Uncertain Dynamic Characteristic Analysis for Structures with Spatially Dependent Random System Parameters

This work presents a robust non-deterministic free vibration analysis for engineering structures with random field parameters in the frame of stochastic finite element method. For this, considering the randomness and spatial correlation of structural physical parameters, a parameter setting model based on random field theory is proposed to represent the random uncertainty of parameters, and the stochastic dynamic characteristics of different structural systems are then analyzed by incorporating the presented parameter setting model with finite element method. First, Gauss random field theory is used to describe the uncertainty of structural material parameters, the random parameters are then characterized as the standard deviation and correlation length of the random field, and the random field parameters are then discretized with the Karhunen–Loeve expansion method. Moreover, based on the discretized random parameters and finite element method, structural dynamic characteristics analysis is addressed, and the probability distribution density function of the random natural frequency is estimated based on multi-dimensional kernel density estimation method. Finally, the random field parameters of the structures are quantified by using the maximum likelihood estimation method to verify the effectiveness of the proposed method and the applicability of the constructed model. The results indicate that (1) for the perspective of maximum likelihood estimation, the parameter setting at the maximum value point is highly similar to the input parameters; (2) the random field considering more parameters reflects a more realistic structure

Connecting Speech Encoder and Large Language Model for ASR

The impressive capability and versatility of large language models (LLMs) have aroused increasing attention in automatic speech recognition (ASR), with several pioneering studies attempting to build integrated ASR models by connecting a speech encoder with an LLM. This paper presents a comparative study of three commonly used structures as connectors, including fully connected layers, multi-head cross-attention, and Q-Former. Speech encoders from the Whisper model series as well as LLMs from the Vicuna model series with different model sizes were studied. Experiments were performed on the commonly used LibriSpeech, Common Voice, and GigaSpeech datasets, where the LLMs with Q-Formers demonstrated consistent and considerable word error rate (WER) reductions over LLMs with other connector structures. Q-Former-based LLMs can generalise well to out-of-domain datasets, where 12% relative WER reductions over the Whisper baseline ASR model were achieved on the Eval2000 test set without using any in-domain training data from Switchboard. Moreover, a novel segment-level Q-Former is proposed to enable LLMs to recognise speech segments with a duration exceeding the limitation of the encoders, which results in 17% relative WER reductions over other connector structures on 90-second-long speech data

Fine-grained Audio-Visual Joint Representations for Multimodal Large Language Models

Audio-visual large language models (LLM) have drawn significant attention, yet the fine-grained combination of both input streams is rather under-explored, which is challenging but necessary for LLMs to understand general video inputs. To this end, a fine-grained audio-visual joint representation (FAVOR) learning framework for multimodal LLMs is proposed in this paper, which extends a text-based LLM to simultaneously perceive speech and audio events in the audio input stream and images or videos in the visual input stream, at the frame level. To fuse the audio and visual feature streams into joint representations and to align the joint space with the LLM input embedding space, we propose a causal Q-Former structure with a causal attention module to enhance the capture of causal relations of the audio-visual frames across time. An audio-visual evaluation benchmark (AVEB) is also proposed which comprises six representative single-modal tasks with five cross-modal tasks reflecting audio-visual co-reasoning abilities. While achieving competitive single-modal performance on audio, speech and image tasks in AVEB, FAVOR achieved over 20% accuracy improvements on the video question-answering task when fine-grained information or temporal causal reasoning is required. FAVOR, in addition, demonstrated remarkable video comprehension and reasoning abilities on tasks that are unprecedented by other multimodal LLMs. An interactive demo of FAVOR is available at https://github.com/BriansIDP/AudioVisualLLM.git, and the training code and model checkpoints will be released soon

Spatial analysis of malaria in Anhui province, China

<p>Abstract</p> <p>Background</p> <p>Malaria has re-emerged in Anhui Province, China, and this province was the most seriously affected by malaria during 2005–2006. It is necessary to understand the spatial distribution of malaria cases and to identify highly endemic areas for future public health planning and resource allocation in Anhui Province.</p> <p>Methods</p> <p>The annual average incidence at the county level was calculated using malaria cases reported between 2000 and 2006 in Anhui Province. GIS-based spatial analyses were conducted to detect spatial distribution and clustering of malaria incidence at the county level.</p> <p>Results</p> <p>The spatial distribution of malaria cases in Anhui Province from 2000 to 2006 was mapped at the county level to show crude incidence, excess hazard and spatial smoothed incidence. Spatial cluster analysis suggested 10 and 24 counties were at increased risk for malaria (<it>P </it>< 0.001) with the maximum spatial cluster sizes at < 50% and < 25% of the total population, respectively.</p> <p>Conclusion</p> <p>The application of GIS, together with spatial statistical techniques, provide a means to quantify explicit malaria risks and to further identify environmental factors responsible for the re-emerged malaria risks. Future public health planning and resource allocation in Anhui Province should be focused on the maximum spatial cluster region.</p

Advances in the Antifungal Effects of Plant Essential Oils

Foods, agricultural by-products and medicinal plants are often contaminated by pathogenic fungi and mycotoxins. Pathogenic fungi not only lead to food deterioration, but also harm people’s health. The commonly used methods for detoxification include physical, chemical and biological methods. A large number of studies have shown that natural plant essential oils inhibit the growth of pathogenic fungi and the accumulation of mycotoxins and have good biodegradability, making them a promising environmental-friendly alternative to traditional synthetic fungicides. In this review, the classification and harm of common pathogenic fungi and mycotoxins, as well as the antifungal components and mechanisms of plant essential oils are introduced, and recent progress on plant essential oils in the control of pathogenic fungi and mycotoxins is summarized. This review provides a theoretical basis for the development and application of plant essential oils

Quantum storage of entangled photons at telecom wavelengths in a crystal

The quantum internet -- in synergy with the internet that we use today -- promises an enabling platform for next-generation information processing, including exponentially speed-up distributed computation, secure communication, and high-precision metrology. The key ingredients for realizing such a global network are the distribution and storage of quantum entanglement. As quantum networks are likely to be based on existing fibre networks, telecom-wavelength entangled photons and corresponding quantum memories are of central interest. Recently, ${\rm ^{167}Er^{3+}}$ ions have been identified as a promising candidate for an efficient, broadband quantum memory at telecom wavelength. However, to date, no storage of entangled photons, the crucial step of quantum memory using these ions, has been reported. Here, we demonstrate the storage and recall of the entangled state of two telecom photons generated from an integrated photonic chip based on silicon nitride. Combining the natural narrow linewidth of the entangled photons and long storage time of ${\rm ^{167}Er^{3+}}$ ions, we achieve storage time of 400 ns, more than one order of magnitude longer than in previous works. Successful storage of entanglement in the crystal is certified by a violation of an entanglement witness by more than 12 standard deviations (-0.161 $\pm$ 0.012) at 400 ns storage time. These results pave the way for realizing quantum networks based on solid-state devices.Comment: 15 pages, 11 figure

Genome Editing of Pik3cd Impedes Abnormal Retinal Angiogenesis

Abnormal angiogenesis is associated with myriad human diseases including proliferative diabetic retinopathy. Signaling transduction via phosphoinositide 3-kinases (PI3Ks) plays a critical role in angiogenesis. Herein, we showed that p110δ, the catalytic subunit of PI3Kδ, was highly expressed in pathological retinal vascular endothelial cells (ECs) in a mouse model of oxygen-induced retinopathy (OIR) and in fibrovascular membranes from patients with proliferative diabetic retinopathy. To explore novel intervention with PI3Kδ expression, we developed a recombinant dual adeno-associated viral (rAAV) system for delivering CRISPR/Cas9 in which Streptococcus pyogenes (Sp) Cas9 expression was driven by an endothelial specific promoter of intercellular adhesion molecule 2 (pICAM2) to edit genomic Pik3cd, the gene encoding p110δ. We then demonstrated that infection of cultured mouse vascular endothelial cells with the dual rAAV1s of rAAV1-pICAM2-SpCas9 and rAAV1-SpGuide targeting genomic Pik3cd resulted in 80% DNA insertion/deletion in the locus of genomic Pik3cd and 70% depletion of p110δ expression. Furthermore, we showed that in the mouse model of OIR editing retinal Pik3cd with the dual rAAV1s resulted in not only a significant decrease in p110δ expression, and Akt activation, but also a dramatic reduction in pathological retinal angiogenesis. These findings reveal that Pik3cd editing is a novel approach to treating abnormal retinal angiogenesis

Correction to: Genome Editing of Pik3cd Impedes Abnormal Retinal Angiogenesis, by Wu et al. Hum Gene Ther 2023;34(1-2):30-41; doi: 10.1089/hum.2022.079

In the January 2023 issue of Human Gene Therapy (vol. 34, no. 1-2; 30–41), the article titled Genome Editing of Pik3cd Impedes Abnormal Retinal Angiogenesis, by Wu et al. requires correction. The author byline originally appeared with the 13th author's name incorrectly published as GuomingZhao Wenyi Wu,1,2,3 Gaoen Ma,4 Hui Qi,5 Lijun Dong,5 Fang Chen,6 Yun Wang,5 Xingxing Mao,5 Xiaoqing Guo,2,3 Jing Cui,7 Joanne Aiko Matsubara,7 Bart Vanhaesebroeck,8 Xiaohe Yan,5Guoming Zhao,5 Shaochong Zhang,5,* and Hetian Lei 5,* The correct spelling of the author's name is GuomingZhang The online version of the article has been corrected to reflect this. The authors apologize for the error