AAEC: An Adversarial Autoencoder-based Classifier for Audio Emotion Recognition

Changzeng Fu, Jiaqi Shi, Chaoran Liu, Carlos Toshinori Ishi, and Hiroshi Ishiguro. 2020. AAEC: An Adversarial Autoencoder-based Classifier for Audio Emotion Recognition. In Proceedings of the 1st International on Multimodal Sentiment Analysis in Real-life Media Challenge and Workshop (MuSe'20). Association for Computing Machinery, New York, NY, USA, 45–51. DOI:https://doi.org/10.1145/3423327.3423669.MM '20: The 28th ACM International Conference on Multimedia [October 16, 2020

Pressure drop characteristics of adjustable slotted distributor in fluidized bed

[EN] In this paper, a fluidized bed with a adjustable slotted gas distributor was used to study fluidization in a 230 mm×200 mm rectangular fluidized bed by adjusting the spacing between the two slotted gas distributors. The pressure drop of the distributor at different inlet gas velocities was obtained and the change law between pressure drop and distance between distributors was summarized. This study provides a theoretical basis for the application of adjustable slotted gas distributor fluidized bed.The authors acknowledge Projects supported by the National Natural Science Foundation of China (Grant No. 31571906 & No.21506163).Tong, Z.; Chaoran, L.; Qing, X.; Zhanyong, L.; W., J. (2018). Pressure drop characteristics of adjustable slotted distributor in fluidized bed. En IDS 2018. 21st International Drying Symposium Proceedings. Editorial Universitat Politècnica de València. 1751-1758. https://doi.org/10.4995/IDS2018.2018.7729OCS1751175

Astrocytic p75NTR expression provoked by ischemic stroke exacerbates the blood-brain barrier disruption

The disruption of the blood–brain barrier (BBB) plays a critical role in the pathology of ischemic stroke. p75 neurotrophin receptor (p75NTR) contributes to the disruption of the blood-retinal barrier in retinal ischemia. However, whether p75NTR influences the BBB permeability after acute cerebral ischemia remains unknown. The present study investigated the role and underlying mechanism of p75NTR on BBB integrity in an ischemic stroke mouse model, middle cerebral artery occlusion (MCAO). After 24 h of MCAO, astrocytes and endothelial cells in the infarct-affected brain area up-regulated p75NTR. Genetic p75NTR knockdown (p75NTR+/ ) or pharmacological inhibition of p75NTR using LM11A-31, a selective inhibitor of p75NTR, both attenuated brain damage and BBB leakage in MCAO mice. Astrocyte-specific conditional knockdown of p75NTR mediated with an adeno-associated virus significantly ameliorated BBB disruption and brain tissue damage, as well as the neurological functions after stroke. Further molecular biological examinations indicated that astrocytic p75NTR activated NF-κB and HIF-1α signals, which upregulated the expression of MMP-9 and vascular endothelial growth factor (VEGF), subsequently leading to tight junction degradation after ischemia. As a result, increased leukocyte infiltration and microglia activation exacerbated brain injury after stroke. Overall, our results provide novel insight into the role of astrocytic p75NTR in BBB disruption after acute cerebral ischemia. The p75NTR may therefore be a potential therapeutic target for the treatment of ischemic stroke

Medical image segmentation based on self-supervised hybrid fusion network

Automatic segmentation of medical images has been a hot research topic in the field of deep learning in recent years, and achieving accurate segmentation of medical images is conducive to breakthroughs in disease diagnosis, monitoring, and treatment. In medicine, MRI imaging technology is often used to image brain tumors, and further judgment of the tumor area needs to be combined with expert analysis. If the diagnosis can be carried out by computer-aided methods, the efficiency and accuracy will be effectively improved. Therefore, this paper completes the task of brain tumor segmentation by building a self-supervised deep learning network. Specifically, it designs a multi-modal encoder-decoder network based on the extension of the residual network. Aiming at the problem of multi-modal feature extraction, the network introduces a multi-modal hybrid fusion module to fully extract the unique features of each modality and reduce the complexity of the whole framework. In addition, to better learn multi-modal complementary features and improve the robustness of the model, a pretext task to complete the masked area is set, to realize the self-supervised learning of the network. Thus, it can effectively improve the encoder’s ability to extract multi-modal features and enhance the noise immunity. Experimental results present that our method is superior to the compared methods on the tested datasets

Smoking Experimentation among Elementary School Students in China: Influences from Peers, Families, and the School Environment

The aim of this study was to investigate experimentation with smoking among primary school students in China. Data were acquired from a recent survey of 4,073 students in grades 4 to 6 (ages 9–12) in 11 primary schools of Ningbo City. The questions were adapted from the Global Youth Tobacco Survey (GYTS). Results suggest that although the Chinese Ministry of Education (MOE) encourages smoke-free schools, experimentation with cigarettes remains a serious problem among primary school students in China. Peers, family members, and the school environment play important roles in influencing smoking experimentation among students. Having a friend who smoked, seeing a family member smoke, and observing a teacher smoking on campus predicted a higher risk of experimentation with smoking; the exposure to anti-tobacco materials at school predicted a lower risk of experimentation with smoking. The evidence suggests that public health practitioners and policymakers should seek to ensure the implementation of smoke-free policies and that intervention should target young people, families, and communities to curb the commencement of smoking among children and adolescents in China

Unique hole-accepting carbon-dots promoting selective carbon dioxide reduction nearly 100% to methanol by pure water

X.L., M.K.B., J.T. acknowledge EPSRC (EP/S018204/2/1), Leverhulme Trust (Grant No: RPG-2017-122) and Newton Advanced Fellowship grant ((NA170422 and NAF\R1\191163).). X.H., Z.X.G. and S.A.S. acknowledge UCL Grace High-Performance Computing Facility (Grace@UCL) and EPSRC (EP/K021192/1, EP/L018330/1). R.G. thanks the FRQNT for postdoctoral funding and NSERC for operational funding. Y.W., J.C. and C.J., acknowledge CSC Scholarship. R.G., J.F.T. and J.R.D. acknowledge ERC AdG Intersolar grant (291482). J.F.T. acknowledges EPSRC CDT (EP/L015277/1). W.Z. thanks EPSRC for Titan Themis S/TEM microscope (EP/L017008/01). We also thank Dr. Jijia Xie for constructive comments on experimental design.Solar-driven CO2 reduction by abundant water to alcohols can supply sustainable liquid fuels and alleviate global warming. However, the sluggish water oxidation reaction has been hardly reported to be efficient and selective in CO2 conversion due to fast charge recombination. Here, using transient absorption spectroscopy, we demonstrate that microwave-synthesised carbon-dots (mCD) possess unique hole-accepting nature, prolonging the electron lifetime (t50%) of carbon nitride (CN) by six folds, favouring a six-electron product. mCD-decorated CN stably produces stoichiometric oxygen and methanol from water and CO2 with nearly 100% selectivity to methanol and internal quantum efficiency of 2.1% in the visible region, further confirmed by isotopic labelling. Such mCD rapidly extracts holes from CN and prevents the surface adsorption of methanol, favourably oxidising water over methanol and enhancing the selective CO2 reduction to alcohols. This work provides a unique strategy for efficient and highly selective CO2 reduction by water to high-value chemicals.Publisher PDFPeer reviewe