GPNet: Simplifying Graph Neural Networks via Multi-channel Geometric Polynomials

Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-world problems on graph-structured data. However, these models usually have at least one of four fundamental limitations: over-smoothing, over-fitting, difficult to train, and strong homophily assumption. For example, Simple Graph Convolution (SGC) is known to suffer from the first and fourth limitations. To tackle these limitations, we identify a set of key designs including (D1) dilated convolution, (D2) multi-channel learning, (D3) self-attention score, and (D4) sign factor to boost learning from different types (i.e. homophily and heterophily) and scales (i.e. small, medium, and large) of networks, and combine them into a graph neural network, GPNet, a simple and efficient one-layer model. We theoretically analyze the model and show that it can approximate various graph filters by adjusting the self-attention score and sign factor. Experiments show that GPNet consistently outperforms baselines in terms of average rank, average accuracy, complexity, and parameters on semi-supervised and full-supervised tasks, and achieves competitive performance compared to state-of-the-art model with inductive learning task.Comment: 15 pages, 15 figure

Analyzing Network Protocols of Application Layer Using Hidden Semi-Markov Model

With the rapid development of Internet, especially the mobile Internet, the new applications or network attacks emerge in a high rate in recent years. More and more traffic becomes unknown due to the lack of protocol specifications about the newly emerging applications. Automatic protocol reverse engineering is a promising solution for understanding this unknown traffic and recovering its protocol specification. One challenge of protocol reverse engineering is to determine the length of protocol keywords and message fields. Existing algorithms are designed to select the longest substrings as protocol keywords, which is an empirical way to decide the length of protocol keywords. In this paper, we propose a novel approach to determine the optimal length of protocol keywords and recover message formats of Internet protocols by maximizing the likelihood probability of message segmentation and keyword selection. A hidden semi-Markov model is presented to model the protocol message format. An affinity propagation mechanism based clustering technique is introduced to determine the message type. The proposed method is applied to identify network traffic and compare the results with existing algorithm

The Evaluation of the thermal storage electric heating system Operation Management

Replacing fossil energy with the high proportion of renewable energy power in the field of enduse energy is the main way to reduce carbon emissions from energy combustion. Building heating is an important component in the field of end-use energy. The thermal storage electric heating system could use wind power during low power load periods at night for building heating. On the one hand, it helps to solve the problem of wind power accommodation, on the other hand it helps to achieve carbon emission reduction in the field of building heating. Based on the background of the thermal storage electric heating system for wind power accommodation, the influencing factors that affect the efficiency and benefits of electric heating system is analysed, and fuzzy comprehensive evaluation method based on analytic hierarchy process (AHP) is used to construct regenerative electric heating system operation management evaluation system

Attenuation of osteoarthritis via blockade of the SDF-1/CXCR4 signaling pathway

This study was performed to evaluate the attenuation of osteoarthritic (OA) pathogenesis via disruption of the stromal cell-derived factor-1 (SDF-1)/C-X-C chemokine receptor type 4 (CXCR4) signaling with AMD3100 in a guinea pig OA model. OA chondrocytes and cartilage explants were incubated with SDF-1, siRNA CXCR4, or anti-CXCR4 antibody before treatment with SDF-1. Matrix metalloproteases (MMPs) mRNA and protein levels were measured with real-time polymerase chain reaction (RT-PCR) and enzyme-linked immunosorbent assay (ELISA), respectively. The 35 9-month-old male Hartley guinea pigs (0.88 kg ± 0.21 kg) were divided into three groups: AMD-treated group (n = 13); OA group (n = 11); and sham group (n = 11). At 3 months after treatment, knee joints, synovial fluid, and serum were collected for histologic and biochemical analysis. The severity of cartilage damage was assessed by using the modified Mankin score. The levels of SDF-1, glycosaminoglycans (GAGs), MMP-1, MMP-13, and interleukin-1 (IL-1β) were quantified with ELISA. SDF-1 infiltrated cartilage and decreased proteoglycan staining. Increased glycosaminoglycans and MMP-13 activity were found in the culture media in response to SDF-1 treatment. Disrupting the interaction between SDF-1 and CXCR4 with siRNA CXCR4 or CXCR4 antibody attenuated the effect of SDF-1. Safranin-O staining revealed less cartilage damage in the AMD3100-treated animals with the lowest Mankin score compared with the control animals. The levels of SDF-1, GAG, MMP1, MMP-13, and IL-1β were much lower in the synovial fluid of the AMD3100 group than in that of control group. The binding of SDF-1 to CXCR4 induces OA cartilage degeneration. The catabolic processes can be disrupted by pharmacologic blockade of SDF-1/CXCR4 signaling. Together, these findings raise the possibility that disruption of the SDF-1/CXCR4 signaling can be used as a therapeutic approach to attenuate cartilage degeneration

Compressive sensing based secret signals recovery for effective image steganalysis in secure communications

Conventional image steganalysis mainly focus on presence detection rather than the recovery of the original secret messages that were embedded in the host image. To address this issue, we propose an image steganalysis method featured in the compressive sensing (CS) domain, where block CS measurement matrix senses the transform coefficients of stego-image to reflect the statistical differences between the cover and stego- images. With multi-hypothesis prediction in the CS domain, the reconstruction of hidden signals is achieved efficiently. Extensive experiments have been carried out on five diverse image databases and benchmarked with four typical stegographic algorithms. The comprehensive results have demonstrated the efficacy of the proposed approach as a universal scheme for effective detection of stegography in secure communications whilst it has greatly reduced the numbers of features requested for secret signal reconstruction

The genome and transcriptome of Japanese flounder provide insights into flatfish asymmetry

Flatfish have the most extreme asymmetric body morphology of vertebrates. During metamorphosis, one eye migrates to the contralateral side of the skull, and this migration is accompanied by extensive craniofacial transformations and simultaneous development of lopsided body pigmentation(1-5). The evolution of this developmental and physiological innovation remains enigmatic. Comparative genomics of two flatfish and transcriptomic analyses during metamorphosis point to a role for thyroid hormone and retinoic acid signaling, as well as phototransduction pathways. We demonstrate that retinoic acid is critical in establishing asymmetric pigmentation and, via cross-talk with thyroid hormones, in modulating eye migration. The unexpected expression of the visual opsins from the phototransduction pathway in the skin translates illumination differences and generates retinoic acid gradients that underlie the generation of asymmetry. Identifying the genetic underpinning of this unique developmental process answers long-standing questions about the evolutionary origin of asymmetry, but it also provides insight into the mechanisms that control body shape in vertebrates.National Natural Science Foundation of China [31130057, 31461163005, 31530078, 31472269, 31472262, 31472273]; State 863 High Technology R&D Project of China [2012AA092203, 2012AA10A408, 2012AA10A403-2]; Education and Research of Guangdong Province [2013B090800017]; Taishan Scholar Climb Project Fund of Shandong of China; Taishan Scholar Project Fund of Shandong of China for Young Scientists; Shanghai Universities First-class Disciplines Project of Fisheries; Program for Professor of Special Appointment (Eastern Scholar) at the Shanghai Institutions of Higher Learning; Shanghai Municipal Science, Special Project on the Integration of Industryinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/publishedVersio

Spatial Heterogeneity of Sustainable Land Use in the Guangdong–Hong Kong–Macao Greater Bay Area in the Context of the Carbon Cycle: GIS-Based Big Data Analysis

The primary object of this study is to survey the spatial heterogeneity of sustainable land use in the Guangdong–Hong Kong–Macao Greater Bay Area, The introduction of GIS technology into the evaluation index system under the traditional concept of circular economy, combined with the “double carbon target” and the methods of entropy weight analysis and superposition analysis led to the establishment of the evaluation index system for sustainable land use in the GIS model. The evaluation’s findings indicate that: (1) Spatially, the horizontal gravity center of sustainable land use in the Guangdong–Hong Kong–Macao Greater Bay Area changed dimensionally from 2010 to 2021, and the spatial gravity center shifted from north to south. (2) In terms of time characteristics, sustainable land use showed a steady upward trend in the 11 years from 2010 to 2021. (3) There were regional differences and uneven development levels in the comprehensive evaluation of sustainable land use in the Guangdong–Hong Kong–Macao Greater Bay Area. It shows that there are great differences in the degree of social and economic development among federation-level cities in the Guangdong–Hong Kong–Macao Greater Bay Area. From the current research on the sustainable use of land resources, the evaluation of sustainable use of land based on the concept of a circular economy is less favorable. Thus far, there has been no case study on land sustainability in the Guangdong–Hong Kong–Macao Greater Bay Area based on carbon cycles. In this study, the results are systematically sorted out, and the influencing factors are analyzed in depth to provide theoretical guidance on the sustainable and circular development of society, culture, and economy in the Guangdong–Hong Kong–Macao Greater Bay Area

The Evaluation of the thermal storage electric heating system Operation Management

Replacing fossil energy with the high proportion of renewable energy power in the field of enduse energy is the main way to reduce carbon emissions from energy combustion. Building heating is an important component in the field of end-use energy. The thermal storage electric heating system could use wind power during low power load periods at night for building heating. On the one hand, it helps to solve the problem of wind power accommodation, on the other hand it helps to achieve carbon emission reduction in the field of building heating. Based on the background of the thermal storage electric heating system for wind power accommodation, the influencing factors that affect the efficiency and benefits of electric heating system is analysed, and fuzzy comprehensive evaluation method based on analytic hierarchy process (AHP) is used to construct regenerative electric heating system operation management evaluation system

Multihop Neighbor Information Fusion Graph Convolutional Network for Text Classification

Graph convolutional network (GCN) is an efficient network for learning graph representations. However, it costs expensive to learn the high-order interaction relationships of the node neighbor. In this paper, we propose a novel graph convolutional model to learn and fuse multihop neighbor information relationships. We adopt the weight-sharing mechanism to design different order graph convolutions for avoiding the potential concerns of overfitting. Moreover, we design a new multihop neighbor information fusion (MIF) operator which mixes different neighbor features from 1-hop to k-hops. We theoretically analyse the computational complexity and the number of trainable parameters of our models. Experiment on text networks shows that the proposed models achieve state-of-the-art performance than the text GCN