GP-NAS-ensemble: a model for NAS Performance Prediction

It is of great significance to estimate the performance of a given model architecture without training in the application of Neural Architecture Search (NAS) as it may take a lot of time to evaluate the performance of an architecture. In this paper, a novel NAS framework called GP-NAS-ensemble is proposed to predict the performance of a neural network architecture with a small training dataset. We make several improvements on the GP-NAS model to make it share the advantage of ensemble learning methods. Our method ranks second in the CVPR2022 second lightweight NAS challenge performance prediction track

Study on the effects of granularity of paprika on physicochemical properties and volatile flavor compounds of chili oil

Objective: This study aimed to investigate the effects of granularity of paprika on the physical and chemical properties and volatile flavor compounds of chili oil. Methods: Chili oil samples（KLD2-KLD5）were prepared from mechanically crushed paprika with different granularity （35, 30, 26, 20 mesh）, and the content of capsaicinoids, chromatic aberration value, and peroxide value of oil samples were determined by high performance liquid chromatography（HPLC）, colorimeter and other methods. The types and contents of volatile flavor compounds were detected and analyzed by gas chromatography-ion mobility spectrometry （GC-IMS） combined with principal component analysis （PCA）, partial least squares discriminant analysis （PLS-DA）, and heat map cluster analysis. In addition, a comparative analysis was performed with the traditional hand-milled chili oil sample （KLD1）. Results: In the KLD2-KLD5 chili oil samples prepared with mechanically crushed paprika, the concentration of capsaicin, dihydrocapsaicin and capsaicinoids, scoville heat units （SHU） and pungency degree decreased with the increase of the granularity of paprika. The peroxide value increases with the increase of the granularity, and the brightness L* increased first and then decreased with the decrease of the granularity, and there was a significant difference（P＜0.05） had been observed. A total of 58 volatile organic compounds （VOCs） were identified by GC-IMS, mainly including: alcohols, aldehydes, ketones, carboxylic acids, esters, heterocyclics and thioethers, with 10, 18, 12, 4, 7, 5 and 2 types respectively. GC-IMS fingerprints combined with the relative percentage of VOCs showed that the types of VOCs in KLD2-KLD5 samples were the same, but the contents were different. The types and content of VOCs in KLD1 were quite different from those in KLD2-KLD5. Fourteen key differential markers of 5 chili oils were screened by PLS-DA. The results of principal component analysis, nearest neighbor analysis, and heat map clustering analysis of VOCs in five kinds of chili oil samples were consistent with the results of GC-IMS fingerprints. These samples could be accurately distinguished and the flavor of KLD1 was the most unique. Conclusion: The granularity of paprika had a significant impact on the dissolution rate of capsaicin and dihydrocapsaicin in chili oil, peroxide value, and brightness L* （P＜0.05）, but has no effect on the types of volatile flavor compounds in chili oil. However, the content of volatile flavor compounds in each sample had a certain difference

Flexible Electrical and Photoelectrical Artificial Synapses for Neuromorphic Systems

Over the past decade, the field of personal electronic systems has trended toward mobile and wearable devices. However, the capabilities of existing electronic systems are overwhelmed by the computing demands at the wearable sensing stage. Two main bottlenecks are encountered. The first bottleneck is located within the computing module, between the processing units and the memory, and is known as the von-Neumann bottleneck. The second bottleneck is located between the sensing module and the computing module of the system. Inspired by neuromorphic computing, an architecture of the sensitive neuromorphic network (SNN) is developed as a candidate for overcoming both bottlenecks. Suitable building blocks, especially in flexible form, must be developed. In this work, starting from the demand analysis and followed by prototype development, performance optimization, and feasibility testing, two kinds of critical devices were developed for fabricating a photosensitive neuromorphic network (PSNN). A high-performance flexible electrical artificial synapse that is based on the electron-trapping mechanism was developed. In addition to the basic memristive features, multiple kinds of synaptic plasticity were also demonstrated, which enriched the collection of possible applications. Furthermore, optimization on multiple performance metrics was easily performed using the intrinsic features and structure of the device. A new photoelectrical artificial synapse was also realized by successfully combining light signal sensing and processing in a single synapse. A flexible dual-mode photoelectrical synapse, which fulfilled the requirements of the designed PSNN working protocol, was demonstrated. The device showed gate-tunable photomemristive features, thereby enabling its application as a photoelectrical artificial synapse. Using the newly developed devices and the proposed network architecture, this work successfully initiated a new area of research, namely, the sensitive neuromorphic network, and provided a valid solution that addresses the current limitations of existing wearable electronic systems.QC 20181008</p

Transcriptome Sequencing Revealed an Inhibitory Mechanism of Aspergillus flavus Asexual Development and Aflatoxin Metabolism by Soy-Fermenting Non-Aflatoxigenic Aspergillus

Aflatoxins (AFs) have always been regarded as the most effective carcinogens, posing a great threat to agriculture, food safety, and human health. Aspergillus flavus is the major producer of aflatoxin contamination in crops. The prevention and control of A. flavus and aflatoxin continues to be a global problem. In this study, we demonstrated that the cell-free culture filtrate of Aspergillus oryzae and a non-aflatoxigenic A. flavus can effectively inhibit the production of AFB1 and the growth and reproduction of A. flavus, indicating that both of the non-aflatoxigenic Aspergillus strains secrete inhibitory compounds. Further transcriptome sequencing was performed to analyze the inhibitory mechanism of A. flavus treated with fermenting cultures, and the results revealed that genes involved in the AF biosynthesis pathway and other biosynthetic gene clusters were significantly downregulated, which might be caused by the reduced expression of specific regulators, such as AflS, FarB, and MtfA. The WGCNA results further revealed that genes involved in the TCA cycle and glycolysis were potentially involved in aflatoxin biosynthesis. Our comparative transcriptomics also revealed that two conidia transcriptional factors, brlA and abaA, were found to be significantly downregulated, which might lead to the downregulation of conidiation-specific genes, such as the conidial hydrophobins genes rodA and rodB. In summary, our research provides new insights for the molecular mechanism of controlling AF synthesis to control the proliferation of A. flavus and AF pollution

Flexible Electrical and Photoelectrical Artificial Synapses for Neuromorphic Systems

Over the past decade, the field of personal electronic systems has trended toward mobile and wearable devices. However, the capabilities of existing electronic systems are overwhelmed by the computing demands at the wearable sensing stage. Two main bottlenecks are encountered. The first bottleneck is located within the computing module, between the processing units and the memory, and is known as the von-Neumann bottleneck. The second bottleneck is located between the sensing module and the computing module of the system. Inspired by neuromorphic computing, an architecture of the sensitive neuromorphic network (SNN) is developed as a candidate for overcoming both bottlenecks. Suitable building blocks, especially in flexible form, must be developed. In this work, starting from the demand analysis and followed by prototype development, performance optimization, and feasibility testing, two kinds of critical devices were developed for fabricating a photosensitive neuromorphic network (PSNN). A high-performance flexible electrical artificial synapse that is based on the electron-trapping mechanism was developed. In addition to the basic memristive features, multiple kinds of synaptic plasticity were also demonstrated, which enriched the collection of possible applications. Furthermore, optimization on multiple performance metrics was easily performed using the intrinsic features and structure of the device. A new photoelectrical artificial synapse was also realized by successfully combining light signal sensing and processing in a single synapse. A flexible dual-mode photoelectrical synapse, which fulfilled the requirements of the designed PSNN working protocol, was demonstrated. The device showed gate-tunable photomemristive features, thereby enabling its application as a photoelectrical artificial synapse. Using the newly developed devices and the proposed network architecture, this work successfully initiated a new area of research, namely, the sensitive neuromorphic network, and provided a valid solution that addresses the current limitations of existing wearable electronic systems.QC 20181008</p

Flexible Electrical and Photoelectrical Artificial Synapses for Neuromorphic Systems

Over the past decade, the field of personal electronic systems has trended toward mobile and wearable devices. However, the capabilities of existing electronic systems are overwhelmed by the computing demands at the wearable sensing stage. Two main bottlenecks are encountered. The first bottleneck is located within the computing module, between the processing units and the memory, and is known as the von-Neumann bottleneck. The second bottleneck is located between the sensing module and the computing module of the system. Inspired by neuromorphic computing, an architecture of the sensitive neuromorphic network (SNN) is developed as a candidate for overcoming both bottlenecks. Suitable building blocks, especially in flexible form, must be developed. In this work, starting from the demand analysis and followed by prototype development, performance optimization, and feasibility testing, two kinds of critical devices were developed for fabricating a photosensitive neuromorphic network (PSNN). A high-performance flexible electrical artificial synapse that is based on the electron-trapping mechanism was developed. In addition to the basic memristive features, multiple kinds of synaptic plasticity were also demonstrated, which enriched the collection of possible applications. Furthermore, optimization on multiple performance metrics was easily performed using the intrinsic features and structure of the device. A new photoelectrical artificial synapse was also realized by successfully combining light signal sensing and processing in a single synapse. A flexible dual-mode photoelectrical synapse, which fulfilled the requirements of the designed PSNN working protocol, was demonstrated. The device showed gate-tunable photomemristive features, thereby enabling its application as a photoelectrical artificial synapse. Using the newly developed devices and the proposed network architecture, this work successfully initiated a new area of research, namely, the sensitive neuromorphic network, and provided a valid solution that addresses the current limitations of existing wearable electronic systems.QC 20181008</p

Time series analysis and long short-term memory neural network to predict landslide displacement

A good prediction of landslide displacement is an essential component for implementing an early warning system. In the Three Gorges Reservoir Area (TGRA), many landslides deform distinctly and in steps from April to September each year under the influence of seasonal rainfall and periodic fluctuation in reservoir water level. The sliding becomes more uniform again from October to April. This landslide deformation pattern leads to accumulated displacement versus time showing a step-wise curve. Most of the existing predictive models express static relationships only. However, the evolution of a landslide is a complex nonlinear dynamic process. This paper proposes a dynamic model to predict landslide displacement, based on time series analysis and long short-term memory (LSTM) neural network. The accumulated displacement was decomposed into a trend term and a periodic term in the time series analysis. A cubic polynomial function was selected to predict the trend displacement. By analyzing the relationships between landslide deformation, rainfall, and reservoir water level, a LSTM model was used to predict the periodic displacement. The LSTM approach was found to properly model the dynamic characteristics of landslides than static models, and make full use of the historical information. The performance of the model was validated with the observations of two step-wise landslides in the TGRA, the Baishuihe landslide and Bazimen landslide. The application of the model to those two landslides demonstrates that the LSTM model provides a good representation of the measured displacements and gives a more reliable prediction of landslide displacement than the static support vector machine (SVM) model. It is concluded that the proposed model can be used to effectively predict the displacement of step-wise landslides in the TGRA.acceptedVersio

Areview of landslide-generated waves risk and practice of management of hazard chain risk from reservoir landslide

As one of the major types of geological hazards in reservoir areas, the risk analysis of landslides has been a top research topic recently. Landslide-generated waves extend the influence area from the landslide source itself to several kilometers upstream and downstream and greatly expand the type and number of elements and disaster damage. The risk evaluation of landslide generated waves is considered to be a difficult component in the evaluation of landslide risk hazard chains, as involving the intersection of different areas. Firstly, the previous research results in recent decades were synthesized from hazard, vulnerability and risk, the current situation of landslide generated waves risk research and common research methods worldwide were outlined, and the key representative research results were reviewed and analyzed. New progress was introduced, which includes experimental studies considering the complexity of actual river topography, coupled numerical simulation methods focusing on landslide-water interaction mechanisms, and a vulnerability assessment system based on multiple hazard-bearing body types. Secondly, the process and consequences of a number of landslide generated waves risk management cases that have occurred in the Three Gorges Reservoir Area in recent years were described in detail. Finally, according to the author's many years of research experience, new directions and ideas were proposed for the study of landslide-landslide generated waves hazard chain risks, and suggestions were given that surge risk and landslide risk evaluation systems should be merged with each other and developed along the direction of quantification, standardization and refinement

Properties and Applications of a New Chemical Grouting Material

The study investigates a new chemical grout by mixing the main agent, auxiliary agent, catalyst, foam stabilizer, solvent, and water, to treat the distress of railway tunnel. The orthogonal design was used to obtain 16 groups of grout proportion schemes, and reasonable proportion parameters were screened using laboratory and field tests. Additionally, this study included detailed research on the grout performance. The test results showed that the proportion schemes of groups 3, 4, and 15 grout were the most reasonable. In particular, for group 3, the viscosity is 663 MPa·s, the curing time is 119 s, the foaming capacity is 1589%, and the compressive strength is 20.16 MPa. For group 4, the viscosity is 663 MPa·s, the curing time is 137 s, the foaming capacity is 1809%, and the compressive strength is 17.76 MPa. For group 15, the viscosity is 281 MPa·s, the curing time is 98 s, the foaming capacity is 1173%, and the compressive strength is 26.79 MPa. Groups 4 and 15 grouts were used to treat the frost boiling and track bed subsidence in existed railway tunnels. Based on this, field monitoring showed that muddy water became clear water with an average depth of only 4 mm in the drainage ditch and that the irregular subsidence of the track bed was also solved after treatment. According to the aforementioned experimental research and analysis, it is proven that new grout not only exhibits a reasonable solidification time, high strength, and excellent waterproofing and impermeability with no pollution of the environment but also can be produced by a safe and convenient synthesis method. Group 4 is suitable for treating tunnel seepage, group 15 is suitable for structural reinforcement, and group 3 confers the advantages of seepage prevention, leakage stoppage, and reinforcement

Study on plugging performance of heterogeneous systems in microchannels

This paper establishes a microchannel model based on the pore scale distribution characteristics of natural cores from Daqing Oilfield. It considers the deformation and flow characteristics of the dispersed and continuous phases in the heterogeneous system， builds a flow model by the phase field method， and solves it by the finite element method. The paper also simulates the generation of dispersed phase particles in the microchannel， realizes particle sorting， and studies the effect of the matching coefficient and pore-throat ratio on the plugging performance of particles in the microscopic pore-throat structure. The results show that when the particles are elastically plugged in the microscopic pore-throat structure， the pressure at the entrance of the pore throat changes periodically with the migration of the particles through the pore throat. The optimal matching coefficient between the particles and the pore throat is ［1.0， 1.4）. In this interval， the particles can be temporarily plugged at the entrance of the pore throat and recover their original shape after deformation and migration through the pore throat. When the pore diameter is the same， a larger matching coefficient and pore-throat ratio indicate greater pressure of particles through the pore throat， and larger particle size reflects a smaller critical value of particles through the pore throat