The inconsistence of firewall/VPN(Virtual Private Network) rule makes a huge maintainable cost. With development of Multinational Company, SOHO office, E-government the number of firewalls/VPN will increase rapidly. R...The inconsistence of firewall/VPN(Virtual Private Network) rule makes a huge maintainable cost. With development of Multinational Company, SOHO office, E-government the number of firewalls/VPN will increase rapidly. Rule table in stand-alone or network will be increased in geometric series accordingly. Checking the consistence of rule table manually is inadequate. A formal approach can define semantic consistence, make a theoretic foundation of intelligent management about rule tables. In this paper, a kind of formalization of host rules and network ones for auto rule-validation based on SET theory were proporsed and a rule validation scheme was defined. The analysis results show the superior performance of the methods and demonstrate its potential for the intelligent management based on rule tables.展开更多
With the rapid development of generative artificial intelligence(GenAI),the task of story visualization,which transforms natural language narratives into coherent and consistent image sequences,has attracted growing r...With the rapid development of generative artificial intelligence(GenAI),the task of story visualization,which transforms natural language narratives into coherent and consistent image sequences,has attracted growing research attention.However,existing methods still face limitations in balancing multi-frame character consistency and generation efficiency,which restricts their feasibility for large-scale practical applications.To address this issue,this study proposes a modular cloud-based distributed system built on Stable Diffusion.By separating the character generation and story generation processes,and integratingmulti-feature control techniques,a cachingmechanism,and an asynchronous task queue architecture,the system enhances generation efficiency and scalability.The experimental design includes both automated and human evaluations of character consistency,performance testing,and multinode simulation.The results show that the proposed system outperforms the baseline model StoryGen in both CLIP-I and human evaluation metrics.In terms of performance,under the experimental environment of this study,dual-node deployment reduces average waiting time by approximately 19%,while the four-node simulation further reduces it by up to 65%.Overall,this study demonstrates the advantages of cloud-distributed GenAI in maintaining character consistency and reducing generation latency,highlighting its potential value inmulti-user collaborative story visualization applications.展开更多
In the era of massive data,the study of distributed data is a significant topic.Model averaging can be effectively applied to distributed data by combining information from all machines.For linear models,the model ave...In the era of massive data,the study of distributed data is a significant topic.Model averaging can be effectively applied to distributed data by combining information from all machines.For linear models,the model averaging approach has been developed in the context of distributed data.However,further investigation is needed for more complex models.In this paper,the authors propose a distributed optimal model averaging approach based on multivariate additive models,which approximates unknown functions using B-splines allowing each machine to have a different smoothing degree.To utilize the information from the covariance matrix of dependent errors in multivariate multiple regressions,the authors use the Mahalanobis distance to construct a Mallows-type weight choice criterion.The criterion can be computed by transmitting information between the local machines and the center machine in two steps.The authors demonstrate the asymptotic optimality of the proposed model averaging estimator when the covariates are subject to uncertainty,and obtain the convergence rate of the weight vector to the theoretically optimal weights.The results remain novel even for additive models with a single response variable.The numerical examples show that the proposed method yields good performance.展开更多
FY-3G is the first polar-orbiting satellite equipped with a precipitation measurement radar(PMR)operating at Ku-andKa-band frequencies in China.In this study,we compare the reflectivity data from the FY-3G PMR Ku prod...FY-3G is the first polar-orbiting satellite equipped with a precipitation measurement radar(PMR)operating at Ku-andKa-band frequencies in China.In this study,we compare the reflectivity data from the FY-3G PMR Ku product and groundbasedradars(GRs)during 2024.Also,the FY-3G PMR is used as a third-party reference to evaluate the reflectivityconsistency among different GRs.The FY-3G PMR and GRs share similarities in their general distribution,characteristics,and intensity of reflectivity in strong precipitation cloud systems,though the former presents less detailed system structure.Systematic deviations between the FY-3G PMR and GRs and between GRs are comparable,albeit the reflectivity of the FY-3G PMR is generally slightly stronger than that of GRs(especially X-band GRs),with a mean bias ranging from 0.7 to 1.7dB.S-band GRs exhibit the smallest systematic deviation(STD=3.09 dB)from the FY-3G PMR,whereas the X-band GRsshow the largest(STD=3.61 dB),indirectly indicating the highest internal consistency among S-band GRs and the lowestamong X-band GRs.Besides,both S-and C-band GRs display similar deviations when paired with the FY-3G PMR as wellas when paired with their adjacent S/C-band GRs,suggesting good consistency between these two bands.In contrast,XbandGRs exhibit relatively poor consistency with S-band GRs and the FY-3G PMR,showing a deviation ranging from 3.0to 4.6 dB.展开更多
Defining science and demarcating it from pseudoscience are longstanding core issues in the philosophy of science.Hilbert’s traditional axiomatic standards(consistency,completeness,independence)struggle with modern co...Defining science and demarcating it from pseudoscience are longstanding core issues in the philosophy of science.Hilbert’s traditional axiomatic standards(consistency,completeness,independence)struggle with modern complex systems,while existing demarcation criteria like replicability and Popper’s falsificationism have practical limitations.Analyzing incompatibilities in complex systems,this paper proposes a revised framework:modified Hilbert axiomatic standards(clearly defined concepts,logical consistency,unrefuted axioms)and dynamic demarcation criteria.Abandoning unattainable traditional requirements of completeness and independence,it emphasizes conceptual clarity,openness,and progressiveness,offering an operational,self-consistent basis for judging scientificity.展开更多
The rapid growth of distributed data-centric applications and AI workloads increases demand for low-latency,high-throughput communication,necessitating frequent and flexible updates to network routing configurations.H...The rapid growth of distributed data-centric applications and AI workloads increases demand for low-latency,high-throughput communication,necessitating frequent and flexible updates to network routing configurations.However,maintaining consistent forwarding states during these updates is challenging,particularly when rerouting multiple flows simultaneously.Existing approaches pay little attention to multi-flow update,where improper update sequences across data plane nodes may construct deadlock dependencies.Moreover,these methods typically involve excessive control-data plane interactions,incurring significant resource overhead and performance degradation.This paper presents P4LoF,an efficient loop-free update approach that enables the controller to reroute multiple flows through minimal interactions.P4LoF first utilizes a greedy-based algorithm to generate the shortest update dependency chain for the single-flow update.These chains are then dynamically merged into a dependency graph and resolved as a Shortest Common Super-sequence(SCS)problem to produce the update sequence of multi-flow update.To address deadlock dependencies in multi-flow updates,P4LoF builds a deadlock-fix forwarding model that leverages the flexible packet processing capabilities of the programmable data plane.Experimental results show that P4LoF reduces control-data plane interactions by at least 32.6%with modest overhead,while effectively guaranteeing loop-free consistency.展开更多
In this paper,the authors propose a class of test procedures to check the fitness of parametric forms of the variance function in regression models when the mean function is unknown.By evaluating the unknown mean func...In this paper,the authors propose a class of test procedures to check the fitness of parametric forms of the variance function in regression models when the mean function is unknown.By evaluating the unknown mean function with the classical kernel estimator,the proposed test statistics are built upon a modified minimum distance between a nonparametric fit and a parametric estimator under the null hypothesis for the variance function.Asymptotic properties of the estimator of the parameters in the variance function are discussed,and the large sample distribution of the test statistics under the null hypothesis is established,as well as the consistency and the power under some local alternative hypotheses.Extensive numerical studies demonstrate that the proposed test procedures have satisfactory finite sample performance.Finally,two real data examples further showcase the effectiveness of the proposed test in real applications.展开更多
Industrial fault diagnosis is a critical challenge in complex systems,where sensor data is often noisy and interdependencies between components are difficult to capture.Traditional methods struggle to effectively mode...Industrial fault diagnosis is a critical challenge in complex systems,where sensor data is often noisy and interdependencies between components are difficult to capture.Traditional methods struggle to effectively model these complexities.This paper presents a novel approach by transforming fault diagnosis into a graph recognition task,using sensor data represented as graph-structured data with the k-nearest neighbors(KNN)algorithm.A Graph Transformer is applied to extract node and graph features,with a combined loss function of cross-entropy and weighted consistency loss to stabilize graph representations.Experiments on the TFF dataset show that Graph Transformer combined with consistency loss outperforms conventional methods in fault diagnosis accuracy,offering a promising solution for enhancing fault detection in industrial systems.展开更多
AIM:To evaluate the differences and consistency of vault measurements obtained by Scheimpflug tomography(Pentacam),anterior segment optical coherence tomography(AS-OCT,CASIA II),and ultrasound biomicroscopy(UBM)follow...AIM:To evaluate the differences and consistency of vault measurements obtained by Scheimpflug tomography(Pentacam),anterior segment optical coherence tomography(AS-OCT,CASIA II),and ultrasound biomicroscopy(UBM)following implantable collamer lens(ICL)V4c implantation.METHODS:Vault measurements were acquired using three modalities:Pentacam,CASIA II AS-OCT,and UBM.Repeated-measures analysis of variance was used to compare the vault values obtained by the three devices.The correlation and consistency of measurements among the three instruments were assessed using the Pearson correlation coefficient,intraclass correlation coefficient(ICC),and Bland-Altman plots.RESULTS:This retrospective study enrolled 210 myopic eyes of 210 patients(158 women and 52 men)who underwent ICL implantation:108 eyes had a myopic ICL V4c implanted,and 102 eyes had a toric ICL V4c implanted.The mean vault values measured by Pentacam,CASIA II,and UBM were 452.64±204.20μm,538.57±203.54μm,and 560.95±227.54μm,respectively,with statistically significant differences among the three groups(P<0.05).Pearson correlation analysis showed strong positive correlations between vault values measured by different instruments(all P<0.001).ICC results indicated good consistency among the three measurement modalities(all P<0.001).Stratified analysis revealed that when the vault value was≤250μm,the correlation and consistency of measurements across the three instruments were lower than those in the medium and high vault subgroups.CONCLUSION:Vault values measured by Pentacam are lower than those obtained by CASIA II and UBM,with UBM yielding the highest mean vault values.Measurements from the three instruments are not interchangeable but can serve as mutual references due to their significant correlation and good overall consistency.Pentacam and CASIA II demonstrate the highest consistency in vault measurement.Notably,when the vault value is≤250μm,the consistency between Pentacam and the other two instruments decreases significantly.展开更多
This paper presents a hierarchical formation control strategy to address the challenges of multiple Unmanned Aerial Vehicles(UAVs)formation control within a cooperative consensus framework.The proposed strategy incorp...This paper presents a hierarchical formation control strategy to address the challenges of multiple Unmanned Aerial Vehicles(UAVs)formation control within a cooperative consensus framework.The proposed strategy incorporates a reference command generation layer,which derives UAV attitude commands based on formation requirements,and a tracking control layer to ensure accurate execution.Collaborative variables,including trajectory position and flight speed,are defined using a three-dimensional track particle and autopilot model,enabling the development of a consensus-based formation control law.Desired attitude angles are computed through altitudehold and coordinated-turn strategies.A sliding surface is designed based on reference models derived from flight quality metrics,while an adaptive controller compensates for aerodynamic model uncertainties.To enhance learning capabilities,a prediction error mechanism based on a series-parallel estimation model is introduced,enabling collaborative learning and the sharing of network weight estimation parameters within the multi-agent system.This facilitates the design of a distributed composite learning law.Lyapunov stability analysis confirms the local exponential stability of the tracking error.The simulations of a twelve-UAV formation,along with comparative analysis of two algorithms,demonstrate the system’s capability for formation maintenance and high-precision tracking control.展开更多
Deep graph contrastive clustering has attracted widespread attentions due to its self-supervised representation learning paradigm and superior clustering performance.Although,two challenges emerge and result in high c...Deep graph contrastive clustering has attracted widespread attentions due to its self-supervised representation learning paradigm and superior clustering performance.Although,two challenges emerge and result in high computational costs.Most existing contrastive methods adopt the data augmentation and then representation learning strategy,where representation learning with trainable graph convolution is coupled with complex and fixed data augmentation,inevitably limiting the efficiency and flexibility.The similarity metric between positive-negative sample pairs is complex and contrastive objective is partial,limiting the discriminability of representation learning.To solve these challenges,a novel wide graph clustering network(WGCN)adhering to representation and then augmentation framework is proposed,which mainly consists of multiorder filter fusion(MFF)and double-level contrastive learning(DCL)modules.Specifically,the MFF module integrates multiorder low-pass filters to extract smooth and multi-scale topological features,utilizing self-attention fusion to reduce redundancy and obtain comprehensive embedding representation.Further,the DCL module constructs two augmented views by the parallel parameter-unshared Siamese encoders rather than complex augmentations on graph.To achieve simple yet effective self-supervised learning,representation self-supervision and structural consistency oriented double-level contrastive loss is designed,where representation self-supervision maximizes the agreement between pairwise augmented embedding representations and structural consistency promotes the mutual information correlation between appending neighborhoods with similar semantics.Extensive experiments on six benchmark datasets demonstrate the superiority of the proposed WGCN,especially highlighting its time-saving characteristic.The code could be available in the https://github.com/Tianxiang Zhao0474/WGCN.展开更多
A 3-dimensional type-K competitive Lotka-Volterra system is considered in this paper.Two discretization schemes are applied to the system with an positive interior fixed point,and two corresponding discrete systems ar...A 3-dimensional type-K competitive Lotka-Volterra system is considered in this paper.Two discretization schemes are applied to the system with an positive interior fixed point,and two corresponding discrete systems are obtained.By analyzing the local dynamics of the corresponding discrete system near the interior fixed point,it is showed that this system is not dynamically consistent with the continuous counterpart system.展开更多
Poor corrosion resistance is a critical barrier to the widespread application of magnesium alloys.Statistically,the literature reported that approximately 70% of as-cast AZ31 magnesium alloys exhibit corrosion rates e...Poor corrosion resistance is a critical barrier to the widespread application of magnesium alloys.Statistically,the literature reported that approximately 70% of as-cast AZ31 magnesium alloys exhibit corrosion rates exceeding 1 mm·y^(-1) in 3.5 wt.%NaCl solution,which is unacceptable for industrial use.Furthermore,there is a considerable discrepancy in the corrosion rates reported by different studies(as-cast alloys ranging from 0.4 to 215 mm·y−1).These phenomena may be attributed to the uncontrollable content of impurity elements in commercial magnesium alloys,which fluctuate widely between batches.In the present work,we prepared as-cast AZ31 magnesium alloys with different impurity contents using two different purities of raw magnesium(Mg-99.9% and Mg-99.99%).The impact of impurity contents on the corrosion resistance of AZ31 magnesium alloys was then analyzed.The AZ31 magnesium alloy prepared with 99.99% raw magnesium showed superior corrosion resistance compared with that prepared with 99.9% raw magnesium,with a reduction in corrosion rate by approximately 98% and a decrease in the fluctuation range of corrosion rate by 91%.Thus,enhancing the purity of raw magnesium is an effective method to improve both the corrosion resistance and consistency of magnesium alloys.展开更多
With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions...With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.展开更多
In order to address the current inability of screen printing to monitor printing pressure online,an online printing pressure monitoring system applied to screen printing machines was designed in this study.In this stu...In order to address the current inability of screen printing to monitor printing pressure online,an online printing pressure monitoring system applied to screen printing machines was designed in this study.In this study,the consistency of printed electrodes was measured by using a confocal microscope and the pressure distribution detected by online pressure monitoring system was compared to investigate the relationship.The results demonstrated the relationship between printing pressure and the consistency of printed electrodes.As printing pressure increases,the ink layer at the corresponding position becomes thicker and that higher printing pressure enhances the consistency of the printed electrodes.The experiment confirms the feasibility of the online pressure monitoring system,which aids in predicting and controlling the consistency of printed electrodes,thereby improving their performance.展开更多
Index tracking is known to be a passive portfolio management strategy by replicating the performance of a real or virtual index.However,the full replication,which considers all the asserts consisted of the index,often...Index tracking is known to be a passive portfolio management strategy by replicating the performance of a real or virtual index.However,the full replication,which considers all the asserts consisted of the index,often suffers from small and illiquid positions and large transaction costs.Thus,it is preferred to purchase sparse portfolios.Besides,existing literature pointed out the phenomenon of the co-movement in assert returns,indicating that the index tracking problems possibly contain group structures together with sparsity.Based on the consideration of the grouping effects and sparsity in index tracking problems,this paper proposes a grouping sparse index tracking model with nonnegative restrictions.We derive a modified version of coordinate decent algorithm for solving the model.The asymptotic properties are also discussed in detail.To show the efficiency of the model,we apply it into the constrained index tracking problem in Shanghai stock market,i.e.tracking SSE 50 Index.By selecting about 10 stocks,the result shows that nonnegative group lasso outperforms nonnegative lasso in assert allocation.展开更多
Facial expression datasets commonly exhibit imbalances between various categories or between difficult and simple samples.This imbalance introduces bias into feature extraction within facial expression recognition(FER...Facial expression datasets commonly exhibit imbalances between various categories or between difficult and simple samples.This imbalance introduces bias into feature extraction within facial expression recognition(FER)models,which hinders the algorithm’s comprehension of emotional states and reduces the overall recognition accuracy.A novel FER model is introduced to address these issues.It integrates rebalancing mechanisms to regulate attention consistency and focus,offering enhanced efficacy.Our approach proposes the following improvements:(i)rebalancing weights are used to enhance the consistency between the heatmaps of an original face sample and its horizontally flipped counterpart;(ii)coefficient factors are incorporated into the standard cross entropy loss function,and rebalancing weights are incorporated to fine-tune the loss adjustment.Experimental results indicate that the FER model outperforms the current leading algorithm,MEK,achieving 0.69%and 2.01%increases in overall and average recognition accuracies,respectively,on the RAF-DB dataset.The model exhibits accuracy improvements of 0.49%and 1.01%in the AffectNet dataset and 0.83%and 1.23%in the FERPlus dataset,respectively.These outcomes validate the superiority and stability of the proposed FER model.展开更多
Passerine mimics often imitate various vocalizations from other bird species and incorporate these sounds into their song repertoires.While a few anecdotes reported that wild songbirds imitated human-associated sounds...Passerine mimics often imitate various vocalizations from other bird species and incorporate these sounds into their song repertoires.While a few anecdotes reported that wild songbirds imitated human-associated sounds,besides captive parrots and songbirds,systemic and quantitative studies on human-made sound mimicry in wild birds remain scarce.In this study,we investigated the mimetic accuracy and consistency of electric moped sounds imitated by an urban bird,the Chinese Blackbird(Turdus mandarinus).We found that:(1)Only one type of electric moped sound was imitated,i.e.,13 of 26 males mimicked the first part of the antitheft alarm,a phrase containing a series of identical notes.(2)The mimicry produced by male Chinese Blackbirds had fewer notes and lower consistency within phrases compared to the model alarms.(3)The mimicry of male Chinese Blackbirds was imperfect,i.e.,most of the acoustic parameters differed from the model alarms.Additionally,mimetic notes were lower in frequency than the models.Mimetic notes from two areas were also different in acoustic structures,suggesting Chinese Blackbirds might learn mimicry mainly from conspecific neighbors within each area respectively rather than electric mopeds,namely the secondary mimicry.Imperfect mimicry of human-made sounds could result from cost and physical constraints,associated with high consistency,frequency,and repetitions.Consequently,Chinese Blackbirds copied a simplified version of electric moped alarms.We recommend further attention to mimic species inhabiting urban ecosystems to better understand vocal mimicry's adaptation to ongoing urbanization.展开更多
The numerical approximation of stochastic partial differential equations(SPDEs),particularly those including q-diffusion,poses considerable challenges due to the requirements for high-order precision,stability amongst...The numerical approximation of stochastic partial differential equations(SPDEs),particularly those including q-diffusion,poses considerable challenges due to the requirements for high-order precision,stability amongst random perturbations,and processing efficiency.Because of their simplicity,conventional numerical techniques like the Euler-Maruyama method are frequently employed to solve stochastic differential equations;nonetheless,they may have low-order accuracy and lower stability in stiff or high-resolution situations.This study proposes a novel computational scheme for solving SPDEs arising from a stochastic SEIR model with q-diffusion and a general incidence rate function.A proposed computational scheme can be used to solve stochastic partial differential equations.For spatial discretization,a compact scheme is chosen.The compact scheme can provide a sixth-order accurate solution.The proposed scheme can be considered an extension of the Euler Maruyama method.Stability and consistency in the mean square sense are also provided.For application purposes,the stochastic SEIR model is considered using q-diffusion effects.The scheme is used to solve the stochastic model and compared with the Euler-Maruyama method.The scheme is also compared with nonstandard finite difference method for solving deterministic models.In both cases,it performs better than existing schemes.Incorporating q-diffusion further enhanced the model’s ability to represent realistic spatial-temporal disease dynamics,especially in scenarios where classical diffusion is insufficient.展开更多
Background:To investigate the consistency level of binary symptom assessment in patients with heart failure and their primary caregivers,and to analyze the related factors influencing consistency.Methods:By using the ...Background:To investigate the consistency level of binary symptom assessment in patients with heart failure and their primary caregivers,and to analyze the related factors influencing consistency.Methods:By using the convenience sampling method,patients with heart failure and their main caregivers in the Department of Cardiology of a tertiary hospital in Suzhou from May to November 2023 were selected as the research subjects.The HFSS scale was used for data collection.The paired t-test or paired Wilcoxon test was used to evaluate the differences in the binary symptom assessment scores of heart failure.The intraclass correlation coefficient was used to assess the consistency level of the binary symptom assessment.The Pearson correlation test was used to examine the correlation of the binary symptom assessment.Regression analysis was employed to explore the factors related to the consistency assessment.Results:A total of 103 pairs of valid questionnaires were collected.The consistency levels of symptom evaluations in patients with heart failure and their primary caregivers were statistically significant(P<0.05).The most frequently reported and severe symptom by patients with heart failure and their primary caregivers is shortness of breath during activity.Both have a high consensus on the severity and urgency of most heart failure symptoms.The patient’s gender,body mass index,number of children,history of diabetes,number of comorbidities,mean arterial pressure,LVEF,number of stents,whether a defibrillator was implanted,as well as the gender,marital status,education level,relationship with the patient,care time,whether they lived together,and communication and interaction situation of the main caregiver were the influencing factors for the consistency of binary symptom assessment of heart failure(P<0.05).Conclusion:The degree of consistency in binary symptom assessment between patients with heart failure and their primary caregivers was moderate or higher,which emphasizes the importance of including binary groups in clinical assessment.展开更多
文摘The inconsistence of firewall/VPN(Virtual Private Network) rule makes a huge maintainable cost. With development of Multinational Company, SOHO office, E-government the number of firewalls/VPN will increase rapidly. Rule table in stand-alone or network will be increased in geometric series accordingly. Checking the consistence of rule table manually is inadequate. A formal approach can define semantic consistence, make a theoretic foundation of intelligent management about rule tables. In this paper, a kind of formalization of host rules and network ones for auto rule-validation based on SET theory were proporsed and a rule validation scheme was defined. The analysis results show the superior performance of the methods and demonstrate its potential for the intelligent management based on rule tables.
文摘With the rapid development of generative artificial intelligence(GenAI),the task of story visualization,which transforms natural language narratives into coherent and consistent image sequences,has attracted growing research attention.However,existing methods still face limitations in balancing multi-frame character consistency and generation efficiency,which restricts their feasibility for large-scale practical applications.To address this issue,this study proposes a modular cloud-based distributed system built on Stable Diffusion.By separating the character generation and story generation processes,and integratingmulti-feature control techniques,a cachingmechanism,and an asynchronous task queue architecture,the system enhances generation efficiency and scalability.The experimental design includes both automated and human evaluations of character consistency,performance testing,and multinode simulation.The results show that the proposed system outperforms the baseline model StoryGen in both CLIP-I and human evaluation metrics.In terms of performance,under the experimental environment of this study,dual-node deployment reduces average waiting time by approximately 19%,while the four-node simulation further reduces it by up to 65%.Overall,this study demonstrates the advantages of cloud-distributed GenAI in maintaining character consistency and reducing generation latency,highlighting its potential value inmulti-user collaborative story visualization applications.
基金supported by Youth Academic Innocation Team Construction project of Capital University of Economics and Business under Grant No.QNTD202303supported by the Beijing Outstanding Young Scientist Program under Grant No.JWZQ20240101027the National Natural Science Foundation of China under Grant Nos.12031016,12531012 and 12426308。
文摘In the era of massive data,the study of distributed data is a significant topic.Model averaging can be effectively applied to distributed data by combining information from all machines.For linear models,the model averaging approach has been developed in the context of distributed data.However,further investigation is needed for more complex models.In this paper,the authors propose a distributed optimal model averaging approach based on multivariate additive models,which approximates unknown functions using B-splines allowing each machine to have a different smoothing degree.To utilize the information from the covariance matrix of dependent errors in multivariate multiple regressions,the authors use the Mahalanobis distance to construct a Mallows-type weight choice criterion.The criterion can be computed by transmitting information between the local machines and the center machine in two steps.The authors demonstrate the asymptotic optimality of the proposed model averaging estimator when the covariates are subject to uncertainty,and obtain the convergence rate of the weight vector to the theoretically optimal weights.The results remain novel even for additive models with a single response variable.The numerical examples show that the proposed method yields good performance.
基金supported by the Innovation and Development Special Project of the China Meteorological Administration(Grant No.CXFZ2024J058)the Guangdong Province Basic and Applied Basic Research Foundation Meteorological Joint Fund Project(Grant No.2024A1515510036)+1 种基金the National Key R&D Program of China(Grant No.2022YFC3004101)the Technical Innovation Team Project of Guangzhou Meteorological Satellite Ground Station(Grant No.CXTD202401).
文摘FY-3G is the first polar-orbiting satellite equipped with a precipitation measurement radar(PMR)operating at Ku-andKa-band frequencies in China.In this study,we compare the reflectivity data from the FY-3G PMR Ku product and groundbasedradars(GRs)during 2024.Also,the FY-3G PMR is used as a third-party reference to evaluate the reflectivityconsistency among different GRs.The FY-3G PMR and GRs share similarities in their general distribution,characteristics,and intensity of reflectivity in strong precipitation cloud systems,though the former presents less detailed system structure.Systematic deviations between the FY-3G PMR and GRs and between GRs are comparable,albeit the reflectivity of the FY-3G PMR is generally slightly stronger than that of GRs(especially X-band GRs),with a mean bias ranging from 0.7 to 1.7dB.S-band GRs exhibit the smallest systematic deviation(STD=3.09 dB)from the FY-3G PMR,whereas the X-band GRsshow the largest(STD=3.61 dB),indirectly indicating the highest internal consistency among S-band GRs and the lowestamong X-band GRs.Besides,both S-and C-band GRs display similar deviations when paired with the FY-3G PMR as wellas when paired with their adjacent S/C-band GRs,suggesting good consistency between these two bands.In contrast,XbandGRs exhibit relatively poor consistency with S-band GRs and the FY-3G PMR,showing a deviation ranging from 3.0to 4.6 dB.
基金supported by the start-up funding from Westlake University under Grant Number 041030150118 and the scientific research project of Westlake University“Theoretical Research and Demonstration Application of Complex Systems and Deep-Sea Technology(Phase I)”under Grant Number WU2025A006.
文摘Defining science and demarcating it from pseudoscience are longstanding core issues in the philosophy of science.Hilbert’s traditional axiomatic standards(consistency,completeness,independence)struggle with modern complex systems,while existing demarcation criteria like replicability and Popper’s falsificationism have practical limitations.Analyzing incompatibilities in complex systems,this paper proposes a revised framework:modified Hilbert axiomatic standards(clearly defined concepts,logical consistency,unrefuted axioms)and dynamic demarcation criteria.Abandoning unattainable traditional requirements of completeness and independence,it emphasizes conceptual clarity,openness,and progressiveness,offering an operational,self-consistent basis for judging scientificity.
基金supported by the National Key Research and Development Program of China under Grant 2022YFB2901501in part by the Science and Technology Innovation leading Talents Subsidy Project of Central Plains under Grant 244200510038.
文摘The rapid growth of distributed data-centric applications and AI workloads increases demand for low-latency,high-throughput communication,necessitating frequent and flexible updates to network routing configurations.However,maintaining consistent forwarding states during these updates is challenging,particularly when rerouting multiple flows simultaneously.Existing approaches pay little attention to multi-flow update,where improper update sequences across data plane nodes may construct deadlock dependencies.Moreover,these methods typically involve excessive control-data plane interactions,incurring significant resource overhead and performance degradation.This paper presents P4LoF,an efficient loop-free update approach that enables the controller to reroute multiple flows through minimal interactions.P4LoF first utilizes a greedy-based algorithm to generate the shortest update dependency chain for the single-flow update.These chains are then dynamically merged into a dependency graph and resolved as a Shortest Common Super-sequence(SCS)problem to produce the update sequence of multi-flow update.To address deadlock dependencies in multi-flow updates,P4LoF builds a deadlock-fix forwarding model that leverages the flexible packet processing capabilities of the programmable data plane.Experimental results show that P4LoF reduces control-data plane interactions by at least 32.6%with modest overhead,while effectively guaranteeing loop-free consistency.
基金supported by the National Natural Science Foundation of China under Grant No.12071267。
文摘In this paper,the authors propose a class of test procedures to check the fitness of parametric forms of the variance function in regression models when the mean function is unknown.By evaluating the unknown mean function with the classical kernel estimator,the proposed test statistics are built upon a modified minimum distance between a nonparametric fit and a parametric estimator under the null hypothesis for the variance function.Asymptotic properties of the estimator of the parameters in the variance function are discussed,and the large sample distribution of the test statistics under the null hypothesis is established,as well as the consistency and the power under some local alternative hypotheses.Extensive numerical studies demonstrate that the proposed test procedures have satisfactory finite sample performance.Finally,two real data examples further showcase the effectiveness of the proposed test in real applications.
基金supported by the National Natural Science Foundation of China under Grants Nos.62573292,62206199 and 62476192National Key Laboratory of Marine Engine Science and Technology under Grant No.LAB-2024-04-WD+2 种基金Young Elite Scientist Sponsorship Program under Grant No.YESS20220409the Hainan Province Science and Technology Special Fund under Grant No.ZDYF2024GXJS003the Natural Science Foundation of Tianjin under Grant No.23JCQNJC02010.
文摘Industrial fault diagnosis is a critical challenge in complex systems,where sensor data is often noisy and interdependencies between components are difficult to capture.Traditional methods struggle to effectively model these complexities.This paper presents a novel approach by transforming fault diagnosis into a graph recognition task,using sensor data represented as graph-structured data with the k-nearest neighbors(KNN)algorithm.A Graph Transformer is applied to extract node and graph features,with a combined loss function of cross-entropy and weighted consistency loss to stabilize graph representations.Experiments on the TFF dataset show that Graph Transformer combined with consistency loss outperforms conventional methods in fault diagnosis accuracy,offering a promising solution for enhancing fault detection in industrial systems.
基金Supported by the National Natural Science Foundation of China(No.82171095)the Project of Shanghai Science and Technology(No.23XD1400500)the Research Fund of Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital(No.24QNPY049).
文摘AIM:To evaluate the differences and consistency of vault measurements obtained by Scheimpflug tomography(Pentacam),anterior segment optical coherence tomography(AS-OCT,CASIA II),and ultrasound biomicroscopy(UBM)following implantable collamer lens(ICL)V4c implantation.METHODS:Vault measurements were acquired using three modalities:Pentacam,CASIA II AS-OCT,and UBM.Repeated-measures analysis of variance was used to compare the vault values obtained by the three devices.The correlation and consistency of measurements among the three instruments were assessed using the Pearson correlation coefficient,intraclass correlation coefficient(ICC),and Bland-Altman plots.RESULTS:This retrospective study enrolled 210 myopic eyes of 210 patients(158 women and 52 men)who underwent ICL implantation:108 eyes had a myopic ICL V4c implanted,and 102 eyes had a toric ICL V4c implanted.The mean vault values measured by Pentacam,CASIA II,and UBM were 452.64±204.20μm,538.57±203.54μm,and 560.95±227.54μm,respectively,with statistically significant differences among the three groups(P<0.05).Pearson correlation analysis showed strong positive correlations between vault values measured by different instruments(all P<0.001).ICC results indicated good consistency among the three measurement modalities(all P<0.001).Stratified analysis revealed that when the vault value was≤250μm,the correlation and consistency of measurements across the three instruments were lower than those in the medium and high vault subgroups.CONCLUSION:Vault values measured by Pentacam are lower than those obtained by CASIA II and UBM,with UBM yielding the highest mean vault values.Measurements from the three instruments are not interchangeable but can serve as mutual references due to their significant correlation and good overall consistency.Pentacam and CASIA II demonstrate the highest consistency in vault measurement.Notably,when the vault value is≤250μm,the consistency between Pentacam and the other two instruments decreases significantly.
基金co-supported in part by the National Natural Science Foundation of China(No.62403131)in part by Jiangsu Funding Program for Excellent Postdoctoral Talent,China(No.2024ZB267)in part by the Shenzhen Science and Technology Program,China(No.JCYJ20230807145500002)。
文摘This paper presents a hierarchical formation control strategy to address the challenges of multiple Unmanned Aerial Vehicles(UAVs)formation control within a cooperative consensus framework.The proposed strategy incorporates a reference command generation layer,which derives UAV attitude commands based on formation requirements,and a tracking control layer to ensure accurate execution.Collaborative variables,including trajectory position and flight speed,are defined using a three-dimensional track particle and autopilot model,enabling the development of a consensus-based formation control law.Desired attitude angles are computed through altitudehold and coordinated-turn strategies.A sliding surface is designed based on reference models derived from flight quality metrics,while an adaptive controller compensates for aerodynamic model uncertainties.To enhance learning capabilities,a prediction error mechanism based on a series-parallel estimation model is introduced,enabling collaborative learning and the sharing of network weight estimation parameters within the multi-agent system.This facilitates the design of a distributed composite learning law.Lyapunov stability analysis confirms the local exponential stability of the tracking error.The simulations of a twelve-UAV formation,along with comparative analysis of two algorithms,demonstrate the system’s capability for formation maintenance and high-precision tracking control.
基金supported by the National Natural Science Foundation of China(62225303,62403043,62433004)the Beijing Natural Science Foundation(4244085)+1 种基金the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(GZC20230203)the China Postdoctoral Science Foundation(2023M740201)。
文摘Deep graph contrastive clustering has attracted widespread attentions due to its self-supervised representation learning paradigm and superior clustering performance.Although,two challenges emerge and result in high computational costs.Most existing contrastive methods adopt the data augmentation and then representation learning strategy,where representation learning with trainable graph convolution is coupled with complex and fixed data augmentation,inevitably limiting the efficiency and flexibility.The similarity metric between positive-negative sample pairs is complex and contrastive objective is partial,limiting the discriminability of representation learning.To solve these challenges,a novel wide graph clustering network(WGCN)adhering to representation and then augmentation framework is proposed,which mainly consists of multiorder filter fusion(MFF)and double-level contrastive learning(DCL)modules.Specifically,the MFF module integrates multiorder low-pass filters to extract smooth and multi-scale topological features,utilizing self-attention fusion to reduce redundancy and obtain comprehensive embedding representation.Further,the DCL module constructs two augmented views by the parallel parameter-unshared Siamese encoders rather than complex augmentations on graph.To achieve simple yet effective self-supervised learning,representation self-supervision and structural consistency oriented double-level contrastive loss is designed,where representation self-supervision maximizes the agreement between pairwise augmented embedding representations and structural consistency promotes the mutual information correlation between appending neighborhoods with similar semantics.Extensive experiments on six benchmark datasets demonstrate the superiority of the proposed WGCN,especially highlighting its time-saving characteristic.The code could be available in the https://github.com/Tianxiang Zhao0474/WGCN.
基金Supported by the Natural Science Foundation of Shandong Province(ZR2009AL010)Project of Shandong Province Higher Educational Science and Technology Program(J09LA51)Program for Innovative Research Team in Ludong University(08-CXB005)
文摘A 3-dimensional type-K competitive Lotka-Volterra system is considered in this paper.Two discretization schemes are applied to the system with an positive interior fixed point,and two corresponding discrete systems are obtained.By analyzing the local dynamics of the corresponding discrete system near the interior fixed point,it is showed that this system is not dynamically consistent with the continuous counterpart system.
基金the support of the National Natural Science Foundation of China(52371122,52031011)Shaanxi Province Foundation for Distinguished Young Scholars(2024JC-JCQN-47)Shaanxi Province Science and Technology Department Project(2023ZSJD-05HZ).
文摘Poor corrosion resistance is a critical barrier to the widespread application of magnesium alloys.Statistically,the literature reported that approximately 70% of as-cast AZ31 magnesium alloys exhibit corrosion rates exceeding 1 mm·y^(-1) in 3.5 wt.%NaCl solution,which is unacceptable for industrial use.Furthermore,there is a considerable discrepancy in the corrosion rates reported by different studies(as-cast alloys ranging from 0.4 to 215 mm·y−1).These phenomena may be attributed to the uncontrollable content of impurity elements in commercial magnesium alloys,which fluctuate widely between batches.In the present work,we prepared as-cast AZ31 magnesium alloys with different impurity contents using two different purities of raw magnesium(Mg-99.9% and Mg-99.99%).The impact of impurity contents on the corrosion resistance of AZ31 magnesium alloys was then analyzed.The AZ31 magnesium alloy prepared with 99.99% raw magnesium showed superior corrosion resistance compared with that prepared with 99.9% raw magnesium,with a reduction in corrosion rate by approximately 98% and a decrease in the fluctuation range of corrosion rate by 91%.Thus,enhancing the purity of raw magnesium is an effective method to improve both the corrosion resistance and consistency of magnesium alloys.
文摘With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.
文摘In order to address the current inability of screen printing to monitor printing pressure online,an online printing pressure monitoring system applied to screen printing machines was designed in this study.In this study,the consistency of printed electrodes was measured by using a confocal microscope and the pressure distribution detected by online pressure monitoring system was compared to investigate the relationship.The results demonstrated the relationship between printing pressure and the consistency of printed electrodes.As printing pressure increases,the ink layer at the corresponding position becomes thicker and that higher printing pressure enhances the consistency of the printed electrodes.The experiment confirms the feasibility of the online pressure monitoring system,which aids in predicting and controlling the consistency of printed electrodes,thereby improving their performance.
基金supported by the Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJQN202400514)the Foundation Project of Chongqing Normal University(Grand No.23XLB020)+1 种基金partly supported by Chongqing Social Science Planning Doctoral Program(Grant No.2022BS064)the Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJQN202301541)。
文摘Index tracking is known to be a passive portfolio management strategy by replicating the performance of a real or virtual index.However,the full replication,which considers all the asserts consisted of the index,often suffers from small and illiquid positions and large transaction costs.Thus,it is preferred to purchase sparse portfolios.Besides,existing literature pointed out the phenomenon of the co-movement in assert returns,indicating that the index tracking problems possibly contain group structures together with sparsity.Based on the consideration of the grouping effects and sparsity in index tracking problems,this paper proposes a grouping sparse index tracking model with nonnegative restrictions.We derive a modified version of coordinate decent algorithm for solving the model.The asymptotic properties are also discussed in detail.To show the efficiency of the model,we apply it into the constrained index tracking problem in Shanghai stock market,i.e.tracking SSE 50 Index.By selecting about 10 stocks,the result shows that nonnegative group lasso outperforms nonnegative lasso in assert allocation.
基金support from the National Natural Science Foundation of China(Grant Number 62477023).
文摘Facial expression datasets commonly exhibit imbalances between various categories or between difficult and simple samples.This imbalance introduces bias into feature extraction within facial expression recognition(FER)models,which hinders the algorithm’s comprehension of emotional states and reduces the overall recognition accuracy.A novel FER model is introduced to address these issues.It integrates rebalancing mechanisms to regulate attention consistency and focus,offering enhanced efficacy.Our approach proposes the following improvements:(i)rebalancing weights are used to enhance the consistency between the heatmaps of an original face sample and its horizontally flipped counterpart;(ii)coefficient factors are incorporated into the standard cross entropy loss function,and rebalancing weights are incorporated to fine-tune the loss adjustment.Experimental results indicate that the FER model outperforms the current leading algorithm,MEK,achieving 0.69%and 2.01%increases in overall and average recognition accuracies,respectively,on the RAF-DB dataset.The model exhibits accuracy improvements of 0.49%and 1.01%in the AffectNet dataset and 0.83%and 1.23%in the FERPlus dataset,respectively.These outcomes validate the superiority and stability of the proposed FER model.
基金supported by the National Key Research and Development Program of China(2022YFC3202104)the Western LightKey Laboratory Cooperative Research Cross-Team Project of Chinese Academy of Sciences(xbzg-zdsys-202207)。
文摘Passerine mimics often imitate various vocalizations from other bird species and incorporate these sounds into their song repertoires.While a few anecdotes reported that wild songbirds imitated human-associated sounds,besides captive parrots and songbirds,systemic and quantitative studies on human-made sound mimicry in wild birds remain scarce.In this study,we investigated the mimetic accuracy and consistency of electric moped sounds imitated by an urban bird,the Chinese Blackbird(Turdus mandarinus).We found that:(1)Only one type of electric moped sound was imitated,i.e.,13 of 26 males mimicked the first part of the antitheft alarm,a phrase containing a series of identical notes.(2)The mimicry produced by male Chinese Blackbirds had fewer notes and lower consistency within phrases compared to the model alarms.(3)The mimicry of male Chinese Blackbirds was imperfect,i.e.,most of the acoustic parameters differed from the model alarms.Additionally,mimetic notes were lower in frequency than the models.Mimetic notes from two areas were also different in acoustic structures,suggesting Chinese Blackbirds might learn mimicry mainly from conspecific neighbors within each area respectively rather than electric mopeds,namely the secondary mimicry.Imperfect mimicry of human-made sounds could result from cost and physical constraints,associated with high consistency,frequency,and repetitions.Consequently,Chinese Blackbirds copied a simplified version of electric moped alarms.We recommend further attention to mimic species inhabiting urban ecosystems to better understand vocal mimicry's adaptation to ongoing urbanization.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2501).
文摘The numerical approximation of stochastic partial differential equations(SPDEs),particularly those including q-diffusion,poses considerable challenges due to the requirements for high-order precision,stability amongst random perturbations,and processing efficiency.Because of their simplicity,conventional numerical techniques like the Euler-Maruyama method are frequently employed to solve stochastic differential equations;nonetheless,they may have low-order accuracy and lower stability in stiff or high-resolution situations.This study proposes a novel computational scheme for solving SPDEs arising from a stochastic SEIR model with q-diffusion and a general incidence rate function.A proposed computational scheme can be used to solve stochastic partial differential equations.For spatial discretization,a compact scheme is chosen.The compact scheme can provide a sixth-order accurate solution.The proposed scheme can be considered an extension of the Euler Maruyama method.Stability and consistency in the mean square sense are also provided.For application purposes,the stochastic SEIR model is considered using q-diffusion effects.The scheme is used to solve the stochastic model and compared with the Euler-Maruyama method.The scheme is also compared with nonstandard finite difference method for solving deterministic models.In both cases,it performs better than existing schemes.Incorporating q-diffusion further enhanced the model’s ability to represent realistic spatial-temporal disease dynamics,especially in scenarios where classical diffusion is insufficient.
文摘Background:To investigate the consistency level of binary symptom assessment in patients with heart failure and their primary caregivers,and to analyze the related factors influencing consistency.Methods:By using the convenience sampling method,patients with heart failure and their main caregivers in the Department of Cardiology of a tertiary hospital in Suzhou from May to November 2023 were selected as the research subjects.The HFSS scale was used for data collection.The paired t-test or paired Wilcoxon test was used to evaluate the differences in the binary symptom assessment scores of heart failure.The intraclass correlation coefficient was used to assess the consistency level of the binary symptom assessment.The Pearson correlation test was used to examine the correlation of the binary symptom assessment.Regression analysis was employed to explore the factors related to the consistency assessment.Results:A total of 103 pairs of valid questionnaires were collected.The consistency levels of symptom evaluations in patients with heart failure and their primary caregivers were statistically significant(P<0.05).The most frequently reported and severe symptom by patients with heart failure and their primary caregivers is shortness of breath during activity.Both have a high consensus on the severity and urgency of most heart failure symptoms.The patient’s gender,body mass index,number of children,history of diabetes,number of comorbidities,mean arterial pressure,LVEF,number of stents,whether a defibrillator was implanted,as well as the gender,marital status,education level,relationship with the patient,care time,whether they lived together,and communication and interaction situation of the main caregiver were the influencing factors for the consistency of binary symptom assessment of heart failure(P<0.05).Conclusion:The degree of consistency in binary symptom assessment between patients with heart failure and their primary caregivers was moderate or higher,which emphasizes the importance of including binary groups in clinical assessment.
