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Brain network is a large-scale complex network with scale-free, small-world, and modularity properties, which largely supports this high-efficiency massive system. In this paper, we propose to synthesize brain-network-inspired interconnections for large-scale network-on-chips. Firstly, we propose a method to generate brain-network-inspired topologies with limited scale-free and power-law small-world properties, which have a low total link length and extremely low average hop count approximately proportional to the logarithm of the network size. In addition, given the large-scale applications and the modular topology, we present an application mapping method, including task mapping and deterministic deadlock-free routing, to minimize the power consumption and hop count. Finally, a cycle-accurate simulator BookSim2 is used to validate the architecture performance with different synthetic traffic patterns and large-scale test cases, including real-world communication networks for the graph processing application. Experiments show that, compared with other topologies and methods, the NoC design generated by the proposed method presents significantly lower average hop count and lower average latency. Especially in graph processing applications with a power-law and tightly coupled inter-core communication, the brain-network-inspired NoC has up to 70% lower average hop count and 75% lower average latency than mesh-based NoCs., Comment: 19 pages, 15 figures, 8 tables, accepted by ACM TODAES

This article is concerned with the synthesis problem of passive mechanical admittances that connect masses in a chain. The mass chain is excited at one boundary point, and the admittances are designed to suppress the disturbance independent of the length of the chain. The scalar transfer functions from the disturbance to a given intermass displacement are studied. The disturbance rejection performance is optimised over a class of arbitrary positive real mechanical admittances. The resulting admittance is synthesised mechanically using springs, dampers, and inerters only.

The last decades of neuroimaging research has revealed atypical development of intrinsic functional connectivity within and between large-scale cortical networks in autism spectrum disorder, but much remains unknown about cortico-subcortical developmental connectivity atypicalities. This study examined cortico-striatal developmental intrinsic functional connectivity changes in autism spectrum disorder and explored how those changes may be correlated with autistic traits. We studied 49 individuals with autism spectrum disorder and 52 age-, sex-, and head motion–matched typically developing individuals (5–30 years old (14.0 ± 5.6)) using resting-state functional magnetic resonance imaging. Age-related differences in striatal intrinsic functional connectivity were compared between the two groups by adopting functional network–based parcellations of the striatum as seeds. Relative to typically developing individuals, autism spectrum disorder individuals showed atypical developmental changes in intrinsic functional connectivities between almost all striatal networks and sensorimotor network/default network, with connectivity increasing with age in the autism spectrum disorder group and decreasing or constant in typically developing individuals. Age-related degree centrality and voxel-mirrored homotopic connectivity atypicalities in sensorimotor network/default network and voxel-mirrored homotopic connectivity disruptions in striatal regions were also observed in autism spectrum disorder. Significant correlations were found between cortico-striatal intrinsic functional connectivities and Autism Diagnostic Observation Schedule communication/repetitive and restricted-behavior subscores in autism spectrum disorder. Our results indicated that developmental atypicalities of cortico-striatal intrinsic functional connectivities might contribute to the neuropathology of autism spectrum disorder. Lay abstract Autism spectrum disorder has long been conceptualized as a disorder of “atypical development of functional brain connectivity (which refers to correlations in activity levels of distant brain regions).” However, most of the research has focused on the connectivity between cortical regions, and much remains unknown about the developmental changes of functional connectivity between subcortical and cortical areas in autism spectrum disorder. We used the technique of resting-state functional magnetic resonance imaging to explore the developmental characteristics of intrinsic functional connectivity (functional brain connectivity when people are asked not to do anything) between subcortical and cortical regions in individuals with and without autism spectrum disorder aged 6–30 years. We focused on one important subcortical structure called striatum, which has roles in motor, cognitive, and affective processes. We found that cortico-striatal intrinsic functional connectivities showed opposite developmental trajectories in autism spectrum disorder and typically developing individuals, with connectivity increasing with age in autism spectrum disorder and decreasing or constant in typically developing individuals. We also found significant negative behavioral correlations between those atypical cortico-striatal intrinsic functional connectivities and autistic symptoms, such as social-communication deficits, and restricted/repetitive behaviors and interests. Taken together, this work highlights that the atypical development of cortico-subcortical functional connectivity might be largely involved in the neuropathological mechanisms of autism spectrum disorder.

For households, water supply, power supply, pipeline gas and centralized heating are necessaries to maintain the normal life of a family. For Chinese urban families, water, electricity, gas, warm services are often provided by different government utility companies. Aimed at the shortage of the manual measurement of the above mentioned services‘ consumptions, a new mechanism using network metering devices is proposed in this paper. Based on the general network measurement mechanism and through analysis, this paper proposes using meter communication network as the public communication network of various metering devices, forming wireless mesh network by metering devices, constituting a virtual private network by the power company's network, and accessing to metering network based on SCEP protocol and certificates of devices. Under this new network measurement mechanism, a network routing optimization strategy, a data acquisition protocol based on IPv6 and a mesh network management system are designed, and preliminary experiments have been carried on. Experimental results show that the proposed mechanism can realize functions through the network, including the control of multi-measuring devices, topology discovery, data acquisition, fulfilling the multi-meter data acquisition system via network.

Efficient utilization of urban road networks has been in the epicenter of researchers’ attention for many decades already. Nowadays, many cities rely on traffic-responsive strategies for the effective management of traffic flows. However, state of the art methodologies, based on Artificial Intelligence (AI), have started challenging the currently implemented solutions. Reinforcement Learning (RL) stands as one of the most promising AI-based methodologies aiming at the optimization of traffic signal controlling. Since the first appearance of relevant RL methodologies, numerous implementation strategies have been suggested. Nonetheless, these approaches are usually evaluated under ideal or simplistic conditions (e.g. toy networks, unrealistic demand patterns, etc.) thus lack the ability to assess the effectiveness of RL-based signaling optimization under realistic conditions. The currently presented study bridges this gap by evaluating a plethora of RL-based implementations under fully realistic urban demand conditions as manifested in the large-scale road network of Nicosia, Cyprus.

Application of the ant algorithm for ensuring the cyber resilience of a distributed system in conditions of various types of cyber attacks is considered. The principle of operation of the ant algorithm is described, a mathematical model of the network infrastructure is developed, and possible types of cyberattacks are determined within the framework of the model. The results of the experimental studies demonstrated the applicability of the ant algorithm for ensuring the cyber resilience of large-scale networks.

Application of connected and automated vehicles (CAVs) is expected to have a significant impact on traffic safety and mobility. Although several studies evaluated the effectiveness of CAVs in a small roadway segment, there is a lack of studies analyzing the impact of CAVs in a large-scale network by considering both freeways and arterials. Therefore, the objective of this study is to analyze the effectiveness of CAVs at the network level by utilizing both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication technologies. Also, the study proposed a new signal control algorithm through V2I technology to elevate the performance of CAVs at intersections. A car-following model named cooperative adaptive cruise control was utilized to approximate the driving behavior of CAVs in the Aimsun Next microsimulation environment. For the testbed, the research team selected Orlando central business district area in Florida, U.S. To this end, the impacts of CAVs were evaluated based on traffic efficiency (e.g., travel time rate [TTR], speed, and average approach delay, etc.) and safety surrogates (e.g., standard deviation of speed, real-time crash-risk models for freeways and arterials, time exposed time-to-collision). The results showed that the application of CAVs reduced TTR significantly compared with the base condition even with the low market penetration level. Also, the proposed signal control algorithm reduced the approach delay for 94% of the total intersections present in the network. Moreover, safety evaluation results showed a significant improvement of traffic safety in the freeways and arterials under CAV conditions with different market penetration rates.

Given a simple undirected graph G=(V, E), the density of a subgraph on vertex set S is defined as a ratio between the number of edges |E(S)| and the number of vertices |S|, where E(S) is the set of edges induced by vertices in S. Finding the maximum density subgraph has become an intense study in recent years, especially in the social network era. Being based on a greedy algorithm that connects with a suitable graph data structure, we have reduced its time complexity by using a randomized binary search tree, also called treap. We make the complexity analysis in both time and memory requirements, including computational experiments in large scale real networks.

We introduce a new method for finding network motifs. Subgraphs are motifs when their frequency in the data is high compared to the expected frequency under a null model. To compute this expectation, a full or approximate count of the occurrences of a motif is normally repeated on as many as 1000 random graphs sampled from the null model; a prohibitively expensive step. We use ideas from the minimum description length literature to define a new measure of motif relevance. With our method, samples from the null model are not required. Instead we compute the probability of the data under the null model and compare this to the probability under a specially designed alternative model. With this new relevance test, we can search for motifs by random sampling, rather than requiring an accurate count of all instances of a motif. This allows motif analysis to scale to networks with billions of links.

Although the traffic and transit assignment processes are intertwined, the interactions between them are usually ignored in practice, especially for large-scale networks. In this paper, we build a simulation-based traffic and transit assignment model that preserves the interactions between the two assignment processes for the large-scale network of the Greater Toronto Area during the morning peak. This traffic assignment model is dynamic, user-equilibrium seeking, and includes surface transit routes. It utilizes the congested travel times, determined by the dynamic traffic assignment, rather than using predefined timetables. Unlike the static transit assignment models, the proposed transit model distinguishes between different intervals within the morning peak by using the accurate demand, transit schedule, and time-based road level-of-service. The traffic and transit assignment models are calibrated against actual field observations. The resulting dynamic model is suitable for testing different demand management strategies that impose dynamic changes on multiple modes simultaneously.

Appropriate pavement maintenance is of great importance due to the increasing deterioration of pavements and limited resources. Nowadays, highway agencies face large-scale networks. The management ...

Time-dependent origin–destination (OD) demand estimation using link traffic data in a large-scale network is a highly underdetermined problem. As a result, providing an accurate initial solution is crucial for obtaining a more reliable estimated demand. In this paper, we discuss the necessity of having a comprehensive demand profiling model that considers the spatial differences of OD pairs and we demonstrate its application in the calibration of large-scale traffic assignment models. First, we apply a departure choice model that adds a time dimension to the OD demand flows concerning their spatial differences. The time-profiled demand is then fed into the time-dependent OD demand estimation problem for further adjustment. Results show that in addition to reducing the error between simulation outputs and the observed link counts, the estimated demand profile more accurately reflects the spatial correlation of the OD pairs in the large-scale network being studied. Results provide practical insights into deployment and calibration of simulation-based dynamic traffic assignment models.

A two-mode network refers to a network where the nodes are classified into two distinct types, and edges can only exist between nodes of different types. In analysis of two-mode networks, one important objective is to explore the relationship between responses of two types of nodes. To this end, we propose a network autoregressive model for two-mode networks. Different network autocorrelation coefficients are allowed. To estimate the model, a quasi-maximum likelihood estimator is developed with high computational cost. To alleviate the computational burden, a least squares estimator is proposed, which is applicable in large-scale networks. The least squares estimator can be viewed as one particular type of generalized methods of moments estimator. The theoretical properties of both estimators are investigated. The finite sample performances are assessed through simulations and a real data example.

Traffic congestion is a major challenge in urban areas, and is associated with longer travel times, increased vehicle emissions, and numerous vehicle crashes. Creating an efficient mobility system is difficult, given that each driver is usually trying to optimize their individual trip within the network without accounting for other road users. However, new technologies in modern vehicles, especially connected vehicle technologies, make it increasingly possible to find solutions to network efficiency problems. Connected technologies allow data sharing between vehicles, allowing for greater system optimization. This work takes advantage of connectivity to develop a global framework to increase transportation network efficiency and address the aforementioned challenges. To enhance mobility, this paper presents a dynamic freeway speed controller based on the sliding mode theory, which uses the fundamental equations governing traffic dynamics in combination with variable speed limit control in order to provide advisory speeds for connected vehicles. Simulation results on a downtown Los Angeles network show significant reductions in trip times and delays both on freeways (where the control was activated) and network-wide (i.e., freeways and other roadways). Specifically, the results for the entire network showed a 12.17% reduction in travel time and a 20.67% reduction in total delay. These results had the secondary effect of reducing fuel consumption and therefore CO2 emissions by 2.6% and 3.3%, respectively. The results for the freeway network alone showed a 20.48% reduction in travel time and a 21.63% reduction in queued vehicles. These results reveal the significant potential benefits of using the proposed speed harmonization controller on real large-scale networks.

Lack of control over significant events may induce a state of learned helplessness that is characterized by cognitive, motivational, and affective deficits. Although highly relevant in the pathogenesis of several mental disorders, the extent of the cognitive deficits induced by experiences of uncontrollability and the neural mechanisms underlying such deficits in humans remain poorly understood. Using functional magnetic resonance imaging (fMRI), we tested here whether uncontrollability over aversive events impairs subsequent working memory performance and, if so, which neural processes are involved in such deficits. We assessed working memory and the involved neurocircuitry in the MRI scanner before and after participants underwent a task in which they could either learn to avoid electric shocks or had no instrumental control over shocks. Our results show that subjective, but not objective, uncontrollability over aversive events impaired working memory performance. This impact of subjective uncontrollability was linked to altered prefrontal and parahippocampal activities and connectivity as well as decreased crosstalk between frontoparietal executive and salience networks. Our findings show that the perceived uncontrollability over aversive events, rather than the aversive events themselves or the actual, objective control over them, disrupts subsequent working memory processes, most likely through altered crosstalk between prefrontal, temporal, and parietal areas.

BACKGROUND Developing mapping tools that allow identification of traditional or non-traditional eloquent areas is necessary to minimize the risk of postoperative neurologic deficits. The objective of our study is to evaluate the use of a novel cloud-based platform that uses machine learning to identify cerebral networks in patients with brain tumors. METHODS We retrospectively included all adult patients who underwent surgery for brain tumor resection or thermal ablation at our Institution between the 16th of February and the 15th of May of 2021. Pre and postoperative contrast-enhanced MRI with T1-weighted and high-resolution Diffusion Tensor Imaging (DTI) sequences were uploaded into the Quicktome platform. After processing the data, we categorized the integrity of seven large-scale brain networks: sensorimotor, visual, ventral attention, central executive, default mode, dorsal attention and limbic. Affected networks were correlated with pre and postoperative clinical data, including neurologic deficits. RESULTS Thirty-five (35) patients were included in the study. The average age of the sample was 63.2 years, and 51.4% (n=18) were females. The most affected network was the central executive network (40%), followed by the dorsal attention and default mode networks (31.4%), while the least affected were the visual (11%) and ventral attention networks (17%). Patients with preoperative deficits showed a significantly higher number of altered networks before the surgery (p=0.021), compared to patients without deficits. In addition, we found that patients without neurologic deficits had an average of 2.06 large-scale networks affected, with 75% of them not being related to traditional eloquent areas as the sensorimotor, language or visual circuits. CONCLUSIONS The Quicktome platform is a practical tool that allows automatic visualization of large-scale brain networks in patients with brain tumors. Although further studies are needed, it may assist in the surgical management of traditional and non-traditional eloquent areas.