Attribution of Biases of Interhemispheric Temperature Contrast in CMIP6 Models

One of the basic characteristics of Earth's modern climate is that the Northern Hemisphere(NH) is climatologically warmer than the Southern Hemisphere(SH). Here, model performances of this basic state are examined using simulation results from 26 CMIP6 models. Results show that the CMIP6 models underestimate the contrast in interhemispheric surface temperatures on average(0.8 K for CMIP6 mean versus 1.4 K for reanalysis data mean), and that there is a large intermodel spread, ranging from -0.7 K to 2.3 K. A box model energy budget analysis shows that the contrast in interhemispheric shortwave absorption at the top of the atmosphere, the contrast in interhemispheric greenhouse trapping, and the crossequatorial northward ocean heat transport, are all underestimated in the multimodel mean. By examining the intermodel spread, we find intermodel biases can be tracked back to biases in midlatitude shortwave cloud forcing in AGCMs. Models with a weaker interhemispheric temperature contrast underestimate the shortwave cloud reflection in the SH but overestimate the shortwave cloud reflection in the NH, which are respectively due to underestimation of the cloud fraction over the SH extratropical ocean and overestimation of the cloud liquid water content over the NH extratropical continents.Models that underestimate the interhemispheric temperature contrast exhibit larger double ITCZ biases, characterized by excessive precipitation in the SH tropics. Although this intermodel spread does not account for the multimodel ensemble mean biases, it highlights that improving cloud simulation in AGCMs is essential for simulating the climate realistically in coupled models.

Gastrointestinal contrast-enhanced ultrasonography for diagnosis and treatment of peptic ulcer in children

BACKGROUND The detection rate of peptic ulcer in children is improving,with development of diagnostic procedures.Gastroscopy is the gold standard for the diagnosis of peptic ulcer,but it is an invasive procedure.Gastrointestinal contrast-enhanced ultrasonography(CEUS)has the advantages of being painless,noninvasive,nonradioactive,easy to use,and safe.AIM To investigate the clinical value of CEUS for diagnosis and treatment of peptic ulcer in children.METHODS We investigated 43 children with digestive tract symptoms in our hospital from January 2021 to June 2022.All children were examined by routine ultrasound,gastrointestinal CEUS,and gastroscopy.The pathological results of gastroscopy were taken as the gold standard.Routine ultrasonography was performed before gastrointestinal CEUS.Conventional ultrasound showed the thickness of the gastroduodenal wall,gastric peristalsis,and the adjacent organs and tissues around the abdominal cavity.Gastrointestinal CEUS recorded the thickness of the gastroduodenal wall;the size,location and shape of the ulcer;gastric peristalsis;and adjacent organs and tissues around the abdominal cavity.The results of routine ultrasound and gastrointestinal ultrasound were compared with those of gastroscopy to evaluate the diagnostic results and coincidence rate of routine ultrasound and gastrointestinal CEUS.All children received informed consent from their guardians for CEUS.This study was reviewed and approved by the hospital medical ethics committee.RESULTS Among the 43 children,17(15 male,2 female)were diagnosed with peptic ulcer by gastroscopy.There were 26 children with nonpeptic ulcer.There were eight cases of peptic ulcer and 35 of nonpeptic ulcer diagnosed by conventional ultrasound.The diagnostic coincidence rate of peptic ulcer in children diagnosed by conventional ultrasound was 79.1%(34/43),which was significantly different from that of gastroscopy(P=0.033).It indicates that the coincidence rate of gastrointestinal contrast-enhanced ultrasound and gastroscope is low.Fifteen cases of peptic ulcer and 28 of nonpeptic ulcer were diagnosed by CEUS.The diagnostic coincidence rate of peptic ulcer in children was 95.3%(41/43).There was no significant difference between CEUS and gastroscopy(P=0.655).It indicates that the coincidence rate of gastrointestinal contrast-enhanced ultrasound and gastroscope is high.CONCLUSION Gastrointestinal CEUS has a high coincidence rate in the diagnosis of peptic ulcer in children,and can be used as a preliminary examination method.

Optical design of a novel near-infrared phase contrast imaging(NI-PCI)diagnostic on the HL-2A tokamak

The optical design of near-infrared phase contrast imaging(NI-PCI)diagnosis on HL-2A is introduced in this paper.This scheme benefits from the great progress of near-infrared laser technology and is a broadening of traditional phase contrast technology.This diagnostic can work as a keen tool to measure plasma wavenumber spectra by inferring string-integrated plasma density fluctuations.Design of both the front optical path which is the path before the laser transmitting into the tokamak plasma and the rear optics which is the path after the laser passing through the plasma is detailed.The 1550 nm laser is chosen as the probe beam and highprecision optical components are designed to fit the laser beam,in which a phase plate with a 194-nm-deep silver groove is the key.Compared with the conventional 10.6μm laser-based PCI system on HL-2A,NI-PCI significantly overcomes the unwanted phase scintillation effect and promotes the measurement capability of high-wavenumber turbulence with an increased maximal measurable wavenumber from 15 cm^(-1)to 32.6 cm^(-1).

Recommendation Method for Contrastive Enhancement of Neighborhood Information

Knowledge graph can assist in improving recommendation performance and is widely applied in various person-alized recommendation domains.However,existing knowledge-aware recommendation methods face challenges such as weak user-item interaction supervisory signals and noise in the knowledge graph.To tackle these issues,this paper proposes a neighbor information contrast-enhanced recommendation method by adding subtle noise to construct contrast views and employing contrastive learning to strengthen supervisory signals and reduce knowledge noise.Specifically,first,this paper adopts heterogeneous propagation and knowledge-aware attention networks to obtain multi-order neighbor embedding of users and items,mining the high-order neighbor informa-tion of users and items.Next,in the neighbor information,this paper introduces weak noise following a uniform distribution to construct neighbor contrast views,effectively reducing the time overhead of view construction.This paper then performs contrastive learning between neighbor views to promote the uniformity of view information,adjusting the neighbor structure,and achieving the goal of reducing the knowledge noise in the knowledge graph.Finally,this paper introduces multi-task learning to mitigate the problem of weak supervisory signals.To validate the effectiveness of our method,experiments are conducted on theMovieLens-1M,MovieLens-20M,Book-Crossing,and Last-FM datasets.The results showthat compared to the best baselines,our method shows significant improvements in AUC and F1.

Laser speckle contrast imaging to predict the effect of temporary spinal cord stimulation in postherpetic neuralgia patients: A prospective observational study

Temporary spinal cord stimulation(tSCS)can effectively reduce the pain and severity of postherpetic neuralgia(PHN).However,there are no effective and objective methods for predicting the effects of tSCS on PHN.Laser speckle contrast imaging(LSCI)is frequently used in neurology to evaluate the effectiveness of treatment.To assess the accuracy of LSCI in predicting the impact of tSCS on PHN,14 adult patients receiving tSCS treatments for spinal nerve-innervated(C6-T2)PHN participated in this observational study.Visual analog scale(VAS)assessments and LSCI bloodflow images of the-ngers were recorded after the tSCS procedure.The results showed that the VAS scores of all patients decreased signi-cantly.Moreover,the bloodflow index(BFI)values were signi-cantly higher than they were before the procedure.Increased bloodflow and pain alleviation were positively correlated.The-ndings indicated that spinal nerve PHN(C6-T2)was signi-cantly reduced by tSCS.Pain alleviation by tSCS was positively correlated with increased bloodflow in the hand.The effect of tSCS on PHN may thus be predicted using an independent and consistent indicator such as LSCI.

Improved Weighted Local Contrast Method for Infrared Small Target Detection

In order to address the problem of high false alarm rate and low probabilities of infrared small target detection in complex low-altitude background,an infrared small target detection method based on improved weighted local contrast is proposed in this paper.First,the ratio information between the target and local background is utilized as an enhancement factor.The local contrast is calculated by incorporating the heterogeneity between the target and local background.Then,a local product weighted method is designed based on the spatial dissimilarity between target and background to further enhance target while suppressing background.Finally,the location of target is obtained by adaptive threshold segmentation.As experimental results demonstrate,the method shows superior performance in several evaluation metrics compared with six existing algorithms on different datasets containing targets such as unmanned aerial vehicles(UAV).

Dynamic contrast enhanced ultrasound in differential diagnosis of hepatocellular carcinoma: A systematic review and meta-analysis

BACKGROUND Non-invasive differential diagnosis between hepatocellular carcinoma(HCC)and other liver cancer(i.e.cholangiocarcinoma or metastasis)is highly challenging and definitive diagnosis still relies on histological exam.The patterns of enhancement and wash-out of liver nodules can be used to stratify the risk of malignancy only in cirrhotic patients and HCC frequently shows atypical features.Dynamic contrast-enhanced ultrasound(DCEUS)with standardized software could help to overcome these obstacles,providing functional and quantitative parameters and potentially improving accuracy in the evaluation of tumor perfusion.AIM To explore clinical evidence regarding the application of DCEUS in the differential diagnosis of liver nodules.METHODS A comprehensive literature search of clinical studies was performed to identify the parameters of DCEUS that could relate to histological diagnosis.In accordance with the study protocol,a qualitative and quantitative analysis of the evidence was planned.RESULTS Rise time was significantly higher in HCC patients with a standardized mean difference(SMD)of 0.83(95%CI:0.48-1.18).Similarly,other statistically significant parameters were mean transit time local with a SMD of 0.73(95%CI:0.20-1.27),peak enhancement with a SMD of 0.37(95%CI:0.03-0.70),area wash-in area under the curve with a SMD of 0.47(95%CI:0.13-0.81),wash-out area under the curve with a SMD of 0.55(95%CI:0.21-0.89)and wash-in and wash-out area under the curve with SMD of 0.51(95%CI:0.17-0.85).SMD resulted not significant in fall time and wash-in rate,but the latter presented a trend towards greater values in HCC compared to intrahepatic cholangiocarcinoma.CONCLUSION DCEUS could improve non-invasive diagnosis of HCC,leading to less liver biopsy and early treatment.This quantitative analysis needs to be applied on larger cohorts to confirm these preliminary results.

Is it a normal phenomenon for pediatric patients to have brain leptomeningeal contrast enhancement on 3-tesla magnetic resonance imaging?

Determining whether sevoflurane sedation in children leads to“pseudo”prominent leptomeningeal contrast enhancement(pLMCE)on 3 Tesla magnetic resonance imaging will help reduce overdiagnosis by radiologists and clarify the pathophysiological changes of pLMCE.

The Inheritance and Application of Chinese Reverse Contrast Typeface Style

This paper discusses the inheritance and application of Chinese character reverse contrast typeface style.It begins by analyzing the visual features of Western reverse contrast typeface styles,with a focus on Caslon Italian and French Clarendon,providing a Western perspective reference for subsequent Chinese character reverse contrast typeface style designs.The paper then traces the origins of the Chinese reverse contrast style,from the calligraphy style"Lacquer Script"to the earliest printing type"フワンテール形",exploring the historical background and cultural significance of the Chinese reverse contrast style.In the methodology section of Chinese character reverse contrast typeface style design,the discussion is conducted from two dimensions:inheritance and application.In terms of inheritance,through an in-depth analysis of"Lacquer Script"and"フワンテール形"typeface style,the paper summarizes three basic theories for modern Chinese character reverse contrast typeface style design.In the application section,it examines in detail the two most influential recent typeface styles,"Ribaasu"and"Basic Artistic",outlining three directions of application:extreme horizontal stroke variations,exaggerated contrast,and diverse decorative strokes,showcasing new directions and possibilities for Chinese character reverse contrast typeface style design.This paper not only reviews the developmental history of the Chinese character reverse contrast typeface style but also analyzes the design methodology of Chinese character reverse contrast typeface style through specific case studies.

Near-Infrared Fluorescence Imaging Contrast Agents for Clinical Research: Limitations and Alternatives

Introduction: Near-infrared fluorescence imaging is a technique that will establish itself in the short term at the international level because it is recognized for its potential to improve the performance of surgical interventions, its moderate investment and operating costs and its portability. Although the technology is now mature, there is currently the problem of the availability of contrast agents to be injected IV. The aim of this methodology article is to propose an alternative solution to the need for contrast agents for clinical research, particularly in oncology. Methodology: They consist of coupling a fluorescent marker in the form of an NHS derivative, such as IR DYE manufactured in compliance with GMP, with therapeutic monoclonal antibodies having marketing authorization for molecular imaging. For a given antibody, the marking procedure must be the subject of a validation file on the final preparation filtered on a sterilizing membrane at 0.22 μm. Once the procedure has been validated, it would be unnecessary to repeat the tests before each clinical research examination. A check of the marking by thin-layer chromatography (TLC) and place it in a sample bank at +4˚C for 1 month of each injected formulation would be sufficient for additional tests if necessary. Conclusion: Molecular near-infrared fluorescence imaging is experiencing development, the process of which could be accelerated by greater availability of clinical contrast agents. Alternative solutions are therefore necessary to promote clinical research in this area. These methods must be shared to make it easier for researchers.

Unilateral contrast-induced encephalopathy with contrast medium exudation:A case report

BACKGROUND Contrast-induced encephalopathy(CIE)is a rare transient,reversible abnormality in the structure or function of the nervous system caused by the intravascular use of contrast agents.CIE can present with a range of neurological manifestations,including focal neurological deficits(hemiplegia,hemianopia,cortical blindness,aphasia,and parkinsonism)and systemic symptoms(confusion,seizures,and coma).However,if not accurately diagnosed and treated in a timely manner,CIE can cause irreversible damage to patients,especially critically ill patients.CASE SUMMARY A male in his 50 s,2 h after digital subtraction angiography,had a progressive disorder of consciousness,mixed aphasia,bilateral pupillary sluggish light reflex,and right limb weakness.Seven hours after the procedure,he developed unconsciousness,high fever(39.5°C),seizures,hemiplegia,neck stiffness(+),and right Babinski signs(+).computed tomography(CT)findings 2 h postprocedure were very confusing and led us to misdiagnose the patient with subarachnoid hemorrhage.Brain CT was performed again 7 h after the procedure.Compared with the CT 2 h after the procedure,the CT 7 h after the procedure showed that the manifestations of subarachnoid hemorrhage in the left cerebral hemisphere had disappeared and were replaced by brain tissue swelling,and the cerebral sulci had disappeared.Combined with the clinical manifestations of the patient and after the exclusion of subarachnoid hemorrhage and cerebrovascular embolism,we diagnosed the patient with CIE,and intravenous fluids were given for adequate hydration,as well as mannitol,albumin dehydration,furosemide and the glucocorticoid methylprednisolone.After 17 d of active treatment,the patient was discharged with no sequelae.CONCLUSION CIE should be taken seriously,but it is easily misdiagnosed,and once CIE is diagnosed,rapid,accurate diagnosis and treatment are critical steps.Whether a follow-up examination using a contrast agent can be performed should be closely evaluated,and the patient should be fully informed of the associated risks.

Contrastive Learning for Blind Super-Resolution via A Distortion-Specific Network

Previous deep learning-based super-resolution(SR)methods rely on the assumption that the degradation process is predefined(e.g.,bicubic downsampling).Thus,their performance would suffer from deterioration if the real degradation is not consistent with the assumption.To deal with real-world scenarios,existing blind SR methods are committed to estimating both the degradation and the super-resolved image with an extra loss or iterative scheme.However,degradation estimation that requires more computation would result in limited SR performance due to the accumulated estimation errors.In this paper,we propose a contrastive regularization built upon contrastive learning to exploit both the information of blurry images and clear images as negative and positive samples,respectively.Contrastive regularization ensures that the restored image is pulled closer to the clear image and pushed far away from the blurry image in the representation space.Furthermore,instead of estimating the degradation,we extract global statistical prior information to capture the character of the distortion.Considering the coupling between the degradation and the low-resolution image,we embed the global prior into the distortion-specific SR network to make our method adaptive to the changes of distortions.We term our distortion-specific network with contrastive regularization as CRDNet.The extensive experiments on synthetic and realworld scenes demonstrate that our lightweight CRDNet surpasses state-of-the-art blind super-resolution approaches.

Ultrasound-guided carotid angioplasty and stenting in a patient with iodinated contrast allergy:A case report

BACKGROUND Ischemic stroke is an entity with high incidence,morbidity,and mortality rates.Carotid artery stenosis is an important and independent risk factor for ischemic stroke.The three current approaches for treating carotid artery stenosis are drug treatment,carotid endarterectomy(CEA),carotid angioplasty and stenting(CAS).The approach is chosen based on the degree of stenosis.CEA or CAS could have been chosen for the current patient,who had severe carotid stenosis and an iodinated contrast allergy.After thoroughly communicating with the patient,the patient chose CAS for treatment.Therefore,we performed ultrasound-guided CAS to avoid the use of iodinated contrast.CASE SUMMARY The main symptoms of the patient were numbness and weakness of the left limb.Computed tomography angiography of the head and neck at another hospital indicated multiple sites of stenosis in the arteries of the head and neck.The patient requested CAS for treatment but was allergic to iodinated contrast media.Thus,routine digital subtraction angiography(DSA)with iodinated contrast could not be used for the procedure.The diagnosis of this patient was as follows:(1)Right parietal lobe cerebral infarction;(2)multiple sites of stenosis in the arteries of the head and neck(severe stenosis of the right internal carotid artery,severe stenosis of the right subclavian artery);(3)right subclavian steal syndrome;and(4)hypertension(stage 3,high risk).The interventions included routine treatment for cerebral infarction,oral administration of clopidogrel(75 mg qd)and aspirin(100 mg qd),ultrasound-guided CAS,and postoperative follow-up.Postoperative color Doppler ultrasound and cerebrovascular magnetic resonance angiography of the carotid artery showed good vascular recovery,and the postoperative follow-up indicated a good prognosis.CONCLUSION This case study suggests that ultrasound-guided endovascular treatment is a potential option for patients with contraindications to the iodinated contrast agents used in DSA-guided surgery,although excellent surgical operating skills are needed.

Transmissive multifocal laser speckle contrast imaging through thick tissue

Laser speckle contrast imaging(LSCI)is a powerful tool for monitoring blood flow changes in tissue or vessels in vivo,but its applications are limited by shallow penetration depth under reflective imaging configuration.The traditional LSCI setup has been used in transmissive imaging for depth extension up to 2l_(t)–3l_(t)(l_(t)is the transport mean free path),but the blood flow estimation is biased due to the depth uncertainty in large depth of field(DOF)images.In this study,we propose a transmissive multifocal LSCI for depth-resolved blood flow in thick tissue,further extending the transmissive LSCI for tissue thickness up to 12lt.The limited-DOF imaging system is applied to the multifocal acquisition,and the depth of the vessel is estimated using a robust visibility parameter V_(r)in the coherent domain.The accuracy and linearity of depth estimation are tested by Monte Carlo simulations.Based on the proposed method,the model of contrast analysis resolving the depth information is established and verified in a phantom experiment.We demonstrated its effectiveness in acquiring depth-resolved vessel structures and flow dynamics in in vivo imaging of chick embryos.

Log Anomaly Detection Based on Hierarchical Graph Neural Network and Label Contrastive Coding

System logs are essential for detecting anomalies,querying faults,and tracing attacks.Because of the time-consuming and labor-intensive nature of manual system troubleshooting and anomaly detection,it cannot meet the actual needs.The implementation of automated log anomaly detection is a topic that demands urgent research.However,the prior work on processing log data is mainly one-dimensional and cannot profoundly learn the complex associations in log data.Meanwhile,there is a lack of attention to the utilization of log labels and usually relies on a large number of labels for detection.This paper proposes a novel and practical detection model named LCC-HGLog,the core of which is the conversion of log anomaly detection into a graph classification problem.Semantic temporal graphs(STG)are constructed by extracting the raw logs’execution sequences and template semantics.Then a unique graph classifier is used to better comprehend each STG’s semantic,sequential,and structural features.The classification model is trained jointly by graph classification loss and label contrastive loss.While achieving discriminability at the class-level,it increases the fine-grained identification at the instance-level,thus achieving detection performance even with a small amount of labeled data.We have conducted numerous experiments on real log datasets,showing that the proposed model outperforms the baseline methods and obtains the best all-around performance.Moreover,the detection performance degrades to less than 1%when only 10%of the labeled data is used.With 200 labeled samples,we can achieve the same or better detection results than the baseline methods.

Solving Geometry Problems via Feature Learning and Contrastive Learning of Multimodal Data

This paper presents an end-to-end deep learning method to solve geometry problems via feature learning and contrastive learning of multimodal data.A key challenge in solving geometry problems using deep learning is to automatically adapt to the task of understanding single-modal and multimodal problems.Existing methods either focus on single-modal ormultimodal problems,and they cannot fit each other.A general geometry problem solver shouldobviouslybe able toprocess variousmodalproblems at the same time.Inthispaper,a shared feature-learning model of multimodal data is adopted to learn the unified feature representation of text and image,which can solve the heterogeneity issue between multimodal geometry problems.A contrastive learning model of multimodal data enhances the semantic relevance betweenmultimodal features and maps them into a unified semantic space,which can effectively adapt to both single-modal and multimodal downstream tasks.Based on the feature extraction and fusion of multimodal data,a proposed geometry problem solver uses relation extraction,theorem reasoning,and problem solving to present solutions in a readable way.Experimental results show the effectiveness of the method.

A Memory-Guided Anomaly Detection Model with Contrastive Learning for Multivariate Time Series

Some reconstruction-based anomaly detection models in multivariate time series have brought impressive performance advancements but suffer from weak generalization ability and a lack of anomaly identification.These limitations can result in the misjudgment of models,leading to a degradation in overall detection performance.This paper proposes a novel transformer-like anomaly detection model adopting a contrastive learning module and a memory block(CLME)to overcome the above limitations.The contrastive learning module tailored for time series data can learn the contextual relationships to generate temporal fine-grained representations.The memory block can record normal patterns of these representations through the utilization of attention-based addressing and reintegration mechanisms.These two modules together effectively alleviate the problem of generalization.Furthermore,this paper introduces a fusion anomaly detection strategy that comprehensively takes into account the residual and feature spaces.Such a strategy can enlarge the discrepancies between normal and abnormal data,which is more conducive to anomaly identification.The proposed CLME model not only efficiently enhances the generalization performance but also improves the ability of anomaly detection.To validate the efficacy of the proposed approach,extensive experiments are conducted on well-established benchmark datasets,including SWaT,PSM,WADI,and MSL.The results demonstrate outstanding performance,with F1 scores of 90.58%,94.83%,91.58%,and 91.75%,respectively.These findings affirm the superiority of the CLME model over existing stateof-the-art anomaly detection methodologies in terms of its ability to detect anomalies within complex datasets accurately.

Colorless to black switching with high contrast ratio via the electrochemical process of a hybrid organic-inorganic perovskite

Colorless‐to‐black switching has attracted widespread attention for smart windows and multifunctional displays because they are more useful to control solar energy.However,it still remains a challenge owing to the tremendous difficulties in the design of completely reverse absorptions in transmissive and colored states.Herein,we report on an electrochemical device that can switch between colorless and black by using the electrochemical process of hybrid organic–inorganic perovskite MAPbBr_(3),which shows a high integrated contrast ratio of up to 73%from 400 to 800 nm.The perovskite solution can be used as the active layer to assemble the device,showing superior transmittance over the entire visible region in neutral states.By applying an appropriate voltage,the device undergoes reversible switching between colorless and black,which is attributed to the formation of lead and Br_(2)in the redox reaction induced by the electron transfer process in MAPbBr_(3).In addition,the contrast ratio can be modulated over the entire visible region by changing the concentration and the applied voltage.These results contribute toward gaining an insightful understanding of the electrochemical process of perovskites and greatly promoting the development of switchable devices.

Contrastive Clustering for Unsupervised Recognition of Interference Signals

Interference signals recognition plays an important role in anti-jamming communication.With the development of deep learning,many supervised interference signals recognition algorithms based on deep learning have emerged recently and show better performance than traditional recognition algorithms.However,there is no unsupervised interference signals recognition algorithm at present.In this paper,an unsupervised interference signals recognition method called double phases and double dimensions contrastive clustering(DDCC)is proposed.Specifically,in the first phase,four data augmentation strategies for interference signals are used in data-augmentation-based(DA-based)contrastive learning.In the second phase,the original dataset’s k-nearest neighbor set(KNNset)is designed in double dimensions contrastive learning.In addition,a dynamic entropy parameter strategy is proposed.The simulation experiments of 9 types of interference signals show that random cropping is the best one of the four data augmentation strategies;the feature dimensional contrastive learning in the second phase can improve the clustering purity;the dynamic entropy parameter strategy can improve the stability of DDCC effectively.The unsupervised interference signals recognition results of DDCC and five other deep clustering algorithms show that the clustering performance of DDCC is superior to other algorithms.In particular,the clustering purity of our method is above 92%,SCAN’s is 81%,and the other three methods’are below 71%when jammingnoise-ratio(JNR)is−5 dB.In addition,our method is close to the supervised learning algorithm.

CoLM^(2)S:Contrastive self‐supervised learning on attributed multiplex graph network with multi‐scale information

Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently.However,there are still two challenges.First,most of the real‐word system are multiple relations,where entities are linked by different types of relations,and each relation is a view of the graph network.Second,the rich multi‐scale information(structure‐level and feature‐level)of the graph network can be seen as self‐supervised signals,which are not fully exploited.A novel contrastive self‐supervised representation learning framework on attributed multiplex graph networks with multi‐scale(named CoLM^(2)S)information is presented in this study.It mainly contains two components:intra‐relation contrast learning and interrelation contrastive learning.Specifically,the contrastive self‐supervised representation learning framework on attributed single‐layer graph networks with multi‐scale information(CoLMS)framework with the graph convolutional network as encoder to capture the intra‐relation information with multi‐scale structure‐level and feature‐level selfsupervised signals is introduced first.The structure‐level information includes the edge structure and sub‐graph structure,and the feature‐level information represents the output of different graph convolutional layer.Second,according to the consensus assumption among inter‐relations,the CoLM^(2)S framework is proposed to jointly learn various graph relations in attributed multiplex graph network to achieve global consensus node embedding.The proposed method can fully distil the graph information.Extensive experiments on unsupervised node clustering and graph visualisation tasks demonstrate the effectiveness of our methods,and it outperforms existing competitive baselines.