Weight Try-Once-Discard Protocol-Based L_2 L_infinity State Estimation for Markovian Jumping Neural Networks with Partially Known Transition Probabilities

It was the L_2 L_infinity performance index that for the first time is initiated into the discussion on state estimation of delayed MJNNs with with partially known transition probabilities, which provides a more general promotion for the estimation error.The WTOD protocol is adopted to dispatch the sensor nodes so as to effectively alleviate the updating frequency of output signals. The hybrid effects of the time delays, Markov chain, and protocol parameters are apparently reflected in the co-designed estimator which can be solved by a combination of comprehensive matrix inequalities

Efficient colour texture image retrieval by combination of colour and texture features in wavelet domain

International audienceA new approach for image retrieval by combination of colour and texture features is proposed. This approach uses the histogram of feature vectors, which are constructed from the coefficients of some subbands of wavelet transform and chosen according to their intrinsic characters. A K-means algorithm is used to quantise feature vectors. The experimental results both on small size databases (40 classes of textures) and large size databases (167 classes of textures) show that, compared with the state-of-the-art approaches, the proposed approach can achieve better retrieval performance

Efficient colour texture image retrieval by combination of colour and texture features in wavelet domain

International audienceA new approach for image retrieval by combination of colour and texture features is proposed. This approach uses the histogram of feature vectors, which are constructed from the coefficients of some subbands of wavelet transform and chosen according to their intrinsic characters. A K-means algorithm is used to quantise feature vectors. The experimental results both on small size databases (40 classes of textures) and large size databases (167 classes of textures) show that, compared with the state-of-the-art approaches, the proposed approach can achieve better retrieval performance

Varying Collimation for Dark-Field Extraction

Although x-ray imaging is widely used in biomedical applications, biological soft tissues have small density changes, leading to low contrast resolution for attenuation-based x-ray imaging. Over the past years, x-ray small-angle scattering was studied as a new contrast mechanism to enhance subtle structural variation within the soft tissue. In this paper, we present a detection method to extract this type of x-ray scattering data, which are also referred to as dark-field signals. The key idea is to acquire an x-ray projection multiple times with varying collimation before an x-ray detector array. The projection data acquired with a collimator of a sufficiently high collimation aspect ratio contain mainly the primary beam with little scattering, while the data acquired with an appropriately reduced collimation aspect ratio include both the primary beam and small-angle scattering signals. Then, analysis of these corresponding datasets will produce desirable dark-field signals; for example, via digitally subtraction. In the numerical experiments, the feasibility of our dark-field detection technology is demonstrated in Monte Carlo simulation. The results show that the acquired dark field signals can clearly reveal the structural information of tissues in terms of Rayleigh scattering characteristics

PCGAN: Partition-Controlled Human Image Generation

Human image generation is a very challenging task since it is affected by many factors. Many human image generation methods focus on generating human images conditioned on a given pose, while the generated backgrounds are often blurred.In this paper,we propose a novel Partition-Controlled GAN to generate human images according to target pose and background. Firstly, human poses in the given images are extracted, and foreground/background are partitioned for further use. Secondly, we extract and fuse appearance features, pose features and background features to generate the desired images. Experiments on Market-1501 and DeepFashion datasets show that our model not only generates realistic human images but also produce the human pose and background as we want. Extensive experiments on COCO and LIP datasets indicate the potential of our method.Comment: AAAI 2019 versio

Arrayed van der Waals Vertical Heterostructures based on 2D GaSe Grown by Molecular Beam Epitaxy

Vertically stacking two dimensional (2D) materials can enable the design of novel electronic and optoelectronic devices and realize complex functionality. However, the fabrication of such artificial heterostructures in wafer scale with an atomically-sharp interface poses an unprecedented challenge. Here, we demonstrate a convenient and controllable approach for the production of wafer-scale 2D GaSe thin films by molecular beam epitaxy. In-situ reflection high-energy electron diffraction oscillations and Raman spectroscopy reveal a layer-by-layer van der Waals epitaxial growth mode. Highly-efficient photodetector arrays were fabricated based on few-layer GaSe on Si. These photodiodes show steady rectifying characteristics and a relatively high external quantum efficiency of 23.6%. The resultant photoresponse is super-fast and robust with a response time of 60 us. Importantly, the device shows no sign of degradation after 1 million cycles of operation. Our study establishes a new approach to produce controllable, robust and large-area 2D heterostructures and presents a crucial step for further practical applications

CSMD: a computational subtraction-based microbiome discovery pipeline for species-level characterization of clinical metagenomic samples

Motivation Microbiome analyses of clinical samples with low microbial biomass are challenging because of the very small quantities of microbial DNA relative to the human host, ubiquitous contaminating DNA in sequencing experiments and the large and rapidly growing microbial reference databases. Results We present computational subtraction-based microbiome discovery (CSMD), a bioinformatics pipeline specifically developed to generate accurate species-level microbiome profiles for clinical samples with low microbial loads. CSMD applies strategies for the maximal elimination of host sequences with minimal loss of microbial signal and effectively detects microorganisms present in the sample with minimal false positives using a stepwise convergent solution. CSMD was benchmarked in a comparative evaluation with other classic tools on previously published well-characterized datasets. It showed higher sensitivity and specificity in host sequence removal and higher specificity in microbial identification, which led to more accurate abundance estimation. All these features are integrated into a free and easy-to-use tool. Additionally, CSMD applied to cell-free plasma DNA showed that microbial diversity within these samples is substantially broader than previously believed. Availability and implementation CSMD is freely available at https://github.com/liuyu8721/csmd

Research on Semiconductor Chip Grade Classification and Real-Time Evaluation Method Based on Hybrid Artificial Intelligence Technology

Semiconductor chips are widely used in various industries, making the classification of their quality grades and real-time evaluation crucial for ensuring optimal performance and reliability. This paper presents a semiconductor chip grade classification and real-time evaluation method based on hybrid artificial intelligence techniques, effectively improving the accuracy and efficiency of the classification process. Through extensive experiments on real-world data sets, the method demonstrated superior performance in terms of classification accuracy, real-time evaluation, and generalization capabilities compared to traditional methods

Pregabalin alleviates postherpetic neuralgia by downregulating spinal TRPV1 channel protein

Purpose: To determine the mechanism involved in pregabalin-induced alleviation of postherpetic neuralgia in a rat model.Methods: Ninety-sixty healthy Sprague-Dawley (SD) rats were assigned to sham, model andpregabalin groups (32 rats per group). A model of postherpetic neuralgia (PN) was established. The expressions of IL-1β and TNF-α in spinal cord tissue were determined 7 days after administration of treatments. The proportions of fluorescence areas in astrocytes in the dorsal horn, prefrontal lobe and hippocampus, and level of spinal cord TRPV1 channel protein in each group were evaluated.Results: Relative to model rats, IL-1β and TNF-α in spinal cord of pregabalin rats were significantly reduced (p < 0.05). The areas of fluorescence in astrocytes in dorsal horn of spinal cord, prefrontal lobe and hippocampus of model group were significantly increased, relative to sham, but were decreased in rats in pregabalin group (p < 0.05).Conclusion: Pregabalin significantly alleviates postherpetic neuralgia via mechanisms which may be related to the inflammatory response of spinal dorsal horn and downregulation of TRPV1 channel protein expression. This finding may be useful in developing new drugs for alleviating postherpetic neuralgia