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As network complexity increases because of the diversification of applications and services based on internet of autonomous things, it is difficult for humans to design the optimal control rule for software-defined networking controllers. Intelligence-defined networking, called IDN, is proposed to overcome this limitation through machine learning algorithms. Since the existing IDN approaches are mostly designed to optimize only the network quality of services, including throughput, jitter, and latency, the controllers don’t consider the importance of data in the applications and the services. This causes the controller to allocate insufficient resources to the crucial data flow, which leads critical problems, such as self-driving car accidents. To prevent this problem, we propose an importance-based IDN architecture that enables network controllers to manage network traffic with importance levels of data flows. Firstly, we devise an importance estimation scheme to set the importance level for the flows. Secondly, a dynamic resource allocation model of the controllers is developed by means of deep learning algorithms in order to make the optimal network resource. Additionally, an online learning mechanism based on weighted auto-labeling is adopted to continue enhancing the adaptability of the resource allocation model on runtime as the network conditions change. The evaluation results of the proposed architecture under various autonomous things scenarios show that the loss rate for data flows of higher importance is reduced by one-quarter compared to the case of a network controller without importance level and that the bandwidth waste ratio is reduced by ten percent compared to the rule-based model.

Long short-term memory (LSTM) applications need fast yet compact models. Neural network compression approaches have been promising for cutting down network complexity by skipping insignificant weights. However, current strategies remain hardware-agnostic and network complexity reduction does not always translate to execution efficiency. We propose a hardware-guided symbiotic training methodology for compact, accurate, yet execution-efficient inference models. It is based on our observation that hardware may introduce substantial non-monotonic behavior, which we call the latency hysteresis effect, when evaluating network size vs. latency. Leveraging hardware-impacted hysteresis effect and sparsity, we enable a symbiosis of model compactness and accuracy with execution efficiency. We have evaluated our approach on language modeling and speech recognition applications. Relative to the traditional LSTM architecture obtained for the Penn Treebank dataset, we reduce the number of parameters by 18.0x (30.5x) and run-time latency by up to 2.4x (5.2x) on Nvidia GPUs (Intel CPUs) without any accuracy degradation. For the DeepSpeech2 architecture obtained for the AN4 dataset, we reduce the model size by 7.0x (19.4x), word error rate from 12.9% to 9.9% (10.4%), and run-time latency by up to 1.7x (2.4x) on Nvidia GPUs (Intel CPUs). Our method consistently outperforms prior art, yielding compact, accurate, yet execution-efficient models.

It has been widely recognized that the efficient training of neural networks (NNs) is crucial to classification performance. While a series of gradient-based approaches have been extensively developed, they are criticized for the ease of trapping into local optima and sensitivity to hyperparameters. Due to the high robustness and wide applicability, evolutionary algorithms (EAs) have been regarded as a promising alternative for training NNs in recent years. However, EAs suffer from the curse of dimensionality and are inefficient in training deep NNs (DNNs). By inheriting the advantages of both the gradient-based approaches and EAs, this article proposes a gradient-guided evolutionary approach to train DNNs. The proposed approach suggests a novel genetic operator to optimize the weights in the search space, where the search direction is determined by the gradient of weights. Moreover, the network sparsity is considered in the proposed approach, which highly reduces the network complexity and alleviates overfitting. Experimental results on single-layer NNs, deep-layer NNs, recurrent NNs, and convolutional NNs (CNNs) demonstrate the effectiveness of the proposed approach. In short, this work not only introduces a novel approach for training DNNs but also enhances the performance of EAs in solving large-scale optimization problems.

Island biotas are in imminent threat from anthropogenic impacts. Of these impacts, the negative effects of exotic species on the taxonomic and functional diversity of the local fauna are of major concern. Exotics may also have a detrimental effect on interspecific interactions which, in turn, can destabilize ecological networks. Species co-occurrence networks can detect species-to-species associations and are used to predict ecological interaction networks and utilized as tools to assess environmental impacts on community structure. Here, we aim to investigate whether or not topological differences of the arthropod co-occurrence networks among native forest fragments from seven Azorean islands can reveal the influence of the abiotic environment and exotic species on these networks. Co-occurrence networks were sensitive to environmental and community dissimilarities, showing a clear separation between islands and pinpointed differences between indigenous and exotic networks. Most exotics were little connected and exotic networks had a large proportion of unconnected species. The resulting decreased connectance and the increased modularity with the increase of the proportions of exotics in the networks suggest that most exotics have too low prevalence to show associations with other species, and only a few dominants drive co-occurrences. The proportion of negative links, as indicators of competition, did not increase with the increase of exotics in the habitats, suggesting that exotics provided new functional roles when they colonized native forest remnants. However, when the theoretical networks consisting of only indigenous species were investigated, connectance decreased and closeness increased with the increase of exotics, suggesting processes of network degradation. Since our study provides ample evidence for the usefulness of co-occurrence network analysis in studying island ecosystems, we recommend the use of this tool for ecosystem assessments, early warning systems and decision-making in island biodiversity conservation. info:eu-repo/semantics/publishedVersion

A key objective of studying biological systems is to design therapeutic intervention strategies for beneficially altering cell dynamics. Derivation of control policies is hindered by the high-dimensional state spaces associated with gene regulatory networks. Hence, it is critical to reduce the network complexity and the paper aims to address this issue by focusing on the distribution of the canalizing power (CP) of the genes in the model. Canalizing genes enforce broad corrective actions on cellular processes and play a crucial role in producing optimal reactions to external stimuli. Therefore, it is critical to reduce the network while preserving the canalizing power of genes. We reduce Boolean networks with perturbation by removing genes with the smallest canalizing power consecutively, and evaluate the stability of canalizing power. A systematic empirical study demonstrates that there are two classes of networks, reducible and irreducible with respect to the preservation of canalizing power of the genes. Based on these observations, we introduce the definition of reducible networks and proceed with the problem of selecting the relevant network features that allow for discriminating networks from the two different classes. We demonstrate the efficacy of the selected features on synthetic and real gene regulatory networks.

A few lightweight convolutional neural network (CNN) models have been recently designed for remote sensing object detection (RSOD). However, most of them simply replace vanilla convolutions with stacked separable convolutions, which may not be efficient due to a lot of precision losses and may not be able to detect oriented bounding boxes (OBB). Also, the existing OBB detection methods are difficult to constrain the shape of objects predicted by CNNs accurately. In this paper, we propose an effective lightweight oriented object detector (LO-Det). Specifically, a channel separation-aggregation (CSA) structure is designed to simplify the complexity of stacked separable convolutions, and a dynamic receptive field (DRF) mechanism is developed to maintain high accuracy by customizing the convolution kernel and its perception range dynamically when reducing the network complexity. The CSA-DRF component optimizes efficiency while maintaining high accuracy. Then, a diagonal support constraint head (DSC-Head) component is designed to detect OBBs and constrain their shapes more accurately and stably. Extensive experiments on public datasets demonstrate that the proposed LO-Det can run very fast even on embedded devices with the competitive accuracy of detecting oriented objects., Comment: 15 pages

Aquatic biodiversity is important in mediating ecosystem functioning, contributing to ecosystem sustainability and human wellbeing. However, how microbial network complexity affects the biodiversity-nutrient cycling relationship in saline freshwater ecosystems remains underexplored. Using high-resolution time-series data, we examined the relationships between microeukaryotic-bacterial community network complexity, biodiversity and multi-nutrient cycling in an urban reservoir undergoing a freshwater salinization-desalinization cycle. We found that low microbial diversity enhanced ecosystem multi-nutrient cycling under high salinity stress. In addition, multi-nutrient cycling declined with increased network complexity. Further, we found a non-linear relationship between salinity-induced shifts in the complexity of the microbial network and biodiversity-nutrient cycling (BNC) relationship of keystone taxa, i.e. the strength of the BNC relationship first became weak and then strong with increased network complexity. Together, these results highlighted the significant insight that there is not always positive relationship between biodiversity/network complexity and multi-nutrient cycling, even between network complexity and BNC relationship in real-world ecosystems, suggesting that preserving microbial association is important in aquatic health managing and evaluating the freshwater salinization problem.

Purpose Soil bacteria and fungi play critical roles in mediating soil nutrient cycling. Exploring the effect of fertilization on soil microbial communities is of great importance to comprehend the sustainability of agricultural ecosystem. Methods In this study, responses of the bacterial and fungal communities in the wheat rhizosphere and bulk soils to four fertilization regimes: no fertilizer (CK), 100% chemical NPK fertilizers (NPK), 50% chemical NPK fertilizers + 50% manure (NPKM), and 100% manure (OM) were investigated. Results Chemical fertilization significantly decreased the bacterial alpha-diversity, while manure fertilization maintained the bacterial diversity in both rhizosphere and bulk soils. Fertilization increased the relative abundance of the bacterial orders Flavobacteriales and Xanthomonadales by 46.0-61.6% and 36.3-59.4% compared with CK in the rhizosphere soils. Application of both chemical fertilizers and manure enhanced the bacterial network complexity and stability. Chemical fertilizer addition significantly weakened the fungal network complexity, while manure fertilization maintained the fungal network complexity and connectivity. Proteobacteria and Bacteroidetes were the two prominent keystone taxa of bacterial networks in CK and NPK, while Actinobacteria was the prominent keystone taxa in NPKM and OM. In addition, Myrothecium became the keystone taxa of fungal networks induced by manure addition. Conclusions Our results suggest that soil bacteria were more sensitive than fungi to fertilization, and manure amendment was beneficial to increase soil microbial network complexity and stability. These findings provide a comprehensive understanding of how fertilization affects the productivity and sustainability of agricultural ecosystems via soil microbial communities.

By combining multilayer perceptrons (MLPs) and radial basis function neural networks (RBF-NNs), an efficient multilayer RBF network is proposed in this work for regression problems. As an extension to the existing multilayer RBF network (RBF-MLP-I), the new multilayer RBF network (RBF-MLP-II) first nonlinearly transforms the multi-dimensional input data by adopting a set of multivariate basis functions. Then, linear weighted sums of these basis functions, i.e., the RBF approximations, are computed in the first hidden layer and used as the features of this layer. Subsequently, in the following hidden layers, each feature of the preceding hidden layer is fed into a univariate RBF characterized by the trainable scalar center and width, and then, RBF approximations are also applied to these basis functions. Finally, the features of the last hidden layer are linearly transformed to approximate the target output data. RBF-MLP-II reduces the number of parameters in basis functions and thus the network complexity of RBF-MLP-I. Verified by four regression problems, it is demonstrated that the proposed RBF-MLP-II exhibits the best approximation accuracy and fastest training convergence compared to conventional MLPs, RBF-NNs, and RBF-MLP-I.

Conventional artificial neural network (ANN) learning algorithms for classification tasks, either derivative-based optimization algorithms or derivative-free optimization algorithms work by training ANN first (or training and validating ANN) and then testing ANN, which are a two-stage and one-pass learning mechanism. Thus, this learning mechanism may not guarantee the generalization ability of a trained ANN. In this article, a novel bilevel learning model is constructed for self-organizing feed-forward neural network (FFNN), in which the training and testing processes are integrated into a unified framework. In this bilevel model, the upper level optimization problem is built for testing error on testing data set and network architecture based on network complexity, whereas the lower level optimization problem is constructed for network weights based on training error on training data set. For the bilevel framework, an interactive learning algorithm is proposed to optimize the architecture and weights of an FFNN with consideration of both training error and testing error. In this interactive learning algorithm, a hybrid binary particle swarm optimization (BPSO) taken as an upper level optimizer is used to self-organize network architecture, whereas the Levenberg-Marquardt (LM) algorithm as a lower level optimizer is utilized to optimize the connection weights of an FFNN. The bilevel learning model and algorithm have been tested on 20 benchmark classification problems. Experimental results demonstrate that the bilevel learning algorithm can significantly produce more compact FFNNs with more excellent generalization ability when compared with conventional learning algorithms.

Medical Waste Management (MWM) is an important and necessary problem in the COVID-19 situation for treatment staff. When the number of infectious patients grows up and amount of MWMs increases day by day. We present Medical Waste Chain Network Design (MWMCND) that contains Health Center (HC), Waste Segregation (WS), Waste Purchase Contractor (WPC) and landfill. We propose to locate WS to decrease waste and recover them and send them to the WPC. Recovering medical waste like metal and plastic can help the environment and return to the production cycle. Therefore, we proposed a novel Viable MWCND by a novel two-stage robust stochastic programming that considers resiliency (flexibility and network complexity) and sustainable (energy and environment) requirements. Therefore, we try to consider risks by Conditional Value at Risk (CVaR) and improve robustness and agility to demand fluctuation and network. We utilize and solve it by GAMS CPLEX solver. The results show that by increasing the conservative coefficient, the confidence level of CVaR and waste recovery coefficient increases cost function and population risk. Moreover, increasing demand and scale of the problem make to increase the cost function.

The theory and experiment of quantum information have been studied extensively in recent years, and the feasibility of quantum communication has been proved. Although the fundamental technology is not yet mature, research on quantum internet should be conducted. To implement quantum internet, an architecture that describes how quantum nodes are linked to form networks and how protocol functions are vertically composed need to be developed urgently. In this paper, we present a novel design of a cluster-based structure to describe how quantum nodes are interconnected, and how the structure can improve the performance of qubit transmission and reduce the network complexity. The idea of the quantum local area network (QLAN) is proposed as an essential component of the quantum internet. Besides, each quantum repeater links to neighboring repeaters to form a core network, and multiple QLANs are connected through the core network. The core network can be grouped into different hierarchical quantum repeater networks according to needed service requirements. For the sake of interoperability and fast prototyping, we adopt the idea of OSI layering model of the current Internet in the design of quantum internet. Finally, we elaborate on the composition of quantum nodes and the realization of end-to-end communication.

Existing research on credit risk contagion of supply chain finance pays more attention to the influence of network internal structure on the process of risk contagion. The spread of COVID-19 has had a huge impact on the supply chain, with a large number of enterprises experiencing difficulties in operation, resulting in increased credit risks in supply chain finance. Under the impact of the epidemic, this paper explores the transmission speed and steady state of credit risk when the supply chain finance network is affected by external impact so that we can have a more complete understanding of the ability of supply chain finance to resist risks. The simulation results show that external shocks of different degrees will increase the number of initial infected enterprises and lead to the increase in credit risk contagion speed but have no significant impact on network steady state;the speed of credit risk contagion is positively correlated with network complexity but not significantly affected by network size;core enterprises infected will increase the rate of credit risk contagion. The intensity of policy intervention has obvious curative effect on the risk caused by external shock. When the supply chain financial network is affected by external shocks, the intensity, time, and pertinence of policy response can effectively prevent the credit risk contagion. © 2021 Wentao Chen et al.

Funder: Alma Mater Studiorum - Università di Bologna, Economic complexity highlights the relationship between interdependence (a positional characteristic of elements belonging to a given network or structure) and connectivity (a functional characteristic of elements belonging to a given field of interaction). Positional interdependence (as the one between pieces in a jigsaw puzzle) is central to studies investigating the architecture of a complex system (Simon) while connectivity is central to the analysis of responsiveness patterns in social networks and strategic action fields. This paper discusses the fundamentals of a structural approach to economic and spatial complexity by highlighting the hierarchical arrangement of network elements as a distinctive feature of system identity. The positional distribution of network elements is a fundamental characteristic of complex networks and a central condition constraining the dynamics of those networks through the principle of relative structural invariance. The paper investigates the role of this principle by connecting it with the aggregation criterion followed in assigning network elements to specific subsystems. The type of aggregation is essential in determining the resilience properties of the network with respect to specific dynamic impulses. The paper concludes highlighting the need to combine the investigation of positional interdependence with the analysis of connectivity since positional interdependence is fundamental in determining which patterns of connection are more likely to arise (and which ones are excluded), due to the role of alternative properties of relative invariance constraining the feasible transformations in the positions of network elements.

Recently, using deep learning(DL) in super-resolution(SR) has ac- hieved great success. These methods combine the convolutional neural network(CNN) to learn a general matrix function for an end-to-end mapping. However, as the width and depth of the network increase, there are two essential problems in the SR tasks. On the one hand, a wider and deeper network will bring better performance but increase the calculating complexity and memory consumption. On the other hand, the expanded architecture will miss the intermediate feature details in the information transmitting process. Hence, a SISR(Single Image Super-Resolution) network with wider feature information blocks(WFIB) is proposed to address these issues by making a balance between the network complexity and performance. Cascade residual block(CRB) helps the network make full use of contextual feature information. Extensive experiments verify that our network achieves better performance and with fewer parameters than the state-of-the-art methods.

During the last decade, deep neural networks have shown a great performance in many machine learning tasks such as classification and clustering. One of the most successful networks is the CNN (Convolutional Neural Network), which has been applied in many application domains such as pattern recognition, medical diagnosis, and signal processing. Despite the very interesting performance of CNNs, their architecture design is still so far a major challenge for researchers and practitioners. Several works have been proposed in the literature with the aim to find optimized architectures such as ResNet and VGGNet. Unfortunately, most of these architectures are either manually defined by experts or automatically designed by greedy induction algorithms. Recent works suggest the use of Evolutionary Algorithms (EAs) thanks to their ability to escape locally-optimal architectures. Despite the fact that EAs have shown interesting performance, researchers in this direction have considered the design task as a single-level optimization problem; which represents the main research gap we tackle in this paper. The main contribution behind our work consists in the fact that CNN architecture design has a hierarchical nature and thus could be seen as a Bi-Level Optimization Problem (BLOP) where: (1) the upper level minimizes the network complexity defined by the number of blocks and the number of nodes per block; and (2) the lower level optimizes the convolution block ‘graphs’ topologies by maximizing the classification accuracy. Motivated by the originality of our observation with respect to the state of the art, we frame for the first time the CNN architecture design problem as a BLOP and then solve it using an adapted version of an existing efficient bi-level EA; through the definition of the solution encoding, the fitness function, and the variation operators at each level. The adapted EA is named BLOP-CNN and is assessed on the image classification task using the commonly employed CIFAR-10 and CIFAR-100 benchmark data sets. The analysis of our experimental results show the merits of our proposed method in providing the user with optimized architectures that outperform many recent and prominent architectures coming from the three different approaches, namely: manual design, reinforcement learning-based generation, and evolutionary optimization. Moreover, to show the applicability of our approach, we have conducted a case study on the detection of the COVID-19 using a set of benchmark chest X-ray and Computed Tomography (CT) images.

It is quite difficult to quantitatively measure heat extraction in a fractured enhanced geothermal system (EGS) reservoir and its impacting factors. This study thus aims to define a characteristic parameter that is failure area percentage for evaluating and optimizing geothermal extraction. Through establishing a thermal-fluid-solid coupling model, the operation lifecycle and economy of an EGS case was evaluated by the proposed parameter. A sensitivity indicator was developed to measure the quantitatively effection of various factors on heat extraction. The results show that heat extraction increases with the three growing-up factors: fracture network complexity, pressure difference, and rock permeability. However, too large value of these factors are adverse to geothermal system smooth operation. Based on the sensitivity indicator analysis, heat extraction is the most sensitive to the variation of fracture network complexity. This study would provide a guidance for optimization of EGS designing.

Development has intrigued humanity since ancient times. Today, the main paradigm in developmental biology and evolutionary developmental biology (evo-devo) is the genetic program, in which development is explained by the interplay and interaction of genes, that is, by the action of gene regulatory networks (GRNs). However, it is not even clear that a GRN, no matter how complex, can be translated into a form. Therefore, the fundamental enigma of development still remains: how is a complex organism formed from a single cell? This question unfolded the historical drama and the dialectical tension between preformation and epigenesis. In order to shed light on these issues, I studied the development of crabs (infraorder Brachyura), as representative of the subphylum Crustacea, using network theory. The external morphology of the different phases of brachyuran metamorphosis were modeled as networks and their main characteristics analyzed. As one could expect, the parameters usually regarded as indicative of network complexity, such as modularity and hierarchy, increased during development. However, when more sophisticated complexity measures were tested, it was evidenced that whereas a group of complexity measures increased during development, another group decreased. This led to consider that two kinds of complexities were being measured. I called them intensive and extensive complexity. In view of these results, I propose that crab development involves a passage from an intensive to an extensive complexity. In other words, crab development can be interpreted as a process of unfolding of an intensive, preexistent complexity., Facultad de Ciencias Naturales y Museo

Over the last decade, wireless devices have developed rapidly until predictions will develop with high complexity and dynamic. So that new capabilities are needed for wireless problems in this problem. Software Defined Network (SDN) is generally a wire-based network, but to meet the needs of users in terms of its implementation, it has begun to introduce a Wireless-based SDN called Software Defined Wireless Network (SDWN) which provides good service quality and reach and higher tools, so as to be able to provide new capabilities to wireless in a high complexity and very dynamic. When SDN is implemented in a wireless network it will require a routing solution that chooses paths due to network complexity. In this paper, SDWN is tested by being applied to mesh topologies of 4,6 and 8 access points (AP) because this topology is very often used in wireless-based networks. To improve network performance, Dijkstra's algorithm is added with the user mobility scheme used is RandomDirection. The Dijkstra algorithm was chosen because it is very effective compared to other algorithms. The performance measured in this study is Quality of Service (QoS), which is a parameter that indicates the quality of data packets in a network. The measurement results obtained show that the QoS value in this study meets the parameters considered by the ITU-T G1010 with a delay value of 1.3 ms for data services and packet loss below 0.1%. When compared with the ITU-T standard, the delay and packet loss fall into the very good category.

Background Liver cancer is the sixth most common cancer worldwide. It is mostly diagnosed with a computed tomography scan. Nowadays deep learning methods have been used for the segmentation of the liver and its tumor from the computed tomography (CT) scan images. This research mainly focused on segmenting liver and tumor from the abdominal CT scan images using a deep learning method and minimizing the effort and time used for a liver cancer diagnosis. The algorithm is based on the original UNet architecture. But, here in this paper, the numbers of filters on each convolutional block were reduced and new batch normalization and a dropout layer were added after each convolutional block of the contracting path. Results Using this algorithm a dice score of 0.96, 0.74, and 0.63 were obtained for liver segmentation, segmentation of tumors from the liver, and the segmentation of tumor from abdominal CT scan images respectively. The segmentation results of liver and tumor from the liver showed an improvement of 0.01 and 0.11 respectively from other works. Conclusion This work proposed a liver and a tumor segmentation method using a UNet architecture as a baseline. Modification regarding the number of filters and network layers were done on the original UNet model to reduce the network complexity and improve segmentation performance. A new class balancing method is also introduced to minimize the class imbalance problem. Through these, the algorithm attained better segmentation results and showed good improvement. However, it faced difficulty in segmenting small and irregular tumors.

The Elman neural network (ElmanNN) is well-known for its capability of processing dynamic information, which has led to successful applications in stock forecasting. In this paper, we introduce direct input-to-output connections (DIOCs) into the ElmanNN and show that the proposed Elman neural network with DIOCs (Elman-DIOCs) significantly out-performs the original ElmanNN without such DIOCs. Four different global stock indices, i.e., the Shanghai Stock Exchange (SSE) Composite Index, the Korea Stock Price Index (KOSPI), the Nikkei 225 Index (Nikkei225), and the Standard & Poor's 500 Index (SPX), are used to demonstrate the affecacy of the Elman-DIOCs in time-series prediction. We systematically evaluate 8 models, depending whether or not there are hidden layer biases, whether or not there are output layer biases, and whether or not there are DIOCs. The experimental results show that DIOCs lead to much better prediction accuracy, while requiring fewer than a half of the hidden neurons. Take the SPX index, for example - the root mean squared error (RMSE) and the mean absolute error (MAE) of the Elman-DIOCs are improved by 44.2% and 41.1%, respectively, compared to the ElmanNN, and 65.6% and 60.8%, respectively, compared to the multi-layer perceptron (MLP). We argue that (1) DIOCs can always help to improve accuracy, while reducing network complexity and computational burden, as long as the problem at hand (either regression or classification) has linear components, and (2) most real-world applications contain linear components. Therefore DIOCs will be almost always beneficial in any types of neural networks for classification or regression. We also point out that in rare cases where the problem at hand is entirely nonlinear, DIOCs should not be used. This study is funded by Joint Research Fund for Overseas Chinese Scholars and Scholars in Hong Kong and Macao (Grant No. 61828601), Natural Science Foundation of Shanxi Province (Grant No. 201801D121141), and Provincial Program on Key Research Projects of Shanxi (Social Development Area) (Grant No. 201903D321003).

With the expansion of the global air cargo transport system, the operating structure of air cargo has become increasingly separate from passenger counterpart, forming an independent organization model. Despite the Chinese air cargo capacity has grown exponentially in the past, its network is still in its infancy. FedEx and UPS have well-established air cargo networks and have operated effectively on both international and domestic scale; thus, understanding the structure and evolution of their air cargo networks is of a high reference value. In conjunction with the division of US regions from the United States Geological Survey (USGS), this paper refers to FedEx and UPS as FEPS and analyzes its topological structure, complexity properties, and air cargo accessibility by using social network analysis (SNA) and accessibility evaluation methods. The results suggest that (1) the structure of the FEPS air cargo network is in the highly developed states and has the typical “small-world” and “scale-free” network characteristics; (2) the degree centrality values for the nodes in the FEPS network suggest that the network complexity has increased; (3) airports in Memphis (MEM), Louisville (SDF), Indianapolis (IDN), and Ontario (ONT) are the major hubs with both high centrality values and air cargo accessibility; and (4) the FEPS network presents a unique hub-and-spoke structure compared with the passenger counterpart.

Narrowband Internet of Things (NB-IoT) has been utilized in a vast range of applications. The conventional NB-IoT is subjected to the limitation of reduced coverage, limited power, and delay. The conventional NB-IoT uses orthogonal frequency-division multiplexing with Long Term Evolution (LTE) is applied, which increases the network complexity. The increase in network complexity increases power consumption and delay. To overcome those limitations, modified Reduced Fast Fourier Transmission (MRFFT) sample generation technique is used in every Uplink transmission for in CAT-0 and CAT-4 based LTE implementation technique. Multiple sample frequency transmission techniques will be required for Up Link (UL) based on control information for getting from Down Link (DL). In this paper, an effective R2MDC—MRFFT technique has been used to combine hardware and data flow complexity. The proposed MRFFT architecture uses “Feedback (R2)-Commutator (R4) MRFFT” is utilized for reducing latency and hardware complexity of the data path flow part of NB-IoT in LTE. The design of feedback-commutator architecture provides the same functionality with increasing throughput (2.4 GSamples) with 30.605 ns latency reduction, lower power consumption, and reduced coverage enhancement. The proposed feedback-commutator based frequency transmission technique is compared with the traditional frequency transmission technique used in CAT-0, CAT-4 devices. This enhanced MRFFT sample generation technique is incorporated into data flow heterogeneous architecture of NB-IoT design procedure to improve the design performances of LTE wireless communication in the perspective of coverage in low mobility.

Air-writing recognition has received wide attention due to its potential application in intelligent systems. To date, some of the fundamental problems in isolated writing have not been addressed effectively. This paper presents a simple yet effective air-writing recognition approach based on deep convolutional neural networks (CNNs). A robust and efficient hand tracking algorithm is proposed to extract air-writing trajectories collected by a single web camera. The algorithm addresses the push-to-write problem and avoids restrictions on the users’ writing without using a delimiter and an imaginary box. A novel preprocessing scheme is also presented to convert the writing trajectory into appropriate forms of data, making the CNNs trained with these forms of data simpler and more effective. Experimental results indicate that the proposed approach not only obtains much higher recognition accuracy but also reduces the network complexity significantly compared to the popular image-based methods.

Traditional tracking algorithms reply on manually extracted features while deep learning algorithms can automatically extract features, which is of great importance for single target tracking in the complex underwater environment that is characterized by poor visibility, low contrasts, and occlusion. The convolutional neural network SiamRPN++ is an advanced deep learning algorithm whose backbone network structure is. However, this algorithm is difficult to be implemented in an underwater platform due to its large amount of computation and high requirement for the computing power of the hardware. To solve this problem, a new backbone network structure NewNet-62 characterized by inverted residual bottleneck block and the SiamRPN++ algorithm are proposed. The depth-wise separable convolution in NewNet-62 simplifies the computation so that the SiamRPN++ algorithm can realize fast and accurate tracking of underwater single targets. The video set in VOT2016 was used to test the algorithm. The results show that the SiamRPN++ algorithm using NewNet-62 as the main network has the great performance. Compared with the algorithms using ResNet-50, the accuracy of the SiamRPN++ algorithm increased to 0.524 and its EAO increased to 0.303. And its tracking speed increased to 73.74. The network complexity reduced to 3.013 billion and the network parameters reduced to 12.538 million, which significantly reduces network complexity and number of network parameters.

With regard to inverse synthetic aperture radar (ISAR) imaging, traditional range-Doppler (RD) algorithm is inapplicable to sparse aperture. Although compressive sensing (CS) algorithm can overcome this problem, the imaging resolution is not high enough. When deep learning (DL) is applied to ISAR imaging, some problems may also occur, such as network complexity, the selection of labels in network training, the influence of noise and sparse aperture, and the loss of weak scattering points in network test. In order to solve the above problems, a joint fast iterative shrinkage-thresholding algorithm (FISTA) and Visual Geometry Group Network (VGGNet) high-resolution imaging method is proposed in this paper. In the proposed method, FISTA is presented to reduce the impact of noise and sparse aperture. The high-resolution processing network (HRPN) is built based on VGGNet. Then, combined with peak extraction technology, the random ideal scattering points are utilized to construct the training/validation set. Meanwhile, the training process of HRPN is analyzed theoretically, and the network test strategy is designed by the differences between the test set and the training/validation set. Extensive experiments based on both simulated and measured data demonstrate that the proposed method has good imaging performance and small network complexity.

In recent years, few-shot learning is proposed to solve the problem of lacking samples in deep learning. However, previous works are mainly concentrated on optimizing neural network structures or augmenting the dataset while ignoring the local relationship of the images. Considering that humans pay more attention to the foreground or prominent features of the images during image recognition, we proposed the foreground feature attention module (FFAM) based on an unsupervised saliency detector for few-shot learning. The FFAM consists of two parts: the foreground extraction module and the features attention module. More specifically, we first extract the foreground images by Robust Background Detector (RBD), one of the best unsupervised saliency detectors. Secondly, we employ the same embedding module to extract the features of both original images and foreground images. Finally, we introduce three improvements to enhance the foreground features and make our network focus on the foreground features without losing background information. Our proposed FFAM is more sensitive to the foreground features than previous approaches. Hence, it effectively recognizes those images with similar backgrounds. Extensive experiments are conducted on miniImagenet and tieredImagenet datasets. It is demonstrated that our proposed FFAM greatly improves the accuracy performance over baseline systems for both one-shot and few-shot classification tasks without increasing the network complexity.

Spatiotemporal analysis has drawn a lot of attentions recently on industrial time series prediction. Most of the existing methods cannot consider the production semantics or recognize the time-order characteristic of different processes in spatial dimension. In this study, a novel multilayer spatiotemporal network based on granular modeling and long short-term memory (LSTM) network is proposed. Regarding the energy data that contain semantic characteristics, a number of information granules are first partitioned based on the process or phase semantics. For representing the spatiotemporal dependencies among the granular sequences, a spatiotemporal topology network is established, where the units are defined specifically by their spatial layers located. To reduce the network complexity, a new gating mechanism is further put forward within the units, with which the network can understand the time-order characteristics of spatial granules at each time step and adjust the impact of input data on the updating procedure accordingly. The performance of the proposed method is validated on the benchmark dataset and the practical energy data with different fluctuation characteristics, using the state-of-the-art comparative algorithms. The results indicate that the proposed method can recognize the spatiotemporal relationship effectively and outperform the comparative ones on prediction accuracy.

Stochastic network modeling is often limited by high computational costs to generate a large number of networks enough for meaningful statistical evaluation. In this study, Deep Convolutional Generative Adversarial Networks (DCGANs) were applied to quickly reproduce drainage networks from the already generated network samples without repetitive long modeling of the stochastic network model, Gibb's model. In particular, we developed a novel connectivity-informed method that converts the drainage network images to the directional information of flow on each node of the drainage network, and then transform it into multiple binary layers where the connectivity constraints between nodes in the drainage network are stored. DCGANs trained with three different types of training samples were compared; 1) original drainage network images, 2) their corresponding directional information only, and 3) the connectivity-informed directional information. Comparison of generated images demonstrated that the novel connectivity-informed method outperformed the other two methods by training DCGANs more effectively and better reproducing accurate drainage networks due to its compact representation of the network complexity and connectivity. This work highlights that DCGANs can be applicable for high contrast images common in earth and material sciences where the network, fractures, and other high contrast features are important., Comment: 16 pages; 9 figures; Python and Matlab scripts used in this paper can be found in https://github.com/saint-kim/RiverDCGANs

Neural Architecture Search (NAS), i.e., the automation of neural network design, has gained much popularity in recent years with increasingly complex search algorithms being proposed. Yet, solid comparisons with simple baselines are often missing. At the same time, recent retrospective studies have found many new algorithms to be no better than random search (RS). In this work we consider the use of a simple Local Search (LS) algorithm for NAS. We particularly consider a multi-objective NAS formulation, with network accuracy and network complexity as two objectives, as understanding the trade-off between these two objectives is arguably among the most interesting aspects of NAS. The proposed LS algorithm is compared with RS and two evolutionary algorithms (EAs), as these are often heralded as being ideal for multi-objective optimization. To promote reproducibility, we create and release two benchmark datasets, named MacroNAS-C10 and -C100, containing 200K saved network evaluations for two established image classification tasks, CIFAR-10 and CIFAR-100. Our benchmarks are designed to be complementary to existing benchmarks, especially in that they are better suited for multi-objective search. We additionally consider a version of the problem with a much larger architecture space. While we find and show that the considered algorithms explore the search space in fundamentally different ways, we also find that LS substantially outperforms RS and even performs nearly as good as state-of-the-art EAs. We believe that this provides strong evidence that LS is truly a competitive baseline for NAS against which new NAS algorithms should be benchmarked.

Image representation learning with encoder-decoder networks plays a fundamental role in multimedia processing. Recent findings show that traditional encoder-decoders can be negatively affected by small visual perturbations. The learned non-smooth feature embedding cannot guarantee to capture semantic-meaningful geometric distance between visually-similar image samples. Inspired by manifold learning, we propose a graph regularized encoder-decoder network, which can preserve local geometric information of the code embedding space. More discriminative feature embedding is learnt to attain both high-level image semantic and neighbor relationship of image clusters. The proposed graph regularizer is formulated upon multi-layer perceptions. It uses the local invariance principle to explicitly reconstruct the geometric similarity graph. Theoretical analysis is provided to show the connection between our deep regularizer and traditional graph Laplacian regularizer. Practically, the network complexity is alleviated by anchor based bipartite graph, and this leverages our method into large scale scenario. Experimental evaluations show the comparable results of the proposed method with state-of-the-art models on different tasks.

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.

Recently, the consolidation of Social Network and the Internet of Things (IoT) acquiring appreciation and consideration among researchers due to its immense and adaptable behavior. Social Internet of Things (SIoT) comprehends the adequacy to support the inventive applications and network services with adept and persuasive demeanors. The author’s exertion for this research provides the subsistence to the SIoT amelioration Currently, the SIoT technique proven to be competent, but the exponential growth of heterogeneous smart devices may arise a challenging scenario. Searching the right object, service, or best path to the informative nodes in such highly convoluted networks is always a challenging task. This research strengthen the SIoT paradigm by consolidating the properties of the individual’s Small World. In addition, this research also advises a Smart Social Agent (SSA) to minimize the human intervention. The absolute objective of this paper is two-fold, (i) transformation of SIoT network into Individual’s Small World SIoT network, (ii) searching the right information efficiently by exploiting SSA. To accomplish the goal we have proposed an efficient Query based Searching algorithm which exploits Service Oriented properties of Individual’s Small World SIoT network and searches information. First result shows the significant alleviation in the network complexity, reduces the average path length, and network diameter. Eventually, it improves the network scalability and navigability. While second result shows that the proposed Query based searching algorithm proves to be efficient with its mean execution time not more than 2.66 ms for complete network on two different machines. Whereas, for Individual’s small world network the mean execution time is not more than 2.47 ms for both machine.

This letter presents a sub-6 GHz cost-effective wideband dual-polarized plane wave generator (PWG) design for the fifth-generation (5G) base station over-the-air test. The proposed nonuniform PWG design is composed of 256 elements and has a dimension of 1.7 m × 1.7 m. Several novel strategies are proposed and implemented to reduce the cost and complexity. To reduce the feeding network complexity and mutual coupling among PWG elements, a novel tapered amplitude-only excitation method (i.e., with the same relative phase excitations among all PWG elements) is proposed. To reduce the number of channels in the phase and amplitude control network, a four-element subarray is applied. Furthermore, the wideband PWG element design and nonuniform configuration are applied to support for ultrawideband operations from 2 to 5 GHz. The proposed PWG design is numerically simulated and experimentally validated. A cylindrical quiet zone of a diameter of 0.9 m and a depth of 0.9 m, with 2.1 m distance to the PWG, is achieved in the measurement for the considered bandwidth, with the typical peak-to-peak E-field amplitude and phase variations less than ±1.25 dB and ±7.5°, respectively, which demonstrated its effectiveness and robustness.

Despite the wide use of LCA for environmental profiling, the approach for determining the system boundary within LCA models continues to be subjective and lacking in mathematical rigor. As a result, life cycle models are often developed in an ad hoc manner, and are difficult to compare. Significant environmental impacts may be inadvertently left out. Overcoming this shortcoming can help elicit greater confidence in life cycle models and their use for decision making. This paper describes a framework for hybrid life cycle model generation by selecting activities based on their importance, parametric uncertainty, and contribution to network complexity. The importance of activities is determined by structural path analysis—which then guides the construction of life cycle models based on uncertainty and complexity indicators. Information about uncertainty is from the available life cycle inventory; complexity is quantified by cost or granularity. The life cycle model is developed in a hierarchical manner by adding the most important activities until error requirements are satisfied or network complexity exceeds user-specified constraints. The framework is applied to an illustrative example for building a hybrid LCA model. Since this is a constructed example, the results can be compared with the actual impact, to validate the approach. This application demonstrates how the algorithm sequentially develops a life cycle model of acceptable uncertainty and network complexity. Challenges in applying this framework to practical problems are discussed. The presented algorithm designs system boundaries between scales of hybrid LCA models, includes or omits activities from the system based on path analysis of environmental impact contribution at upstream network nodes, and provides model quality indicators that permit comparison between different LCA models.

For the detection of marine ship objects in radar images, large-scale networks based on deep learning are difficult to be deployed on existing radar-equipped devices. This paper proposes a lightweight convolutional neural network, LiraNet, which combines the idea of dense connections, residual connections and group convolution, including stem blocks and extractor modules. The designed stem block uses a series of small convolutions to extract the input image features, and the extractor network adopts the designed two-way dense connection module, which further reduces the network operation complexity. Mounting LiraNet on the object detection framework Darknet, this paper proposes Lira-you only look once (Lira-YOLO), a lightweight model for ship detection in radar images, which can easily be deployed on the mobile devices. Lira-YOLO's prediction module uses a two-layer YOLO prediction layer and adds a residual module for better feature delivery. At the same time, in order to fully verify the performance of the model, mini-RD, a lightweight distance Doppler domain radar images dataset, is constructed. Experiments show that the network complexity of Lira-YOLO is low, being only 2.980 Bflops, and the parameter quantity is smaller, which is only 4.3 MB. The mean average precision (mAP) indicators on the mini-RD and SAR ship detection dataset (SSDD) reach 83.21% and 85.46%, respectively, which is comparable to the tiny-YOLOv3. Lira-YOLO has achieved a good detection accuracy with less memory and computational cost.

In existing works of deep learning-based resource allocation, the scalability degrades heavily with the increase of network complexity, which is due to their limited learning ability of shallow neural networks and insufficient knowledge of network. Nowadays, to address the growth of cell density, cooperative beamforming in user-centric network (UCN) is emerged, where the additional degrees of freedom of multi-antenna and cell coordination aggravate the challenges. This letter proposes a deep residual learning framework, UcnBeamNet, to enhance the ability of approximating the iterative algorithm for sum rate maximization, where multi-branch subnets are connected in parallel to extract extra information. Specifically, a weighted minimum mean square error (WMMSE)-based algorithm is derived to determine the optimal clusters and beamforming matrices; then UcnBeamNet is trained to learn the input-output mapping and provide direct insight of UCN from association matrices in addition to plural inputs. Extensive experiments demonstrate UcnBeamNet still reaches 90.38% sum-rate relative to conventional algorithm even with a large network size, and achieves more than 50, $000\times $ speed up in computational efficiency.

Deep neural networks are well known to achieve outstanding results in many domains. Recently, many researchers have introduced deep neural networks into remote sensing (RS) data processing. However, typical RS data usually possesses enormous scale. Processing RS data with deep neural networks requires a rather demanding computing hardware. Most high-performance deep neural networks are associated with highly complex network structures with many parameters. This restricts their deployment for real-time processing in satellites. Many researchers have attempted overcoming this obstacle by reducing network complexity. One of the promising approaches able to reduce network computational complexity and memory usage dramatically is network binarization. In this letter, through analyzing the learning behavior of binarized neural networks (BNNs), we propose several novel strategies for improving the performance of BNNs. Empirical experiments prove these strategies to be effective in improving BNN performance for image classification tasks on both small- and large-scale data sets. We also test BNN on a remote sense data set with positive results. A detailed discussion and preliminary analysis of the strategies used in the training are provided.

Software-Defined Networking (SDN) is an innovational network architecture which gives network administrators the ability to directly control the whole network by programming on a centralized controller. Due to network complexity, networks are unlikely to be bug-free. The ability to verify data plane properties will make network management easier for network administrators in SDN. In this paper, we present a novel atomic predicates based data plane properties verification method for SDN using Spark which is a big data processing framework. First, we verify packet reachability which is a fundamental data plane property. Then, we verify other data plane properties such as loop-freedom and nonexistence of black holes. In addition, the proposed method can detect a security threat existing in SDN called firewall bypass threat with packet reachability verification. By adopting atomic predicates, we achieve less computational and storage overhead. We implement the methods and study the performance. The results of experiments show that we can efficiently and accurately detect loops, black holes and firewall bypass threats.

Era of computation intelligence leads to various kinds of systems that evolve. Usually, an evolving system contains evolving interconnected entities (or components) that make evolving networks for the system State Series ${\text{SS}}\,= \,\{S_{1},\,S_{2}\ldots \,S_{N}\}$ created over time, where S $_i$ represents the i th system state. In this paper, we introduce an approach for mining Network Evolution Subgraphs such as Network Evolution Graphlets (NEGs) and Network Evolution Motifs (NEMs) from a set of evolving networks. We used graphlets information of a state to calculate System State Complexity (SSC). The System State Complexities (SSCs) represent time-varying complexities of multiple states. Additionally, we also used the NEGs information to calculate Evolving System Complexity (ESC) for a state series over time. We proposed an algorithm named System Network Complexity (SNC) for mining NEGs, SSCs, and ESC, which analyzes a pre-evolved state series of an evolving system. We prototyped the technique as a tool named SNC-Tool , which is applied to six real-world evolving systems collected from open-internet repositories of four different domains: software system, natural language system, retail market basket system, and IMDb movie genres system. This is demonstrated as experimentation reports containing retrieved—NEGs, NEMs, SSCs, and ESC—for each evolving system.

Mobile network operators (MNOs) are in the process of overlaying their conventional macro cellular networks with shorter range cells such as outdoor pico cells. The resultant increase in network complexity creates substantial overhead in terms of operating expenses, time, and labor for their planning and management. Artificial intelligence (AI) offers the potential for MNOs to operate their networks in a more organic and cost-efficient manner. We argue that deploying AI in fifth generation (5G) and beyond will require surmounting significant technical barriers in terms of robustness, performance, and complexity. We outline future research directions, identify top five challenges, and present a possible roadmap to realize the vision of AI-enabled cellular networks for Beyond- 5G and sixth generation (6G) networks.

Understanding the mechanisms of biological invasion is critical to biodiversity protection. Previous studies have produced inconsistent relationships between native species richness and invasibility, referred to as the invasion paradox. Although facilitative interactions among species have been proposed to explain the non-negative diversity–invasibility relationship, little is known about the facilitation of plant-associated microbes in invasions. We established a two-year field biodiversity experiment with a native plant species richness gradient (1, 2, 4, or 8 species) and analyzed the effects of community structure and network complexity of leaf bacteria on invasion success. Our results indicated a positive relationship between invasibility and network complexity of leaf bacteria of the invader. Consistent with previous studies, we also found that native plant species richness increased the leaf bacterial diversity and network complexity. Moreover, the results of the leaf bacteria community assembly of the invader suggested that the complex bacteria community resulted from higher native diversity rather than higher invader biomass. We concluded that increased leaf bacterial network complexity along the native plant diversity gradient likely facilitated plant invasion. Our findings provided evidence of a potential mechanism by which microbes may affect the plant community invasibility, hopefully helping to explain the non-negative relationship between native diversity and invasibility.

The use of information–theoretical methodologies to assess graph-based systems has received a significant amount of attention. Evaluating a graph’s structural information content is a classic issue in fields such as cybernetics, pattern recognition, mathematical chemistry, and computational physics. Therefore, conventional methods for determining a graph’s structural information content rely heavily on determining a specific partitioning of the vertex set to obtain a probability distribution. A network’s entropy based on such a probability distribution is obtained from vertex partitioning. These entropies produce the numeric information about complexity and information processing which, as a consequence, increases the understanding of the network. In this paper, we study the Benes network and its novel-derived classes via different entropy measures, which are based on information functionals. We construct different partitions of vertices of the Benes network and its novel-derived classes to compute information functional dependent entropies. Further, we present the numerical applications of our findings in understanding network complexity. We also classify information functionals which describe the networks more appropriately and may be applied to other networks.

In this article, we propose a variant of the electric vehicle routing problem considering explicitly the intermediate nodes. Ultimately aiming to provide an optimal routing plan for the shuttle fleet that serves to a university settlement internally, we consider a real road network by explicitly taking into account in the formulation the entire intersections existing and the time-varying passenger demand at shuttle stops, as well as the vehicle dynamics, battery, and recharging features. On purpose, a mathematical program to obtain the joint minimization of a number of objectives in terms of cost, i.e., vehicle operating, battery recharging, and recharging station purchasing, is formulated. Solutions employing an exact method are sought using models of mixed integer program within scenarios involving a number of features including the campus-wide passenger demand, seat capacity of shuttles, battery capacity of shuttles, and recharging station location. Further solutions to our model formulation have been obtained using a benchmark set of instances designed for a large-scale real network. Our findings show that considering a real road network as it is, is significant in exact routing solutions despite the fact that the level of network complexity is an issue. We suggest that a trade-off among the actuality of the network topology and the consequent computational load should be carefully made in order to obtain solutions using exact methods. As is shown, there is room to investigate further in details the dynamics of routing considering especially the effects of changes in flow conditions at intermediate nodes using our formulation.

12 páginas.- 4 figuras.- 4 tablas.- 114 referencias.- Supplementary data to this article can be found online at https://doi.org/10.1016/j.soilbio.2022.108560., The loss of tree health is a global concern with many unknown cascading impacts on the diversity and function of forest ecosystems. Specifically, it is uncertain how the process of tree decline and mortality driven by exotic pathogens might alter the soil microbiome. Here we combined high-throughput sequencing, neighborhood models, and network analysis to explore the impacts of the decline of a Mediterranean tree species, Quercus suber, on the diversity, composition and network structure of soil fungal and bacterial communities in forests invaded by the exotic pathogen Phytophthora cinnamomi. The belowground footprint of pathogen-driven tree decline implied an increase in the taxonomic and phylogenetic diversity of both bacteria and fungi, but also a severe reduction of tree-symbiotic fungi and Proteobacteria known to have positive effects on plant growth and disease suppression. Moreover, we detected alterations of the topology of soil microbial networks in declining tree neighborhoods (lower connectivity, higher modularity), with implications for ecosystem function. Our findings reveal the large impacts that moderate levels of tree decline can have on the soil microbiome of invaded forests, and highlight the recovery of a functionally diverse and highly connected soil microbiome as a key target in the restoration of these disturbed systems., This research was supported by the Ministerio de Ciencia e Innovación (MICINN) projects INTERCAPA (CGL2014-56739-R) and MICROFUN (RTI2018-094394-B-I00). AJMP was supported by a post-doctoral fellowship funded by the Portuguese Foundation for Science and Technology (SFRH/BPD/111015/2015), JDB by a FPI-MEC grant (BES-2012-055113) and EVS by a FPU-MICINN grant (FPU16/04616).

Throughout the years, measuring the complexity of networks and graphs has been of great interest to scientists. The Kolmogorov complexity is known as one of the most important tools to measure the complexity of an object. We formalized a method to calculate an upper bound for the Kolmogorov complexity of graphs and networks. Firstly, the most simple graphs possible, those with O(1) Kolmogorov complexity, were identified. These graphs were then used to develop a method to estimate the complexity of a given graph. The proposed method utilizes the simple structures within a graph to capture its non-randomness. This method is able to capture features that make a network closer to the more non-random end of the spectrum. The resulting algorithm takes a graph as an input and outputs an upper bound to its Kolmogorov complexity. This could be applicable in, for example evaluating the performances of graph compression methods.

Urban parks are essential components of urban ecosystems, providing recreation and relaxation places to residents. Measuring the spatial accessibility to urban parks serves as an initial step in urban planning and developing urban development strategies to improve social and environmental justice. This study aims to evaluate measures of spatial accessibility to urban parks by comparing three geographic information systems (GIS)-based approaches, accounting for network complexity, transport modes, distance thresholds, and destination choices. Taking Ipswich City (Australia) and Enschede (the Netherlands) as two testbeds, we examine the spatial patterns of a total of 21 accessibility measures in the two cities and conduct a correlation and principal component analysis to unravel the interrelationship between these measures. The results suggest that among all measures under the three approaches, the selection of distance thresholds and transport modes matter more to accessibility measures than the destination choices. Furthermore, when distance threshold and transport mode are held constant, the network-based and entrance-based methods provide more realistic accessibility measures than other methods. We also discuss the generality of the entrance-based method we propose and suggest ways to choose the most appropriate accessibility measure for use in different contexts.