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.

Contribution Tracking Feature Selection (CTFS) Based on the Fusion of Sparse Autoencoder and Mutual Information

For data mining tasks on large-scale data,feature selection is a pivotal stage that plays an important role in removing redundant or irrelevant features while improving classifier performance.Traditional wrapper feature selection methodologies typically require extensive model training and evaluation,which cannot deliver desired outcomes within a reasonable computing time.In this paper,an innovative wrapper approach termed Contribution Tracking Feature Selection(CTFS)is proposed for feature selection of large-scale data,which can locate informative features without population-level evolution.In other words,fewer evaluations are needed for CTFS compared to other evolutionary methods.We initially introduce a refined sparse autoencoder to assess the prominence of each feature in the subsequent wrapper method.Subsequently,we utilize an enhanced wrapper feature selection technique that merges Mutual Information(MI)with individual feature contributions.Finally,a fine-tuning contribution tracking mechanism discerns informative features within the optimal feature subset,operating via a dominance accumulation mechanism.Experimental results for multiple classification performance metrics demonstrate that the proposed method effectively yields smaller feature subsets without degrading classification performance in an acceptable runtime compared to state-of-the-art algorithms across most large-scale benchmark datasets.

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.

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.

International Exhibitions of Cultural Property Fostering Exchanges and Mutual Learning among Civilizations China Tour of Masterpieces in Miniature:Treasures from the Rosalinde and Arthur Gilbert Collection in the Victoria and Albert Museum

The two-year"Masterpieces in Miniature"tour across seven major museums in China has fully demonstrated the role of museums as grand platforms for intercultural dialogue and the blending of world civilizations.This tour has successully opened a new chapter in cultural exchanges between China and the UK,contributing to global museum collaboration and the mutual enrichment of world civilizations.

Strengthening Exchange and Mutual Learning Between China and the Rest of the World

CPPCC member Du Zhanyuan gives an exclusive interview to China Today on how to enhance international communication and tell positive stories of Chinese modernization.AS an ancient Chinese philosopher said,“Amity between peoples holds the key to sound state-to-state relations.”This is often cited by President Xi Jinping to emphasize the importance of exchanges and mutual learning of civilizations in the development of international relations.

China-Russian Managed to Achieve Closer Collaboration with Mutual Complementarity

This year marks the 75th anniversary of China-Russia diplomatic relations.Russian President Vladimir Putin paid a state visit to China in May.The heads of state of both China and Russia held a sincere,friendly and informative meeting,signed and issued a joint statement,and witnessed the signing of more than 10 important cooperation documents.These bilateral relations have shown a sound momentum of mutual benefit,in-depth coordination and long-term stability.

BRIGHT PROSPECTS FOR MUTUALLY BENEFICIAL COOPERATION BETWEEN CHINA AND CAMBODIA--Exclusive interview with Chinese Ambassador to Cambodia Wang Wenbin

Exchanges between China and Cambodia date back at least 2,000 years.The time-honored traditional friendship is a valuable heritage of the two peoples and an important basis for mutually beneficial cooperation between the two countries in the new era.In 2024,China-Cambodia cooperation in the fields of economics,trade,energy,and people-to-people exchange has developed vigorously and delivered fruitful results.Cambodia’s vision to become an upper-middle income country by 2030 and a high-income country by 2050 aligns nicely with China’s second centenary goal of building itself into a modern socialist country in all respects by the middle of the 21st Century.

Exchanges,Mutual Learning and Looking Forward to the Future The Exhibition of Oil Paintings of China and Five Central Asian Countries

Exchanges and mutual learning between the Chinese and foreign civilizations are an important part of human cultural development.The Chinese civilization adopts an all-embracing,open and inclusive attitude to understand the different views of different civilizations on the connotation of values.The ancient Silk Road has opened a new window of friendship between countries and written a new chapter in the development of mankind.

Mutual Respect and Win-Win Cooperation 50 years of China-Malaysia relations

Friendly exchanges between China and Malaysia date back more than 2,000 years,and a profound traditional friendship has been forged between the two countries.By adhering to the principles of mutual trust,win-win cooperation,and equal assistance,China and Malaysia have become good neighbors,good friends,and good partners.In 2013,China and Malaysia established a comprehensive strategic partnership,accelerating the development of bilateral relations.

Degree of Freedom Analysis for Holographic MIMO Based on a Mutual-Coupling-Compliant Channel Model

Degree of freedom(DOF)is a key indicator for spatial multiplexing layers of a wireless channel.Traditionally,the channel of a multiple-input multiple-output(MIMO)half-wavelength dipole array has a DOF that equals the antenna number.However,recent studies suggest that the DOF could be less than the antenna number when strong mutual coupling is considered.We utilize a mutual-coupling-compliant channel model to investigate the DOF of the holographic MIMO(HMIMO)channel and give a upper bound of the DOF with strong mutual coupling.Our numerical simulations demonstrate that a dense array can support more DOF per unit aperture as compared with a half-wavelength MIMO system.

Boundless Potential China-Africa cooperation in the space sector exemplifies mutually beneficial partnerships

Exports of Chinese satellites,joint construction of space infrastructure,and joint research and development of satellites...The recent years have seen substantial growth in China-Africa cooperation in the space sector,which is bringing tangible benefits to both sides.

The Study of Mutual Benefits and Potential in China-Mongolia Agricultural Trade

This article aims to explore the mutual benefits and potential of agricultural trade between China and Mongolia,analyzing the current situation of agricultural resources,trade history,and policy environment of the two countries.The study finds that China-Mongolia agricultural trade shows significant economic complementarity.Trade activities not only promote economic growth in both countries but also deepen social and cultural exchanges.However,technical standards,political risks,and logistics costs remain major challenges facing trade cooperation.By strengthening policy coordination,investing in agricultural technology and infrastructure,and expanding the variety and markets of trade,the potential for cooperation in China-Mongolia agricultural trade can be further enhanced.These measures will help improve the efficiency and benefits of bilateral trade,achieving a win-win development.

资源支持、组织能力与价值创造:社区互助养老服务可持续生产的驱动路径

社区互助养老服务何以可持续生产,基于“环境-组织-价值”分析框架,以我国37个社区互助养老典型案例为样本,运用模糊集定性比较分析方法;(fsQCA)探究社区互助养老服务可持续生产的组态驱动路径。研究表明:;(1)外部支持、社区资源、主体协同、制度化水平、技术赋能与价值创造通过组态效应影响社区互助养老服务的可持续生产;(2)外部支持与社区资源、制度化水平与技术赋能具有潜在替代性,社区资源和价值创造是组态形成的关键因素;(3)党政统合型、社区自组织型、技术嵌入型和协同驱动型是驱动社区互助养老服务可持续生产的4种优化路径。实现社区互助养老服务高水平的可持续生产应坚持系统联动思维,释放多元组态的复合驱动效应,重视社区资源和价值创造的关键作用,选择适宜不同地区发展基础的互助养老模式。