Based on the generalized bilinear method, diversity of exact solutions of the (3 + 1)-dimensional Kadomtsev-Petviashvili-Boussinesq-like equation is successfully derived by using symbolic computation with Maple. These...Based on the generalized bilinear method, diversity of exact solutions of the (3 + 1)-dimensional Kadomtsev-Petviashvili-Boussinesq-like equation is successfully derived by using symbolic computation with Maple. These new solutions, named three-wave solutions and periodic wave have greatly enriched the existing literature. Via the three-dimensional images, density images and contour plots, the physical characteristics of these waves are well described. The new three-wave solutions and periodic solitary wave solutions obtained in this paper, will have a wide range of applications in the fields of physics and mechanics.展开更多
The purpose of this paper is to study the maximum trigonometric degree of the quadrature formula associated with m prescribed nodes and n unknown additional nodes in the interval（-π, π]. We show that for a fixed n,...The purpose of this paper is to study the maximum trigonometric degree of the quadrature formula associated with m prescribed nodes and n unknown additional nodes in the interval（-π, π]. We show that for a fixed n, the quadrature formulae with m and m ＋ 1 prescribed nodes share the same maximum degree if m is odd. We also give necessary and sufficient conditions for all the additional nodes to be real, pairwise distinct and in the interval（-π, π] for even m, which can be obtained constructively. Some numerical examples are given by choosing the prescribed nodes to be the zeros of Chebyshev polynomials of the second kind or randomly for m ≥ 3.展开更多
The error propagation among estimated parameters reflects the correlation among the parameters.We study the capability of machine learning of"learning"the correlation of estimated parameters.We show that mac...The error propagation among estimated parameters reflects the correlation among the parameters.We study the capability of machine learning of"learning"the correlation of estimated parameters.We show that machine learning can recover the relation between the uncertainties of different parameters,especially,as predicted by the error propagation formula.Gravitational lensing can be used to probe both astrophysics and cosmology.As a practical application,we show that the machine learning is able to intelligently find the error propagation among the gravitational lens parameters(effective lens mass ML and Einstein radiusθ_(E))in accordance with the theoretical formula for the singular isothermal ellipse(SIE)lens model.The relation of errors of lens mass and Einstein radius,(e.g.,the ratio of standard deviations F=σ_(ML)/σ_(θ_(E)))predicted by the deep convolution neural network are consistent with the error propagation formula of the SIE lens model.As a proof-of-principle test,a toy model of linear relation with Gaussian noise is presented.We found that the predictions obtained by machine learning indeed indicate the information about the law of error propagation and the distribution of noise.Error propagation plays a crucial role in identifying the physical relation among parameters,rather than a coincidence relation,therefore we anticipate our case study on the error propagation of machine learning predictions could extend to other physical systems on searching the correlation among parameters.展开更多
In order to secure the massive heterogeneous medical data for the complex scenarios and improve the information sharing efficiency in healthcare system,a distributed medical data ledger model(DMDL)is proposed in this ...In order to secure the massive heterogeneous medical data for the complex scenarios and improve the information sharing efficiency in healthcare system,a distributed medical data ledger model(DMDL)is proposed in this paper.This DMDL model has adopted the blockchain technology including the function decoupling,the distributed consensus,smart contract as well as multi-channel communication structure of consortium blockchain.The DMDL model not only has high adaptability,but also meets the requirements of the medical treatment processes which generally involve multientities,highly private information and secure transaction.The steps for processing the medical data are also introduced.Additionally,the methods for the definition and application of the DMDL model are presented for three specific medical scenarios,i.e.,the management of the heterogeneous data,copyright protection for medical data and the secure utilization of sensitive data.The advantage of the proposed DMDL model is demonstrated by comparing with the models which are being currently adopted in healthcare system.展开更多
Oates is regarded as one of the most prolific modern American writers.Wonderland is one of her most representative novels, which is regarded as a"family tragedy"novel.It is Willard Harte who begins the most ...Oates is regarded as one of the most prolific modern American writers.Wonderland is one of her most representative novels, which is regarded as a"family tragedy"novel.It is Willard Harte who begins the most horrible family tragedy. The author of the paper tries to analyze the desperate destroyer, Willard Harte, and his motive for the killing expecting to attract more Chinese scholars' attention.展开更多
Fog computing is introduced to relieve the problems triggered by the long distance between the cloud and terminal devices. In this paper, considering the mobility of terminal devices represented as mobile multimedia u...Fog computing is introduced to relieve the problems triggered by the long distance between the cloud and terminal devices. In this paper, considering the mobility of terminal devices represented as mobile multimedia users(MMUs) and the continuity of requests delivered by them, we propose an online resource allocation scheme with respect to deciding the state of servers in fog nodes distributed at different zones on the premise of satisfying the quality of experience(QoE) based on a Stackelberg game. Specifically, a multi-round of a predictably\unpredictably dynamic scheme is derived from a single-round of a static scheme. The optimal allocation schemes are discussed in detail, and related experiments are designed. For simulations, comparing with non-strategy schemes, the performance of the dynamic scheme is better at minimizing the cost used to maintain fog nodes for providing services.展开更多
This study analyzes and summarizes seven main characteristics of the marine data sampled by multiple underwater gliders. These characteristics such as the big data volume and data sparseness make it extremely difficul...This study analyzes and summarizes seven main characteristics of the marine data sampled by multiple underwater gliders. These characteristics such as the big data volume and data sparseness make it extremely difficult to do some meaningful applications like early warning of marine environment. In order to make full use of the sea trial data, this paper gives the definition of two types of marine data cube which can integrate the big marine data sampled by multiple underwater gliders along saw-tooth paths, and proposes a data fitting algorithm based on time extraction and space compression(DFTS) to construct the temperature and conductivity data cubes. This research also presents an early warning algorithm based on data cube(EWDC) to realize the early warning of a new sampled data file.Experiments results show that the proposed methods are reasonable and effective. Our work is the first study to do some realistic applications on the data sampled by multiple underwater vehicles, and it provides a research framework for processing and analyzing the big marine data oriented to the applications of underwater gliders.展开更多
Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)framework.Yet,delays in data retrieval can markedly hinder the eff...Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)framework.Yet,delays in data retrieval can markedly hinder the efficacy of big data awareness detection systems.We advocate for a collaborative caching approach involving edge devices and cloud networks to combat this.This strategy is devised to streamline the data retrieval path,subsequently diminishing network strain.Crafting an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation tactics.This paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge devices.The system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health anomalies.Given the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during crises.This system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response times.Furthermore,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize utility.Addressing the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a solution.Simulations reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark algorithms.The empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring solution.To encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection system.Our methodology yields enhanced cache efficiency and data freshness.The corroborative numerical data accentuates the feasibility and relevance of our model,casting a beacon for the future trajectory of real-time health data monitoring systems.展开更多
We propose a newmethod to generate surface quadrilateralmesh by calculating a globally defined parameterization with feature constraints.In the field of quadrilateral generation with features,the cross field methods a...We propose a newmethod to generate surface quadrilateralmesh by calculating a globally defined parameterization with feature constraints.In the field of quadrilateral generation with features,the cross field methods are wellknown because of their superior performance in feature preservation.The methods based on metrics are popular due to their sound theoretical basis,especially the Ricci flow algorithm.The cross field methods’major part,the Poisson equation,is challenging to solve in three dimensions directly.When it comes to cases with a large number of elements,the computational costs are expensive while the methods based on metrics are on the contrary.In addition,an appropriate initial value plays a positive role in the solution of the Poisson equation,and this initial value can be obtained from the Ricci flow algorithm.So we combine the methods based on metric with the cross field methods.We use the discrete dynamic Ricci flow algorithm to generate an initial value for the Poisson equation,which speeds up the solution of the equation and ensures the convergence of the computation.Numerical experiments show that our method is effective in generating a quadrilateral mesh for models with features,and the quality of the quadrilateral mesh is reliable.展开更多
Video steganography plays an important role in secret communication that conceals a secret video in a cover video by perturbing the value of pixels in the cover frames.Imperceptibility is the first and foremost requir...Video steganography plays an important role in secret communication that conceals a secret video in a cover video by perturbing the value of pixels in the cover frames.Imperceptibility is the first and foremost requirement of any steganographic approach.Inspired by the fact that human eyes perceive pixel perturbation differently in different video areas,a novel effective and efficient Deeply‐Recursive Attention Network(DRANet)for video steganography to find suitable areas for information hiding via modelling spatio‐temporal attention is proposed.The DRANet mainly contains two important components,a Non‐Local Self‐Attention(NLSA)block and a Non‐Local Co‐Attention(NLCA)block.Specifically,the NLSA block can select the cover frame areas which are suitable for hiding by computing the correlations among inter‐and intra‐cover frames.The NLCA block aims to effectively produce the enhanced representations of the secret frames to enhance the robustness of the model and alleviate the influence of different areas in the secret video.Furthermore,the DRANet reduces the model parameters by performing similar operations on the different frames within an input video recursively.Experimental results show the proposed DRANet achieves better performance with fewer parameters than the state‐of‐the‐art competitors.展开更多
The diversified development of the service ecosystem,particularly the rapid growth of services like cloud and edge computing,has propelled the flourishing expansion of the service trading market.However,in the absence...The diversified development of the service ecosystem,particularly the rapid growth of services like cloud and edge computing,has propelled the flourishing expansion of the service trading market.However,in the absence of appropriate pricing guidance,service providers often devise pricing strategies solely based on their own interests,potentially hindering the maximization of overall market profits.This challenge is even more severe in edge computing scenarios,as different edge service providers are dispersed across various regions and influenced by multiple factors,making it challenging to establish a unified pricing model.This paper introduces a multi-participant stochastic game model to formalize the pricing problem of multiple edge services.Subsequently,an incentive mechanism based on Pareto improvement is proposed to drive the game towards Pareto optimal direction,achieving optimal profits.Finally,an enhanced PSO algorithm was proposed by adaptively optimizing inertia factor across three stages.This optimization significantly improved the efficiency of solving the game model and analyzed equilibrium states under various evolutionary mechanisms.Experimental results demonstrate that the proposed pricing incentive mechanism promotes more effective and rational pricing allocations,while also demonstrating the effectiveness of our algorithm in resolving game problems.展开更多
Joyce Carol Oates is one of the most productive writers in American literary circles,reputed as a representative writer of"psychological realism".For nearly 50 years,her works are various genres with profoun...Joyce Carol Oates is one of the most productive writers in American literary circles,reputed as a representative writer of"psychological realism".For nearly 50 years,her works are various genres with profound theme,which get a lot of attention by Chinese and foreign academic circles.The author of the paper introduces Oates to readers and analyzes her literary career in detail expecting to attract more Chinese scholars’attention.展开更多
We digitally reproduce the process of resource collaboration,design creation,and visual presentation of Chinese seal-carving art.We develop an intelligent seal-carving art-generation system(Zhejiang University Intelli...We digitally reproduce the process of resource collaboration,design creation,and visual presentation of Chinese seal-carving art.We develop an intelligent seal-carving art-generation system(Zhejiang University Intelligent Seal-Carving System,http://www.next.zju.edu.cn/seal/;the website of the seal-carving search and layout system is http://www.next.zju.edu.cn/seal/search_app/)to deal with the difficulty in using a visual knowledge guided computational art approach.The knowledge base in this study is the Qiushi Seal-Carving Database,which consists of open datasets of images of seal characters and seal stamps.We propose a seal character generation method based on visual knowledge,guided by the database and expertise.Furthermore,to create the layout of the seal,we propose a deformation algorithm to adjust the seal characters and calculate layout parameters from the database and knowledge to achieve an intelligent structure.Experimental results show that this method and system can effectively deal with the difficulties in the generation of seal carving.Our work provides theoretical and applied references for the rebirth and innovation of seal-carving art.展开更多
The phenomenon of cooperation is prevalent in both nature and human society. In this paper a simulative model is developed to examine how the strategy continuity influences cooperation in the spatial prisoner＇s games...The phenomenon of cooperation is prevalent in both nature and human society. In this paper a simulative model is developed to examine how the strategy continuity influences cooperation in the spatial prisoner＇s games in which the players migrate through the success-driven migration mechanism. Numerical simulations illustrate that the strategy continuity promotes cooperation at a low rate of migration, while impeding cooperation when the migration rate is higher. The influence of strategy continuity is also dependent on the game types. Through a more dynamic analysis, the different effects of the strategy continuity at low and high rates of migration are explained by the formation, expansion, and extinction of the self-assembled clusters of ＂partial-cooperators＂ within the gaming population.展开更多
In this paper,we study the influence of the size of interaction neighbors(k) on the evolution of cooperation in the spatial snowdrift game.At first,we consider the effects of noise K and cost-to-benefit ratio r,the si...In this paper,we study the influence of the size of interaction neighbors(k) on the evolution of cooperation in the spatial snowdrift game.At first,we consider the effects of noise K and cost-to-benefit ratio r,the simulation results indicate that the evolution of cooperation depends on the combined action of noise and cost-to-benefit ratio.For a lower r,the cooperators are multitudinous and the cooperation frequency ultimately increases to 1 as the increase of noise.However,for a higher r,the defectors account for the majority of the game and dominate the game if the noise is large enough.Then we mainly investigate how k influences the evolution of cooperation by varying the noise in detail.We find that the frequency of cooperators is closely related to the size of neighborhood and cost-to-benefit ratio r.In the case of lower r,the augmentation of k plays no positive role in promoting the cooperation as compared with that of k = 4,while for higher r the cooperation is improved for a growing size of neighborhood.At last,based on the above discussions,we explore the cluster-forming mechanism among the cooperators.The current results are beneficial to further understand the evolution of cooperation in many natural,social and biological systems.展开更多
Recent studies employing deep learning to solve the traveling salesman problem(TSP)have mainly focused on learning construction heuristics.Such methods can improve TSP solutions,but still depend on additional programs...Recent studies employing deep learning to solve the traveling salesman problem(TSP)have mainly focused on learning construction heuristics.Such methods can improve TSP solutions,but still depend on additional programs.However,methods that focus on learning improvement heuristics to iteratively refine solutions remain insufficient.Traditional improvement heuristics are guided by a manually designed search strategy and may only achieve limited improvements.This paper proposes a novel framework for learning improvement heuristics,which automatically discovers better improvement policies for heuristics to iteratively solve the TSP.Our framework first designs a new architecture based on a transformer model to make the policy network parameterized,which introduces an action-dropout layer to prevent action selection from overfitting.It then proposes a deep reinforcement learning approach integrating a simulated annealing mechanism(named RL-SA)to learn the pairwise selected policy,aiming to improve the 2-opt algorithm's performance.The RL-SA leverages the whale optimization algorithm to generate initial solutions for better sampling efficiency and uses the Gaussian perturbation strategy to tackle the sparse reward problem of reinforcement learning.The experiment results show that the proposed approach is significantly superior to the state-of-the-art learning-based methods,and further reduces the gap between learning-based methods and highly optimized solvers in the benchmark datasets.Moreover,our pre-trained model M can be applied to guide the SA algorithm(named M-SA(ours)),which performs better than existing deep models in small-,medium-,and large-scale TSPLIB datasets.Additionally,the M-SA(ours)achieves excellent generalization performance in a real-world dataset on global liner shipping routes,with the optimization percentages in distance reduction ranging from3.52%to 17.99%.展开更多
The rise of artificial intelligence generated content(AIGC)has been remarkable in the language and image fields,but artificial intelligence(AI)generated three-dimensional(3D)models are still under-explored due to thei...The rise of artificial intelligence generated content(AIGC)has been remarkable in the language and image fields,but artificial intelligence(AI)generated three-dimensional(3D)models are still under-explored due to their complex nature and lack of training data.The conventional approach of creating 3D content through computer-aided design(CAD)is labor-intensive and requires expertise,making it challenging for novice users.To address this issue,we propose a sketch-based 3D modeling approach,Deep3DSketch-im,which uses a single freehand sketch for modeling.This is a challenging task due to the sparsity and ambiguity.Deep3DSketch-im uses a novel data representation called the signed distance field(SDF)to improve the sketch-to-3D model process by incorporating an implicit continuous field instead of voxel or points,and a specially designed neural network that can capture point and local features.Extensive experiments are conducted to demonstrate the effectiveness of the approach,achieving state-of-the-art(SOTA)performance on both synthetic and real datasets.Additionally,users show more satisfaction with results generated by Deep3DSketch-im,as reported in a user study.We believe that Deep3DSketch-im has the potential to revolutionize the process of 3D modeling by providing an intuitive and easy-to-use solution for novice users.展开更多
In this paper,we introduce the large language model and domain-specific model collaboration(LDMC)framework designed to enhance smart education.The LDMC framework leverages the comprehensive and versatile knowledge of ...In this paper,we introduce the large language model and domain-specific model collaboration(LDMC)framework designed to enhance smart education.The LDMC framework leverages the comprehensive and versatile knowledge of large domain-general models,combines it with the specialized and disciplinary knowledge from small domainspecific models(DSMs),and incorporates pedagogy knowledge from learning theory models.This integration yields multiple knowledge representations,fostering personalized and adaptive educational experiences.We explore various applications of the LDMC framework in the context of smart education.展开更多
Travel time estimation(TTE)is a fundamental task to build intelligent transportation systems.However,most existing TTE solutions design models upon simple homogeneous graphs and ignore the heterogeneity of traffic net...Travel time estimation(TTE)is a fundamental task to build intelligent transportation systems.However,most existing TTE solutions design models upon simple homogeneous graphs and ignore the heterogeneity of traffic networks,where,e.g.,main roads typically contribute differently from side roads.In terms of spatial dimension,few studies consider the dynamic spatial correlations across road segments,e.g.,the traffic speed/volume on road segment A may correlate with the traffic speed/volume on road segment B,where A and B could be adjacent or non-adjacent,and such correlations may vary across time.In terms of temporal dimension,even fewer studies consider the dynamic temporal dependences,where,e.g.,the historical states of road A may directly correlate with the recent state of A,and may also indirectly correlate with the recent state of road B.To track all aforementioned issues of existing TTE approaches,we provide HDTTE,a solution that employs heterogeneous and dynamic spatio-temporal predictive learning.Specifically,we first design a general multi-relational graph constructor that extracts hidden heterogeneous information of road segments,where we model road segments as nodes and model correlations as edges in the multi-relational graph.Next,we propose a dynamic graph attention convolution module that aggregates dynamic spatial dependence of neighbor roads to focal roads.We also present a novel correlation-augmented temporal convolution module to capture the influence of states at past time steps on current traffic states.Finally,in view of the periodic dependence of traffic,we develop a multi-scale adaptive fusion layer to enable HDTTE to exploit periodic patterns from recent,daily,and weekly traffic states.An experimental study using real-life highway and urban datasets demonstrates the validity of the approach and its advantage over others.展开更多
Existing lip synchronization(lip-sync)methods generate accurately synchronized mouths and faces in a generated video.However,they still confront the problem of artifacts in regions of non-interest(RONI),e.g.,backgroun...Existing lip synchronization(lip-sync)methods generate accurately synchronized mouths and faces in a generated video.However,they still confront the problem of artifacts in regions of non-interest(RONI),e.g.,background and other parts of a face,which decreases the overall visual quality.To solve these problems,we innovatively introduce diverse image inpainting to lip-sync generation.We propose Modulated Inpainting Lip-sync GAN(MILG),an audio-constraint inpainting network to predict synchronous mouths.MILG utilizes prior knowledge of RONI and audio sequences to predict lip shape instead of image generation,which can keep the RONI consistent.Specifically,we integrate modulated spatially probabilistic diversity normalization(MSPD Norm)in our inpainting network,which helps the network generate fine-grained diverse mouth movements guided by the continuous audio features.Furthermore,to lower the training overhead,we modify the contrastive loss in lipsync to support small-batch-size and few-sample training.Extensive experiments demonstrate that our approach outperforms the existing state-of-the-art of image quality and authenticity while keeping lip-sync.展开更多
文摘Based on the generalized bilinear method, diversity of exact solutions of the (3 + 1)-dimensional Kadomtsev-Petviashvili-Boussinesq-like equation is successfully derived by using symbolic computation with Maple. These new solutions, named three-wave solutions and periodic wave have greatly enriched the existing literature. Via the three-dimensional images, density images and contour plots, the physical characteristics of these waves are well described. The new three-wave solutions and periodic solitary wave solutions obtained in this paper, will have a wide range of applications in the fields of physics and mechanics.
基金The NSF (61033012,10801023,10911140268 and 10771028) of China
文摘The purpose of this paper is to study the maximum trigonometric degree of the quadrature formula associated with m prescribed nodes and n unknown additional nodes in the interval（-π, π]. We show that for a fixed n, the quadrature formulae with m and m ＋ 1 prescribed nodes share the same maximum degree if m is odd. We also give necessary and sufficient conditions for all the additional nodes to be real, pairwise distinct and in the interval（-π, π] for even m, which can be obtained constructively. Some numerical examples are given by choosing the prescribed nodes to be the zeros of Chebyshev polynomials of the second kind or randomly for m ≥ 3.
基金supported by the National Natural Science Foundation of China(grant No.11922303)the Natural Science Foundation of Chongqing(grant No.CSTB2023NSCQ-MSX0103)+1 种基金the Key Research Program of Xingtai 2020ZC005the Fundamental Research Funds for the Central Universities(grant No.2042022kf1182)。
文摘The error propagation among estimated parameters reflects the correlation among the parameters.We study the capability of machine learning of"learning"the correlation of estimated parameters.We show that machine learning can recover the relation between the uncertainties of different parameters,especially,as predicted by the error propagation formula.Gravitational lensing can be used to probe both astrophysics and cosmology.As a practical application,we show that the machine learning is able to intelligently find the error propagation among the gravitational lens parameters(effective lens mass ML and Einstein radiusθ_(E))in accordance with the theoretical formula for the singular isothermal ellipse(SIE)lens model.The relation of errors of lens mass and Einstein radius,(e.g.,the ratio of standard deviations F=σ_(ML)/σ_(θ_(E)))predicted by the deep convolution neural network are consistent with the error propagation formula of the SIE lens model.As a proof-of-principle test,a toy model of linear relation with Gaussian noise is presented.We found that the predictions obtained by machine learning indeed indicate the information about the law of error propagation and the distribution of noise.Error propagation plays a crucial role in identifying the physical relation among parameters,rather than a coincidence relation,therefore we anticipate our case study on the error propagation of machine learning predictions could extend to other physical systems on searching the correlation among parameters.
文摘In order to secure the massive heterogeneous medical data for the complex scenarios and improve the information sharing efficiency in healthcare system,a distributed medical data ledger model(DMDL)is proposed in this paper.This DMDL model has adopted the blockchain technology including the function decoupling,the distributed consensus,smart contract as well as multi-channel communication structure of consortium blockchain.The DMDL model not only has high adaptability,but also meets the requirements of the medical treatment processes which generally involve multientities,highly private information and secure transaction.The steps for processing the medical data are also introduced.Additionally,the methods for the definition and application of the DMDL model are presented for three specific medical scenarios,i.e.,the management of the heterogeneous data,copyright protection for medical data and the secure utilization of sensitive data.The advantage of the proposed DMDL model is demonstrated by comparing with the models which are being currently adopted in healthcare system.
文摘Oates is regarded as one of the most prolific modern American writers.Wonderland is one of her most representative novels, which is regarded as a"family tragedy"novel.It is Willard Harte who begins the most horrible family tragedy. The author of the paper tries to analyze the desperate destroyer, Willard Harte, and his motive for the killing expecting to attract more Chinese scholars' attention.
基金supported by the National Natural Science Foundation of China under grant No. 61501080, 61572095, 61871064, and 61877007
文摘Fog computing is introduced to relieve the problems triggered by the long distance between the cloud and terminal devices. In this paper, considering the mobility of terminal devices represented as mobile multimedia users(MMUs) and the continuity of requests delivered by them, we propose an online resource allocation scheme with respect to deciding the state of servers in fog nodes distributed at different zones on the premise of satisfying the quality of experience(QoE) based on a Stackelberg game. Specifically, a multi-round of a predictably\unpredictably dynamic scheme is derived from a single-round of a static scheme. The optimal allocation schemes are discussed in detail, and related experiments are designed. For simulations, comparing with non-strategy schemes, the performance of the dynamic scheme is better at minimizing the cost used to maintain fog nodes for providing services.
基金financially supported by the National Natural Science Foundation of China(Grant Nos.U1709202 and No.61502069)the Foundation of State Key Laboratory of Robotics(Grant No.2015-o03)the Fundamental Research Funds for the Central Universities(Grant Nos.DUT18JC39 and DUT17JC45)
文摘This study analyzes and summarizes seven main characteristics of the marine data sampled by multiple underwater gliders. These characteristics such as the big data volume and data sparseness make it extremely difficult to do some meaningful applications like early warning of marine environment. In order to make full use of the sea trial data, this paper gives the definition of two types of marine data cube which can integrate the big marine data sampled by multiple underwater gliders along saw-tooth paths, and proposes a data fitting algorithm based on time extraction and space compression(DFTS) to construct the temperature and conductivity data cubes. This research also presents an early warning algorithm based on data cube(EWDC) to realize the early warning of a new sampled data file.Experiments results show that the proposed methods are reasonable and effective. Our work is the first study to do some realistic applications on the data sampled by multiple underwater vehicles, and it provides a research framework for processing and analyzing the big marine data oriented to the applications of underwater gliders.
基金supported by National Natural Science Foundation of China(NSFC)under Grant Number T2350710232.
文摘Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)framework.Yet,delays in data retrieval can markedly hinder the efficacy of big data awareness detection systems.We advocate for a collaborative caching approach involving edge devices and cloud networks to combat this.This strategy is devised to streamline the data retrieval path,subsequently diminishing network strain.Crafting an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation tactics.This paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge devices.The system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health anomalies.Given the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during crises.This system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response times.Furthermore,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize utility.Addressing the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a solution.Simulations reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark algorithms.The empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring solution.To encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection system.Our methodology yields enhanced cache efficiency and data freshness.The corroborative numerical data accentuates the feasibility and relevance of our model,casting a beacon for the future trajectory of real-time health data monitoring systems.
基金supported by NSFC Nos.61907005,61720106005,61936002,62272080.
文摘We propose a newmethod to generate surface quadrilateralmesh by calculating a globally defined parameterization with feature constraints.In the field of quadrilateral generation with features,the cross field methods are wellknown because of their superior performance in feature preservation.The methods based on metrics are popular due to their sound theoretical basis,especially the Ricci flow algorithm.The cross field methods’major part,the Poisson equation,is challenging to solve in three dimensions directly.When it comes to cases with a large number of elements,the computational costs are expensive while the methods based on metrics are on the contrary.In addition,an appropriate initial value plays a positive role in the solution of the Poisson equation,and this initial value can be obtained from the Ricci flow algorithm.So we combine the methods based on metric with the cross field methods.We use the discrete dynamic Ricci flow algorithm to generate an initial value for the Poisson equation,which speeds up the solution of the equation and ensures the convergence of the computation.Numerical experiments show that our method is effective in generating a quadrilateral mesh for models with features,and the quality of the quadrilateral mesh is reliable.
基金supported in part by NSFC(62002320,U19B2043,61672456)the Key R&D Program of Zhejiang Province,China(2021C01119).
文摘Video steganography plays an important role in secret communication that conceals a secret video in a cover video by perturbing the value of pixels in the cover frames.Imperceptibility is the first and foremost requirement of any steganographic approach.Inspired by the fact that human eyes perceive pixel perturbation differently in different video areas,a novel effective and efficient Deeply‐Recursive Attention Network(DRANet)for video steganography to find suitable areas for information hiding via modelling spatio‐temporal attention is proposed.The DRANet mainly contains two important components,a Non‐Local Self‐Attention(NLSA)block and a Non‐Local Co‐Attention(NLCA)block.Specifically,the NLSA block can select the cover frame areas which are suitable for hiding by computing the correlations among inter‐and intra‐cover frames.The NLCA block aims to effectively produce the enhanced representations of the secret frames to enhance the robustness of the model and alleviate the influence of different areas in the secret video.Furthermore,the DRANet reduces the model parameters by performing similar operations on the different frames within an input video recursively.Experimental results show the proposed DRANet achieves better performance with fewer parameters than the state‐of‐the‐art competitors.
基金supported in part by the National Key R&D Program of China(No.2022YFF0902702)in part by the Major Program of National Natural Science Foundation of Zhejiang(No.LD24F020014)+3 种基金in part by the Zhejiang Pioneer Project(No.2024C01032)in part by the Key R&D Program of Ningbo(No.2023Z235)in part by the Ningbo Yongjiang Talent Programme(No.2023A-198-G)in part by the Beijing Life Science Academy(No.BLSA:2023000CB0020).
文摘The diversified development of the service ecosystem,particularly the rapid growth of services like cloud and edge computing,has propelled the flourishing expansion of the service trading market.However,in the absence of appropriate pricing guidance,service providers often devise pricing strategies solely based on their own interests,potentially hindering the maximization of overall market profits.This challenge is even more severe in edge computing scenarios,as different edge service providers are dispersed across various regions and influenced by multiple factors,making it challenging to establish a unified pricing model.This paper introduces a multi-participant stochastic game model to formalize the pricing problem of multiple edge services.Subsequently,an incentive mechanism based on Pareto improvement is proposed to drive the game towards Pareto optimal direction,achieving optimal profits.Finally,an enhanced PSO algorithm was proposed by adaptively optimizing inertia factor across three stages.This optimization significantly improved the efficiency of solving the game model and analyzed equilibrium states under various evolutionary mechanisms.Experimental results demonstrate that the proposed pricing incentive mechanism promotes more effective and rational pricing allocations,while also demonstrating the effectiveness of our algorithm in resolving game problems.
文摘Joyce Carol Oates is one of the most productive writers in American literary circles,reputed as a representative writer of"psychological realism".For nearly 50 years,her works are various genres with profound theme,which get a lot of attention by Chinese and foreign academic circles.The author of the paper introduces Oates to readers and analyzes her literary career in detail expecting to attract more Chinese scholars’attention.
基金the Natural Science Foundation of Zhejiang Province,China(No.LZ19F020002)the Key R&D Program of Zhejiang Province,China(No.2022C03126)。
文摘We digitally reproduce the process of resource collaboration,design creation,and visual presentation of Chinese seal-carving art.We develop an intelligent seal-carving art-generation system(Zhejiang University Intelligent Seal-Carving System,http://www.next.zju.edu.cn/seal/;the website of the seal-carving search and layout system is http://www.next.zju.edu.cn/seal/search_app/)to deal with the difficulty in using a visual knowledge guided computational art approach.The knowledge base in this study is the Qiushi Seal-Carving Database,which consists of open datasets of images of seal characters and seal stamps.We propose a seal character generation method based on visual knowledge,guided by the database and expertise.Furthermore,to create the layout of the seal,we propose a deformation algorithm to adjust the seal characters and calculate layout parameters from the database and knowledge to achieve an intelligent structure.Experimental results show that this method and system can effectively deal with the difficulties in the generation of seal carving.Our work provides theoretical and applied references for the rebirth and innovation of seal-carving art.
基金Supported by the National Natural Science Foundation of China(61702076,71371040,71533001,71371040)the Fundamental Research Funds for the Central Universities(DUT17RW131)
文摘The phenomenon of cooperation is prevalent in both nature and human society. In this paper a simulative model is developed to examine how the strategy continuity influences cooperation in the spatial prisoner＇s games in which the players migrate through the success-driven migration mechanism. Numerical simulations illustrate that the strategy continuity promotes cooperation at a low rate of migration, while impeding cooperation when the migration rate is higher. The influence of strategy continuity is also dependent on the game types. Through a more dynamic analysis, the different effects of the strategy continuity at low and high rates of migration are explained by the formation, expansion, and extinction of the self-assembled clusters of ＂partial-cooperators＂ within the gaming population.
基金Supported by the National Natural Science Foundation of China under Grant Nos. 60904063 and 60673046Tianjin municipal Natural Science Foundation under Grant No. 11JCYBJC06600the Development Fund of Science and Technology for the Higher Education in Tianjin under Grant No. 20090813
文摘In this paper,we study the influence of the size of interaction neighbors(k) on the evolution of cooperation in the spatial snowdrift game.At first,we consider the effects of noise K and cost-to-benefit ratio r,the simulation results indicate that the evolution of cooperation depends on the combined action of noise and cost-to-benefit ratio.For a lower r,the cooperators are multitudinous and the cooperation frequency ultimately increases to 1 as the increase of noise.However,for a higher r,the defectors account for the majority of the game and dominate the game if the noise is large enough.Then we mainly investigate how k influences the evolution of cooperation by varying the noise in detail.We find that the frequency of cooperators is closely related to the size of neighborhood and cost-to-benefit ratio r.In the case of lower r,the augmentation of k plays no positive role in promoting the cooperation as compared with that of k = 4,while for higher r the cooperation is improved for a growing size of neighborhood.At last,based on the above discussions,we explore the cluster-forming mechanism among the cooperators.The current results are beneficial to further understand the evolution of cooperation in many natural,social and biological systems.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.72101046 and 61672128)。
文摘Recent studies employing deep learning to solve the traveling salesman problem(TSP)have mainly focused on learning construction heuristics.Such methods can improve TSP solutions,but still depend on additional programs.However,methods that focus on learning improvement heuristics to iteratively refine solutions remain insufficient.Traditional improvement heuristics are guided by a manually designed search strategy and may only achieve limited improvements.This paper proposes a novel framework for learning improvement heuristics,which automatically discovers better improvement policies for heuristics to iteratively solve the TSP.Our framework first designs a new architecture based on a transformer model to make the policy network parameterized,which introduces an action-dropout layer to prevent action selection from overfitting.It then proposes a deep reinforcement learning approach integrating a simulated annealing mechanism(named RL-SA)to learn the pairwise selected policy,aiming to improve the 2-opt algorithm's performance.The RL-SA leverages the whale optimization algorithm to generate initial solutions for better sampling efficiency and uses the Gaussian perturbation strategy to tackle the sparse reward problem of reinforcement learning.The experiment results show that the proposed approach is significantly superior to the state-of-the-art learning-based methods,and further reduces the gap between learning-based methods and highly optimized solvers in the benchmark datasets.Moreover,our pre-trained model M can be applied to guide the SA algorithm(named M-SA(ours)),which performs better than existing deep models in small-,medium-,and large-scale TSPLIB datasets.Additionally,the M-SA(ours)achieves excellent generalization performance in a real-world dataset on global liner shipping routes,with the optimization percentages in distance reduction ranging from3.52%to 17.99%.
基金Project supported by the National Key R&D Program of China(No.2022YFB3303301)the National Natural Science Foundation of China(Nos.62006208,62107035,and 62207024)the Public Welfare Research Program of Huzhou Science and Technology Bureau,China(No.2022GZ01)。
文摘The rise of artificial intelligence generated content(AIGC)has been remarkable in the language and image fields,but artificial intelligence(AI)generated three-dimensional(3D)models are still under-explored due to their complex nature and lack of training data.The conventional approach of creating 3D content through computer-aided design(CAD)is labor-intensive and requires expertise,making it challenging for novice users.To address this issue,we propose a sketch-based 3D modeling approach,Deep3DSketch-im,which uses a single freehand sketch for modeling.This is a challenging task due to the sparsity and ambiguity.Deep3DSketch-im uses a novel data representation called the signed distance field(SDF)to improve the sketch-to-3D model process by incorporating an implicit continuous field instead of voxel or points,and a specially designed neural network that can capture point and local features.Extensive experiments are conducted to demonstrate the effectiveness of the approach,achieving state-of-the-art(SOTA)performance on both synthetic and real datasets.Additionally,users show more satisfaction with results generated by Deep3DSketch-im,as reported in a user study.We believe that Deep3DSketch-im has the potential to revolutionize the process of 3D modeling by providing an intuitive and easy-to-use solution for novice users.
基金Project supported by the National Key R&D Program of China(No.2020AAA0108800)the National Natural Science Foundation of China(Nos.62293554,62206249,and U2336212)+1 种基金the Natural Science Foundation of Zhejiang Province,China(No.LZ24F020002)the Young Elite Scientists Sponsorship Program by CAST(No.2023QNRC001)。
文摘In this paper,we introduce the large language model and domain-specific model collaboration(LDMC)framework designed to enhance smart education.The LDMC framework leverages the comprehensive and versatile knowledge of large domain-general models,combines it with the specialized and disciplinary knowledge from small domainspecific models(DSMs),and incorporates pedagogy knowledge from learning theory models.This integration yields multiple knowledge representations,fostering personalized and adaptive educational experiences.We explore various applications of the LDMC framework in the context of smart education.
基金supported by the National Key Research and Development Program of China under Grant No.2021YFC3300303the National Natural Science Foundation of China under Grant Nos.62025206,61972338,and 62102351.
文摘Travel time estimation(TTE)is a fundamental task to build intelligent transportation systems.However,most existing TTE solutions design models upon simple homogeneous graphs and ignore the heterogeneity of traffic networks,where,e.g.,main roads typically contribute differently from side roads.In terms of spatial dimension,few studies consider the dynamic spatial correlations across road segments,e.g.,the traffic speed/volume on road segment A may correlate with the traffic speed/volume on road segment B,where A and B could be adjacent or non-adjacent,and such correlations may vary across time.In terms of temporal dimension,even fewer studies consider the dynamic temporal dependences,where,e.g.,the historical states of road A may directly correlate with the recent state of A,and may also indirectly correlate with the recent state of road B.To track all aforementioned issues of existing TTE approaches,we provide HDTTE,a solution that employs heterogeneous and dynamic spatio-temporal predictive learning.Specifically,we first design a general multi-relational graph constructor that extracts hidden heterogeneous information of road segments,where we model road segments as nodes and model correlations as edges in the multi-relational graph.Next,we propose a dynamic graph attention convolution module that aggregates dynamic spatial dependence of neighbor roads to focal roads.We also present a novel correlation-augmented temporal convolution module to capture the influence of states at past time steps on current traffic states.Finally,in view of the periodic dependence of traffic,we develop a multi-scale adaptive fusion layer to enable HDTTE to exploit periodic patterns from recent,daily,and weekly traffic states.An experimental study using real-life highway and urban datasets demonstrates the validity of the approach and its advantage over others.
基金partially funded by the National Key Research and Development Program of China(Grant No.2020AAA0140004).
文摘Existing lip synchronization(lip-sync)methods generate accurately synchronized mouths and faces in a generated video.However,they still confront the problem of artifacts in regions of non-interest(RONI),e.g.,background and other parts of a face,which decreases the overall visual quality.To solve these problems,we innovatively introduce diverse image inpainting to lip-sync generation.We propose Modulated Inpainting Lip-sync GAN(MILG),an audio-constraint inpainting network to predict synchronous mouths.MILG utilizes prior knowledge of RONI and audio sequences to predict lip shape instead of image generation,which can keep the RONI consistent.Specifically,we integrate modulated spatially probabilistic diversity normalization(MSPD Norm)in our inpainting network,which helps the network generate fine-grained diverse mouth movements guided by the continuous audio features.Furthermore,to lower the training overhead,we modify the contrastive loss in lipsync to support small-batch-size and few-sample training.Extensive experiments demonstrate that our approach outperforms the existing state-of-the-art of image quality and authenticity while keeping lip-sync.
