To obtain complets that are highly descriptive to image compositions, a weakly supervised complet ranking algorithm is designed by quantifying the quality of each complet.  https://www.selleckchem.com/products/leupeptin-hemisulfate.html  The algorithm seamlessly encodes three factors the image-level quality discrimination, weakly supervised constraint, and complet geometry of each image. Based on the top-ranking complets, a novel multi-column convolutional neural network (CNN) called SDA-Net is designed, which supports input segments with arbitrary shapes. The key is a dual-aggregation mechanism that fuses the intracomplet deep features and the intercomplet deep features under a unified framework. Thorough experimental validations on a series of benchmark data sets demonstrated the superiority of our method.Tactile representation on touchscreens plays an important role in improving realism and richness of users' interaction experience. The dynamic lateral force range and the efficient feedback dimensions are very critical in determining the fidelity of tactile displays. This study develops a tri-modal Electrovibration, Ultrasonic Vibraiton and Mechanical Vibraiton (EUMV) tactile display integrating three types of representative principles, which enhances the dynamic lateral force range by leveraging electrostatic and ultrasonic vibrations stimuli, and induces the normal feedback dimension by utilizing mechanical vibration stimulus. Then, a tactile perception scheme with the EUMV display is proposed for simultaneously rendering contour and texture roughness features of visualized surfaces, in which the contour gradient-lateral force model and the texture gradient-perceived roughness model are determined respectively. Objective and subjective evaluations with twenty participants show that the novel scheme establishes significant improvements in both correct recognition ratios of geometric shapes and tactile perception realism of visualized images than the previous studies.A multi-channel analog front-end (AFE) ASIC for wearable EEG recording application is presented in this paper. Two techniques, namely chopping stabilization (CS) and time-division-multiplexing (TDM) are combined in a unified manner to improve the input-referred noise and the system level common-mode rejection ratio (CMRR) for multi-channel AFE. With the proposed TDM/CS structure, multiple channels can share single second-stage amplifier for significant reduction in chip size and power consumption. Dual feedback loops for input impedance boosting as well as electrode offset cancellation are incorporated in the system. Implemented in a 0.18-μm CMOS process, the AFE consumes 24 μW under 1 V supply. The input referred noise is 0.63 μVrms in 0.5 Hz - 100 Hz and the input impedance is boosted to 560 MΩ at 50 Hz. The measured amplifier intrinsic CMRR and system-level AFE CMRR are 89 dB and 82 dB, respectively.Conditions play an essential role in biomedical statements. However, existing biomedical knowledge graphs (BioKGs) only focus on factual knowledge, organized as a flat relational network of biomedical concepts. These BioKGs ignore the conditions of the facts being valid, which loses essential contexts for knowledge exploration and inference. We consider both facts and their conditions in biomedical statements and proposed a three-layered information-lossless representation of BioKG. The first layer has biomedical concept nodes, attribute nodes. The second layer represents both biomedical fact and condition tuples by nodes of the relation phrases, connecting to the subject and object in the first layer. The third layer has nodes of statements connecting to a set of fact tuples and/or condition tuples in the second layer. We transform the BioKG construction problem into a sequence labeling problem based on a novel designed tag schema. We design a Multi-Input Multi-Output sequence labeling model (MIMO) that learns from multiple input signals and generates proper number of multiple output sequences for tuple extraction. Experiments on a newly constructed dataset show that MIMO outperforms the existing methods. Further case study demonstrates that the BioKGs constructed provide a good understanding of the biomedical statements.The advent of single-cell RNA sequencing (scRNA-seq) techniques opens up new opportunities for studying the cell-specific changes in the transcriptomic data. An important research problem related with scRNA-seq data analysis is to identify cell subpopulations with distinct functions. However, the expression profiles of individual cells are usually measured over tens of thousands of genes, and it remains a difficult problem to effectively cluster the cells based on the high-dimensional profiles. An additional challenge of performing the analysis is that, the scRNA-seq data are often noisy and sometimes extremely sparse due to technical limitations and sampling deficiencies. In this paper, we propose a biclustering-based framework called DivBiclust that effectively identifies the cell subpopulations based on the high-dimensional noisy scRNA-seq data. Compared with nine state-of-the-art methods, DivBiclust excels in identifying cell subpopulations with high accuracy as evidenced by our experiments on ten real scRNA-seq datasets with different size and diverse dropout rates. The supplemental materials of DivBiclust, including the source codes, data, and a supplementary document, are available at https//www.github.com/Qiong-Fang/DivBiclust.Spinal Cord Injury (SCI) is a serious condition that can result in loss of motor and sensory functions by disrupting communication among neurons, i.e., neuro-spike communication. Future information and communication technology (ICT) based treatment techniques for SCI are expected to rely on nano networks, deployed inside the body. In this respect, modeling neuro-spike communication channels in the spinal cord and revealing the relationship between channel metrics and SCI are required to realize these treatment techniques and diagnosis tools such as replacement neural implants, high-performance diagnosis tools, which are based on ICT metrics instead of large medical data. Therefore, in this study, we focus on a spinal cord network, namely the descending spinal cord pathway, which is responsible for the transmission of brain motor signals to the spinal cord. We aim to analyze the rate of motor information flow to the corresponding muscle. To this end, we model the spinal cord motor network as a layered network consisting of a cascade of two independent neuro-spike channels, which are brain-spinal cord network and spinal cord interneuron-spinal cord motoneuron network.