Intervention against information diffusion in static and temporal coupling networks

Information diffusion in complex networks has become quite an active research topic.As an important part of this field,intervention against information diffusion processes is attracting ever-increasing attention from network and control engineers.In particular,it is urgent to design intervention schemes for the coevolutionary dynamics between information diffusion processes and coupled networks.For this purpose,we comprehensively study the problem of information diffusion intervention over static and temporal coupling networks.First,individual interactions are described by a modified activitydriven network(ADN)model.Then,we establish a novel node-based susceptible-infected-recovered-susceptible(SIRS)model to characterize the information diffusion dynamics.On these bases,three synergetic intervention strategies are formulated.Second,we derive the critical threshold of the controlled-SIRS system via stability analysis.Accordingly,we exploit a spectral optimization scheme to minimize the outbreak risk or the required budget.Third,we develop an optimal control scheme of dynamically allocating resources to minimize both system loss and intervention expense,in which the optimal intervention inputs are obtained through optimal control theory and a forward-backward sweep algorithm.Finally,extensive simulation results validate the accuracy of theoretical derivation and the performance of our proposed intervention schemes.

A simulation study of polarization characteristics of ultrathin CsPbBr3 nanowires with different cross-section shapes and sizes

The polarization characteristics of ultrathin CsPbBr3nanowires are investigated. Especially, for the height of crosssection of nanowires between 2 nm and 25 nm, the normalized intensity and polarization ratio ρ of CsPbBr3nanowires with triangular, square and hexagonal cross-section shapes are compared. The results show that, along with the increase of the height of cross-section, the polarization ratios of these three nanowires decrease until T = 15 nm, and increase afterwards.Also, along with the increase of the cross-section area up to 100 nm~2, the polarization ratios of these three nanowires increase too. In general, for the same height or area, the polarization ratio ρ of these nanowires follows ρhexagon> ρsquare>ρtriangle. Therefore, the nanowire with the hexagonal cross-section should be chosen, where for a cross-section height of 2 nm and a length-height ratio of 20 : 1, the maximal polarization ratio is 0.951 at the longitudinal center of the NW. Further,for the hexagonal NW with a cross-section height of 10 nm, the hexagonal NW with a length-height ratio of 45 : 1 exhibits the maximal polarization ratio at the longitudinal center of the NW. These simulation results predict the feasible size and shape of CsPbBr3nanowire devices with high polarization ratios.

Robust description and recognition of various viewpoint dynamic textures

The problem of recognizing natural scenes, such as water, smoke, fire, wind-blown vegetation and a flock of flying birds, is considered. These scenes exhibit the characteristic dynamic pattern, but have stochastic extent. They are referred to as dynamic texture（DT）. In reality, the diversity of DTs on different viewpoints and scales are very common, which also bring great difficulty to recognize DTs. In the previous studies, due to no considering of the deformable and transient nature of elements in DT, the motion estimation method is based on brightness constancy assumption,which seem inappropriate for aggregate and complex motions. A novel motion model based on relative motion in the neighborhood of two-dimensional motion fields is proposed. The estimation of non-rigid motion of DTs is based on the continuity equation, and then the local vector difference（LVD） is proposed to characterize DT local relative motion. Spatiotemporal statistics of the LVDs is used as the representation of DT sequences. Excellent performances of classifying all DTs in UCLA database demonstrate the capability of the proposed method in describing DT.

Multiscale Fusion Transformer Network for Hyperspectral Image Classification

Convolutional neural network(CNN)has excellent ability to model locally contextual information.However,CNNs face challenges for descripting long-range semantic features,which will lead to relatively low classification accuracy of hyperspectral images.To address this problem,this article proposes an algorithm based on multiscale fusion and transformer network for hyperspectral image classification.Firstly,the low-level spatial-spectral features are extracted by multi-scale residual structure.Secondly,an attention module is introduced to focus on the more important spatialspectral information.Finally,high-level semantic features are represented and learned by a token learner and an improved transformer encoder.The proposed algorithm is compared with six classical hyperspectral classification algorithms on real hyperspectral images.The experimental results show that the proposed algorithm effectively improves the land cover classification accuracy of hyperspectral images.

Securing Sensor Networks Based on Optimization of Weighted Confidence

In this paper, an optimized rmlicious nodes detection algorithm, based on Weighted Confidence Filter （WCF）, is proposed to protect sensor networks from attacks. In this algorithm, each cluster head in a cluster-based hierarchical network figures out an average confidence degree by means of messages from its child nodes. The cluster head only accepts a message from the child node whose confidence degree is higher than the average. Meanwhile, it updates the confidence degrees for each of its child nodes by comparing the aggregation value and the received messages, and regards them as the weight of exactness of messages from nodes. A sensor node is judged to be rmlicious if its weight value is lower than the predefined threshold. Comparative simulation results verify that the proposed WCF algorithm is better than the Weighted Trust Evaluation （WTE） in terms of the detection ratio and the false alarm ratio. More specifically, with the WCF, the detection ratio is significantly improved and the false alarm ratio is observably reduced, especially when the malicious node ratio is 0.25 or greater. When 40% of 100 sensors are malicious, the detection accuracy is above 90% and the false alarm ratio is nearly only 1.8%.

A Distributed User Association and Resource Allocation Method in Cache-Enabled Small Cell Networks

Cache-enabled small cell networks have been regarded as a promising approach for network operators to cope with the explosive data traffic growth in future 5 G networks. However, the user association and resource allocation mechanism has not been thoroughly studied under given content placement situation. In this paper, we formulate the joint optimization problem of user association and resource allocation as a mixed integer nonlinear programming(MINLP) problem aiming at deriving a balance between the total utility of data rates and the total data rates retrieved from caches. To solve this problem, we propose a distributed relaxing-rounding method. Simulation results demonstrate that the distributed relaxing-rounding method outperforms traditional max-SINR method and range-expansion method in terms of both total utility of data rates and total data rates retrieved from caches in practical scenarios. In addition, effects of storage and backhaul capacities on the performance are also studied.

Anisotropic self-diffusion of fluorinated poly(methacrylate) in metal-organic frameworks assessed with molecular dynamics simulation

Utilizing the periodically structured metal-organic framework （MOF） as the reaction vessel is a promising technique to achieve the aligned polymer molecular chains, where the diffusion procedure of the polymer monomer inside MOF is one of the key mechanisms. To investigate the diffusion mechanism of fluorinated polymer monomers in MOFs, in this paper the molecular dynamics simulations combined with the density functional theory and the Monte Carlo method are used and the all-atom models of TFMA （trifluoroethyl methacrylate） monomer and two types of MOFs,[Zn2（BDC）2（TED）]n and[Zn2（BPDC）2（TED）]n, are established. The diffusion behaviors of TFMA monomer in these two MOFs are simulated and the main influencing factors are analyzed. The obtained results are as follows. First, the electrostatic interactions between TFMA monomers and MOFs cause the monomers to concentrate in the MOF channel, which slows down the monomer diffusion. Second, the anisotropic shape of the one-dimensional MOF channel leads to different diffusion speeds of monomers in different directions. Third, MOF with a larger pore diameter due to a longer organic ligand,[Zn2（BPDC）2（TED）]n in this paper, facilitates the diffusion of monomers in the MOF channel. Finally, as the number of monomers increases, the self-diffusion coefficient is reduced by the steric effect.

Simulation Research on Ran-Assisted WLAN Offloading Scheme

Wireless Local Area Network (WLAN) offloading is an important approach to address the data traffic challenge faced by Long Term Evolution (LTE) network. Legacy offloading solutions based on the core network suffer from the limitations of load unbalance and user experience degradation. To solve this problem, 3GPP has recently proposed a radio access network (RAN) assisted WLAN offloading scheme. In this article, we thoroughly analyze the performance of this new scheme, with emphasis on the RAN auxiliary parameters and control rules defined in it. Furthermore, two kinds of offloading algorithms based on this scheme are proposed and compared with the traditional solutions by simulation. Results show that the RAN-assisted offloading scheme can increase average user throughput and leverage resources available in both LTE and WLAN systems.

Dynamic properties of rumor propagation model induced by L´evy noise on social networks

Social networks are inevitably subject to disruptions from the physical world,such as sudden internet outages that sever local connections and impede information flow.While Gaussian white noise,commonly used to simulate stochastic disruptions,only fluctuates within a narrow range around its mean and fails to capture large-scale variations,L´evy noise can effectively compensate for this limitation.Therefore,a susceptible–infected–removed rumor propagation model with L´evy noise is constructed on homogeneous and heterogeneous networks,respectively.Then,the existence of a global positive solution and the asymptotic path-wise of the solution are derived on heterogeneous networks,and the sufficient conditions of rumor extinction and persistence are investigated.Subsequently,theoretical results are verified through numerical calculations and the sensitivity analysis related to the threshold is conducted on the model parameters.Through simulation experiments on Watts–Strogatz(WS)and Barab´asi–Albert networks,it is found that the addition of noise can inhibit the spread of rumors,resulting in a stochastic resonance phenomenon,and the optimal noise intensity is obtained on the WS network.The validity of the model is verified on three real datasets by particle swarm optimization algorithm.

Prediction Method for Network Traffic Based on Maximum Correntropy Criterion

This paper proposes a method for improving the precision of Network Traffic Prediction based on the Maximum Correntropy Criterion(NTPMCC),where the nonlinear characteristics of network traffic are considered.This method utilizes the MCC as a new error evaluation criterion or named the cost function(CF)to train neural networks(NN).MCC is based on a new similarity function(Generalized correlation entropy function,Correntropy),which has as its foundation the Parzen window evaluation and Renyi entropy of error probability density function.At the same time,by combining the MCC with the Mean Square Error(MSE),a mixed evaluation criterion with MCC and MSE is proposed as a cost function of NN training.According to the traffic network characteristics including the nonlinear,non-Gaussian,and mutation,the Elman neural network is trained by MCC and MCC-MSE,and then the trained neural network is used as the model for predicting network traffic.The simulation results based on the evaluation by Mean Absolute Error(MAE),MSE,and Sum Squared Error(SSE)show that the accuracy of the prediction based on MCC is superior to the results of the Elman neural network with MSE.The overall performance is improved by about 0.0131.

Robust Digital Non-Linear Self-Interference Cancellation in Full Duplex Radios with Maximum Correntropy Criterion

Full duplex radio increases the frequency efficiency but its performance is limited by the self-interference （SI）. We first analyze the multiple noises in the full duplex radio system and model such noises as an α - stable distribution. Then we formulate a novel non-Gaussian SI problem. Under the maximum correntropy criterion （MCC）, a robust digital non-linear self-interference cancellation algorithm is proposed for the SI channel estimation. A gradient descent based algorithm is derived to search the optimal solution. Simulation results show that the proposed algorithm can achieve a smaller estimation error and a higher pseudo signal to interference plus noise ratio （PSINR） than the well-known least mean square （LMS） algorithm and least square （LS） algorithm.

A fluorescent Eu(Ⅲ) MOF for highly selective and sensitive sensing of picric acid

A metal-organic framework [Eu_3L_3(CH_3COO)_2(H_2O)_2(μ_3-OH)]·3 DMF,(EuL, H_2L=9H-carbazole-2,7-dicarboxylic acid,DMF=N,N-dimethylformamide) has been synthesized under solvothermal conditions and structurally characterized. In EuL,Eu_6O_8 clusters are four-bridged by carboxylates to form parallel-aligned Eu–O–C chains, which are further linked by the carbazole moieties of L^(2-) ligands to form the three-dimensional framework with rhombic channels. The EuL material with characteristic emission of Eu^(3+) ion exhibits significant luminescence quenching response for picric acid(PA) and the linear Stern-Volmer plot was observed in the concentration range of 0.05–0.15 mM with K_(sv) of 98074 M^(-1). As far as we know, this Ksv is among the highest values for COFs and MOFs in detection of PA. The excellent anti-interference ability and repeatability were also verified by experiments. Lastly, we investigated the luminescence quenching mechanism in the EuL sensing system.

NOVEL SPECTRUM SENSING METHOD BASED ON THE SPATIAL SPECTRUM FOR COGNITIVE RADIO SYSTEMS

Spectrum sensing is the key problem for Cognitive Radio(CR) systems.A method based on the Peak-to-Average Amplitude-Ratio(PAAR) of the Spatial Spectrum(SS) of the received signals is proposed to sense the available spectrum for the cognitive users with the help of the multiple antennas at the receiver of the cognitive users.The greatest advantage of the new method is that it requires no information of the noise power and is free of the noise power uncertainty.Both the simulation and the analytical results show that the proposed method is robust to noise uncertainty,and greatly outperform the classical Energy Detector(ED) method.

Rapid identifying high-influence nodes in complex networks

A tiny fraction of influential individuals play a critical role in the dynamics on complex systems. Identifying the influential nodes in complex networks has theoretical and practical significance. Considering the uncertainties of network scale and topology, and the timeliness of dynamic behaviors in real networks, we propose a rapid identifying method（RIM）to find the fraction of high-influential nodes. Instead of ranking all nodes, our method only aims at ranking a small number of nodes in network. We set the high-influential nodes as initial spreaders, and evaluate the performance of RIM by the susceptible-infected-recovered（SIR） model. The simulations show that in different networks, RIM performs well on rapid identifying high-influential nodes, which is verified by typical ranking methods, such as degree, closeness, betweenness,and eigenvector centrality methods.

User-Oriented Graph Based Frequency Allocation Algorithm for Densely Deployed Femtocell Network

Femtocell is a promising technology for improving indoor coverage and offloading the macrocell.Femtocells tend to be densely deployed in populated areas such as the dormitories.However,the inter-tier interference seriously exists in the co-channel Densely Deployed Femtocell Network(DDFN).Since the Femtocell Access Points(FAPs) are randomly deployed by their customers,the interference cannot be predicted in advance.Meanwhile,new characteristics such as the short radius of femtocell and the small number of users lead to the inefficiency of the traditional frequency reuse algorithms such as Fractional Frequency Reuse(FFR).Aiming for the downlink interference coordination in the DDFN,in this paper,we propose a User-oriented Graph based Frequency Allocation(UGFA)algorithm.Firstly,we construct the interference graph for users in the network.Secondly,we study the conventional graph based resources allocation algorithm.Then an improved two steps graph based frequency allocation mechanism is proposed.Simulation results show that UGFA has a high frequency reuse ratio mean while guarantees a better throughput.

Rice Plant Counting,Locating,and Sizing Method Based on High-Throughput UAV RGB Images

Rice plant counting is crucial for many applications in rice production,such as yield estimation,growth diagnosis,disaster loss assessment,etc.Currently,rice counting still heavily relies on tedious and time-consuming manual operation.To alleviate the workload of rice counting,we employed an UAV(unmanned aerial vehicle)to collect the RGB images of the paddy field.Then,we proposed a new rice plant counting,locating,and sizing method(RiceNet),which consists of one feature extractor frontend and 3 feature decoder modules,namely,density map estimator,plant location detector,and plant size estimator.In RiceNet,rice plant attention mechanism and positive–negative loss are designed to improve the ability to distinguish plants from background and the quality of the estimated density maps.To verify the validity of our method,we propose a new UAV-based rice counting dataset,which contains 355 images and 257,793 manual labeled points.Experiment results show that the mean absolute error and root mean square error of the proposed RiceNet are 8.6 and 11.2,respectively.Moreover,we validated the performance of our method with two other popular crop datasets.On these three datasets,our method significantly outperforms state-of-the-art methods.Results suggest that RiceNet can accurately and efficiently estimate the number of rice plants and replace the traditional manual method.

Robust Visual Tracking with Hierarchical Deep Features Weighted Fusion

To solve the problem of low robustness of trackers under significant appearance changes in complex background,a novel moving target tracking method based on hierarchical deep features weighted fusion and correlation filter is proposed.Firstly,multi-layer features are extracted by a deep model pre-trained on massive object recognition datasets.The linearly separable features of Relu3-1,Relu4-1 and Relu5-4 layers from VGG-Net-19 are especially suitable for target tracking.Then,correlation filters over hierarchical convolutional features are learned to generate their correlation response maps.Finally,a novel approach of weight adjustment is presented to fuse response maps.The maximum value of the final response map is just the location of the target.Extensive experiments on the object tracking benchmark datasets demonstrate the high robustness and recognition precision compared with several state-of-the-art trackers under the different conditions.

Kernel Least Mean Kurtosis Based Online Chaotic Time Series Prediction

Based on the kernel methods and the nonlinear feature of chaotic time series,we develop a new algorithm called kernel least mean kurtosis(KLMK)by applying the kernel trick to the least mean kurtosis(LMK)algorithm,which maps the input data to a high dimensional feature space.The KLMK algorithm can overcome the shortcomings of the original LMK for nonlinear time series prediction,and it is easy to implement a sample by sample adaptation procedure.Theoretical analysis suggests that the KLMK algorithm may converge in a mean square sense in nonlinear chaotic time series prediction under certain conditions.Simulation results show that the performance of KLMK is better than those of LMK and the kernel least mean square(KLMS)algorithm.

Disclosing incoherent sparse and low-rank patterns inside homologous GPCR tasks for better modelling of ligand bioactivities

There are many new and potential drug targets in G protein-coupled receptors(GPCRs)without sufficient ligand associations,and accurately predicting and interpreting ligand bioactivities is vital for screening and optimizing hit compounds targeting these GPCRs.To efficiently address the lack of labeled training samples,we proposed a multi-task regression learning with incoherent sparse and low-rank patterns(MTR-ISLR)to model ligand bioactivities and identify their key substructures associated with these GPCRs targets.That is,MTR-ISLR intends to enhance the performance and interpretability of models under a small size of available training data by introducing homologous GPCR tasks.Meanwhile,the low-rank constraint term encourages to catch the underlying relationship among homologous GPCR tasks for greater model generalization,and the entry-wise sparse regularization term ensures to recognize essential discriminative substructures from each task for explanative modeling.We examined MTR-ISLR on a set of 31 important human GPCRs datasets from 9 subfamilies,each with less than 400 ligand associations.The results show that MTR-ISLR reaches better performance when compared with traditional single-task learning,deep multi-task learning and multi-task learning with joint feature learning-based models on most cases,where MTR-ISLR obtains an average improvement of 7%in correlation coefficient(r2)and 12%in root mean square error(RMSE)against the runner-up predictors.The MTR-ISLR web server appends freely all source codes and data for academic usages.^(1))

Non-cooperative game for network access selection in heterogeneous integrated networks

The integration of different heterogeneous access networks is one of the remarkable characteristics of the next generation network,in which users with multi-network interface terminals can independently select access network to obtain the most desired service.A kind of unified quantification model of non-monotone quality of service(QoS) and a model of non-cooperative game between users and networks are proposed for heterogeneous network access selection.An optimal network pricing mechanism could be formulated by using a novel strategy which is used in this non-cooperative game model to balance the interests of both the users and the networks.This access network selection mechanism could select the most suitable network for users,and it also could provide the basis when formulating QoS standards in heterogeneous integrated networks.The simulation results show that this network selection decision-making algorithm can meet the users' demand for different levels service in different scenes and it can also avoid network congestion caused by unbalanced load.