Pair Of Nodes Research Articles

SWIPT Enabled Cooperative Cognitive Radio Sensor Network With Non-Linear Power Amplifier

In this work, performance of a simultaneous wireless information and power transfer (SWIPT) enabled cooperative cognitive radio sensor network (CRSN) is explored over generalized Nakagami-m faded channels. A time switching (TS) protocol for SWIPT is used and the impact of TS factor on energy harvesting and information transmission phases, and information processing and broadcasting phases are considered. The cooperative CRSN consists of a pair of primary nodes (PNs) and sensor nodes (SNs), where one of the SWIPT enabled SN provides cooperative relaying for PNs communication by utilizing amplify-and-forward protocol. The overlay spectrum sharing protocol is used at the SN, which supports PNs communication along with its own transmission and resolves the spectrum scarcity issue. The impact of a non-linear power amplifier (NLPA) is also considered on the network performance. The analytical expressions of outage probability (OP), asymptotic OP, system throughput, and energy efficiency are obtained over Nakagami-m faded channels for arbitrary m. Further, the analytical expressions of ergodic capacity (EC) and asymptotic EC are obtained. The impact of NLPA and other system parameters on the performance of the considered network are highlighted. Finally, the obtained analytical expressions are validated through Monte-Carlo simulations.

Zeeman Field-Induced Two-Dimensional Weyl Semimetal Phase in Cadmium Arsenide.

We report a topological phase transition in quantum-confined cadmium arsenide (Cd_{3}As_{2}) thin films under an in-plane Zeeman field when the Fermi level is tuned into the topological gap via an electric field. Symmetry considerations in this case predict the appearance of a two-dimensional Weyl semimetal (2D WSM), with a pair of Weyl nodes of opposite chirality at charge neutrality that are protected by space-time inversion (C_{2}T) symmetry. We show that the 2D WSM phase displays unique transport signatures, including saturated resistivities on the order of h/e^{2} that persist over a range of in-plane magnetic fields. Moreover, applying a small out-of-plane magnetic field, while keeping the in-plane field within the stability range of the 2D WSM phase, gives rise to a well-developed odd integer quantum Hall effect, characteristic of degenerate, massive Weyl fermions. A minimal four-band k·p model of Cd_{3}As_{2}, which incorporates first-principles effective g factors, qualitatively explains our findings.

Integrating Node Importance and Network Topological Properties for Link Prediction in Complex Network

Link prediction is one of the most important and challenging tasks in complex network analysis, which aims to predict the existence of unknown links based on the known information in the network. As critical topological properties in the network, node’s degree and clustering coefficient are well-suited for describing the tightness of connection between nodes. The importance of node can affect the possibility of link existence to a certain extent. By analyzing the impact of different centrality on links, which concluded that the degree centrality and proximity centrality have the greatest influence on network link prediction. A link prediction algorithm combines importance of node and network topological properties, called DCCLP, is proposed in this paper, the symmetry of the adjacency matrix is considered in the DCCLP link prediction algorithm to further describe the structural similarity of network nodes. In the training phase of the DCCLP algorithm, the maximized AUC indicator in the training set as the objective, and the optimal parameters are estimated by utilizing the White Shark Optimization algorithm. Then the prediction accuracy of the DCCLP algorithm is evaluated in the test set. By experimenting on twenty-one networks with different scales, and comparing with existing algorithms, the experimental results show that the effectiveness and feasibility of DCCLP algorithm, and further illustrate the importance of the degree centrality of node pairs and proximity centrality of nodes to improve the prediction accuracy of link prediction.

Electrophysiological markers for anticipatory processing of nocebo-augmented pain.

Nocebo effects on pain are widely thought to be driven by negative expectations. This suggests that anticipatory processing, or some other form of top-down cognitive activity prior to the experience of pain, takes place to form sensory-augmenting expectations. However, little is known about the neural markers of anticipatory processing for nocebo effects. In this event-related potential study on healthy participants (n = 42), we tested whether anticipatory processing for classically conditioned nocebo-augmented pain differed from pain without nocebo augmentation using stimulus preceding negativity (SPN), and Granger Causality (GC). SPN is a slow-wave ERP component thought to measure top-down processing, and GC is a multivariate time series analysis used to measure functional connectivity between brain regions. Fear of pain was assessed with the Fear of Pain Questionnaire-III and tested for correlation with SPN and GC metrics. We found evidence that both anticipatory processing measured with SPN and functional connectivity from frontal to temporoparietal brain regions measured with GC were increased for nocebo pain stimuli relative to control pain stimuli. Other GC node pairs did not yield significant effects, and a lag in the timing of nocebo pain stimuli limited interpretation of the results. No correlations with trait fear of pain measured after the conditioning procedure were detected, indicating that while differences in neural activity could be detected between the anticipation of nocebo and control pain trials, they likely were not related to fear. These results highlight the role that top-down processes play in augmenting sensory perception based on negative expectations before sensation occurs.

Enhanced contrastive learning with multi-aspect information for recommender systems

In recent years, graph neural networks (GNNs), as the state-of-the-art technology have advanced the solutions of recommender systems (RSs). However, the representations of nodes obtained from GNN-based models are always suboptimal when facing sparse interaction data. Contrastive learning on graphs has been proven to be effective in alleviating the influence of data sparsity. Most related works perform contrastive learning through self-discrimination. As there is extensive homophily among nodes in RSs, we consider it irrational to ignore the self-supervised signal from neighboring nodes, which is crucial for optimizing the embedding representation of nodes. To address these issues, we propose a model named enhanced contrastive learning with multiaspect information (ECMI) for RSs, which aims to capture the self-supervised signals from both the nodes themselves and their neighbors. Specifically, we conducted contrastive learning in a two-layer space separately. We consider structurally connected homophilic node pairs as positive samples in explicit space while introducing a social relationship network and an item entity network in implicit space. These two networks can be combined with the interaction network to conduct a two-channel training procedure. This procedure can build more effective contrastive learning by exploring the similar potential neighbors of nodes. We conduct experiments on four public datasets, and the results consistently validate the effectiveness of the ECMI.

Enhanced energy efficient sleep awake aware intelligent sensor network routing protocol for wireless sensor network

One of the major challenge in Wireless Sensor Networks (WSN’s) deployment is efficient energy consumption. Critical distance, proper routing algorithm and error control coding techniques can be used for energy optimization. As WSN contains a large number of power constrained sensors, the sensed data from the environment should be transmitted in a cooperative way to the base station (BS). The pattern of the clustering structure can extend the sensor network life time, reduce the total consumed energy and regulate the data transmission. Clustering concept combines group of sensors which are located in the same communication range. Some of the routing protocol like, SEED, LEACH, SEP, Z-SEP etc., suffers from idle listening problem, which cannot cope with an environment with sensor nodes. It leads to energy wastage across the network. To manage energy efficiency and traffic heterogeneity issues, a new routing protocol called enhanced energy efficient sleep awake aware intelligent sensor network (EEESAA) is proposed. Here, one sensor in each group will be in active mode whereas other sensors entered in sleep mode. Based on the nodes energy, sleep and awake node pairs will be altered. In the proposed method, one slot is allotted for group of pairs. The proposed approach is evaluated and compared against LEACH, SEP and Z-SEP protocols. Simulation results show that EEESAA protocol performs better than LEACH, SEP, Z-SEP in terms of cluster head selection, throughput, number of alive & dead nodes and network lifetime.

Robustness of interdependent higher-order networks

In real complex systems, interactions occur not only between a pair of nodes, but also in groups of three or more nodes, which can be abstracted as higher-order structures in the networks. The simplicial complex is one of a model to represent systems with both low-order and higher-order structures. In this paper, we study the robustness of interdependent simplicial complexes under random attacks, where the complementary effects of the higher-order structure are introduced. When a higher-order node in a 2-simplex fails, its dependent node in the other layer survives with a certain probability due to the complementary effects from the 2-simplex. By using the percolation method, we derive the percolation threshold and the size of the giant component when the cascading failure reaches its steady state. The simulation results agree well with analytical predictions. We find that the type of phase transition changes from the first-order to the second-order when the complementary effect of the higher-order structure on the dependent node increases or the number of 2-simplices in the interdependent simplicial complex increases. While the interlayer coupling strength increases, the type of phase transition changes from the second-order to the first-order. In particular, even if the higher-order interactions do not provide complementary effects for dependent nodes, the robustness of the interdependent heterogeneous simplicial complex is higher than that of the ordinary interdependent network with the same average degree due to the existence of 2-simplices in the system. This study furthers our understanding in the robustness of interdependent higher-order networks.

Graph Representation Learning With Adaptive Metric

Contrastive learning has been widely used in graph representation learning, which extracts node or graph representations by contrasting positive and negative node pairs. It requires node representations (embeddings) to reflect their correlations in topology, increasing the similarities between an anchor node and its positive nodes, or reducing the similarities with its negative nodes in embedding space. However, most existing contrastive models measure similarities through a fixed metric that equally scores all sample pairs in a specific feature space, but ignores the varieties of node attributes and network topologies. Moreover, these fixed metrics are always defined explicitly and manually, which makes them unsuitable for applying to all graphs and networks. To solve these problems, we propose a novel graph representation learning model with an adaptive metric, called GRAM, which produces appropriate similarity scores of node pairs according to the different significance of each dimension in their embedding vectors and adaptive metrics based on data distribution. With these scores, it is better to train a graph encoder and obtain representative embeddings. Experimental results show that GRAM has strong competitiveness in multiple tasks.

Attribute Augmented Network Embedding Based on Generative Adversarial Nets.

Network embedding is to learn low-dimensional representations of nodes while preserving necessary information for network analysis tasks. Though representations preserving both structure and attribute features have achieved in many real-world applications, learning these representations for networks with attribute information is difficult due to the heterogeneity between structure and attribute information. Many existing methods have been proposed to preserve explicit proximities between nodes, with optimization limited to node pairs with large structure and attribute proximities, which may lead to overfitting. To address the above problems, we adopt an attribute augmented network to represent attribute and structure information in a unified framework. Specifically, we study the problem of attribute augmented network embedding that exploits the strength of generative adversarial nets (ANGANs) in capturing the latent distribution of data to learn robust and informative representations of nodes. The ANGAN method obtains the low-dimensional representations of nodes through adversarial learning between the generative and discriminative models. The generative model approximates the underlying connectivity and attributes distributions of nodes by using the distributions generated from the learned representations. It is implemented by utilizing the properties of the attribute augmented network to improve the traditional Skip-gram model. The discriminative model is designed as a binary classifier to distinguish the truly connected node pairs from the generated ones. The pre-training algorithm and the teacher forcing approach are adopted to improve training efficiency and stability. Empirical results show that ANGAN generally outperforms state-of-the-art methods in various real-world applications, which demonstrates the effectiveness and generality of our method.

Cellular dynamics in tumour microenvironment along with lung cancer progression underscore spatial and evolutionary heterogeneity of neutrophil.

The cellular dynamics in the tumour microenvironment (TME) along with non-small cell lung cancer (NSCLC) progression remain unclear. Multiplex immunofluorescence test detecting 10 immune-related markers on 553 primary tumour (PT) samples of NSCLC was conducted and spatial information in TME was assessed by the StarDist depth learning model. The single-cell transcriptomic atlas of PT (n=4) and paired tumour-draining lymph nodes (TDLNs) (n=5 for tumour-invaded, n=3 for tumour-free) microenvironment was profiled. Various bioinformatics analyses based on Gene Expression Omnibus, TCGA and Array-Express databases were also used to validate the discoveries. Spatial distances of CD4+ T cells-CD38+ T cells, CD4+ T cells-neutrophils and CD38+ T cells-neutrophils prolonged and they were replaced by CD163+ macrophages in PT along with tumour progression. Neutrophils showed unique stage and location-dependent prognostic effects. A high abundance of stromal neutrophils improved disease-free survival in the early-stage, whereas high intratumoural neutrophil infiltrates predicted poor prognosis in the mid-to-late-stage. Significant molecular and functional reprogramming in PT and TDLN microenvironments was observed. Diverse interaction networks mediated by neutrophils were found between positive and negative TDLNs. Five phenotypically and functionally heterogeneous subtypes of tumour-associated neutrophil (TAN) were further identified by pseudotime analysis, including TAN-0 with antigen-presenting function, TAN-1 with strong expression of interferon (IFN)-stimulated genes, the pro-tumour TAN-2 subcluster, the classical subset (TAN-3) and the pro-inflammatory subtype (TAN-4). Loss of IFN-stimulated signature and growing angiogenesis activity were discovered along the transitional trajectory. Eventually, a robust six neutrophil differentiation relevant genes-based model was established, showing that low-risk patients had longer overall survival time and may respond better to immunotherapy. The cellular composition, spatial location, molecular and functional changes in PT and TDLN microenvironments along with NSCLC progression were deciphered, highlighting the immunoregulatory roles and evolutionary heterogeneity of TANs.

Penerapan Algoritma Floyd-Warshall pada Jalur Evakuasi Korban Kecelakaan di Boyolali

The Floyd-Warshall algorithm is one of the algorithms that can be used to solve the shoetest route problem and is the easiests to apply because it can find all the shortest routes between each possible pair of position and is part of a dynamic program that is very efficient in solving optimal route promblems. The Floyd-Warshall algorithm works by comparing each possible path on the graph for each node pair and checking the resulting node combination. The problem of the shortest route in daily life is the evacuation of victims of traffic accidents in Boyolali, Central Java. For evacuation to be more effective, a nearby route to the nearest hospital is needed. Based on the results of the research that has been done, it can be concluded that the study produced reference matrix in the form of the shortest trajectory used to determine the shortest route to nearest hospital in Boyolali, Central Java.

Barycentric coordinate-based distributed localization for wireless sensor networks subject to random lossy links

Distributed localization in wireless sensor networks is essential for verifying the positions of the sensor nodes deployed in the sensing field based on information interactions. However, the information transmitted from the controller to the actuator for each sensor node may be lost owing to communication noise, channel interference, or network congestion. With this in mind, this paper aims at investigating the distributed localization for wireless sensor networks subject to random lossy links. The barycentric coordinate, which can be determined by the distance measurements between node pairs in the network, is used as the basic scheme for range-based localization. First, based on the characterization of random link loss in wireless sensor networks with a Bernoulli random variable, a distributed iterative localization algorithm is proposed. And then, the global convergence of the proposed localization algorithm which ensures accurate localization is theoretically proved by using the convergence of sub-stochastic matrices’ product. Finally, numerical examples are conducted to illustrate the effectiveness of the proposed localization algorithm.

Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning Approach

We study the problem of graph structure identification, i.e., of recovering the graph of dependencies among time series. We model these time series data as components of the state of linear stochastic networked dynamical systems. We assume partial observability, where the state evolution of only a subset of nodes comprising the network is observed. We propose a new feature-based paradigm: to each pair of nodes, we compute a feature vector from the observed time series. We prove that these features are linearly separable, i.e., there exists a hyperplane that separates the cluster of features associated with connected pairs of nodes from those of disconnected pairs. This renders the features amenable to train a variety of classifiers to perform causal inference. In particular, we use these features to train Convolutional Neural Networks (CNNs). The resulting causal inference mechanism outperforms state-of-the-art counterparts w.r.t. sample-complexity. The trained CNNs generalize well over structurally distinct networks (dense or sparse) and noise-level profiles. Remarkably, they also generalize well to real-world networks while trained over a synthetic network -- namely, a particular realization of a random graph.

Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating

Unsupervised graph representation learning (UGRL) has drawn increasing research attention and achieved promising results in several graph analytic tasks. Relying on the homophily assumption, existing UGRL methods tend to smooth the learned node representations along all edges, ignoring the existence of heterophilic edges that connect nodes with distinct attributes. As a result, current methods are hard to generalize to heterophilic graphs where dissimilar nodes are widely connected, and also vulnerable to adversarial attacks. To address this issue, we propose a novel unsupervised Graph Representation learning method with Edge hEterophily discriminaTing (GREET) which learns representations by discriminating and leveraging homophilic edges and heterophilic edges. To distinguish two types of edges, we build an edge discriminator that infers edge homophily/heterophily from feature and structure information. We train the edge discriminator in an unsupervised way through minimizing the crafted pivot-anchored ranking loss, with randomly sampled node pairs acting as pivots. Node representations are learned through contrasting the dual-channel encodings obtained from the discriminated homophilic and heterophilic edges. With an effective interplaying scheme, edge discriminating and representation learning can mutually boost each other during the training phase. We conducted extensive experiments on 14 benchmark datasets and multiple learning scenarios to demonstrate the superiority of GREET.

A High-Reliability 12T SRAM Radiation-Hardened Cell for Aerospace Applications.

The static random-access memory (SRAM) cells used in the high radiation environment of aerospace have become highly vulnerable to single-event effects (SEE). Therefore, a 12T SRAM-hardened circuit (RHB-12T cell) for the soft error recovery is proposed using the radiation hardening design (RHBD) concept. To verify the performance of the RHB-12T, the proposed cell is simulated by the 28 nm CMOS process and compared with other hardened cells (Quatro-10T, WE-Quatro-12T, RHM-12T, RHD-12T, and RSP-14T). The simulation results show that the RHB-12T cell can recover not only from single-event upset caused by their sensitive nodes but also from single-event multi-node upset caused by their storage node pairs. The proposed cell exhibits 1.14×/1.23×/1.06× shorter read delay than Quatro-10T/WE-Quatro-12T/RSP-14T and 1.31×/1.11×/1.18×/1.37× shorter write delay than WE-Quatro-12T/RHM-12T/RHD-12T/RSP-14T. It also shows 1.35×/1.11×/1.04× higher read stability than Quatro-10T/RHM-12T/RHD-12T and 1.12×/1.04×/1.09× higher write ability than RHM-12T/RHD-12T/RSP-14T. All these improvements are achieved at the cost of a slightly larger area and power consumption.

The Impact of Higher-Order Interactions on the Synchronization of Hindmarsh–Rose Neuron Maps under Different Coupling Functions

In network analysis, links depict the connections between each pair of network nodes. However, such pairwise connections fail to consider the interactions among more agents, which may be indirectly connected. Such non-pairwise or higher-order connections can be signified by involving simplicial complexes. The higher-order connections become even more noteworthy when it comes to neuronal network synchronization, an emerging phenomenon responsible for the many biological processes in real-world phenomena. However, involving higher-order interactions may considerably increase the computational costs. To confound this issue, map-based models are more suitable since they are faster, simpler, more flexible, and computationally more optimal. Therefore, this paper addresses the impact of pairwise and non-pairwise neuronal interactions on the synchronization state of 10 coupled memristive Hindmarsh–Rose neuron maps. To this aim, electrical, inner linking, and chemical synaptic functions are considered as two- and three-body interactions in three homogeneous and two heterogeneous cases. The results show that through chemical pairwise and non-pairwise synapses, the neurons achieve synchrony with the weakest coupling strengths.

A Partitioned Rigid-Element and Interface-Element Method for Rock-Slope-Stability Analysis

The stability analysis of rock slopes has been a prominent topic in the field of rock mechanics, primarily due to the widespread occurrence of discontinuous structural planes in rock masses. Based on this complex characteristic of rock slopes, this paper proposes a novel numerical method, the Partitioned-Rigid-Element and Interface-Element (PRE-IE) method. In the PRE-IE method, the structure is modeled as several rigid bodies and discontinuous structural planes, which are, respectively, divided into partitioned rigid elements and interface elements. Taking the contact force of node pairs and the displacement of the rigid body centroid as mixed variables, according to the principle of minimum potential energy, the governing equations of PRE-IE can be established using the Lagrange multiplier method and then solved using the nonlinear contact iterative method and the incremental method. A classic case study demonstrates that using the failure of all contact node pairs as the criterion for slope failure is appropriate. This criterion is objective and avoids the potential impact of personal bias on safety factor calculations. Two numerical examples of differently shaped slopes are provided to verify the correctness and validity of the PRE-IE method. By comparing the safety factor calculated using the PRE-IE method with those obtained from other different methods, as well as comparing the computational time, it is shown that the PRE-IE method, in combination with the SRM, can accurately and efficiently analyze the stability problems of rock slopes.

A Hybrid Continuous-Time Dynamic Graph Representation Learning Model by Exploring Both Temporal and Repetitive Information

Recently, dynamic graph representation learning has attracted more and more attention from both academic and industrial communities due to its capabilities of capturing different real-world phenomena. For a dynamic graph represented as a sequence of timestamped events, there are two kinds of evolutionary essences: temporal and repetitive information. At present, the temporal information of interactions (e.g., timestamps) have been deeply explored. However, as another vital nature of dynamic graphs, the repetitive information of interactions between two nodes is neglected, which may lead to inaccurate node representation. To address this issue, we propose a novel continuous-time dynamic graph representation learning model, which consists of a node-level-memory based module, a historical high-order neighborhood based vertical aggregation module and a repetitive-topological information based horizontal aggregation module. In particular, to characterize the evolving pattern of the repetitive information of interactions between a pair of nodes, we put forward a repetitive-interaction based attention mechanism to integrate the two key attributes (i.e., the content and the number of interactions) of repetitive interactions at different moments, based on the insight that the repetitive behaviors of nodes are widespread and essential. We conduct extensive experiments including future link prediction tasks (for transductive and inductive learning) and dynamic node classification task, and results on three real-life dynamic graph datasets demonstrate that the proposed method significantly outperforms state-of-the-art baselines, for both observed nodes and new ones.

Shortest Paths Discovery in Uncertain Networks via Transfer Learning

Due to various reasons such as noisy measurement and privacy preservation, a network/graph is often uncertain such that each edge in the network has a probability of existence. In this paper, we study finding the most probable shortest path which has the highest probability of being the shortest path between a given pair of nodes in an uncertain network. Despite significant progress being made, this problem still suffers from the efficiency and scalability issue. To solve this problem, the state-of-the-art adopts a two-phase approach where Phase 1 generates some candidate paths and Phase 2 estimates their probabilities of being the shortest path and returns the one with the highest probability as the solution. Notably, Phase 2 requires a large number of simulations over all edges in the network and can easily dominate the cost of the whole process. In this paper, we aim to resolve the efficiency and scalability issue by optimizing Phase 2. Specifically, we first propose a non-learning based fast approximation technique which significantly reduces the number of samples for the probability estimation in each simulation. Afterwards, we further propose a learning-based method which can directly estimate the probability of each candidate path without costly simulations. Extensive experiments show that (1) compared to the state-of-the-art, our fast approximation technique and learning-based method can achieve up to 5x and 210x speedups in Phase 2 respectively while maintaining highly competitive or even equivalent results, (2) the training process is highly scalable and (3) the prediction function can work effectively under the problem settings different from the one it was trained.

Deep attention fuzzy cognitive maps for interpretable multivariate time series prediction

Although time series prediction is widely used to estimate the future state of complex systems in various industries, accurate, interpretable and generalizable methods are still limited when used to make long-term nonstationary predictions. To this end, this article proposes deep attention fuzzy cognitive maps (DAFCM), which is composed of spatiotemporal fuzzy cognitive maps (STFCM), long short-term memory (LSTM) neural network, temporal fuzzy cognitive maps (TFCM) and residual structures. First, an improved attention mechanism is used to build spatiotemporal fuzzy cognitive maps that capture the spatial correlation in pairs of nodes and the temporal correlation of respective nodes. Second, the node state updated through the STFCM is input to the LSTM to capture the long-term trend of these series, and the TFCM with improved time attention is applied for the nonstationary problem in the time series. Finally, we add the state values of previous nodes into the DAFCM and build residual structures through linear transformation to prevent gradient explosion and gradient disappearance in long-term backpropagation. By combining the interpretability of fuzzy cognitive maps (FCM) and the high prediction accuracy of deep learning, the DAFCM can be used to accomplish tasks such as multivariate long-term nonstationary time series forecasting in multiple domains, and its efficiency is validated with 6 public datasets across 9 baselines.