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Music recommendation systems have emerged as a vital component to enhance user experience and satisfaction for the music streaming services, which dominates music consumption. The key challenge in improving these recommender systems lies in comprehending the complexity of music data, specifically for the underpinning music genre classification. The limitations of manual genre classification have highlighted the need for a more advanced system, namely the Automatic Music Genre Classification (AMGC) system. While traditional machine learning techniques have shown potential in genre classification, they heavily rely on manually engineered features and feature selection, failing to capture the full complexity of music data. On the other hand, deep learning classification architectures like the traditional Convolutional Neural Networks (CNN) are effective in capturing the spatial hierarchies but struggle to capture the temporal dynamics inherent in music data. To address these challenges, this study proposes a novel approach using visual spectrograms as input, and propose a hybrid model that combines the strength of the Residual neural Network (ResNet) and the Gated Recurrent Unit (GRU). This model is designed to provide a more comprehensive analysis of music data, offering the potential to improve the music recommender systems through achieving a more comprehensive analysis of music data and hence potentially more accurate genre classification.

3'UTR Sequencing of Breast cancer cells RNA Seq of NCI H1975 WT, Vector, desARE3'UTR ERBB2-3,30 Optical genome mapping data of NCI H1975 WT, Vector, desARE3'UTRERBB2-3,30.

In engineering optimization problems, multiple objectives with a large number of variables under highly nonlinear constraints are usually required to be simultaneously optimized. Significant computing effort are required to find the Pareto front of a nonlinear multi-objective optimization problem. Swarm intelligence based metaheuristic algorithms have been successfully applied to solve multi-objective optimization problems. Recently, an individual intelligence based algorithm called beetle antennae search algorithm was proposed. This algorithm was proved to be more computationally efficient. Therefore, we extended this algorithm to solve multi-objective optimization problems. The proposed multi-objective beetle antennae search algorithm is tested using four well-selected benchmark functions and its performance is compared with other multi-objective optimization algorithms. The results show that the proposed multi-objective beetle antennae search algorithm has higher computational efficiency with satisfactory accuracy., Comment: 5 figures and 1 table

JunFei Zhang, Jia Liu, JiaLi Wu, WenFeng Li, ZhongWei Chen, LiShan YangDepartment of Emergency Medical, General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750000, People’s Republic of ChinaAbstract: CRYAB is a member of the small heat shock protein family, first discovered in the lens of the eye, and involved in various diseases, such as eye and heart diseases and even cancers, for example, breast cancer, lung cancer, prostate cancer, and ovarian cancer. In addition, CRYAB proteins are involved in a variety of signaling pathways including apoptosis, inflammation, and oxidative stress. This review summarizes the recent progress concerning the role of CRYAB in signaling pathways and diseases. Therefore, the role of CRYAB in signaling pathways and cancers is urgently needed. This article reviews the regulation of CRYAB in the apoptotic inflammatory signaling pathway and its role in cancers progression and as a key role in anti-cancer therapy targeting CRYAB in an effort to improve outcomes for patients with metastatic disease.Keywords: CRYAB, signaling pathways, cancers

Recently, there has been growing interest in developing learning-based methods to detect and utilize salient semi-global or global structures, such as junctions, lines, planes, cuboids, smooth surfaces, and all types of symmetries, for 3D scene modeling and understanding. However, the ground truth annotations are often obtained via human labor, which is particularly challenging and inefficient for such tasks due to the large number of 3D structure instances (e.g., line segments) and other factors such as viewpoints and occlusions. In this paper, we present a new synthetic dataset, Structured3D, with the aim of providing large-scale photo-realistic images with rich 3D structure annotations for a wide spectrum of structured 3D modeling tasks. We take advantage of the availability of professional interior designs and automatically extract 3D structures from them. We generate high-quality images with an industry-leading rendering engine. We use our synthetic dataset in combination with real images to train deep networks for room layout estimation and demonstrate improved performance on benchmark datasets., Comment: Accepted to ECCV 2020. Project website: https://structured3d-dataset.org

This paper presents a high-speed medical image acquisition method of the Digital Subtract Angiography system based on ActiveMIL groupware. This system adopts two grab buffer to grab medical image based on card Matrox Meteor II/Digital and presents a algorithm for DSA Process. Finally this system realizes more DSA images process function effects.