Research of Innovation Diffusion on Industrial Networks

The real value of innovation consists in its diffusion on industrial network. The factors which affect the diffusion of innovation on industrial network are the topology of industrial network and rules of diffusion. Industrial network is a complex network which has scale-free and small-world characters; its structure has some affection on threshold, length of path, enterprise’s status, and information share of innovation diffusion. Based on the cost and attitude to risk of technical innovation, we present the “avalanche” diffusing model of technical innovation on industrial network

De novo assembly and characterization of transcriptome using Illumina paired-end sequencing and identification of CesA gene in ramie (Boehmeria nivea L. Gaud)

BACKGROUND: Ramie fiber, extracted from vegetative organ stem bast, is one of the most important natural fibers. Understanding the molecular mechanisms of the vegetative growth of the ramie and the formation and development of bast fiber is essential for improving the yield and quality of the ramie fiber. However, only 418 expressed tag sequences (ESTs) of ramie deposited in public databases are far from sufficient to understand the molecular mechanisms. Thus, high-throughput transcriptome sequencing is essential to generate enormous ramie transcript sequences for the purpose of gene discovery, especially genes such as the cellulose synthase (CesA) gene. RESULTS: Using Illumina paired-end sequencing, about 53 million sequencing reads were generated. De novo assembly yielded 43,990 unigenes with an average length of 824 bp. By sequence similarity searching for known proteins, a total of 34,192 (77.7%) genes were annotated for their function. Out of these annotated unigenes, 16,050 and 13,042 unigenes were assigned to gene ontology and clusters of orthologous group, respectively. Searching against the Kyoto Encyclopedia of Genes and Genomes Pathway database (KEGG) indicated that 19,846 unigenes were mapped to 126 KEGG pathways, and 565 genes were assigned to http://starch and sucrose metabolic pathway which was related with cellulose biosynthesis. Additionally, 51 CesA genes involved in cellulose biosynthesis were identified. Analysis of tissue-specific expression pattern of the 51 CesA genes revealed that there were 36 genes with a relatively high expression levels in the stem bark, which suggests that they are most likely responsible for the biosynthesis of bast fiber. CONCLUSION: To the best of our knowledge, this study is the first to characterize the ramie transcriptome and the substantial amount of transcripts obtained will accelerate the understanding of the ramie vegetative growth and development mechanism. Moreover, discovery of the 36 CesA genes with relatively high expression levels in the stem bark will present an opportunity to understand the ramie bast fiber formation and development mechanisms

Improving Viewpoint Robustness for Visual Recognition via Adversarial Training

Viewpoint invariance remains challenging for visual recognition in the 3D world, as altering the viewing directions can significantly impact predictions for the same object. While substantial efforts have been dedicated to making neural networks invariant to 2D image translations and rotations, viewpoint invariance is rarely investigated. Motivated by the success of adversarial training in enhancing model robustness, we propose Viewpoint-Invariant Adversarial Training (VIAT) to improve the viewpoint robustness of image classifiers. Regarding viewpoint transformation as an attack, we formulate VIAT as a minimax optimization problem, where the inner maximization characterizes diverse adversarial viewpoints by learning a Gaussian mixture distribution based on the proposed attack method GMVFool. The outer minimization obtains a viewpoint-invariant classifier by minimizing the expected loss over the worst-case viewpoint distributions that can share the same one for different objects within the same category. Based on GMVFool, we contribute a large-scale dataset called ImageNet-V+ to benchmark viewpoint robustness. Experimental results show that VIAT significantly improves the viewpoint robustness of various image classifiers based on the diversity of adversarial viewpoints generated by GMVFool. Furthermore, we propose ViewRS, a certified viewpoint robustness method that provides a certified radius and accuracy to demonstrate the effectiveness of VIAT from the theoretical perspective.Comment: 14 pages, 12 figures. arXiv admin note: substantial text overlap with arXiv:2307.1023

Towards Viewpoint-Invariant Visual Recognition via Adversarial Training

Visual recognition models are not invariant to viewpoint changes in the 3D world, as different viewing directions can dramatically affect the predictions given the same object. Although many efforts have been devoted to making neural networks invariant to 2D image translations and rotations, viewpoint invariance is rarely investigated. As most models process images in the perspective view, it is challenging to impose invariance to 3D viewpoint changes based only on 2D inputs. Motivated by the success of adversarial training in promoting model robustness, we propose Viewpoint-Invariant Adversarial Training (VIAT) to improve viewpoint robustness of common image classifiers. By regarding viewpoint transformation as an attack, VIAT is formulated as a minimax optimization problem, where the inner maximization characterizes diverse adversarial viewpoints by learning a Gaussian mixture distribution based on a new attack GMVFool, while the outer minimization trains a viewpoint-invariant classifier by minimizing the expected loss over the worst-case adversarial viewpoint distributions. To further improve the generalization performance, a distribution sharing strategy is introduced leveraging the transferability of adversarial viewpoints across objects. Experiments validate the effectiveness of VIAT in improving the viewpoint robustness of various image classifiers based on the diversity of adversarial viewpoints generated by GMVFool.Comment: Accepted by ICCV 202

Metadata Caching in Presto: Towards Fast Data Processing

Presto is an open-source distributed SQL query engine for OLAP, aiming for "SQL on everything". Since open-sourced in 2013, Presto has been consistently gaining popularity in large-scale data analytics and attracting adoption from a wide range of enterprises. From the development and operation of Presto, we witnessed a significant amount of CPU consumption on parsing column-oriented data files in Presto worker nodes. This blocks some companies, including Meta, from increasing analytical data volumes. In this paper, we present a metadata caching layer, built on top of the Alluxio SDK cache and incorporated in each Presto worker node, to cache the intermediate results in file parsing. The metadata cache provides two caching methods: caching the decompressed metadata bytes from raw data files and caching the deserialized metadata objects. Our evaluation of the TPC-DS benchmark on Presto demonstrates that when the cache is warm, the first method can reduce the query's CPU consumption by 10%-20%, whereas the second method can minimize the CPU usage by 20%-40%.Comment: 5 pages, 8 figure

Deep learning for the rapid automatic segmentation of forearm muscle boundaries from ultrasound datasets

Ultrasound (US) is widely used in the clinical diagnosis and treatment of musculoskeletal diseases. However, the low efficiency and non-uniformity of artificial recognition hinder the application and popularization of US for this purpose. Herein, we developed an automatic muscle boundary segmentation tool for US image recognition and tested its accuracy and clinical applicability. Our dataset was constructed from a total of 465 US images of the flexor digitorum superficialis (FDS) from 19 participants (10 men and 9 women, age 27.4 ± 6.3 years). We used the U-net model for US image segmentation. The U-net output often includes several disconnected regions. Anatomically, the target muscle usually only has one connected region. Based on this principle, we designed an algorithm written in C++ to eliminate redundantly connected regions of outputs. The muscle boundary images generated by the tool were compared with those obtained by professionals and junior physicians to analyze their accuracy and clinical applicability. The dataset was divided into five groups for experimentation, and the average Dice coefficient, recall, and accuracy, as well as the intersection over union (IoU) of the prediction set in each group were all about 90%. Furthermore, we propose a new standard to judge the segmentation results. Under this standard, 99% of the total 150 predicted images by U-net are excellent, which is very close to the segmentation result obtained by professional doctors. In this study, we developed an automatic muscle segmentation tool for US-guided muscle injections. The accuracy of the recognition of the muscle boundary was similar to that of manual labeling by a specialist sonographer, providing a reliable auxiliary tool for clinicians to shorten the US learning cycle, reduce the clinical workload, and improve injection safety

Guests mediated supramolecule-modified gold nanoparticles network for mimic enzyme application

1434-1441Supramolecules mediated porous metal nanostructures are meaningful materials because of their specific properties and wide range of applications. Here, we describe a general and simple strategy for building Au-networks based on the guest-induced 3D assembly of Au nanoparticles (Au-NPs) resulted in host-guest interaction resolved sulfonatocalix[4]arene (pSC4)-modified Au-NPs aggregate. The diverse guest molecules induced different porous network structures resulting in their different oxidize ability toward glucose. Among three different kinds of guest, hexamethylenediamine-pSC4-Au-NPs have high sensitivity, wide linear range and good stability. By surface characterization and calculating the electrochemical properties of the Au-NPs networks modified glassy carbon electrodes, the giving Au-NPs network reveals good porosity, high surface areas and increased conductance and electron transfer for the electrocatalysis. The synthesized nano-structures afford fast transport of glucose and ensure contact with a larger reaction surface due to high surface area. The fabricated sensor provides a platform for developing a more stable and efficient glucose sensor based on supramolecules mediated Au-NPs networks

Hybrid Impulsive Control for Closed Quantum Systems

The state transfer problem of a class of nonideal quantum systems is investigated. It is known that traditional Lyapunov methods may fail to guarantee convergence for the non-ideal case. Hence, a hybrid impulsive control is proposed to accomplish a more accurate convergence. In particular, the largest invariant sets are explicitly characterized, and the convergence of quantum impulsive control systems is analyzed accordingly. Numerical simulation is also presented to demonstrate the improvement of the control performance

Downregulation of Long Non-coding RNA FALEC Inhibits Gastric Cancer Cell Migration and Invasion Through Impairing ECM1 Expression by Exerting Its Enhancer-Like Function

Long non-coding RNAs (lncRNAs) have been shown to play important roles in many human diseases. However, their functions and mechanisms in tumorigenesis and development remain largely unknown. Here, we demonstrated that focally amplified lncRNA in epithelial cancer (FALEC) was upregulated and significantly correlated with lymph node metastasis, TNM stage in gastric cancer (GC). Further experiments revealed that FALEC knockdown significantly inhibited GC cells migration and invasion in vitro. Mechanistic investigations demonstrated that small interfering RNA-induced silencing of FALEC decreased expression of the nearby gene extracellular matrix protein 1 (ECM1) in cis. Additionally, ECM1 and FALEC expression were positively correlated, and high levels of ECM1 predicted shorter survival time in GC patients. Our results suggest that the downregulation of FALEC significantly inhibited the migration and invasion of GC cells through impairing ECM1 expression by exerting an enhancer-like function. Our work provides valuable information and a novel promising target for developing new therapeutic strategies in GC