Hierarchical Synchronization Research Articles

Emergence of phase clusters and coexisting states reveals the structure-function relationship.

The Brain Connectome Project has made significant strides in uncovering the structural connections within the brain on various levels. This has led to the question of how brain structure and function are related. Our research explores this relationship in an adaptive neural network in which synaptic conductance between neurons follows spike-time synaptic plasticity rules. By adjusting the plasticity boundary, the network exhibits diverse collective behaviors, including phase synchronization, phase locking, hierarchical synchronization (phase clusters), and coexisting states. Using graph theory, we found that hierarchical synchronization is related to the community structure, while coexisting states are related to the hierarchical self-organizing and core-periphery structure. The network evolves into several tightly connected modules, with sparsely intermodule connections resulting in the formation of phase clusters. In addition, the hierarchical self-organizing structure facilitates the emergence of coexisting states. The coexistence state promotes the evolution of the core-periphery structure. Our results point towards the equivalence between function and structure, with function emerging from structure, and structure being influenced by function in a complex dynamic process.

Social bonding in groups of humans selectively increases inter-status information exchange and prefrontal neural synchronization.

Social groups in various social species are organized with hierarchical structures that shape group dynamics and the nature of within-group interactions. In-group social bonding, exemplified by grooming behaviors among animals and collective rituals and team-building activities in human societies, is recognized as a practical adaptive strategy to foster group harmony and stabilize hierarchical structures in both human and nonhuman animal groups. However, the neurocognitive mechanisms underlying the effects of social bonding on hierarchical groups remain largely unexplored. Here, we conducted simultaneous neural recordings on human participants engaged in-group communications within small hierarchical groups (n = 528, organized into 176 three-person groups) to investigate how social bonding influenced hierarchical interactions and neural synchronizations. We differentiated interpersonal interactions between individuals of different (inter-status) or same (intra-status) social status and observed distinct effects of social bonding on inter-status and intra-status interactions. Specifically, social bonding selectively increased frequent and rapid information exchange and prefrontal neural synchronization for inter-status dyads but not intra-status dyads. Furthermore, social bonding facilitated unidirectional neural alignment from group leader to followers, enabling group leaders to predictively align their prefrontal activity with that of followers. These findings provide insights into how social bonding influences hierarchical dynamics and neural synchronization while highlighting the role of social status in shaping the strength and nature of social bonding experiences in human groups.

Hierarchical synchronization with structured multi-granularity interaction for video question answering

Video Question Answering (VideoQA) requires a thorough comprehension of linguistic and visual modalities. However, recent methods confront two problems: (1) Synchronous modeling of object action and frame scene instead of a step-by-step manner, which can better mine potential semantic attributes of videos, lacks research; (2) The relationship between cross-modal alignments at different granularity of abstraction is not fully utilized. Based on these insights, we propose a novel method named hierarchical synchronization with structured multi-granularity interaction (HSSMI) for VideoQA. First, a hierarchical synchronous reasoning module is put forward to model objects’ relations and dynamics and synchronously capture their synergistic influences over time when analyzing whole frames. It is seen as multiple Object ConvLSTMs (O-CLSTMs) in isolation or a Frame ConvLSTM (F-CLSTM) in collectivity. Specifically, O-CLSTM learns the object-level action states under neighboring spatial interplays. Meanwhile, F-CLSTM learns the frame-level scene state, where action information from O-CLSTMs is selectively aggregated into a common memory cell of F-CLSTM as instructed by questions. Besides, a boundary detector is equipped to discover scene discontinuities, enabling F-CLSTM to alter its time connectivity and adapt its sequential encoding process to videos. Thereafter, we develop a conditional VLAD with topic constraints for discriminative modality summarization. Last, a structured multi-granularity interaction module is proposed to integrate complemented clues on the global alignment between scene summary and full question and the local alignments between action summaries and words. This module encourages useful information passing through compositional syntactical topologies of questions to predict answers. Experiments on three public benchmark datasets demonstrate the superiority of our HSSMI against other state-of-the-art methods. Codes will be publicly available at https://github.com/Qiss33/HSSMI.

Spatial-Temporal Analysis of Residential Housing, Office Property, and Retail Property Price Index Correlations: Evidence from Ten Chinese Cities

Using correlation-based hierarchical analysis and synchronization analysis, this study focuses on monthly price indices for residential homes, office buildings, and retail properties in ten major Chinese cities for the years 2005 to 2021. Through these analyses, one can identify interactions and interdependence among the price indices, heterogeneous patterns in synchronizations of the price indices, and their evolving paths with time. Empirical findings suggest that the degree of real estate price comovements across all property types and cities is relatively low and stable from January 2017 to February 2020, followed by significant increases during the COVID-19 pandemic from March 2020 to January 2021 and significant decreases since February 2021 with the recovery of the economy. Several groups of property types and cities are determined in this study, each of which having its members reveal rather strong but volatile synchronizations of price indices. Rolling importance analysis does not suggest persistent increasing or decreasing trends for the real estate price associated with a specific property type and city. Policy studies on real estate price comovements may benefit from these findings here.

High-Performance Low-Complexity Hierarchical Frequency Synchronization for Distributed Massive MIMO-OFDMA Systems

We propose a high-performance yet low-complexity hierarchical frequency synchronization scheme for orthogonal frequency-division multiple-access (OFDMA) aided distributed massive multi-input multi-output (MIMO) systems, where multiple carrier frequency offsets (CFOs) have to be estimated in the uplink. To solve this multi-CFO estimation problem efficiently, we classify the active antenna units (AAUs) as the master and the slaves. Then, we split the scheme into two stages. During the first stage the distributed slave AAUs are synchronized with the master AAU, while the user equipment (UE) is synchronized with the closest slave AAU during the second stage. The mean square error (MSE) performance of our scheme is better than that of the representative state-of-the-art baseline schemes, while its computational complexity is substantially lower.

Entrainment of Mutually Synchronized Spatially Distributed 24 GHz Oscillators

Synchronization is one of the most challenging aspects of distributed systems in terms of their scalability. Minimal uncertainties can lead to problems or failures regarding data consistency in globally operating data centers or in distributed sensor arrays. Existing approaches to address these challenges are based on hierarchical synchronization concepts which are well understood and have reached technical maturity, but have the disadvantage of having a single point of failure. However, especially for critical infrastructure or backup more resilient solutions are required. Mutual synchronization where oscillators in a network are coupled bidirectionally without a reference have been considered. Due to the flat hierarchy such systems do not have a single point of failure. This work studies how hierarchical synchronization can be combined with architectures implementing mutual synchronization. A network of three mutually coupled 24 GHz oscillators is used to study how injecting a reference signal into one oscillator affects the dynamics. This can be quantified by analyzing in which range of frequencies the network of mutually coupled oscillators can follow the reference frequency. Measurements on a ring and chain network topology forced by an external reference oscillator shown here are in good agreement with the predictions of a nonlinear dynamical model.

Research on Clock Acquisition and Correction Technology of Measurement Equipment

Comprehensive energy metering and electricity market-oriented transactions under the dual carbon goals put forward higher requirements on the accuracy of metering equipment clocks. The existing inventory of metering equipment has complex specifications, complex communication methods, and difficult management. Because of different production years, the old version of the metering equipment has poor clock retention capability and insufficient automatic time synchronization function. In order to solve the problems of clock accuracy and time management of metering equipment, a wide-area hierarchical collection and synchronization technology of clocks is designed, a technical framework for clock collection and synchronization of equipment at all levels of the system is constructed, and the normal state of system equipment is strengthened and maintained through the method of serial broadcast time calibration. Clock, designed a time-calibration protocol for object-oriented metering equipment. This technology has been applied to the clock management of on-site metering equipment, and the qualified rate of metering equipment clocks has reached more than 95%, which has improved the automation level of metering equipment clock management, the efficiency of time schooling, and the timely rate of time schooling.

Network Analysis of Price Comovements Among Corn Futures and Cash Prices

Abstract Due to significant implications for resource and food sectors that directly influence social well-being, commodity price comovements represent an important issue in agricultural economics. In this study, we approach this issue by concentrating on daily prices of the corn futures market and 496 cash markets from 16 states in the United States for the period of July 2006 – February 2011 through correlation based hierarchical analysis and synchronization analysis, which allow for determining interactions and interdependence among these prices, heterogeneities in price synchronization, and their changing patterns over time. As the first study of the issue focusing on prices of the futures and hundreds of spatially dispersed cash markets for a commodity of indubitable economic significance, empirical findings show that the degree of comovements is generally higher after March 2008 but no persistent increase is observed. Different groups of cash markets are identified, each of which has its members exhibit relatively stable price synchronization over time that is generally at a higher level than the synchronization among the futures and all of the 496 cash markets. The futures is not found to show stable price synchronization with any cash market. Certain cash markets have potential of serving as cash price leaders. Results here benefit resource and food policy analysis and design for economic welfare. The empirical framework has potential of being adapted to network analysis of prices of different commodities.

A Pulse Delay Control Method by External Trigger to Underwater Illumination

Aiming at the problem of high precision pulse synchronization control in digital signal processing, we proposed an external trigger synchronization control method by double-layer hierarchical delay. The method consists of hardware circuit and control algorithm. The hardware circuit employs the main control unit and the digital delay unit, in which the main control unit makes the coordination between the digital delay unit and the peripheral circuit, and the two-layer digital delay unit performs multiple fixed period delay operation according to the instruction of the main control unit. The control algorithm adopts the two-layer hierarchical control mode, decomposes the total delay time according to the two-layer delay precision, and performs the combined delay in the two-layer digital delay unit successively. The delay result achieves a fixed delay of less than 40 ns, a delay step of 0.25 ns and a delay range of less than 1000 ns. The method adopts the combination of hardware and software to realize the external trigger double-layer hierarchical delay synchronization control, which has the characteristics of high delay step and pulse width adjustment accuracy and small cumulative delay error. It is suitable for the application scenarios of lidar echo signal and ultra-wide spectrum power source synthesis.

Hierarchical Synchronization Estimation of Low- and High-Order Functional Connectivity Based on Sub-Network Division for the Diagnosis of Autism Spectrum Disorder.

Functional connectivity network (FCN) calculated by resting-state functional magnetic resonance imaging (rs-fMRI) plays an increasingly important role in the exploration of neurologic and mental diseases. Among the presented researches, the method of constructing FCN based on Matrix Variate Normal Distribution (MVND) theory provides a novel perspective, which can capture both low- and high-order correlations simultaneously with a clear mathematical interpretability. However, when fitting MVND model, the dimension of the parameters (i.e., population mean and population covariance) to be estimated is too high, but the number of samples is relatively quite small, which is insufficient to achieve accurate fitting. To address the issue, we divide the brain network into several sub-networks, and then the MVND based FCN construction algorithm is implemented in each sub-network, thus the spatial dimension of MVND is reduced and more accurate estimates of low- and high-order FCNs is obtained. Furthermore, for making up the functional connectivity which is lost because of the sub-network division, the rs-fMRI mean series of all sub-networks are calculated, and the low- and high-order FCN across sub-networks are estimated with the MVND based FCN construction method. In order to prove the superiority and effectiveness of this method, we design and conduct classification experiments on ASD patients and normal controls. The experimental results show that the classification accuracy of “hierarchical sub-network method” is greatly improved, and the sub-network found most related to ASD in our experiment is consistent with other related medical researches.

Network Synchronization Revisited: Time Delays in Mutually Coupled Oscillators

Coordinated and efficient operation in large, complex systems requires the synchronization of the rhythms of spatially distributed components. Such systems are the basis for critical infrastructure such as satellite navigation, mobile communications, and services like the precision time protocol and Universal Coordinated Time. Different concepts for the synchronization of oscillator networks have been proposed, in particular mutual synchronization without and hierarchical synchronization from a reference clock. Established network synchronization models in electrical engineering address the role of inevitable cross-coupling time delays for network synchronization. Mutual synchronization has been studied using linear approximations of the coupling functions of these models. We review previous work and present a general model in which we study synchronization taking into account nonlinearities and finite time delays. As a result, dynamical phenomena in networks of coupled electronic oscillators induced by time delays, such as the multistability and stabilization of synchronized states can be predicted and observed. We study the linear stability of nonlinear states and predict for which system parameters synchronized states can be stable. We use these results to discuss the implementation of mutual synchronization for complex system architectures. A key finding is that mutual synchronization can result in stable in- and anti-phase synchronized states in the presence of large time delays. We provide the condition for which such synchronized states are guaranteed to be stable.

Network analysis of corn cash price comovements

Commodity price comovements are an important issue in economics given their significant implications for food and resource sectors that directly influence social well-being. This study approaches this issue by focusing on daily cash prices of 182 corn markets from the seven largest harvest states in the United States for 2006–2011 by using correlation based hierarchical analysis and synchronization analysis, through which we can determine interactions and interdependence among these prices, heterogeneities in price synchronization, and their changing patterns over time. As the first study of the issue concentrating on prices of hundreds of spatially dispersed markets for a commodity of indubitable economic significance, empirical findings show that the degree of comovements is generally higher after November 2006 but no persistent increase is observed. Different groups of markets are identified, each of which has its members exhibit similar price dynamics. Certain markets show potential of serving as price leaders. Results here benefit food and resource policy analysis and design for economic welfare. The empirical framework has potential of being adapted to network analysis of prices of different commodities.

Aperiodic sleep networks promote memory consolidation.

Hierarchical synchronization of sleep oscillations establishes communication pathways to support memory reactivation, transfer, and consolidation. From an information-theoretical perspective, oscillations constitute highly structured network states that provide limited information-coding capacity. Recent findings indicate that sleep oscillations occur in transient bursts that are interleaved with aperiodic network states, which were previously considered to be random noise. We argue that aperiodic activity exhibits unique and variable spatiotemporal patterns, providing an ideal information-rich neurophysiological substrate for imprinting new mnemonic patterns onto existing circuits. We discuss novel avenues in conceptualizing and quantifying aperiodic network states during sleep to further understand their relevance and interplay with sleep oscillations in support of memory consolidation.

Evaluation of the Effects of Mobile Smart Object to Boost IoT Network Synchronization.

This paper deals with the synchronization of Mobile Smart Objects (MSOs). Today, this scenario is becoming typical in Industrial IoT applications due to the plethora of MSOs available as robots, drones and wearables, equipped by sensors making them measurement instruments cooperating in distributed measurement systems. In this context, the synchronization accuracy is directly tied with the accuracy of the performed measurements. In hierarchical synchronization approaches, the presence of an MSO makes the network topology time varying, and this could prevent the synchronization of the whole network. Peer to peer approaches do not need node hierarchy to synchronize but could not converge to a common sense of time. To overcome these challenges, this paper proposes a consensus-based approach for which the convergence to a common sense of time is here demonstrated. The proposal deploys the MSO to bring the common sense of time from an SO to another, establishing new paths among SOs. The new paths are temporary and depend on the MSO’s route. In the paper, the influence of the MSO’s route on the synchronization accuracy σ and the time interval to synchronize all the SOs ∆TIS is investigated, also. The mathematical proof, the simulations and the experimental tests confirm that the MSO can reduce both the values of σ and ∆TIS, because the new connections introduced by the MSO can boost the exchange of information among SOs. Consequently, the criteria to a priori select the route ameliorating σ and ∆TIS values are proposed.

Hierarchical Optimal Synchronization for Linear Systems via Reinforcement Learning: A Stackelberg-Nash Game Perspective.

Considering the fact that in the real world, a certain agent may have some sort of advantage to act before others, a novel hierarchical optimal synchronization problem for linear systems, composed of one major agent and multiple minor agents, is formulated and studied in this article from a Stackelberg-Nash game perspective. The major agent herein makes its decision prior to others, and then, all the minor agents determine their actions simultaneously. To seek the optimal controllers, the Hamilton-Jacobi-Bellman (HJB) equations in coupled forms are established, whose solutions are further proven to be stable and constitute the Stackelberg-Nash equilibrium. Due to the introduction of the asymmetric roles for agents, the established HJB equations are more strongly coupled and more difficult to solve than that given in most existing works. Therefore, we propose a new reinforcement learning (RL) algorithm, i.e., a two-level value iteration (VI) algorithm, which does not rely on complete system matrices. Furthermore, the proposed algorithm is shown to be convergent, and the converged values are exactly the optimal ones. To implement this VI algorithm, neural networks (NNs) are employed to approximate the value functions, and the gradient descent method is used to update the weights of NNs. Finally, an illustrative example is provided to verify the effectiveness of the proposed algorithm.

Hierarchical Synchronization Control Strategy of Active Rear Axle Independent Steering System

The synchronization error of the left and right steering-wheel-angles and the disturbances rejection of the synchronization controller are of great significance for the active rear axle independent steering (ARIS) system under complex driving conditions and uncertain disturbances. In order to reduce synchronization error, a novel hierarchical synchronization control strategy based on virtual synchronization control and linear active disturbance rejection control (LADRC) is proposed. The upper controller adopts the virtual synchronization controller based on the dynamic model of the virtual rear axle steering mechanism to reduce the synchronization error between the rear wheel steering angles of the ARIS system; the lower controller is designed based on an LADRC algorithm to realize an accurate tracking control of the steering angle for each wheels. Experiments based on a prototype vehicle are conducted to prove that the proposed hierarchical synchronization control strategy for the ARIS system can improve the control accuracy significantly and has the properties of better disturbances rejection and stronger robustness.

Zero-lag chaos synchronization properties in a hierarchical tree-type network consisting of mutually coupled semiconductor lasers

Chaos synchronization properties in a novel hierarchical tree-type optical network consisting of semiconductor lasers (SLs) are investigated numerically. In such network, each child node is asymmetrically mutually coupled with the parent node. Zero-lag synchronization is found among the nodes belonging to the same layer, while the nodes belonging to different layers are not synchronized, which is denoted as hierarchical chaos synchronization (HCS). The effects of injection strength, asymmetrical injection parameters, bias currents and frequency detuning on the performance of HCS and chaotic complexity are studied in detail. Our results show that high-quality HCS can be obtained under moderate parameters. The stability of the network is also investigated systematically. Further, we explore the robustness of HCS by considering an asymmetrical network. Finally, output bit streams are generated from each SL at a tunable rate of up to about 10 Gbps with verified randomness. These results are enlightening to the multi-user chaotic communication and the synchronization mechanism in biological neuronal networks.

Scaling out NUMA-Aware Applications with RDMA-Based Distributed Shared Memory

The multicore evolution has stimulated renewed interests in scaling up applications on shared-memory multiprocessors, significantly improving the scalability of many applications. But the scalability is limited within a single node; therefore programmers still have to redesign applications to scale out over multiple nodes. This paper revisits the design and implementation of distributed shared memory (DSM) as a way to scale out applications optimized for non-uniform memory access (NUMA) architecture over a well-connected cluster. This paper presents MAGI, an efficient DSM system that provides a transparent shared address space with scalable performance on a cluster with fast network interfaces. MAGI is unique in that it presents a NUMA abstraction to fully harness the multicore resources in each node through hierarchical synchronization and memory management. MAGI also exploits the memory access patterns of big-data applications and leverages a set of optimizations for remote direct memory access (RDMA) to reduce the number of page faults and the cost of the coherence protocol. MAGI has been implemented as a user-space library with pthread-compatible interfaces and can run existing multithreaded applications with minimized modifications. We deployed MAGI over an 8-node RDMAenabled cluster. Experimental evaluation shows that MAGI achieves up to 9.25x speedup compared with an unoptimized implementation, leading to a scalable performance for large-scale data-intensive applications.

A novel robust compression-encryption of images based on SPIHT coding and fractional-order discrete-time chaotic system

In this article, a novel robust compression-encryption of images based on Set partitioning in Hierarchical Trees (SPIHT) coding-decoding algorithm and synchronization of chaotic maps with non-integer order is proposed. The essential innovation in the proposed communication system is that the source image is compressed and encrypted by several secret keys. The developed transmission scheme is designed to take profit of two advantages. The first is the use of standard SPIHT coding which is an efficient and computationally very fast technique for image compression. The second is to introduce farther complexity to the cryptosystem through the use of fractional-order chaotic maps which has many advantages such as the generation of supplementary secret keys with respect to the discrete-time chaotic systems. From the obtained simulation results, we can see that the original image has been well encoded in the cipher text and retrieved with success through the chaotic signals. Further, to verify the high confidentiality of the presented cryptosystem, cryptanalysis is studied in detail through the various tests.

On construction of a virtual GPU cluster with InfiniBand and 10 Gb Ethernet virtualization

Due to increasing requirement of computing capability, the graphics processor unit and CUDA are used to build a higher-performance computing environment. The graphics processing unit (GPU) is necessary for building the high-performance computing environment because of its high computing performance. CUDA, a parallel computing platform and programming model created by NVIDIA, utilizes some parallel construction concepts to upgrade performance, such as hierarchical thread blocks, shared memory, and barrier synchronization. The GPU and CUDA are also used in cloud computing, because they can provide high-performance computing capabilities. Virtualization plays a very important part in the cloud architecture. Virtual machines built with the NVIDIA graphics card can use CUDA to provide virtual machine computing capability. This makes virtual machine have not only virtual CPUs, but also physical graphics processors to do computations. InfiniBand is faster than Ethernet as the transmission medium. In the past, virtual machine cannot use direct InfiniBand, but now, many virtualization platforms can do it, that brings transmission speed improvement between virtual machines. In this work, we use many graphics processing units to build a high-performance computing cloud cluster. Then, we compare performance of using direct InfiniBand with that of using indirect InfiniBand transmission performance by running High Performance Linpack benchmark. In this work, we use a well-known virtualization platform, i.e., VMware to do experiments in this paper. And then, we use GPU passthrough and InfiniBand virtual and 10 Gb Ethernet passthrough to improve performance of the virtual cluster.