Symmetric Network Research Articles

Hemispheric Network Expression in Parkinson's Disease: Relationship to Dopaminergic Asymmetries.

Parkinson's disease (PD) is characterized by brain metabolic networks, specifically associated with motor and cognitive manifestations. Few studies have investigated network changes in cerebral hemispheres ipsilateral and contralateral to the clinically more affected body side. We examined hemispheric network abnormalities and their relationship to striatal dopaminergic deficits in PD patients at different stages. 45 PD patients underwent dual-tracer positron emission tomography (PET) with 18F-fluorodeoxyglucose (FDG) and 18F-fluorodopa (FDOPA) in a high-resolution PET scanner. In all patients, we computed expression levels for the PD-related motor/cognition metabolic patterns (PDRP/PDCP) as well as putamen/caudate FDOPA uptake values in both hemispheres. Resulting hemispheric measures in the PD group were compared with corresponding healthy control values and assessed across disease stages. Hemispheric PDRP and PDCP expression was significantly elevated contralateral and ipsilateral to the more affected body side in patients with unilateral symptoms (H&Y 1: p < 0.01) and in patients with bilateral limb involvement (H&Y 2-3: p < 0.001; H&Y 4: p < 0.003). Elevations in pattern expression were symmetrical at all disease stages. By contrast, FDOPA uptake in the caudate and putamen was reduced bilaterally (p < 0.002), with lower values on both sides at more advanced disease stages. Hemispheric uptake was asymmetrical in both striatal regions, with lower contralateral values at all disease stages. The magnitude of hemispheric uptake asymmetry was smaller with more advanced disease, reflecting greater change ipsilaterally. Symmetrical network expression in PD represents bilateral functional effects unrelated to nigrostriatal dopaminergic asymmetries.

Distributed changes of the functional connectome in patients with glioblastoma

Glioblastoma might have widespread effects on the neural organization and cognitive function, and even focal lesions may be associated with distributed functional alterations. However, functional changes do not necessarily follow obvious anatomical patterns and the current understanding of this interrelation is limited. In this study, we used resting-state functional magnetic resonance imaging to evaluate changes in global functional connectivity patterns in 15 patients with glioblastoma. For six patients we followed longitudinal trajectories of their functional connectome and structural tumour evolution using bi-monthly follow-up scans throughout treatment and disease progression. In all patients, unilateral tumour lesions were associated with inter-hemispherically symmetric network alterations, and functional proximity of tumour location was stronger linked to distributed network deterioration than anatomical distance. In the longitudinal subcohort of six patients, we observed patterns of network alterations with initial transient deterioration followed by recovery at first follow-up, and local network deterioration to precede structural tumour recurrence by two months. In summary, the impact of focal glioblastoma lesions on the functional connectome is global and linked to functional proximity rather than anatomical distance to tumour regions. Our findings further suggest a relevance for functional network trajectories as a possible means supporting early detection of tumour recurrence.

A node-based informed modularity strategy to identify organizational modules in anatomical networks.

ABSTRACTThe study of morphological modularity using anatomical networks is growing in recent years. A common strategy to find the best network partition uses community detection algorithms that optimize the modularity Q function. Because anatomical networks and their modules tend to be small, this strategy often produces two problems. One is that some algorithms find inexplicable different modules when one inputs slightly different networks. The other is that algorithms find asymmetric modules in otherwise symmetric networks. These problems have discouraged researchers to use anatomical network analysis and boost criticisms to this methodology. Here, I propose a node-based informed modularity strategy (NIMS) to identify modules in anatomical networks that bypass resolution and sensitivity limitations by using a bottom-up approach. Starting with the local modularity around every individual node, NIMS returns the modular organization of the network by merging non-redundant modules and assessing their intersection statistically using combinatorial theory. Instead of acting as a black box, NIMS allows researchers to make informed decisions about whether to merge non-redundant modules. NIMS returns network modules that are robust to minor variation and does not require optimization of a global modularity function. NIMS may prove useful to identify modules also in small ecological and social networks.

User Behavior on Online Social Networks: Relationships among Social Activities and Satisfaction

Social networking sites (SNSs) are now ubiquitous communities for constant online interpersonal interactions that trigger symmetric or asymmetric effects on our everyday life. Recent studies advocate in favor of the significant role that SNSs have in promoting well-being and, more importantly, in disseminating reliable information during a global crisis, such as the current COVID-19 pandemic. Based on the growing importance of SNSs to the global framework, the main purpose of this study is to empirically assess the link between the use of symmetric social networks such as Facebook, or asymmetric social networks, like Instagram, and the level of satisfaction, employing the methodology of structural equation modeling. The results of the research validate the hypothesis that SNS activities increase the level of satisfaction, and therefore, that there is a direct link between the number of posts and comments and the level of satisfaction. Furthermore, based on the reversible and significant link between the level of satisfaction and the importance attributed to SNSs, the main conclusion of the study is that the higher the importance of the SNS, the greater the level of dissatisfaction experienced by users. Also, public activities on social networks positively affect social network satisfaction, while private activities have a direct negative relationship with the importance of social networks.

BSN MOT: A Fast and Cost-efficient Optoelectronic Architecture

Recently, an efficient recursive and symmetrical optoelectronic interconnection network based on biswapped framework named “Biswapped-Hyper Hexa-Cell (BSN HHC)” is reported. In spite of containing a prime advantage concerning network diameter compared to its competitive Biswapped-Mesh (BSN Mesh) optoelectronic architecture; a major limitation of the network is its high cost. In a search for a competitive alternative variation of this network, a fast and a cost-efficient member of biswapped family named “Biswapped-Mesh of Trees (BSN MOT)” is proposed here. The suggested BSN MOT optoelectronic network claims to be a better alternative of counter-part OTIS-Mesh of Trees (OTIS MOT) optoelectronic network, claims advantages concerning diameter, minimum node degree, and network total and optical cost (total cost drops about 10–30% and optical cost drops about 50%). More specifically, compared to competitive Biswapped-Mesh and BSN HHC optoelectronic networks, the proposed network has a major advantage concerning network diameter. Moreover, BSN MOT is a cost-efficient architecture, where total network cost drops about 30–45% than that of BSN HHC. Besides, it is a better alternative for processing numerical problems that require network with “low static maximum node degree”. Thus, on the basis of above-mentioned properties, the proposed network claims to be the most economical and the fastest variation of biswapped framework.

Subspace controllability of symmetric spin networks

Controlling the dynamics of quantum systems has been a long-standing goal of quantum physics. To understand the controllability of a finite-dimensional quantum system is a fundamental problem in quantum control theory, but can be very challenging in many cases. In particular, results on controllability of open-loop coherently controlled closed quantum systems under symmetry constraints have been elusive. In this paper, we study the controllability of spin systems with some specific symmetry where spins are coupled via Ising interaction and manipulated collectively by a global control field. Based on Lie group and Lie algebra techniques, we find the conditions under which the system is universally controllable in its irreducible subspaces. We believe that the results obtained here will contribute to quantum controlling on symmetric quantum systems.

Extraordinary characteristics of one-dimensional PT-symmetric ring optical waveguide networks with near-isometric and isometric arms

We construct one-dimensional -symmetric ring optical waveguide networks with near-isometric and isometric arms and investigate their extraordinary optical properties. It is found that the features for the former and the latter are quite different, although extraordinary ultrastrong transmission and reflection exist in both of them. When the former changes to be the latter, the extraordinary optical properties will all change remarkably, and the fitting formulae are obtained. The bidirectional invisibility only exists in the former and the principle is analyzed. This work may be helpful for designing ultra-narrow optical filters, ultra-sensitive optical switches, and resonant component for high-monochromatic lasing.

Fisheye Rectified Image Super-resolution Using Deep Symmetrical Neural Network

Influenced by the fast developments of imaging technologies, fisheye cameras have been widely applied. But the resolution of images captured by fisheye cameras is limited due to lens distortions. In our work, we use a deep symmetrical network model to deal with single fisheye rectified image super-resolution task. It is shown by the experiments that the proposed method can achieve satisfactory results for fisheye rectified image super-resolution.

Collective Dynamics and Bifurcations in Symmetric Networks of Phase Oscillators. I

The present paper is a brief survey of the history and development of the famous Kuramoto model of coupled phase oscillators. We consider several systems generalizing the classical Kuramoto model and given on symmetric oscillatory networks with different functions of interaction between the elements. We describe the collective dynamics and bifurcations of transitions between different modes of interacting elements, namely: partial and complete synchronization, global antiphase mode, slow switching, and chimera states. We show the relationship between the symmetries of networks and the existence of invariant manifolds of the system, cluster states, and more complicated collective behaviors. In part II, we plan to consider several models with nonglobal symmetric coupling.

Research on Accurate House Price Analysis by Using GIS Technology and Transport Accessibility: A Case Study of Xi’an, China

Based on the symmetrical public transportation network data of Xi’an, China obtained by geographic information system (GIS) technology in 2019, three urban public transportation indexes of walking accessibility, bus accessibility, and metro accessibility were established, and a real estate price prediction model was built by using several machine learning algorithms to predict and analysis the housing price in Xi’an, China. Firstly, the symmetrical road network data and real estate property data of Xi’an were collected and preprocessed, secondly, the spatial syntax theory and distance calculation method were applied to establish three indexes of traffic accessibility; finally, taking the house property data and the calculated traffic accessibility indexes as the characteristic index, the real estate price prediction model of Xi’an was constructed by using the random forest algorithm (RF), lightweight gradient lift algorithm (LGBM), and gradient lifting regression tree algorithm (GBDT). The prediction accuracy of the final model is 89.2% and the root-mean-square error is 1761.84. The results show that the accessibility of bus and metro to some extent represent the convenience of public transportation in different areas of urban space. The higher the accessibility index is, the more convenient the traffic is. The real estate price model has high prediction accuracy and can reflect the real situation of urban real estate price. The importance of the three accessibility features to the real estate price prediction model are nearly more than 20%, which indicates that the accessibility of urban public transportation has an important impact on the change of urban real estate price, and the development of urban public transportation plays an important role in the real estate economy.

Half-duplex multiple-relay multiple-antenna diamond networks with perfect full-duplex performance

Diversity-multiplexing tradeoff (DMT) of a full-duplex diamond network where the inter-relay interference (IRI) is suppressed, global channel state information (CSI) is available in all nodes, and perfect self-interference cancellation (SIC) technique is used, which we call it the perfect full-duplex diamond network, is an upper bound for the DMT of the equivalent half-duplex diamond network, i.e., the perfect full-duplex diamond network outperforms the half-duplex case. Nevertheless, the implementation of the perfect full-duplex diamond network is a high-cost operation and requires significant hardware changes in current systems. The main question addressed in this paper is: under which condition (or perhaps conditions) does a prevalent half-duplex diamond network provide the same performance as the perfect full-duplex one? In this regard, first, we calculate the DMT of a perfect full-duplex multiple-relay and multiple-antenna (MRMA) symmetric diamond network. Then, we demonstrate that for the same network but with half-duplex relays, when the number of antennas in relays satisfies a specific condition, there exists a fixed listen-transmit schedule combined with quantize-map-and-forward (QMF) relaying to achieve the optimal DMT. Also, we attain a lower bound for the total number of antennas of the MRMA symmetric diamond network to achieve the optimal DMT.

Symmetrical Graph Neural Network for Quantum Chemistry with Dual Real and Momenta Space.

Most of the current neural network models in quantum chemistry (QC) exclude the molecular symmetry and separate the well-correlated real space (R space) and momenta space (K space) into two individuals, which lack the essential physics in molecular chemistry. In this work, by endorsing the molecular symmetry and elementals of group theory, we propose a comprehendible method to apply symmetry in the graph neural network (SY-GNN), which extends the property-predicting coverage to orbital symmetry for both ground and excited states. SY-GNN is an end-to-end model that can predict multiple properties in both K and R space within a single model, and it shows excellent performance in predicting both the absolute and relative R and K space quantities. Besides the numerical properties, SY-GNN can also predict orbital properties, providing the active regions of chemical reactions. We believe the symmetry-endorsed deep learning scheme covers the significant physics inside and is essential for the application of neural networks in QC and many other research fields in the future.

Secure impulsive synchronization in Lipschitz-type multi-agent systems subject to deception attacks

Cyber attacks pose severe threats on synchronization of multi-agent systems. Deception attack, as a typical type of cyber attack, can bypass the surveillance of the attack detection mechanism silently, resulting in a heavy loss. Therefore, the problem of mean-square bounded synchronization in multi-agent systems subject to deception attacks is investigated in this paper. The control signals can be replaced with false data from controller-to-actuator channels or the controller. The success of the attack is measured through a stochastic variable. A distributed impulsive controller using a pinning strategy is redesigned, which ensures that mean-square bounded synchronization is achieved in the presence of deception attacks. Some sufficient conditions are derived, in which upper bounds of the synchronization error are given. Finally, two numerical simulations with symmetric and asymmetric network topologies are given to illustrate the theoretical results.

Isolation Improvement of Compact Microbase Station Antenna Based on Metasurface Spatial Filtering

A compact high-isolation multiple-input multiple-output (MIMO) microbase station antenna consisting of two dual-polarized antenna elements is presented. The symmetrical magnetic coupling feeding network is used to feed the radiation patch of the dual-polarized antenna and to improve the isolation of the antenna element. By introducing parasitic square patches, the radiation characteristics and isolation of the dual-polarized antenna are improved. In addition, a modified closed-loop metasurface unit is designed and used to construct an isolation wall. When dual-polarized antenna elements are used to assemble a MIMO antenna, the proposed metasurface structure is inserted between them to reduce the coupling between MIMO antenna elements, regarded as a spatial band-stop filter to suppress the spatial electromagnetic waveform of the elements. After loading the metasurface isolation wall, the maximum increase of isolation is 9.17 dB at the center frequency point of 2.6 GHz. For antenna element ports and cross-polarized ports, the isolations are greater than 30 dB, and the isolation of the same polarized ports is better than 25 dB within the frequency band from 2.45 to 2.8 GHz. At the same time, the reflection coefficients of the ports of the elements are less than -15 dB. Finally, a prototype of the proposed MIMO microbase station antenna was fabricated and tested, and the experimental results are proximity with the simulation responses.

A network model for stemflow solute transport

While the role of stemflow in directing and concentrating water and nutrients at the tree base is rarely in dispute, its mathematical representation remains a subject of inquiry and research. A network model that seeks to estimate stemflow solute concentration and leaching is proposed. The model accommodates the physico-chemical properties of individual furrows embedded within the tree bark and their interconnections. The within-furrow equations for water and solute transport that include leaching are first developed and integrated along a rough-bark network topology to describe solute concentration and fluxes out of the network. The model is parameterized using published data on stemflow, field measurements of bark geometry, and laboratory experiments on bark leaching for potassium, magnesium, and calcium. The parameterization is intended to impose plausibility constraints and not to test model predictions at a particular site, a single event, or an individual experiment. The outflow concentration is then analyzed as a function of the network complexity that includes asymmetry in the lengths or subpaths connecting network nodes. For a symmetric network, an effective ’channel-flow’ analogy may be used to represent solute concentration at the outflow. However, as the asymmetry increases in subpath lengths, the efficiency of the bark network at moving solutes diminishes for the same rainfall input onto the stem. The network representation featured here is by no means offering a ’finality’ to the stemflow mathematical representation. It must be viewed as an embryonic step that opens up the possibility of using modern advances in network theories to link rainfall properties to stemflow water and solute input from a variety of tree species with differing bark microrelief configurations into the soil.

Semi‐supervised long short‐term memory for human action recognition

In real human action recognition task, it is a common phenomenon that there are many unlabelled samples and few labelled samples. How to make good use of unlabelled samples to improve the generalisation ability of models is the focus of semi-supervised learning research. In this study, the authors present two semi-supervised methods based on long short-term memory (LSTM) to learn discriminative hidden features. One is the LSTM ladder network, the other is the Symmetrical LSTM network. By them unlabelled samples can be used automatically to improve learning performance without relying on external interaction. Both on the NTU-RGB + D dataset and the Kinetics dataset, their methods achieve >10 and 5% improvements, separately.

Cloud Detection for Satellite Imagery Using Attention-Based U-Net Convolutional Neural Network

Cloud detection is an important and difficult task in the pre-processing of satellite remote sensing data. The results of traditional cloud detection methods are often unsatisfactory in complex environments or the presence of various noise disturbances. With the rapid development of artificial intelligence technology, deep learning methods have achieved great success in many fields such as image processing, speech recognition, autonomous driving, etc. This study proposes a deep learning model suitable for cloud detection, Cloud-AttU, which is based on a U-Net network and incorporates an attention mechanism. The Cloud-AttU model adopts the symmetric Encoder-Decoder structure, which achieves the fusion of high-level features and low-level features through the skip-connection operation, making the output results contain richer multi-scale information. This symmetrical network structure is concise and stable, significantly enhancing the effect of image segmentation. Based on the characteristics of cloud detection, the model is improved by introducing an attention mechanism that allows model to learn more effective features and distinguish between cloud and non-cloud pixels more accurately. The experimental results show that the method proposed in this paper has a significant accuracy advantage over the traditional cloud detection method. The proposed method is also able to achieve great results in the presence of snow/ice disturbance and other bright non-cloud objects, with strong resistance to disturbance. The Cloud-AttU model proposed in this study has achieved excellent results in the cloud detection tasks, indicating that this symmetric network architecture has great potential for application in satellite image processing and deserves further research.

Depth image super-resolution using correlation-controlled color guidance and multi-scale symmetric network

Depth image super-resolution (DISR) is an effective solution to improve the quality of depth images captured by real world low-cost cameras. In this paper, we propose a multi-scale symmetric network with the correlation-controlled color guidance block (CCGB) for DISR. The proposed network consists of two multi-scale sub-networks to respectively provide guidance and estimate depth. A symmetric unit (SU), which is a mini-encoder-decoder structure with residual learning, is designed and used as a basic network atom. The encoder part in SU aims to extract essential features, while the decoder part works to restore edge details. The way the SU processes information matches well with the textureless and sharp-edge characteristics of depth images. The two sub-networks present a high-level symmetric structure connected by dense guidance links in between. Based on the correlation analyses between the two sub-networks, each guidance link will transfer information trough a CCGB designed to implement channel-wise re-weighting mechanism. The accurate color guidance from CCGB helps avoiding artifacts introduced by non-co-occurrence of depth discontinuities and color edges. Experimental results demonstrate the superiority of the proposed method over several state-of-the-art DISR works.

Functional imaging evidence for task-induced deactivation and disconnection of a major default mode network hub in the mouse brain

The default mode network (DMN) has been defined in functional brain imaging studies as a set of highly connected brain areas, which are active during wakeful rest and inactivated during task-based stimulation. DMN function is characteristically impaired in major neuropsychiatric diseases, emphasizing its interest for translational research. However, in the mouse, a major preclinical rodent model, there is still no functional imaging evidence supporting DMN deactivation and deconnection during high-demanding cognitive/sensory tasks. Here we have developed functional ultrasound (fUS) imaging to properly visualize both activation levels and functional connectivity patterns, in head-restrained awake and behaving mice, and investigated their modulation during a sensory-task, whisker stimulation. We identified reproducible and highly symmetric resting-state networks, with overall connectivity strength directly proportional to the wakefulness level of the animal. We show that unilateral whisker stimulation leads to the expected activation of the contralateral barrel cortex in lightly sedated mice, while interhemispheric inhibition reduces activity in the ipsilateral barrel cortex. Whisker stimulation also leads to elevated bilateral connectivity in the hippocampus. Importantly, in addition to functional changes in these major hubs of tactile information processing, whisker stimulation during genuine awake resting-state periods leads to highly specific reductions both in activation and interhemispheric correlation within the restrosplenial cortex, a major hub of the DMN. These results validate an imaging technique for the study of activation and connectivity in the lightly sedated awake mouse brain and provide evidence supporting an evolutionary preserved function of the DMN, putatively improving translational relevance of preclinical models of neuropsychiatric diseases.

Flow field reconstruction and prediction of the supersonic cascade channel based on a symmetry neural network under complex and variable conditions

A data-driven model containing a symmetrical deep neural network is proposed to reconstruct the flow field structure in a cascade channel by measuring discrete pressure values on the wall of the supersonic cascade channel. The model designed is to demonstrate that the deep neural network can realize the reconstruction and prediction of the flow field structure in the supersonic cascade channel under complicated and changing working conditions. The dataset used for model training is derived from numerical simulation of the supersonic cascade channel. The symmetrical model includes a transposed convolution part and a conventional convolution part, which, respectively, implement up-sampling of the pressure data and further extraction of features. The generalization ability and scalability of the model are analyzed from the contour plots of the pressure and density gradient. In order to verify the ability of the model to reconstruct unknown operating conditions, the organizational form of the training set and testing set has been specially designed to achieve the ability of interpolating outwards. In the testing set, the symmetrical model has a certain ability to realize extrapolation and prediction, and the flow field structure can be accurately reconstructed by using the discrete pressure values on the wall surface of the cascade channel. Moreover, to accurately evaluate the regression model proposed by this study, the correlation analysis was also applied in this study. The results show that the worst linear correlation coefficient is 0.9848 in the testing set, indicating that the model has satisfactory ability to reconstruct and predict the flow field.