveness. However, the lack of efficient algorithms for optimizing the welfare fairness objective function restricts its application to small-scale networks with only a few hundred nodes. In this paper, we adopt the objective function of welfare fairness to maximize the exponentially weighted summation over the influenced fraction of all communities. We first introduce an unbiased estimator for the fractional power of the arithmetic mean. Then, by adapting the reverse influence sampling (RIS) approach, we convert the optimization problem to a weighted maximum coverage problem. We also analyze the number of reverse reachable sets needed to approximate the fair influence at a high probability. Further, we present an efficient algorithm that guarantees $1-1/e - \varepsilon$ approximation

Characterization of blaOxA-23 gene regions in isolates of Acinetobacter baumannii

Background/purposeTo investigate the characterization of blaOxA-23 gene regions in isolates of Acinetobacter baumannii from Taizhou Municipal Hospital.MethodsFifty-nine non-repetitive, multiresistant (including imipenem-resistant) isolates of A. baumannii were recovered from clinical infections in hospitalized patients from January 2010 to August 2011 in Taizhou Municipal Hospital (affiliated with Taizhou University) in China. These isolates were genotyped using pulsed-field gel electrophoresis (PFGE). blaOxA-23 β-lactamase and associated genetic structures were analyzed using polymerase chain reaction (PCR), and recombination plasmids were analyzed by BamHI- or SacI- restriction enzyme digestion; predicted promoter structures of blaOxA-23 genes were determined and compared using protein-protein BLAST analysis.ResultsFifteen out of 59 isolates expressing imipenem-resistant A. baumannii clinical isolates acquired either a blaOxA-23 β-lactamase gene. A new gene cluster (ISAba1-blaOxA-23-AMP) with three previously identified transposons (Tn2006, Tn2007, and Tn2008) and one previously identified gene cluster (ISAba1- blaOxA-23) was found in the isolates. Recombination plasmids were analyzed by restriction enzyme digestion.ConclusionOur results indicate that pattern A was the most prevalent molecular type based on PFGE, and that different clones might be widespread with a majority of ISAba1-blaOxA-23 clonal lineages in the 15 PCR positive isolates of A. baumannii in the hospital

MLIC: Multi-Reference Entropy Model for Learned Image Compression

Recently, learned image compression has achieved remarkable performance. The entropy model, which estimates the distribution of the latent representation, plays a crucial role in boosting rate-distortion performance. However, most entropy models only capture correlations in one dimension, while the latent representation contain channel-wise, local spatial, and global spatial correlations. To tackle this issue, we propose the Multi-Reference Entropy Model (MEM) and the advanced version, MEM$^+$. These models capture the different types of correlations present in latent representation. Specifically, We first divide the latent representation into slices. When decoding the current slice, we use previously decoded slices as context and employ the attention map of the previously decoded slice to predict global correlations in the current slice. To capture local contexts, we introduce two enhanced checkerboard context capturing techniques that avoids performance degradation. Based on MEM and MEM$^+$, we propose image compression models MLIC and MLIC$^+$. Extensive experimental evaluations demonstrate that our MLIC and MLIC+ models achieve state-of-the-art performance, reducing BD-rate by $8.05\%$ and $11.39\%$ on the Kodak dataset compared to VTM-17.0 when measured in PSNR.Comment: Fixed some typos and re-organized the pape