Understanding the Predication Mechanism of Deep Learning through Error Propagation among Parameters in Strong Lensing Case

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.

Diversity of New Three-Wave Solutions and New Periodic Waves for the (3 + 1)-Dimensional Kadomtsev-Petviashvili-Boussinesq-Like Equation

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.

The Maximum Trigonometric Degrees of Quadrature Formulae with Prescribed Nodes

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.

Secure Model of Medical Data Sharing for Complex Scenarios

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.

Character Analysis of Oates'Wonderland--The Source of Tragedy:Wil-lard Harte

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.

Towards Cache-Assisted Hierarchical Detection for Real-Time Health Data Monitoring in IoHT

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.

Feature Preserving Parameterization for Quadrilateral Mesh Generation Based on Ricci Flow and Cross Field

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.

Deeply‐Recursive Attention Network for video steganography

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.

Game-Theoretic Online Resource Allocation Scheme on Fog Computing for Mobile Multimedia Users

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.

A Marine Environment Early Warning Algorithm Based on Marine Data Sampled by Multiple Underwater Gliders

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.

General Introduction to Joyce Carol Oates' Literary Career

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.

Deep3DSketch-im:rapid high-fidelity AI 3D model generation by single freehand sketches

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.

Large language model and domain-specific model collaboration for smart education

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.

MILG:Realistic lip-sync video generation with audio-modulated image inpainting

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.

Autoencoder-based conditional optimal transport generative adversarial network for medical image generation

Medical image generation has recently garnered significant interest among researchers.However,the primary generative models,such as Generative Adversarial Networks(GANs),often encounter challenges during training,including mode collapse.To address these issues,we proposed the AECOT-GAN model(Autoencoder-based Conditional Optimal Transport Generative Adversarial Network)for the generation of medical images belonging to specific categories.The training process of our model comprises three fundamental components.The training process of our model encompasses three fundamental components.First,we employ an autoencoder model to obtain a low-dimensional manifold representation of real images.Second,we apply extended semi-discrete optimal transport to map Gaussian noise distribution to the latent space distribution and obtain corresponding labels effectively.This procedure leads to the generation of new latent codes with known labels.Finally,we integrate a GAN to train the decoder further to generate medical images.To evaluate the performance of the AE-COT-GAN model,we conducted experiments on two medical image datasets,namely DermaMNIST and BloodMNIST.The model’s performance was compared with state-of-the-art generative models.Results show that the AE-COT-GAN model had excellent performance in generating medical images.Moreover,it effectively addressed the common issues associated with traditional GANs.

Visual knowledge guided intelligent generation of Chinese seal carving

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.

Influence of Strategy Continuity on Cooperation in Spatial Prisoner’s Dilemma Games with Migrating Players

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.

Effect of Growing Size of Interaction Neighbors on the Evolution of Cooperation in Spatial Snowdrift Game

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.

A Blockchain-Based Scheme of Data Sharing for Housing Provident Fund

This paper proposes a secure,reliable and collaborative data-sharing system for China’s housing provident fund based on blockchain.Firstly,federal computingwas introduced to realize“available but invisible”sharing of data about housing provident fund,which reduces the data leakage risk and improves the data availability.Secondly,four data sharing modes were proposed to deal with different situations with different amount of data provider and data.Lastly,to realize individual data deep sharing on the premise of security,an enterprise and personal information query authorization mechanism was established to provide solutions to personal and institutional authorization.This system helps to realize both the internal and external data sharing of the housing provident fund system under the premise of security and privacy protection.This system improves the efficiency of housing provident fund issue,and fully taps the value of data comprehensively.

Federatedmutual learning:a collaborative machine learning method for heterogeneous data,models,and objectives

Federated learning(FL)is a novel technique in deep learning that enables clients to collaboratively train a shared model while retaining their decentralized data.However,researchers working on FL face several unique challenges,especially in the context of heterogeneity.Heterogeneity in data distributions,computational capabilities,and scenarios among clients necessitates the development of customized models and objectives in FL.Unfortunately,existing works such as FedAvg may not effectively accommodate the specific needs of each client.To address the challenges arising from heterogeneity in FL,we provide an overview of the heterogeneities in data,model,and objective(DMO).Furthermore,we propose a novel framework called federated mutual learning(FML),which enables each client to train a personalized model that accounts for the data heterogeneity(DH).A“meme model”serves as an intermediary between the personalized and global models to address model heterogeneity(MH).We introduce a knowledge distillation technique called deep mutual learning(DML)to transfer knowledge between these two models on local data.To overcome objective heterogeneity(OH),we design a shared global model that includes only certain parts,and the personalized model is task-specific and enhanced through mutual learning with the meme model.We evaluate the performance of FML in addressing DMO heterogeneities through experiments and compare it with other commonly used FL methods in similar scenarios.The results demonstrate that FML outperforms other methods and effectively addresses the DMO challenges encountered in the FL setting.