Miracle-dz/UniFAR-FTUs huggingface dataset:
ISSR Analysis on genetic diversity of Panax ginseng landraces

Objective To discuss the genetic diversity and relationship of Panax ginseng landraces and provide genetic references for ginseng cultivation, selection and breeding. Methods The genetic diversity of 120 individuals from seven populations, representing five P. ginseng landraces, was analyzed by inter-simple sequence repeat (ISSR) markers. Results P. ginseng landraces held higher genetic diversity and the average percentage of polymorphic loci was 48.85%. Genetic diversity levels were very different among various landraces. The landraces of Zhujielu and Changbo showed relatively low genetic variation as compared with the others and their similarity coefficient was up to 97%. A large genetic variation existed in the same landraces but cultivated in different habitats. Conclusion This study indicates that the large genetic variations mainly exists within each landrace, but even more variations might exist within different populations from a same race. For promoting the breeding and selecting of P. ginseng varieties, it is necessary to supply germplasm of the same race from different habitats to the currently cultivated populations.
Two terpene synthases in resistant Pinus massoniana contribute to defense against Bursaphelenchus xylophilus.

Pine wood nematode (PWN; Bursaphelenchus xylophilus), a destructive pest of Pinus massoniana, is causing a severe epidemic of pine wilt disease in China. When invaded by PWN, resistant P. massoniana secrete an abundance of oleoresin terpenoids as a defensive strategy. However, regulatory mechanisms of this defense in resistant P. massoniana have yet to be elucidated. Here, we characterized two terpene synthase genes, α-pinene synthase (PmTPS4) and longifolene synthase (PmTPS21) identified in resistant P. massoniana and investigate the contribution of these genes to the oleoresin defense strategy in resistant masson pines. Upregulation of these two genes in the stem supported their involvement in terpene biosynthesis as part of the defense against PWN. Recombinant protein expression revealed catalytic activity for the two PmTPSs, with PmTPS4 primarily producing α-pinene, while PmTPS21 produced α-pinene and longifolene simultaneously. The major enzymatic products of the two terpene synthases had inhibitory effects on PWN in vitro. We demonstrated that PmTPS4 and PmTPS21 played positive roles in terpene-defense mechanisms against PWN infestation. The major products of these terpene synthases could directly inhibit the survival rate of PWN in vitro. We revealed that PmTPS21 was a novel bifunctional enzyme capable of simultaneous production of both monoterpene and sesquiterpene. This article is protected by copyright. All rights reserved.
DCSegNet: Deep Learning Framework Based on Divide-and-Conquer Method for Liver Segmentation

Image segmentation plays a vital role in the medical diagnosis and intervention field. The segmentation methods can be classified as fully automated, semiautomated or manual. Among them, manual segmentation can best improve the quality of the results, but it is time-consuming and tedious, and it may lead to operator bias. A continuity-aware probabilistic network based on the divide-and-conquer method was proposed in the current work. The proposed network comprised backbone network, local segmentation and a weight network. The backbone network extracts the features from image. The local segmentation divides the data space, whereas the weight network provides the continuity-aware weights. Therefore, combining those results of the weighted segments can eventually yield precise estimations. In this study, the proposed model was evaluated against several recent methods on the three datasets, and a several performance indexes of segmentation were evaluated for liver segmentation, the results showing that it is the most advanced liver segmentation approach. The source code of this work is publicly shared at https://github.com/licongsheng/DCSegNet for others to easily reproduce the work and build their own models with the introduced mechanisms.
Deep Learning for Social Network Information Cascade Analysis: a survey

The phenomenon of information dissemination in social networks is widespread, and Social Network Information Cascade Analysis (SNICA) aims to acquire valuable knowledge in the process of information dissemination in social networks. As the number, volume, and resolution of social network data increase rapidly, traditional social network data analysis methods, especially the analysis method of social network graph (SNG) data becoming overwhelmed in SNICA. Recently, deep learning models have changed this situation, and it has achieved success in SNICA with its powerful implicit feature extraction capabilities. In this paper, we provide a comprehensive survey of recent progress in applying deep learning techniques for SNICA. We first introduce related concepts and summarize the advantages of deep learning technology in SNICA. Then, we propose a general framework for deep learning technology, which applies to SNICA. Then, different SNICA application scenarios in the framework are classified and discussed according to user behavior analysis, information cascade analysis, rumor detection, and social network event analysis. Finally, we discuss the limitations of current work and suggest future directions.
Architecture Trades for Accessing Small Bodies with an Autonomous Small Spacecraft

Characterizing the composition, properties, and environments of Small Bodies is key to understanding the origins and processes of the Solar System. Traditionally, our knowledge has been limited to ground observations and selected few missions which cannot fully characterize the diversity of Small Bodies. Advances in miniaturized spacecraft technologies have recently enabled small spacecraft to perform missions in deep space, as demonstrated by Mars Cube One in 2018. Additional missions are being developed to further mature these technologies and expand their capabilities. We investigate a new approach to exploring Small Bodies, where standalone small spacecraft can be used as a more affordable approach to autonomously navigate, rendezvous, and characterize them. We review relevant mission concepts, available targets, architecture trade-offs, and required technologies for baseline mission design options. Using near-term technologies that will be available in less than 3 years, Our results indicate that it is possible for standalone small spacecraft to rendezvous one of several Small Bodies. It was found that a 24 kg and 180 kg spacecraft would be capable of delivering payloads of 1.5 kg and 10 kg, respectively, to several near-Earth asteroids candidates. With a departure window of 2022 to 2030, the number of available targets for the 24 kg and 180 kg architectures are 9 to 177 and 104 to 1132 respectively. The number of targets depends on additional requirements, such as orbit uncertainty, composition, and diameter. Enabling technologies include high-delta-V (>3 km/s) miniature electric propulsion system, high-efficiency (>100 W/kg) deployable solar arrays, and improved computational hardware and software for autonomy. Advances in miniaturized instruments, high-performance radiation-tolerant avionics, and interplanetary communications systems can also be leveraged. In the long term, a standardized autonomous small spacecraft architecture could enable a fleet of spacecraft to perform a cursory exploration of a representative sample of Small Bodies.
Identification of metastasis-associated genes in prostate cancer

ii TABLE OF CONTENTS . iv LIST OF TABLES . viii LIST OF FIGURES . ix LIST OF ABBREVIATIONS . xi ACKNOWLEDGEMENTS . xiv DEDICATION . xv CO-AUTHORSHIP STATEMENT.xvi CHAPTER
Neighborhood Activity-Driven Representation for Hyperspectral Imagery Classification

In the classic sparse representation (SR)-based models and their improved versions with the spatial consistency, such as joint representation (JR)-based frameworks, the sparse coefficient is generally considered with the dictionary together for representation. In fact, there is latent significance and property under the sparse coefficient which can be further exploited for classification. In this article, we first introduce two important definitions. One is the activity degree (AD) into the coefficient vector, and the other one is the neighborhood activity degree (NAD) into the coefficient matrix. Through the estimation of AD, we establish a simplified and equivalent model to the classic SR-based classifier called AD-driven representation-based classifier (ADRC). Based on the evaluation of NAD, we propose a novel classifier as an extension to ADRC, named NAD-driven representation-based classifier, including the spatial coherence. The proposed methods take advantages of the sparse idea for effective and concise utilization of individual and overall sparsity. Experimental results on three real hyperspectral datasets demonstrate their efficiency and improvements over the SR-based models and their spatial variants.
A New Approach to Color Edge Detection by Means of Transforming RGB Images into an 8-Dimension Color Space

In this paper, different ways of aggregating color information in edge extraction task are explored. One of these approaches is based on a previous transformation of the RGB images into a new color space of 8 dimensions. This increases the number of dimensions, making differences between colors easier to detect, and in this way improving the performance of edge detection task. Sobel and Canny algorithms are employed over the RGB images and its transformed version of 8 dimensions -that we have named Super8 image-. The set of employed images is the one from Berkeley’s dataset. In order to evaluate the performance, precision, recall and F measure are computed. The way of aggregating the color information is showed to be relevant. For some well-known algorithms, the new 8 dimension color space overtakes RGB’s for edge detection problem.
Design and Implementation of On-orbit Valuable Image Extraction for the TianZhi-1 Satellite

Recently, software-defined satellite has become a research hotspot in the aerospace. Based on an advanced computing platform with open system architecture, researchers can upload software for specific tasks even the satellite has been launched into space. This paper we have designed an on-orbit application for China’s first software-defined satellite TianZhi-1, which use Android smartphone as a system platform. Two main tasks are focused on our work, one is to reduce data redundancy and the other is to compress the size of the software. First, a light-weight and extensible framework is designed to support different image processing algorithms. Following this, we propose a three-step approach for on-orbit valuable image extraction, include image denoising, stitching, and salient object extraction. Experiments on the real satellite achieve outstanding results.
A Novel Method for Induction Motor Fault Identification Based on MSST and LightGBM

Induction motors are widely used in industries and daily lives. The sudden occurrence of motor faults can cause huge economic losses and even inflict human injuries. To identify the motor faults promptly and improve the operation reliability. This paper presents a novel method for induction motor fault identification. The method consists of five main steps: multi-synchrosqueezing transform (MSST) analysis, signal texture feature extraction, multi-domain feature vector construction, feature dimension reduction with Linear Discriminant Analysis (LDA), and fault identification with light gradient boosting machine (LightGBM) algorithm. In the process, MSST is first utilized to get an energy-concentrated time-frequency (TF) representation of the signal, the texture features of TF images are calculated with gray level co-occurrence matrix (GLCM), then combined with time-domain and frequency domain-features, a multi-domain feature matrix is constructed and a low-dimension feature matrix is obtained by LDA to improve the feature quality. After that, a LightGBM model is trained with the new low dimension feature matrix to identify the motor faults. The proposed method is validated by the experiment dataset of the drivetrain dynamics simulator, and the results proved that the approach can accurately classify different induction motor faults.
Handover Decision in LTE & LTE-A based Hetnets using Regression Heuristics of Quality Metrics (RHQM) for Optimal Load Balancing

Load balancing in the communication networks based on LTE-A & LTE are more significant because of the remarkable increase in consumers & load requirements for user interaction. The existing models of recent past literature have attempted to balance the load to offer optimum QoS by respective networks. Nevertheless, handoff schemes portrayed or adapted in several existing methods prioritized for balancing load under the QoS parameter, which is particular to the target network context. In the context of quality-aware handoff decision strategy, a new handoff scheme for heterogeneous networks has proposed, which attains optimum load balancing under diversified quality factors. This proposal is to search scheme for optimizing the handoff procedure intricacy. The simulation study has scaled the competency of the proposed model by comparing it with existing models under distinct performance parameters.
Modeling and Design of High Bandwidth Feedback Loop for dv/dt Control in CMOS AGD for GaN

The objective of this work is to show the intrinsic limitations of a CMOS technology for the realization of an Active Gate Driver (AGD) with an active dv/dt control loop. Due to a theoretical study using first order models of CMOS submicron transistors, the main equations providing the link between feedback loop bandwidth and specific technology parameters are obtained. This optimization study allows us to determine the theoretical limits in terms of bandwidth and silicon area. Then, it becomes possible to determine the most appropriate switching control method to implement depending on the application requirements (high efficiency, low EMI), i.e. active feedback with adjustable gain, while ensuring suitable time delays. A feedback loop bandwidth of 1. 59GHz using a 1pF integrated capacitor to address a switching speed of 175V/ns is demonstrated. Experimental results and simulations using accurate technology models confirm the theory.
NAD: Neuron Activation based Divergence Maps for Weakly Supervised Object Localization

Convolutional neutral networks (CNNs) have brought about massive improvements in the field of computer vision in solving some of the most conmplex problems like object detection, image captioning, semantic segmentation etc. These networks perform very well for such tasks but very little is known about why they do so. Their lack of transparency makes them difficult to interpret and that is why they are considered as black boxes. In this paper, we have proposed an approach in which we carry out weakly supervised object localization in images which eventually helps us understand the functioning of CNNs by providing us with the visual explanations for the predictions of CNNs. The proposed work focuses on exploiting the learned feature dependencies between consecutive layers of CNN. Different strategies are employed for different types of layers (Fully Connected layer, Convolutional layer etc.) to compute a binary value signifying neuron relevance. Moreover, we employ a method in which the computed activation maps corresponding to the non-target class are discounted from those of the target class in order to eliminate the irrelevant neurons and amplify the most discriminative neurons. This process highlights the most significant neurons of the CNN which have contributed the most in the prediction of a particular object. Our proposed approach performs better than the previously developed techniques with a better accuracy.
Weighted t-Schatten-p Norm Minimization for Real Color Image Denoising

In this paper, to fully exploit the spatial and spectral correlation information, we present a new real color image denoising scheme using tensor Schatten-<inline-formula> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula> norm (t-Schatten-<inline-formula> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula> norm) minimization based on t-SVD to recover the underlying low-rank tensor. Similar to matrix Schatten-<inline-formula> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula> norm, using non-convex t-Schatten-<inline-formula> <tex-math notation="LaTeX">${p}~({0 < {p} < {1}})$ </tex-math></inline-formula> norm minimization could obtain better results than the tensor nuclear norm minimization which is a convex relaxation of the nonconvex tensor tubal rank. To avoid over-shrink the tensor tubal rank components, a flexible weighted t-Schatten-<inline-formula> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula> norm model is proposed with weights assigned to different elements of tensor singular tubes. We adopt the generalized iterated shrinkage algorithm to solve the minimization problem efficiently. Extensive experiments on one synthetic and two realistic datasets demonstrate the effectiveness of our proposed method to remove noise both quantitatively and qualitatively.
Deep Attention and Multi-Scale Networks for Accurate Remote Sensing Image Segmentation

Remote sensing image segmentation is a challenging task in remote sensing image analysis. Remote sensing image segmentation has great significance in urban planning, crop planting, and other fields that need plentiful information about the land. Technically, this task suffers from the ultra-high resolution, large shooting angle, and feature complexity of the remote sensing images. To address these issues, we propose a deep learning-based network called ATD-LinkNet with several customized modules. Specifically, we propose a replaceable module named AT block using multi-scale convolution and attention mechanism as the building block in ATD-LinkNet. AT block fuses different scale features and effectively utilizes the abundant spatial and semantic information in remote sensing images. To refine the nonlinear boundaries of internal objects in remote sensing images, we adopt the dense upsampling convolution in the decoder part of ATD-LinkNet. Experimentally, we enforce sufficient comparative experiments on two public remote sensing datasets (Potsdam and DeepGlobe Road Extraction). The results show our ATD-LinkNet achieves better performance against most state-of-the-art networks. We obtain 89.0% for pixel-level accuracy in the Potsdam dataset and 62.68% for mean Intersection over Union in the DeepGlobe Road Extraction dataset.
Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

The global concern with power quality is increasing due to the penetration of renewable energy (RE) sources to cater the energy demands and meet de-carbonization targets. Power quality (PQ) disturbances are found to be more predominant with RE penetration due to the variable outputs and interfacing converters. There is a need to recognize and mitigate PQ disturbances to supply clean power to the consumer. This article presents a critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration. The broad perspective of this review paper is to provide various concepts utilized for extraction of the features to detect and classify the PQ disturbances even in the noisy environment. More than 220 research publications have been critically reviewed, classified and listed for quick reference of the engineers, scientists and academicians working in the power quality area.
A Conceptual Design of an Inattention Management Middleware with Adaptive Target Saliency

We present a conceptual design of an inattention management middleware with adaptive target saliency. The design objective is to provide mechanisms for managing operator inattention in multi-display multitasking applications. The conceptual design integrates ideas from situational awareness into its mechanisms to provide dynamic target saliency as a means to 1) guide operators through sub-tasks by drawing attention to high priority targets; and 2) guide operators on how to efficiently split their attention between tasks. We motivate the design by analysis of the results following a formative study with a prototype version of the conceptual design.
Numerical Investigation of Reconfigurable Photonic Crystal Switch Based on Phase Change Nanomaterial

In this article, the novel reconfigurable all-optical switches are designed with ultracompact size, fast response time and high extinction ratio. The hybrid silicon (Si) and phase change material of Germanium−Antimony−Tellurides (GST) based nanoresonator coupled photonic crystal platform is used to realize the 1 × 1, 2 × 2, 3 × 3, and 4 × 4 switches. The GST-based nano-rods are playing a very significant role in “ON” and “OFF” states of the optical switch which can be triggered by changing the phase of the material. The introduction of GST rods within nanocavity provide high output power at the amorphous state and very low output power at the crystalline state, which in turn provide a very high extinction ratio. The designed nanoswitch is optimized through the Finite-Difference-Time-Domain (FDTD) method. The non-volatile 1 × 1 nanoswitch is designed with a low footprint of 44 µm2, the low delay time of 293.86 femtoseconds (fs) and high data rate of 3.40 Tbps. Hence, the reported switching platform is more appropriate for photonic interconnects and quantum computing.
Official Release of The Radioisotope Power System Dose Estimation Tool (RPS-DET)

The Radioisotope Power System Dose Estimation Tool (RPS-DET) is a software simulation application that serves as a one-stop shop for simulating and analyzing the radiation effects from radioisotope power systems (RPSs). RPS-DET includes a graphical user interface that allows the user to select from multiple RPS designs, place them in various terrestrial, planetary, or deep-space environments, and customize the plutonium oxide (PuO2) fuel. These user selections are combined into an input file that is automatically sent to the SCALE software suite, where simulation-specific neutron and gamma source terms are written prior to performing a Monte Carlo particle transport process on the chosen geometries. Simulation results represent three-dimensional instantaneous particle fluxes and dose rates or time-integrated particle fluences or doses according to pre-defined, customizable responses. The literature regarding RPS-DET addresses the early development and methodologies of this effort, while this paper outlines the official release of the software, instructions for ordering, final features, and current design.
Drug resistance of Vibrio anguillarum isolated from Ayu between 1989 and 1991

One hundred and fourteen strains of Vibrio anguillarum collected from cultured ayu Plecoglos-sus altivelis between 1989 and 1991 were studied for their sensitivities to 10 chemotherapeutants. Among the drugs tested florfenicol (FF) was the most effective against all strains. Only one strain was found to be sensitive to all the drugs (ampicillin, chloramphenicol (CP), FF, furazolidone, kanamycin (KM), nalidixic acid, streptomycin (SM), sulfamonomethoxine (SA), tetracycline (TC), and trimethoprim (TMP). The remaining strains showed resistance to various combinations of drugs. Transferable R plasmids were detected from 21 strains encoded with resistance to 7 to 9 drugs. All detected R plasmids encoded with resistance to CP, KM, SA, SM, TC, and TMP. Identical endonuclease digestion patterns were observed in R plasmids detected from various areas, and strong homology among these R plasmid DNAs were also observed. The DNA structure of the R plasmids detected from 1989 to 1991 was not similar to that of previously detected R plasmids from V. anguillarum. The results indicate that multiple drug resistant strains of V. anguillarum were common in ayu farms between 1989 and 1991.