Network Asymmetry Research Articles

Industry emergence and market capture: The rise of autonomous vehicles

In this paper we examine how the rise of Autonomous Vehicles (AV) is reshaping the power dynamics in the automobile industry through competition from the information technology (IT) industry. By introducing the concept of market capture, we analyze how incumbents and newcomers pursue control over the definition, evolution, and technology of a newly emerging market. We demonstrate this with an analysis of technological acquisitions and alliance networks for 2011–2018, a period where the AV industry first coalesced and then experienced dramatic growth. By examining firm transactions through their geographic locations and an array of centrality measures, we shed light on inter-firm dynamics shaping the emergence and evolution of the AV industry. This research demonstrates how market capture can serve as a useful framework to analyze competition, cooperation, geographic patterns, and power asymmetries in the transaction network of an emerging industry.

Large-scale distributed networks and cerebral hemispheres

The two main large-scale distributed networks, Central Executive (CEN) and Default Mode (DMN) have been extensively studied, but their relationship to hemispheric specialization has not been comprehensively addressed. We present evidence that they are neuroanatomically asymmetric: the CEN components are volumetrically larger in the right hemisphere, and DMN components are volumetrically larger in the left hemisphere. Based on this, the possibility that CEN and DMN are also functionally asymmetric is introduced and implications of the putative functional asymmetry of large-scale distributed networks for refining our understanding of hemispheric specialization are examined.

Subtly altered topological asymmetry of brain structural covariance networks in autism spectrum disorder across 43 datasets from the ENIGMA consortium

Small average differences in the left-right asymmetry of cerebral cortical thickness have been reported in individuals with autism spectrum disorder (ASD) compared to typically developing controls, affecting widespread cortical regions. The possible impacts of these regional alterations in terms of structural network effects have not previously been characterized. Inter-regional morphological covariance analysis can capture network connectivity between different cortical areas at the macroscale level. Here, we used cortical thickness data from 1455 individuals with ASD and 1560 controls, across 43 independent datasets of the ENIGMA consortium’s ASD Working Group, to assess hemispheric asymmetries of intra-individual structural covariance networks, using graph theory-based topological metrics. Compared with typical features of small-world architecture in controls, the ASD sample showed significantly altered average asymmetry of networks involving the fusiform, rostral middle frontal, and medial orbitofrontal cortex, involving higher randomization of the corresponding right-hemispheric networks in ASD. A network involving the superior frontal cortex showed decreased right-hemisphere randomization. Based on comparisons with meta-analyzed functional neuroimaging data, the altered connectivity asymmetry particularly affected networks that subserve executive functions, language-related and sensorimotor processes. These findings provide a network-level characterization of altered left-right brain asymmetry in ASD, based on a large combined sample. Altered asymmetrical brain development in ASD may be partly propagated among spatially distant regions through structural connectivity.

Improving QoS through network isolation in PaaS

The lack of knowledge about the architecture and dynamics of the hosted applications represents a significant limit for the QoS management, both on IaaS- and PaaS-based clouds. The interference among virtual machines, the heterogeneity of cloud clusters, the different resource requirements of micro-services based applications, and the network asymmetry make it difficult to find an effective mapping between the applications and the clouds’ resources.This paper aims to show how to improve the estimation of an applications’ dynamics within the cloud, combining the exploitation of the inherent flexibility of the containerization architecture with the availability of information about the structure of workflows.The authors propose two parameters, isolation index and closeness, to measure the degree of mutual influence among the components of distributed applications. These parameters will be the base for a deployment strategy aimed at minimizing the mutual interference among the applications and making their performance more predictable. The problem will be formulated through a IQP-based algorithm and its effectiveness will be shown by empirical tests and numerical simulations.

Association Between Microstructural Asymmetry of Temporal Lobe White Matter and Memory Decline After Anterior Temporal Lobectomy.

Risk for memory decline is a substantial concern in patients with temporal lobe epilepsy (TLE) undergoing anterior temporal lobectomy (ATL). Although prior studies have identified associations between memory and integrity of white matter (WM) networks within the medial temporal lobe (MTL) preoperatively, we contribute a study examining whether microstructural asymmetry of deep and superficial WM networks within the MTL predicts postoperative memory decline. Patients with drug-resistant TLE were recruited from 2 epilepsy centers in a prospective longitudinal study. All patients completed preoperative T1 and diffusion-weighted MRI (DWI) as well as preoperative and postoperative neuropsychological testing. Preoperative fractional anisotropy (FA) of the WM directly beneath the neocortex (i.e., superficial WM [SWM]) and of deep WM tracts associated with memory were calculated. Asymmetry was calculated for hippocampal volume and FA of each WM tract or region and examined in linear and logistic regressions with preoperative to postoperative memory change as the primary outcome. Data were analyzed from 42 patients with TLE (19 left TLE [LTLE], 23 right TLE [RTLE]) who underwent ATL. Leftward FA asymmetry of the entorhinal SWM was associated with decline on prose and associative recall in LTLE, whereas leftward FA asymmetry of the uncinate fasciculus (UNC) was associated with decline on prose recall only. After controlling for preoperative memory score and hippocampal volume, leftward FA asymmetry of the entorhinal SWM uniquely contributed to decline in both prose and associative recall (β = -0.46; SE 0.14 and β = -0.68; SE 0.22, respectively) and leftward FA asymmetry of the UNC uniquely contributed to decline in prose recall (β = -0.31; SE 0.14). A model combining asymmetry of hippocampal volume and entorhinal FA correctly classified memory outcomes in 79% of patients with LTLE for prose (area under the curve [AUC] 0.89; sensitivity 82%; specificity 75%) and 81% of patients for associative (AUC 0.79; sensitivity 83%; specificity 80%) recall. Entorhinal SWM asymmetry was the strongest predictor in both models. Preoperative asymmetry of deep WM and SWM integrity within the MTL is a strong predictor of postoperative memory decline in TLE, suggesting that surgical decision-making may benefit from considering each patient's WM network adequacy and reserve in addition to hippocampal integrity. This study provides Class II evidence that preoperative asymmetry of deep WM and SWM integrity within the MTL is a predictor of postoperative memory decline.

Effect of Asymmetry in the Wing Vein Network

A wing vein network of a fore wing of a Drosophila fly has asymmetry. One vein breaking symmetry of the wing vein network was found via application of our topological transformation. This asymmetry-creating wing vein can be a strategy to improve functionality of the wing veins. As one of multiple functions of the wing veins, they are pipes to transport blood according to the entomological knowledge. However, there have been no engineering attention to the function of vein network as pipe network for blood transportation and are no understandings of how blood circulation is achieved in such a network consisting of extremely narrow pipes based on physics. In this study, we revealed the effect of the asymmetric vein network structure on the blood circulation via comparison with symmetric vein network without the asymmetry-creating vein. A numerical simulation based on the Hardy-Cross method allowed us to demonstrate how addition of this vein influences blood circulation in the wing. The result of that showed the asymmetrical structure improves blood flow distribution in the posterior region of the wing and energy consumption by pumping organ for circulation. Our results interpreted importance of the asymmetry caused by one irregularly positioned vein for the efficient maintenance of the wing via circulation.

RMC: Reordering Marking and Coding for Fine-Grained Load Balancing in Data Centers

Data center networks typically adopt multi-rooted tree topologies to provide high bisection bandwidth. Various fine-grained load balancing schemes have been proposed to split flows across multiple paths. However, data center networks suffer from many uncertainties such as highly dynamic traffic. These uncertainties easily make network become asymmetric, resulting in significant packet reordering. Unfortunately, existing solutions passively deal with packet reordering based on a threshold and hardly adapt to asymmetric networks because of lacking the explicit reordering feedback. These solutions either fail to quickly respond to packet loss or cause unnecessary fast retransmission, which reduces link utilization and increases flow completion time. In this paper, we propose a fine-grained load balancing scheme RMC to eliminate the impact of packet reordering and handle uncertainties in asymmetric networks. To avoid unnecessary fast retransmission, the switch proactively identifies reordered packet according to local queue length and global path latency. Furthermore, we employ a coding technique with redundancy optimization to reduce long-tailed flow completion time under network asymmetry. Through a series of large-scale NS2 simulations and testbed experiments, we demonstrate that RMC effectively avoids unnecessary fast retransmission under different network scenarios and reduces flow completion time by up to 72% compared with state-of-the-art schemes.

Market interaction structure and equilibrium price heterogeneity in monopolistic competition

This paper investigates the extent to which the structure of the interaction network between suppliers and buyers affects equilibrium price heterogeneity. An incomplete interaction structure leads to uneven information flows and different information bases for consumers which is then taken into account by price setting producers. This results in heterogenous prices even if producers are identical in all other respects. The complete interaction network serves as a special case resembling standard monopolistic competition models. We show that a slight deviation from the complete network results in heterogeneous prices, although this heterogeneity becomes economically significant only under sparse or small networks. Sparsity as the main determinant of price heterogeneity dominates network asymmetry: relatively dense networks show minimal price dispersion even if its degree distribution follows a power law.

Application of a brain-inspired deep imitation learning algorithm in autonomous driving

<h2>Abstract</h2> Autonomous driving has attracted great attention from both academics and industries. To realise autonomous driving, Deep Imitation Learning (DIL) is treated as one of the most promising solutions, because it improves autonomous driving systems by automatically learning a complex mapping from human driving data, compared to manually designing the driving policy. However, existing DIL methods cannot generalise well across domains, that is, a network trained on the data of source domain gives rise to poor generalisation on the data of target domain. In the present study, we propose a novel brain-inspired deep imitation method that builds on the evidence from human brain functions, to improve the generalisation ability of DNN so that autonomous driving systems can perform well in various scenarios. Specifically, humans have a strong generalisation ability which is beneficial from the structural and functional asymmetry of the two sides of the brain. Here, we design dual Neural Circuit Policy (NCP) architectures in DNN based on the asymmetry of human neural networks. Experimental results demonstrate that our brain-inspired method outperforms existing methods regarding generalisation when dealing with unseen data. Our source codes and pretrained models are available at https://github.com/Intenzo21/Brain-Inspired-Deep-Imitation-Learning-for-Autonomous-Driving-Systems.

Prediction of Deep Brain Stimulation Outcome in Parkinson's Disease With Connectome Based on Hemispheric Asymmetry.

Parkinson’s disease (PD) is a neurodegenerative disease that is associated with motor and non-motor symptoms and caused by lack of dopamine in the substantia nigra of the brain. Subthalamic nucleus deep brain stimulation (STN-DBS) is a widely accepted therapy of PD that mainly inserts electrodes into both sides of the brain. The effect of STN-DBS was mainly for motor function, so this study focused on the recovery of motor function for PD after DBS. Hemispherical asymmetry in the brain network is considered to be a potential indicator for diagnosing PD patients. This study investigated the value of hemispheric brain connection asymmetry in predicting the DBS surgery outcome in PD patients. Four types of brain connections, including left intra-hemispheric (LH) connection, right intra-hemispheric (RH) connection, inter-hemispheric homotopic (Ho) connection, and inter-hemispheric heterotopic (He) connection, were constructed based on the resting state functional magnetic resonance imaging (rs-fMRI) performed before the DBS surgery. We used random forest for selecting features and the Ridge model for predicting surgical outcome (i.e., improvement rate of motor function). The functional connectivity analysis showed that the brain has a right laterality: the RH networks has the best correlation (r = 0.37, p = 5.68E-03) between the predicted value and the true value among the above four connections. Moreover, the region-of-interest (ROI) analysis indicated that the medioventral occipital cortex (MVOcC)–superior temporal gyrus (STG) and thalamus (Tha)–precentral gyrus (PrG) contributed most to the outcome prediction model for DBS without medication. This result provides more support for PD patients to evaluate DBS before surgery.

Southern actors and the governance of labour standards in global production networks: The case of South African fruit and wine

Recent studies highlight the emergence of standards, including multi-stakeholder initiatives developed and applied within the global South where supplier firms are usually based. This trend has created a complex ethical terrain whereby transnational standards flow through global production networks and intersect with domestic initiatives at places of production. The paper complements global production network analysis with the concepts of ‘space of flows’ and ‘space of places’ and insights from relational economic geography, to examine how some multi-stakeholder initiatives in the global South can shape the broader governance of labour standards in global production networks. The following questions are addressed: How is the governance of labour standards in global production networks shaped by dynamic spatial interactions between actors? What role have diverse Southern multi-stakeholder initiatives played in influencing the governance of South African fruit and wine? We draw on research conducted over seven years into two standards in South Africa, the Wine and Agriculture Ethical Trade Association and Sustainability Initiative of South Africa. Our analysis shows that these two Southern-based multi-stakeholder initiatives contributed to shaping the broader governance of labour standards through dynamic non-linear waves of interaction over time, involving both collaborative and contested exchanges between actors across space of flows and places. We further argue that despite the development of multi-stakeholder initiatives by Southern actors, commercial power asymmetries in global production networks limit their ability to promote significant improvements for producers and workers.

An Overview of the Geomorphological Characteristics of the Pergamon Micro-Region (Bakırçay and Madra River Catchments, Aegean Region, West Turkey)

Current landforms are the basis for understanding past geomorphodynamics and human activities. Based on multiple materials and methods, including geomorphometric analyses of a digital elevation model and visual interpretations of satellite images, different landscape units of the Bakırçay and Madra River catchments in the environs of ancient Pergamon are described. The area was mainly shaped by tectonics that formed a horst-and-graben structure; small Miocene horsts and NE-trending subgrabens in the Kozak and Yunt Dağı Mountains were separated by the NEE-trending Plio-Pleistocene Bergama Graben. The asymmetry in relief, drainage network, and sediment accumulation between the Kozak Mountains to the north and the Yunt Dağı Mountains to the south of the lower Bakırçay plain characterize the Pergamon Micro-Region. The regional relief characteristics, with wide flat basin and plateau areas, are suitable for agriculture. Complemented by its richness in natural resources, the Pergamon Micro-Region became a preferred settlement area, with evidence of human impact since the Hellenistic–Roman period at the latest. As a consequence of settlement activities, several landscape engineering measures were implemented simultaneously with a parallel change in morphodynamics.

Mitigating Packet Reordering for Random Packet Spraying in Data Center Networks

Modern data center networks are usually constructed in multi-rooted tree topologies, which require the highly efficient multi-path load balancing to achieve high link utilization. Recent packet-level load balancer obtains high throughput by spraying packets to all paths, but it easily leads to the packet reordering under network asymmetry. The flow-level or flowlet-level load balancer avoids the packet reordering, while reducing the link utilization due to their inflexibility. To solve these problems, we design a Queueing Delay Aware Packet Spraying (QDAPS), that effectively mitigates the packet reordering for packet-level load balancer. QDAPS selects paths for packets according to the queueing delay of output buffer, and lets the packet arriving earlier be forwarded before the later packets to avoid packet reordering. Moreover, we adopt the “power-of- n-choices” paradigm on QDAPS to alleviate the impact of herd behavior under multiple forwarding engines. We compare QDAPS with ECMP, LetFlow and RPS through NS2 simulation and Mininet implementation. The test results show that QDAPS reduces flow completion time (FCT) by ~30%-50% over the state-of-the-art load balancing mechanism.

Power asymmetry and value creation in B2C relationship networks

Abstract The purpose of this paper is to explore the problem of power distribution within networks of relationships between companies and consumers (business-to-consumer (B2C) networks) and to examine the ways in which value is created and captured in such structures. To this end, we applied the network approach to multiple theoretical constructs describing collective consumer phenomena, carried over from the field of sociology to management science. Based on the literature and case study analysis, we managed to define a typology of B2C networks consisting of three types: (1) publics – centered around and dominated by a company, with no relationships between consumers themselves, creating value through crowd-sourcing; (2) communities – also centered around a company, but independent to a degree and more focused on consumer-to-consumer (C2C) relationships, creating value through consumer-managed projects; and (3) tribes – where companies serve only as peripheral actors, and their products – as potential symbols of affiliation, with value being created through creation and reinterpretation of the said products’ meanings (sign value).

Ocular Dominance and Functional Asymmetry in Visual Attention Networks.

PurposeThe dorsal attention network (DAN) and the ventral attention network (VAN) are known to support visual attention, but the influences of ocular dominance on the attention networks are unclear. We aimed to explore how visual cortical asymmetry of the attention networks correlate with neurophysiological oscillation and connectivity markers of attentional processes.MethodsAn oddball task with concentric circle stimuli of three different sizes (i.e., spot size of 5°, 20°, or 30° of visual angle) was used to vary task difficulty. Event-related oscillations and interareal communication were tested with an electroencephalogram-based visual evoked components as a function of ocular dominance in 30 healthy subjects.ResultsAccuracy rates were higher in the dominant eyes compared with the nondominant eyes. Compared with the nondominant eyes, the dominant eyes had higher theta, low-alpha, and low-beta powers and lower high-alpha powers within the nodes of VAN and DAN. Furthermore, visual information processed by the dominant and nondominant eye had different fates, that is, the dominant eyes mainly relied on theta and low-alpha connectivity within both the VAN and the DAN, whereas the nondominant eyes mainly relied on theta connectivity within the VAN and high-alpha connectivity within the DAN. The difference in accuracy rate between the two eyes was correlated with the low-alpha oscillations in the anterior DAN area and low-alpha connectivity of the left DAN.ConclusionsThe ocular dominance processing and interareal communication reveal a cortical asymmetry underlying attention, and this reflects a two-way modulatory mechanism within attention networks in the human brain.

The network asymmetry caused by the degree correlation and its effect on the bimodality in control

Our ability to control a whole network can be achieved via a small set of driver nodes. While the minimum number of driver nodes needed for control is fixed in a given network, there are multiple choices for the driver node set. A quantity used to investigate this multiplicity is the fraction of redundant nodes in the network, referring to nodes that do not need any external control. Previous work has discovered a bimodality feature characterized by a bifurcation diagram: networks with the same statistical property would stay with equal probability to have a large or small fraction of redundant nodes. Here we find that this feature is rooted in the symmetry of the directed network, where both the degree distribution and the degree correlation can play a role. The in-in and out-out degree correlation will suppress the bifurcation, as networks with such degree correlations are asymmetric under network transpose. The out-in and in-out degree correlation do not change the network symmetry, hence the bimodality feature is preserved. However, the out-in degree correlation will change the critical average degree needed for the bifurcation. Hence by fixing the average degree of networks and tuning out-in degree correlation alone, we can observe a similar bifurcation diagram. We conduct analytical analyses that adequately explain the emergence of bimodality caused by out-in degree correlation. We also propose a quantity, taking both degree distribution and degree correlation into consideration, to predict if a network would be at the upper or lower branch of the bifurcation. As is well known that most real networks are not neutral, our results extend our understandings of the controllability of complex networks.

Cognitive Radio using Spectrum Sensing by Cooperative Two Secondary Users

In cognitive radio systems, users that are cognitive (unlicensed) must constantly scan the spectrum for primary (licensed) users to be present. In this article, the researchers display the benefits of cognitive radio collaboration. It is demonstrate that detection times can be reduced by motivating cognitive users to cooperate in the same band, thus increasing overall agility. Next, a cognitive two-user radio network is depicted and a method to exploit the inherent network asymmetry to improve agility. It is shown that our cooperation program improves the efficiency of cognitive users by 35%. The researchers then extend the cooperation scheme to two user-per-carrier multi-corporate networks and calculate the gain in asymptotic agility.

Asymmetry of Regional Phase Synchrony Cortical Networks Under Cognitive Alertness and Vigilance Decrement States.

This study investigates intra-regional connectivity and regional hemispheric asymmetry under two vigilance states: alertness and vigilance decrement. The vigilance states were induced on nine healthy subjects while performing 30 min in-congruent Stroop color-word task (I-SCWT). We measured brain activity using Electroencephalography (EEG) signals with 64-channels. We quantified the regional network connectivity using the phase-locking value (PLV) with graph theory analysis (GTA) and Support Vector Machines (SVM). Results showed that the vigilance decrement state was associated with impaired information processing within the frontal and central regions in delta and theta frequency bands. Meanwhile, the hemispheric asymmetry results showed that the laterality shifted to the right-temporal in delta, right-central, parietal, and left frontal in theta, right-frontal and left-central, temporal and parietal in alpha, and right-parietal and left temporal in beta frequency bands. These findings represent the first demonstration of intra-regional connectivity and hemispheric asymmetry changes as a function of cognitive vigilance states. The overall results showed that vigilance decrement is region and frequency band-specific. Our SVM model achieved the highest classification accuracy of 99.73% in differentiating between the two vigilance states based on the frontal and central connectivity networks measures.

Emergent cell-free layer asymmetry and biased haematocrit partition in a biomimetic vascular network of successive bifurcations.

Blood is a vital soft matter, and its normal circulation in the human body relies on the distribution of red blood cells (RBCs) at successive bifurcations. Understanding how RBCs are partitioned at bifurcations is key for the optimisation of microfluidic devices as well as for devising novel strategies for diagnosis and treatment of blood-related diseases. We report the dynamics of RBC suspensions flowing through a biomimetic vascular network incorporating three generations of microchannels and two classical types of bifurcations at the arteriole level. Our microfluidic experiments with dilute and semidilute RBC suspensions demonstrate the emergence of excessive heterogeneity of RBC concentration in downstream generations upon altering the network's outflow rates. Through parallel simulations using the immersed-boundary-lattice-Boltzmann method, we reveal that the heterogeneity is attributed to upstream perturbations in the cell-free layer (CFL) and lack of its recovery between consecutive bifurcations owing to suppressed hydrodynamic lift under reduced flow conditions. In the dilute/semidilute regime, this perturbation dominates over the effect of local fractional flow at the bifurcation and can lead to inherently unfavourable child branches that are deprived of RBCs even for equal flow split. Our work highlights the importance of CFL asymmetry cascading down a vascular network, which leads to biased phase separation that deviates from established empirical predictions.

Asymmetric Interactions of Seed–Predation Network Contribute to Rare‐Species Advantage

Tephritid flies (Diptera, Tephritidae) are common predispersal seed predators of Asteraceae plants. Adult females lay eggs into budding flowerheads, and their larvae consume developing seeds before emergence. After investigating which fly species fed on which plant species and estimating how much damage a fly species incurred in each plant species in an alpine meadow, we constructed a plant-fly network and calculated network asymmetry and seed loss in each species. Results show that asymmetric interactions between plant and fly species result in a higher seed loss in dominant plant species than in rare species. Thus, a rare-species advantage exists, which facilitates species coexistence in local communities. These photographs illustrate the article “Asymmetric interactions of seed–predation network contribute to rare-species advantage” by Xi, X., Y., Yang, J. M., Tylianakis, S., Yang, Y., Dong, and S., Sun published in Ecology. https://doi.org/10.1002/ecy.3050