Gate-controlled suppression of light-driven proton transport through graphene electrodes.

Recent experiments demonstrated that proton transport through graphene electrodes can be accelerated by over an order of magnitude with low intensity illumination. Here we show that this photo-effect can be suppressed for a tuneable fraction of the infra-red spectrum by applying a voltage bias. Using photocurrent measurements and Raman spectroscopy, we show that such fraction can be selected by tuning the Fermi energy of electrons in graphene with a bias, a phenomenon controlled by Pauli blocking of photo-excited electrons.

Optimal Mapping of Spiking Neural Network to Neuromorphic Hardware for Edge-AI.

Neuromorphic hardware, the new generation of non-von Neumann computing system, implements spiking neurons and synapses to spiking neural network (SNN)-based applications. The energy-efficient property makes the neuromorphic hardware suitable for power-constrained environments where sensors and edge nodes of the internet of things (IoT) work. The mapping of SNNs onto neuromorphic hardware is challenging because a non-optimized mapping may result in a high network-on-chip (NoC) latency and energy consumption.

Towards Convolutional Neural Network Acceleration and Compression Based on -Means.

Convolutional Neural Networks (CNNs) are popular models that are widely used in image classification, target recognition, and other fields. Model compression is a common step in transplanting neural networks into embedded devices, and it is often used in the retraining stage. However, it requires a high expenditure of time by retraining weight data to atone for the loss of precision.

NC-OFDM Satellite Communication Based on Compressed Spectrum Sensing.

With the fast development of giant LEO constellations, the effective spectrum utilization has been regarded as one of the key orientations for satellite communications. This paper focuses on improving the spectrum utilization efficiency of satellite communications by proposing a non-continuous orthogonal frequency division multiplexing (NC-OFDM) method. Based on the models of NC-OFDM system, we first propose a sub-carrier allocation method by using spectrum sensing to efficiently perceive and utilize the spectrum holes in the satellite communication system.

Plastic Crushing Failure of Bio-Inspired Cellular Hierarchical Topological Sandwich Core.

Bio-inspired self-similar hierarchical honeycombs are multifunctional cellular topologies used for resisting various loadings. However, the crushing behavior under large plastic deformation is still unknown. This paper investigates the in-plane compressive response of selective laser melting (SLM) fabricated hierarchical honeycombs.

[Summary and prospect of early warning models and systems for infectious disease outbreaks].

This paper summarizes the basic principles and models of early warning for infectious disease outbreaks, introduces the early warning systems for infectious disease based on different data sources and their applications, and discusses the application potential of big data and their analysing techniques, which have been studied and used in the prevention and control of COVID-19 pandemic, including internet inquiry, social media, mobile positioning, in the early warning of infectious diseases in order to provide reference for the establishment of an intelligent early warning mechanism and platform for infectious diseases based on multi-source big data.

Mining a stroke knowledge graph from literature.

Background: Stroke has an acute onset and a high mortality rate, making it one of the most fatal diseases worldwide. Its underlying biology and treatments have been widely studied both in the "Western" biomedicine and the Traditional Chinese Medicine (TCM). However, these two approaches are often studied and reported in insolation, both in the literature and associated databases.

A Novel Encoder-Decoder Knowledge Graph Completion Model for Robot Brain.

With the rapid development of artificial intelligence, Cybernetics, and other High-tech subject technology, robots have been made and used in increasing fields. And studies on robots have attracted growing research interests from different communities. The knowledge graph can act as the brain of a robot and provide intelligence, to support the interaction between the robot and the human beings.

gene expression signature analysis revealed bosutinib as a radiosensitizer of breast cancer cells by targeting eIF4G1.

Radioresistance is the predominant cause for radiotherapy failure and disease progression, resulting in increased breast cancer‑associated mortality. Using gene expression signature analysis of the Library of Integrated Network‑Based Cellular Signatures (LINCS) and Gene Expression Omnibus (GEO), the aim of the present study was to systematically identify potential candidate radiosensitizers from known drugs. The similarity of integrated gene expression signatures between irradiated eukaryotic translation initiation factor 4 γ 1 (eIF4G1)‑silenced breast cancer cells and known drugs was measured using enrichment scores (ES).

Gyroscope Sensor Based Finger Axes of Rotation Identification Using Screw Displacement.

This paper presents a low-cost, efficient, and portable method for identifying axes of rotation of the proximal interphalangeal and distal interphalangeal joints in an index finger. The approach is associated with the screw displacement representation of rigid body motion. Using the matrix exponential method, a detailed derivation of general spatial displacement of a rigid body in the form of screw displacement including the Rodrigues' formulae for rotation is presented.

A Potential Mechanism of Sodium Channel Mediating the General Anesthesia Induced by Propofol.

General anesthesia has revolutionized healthcare over the past 200 years and continues to show advancements. However, many phenomena induced by general anesthetics including paradoxical excitation are still poorly understood. Voltage-gated sodium channels (Na ) were believed to be one of the proteins targeted during general anesthesia.

Spectral Convolution Feature-Based SPD Matrix Representation for Signal Detection Using a Deep Neural Network.

Convolutional neural networks have powerful performances in many visual tasks because of their hierarchical structures and powerful feature extraction capabilities. SPD (symmetric positive definition) matrix is paid attention to in visual classification, because it has excellent ability to learn proper statistical representation and distinguish samples with different information. In this paper, a deep neural network signal detection method based on spectral convolution features is proposed.

Spectral-Based SPD Matrix Representation for Signal Detection Using a Deep Neutral Network.

The symmetric positive definite (SPD) matrix has attracted much attention in classification problems because of its remarkable performance, which is due to the underlying structure of the Riemannian manifold with non-negative curvature as well as the use of non-linear geometric metrics, which have a stronger ability to distinguish SPD matrices and reduce information loss compared to the Euclidean metric. In this paper, we propose a spectral-based SPD matrix signal detection method with deep learning that uses time-frequency spectra to construct SPD matrices and then exploits a deep SPD matrix learning network to detect the target signal. Using this approach, the signal detection problem is transformed into a binary classification problem on a manifold to judge whether the input sample has target signal or not.

Interval-valued intuitionistic fuzzy MADM method based on TOPSIS and grey correlation analysis.

In this paper, we propose an interval-valued intuitionistic fuzzy Multi-Attribute Decision Making (MADM) method based on improved TOPSIS and Grey Correlation Analysis (GCA), in which the attribute values are interval-valued intuitionistic fuzzy numbers. So that we can deal with imprecise information in fuzzy and rough form in MADM problems by using interval-valued intuitionistic fuzzy numbers Firstly, the concept of interval intuitionistic fuzzy entropy is introduced to calculate the entropy weight of attributes. And the combined weight is calculated by combining the entropy weight with the subjective weight.

Rumor detection based on propagation graph neural network with attention mechanism.

Rumors on social media have always been an important issue that seriously endangers social security. Researches on timely and effective detection of rumors have aroused lots of interest in both academia and industry. At present, most existing methods identify rumors based solely on the linguistic information without considering the temporal dynamics and propagation patterns.

DeepHiC: A generative adversarial network for enhancing Hi-C data resolution.

Hi-C is commonly used to study three-dimensional genome organization. However, due to the high sequencing cost and technical constraints, the resolution of most Hi-C datasets is coarse, resulting in a loss of information and biological interpretability. Here we develop DeepHiC, a generative adversarial network, to predict high-resolution Hi-C contact maps from low-coverage sequencing data.

Simultaneous size manipulation and red upconversion luminescence enhancement of CaF:Yb/Ho nanoparticles by doping with Ce ions.

Harnessing the color tuning capability of upconversion nanoparticles (UCNPs) is of great significance in the field of advanced bioimaging and color display. Here, we report the tunable size and upconversion luminescence (UCL) multicolor in CaF:Yb/Ho/Ce UCNPs, which were synthesized by a facile hydrothermal method. It was found that the size of these UCNPs could be controlled (from 600 to 30 nm) by varying the concentration of Ce ions.

Ultrafast exciton transfer in perovskite CsPbBr quantum dots and topological insulator BiSe film heterostructure.

Recently, topological insulator based heterostructures (HSs) have attracted tremendous research interest, due to their efficient carrier transfer features at the heterointerface induced by metallic surface states. Here, a novel HS comprising 0D perovskite CsPbBr quantum dots (QDs) and 2D material topological insulator BiSe film is proposed and experimentally investigated. Specifically, steady state and time-resolved photoluminescence (PL) measurements are employed, from which a significant quenching behaviour is observed in the HS, with an average quenching factor of 93.

One-Stage Multi-Sensor Data Fusion Convolutional Neural Network for 3D Object Detection.

Three-dimensional (3D) object detection has important applications in robotics, automatic loading, automatic driving and other scenarios. With the improvement of devices, people can collect multi-sensor/multimodal data from a variety of sensors such as Lidar and cameras. In order to make full use of various information advantages and improve the performance of object detection, we proposed a Complex-Retina network, a convolution neural network for 3D object detection based on multi-sensor data fusion.

Sensor Fusion-Based Cooperative Trail Following for Autonomous Multi-Robot System.

Autonomously following a man-made trail in the wild is a challenging problem for robotic systems. Recently, deep learning-based approaches have cast the trail following problem as an image classification task and have achieved great success in the vision-based trail-following problem. However, the existing research only focuses on the trail-following task with a single-robot system.

ConnectedAlign: a PPI network alignment method for identifying conserved protein complexes across multiple species.

Background: In bioinformatics, network alignment algorithms have been applied to protein-protein interaction (PPI) networks to discover evolutionary conserved substructures at the system level. However, most previous methods aim to maximize the similarity of aligned proteins in pairwise networks, while concerning little about the feature of connectivity in these substructures, such as the protein complexes.

Results: In this paper, we identify the problem of finding conserved protein complexes, which requires the aligned proteins in a PPI network to form a connected subnetwork.

Clinical significance of fibrotic, haemostatic and endotoxic changes in patients with liver cirrhosis.

Background And Study Aims: To investigate the relationship among fibrotic, haemostatic and endotoxic changes in patients with different degrees of liver cirrhosis.

Patients And Methods: Liver fibrotic markers, including hyaluronic acid (HA), Ccollagen IV (Col-IV), laminin (LN), and N-terminal pro-peptide of collagen type III (PIIINP), were determined by radioimmunoassay. A series of haemostatic tests, including prothrombin time (PT), international normalized ratio, activated partial thromboplastin time, antithrombin-III, thrombin time, fibrinogen, fibrin(ogen) degradation product and D-dimer were determined using an automatic coagulation analyszer.