Learning the parameters of a class of stochastic Lotka-Volterra systems with neural networks

In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.

Effects of Rice-duck Mutualism on Diseases Insect Pests and Weeds and Economic Benefits

[Objective] Study on the effects of rice-duck mutualism on weeds and insects pests and economic benefits of paddy field. [Method] Comparison of the incidence of weeds, sheath blight and insects pest under rice-duck mutualism, conventional cultivation and control treatment, the yield and economic benefits were analyzed under the 3 treatments. [Result] Average occurrence of weeds in rice-duck mutualism group decreased by 2.33 and 52.0g ind/m^2 compared with that in conventional cultivation and control treatment ; the control rate of mutualism was up to 75% against rice hopper, but just between 25% -60% against rice leaf roller and Chilo suppressalis. The rates of diseased plant and diseased bell against rice sheath blight were higher and disease indices were lower compared with control group. The yield of mutualism group was identical with conventional cultivation, Which was greatly higher than that of control group. The results suggested an higher economic benefits and lower cost benefit ratio for rice-duck mutualism treatment. [Conclusion] Rice-duck mutualism gives birth to a positive effect to control the diseases, insect pests and weeds, as well as to economic benefits, providing basis on extension of rice-duck mutualism system.

基于Lotka-Volterra模型的数字经济与实体经济共生模式研究

为推进数字经济与实体经济深度融合,促进区域经济高质量发展,依据共生理论,论文从资源环境承载力角度,构建数字经济与实体经济的共生系统,基于Lotka-Volterra模型分析数字经济与实体经济的共生模式,并运用熵值-DEMATEL法探析实现数字经济与实体经济互惠共生的关键因素。实证研究结果表明:存在以互惠共生为主的多种共生模式,以实体经济的促进作用为主要表现形式;共生模式与数字经济、实体经济发展水平无明显关联;数字经济与实体经济的共生呈现出互惠共生—单利共生/竞争互害—互惠共生的交替上升规律;数字经济发展类因素是实现数字经济与实体经济互惠共生的关键因素,生态环境类因素是实现互惠共生发展中最为明显的被影响因素。

AN OPTIMAL CONTROL PROBLEM FOR A LOTKA-VOLTERRA COMPETITION MODEL WITH CHEMO-REPULSION

In this paper we study a bilinear optimal control problem for a diffusive Lotka-Volterra competition model with chemo-repulsion in a bounded domain of ℝ^(ℕ),N=2,3.This model describes the competition of two species in which one of them avoid encounters with rivals through a chemo-repulsion mechanism.We prove the existence and uniqueness of weak-strong solutions,and then we analyze the existence of a global optimal solution for a related bilinear optimal control problem,where the control is acting on the chemical signal.Posteriorly,we derive first-order optimality conditions for local optimal solutions using the Lagrange multipliers theory.Finally,we propose a discrete approximation scheme of the optimality system based on the gradient method,which is validated with some computational experiments.

A Study on the Rebirth of Buck Based on the Repetitive Breakage and Es⁃tablishment of Mutualism

In the Book The Call of the Wild,Buck experiences numerous ups and downs in his life.He changes from a tamed dog to a most powerful and persevered leader of wolves in the primitive forest.He finally answers the call of his ancestor and the call of nature,returning to the place where he is destined to be.The naturalism as well as symbolism is heated discussed in this book to date.However,the study on the rebirth of Buck from the perspective of mutualism still remains a relative blank area.This article further analyzes the rebirth with the analyzing method of mutualism and the concrete context.The repetitive breakage and establish⁃ment of various mutual relationships for Buck gradually reshape some of his features and wakes up something inherited,deep root⁃ed but once hidden in his soul.These features eventually pushes him to reach the peak of his life—the life he is meant to be and in which he becomes a true warrior.

一类分数阶Lotka-Volterra系统的动力学研究

本文建立了一个新的分数阶Lotka-Volterra模型.首先,我们考虑所涉及的分数阶Lotka-Volterra模型的存在性、唯一性和非负性.其次,通过分析特征方程将时滞视为分岔参数,建立一个新的充分条件来保证系统的稳定性和Hopf分岔的出现.

Synchronization in a Mutualism Ecosystem Induced by Noise Correlation

Understanding the cause of the synchronization of population evolution is an important issue for ecologicalimprovement.Here we present a Lotka-Volterra-type model driven by two correlated environmental noises and show,via theoretical analysis and direct simulation,that noise correlation can induce a synchronization of the mutualists.Thetime series of mutual species exhibit a chaotic-like fluctuation,which is independent of the noise correlation,however,the chaotic fluctuation of mutual species ratio decreases with the noise correlation.A quantitative parameter defined forcharacterizing chaotic fluctuation provides a good approach to measure when the complete synchronization happens.

A Theory of Ratio Selection—Lattice Model for Obligate Mutualism

Mutualisms are cooperative interactions between members of different species. We focus on obligate mutualism, where each species cannot survive without the other. From a theoretical aspect, obligate mutualism is similar to the relationship between male and female. Empirical data indicate a sex-ratio selection: male and female have a specific ratio in their population sizes. In the present paper, we apply lattice model to obligate mutualism between two species, and present a theory of “ratio selection” which is a generalization of sex-ratio selection. Computer simulations are carried out by two methods: local and global interactions. In the former, interactions occur between neighbouring cells, while in the latter they occur between any pair of cells. Simulations in both interactions show the so-called Allee effect: both species can survive, when both densities are large in some extent. However, we find a large difference between local and global simulations. In the case of local interaction, restriction for survival is found to be extremely severe compared to global interaction. Both species require a proper ratio for their sustainability. This result leads to the theory of ratio selection: when interaction occurs locally, the ratio of both species is uniquely determined. We discuss that the ratio selection explains not only the evolution of endosymbionts from free-living ancestors but also the evolution from endosymbionts to organelles.

具有时滞和线性收获项的三维Lotka-Volterra合作系统的Hopf分支

针对三维Lotka-Volterra合作系统的Hopf分支进行了研究。首先,在三维Lotka-Volterra合作系统的基础上,引入时滞项及线性收获项对该系统进行改进;其次,以时滞τ作为分支参数,对改进后系统的局部Hopf分支的存在性进行分析,给出使系统在正平衡点处产生Hopf分支的临界值;然后,利用中心流形定理和规范型理论计算出Hopf分支方向及周期解稳定性的公式;最后,进行数值模拟,根据数值模拟的结果,验证了理论的可行性。结果表明,当时滞τ从0一直增加到超过临界值时,系统的正平衡点由稳定状态变为不稳定状态,且在正平衡点处产生Hopf分支。说明时滞影响了系统的动力学行为,使其动力学性质更加复杂。

基于Lotka-Volterra种群动态模型的水生动物性别比例变化研究

水生动物的性别比例会受遗传因素与外界环境、激素的影响,其具有适应性别比例变异的特征,且性别比例变化对种群自身及其他物种都有一定影响。主要从物种间的内部机制与外部资源环境因素两方面论述。通过已知性别决定因素,构造性别比例变化与资源可用性相关函数,考虑性别比例变化与生存环境面积的影响,建立基于Lotka-Volterra的系统动态模型。本文以海七鳃鳗为例,运用模型进行研究,研究得到其在性别比例变化下种群数量变化曲线随时间呈周期性,且猎食者的数量波动相比于猎物的数量呈现约四分之一个周期的滞后效应。

Mutual Information Maximization via Joint Power Allocation in Integrated Sensing and Communications System

In this paper, we focus on the power allocation of Integrated Sensing and Communication(ISAC) with orthogonal frequency division multiplexing(OFDM) waveform. In order to improve the spectrum utilization efficiency in ISAC, we propose a design scheme based on spectrum sharing, that is,to maximize the mutual information(MI) of radar sensing while ensuring certain communication rate and transmission power constraints. In the proposed scheme, three cases are considered for the scattering off the target due to the communication signals,as negligible signal, beneficial signal, and interference signal to radar sensing, respectively, thus requiring three power allocation schemes. However,the corresponding power allocation schemes are nonconvex and their closed-form solutions are unavailable as a consequence. Motivated by this, alternating optimization(AO), sequence convex programming(SCP) and Lagrange multiplier are individually combined for three suboptimal solutions corresponding with three power allocation schemes. By combining the three algorithms, we transform the non-convex problem which is difficult to deal with into a convex problem which is easy to solve and obtain the suboptimal solution of the corresponding optimization problem. Numerical results show that, compared with the allocation results of the existing algorithms, the proposed joint design algorithm significantly improves the radar performance.

Quantized Decoders that Maximize Mutual Information for Polar Codes

In this paper,we innovatively associate the mutual information with the frame error rate(FER)performance and propose novel quantized decoders for polar codes.Based on the optimal quantizer of binary-input discrete memoryless channels(BDMCs),the proposed decoders quantize the virtual subchannels of polar codes to maximize mutual information(MMI)between source bits and quantized symbols.The nested structure of polar codes ensures that the MMI quantization can be implemented stage by stage.Simulation results show that the proposed MMI decoders with 4 quantization bits outperform the existing nonuniform quantized decoders that minimize mean-squared error(MMSE)with 4 quantization bits,and yield even better performance than uniform MMI quantized decoders with 5 quantization bits.Furthermore,the proposed 5-bit quantized MMI decoders approach the floating-point decoders with negligible performance loss.

New construction of mutually unbiased bases for odd-dimensional state space

We study the construction of mutually unbiased bases in Hilbert space for composite dimensions d which are not prime powers.We explore the results for composite dimensions which are true for prime power dimensions.We then provide a method for selecting mutually unbiased vectors from the eigenvectors of generalized Pauli matrices to construct mutually unbiased bases.In particular,we present four mutually unbiased bases in C^(15).

Analytical modeling and approaches of multihelix cables incorporating with interwire mutual contacts

This study aims to develop an analytical model based on the curve beam theory to capture the mechanical response of a multihelix cable considering the internal contact displacements.Accordingly,a double-helix cable subjected to axial tension and torsion is analyzed,and both the line and point contacts between the neighboring wires and strands are considered via an equivalent homogenized approach.Then,the proposed theoretical model is extended to a hierarchical multihelix cable with mutual contact displacements by constructing a recursive relationship between the high-and low-level multihelix structures.The global tensile stiffness and torsional stiffness of the double-helix cable are successfully evaluated.The results are validated by a finite element(FE)model,and are found to be consistent with the findings of previous studies.It is shown that the contact deformations in multihelix cables significantly affect their equivalent mechanical stiffness,and the contact displacements are remarkably enhanced as the helix angles increase.This study provides insights into the interwire/interstrand mutual contact effects on global and local responses.

Robust and Discriminative Feature Learning via Mutual Information Maximization for Object Detection in Aerial Images

Object detection in unmanned aerial vehicle(UAV)aerial images has become increasingly important in military and civil applications.General object detection models are not robust enough against interclass similarity and intraclass variability of small objects,and UAV-specific nuisances such as uncontrolledweather conditions.Unlike previous approaches focusing on high-level semantic information,we report the importance of underlying features to improve detection accuracy and robustness fromthe information-theoretic perspective.Specifically,we propose a robust and discriminative feature learning approach through mutual information maximization(RD-MIM),which can be integrated into numerous object detection methods for aerial images.Firstly,we present the rank sample mining method to reduce underlying feature differences between the natural image domain and the aerial image domain.Then,we design a momentum contrast learning strategy to make object features similar to the same category and dissimilar to different categories.Finally,we construct a transformer-based global attention mechanism to boost object location semantics by leveraging the high interrelation of different receptive fields.We conduct extensive experiments on the VisDrone and Unmanned Aerial Vehicle Benchmark Object Detection and Tracking(UAVDT)datasets to prove the effectiveness of the proposed method.The experimental results show that our approach brings considerable robustness gains to basic detectors and advanced detection methods,achieving relative growth rates of 51.0%and 39.4%in corruption robustness,respectively.Our code is available at https://github.com/cq100/RD-MIM(accessed on 2 August 2024).

Automatic Extraction of Medical Latent Variables from ECG Signals Utilizing a Mutual Information-Based Technique and Capsular Neural Networks for Arrhythmia Detection

From a medical perspective,the 12 leads of the heart in an electrocardiogram(ECG)signal have functional dependencies with each other.Therefore,all these leads report different aspects of an arrhythmia.Their differences lie in the level of highlighting and displaying information about that arrhythmia.For example,although all leads show traces of atrial excitation,this function is more evident in lead II than in any other lead.In this article,a new model was proposed using ECG functional and structural dependencies between heart leads.In the prescreening stage,the ECG signals are segmented from the QRS point so that further analyzes can be performed on these segments in a more detailed manner.The mutual information indices were used to assess the relationship between leads.In order to calculate mutual information,the correlation between the 12 ECG leads has been calculated.The output of this step is a matrix containing all mutual information.Furthermore,to calculate the structural information of ECG signals,a capsule neural network was implemented to aid physicians in the automatic classification of cardiac arrhythmias.The architecture of this capsule neural network has been modified to perform the classification task.In the experimental results section,the proposed model was used to classify arrhythmias in ECG signals from the Chapman dataset.Numerical evaluations showed that this model has a precision of 97.02%,recall of 96.13%,F1-score of 96.57%and accuracy of 97.38%,indicating acceptable performance compared to other state-of-the-art methods.The proposed method shows an average accuracy of 2%superiority over similar works.

Generative Multi-Modal Mutual Enhancement Video Semantic Communications

Recently,there have been significant advancements in the study of semantic communication in single-modal scenarios.However,the ability to process information in multi-modal environments remains limited.Inspired by the research and applications of natural language processing across different modalities,our goal is to accurately extract frame-level semantic information from videos and ultimately transmit high-quality videos.Specifically,we propose a deep learning-basedMulti-ModalMutual Enhancement Video Semantic Communication system,called M3E-VSC.Built upon a VectorQuantized Generative AdversarialNetwork(VQGAN),our systemaims to leverage mutual enhancement among different modalities by using text as the main carrier of transmission.With it,the semantic information can be extracted fromkey-frame images and audio of the video and performdifferential value to ensure that the extracted text conveys accurate semantic information with fewer bits,thus improving the capacity of the system.Furthermore,a multi-frame semantic detection module is designed to facilitate semantic transitions during video generation.Simulation results demonstrate that our proposed model maintains high robustness in complex noise environments,particularly in low signal-to-noise ratio conditions,significantly improving the accuracy and speed of semantic transmission in video communication by approximately 50 percent.

Performance analysis of the mutual coupling effect on Phased Array Feeds

A phased array feed(PAF)is a type of receiving array that places phased array antennas on the focal plane of a radio telescope to expand its field of view and improve observation efficiency.Owing to the mutual coupling effect between elements caused by a tightly arranged feed array,which changes the performance of a PAF,this paper presents a 7×7 rectangular feed array model for a 25 m reflector telescope.By adjusting the element spacings,the performance of a PAF with different spacings is comprehensively analyzed with respect to the mutual coupling effect via performance statistics and comparison.This research aims to provide a reference for the preliminary design of a related PAF.

Mutual effects between a gliding arc discharge and a premixed flame

Mutual effects between a gliding arc(GA)discharge at atmospheric pressure and a premixed CH_(4)/air flame were experimentally investigated.Effects of the flame on the GA were studied using simultaneous measurements of the current,the voltage,and the instantaneous images of the plasma columns.The GA in the flame has a thicker and more diffusive plasma column,and it is more frequently ignited at a smaller breakdown voltage than that in the air.The GA extension velocity and the gliding velocity in the flame are larger than those in the air.The electrode voltage drop of the GA discharge in the flame is about 160 V,whereas that in the air is about 220 V.Compared with the GA in the air,the different features of the GA in the flame can be explained by high-temperature,weakly ionized,and species-abundant environment that are generated by the premixed CH_(4)/air flame.Effects of the gliding arc discharge on the premixed flames were demonstrated using planar laser-induced fluorescence of hydroxyl radicals(OH)and formaldehyde(CH_(2)O).OH and CH_(2)O can be formed in the CH_(4)/air mixture in the presence of the GA due to kinetic effects,and the increase of OH and CH_(2)O shows the great potential of the GA for combustion enhancement.

A novel fracture-cavity reservoir outcrop geological knowledge base construction method considering parameter collection and processing,mutual transformation of data-knowledge,application and update

This study endeavors to formulate a comprehensive methodology for establishing a Geological Knowledge Base(GKB)tailored to fracture-cavity reservoir outcrops within the North Tarim Basin.The acquisition of quantitative geological parameters was accomplished through diverse means such as outcrop observations,thin section studies,unmanned aerial vehicle scanning,and high-resolution cameras.Subsequently,a three-dimensional digital outcrop model was generated,and the parameters were standardized.An assessment of traditional geological knowledge was conducted to delineate the knowledge framework,content,and system of the GKB.The basic parameter knowledge was extracted using multiscale fine characterization techniques,including core statistics,field observations,and microscopic thin section analysis.Key mechanism knowledge was identified by integrating trace elements from filling,isotope geochemical tests,and water-rock simulation experiments.Significant representational knowledge was then extracted by employing various methods such as multiple linear regression,neural network technology,and discriminant classification.Subsequently,an analogy study was performed on the karst fracture-cavity system(KFCS)in both outcrop and underground reservoir settings.The results underscored several key findings:(1)Utilization of a diverse range of techniques,including outcrop observations,core statistics,unmanned aerial vehicle scanning,high-resolution cameras,thin section analysis,and electron scanning imaging,enabled the acquisition and standardization of data.This facilitated effective management and integration of geological parameter data from multiple sources and scales.(2)The GKB for fracture-cavity reservoir outcrops,encompassing basic parameter knowledge,key mechanism knowledge,and significant representational knowledge,provides robust data support and systematic geological insights for the intricate and in-depth examination of the genetic mechanisms of fracture-cavity reservoirs.(3)The developmental characteristics of fracturecavities in karst outcrops offer effective,efficient,and accurate guidance for fracture-cavity research in underground karst reservoirs.The outlined construction method of the outcrop geological knowledge base is applicable to various fracture-cavity reservoirs in different layers and regions worldwide.