Essential proteins identification method based on four-order distances and subcellular localization information

Essential proteins are inseparable in cell growth and survival. The study of essential proteins is important for understanding cellular functions and biological mechanisms. Therefore, various computable methods have been proposed to identify essential proteins. Unfortunately, most methods based on network topology only consider the interactions between a protein and its neighboring proteins, and not the interactions with its higher-order distance proteins. In this paper, we propose the DSEP algorithm in which we integrated network topology properties and subcellular localization information in protein–protein interaction(PPI) networks based on four-order distances, and then used random walks to identify the essential proteins. We also propose a method to calculate the finite-order distance of the network, which can greatly reduce the time complexity of our algorithm. We conducted a comprehensive comparison of the DSEP algorithm with 11 existing classical algorithms to identify essential proteins with multiple evaluation methods. The results show that DSEP is superior to these 11 methods.

Self-supervised recalibration network for person re-identification

The attention mechanism can extract salient features in images,which has been proved to be effective in improving the performance of person re-identification(Re-ID).However,most of the existing attention modules have the following two shortcomings:On the one hand,they mostly use global average pooling to generate context descriptors,without highlighting the guiding role of salient information on descriptor generation,resulting in insufficient ability of the final generated attention mask representation;On the other hand,the design of most attention modules is complicated,which greatly increases the computational cost of the model.To solve these problems,this paper proposes an attention module called self-supervised recalibration(SR)block,which introduces both global and local information through adaptive weighted fusion to generate a more refined attention mask.In particular,a special"Squeeze-Excitation"(SE)unit is designed in the SR block to further process the generated intermediate masks,both for nonlinearizations of the features and for constraint of the resulting computation by controlling the number of channels.Furthermore,we combine the most commonly used Res Net-50 to construct the instantiation model of the SR block,and verify its effectiveness on multiple Re-ID datasets,especially the mean Average Precision(m AP)on the Occluded-Duke dataset exceeds the state-of-the-art(SOTA)algorithm by 4.49%.

Novel detection method for infrared small targets using weighted information entropy

This paper presents a method for detecting the small infrared target under complex background. An algorithm, named local mutation weighted information entropy （LMWIE）, is proposed to suppress background. Then, the grey value of targets is enhanced by calculating the local energy. Image segmentation based on the adaptive threshold is used to solve the problems that the grey value of noise is enhanced with the grey value improvement of targets. Experimental results show that compared with the adaptive Butterworth high-pass filter method, the proposed algorithm is more effective and faster for the infrared small target detection.

Set pair three-way overlapping community discovery algorithm for weighted social internet of things

There are many problems in Social Internet of Things(IoTs),such as complex topology information,different degree of association between nodes and overlapping communities.The idea of set pair information grain computing and clustering is introduced to solve the above problems so as to accurately describe the similarity between nodes and fully explore the multi-community structure.A Set Pair Three-Way Overlapping Community Discovery Algorithm for Weighted Social Internet of Things(WSIoT-SPTOCD)is proposed.In the local network structure,which fully considers the topological information between nodes,the set pair connection degree is used to analyze the identity,difference and reverse of neighbor nodes.The similarity degree of different neighbor nodes is defined from network edge weight and node degree,and the similarity measurement method of set pair between nodes based on the local information structure is proposed.According to the number of nodes'neighbors and the connection degree of adjacent edges,the clustering intensity of nodes is defined,and an improved algorithm for initial value selection of k-means is proposed.The nodes are allocated according to the set pair similarity between nodes and different communities.Three-way community structures composed of a positive domain,boundary domain and negative domain are generated iteratively.Next,the overlapping node set is generated according to the calculation results of community node membership.Finally,experiments are carried out on artificial networks and real networks.The results show that WSIoT-SPTOCD performs well in terms of standardized mutual information,overlapping community modularity and F1.

SUNDER:Self-organized grouping and entrapping method for swarms in multitarget environments

For swarm robots moving in a harsh or uncharted outdoor environment without GPS guidance and global communication,algorithms that rely on global-based information are infeasible.Typically,traditional gene regulatory networks(GRNs)that achieve superior performance in forming trapping pattern towards targets require accurate global positional information to guide swarm robots.This article presents a gene regulatory network with Self-organized grouping and entrapping method for swarms(SUNDER-GRN)to achieve adequate trapping performance with a large-scale swarm in a confined multitarget environment with access to only local information.A hierarchical self-organized grouping method(HSG)is proposed to structure subswarms in a distributed way.In addition,a modified distributed controller,with a relative coordinate system that is established to relieve the need for global information,is leveraged to facilitate subswarms entrapment toward different targets,thus improving the global multi-target entrapping performance.The results demonstrate the superiority of SUNDERGRN in the performance of structuring subswarms and entrapping 10 targets with 200 robots in an environment confined by obstacles and with only local information accessible.

Local quantum Fisher information and quantum correlation in the mixed-spin Heisenberg XXZ chain

We study the local quantum Fisher information(LQFI)in the mixed-spin Heisenberg XXZ chain.Both the maximal and minimal LQFI are studied and the former is essential to determine the accuracy of the quantum parameter estimation,the latter can be well used to characterize the discord-type quantum correlations.We investigate the effects of the temperature and the anisotropy parameter on the maximal LQFI and thus on the accuracy of the parameter estimation.Then we make use of the minimal LQFI to study the discord-type correlations of different site pairs.Different dimensions of the subsystems cause different values of the minimal LQFI which reflects the asymmetry of the discord-type correlation.In addition,the site pairs at different positions of the spin chains have different minimal LQFI,which reveals the influence of the surrounding spins on the bipartite quantum correlation.Our results show that the LQFI obtained through a simple calculation process provides a convenient way to investigate the discord-type correlation in high-dimensional systems.

Towards No-Reference Image Quality Assessment Based on Multi-Scale Convolutional Neural Network

Image quality assessment has become increasingly important in image quality monitoring and reliability assuring of image processing systems.Most of the existing no-reference image quality assessment methods mainly exploit the global information of image while ignoring vital local information.Actually,the introduced distortion depends on a slight difference in details between the distorted image and the non-distorted reference image.In light of this,we propose a no-reference image quality assessment method based on a multi-scale convolutional neural network,which integrates both global information and local information of an image.We first adopt the image pyramid method to generate four scale images required for network input and then provide two network models by respectively using two fusion strategies to evaluate image quality.In order to better adapt to the quality assessment of the entire image,we use two different loss functions in the training and validation phases.The superiority of the proposed method is verified by several different experiments on the LIVE datasets and TID2008 datasets.

A Novel Unsupervised Change Detection Method with Structure Consistency and GFLICM Based on UAV Images

With the rapid development of Unmanned Aerial Vehicle(UAV)technology,change detection methods based on UAV images have been extensively studied.However,the imaging of UAV sensors is susceptible to environmental interference,which leads to great differences of same object between UAV images.Overcoming the discrepancy difference between UAV images is crucial to improving the accuracy of change detection.To address this issue,a novel unsupervised change detection method based on structural consistency and the Generalized Fuzzy Local Information C-means Clustering Model(GFLICM)was proposed in this study.Within this method,the establishment of a graph-based structural consistency measure allowed for the detection of change information by comparing structure similarity between UAV images.The local variation coefficient was introduced and a new fuzzy factor was reconstructed,after which the GFLICM algorithm was used to analyze difference images.Finally,change detection results were analyzed qualitatively and quantitatively.To measure the feasibility and robustness of the proposed method,experiments were conducted using two data sets from the cities of Yangzhou and Nanjing.The experimental results show that the proposed method can improve the overall accuracy of change detection and reduce the false alarm rate when compared with other state-of-the-art change detection methods.

Novel Active Contour Model for Image Segmentation Based on Local Fuzzy Gaussian Distribution Fitting

A novel active contour model is proposed, which incorporates local information distributions in a fuzzy energy function to effectively deal with the intensity inhomogeneity. Moreover, the proposed model is convex with respect to the variable which is used for extracting the contour. This makes the model independent on the initial condition and suitable for an automatic segmentation. Furthermore, the energy function is minimized in a computationally efficient way by calculating the fuzzy energy alterations directly. Experiments are carried out to prove the performance of the proposed model over some existing methods. The obtained results confirm the efficiency of the method.

The UNIX Localization and Chinese Information Processing System

To facilitate the wider use of computers all over the world,it is necessary to provide National Language Support in the computer systems.This paper introduces some aspects of design and implementation of the UNIX-based Chinese Information Processing Systems(CIPS). Due to the special nature of the Oriental languages,and in order to be able to share resources and ex- change information between different countries,it is necessary to create a standard of multilingual informa- tion exchange code.The Unified Chinese/Japanese/Korean character code,Han Character Collec- lion(HCC),was proposed to ISO/IEC JTC1/SC2/ WG2 by China Computer and Information Pro- cessing Standardization Technical Committee.Based on this character set and the corresponding coding sys- tem,it is possible to create a true Internationalized UNIX System.

Nonclassical correlations in two-dimensional graphene lattices

We investigate nonclassical correlations via negativity,local quantum uncertainty(LQU)and local quantum Fisher information(LQFI)for two-dimensional graphene lattices.The explicitly analytical expressions for negativity,LQU and LQFI are given.The close forms of LQU and LQFI confirm the inequality between the quantum Fisher information and skew information,where the LQFI is always greater than or equal to the LQU,and both show very similar behavior with different amplitudes.Moreover,the effects of the different system parameters on the quantified quantum correlation are analyzed.The LQFI reveals more nonclassical correlations than LQU in a two-dimensional graphene lattice system.

Fuzzy c-means clustering with non local spatial information for noisy image segmentation

As an effective image segmentation method, the standard fuzzy c-means （FCM） clustering algorithm is very sensitive to noise in images. Several modified FCM algorithms, using local spatial information, can overcome this problem to some degree. However, when the noise level in the image is high, these algorithms still cannot obtain satisfactory segmentation performance. In this paper, we introduce a non local spatial constraint term into the objective function of FCM and propose a fuzzy c- means clustering algorithm with non local spatial information （FCM_NLS）. FCM_NLS can deal more effectively with the image noise and preserve geometrical edges in the image. Performance evaluation experiments on synthetic and real images, especially magnetic resonance （MR） images, show that FCM NLS is more robust than both the standard FCM and the modified FCM algorithms using local spatial information for noisy image segmentation.

Unsupervised Nonlinear Adaptive Manifold Learning for Global and Local Information

In this paper,we propose an Unsupervised Nonlinear Adaptive Manifold Learning method(UNAML)that considers both global and local information.In this approach,we apply unlabeled training samples to study nonlinear manifold features,while considering global pairwise distances and maintaining local topology structure.Our method aims at minimizing global pairwise data distance errors as well as local structural errors.In order to enable our UNAML to be more efficient and to extract manifold features from the external source of new data,we add a feature approximate error that can be used to learn a linear extractor.Also,we add a feature approximate error that can be used to learn a linear extractor.In addition,we use a method of adaptive neighbor selection to calculate local structural errors.This paper uses the kernel matrix method to optimize the original algorithm.Our algorithm proves to be more effective when compared with the experimental results of other feature extraction methods on real face-data sets and object data sets.

A Reliable Neighbor-Based Method for Identifying Essential Proteins by Integrating Gene Expressions, Orthology,and Subcellular Localization Information

Essential proteins are those necessary for the survival or reproduction of species and discovering such essential proteins is fundamental for understanding the minimal requirements for cellular life, which is also meaningful to the disease study and drug design. With the development of high-throughput techniques, a large number of Protein-Protein Interactions（PPIs） can be used to identify essential proteins at the network level. Up to now, though a series of network-based computational methods have been proposed, it is still a challenge to improve the prediction precision as the high false positives in PPI networks. In this paper, we propose a new method GOS to identify essential proteins by integrating the Gene expressions, Orthology, and Subcellular localization information.The gene expressions and subcellular localization information are used to determine whether a neighbor in the PPI network is reliable. Only reliable neighbors are considered when we analyze the topological characteristics of a protein in a PPI network. We also analyze the orthologous attributes of each protein to reflect its conservative features, and use a random walk model to integrate a protein＇s topological characteristics and its orthology. The experimental results on the yeast PPI network show that the proposed method GOS outperforms the ten existing methods DC, BC, CC, SC, EC, IC, NC, Pe C, ION, and CSC.

A Novel Attention-based Global and Local Information Fusion Neural Network for Group Recommendation

Due to the popularity of group activities in social media,group recommendation becomes increasingly significant.It aims to pursue a list of preferred items for a target group.Most deep learning-based methods on group recommendation have focused on learning group representations from single interaction between groups and users.However,these methods may suffer from data sparsity problem.Except for the interaction between groups and users,there also exist other interactions that may enrich group representation,such as the interaction between groups and items.Such interactions,which take place in the range of a group,form a local view of a certain group.In addition to local information,groups with common interests may also show similar tastes on items.Therefore,group representation can be conducted according to the similarity among groups,which forms a global view of a certain group.In this paper,we propose a novel global and local information fusion neural network(GLIF)model for group recommendation.In GLIF,an attentive neural network(ANN)activates rich interactions among groups,users and items with respect to forming a group′s local representation.Moreover,our model also leverages ANN to obtain a group′s global representation based on the similarity among different groups.Then,it fuses global and local representations based on attention mechanism to form a group′s comprehensive representation.Finally,group recommendation is conducted under neural collaborative filtering(NCF)framework.Extensive experiments on three public datasets demonstrate its superiority over the state-of-the-art methods for group recommendation.

Fault Tolerant Data Aggregation Scheduling with Local Information in Wireless Sensor Networks

We study the problem of efficient data aggregation in unreliable wireless sensor networks by designing a fault tolerant data aggregation protocol. A fault tolerant data aggregation protocol consists of two parts： basic aggregation scheduling and amendment strategies. On default, data is aggregated according to the basic aggregation scheduling strategy. The amendment strategy will start automatically when a middle sensor node is out of service. We focus our attention on the amendment strategies and assume that the network adopts a connected dominating set （CDS） based aggregation scheduling as its basic aggregation scheduling strategy. The amendment scheme includes localized aggregation tree repairing algorithms and distributed rescheduling algorithms. The former are used to find a new aggregation tree for every child of the corrupted node, whereas the latter are used to achieve interference free data aggregation scheduling after the amendment. These amendment strategies impact only a very limited number of nodes near the corrupted node and the amendment process is transparent to all the other nodes. Theoretical analyses and simulations show that the scheme greatly improves the efficiency of the data aggregation operation by reducing both message and time costs compared to rebuilding the aggregation tree and rescheduling the en- tire network.

Novel Model Using Kernel Function and Local Intensity Information for Noise Image Segmentation

It remains a challenging task to segment images that are distorted by noise and intensity inhomogeneity.To overcome these problems, in this paper, we present a novel region-based active contour model based on local intensity information and a kernel metric. By introducing intensity information about the local region, the proposed model can accurately segment images with intensity inhomogeneity. To enhance the model＇s robustness to noise and outliers, we introduce a kernel metric as its objective functional. To more accurately detect boundaries, we apply convex optimization to this new model, which uses a weighted total-variation norm given by an edge indicator function. Lastly, we use the split Bregman iteration method to obtain the numerical solution. We conducted an extensive series of experiments on both synthetic and real images to evaluate our proposed method, and the results demonstrate significant improvements in terms of efficiency and accuracy, compared with the performance of currently popular methods.

Enhanced kernel-based fuzzy local information clustering integrating neighborhood membership

To enhance the segmentation performance and robustness of kernel weighted fuzzy local information C-means(KWFLICM) clustering for image segmentation in the presence of high noise, an improved KWFLICM algorithm aggregating neighborhood membership information is proposed. This algorithm firstly constructs a linear weighted membership function by combining the membership degrees of current pixel and its neighborhood pixels. Then it is normalized to meet the constraint that the sum of membership degree of pixel belonging to different classes is 1. In the end, normalized membership is used to update the clustering centers of KWFLICM algorithm. Experimental results show that the proposed adaptive KWFLICM(AKWFLICM) algorithm outperforms existing state of the art fuzzy clustering-related segmentation algorithms for image with high noise.

Multitarget tracking control algorithm under local information selection interaction mechanism

This study focuses on the problem of multitarget tracking.To address the existing problems of current tracking algorithms,as manifested by the time consumption of subgroup separation and the uneven group size of unmanned aerial vehicles(UAVs)for target tracking,a multitarget tracking control algorithm under local information selection interaction is proposed.First,on the basis of location,number,and perceived target information of neighboring UAVs,a temporary leader selection strategy is designed to realize the local follow-up movement of UAVs when the UAVs cannot fully perceive the target.Second,in combination with the basic rules of cluster movement and target information perception factors,distributed control equations are designed to achieve a rapid gathering of UAVs and consistent tracking of multiple targets.Lastly,the simulation experiments are conducted in two-and three-dimensional spaces.Under a certain number of UAVs,clustering speed of the proposed algorithm is less than 3 s,and the equal probability of the UAV subgroup size after group separation is over 78%.

Adaptive LII-RMPLS based data-driven process monitoring scheme for quality-relevant fault detection

The partial least squares(PLS)method has been successfully applied for fault diagnosis in indus-trial production.Compared with the traditional PLS methods,the modified PLS(MPLS)approach is available for slow-time-varying data processing and quality-relevant fault detecting.How-ever,it encounters heavy computational load in model updating,and the static control limits often lead to the low fault detection rate(FDR)or high false alarm rate(FAR).In this article,we first introduce the recursive MPLS(RMPLS)method for quality-relevant fault detection and computational complexity reducing,and then combine the local information increment(LII)method to obtain the time-varying control limits.First,the proposed LII-RMPLS method is capa-ble of quality-relevant faults detection.Second,the adaptive threshold leads to higher FDRs and lower FARs compared with traditional methods.Third,the adaptive parameter-matrices-based model updating approach ensures that the proposed method has better robustness and lower computational complexity when dealing with time-varying factors.