Analysis acoustic target strength of anechoic coatings in low frequency based on equivalent parameter inversion

It’s generally different to predict the acoustic target strength of a submarine with anechoic coatings by conventional finite element method (FEM). Parameter inversion is a common method to solve this problem. Some researchers have studied the parameter inversion about normal incidence of anechoic coatings. In this paper, the FEM of impedance tube is used to obtain acoustic reflection coefficient of anechoic coatings. Then the genetic algorithm is applied to acquire the physical parameters of viscoelastic material which is equivalent to anechoic coatings. The LMS Virtual Lab is used to construct finite element model of the anechoic coatings to validate the parameter inversion with normal incidence

Aqua­[4-chloro-2-(2-pyridylmethyl­imino­meth­yl)phenolato]copper(II) nitrate monohydrate

In the title mononuclear complex, [Cu(C13H10ClN2O)(H2O)]­NO3·H2O, the CuII atom is four-coordinated by two N atoms and one O atom of the tridentate Schiff base ligand and one O atom from the coordinated water mol­ecule in a slightly distorted square-planar configuration. The nitrate ion inter­acts with the copper center [Cu1⋯O3 = 2.579 (4) Å]. In the crystal, the cations, anions and water mol­ecules are linked by O—H⋯O and O—H⋯N hydrogen bonds

A Dynamic Graph Interactive Framework with Label-Semantic Injection for Spoken Language Understanding

Multi-intent detection and slot filling joint models are gaining increasing traction since they are closer to complicated real-world scenarios. However, existing approaches (1) focus on identifying implicit correlations between utterances and one-hot encoded labels in both tasks while ignoring explicit label characteristics; (2) directly incorporate multi-intent information for each token, which could lead to incorrect slot prediction due to the introduction of irrelevant intent. In this paper, we propose a framework termed DGIF, which first leverages the semantic information of labels to give the model additional signals and enriched priors. Then, a multi-grain interactive graph is constructed to model correlations between intents and slots. Specifically, we propose a novel approach to construct the interactive graph based on the injection of label semantics, which can automatically update the graph to better alleviate error propagation. Experimental results show that our framework significantly outperforms existing approaches, obtaining a relative improvement of 13.7% over the previous best model on the MixATIS dataset in overall accuracy.Comment: Submitted to ICASSP 202

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

ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding

Spoken language understanding (SLU) is a fundamental task in the task-oriented dialogue systems. However, the inevitable errors from automatic speech recognition (ASR) usually impair the understanding performance and lead to error propagation. Although there are some attempts to address this problem through contrastive learning, they (1) treat clean manual transcripts and ASR transcripts equally without discrimination in fine-tuning; (2) neglect the fact that the semantically similar pairs are still pushed away when applying contrastive learning; (3) suffer from the problem of Kullback-Leibler (KL) vanishing. In this paper, we propose Mutual Learning and Large-Margin Contrastive Learning (ML-LMCL), a novel framework for improving ASR robustness in SLU. Specifically, in fine-tuning, we apply mutual learning and train two SLU models on the manual transcripts and the ASR transcripts, respectively, aiming to iteratively share knowledge between these two models. We also introduce a distance polarization regularizer to avoid pushing away the intra-cluster pairs as much as possible. Moreover, we use a cyclical annealing schedule to mitigate KL vanishing issue. Experiments on three datasets show that ML-LMCL outperforms existing models and achieves new state-of-the-art performance

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

Unify, Align and Refine: Multi-Level Semantic Alignment for Radiology Report Generation

Automatic radiology report generation has attracted enormous research interest due to its practical value in reducing the workload of radiologists. However, simultaneously establishing global correspondences between the image (e.g., Chest X-ray) and its related report and local alignments between image patches and keywords remains challenging. To this end, we propose an Unify, Align and then Refine (UAR) approach to learn multi-level cross-modal alignments and introduce three novel modules: Latent Space Unifier (LSU), Cross-modal Representation Aligner (CRA) and Text-to-Image Refiner (TIR). Specifically, LSU unifies multimodal data into discrete tokens, making it flexible to learn common knowledge among modalities with a shared network. The modality-agnostic CRA learns discriminative features via a set of orthonormal basis and a dual-gate mechanism first and then globally aligns visual and textual representations under a triplet contrastive loss. TIR boosts token-level local alignment via calibrating text-to-image attention with a learnable mask. Additionally, we design a two-stage training procedure to make UAR gradually grasp cross-modal alignments at different levels, which imitates radiologists' workflow: writing sentence by sentence first and then checking word by word. Extensive experiments and analyses on IU-Xray and MIMIC-CXR benchmark datasets demonstrate the superiority of our UAR against varied state-of-the-art methods.Comment: 8 pages,6 figures,4 table

Aqua­bis(2,3-dimethyl-4-oxo-4H-pyrido[1,2-a]pyrimidin-9-olato)nickel(II)

In the crystal structure of the mononuclear title complex, [Ni(C10H9N2O2)2(H2O)], the NiII ion is five-coordinated in a distorted square-pyramidal geometry by two N atoms and two O atoms from 2,3-dimethyl-4-oxopyrido[1,2-a]pyrimidin-9-olate ligands and one O atom from a water mol­ecule. O—H⋯O hydrogen bonds between the coordinated water mol­ecule and the ligand connect adjacent mol­ecules, forming a ribbon parallel to the b axis

Association between exposure to noise and risk of hypertension: A meta-analysis of observational epidemiological studies

Background and Objective: An increasing amount of original studies suggested that exposure to noise could be associated with the risk of hypertension, but the results remain inconsistent and inconclusive. We aimed to synthesize available epidemiological evidence about the relationship between various types of noise and hypertension, and to explore the potential dose-response relationship between them in an up-to-date meta-analysis. Methods: We conducted a literature search of PubMed and Embase from these databases’ inception through December 2016 to identify observational epidemiological studies examining the association between noise and risk of hypertension. A Random-effects model was used to combine the results of included studies. Dose-response meta-analysis was conducted to examine the potential dose-response relationship. Results: Thirty-two studies (five cohort studies, one case-control study, and twenty-six cross-section Studies) involving 264,678 participants were eligible for inclusion. Pooled result showed that living or working in environment with noise exposure was significantly associated with increase risk of hypertension (OR 1.62; 95% CI: 1.40 to 1.88). We found no evidence of a curve linear association between noise and risk of hypertension. Dose-response analysis suggested that, for an increment of per 10 dB(A) of noise, the combined odds ratio of hypertension was 1.06 (95% CI: 1.04 to 1.08). Conclusions: Integrated epidemiological evidence supports the hypothesis that exposure to noise may be a risk factor of hypertension, and there is a positive dose-response association between them

Metabolism of Functional Oligosaccharides and Antagonism of Foodborne Pathogens by Resting Cells of Limosilactobacillus reuteri

Based on the metabolic activity and biotransformation capacity of the resting cells of Limosilactobacillus reuteri, its ability to metabolize functional oligosaccharides and antagonize foodborne pathogens was explored. A phylogenetic tree for L. reuteri was constructed based on its 16S rRNA gene sequence. The environmental tolerance of the resting cells of L. reuteri was analyzed by acid and bile salt tolerance, and antibiotic sensitivity assays. The effects of functional oligosaccharides on the metabolism, surface hydrophobicity, adhesion capacity and reuterin (a broad-spectrum antibacterial) production ability of L. reuteri were evaluated by using modified media. Then, the inhibitory effect of the resting cells of L. reuteri on the adhesion of Staphylococcus aureus to Caco-2 cells and its growth in skim milk was explored. Results showed that both L. reuteri strains tested could tolerate extreme acidic conditions and bile salts for more than 3 h and were susceptible to common antibiotics. All functional oligosaccharides tested except xylo-oligosaccharides could be metabolized by the resting cells, and its growth, surface hydrophobicity and adhesion capacity were improved significantly by raffinose (P < 0.05). Raffinose could promote the production of reuterin from L. reuteri HLRE13 by utilizing glycerol, attaining a concentration of (1.34 ± 0.03) g/L. Glycerol promoted the inhibitory effect of L. reuteri HLRE13 on a variety of foodborne pathogens significantly (P < 0.001), and inhibited the adhesion of S. aureus to Caco-2 cells by 41.67% through exclusion. Furthermore, glycerol could promote L. reuteri HLRE13 to reduce the number of S. aureus to 103 CFU/mL during co-culture. This study will provide a scientific basis for the functional development of resting cells of L. reuteri and its synergistic efficacy with functional oligosaccharides