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

Bayesian Recursive Information Optical Imaging: A Ghost Imaging Scheme Based on Bayesian Filtering

Computational imaging~(CI) has been attracting a lot of interest in recent years for its superiority over traditional imaging in various applications. In CI systems, information is generally acquired in an encoded form and subsequently decoded via processing algorithms, which is quite in line with the information transmission mode of modern communication, and leads to emerging studies from the viewpoint of information optical imaging. Currently, one of the most important issues to be theoretically studied for CI is to quantitatively evaluate the fundamental ability of information acquisition, which is essential for both objective performance assessment and efficient design of imaging system. In this paper, by incorporating the Bayesian filtering paradigm, we propose a framework for CI that enables quantitative evaluation and design of the imaging system, and demonstate it based on ghost imaging. In specific, this framework can provide a quantitative evaluation on the acquired information through Fisher information and Cram\'er-Rao Lower Bound (CRLB), and the intrinsic performance of the imaging system can be accessed in real-time. With simulation and experiments, the framework is validated and compared with existing linear unbiased algorithms. In particular, the image retrieval can reach the CRLB. Furthermore, information-driven adaptive design for optimizing the information acquisition procedure is also achieved. By quantitative describing and efficient designing, the proposed framework is expected to promote the practical applications of CI techniques

Photonic RF Channelization Based on Microcombs

In recent decades, microwave photonic channelization techniques have developed significantly. Characterized by low loss, high versatility, large instantaneous bandwidth, and immunity to electromagnetic interference, microwave photonic channelization addresses the requirements of modern radar and electronic warfare for receivers. Microresonator-based optical frequency combs are promising devices for photonic channelized receivers, enabling full advantage of multicarriers, large bandwidths, and accelerating the integration process of microwave photonic channelized receivers. In this paper, we review the research progress and trends in microwave photonic channelization, focusing on schemes that utilize integrated microcombs. We discuss the potential of microcomb-based RF channelization, as well as their challenges and limitations, and provide perspectives for their future development in the context of on-chip silicon-based photonics.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

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