An Unsupervised Writer Identification Based on Generating Clusterable Embeddings

The writer identification system identifies individuals based on their handwriting is a frequent topic in biometric authentication and verification systems.Due to its importance,numerous studies have been conducted in various languages.Researchers have established several learning methods for writer identification including supervised and unsupervised learning.However,supervised methods require a large amount of annotation data,which is impossible in most scenarios.On the other hand,unsupervised writer identification methods may be limited and dependent on feature extraction that cannot provide the proper objectives to the architecture and be misinterpreted.This paper introduces an unsupervised writer identification system that analyzes the data and recognizes the writer based on the inter-feature relations of the data to resolve the uncertainty of the features.A pairwise architecturebased Autoembedder was applied to generate clusterable embeddings for handwritten text images.Furthermore,the trained baseline architecture generates the embedding of the data image,and the K-means algorithm is used to distinguish the embedding of individual writers.The proposed model utilized the IAM dataset for the experiment as it is inconsistent with contributions from the authors but is easily accessible for writer identification tasks.In addition,traditional evaluation metrics are used in the proposed model.Finally,the proposed model is compared with a few unsupervised models,and it outperformed the state-of-the-art deep convolutional architectures in recognizing writers based on unlabeled data.

基于Blending-Clustering集成学习的大坝变形预测模型

【目的】变形是反映大坝结构性态最直观的效应量,构建科学合理的变形预测模型是保障大坝安全健康运行的重要手段。针对传统大坝变形预测模型预测精度低、误报率高等问题导致的错误报警现象,【方法】选取不同预测模型和聚类算法集成,构建了一种Blending-Clustering集成学习的大坝变形预测模型,该模型以Blending对单一预测模型集成提升预测精度为核心,并通过Clustering聚类优选预测值改善模型稳定性。以新疆某面板堆石坝变形监测数据为实例分析,通过多模型预测性能比较,对所提出模型的预测精度和稳定性进行全面评估。【结果】结果显示:Blending-Clustering模型将预测模型和聚类算法集成,均方根误差(RMSE)和归一化平均百分比误差(nMAPE)明显降低,模型的预测精度得到显著提高;回归相关系数(R~2)得到提升,模型具备更强的拟合能力;在面板堆石坝上22个测点变形数据集上的预测评价指标波动范围更小,模型的泛化性和稳定性得到有效增强。【结论】结果表明:Blending-Clustering集成预测模型对于预测精度、泛化性和稳定性均有明显提升,在实际工程具有一定的应用价值。

Robust and Trustworthy Data Sharing Framework Leveraging On-Chain and Off-Chain Collaboration

The proliferation of Internet of Things(IoT)systems has resulted in the generation of substantial data,presenting new challenges in reliable storage and trustworthy sharing.Conventional distributed storage systems are hindered by centralized management and lack traceability,while blockchain systems are limited by low capacity and high latency.To address these challenges,the present study investigates the reliable storage and trustworthy sharing of IoT data,and presents a novel system architecture that integrates on-chain and off-chain data manage systems.This architecture,integrating blockchain and distributed storage technologies,provides high-capacity,high-performance,traceable,and verifiable data storage and access.The on-chain system,built on Hyperledger Fabric,manages metadata,verification data,and permission information of the raw data.The off-chain system,implemented using IPFS Cluster,ensures the reliable storage and efficient access to massive files.A collaborative storage server is designed to integrate on-chain and off-chain operation interfaces,facilitating comprehensive data operations.We provide a unified access interface for user-friendly system interaction.Extensive testing validates the system’s reliability and stable performance.The proposed approach significantly enhances storage capacity compared to standalone blockchain systems.Rigorous reliability tests consistently yield positive outcomes.With average upload and download throughputs of roughly 20 and 30 MB/s,respectively,the system’s throughput surpasses the blockchain system by a factor of 4 to 18.

Clustered张拉整体结构的动力学建模

针对clustered张拉整体结构,提出了一种基于任意拉格朗日-欧拉(arbitrary Lagrangian-Eulerian, ALE)法的多体动力学模型。相较于传统的拉格朗日法模型,该研究中的模型具有更为简单的运动学约束。首先,引入了一种ALE时变长度索单元,其网格节点与物质点可以独立运动,提供了一种自然的方式描述结构中运动的滑轮和滑动绳索;其次,利用达朗贝尔原理,推导了该单元的广义力向量并计算了相应的雅可比矩阵;然后,建立了张拉整体系统的动力学方程,并利用广义α算法对其进行求解,选取节点的全局位置坐标和物质坐标作为广义坐标,其中全局位置坐标可以被不同物体共享,以减少动力学方程的自由度数和消除物体间的约束;最后,展示了一个数值算例,10层可折叠张拉整体塔架,对其折叠过程进行了准静态和动力学仿真,验证了模型的有效性。所提出的模型和算法可为clustered张拉整体结构的设计提供理论指导,具有工程意义。

Weak Merging Scenario of CLASH Cluster A209

We study the structural and dynamical properties of A209 based on Chandra and XMM-Newton observations.We obtain detailed temperature,pressure,and entropy maps with the contour binning method,and find a hot region in the NW direction.The X-ray brightness residual map and corresponding temperature profiles reveal a possible shock front in the NW direction and a cold front feature in the SE direction.Combined with the galaxy luminosity density map we propose a weak merger scenario.A young sub-cluster passing from the SE to NW direction could explain the optical subpeak,the intracluster medium temperature map,the X-ray surface brightness excess,and the X-ray peak offset together.

Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering

In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data,particularly in the field of medical imaging.Traditional deep subspace clustering algorithms,which are mostly unsupervised,are limited in their ability to effectively utilize the inherent prior knowledge in medical images.Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process,thereby enhancing the discriminative power of the feature representations.Additionally,the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data,resulting in more accurate clustering performance.To address the difficulty of hyperparameter selection in deep subspace clustering,this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering,prior knowledge constraints,and model loss weights.Extensive experiments on standard clustering datasets,including ORL,Coil20,and Coil100,validate the effectiveness of the MAS-DSC algorithm.The results show that with its multi-scale network structure and Bayesian hyperparameter optimization,MAS-DSC achieves excellent clustering results on these datasets.Furthermore,tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework.

Cluster DetectionMethod of Endogenous Security Abnormal Attack Behavior in Air Traffic Control Network

In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set for ATC cybersecurity attacks is constructed by setting the feature states,adding recursive features,and determining the feature criticality.The expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature data.An autoencoder is introduced into the AI(artificial intelligence)algorithm to encode and decode the characteristics of ATC network security attack behavior to reduce the dimensionality of the ATC network security attack behavior data.Based on the above processing,an unsupervised learning algorithm for clustering detection of ATC network security attacks is designed.First,determine the distance between the clustering clusters of ATC network security attack behavior characteristics,calculate the clustering threshold,and construct the initial clustering center.Then,the new average value of all feature objects in each cluster is recalculated as the new cluster center.Second,it traverses all objects in a cluster of ATC network security attack behavior feature data.Finally,the cluster detection of ATC network security attack behavior is completed by the computation of objective functions.The experiment took three groups of experimental attack behavior data sets as the test object,and took the detection rate,false detection rate and recall rate as the test indicators,and selected three similar methods for comparative test.The experimental results show that the detection rate of this method is about 98%,the false positive rate is below 1%,and the recall rate is above 97%.Research shows that this method can improve the detection performance of security attacks in air traffic control network.

Perception of fundamental science to boost lithium metal anodes toward practical application

As a key material for lithium metal batteries(LMBs),lithium metal is one of the most promising anode materials to break the bottleneck of battery energy density and a commonly used active material for reference electrodes.Although lithium anodes are regarded as the holy grail of lithium batteries,decades of exploration have not led to the successful commercialization of LMBs,due mainly to the challenges related to the inherent properties of lithium metal.To pave the way for further investigation,herein,a comprehensive review focusing on the fundamental science of lithium are provided.Firstly,the natures of lithium atoms and their isotopes,lithium clusters and lithium crystals are revisited,especially their structural and energetic properties.Subsequently,the electrochemical properties of lithium metal are reviewed.Numerous important concepts and scientific questions,including the electronic structure of lithium,influence of high pressure and low temperature on the properties of lithium,factors influencing lithium deposition,generation of lithium dendrites,and electrode potential of lithium in different electrolytes,are explained and analyzed in detail.Approaches to improve the performance of lithium anodes and thoughtfulness about the electrode potential in lithium battery research are proposed.

Heterostructured Pt-Ni_(3)Mo_(3)N formed via ammonia-containing polyoxometalates for highly efficient electrocatalytic hydrogen evolution in acid medium

Constructing heterostructured nanohybrid is considered as a prominent route to fabricate alternative electrocatalysts to commercial Pt/C for hydrogen evolution reaction(HER).In this work,(NH_(4))_(4)[NiH_(6)Mo_(6)O_(4)]·5H_(2)O polyoxometalates(NiMo_(6))are adopted as the cluster precursors for simple fabrication of heterostructured Pt-Ni_(3)Mo_(3)N nanohybrids supported by carbon black(Pt-Ni_(3)Mo_(3)N/C)without using additional N sources.The improved porosity and enhanced electronic interaction of Pt-Ni_(3)Mo_(3)N/C should be attributed to the integration of Pt with NiMo_(6),which favors the mass transport,promotes the formation of exposed catalytic sites,and benefits the regulation of intrinsic activity.Thus,the as-obtained Pt-Ni_(3)Mo_(3)N/C exhibits impressive and durable HER performance as indicated by the low overpotential of 13.7 mV at the current density of 10 mA cm^(-2) and the stable overpotential during continuous working at 100 mA cm^(-2) for 100 h.This work provides significant insights for the synthesis of new highly active heterostructured electrocatalysts for renewable energy devices.

Formation and transformation of metastable LPSO building blocks clusters in Mg-Gd-Y-Zn-Zr alloys by spinodal decomposition and heterogeneous nucleation

To study the formation and transformation mechanism of long-period stacked ordered(LPSO)structures,a systematic atomic scale analysis was conducted for the structural evolution of long-period stacked ordered(LPSO)structures in the Mg-Gd-Y-Zn-Zr alloy annealed at 300℃~500℃.Various types of metastable LPSO building block clusters were found to exist in alloy structures at different temperatures,which precipitate during the solidification and homogenization process.The stability of Zn/Y clusters is explained by the first principles of density functional theory.The LPSO structure is distinguished by the arrangement of its different Zn/Y enriched LPSO structural units,which comprises local fcc stacking sequences upon a tightly packed plane.The presence of solute atoms causes local lattice distortion,thereby enabling the rearrangement of Mg atoms in the different configurations in the local lattice,and local HCP-FCC transitions occur between Mg and Zn atoms occupying the nearest neighbor positions.This finding indicates that LPSO structures can generate necessary Schockley partial dislocations on specific slip surfaces,providing direct evidence of the transition from 18R to 14H.Growth of the LPSO,devoid of any defects and non-coherent interfaces,was observed separately from other precipitated phases.As a result,the precipitation sequence of LPSO in the solidification stage was as follows:Zn/Ycluster+Mg layers→various metastable LPSO building block clusters→18R/24R LPSO;whereas the precipitation sequence of LPSO during homogenization treatment was observed to be as follows:18R LPSO→various metastable LPSO building block clusters→14H LPSO.Of these,14H LPSO was found to be the most thermodynamically stable structure.

过一硫酸盐体系中铜分布模式控制活性物种的产生和污染物降解路径

The distribution pattern of metals as active centers on a substrate can influence the peroxymonosulfate(PMS)activation and contaminants degradation.Herein,atomic layer deposition is applied to prepare Cu single atom(SA-Cu),cluster(C-Cu),and film(F-Cu)decorated MXene catalysts by regulating the number of deposition cycles.In comparison with SA-Cu-MXene(adsorption energy(E_(ads))=-4.236 eV)and F-Cu-MXene(E_(ads)=-3.548 eV),PMS is shown to adsorb preferably on the C-Cu-MXene surface for activation(E_(ads)=-5.435 eV),realizing higher utilization efficiency.More SO_(4)^(·-)are generated in C-Cu-MXene/PMS system with steady-state concentration and 1–3 orders of magnitude higher than those in the SA-Cu-MXene and F-Cu-MXene activated PMS systems.Particularly,the contribution of SO_(4)^(·-)oxidation to sulfamethoxazole(SMX)degradation followed the order,C-Cu-MXene(97.3%)>SA-Cu-MXene(90.4%)>FCu-MXene(71.9%),realizing the larger SMX degradation rate in the C-Cu-MXene/PMS system with the degradation rate constants(k)at 0.0485 min^(-1).Additionally,SMX degradation routes in C-Cu-MXene/PMS system are found with fewer toxic intermediates.Through this work,we highlighted the importance of guided design of heterogeneous catalysts in the PMS system.Appropriate metal distribution patterns need to be selected according to the actual water treatment demand.Metal sites could be then fully utilized to produce specific active species to improve the utilization efficiency of the oxidants.

Fixed-Time Cluster Optimization for Multi-Agent Systems Based on Piecewise Power-Law Design

Dear Editor,This letter focuses on the fixed-time(FXT)cluster optimization problem of first-order multi-agent systems(FOMASs)in an undirected network,in which the optimization objective is the sum of the objective functions of all clusters.A novel piecewise power-law control protocol with cooperative-competition relations is proposed.Furthermore,a sufficient condition is obtained to ensure that the FOMASs achieve the cluster consensus within an FXT.

A Shared Natural Neighbors Based-Hierarchical Clustering Algorithm for Discovering Arbitrary-Shaped Clusters

In clustering algorithms,the selection of neighbors significantly affects the quality of the final clustering results.While various neighbor relationships exist,such as K-nearest neighbors,natural neighbors,and shared neighbors,most neighbor relationships can only handle single structural relationships,and the identification accuracy is low for datasets with multiple structures.In life,people’s first instinct for complex things is to divide them into multiple parts to complete.Partitioning the dataset into more sub-graphs is a good idea approach to identifying complex structures.Taking inspiration from this,we propose a novel neighbor method:Shared Natural Neighbors(SNaN).To demonstrate the superiority of this neighbor method,we propose a shared natural neighbors-based hierarchical clustering algorithm for discovering arbitrary-shaped clusters(HC-SNaN).Our algorithm excels in identifying both spherical clusters and manifold clusters.Tested on synthetic datasets and real-world datasets,HC-SNaN demonstrates significant advantages over existing clustering algorithms,particularly when dealing with datasets containing arbitrary shapes.

Automatic velocity picking based on optimal key points tracking algorithm

Picking velocities from semblances manually is laborious and necessitates experience. Although various methods for automatic velocity picking have been developed, there remains a challenge in efficiently incorporating information from nearby gathers to ensure picked velocity aligns with seismic horizons while also improving picking accuracy. The conventional method of velocity picking from a semblance volume is computationally demanding, highlighting a need for a more efficient strategy. In this study, we introduce a novel method for automatic velocity picking based on multi-object tracking. This dynamic tracking process across different semblance panels can integrate information from nearby gathers effectively while maintaining computational efficiency. First, we employ accelerated density clustering on the velocity spectrum to discern cluster centers without the requirement for prior knowledge regarding the number of clusters. These cluster centers embody the maximum likelihood velocities of the main subsurface structures. Second, our proposed method tracks key points within the semblance volume. Kalman filter is adopted to adjust the tracking process, followed by interpolation on these tracked points to construct the final velocity model. Our synthetic data example demonstrates that our proposed algorithm can effectively rectify the picking errors of the clustering algorithm. We further compare the performances of the clustering method(CM), the proposed tracking method(TM), and the variational method(VM) on a field dataset from the Gulf of Mexico. The results attest that our method offers superior accuracy than CM, achieves comparable accuracy with VM, and benefits from a reduced computational cost.

Screening the optimal Co_(x)/CeO_(2)(110)(x=1–6)catalyst for methane activation in coalbed gas

The challenges posed by energy and environmental issues have forced mankind to explore and utilize unconventional energy sources.It is imperative to convert the abundant coalbed gas(CBG)into high value-added products,i.e.,selective and efficient conversion of methane from CBG.Methane activation,known as the“holy grail”,poses a challenge to the design and development of catalysts.The structural complexity of the active metal on the carrier is of particular concern.In this work,we have studied the nucleation growth of small Co clusters(up to Co_(6))on the surface of CeO_(2)(110)using density functional theory,from which a stable loaded Co/CeO_(2)(110)structure was selected to investigate the methane activation mechanism.Despite the relatively small size of the selected Co clusters,the obtained Co_(x)/CeO_(2)(110)exhibits interesting properties.The optimized Co_(5)/CeO_(2)(110)structure was selected as the optimal structure to study the activation mechanism of methane due to its competitive electronic structure,adsorption energy and binding energy.The energy barriers for the stepwise dissociation of methane to form CH3^(*),CH2^(*),CH^(*),and C^(*)radical fragments are 0.44,0.55,0.31,and 1.20 eV,respectively,indicating that CH^(*)dissociative dehydrogenation is the rate-determining step for the system under investigation here.This fundamental study of metal-support interactions based on Co growth on the CeO_(2)(110)surface contributes to the understanding of the essence of Co/CeO_(2) catalysts with promising catalytic behavior.It provides theoretical guidance for better designing the optimal Co/CeO_(2) catalyst for tailored catalytic reactions.

Correlation Study on Grain Size Characteristics and Geotechnical Properties of Surface Sediments in Qingdao Offshore Area

The comprehension of sediment grain size parameters and the corresponding sedimentary environment holds paramount importance in elucidating the engineering geological attributes of the subaqueous seabed.This study delineated the sedimentary environment zoning in the northern sea area of Qingdao through cluster analysis of grain size parameters derived from 123 surface sediment samples.The study analyzed the correlation between sediment geotechnical indices and grain size parameters across diverse sedimentary environments.A correlation equation was established for samples exhibiting a strong correlation.The study found four distinct sedimentary environments in the study area:coastal,transitional,shallow sea,and residual.Within the same sedimentary environment,the average grain size and sorting coefficient exhibit significant correlations with geotechnical indices such as water content,density,shear strength,plastic limit,liquid limit,and plastic index.However,notable disparities in the correlation between grain size parameters and geotechnical indices emerge across different sedimentary environments.

Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN

Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost.One of them is a sensor network with embedded sensors working as the primary nodes,termed Wireless Sensor Networks(WSNs),in which numerous sensors are connected to at least one Base Station(BS).These sensors gather information from the environment and transmit it to a BS or gathering location.WSNs have several challenges,including throughput,energy usage,and network lifetime concerns.Different strategies have been applied to get over these restrictions.Clustering may,therefore,be thought of as the best way to solve such issues.Consequently,it is crucial to analyze effective Cluster Head(CH)selection to maximize efficiency throughput,extend the network lifetime,and minimize energy consumption.This paper proposed an Accelerated Particle Swarm Optimization(APSO)algorithm based on the Low Energy Adaptive Clustering Hierarchy(LEACH),Neighboring Based Energy Efficient Routing(NBEER),Cooperative Energy Efficient Routing(CEER),and Cooperative Relay Neighboring Based Energy Efficient Routing(CR-NBEER)techniques.With the help of APSO in the implementation of the WSN,the main methodology of this article has taken place.The simulation findings in this study demonstrated that the suggested approach uses less energy,with respective energy consumption ranges of 0.1441 to 0.013 for 5 CH,1.003 to 0.0521 for 10 CH,and 0.1734 to 0.0911 for 15 CH.The sending packets ratio was also raised for all three CH selection scenarios,increasing from 659 to 1730.The number of dead nodes likewise dropped for the given combination,falling between 71 and 66.The network lifetime was deemed to have risen based on the results found.A hybrid with a few valuable parameters can further improve the suggested APSO-based protocol.Similar to underwater,WSN can make use of the proposed protocol.The overall results have been evaluated and compared with the existing approaches of sensor networks.

Improved Data Stream Clustering Method: Incorporating KD-Tree for Typicality and Eccentricity-Based Approach

Data stream clustering is integral to contemporary big data applications.However,addressing the ongoing influx of data streams efficiently and accurately remains a primary challenge in current research.This paper aims to elevate the efficiency and precision of data stream clustering,leveraging the TEDA(Typicality and Eccentricity Data Analysis)algorithm as a foundation,we introduce improvements by integrating a nearest neighbor search algorithm to enhance both the efficiency and accuracy of the algorithm.The original TEDA algorithm,grounded in the concept of“Typicality and Eccentricity Data Analytics”,represents an evolving and recursive method that requires no prior knowledge.While the algorithm autonomously creates and merges clusters as new data arrives,its efficiency is significantly hindered by the need to traverse all existing clusters upon the arrival of further data.This work presents the NS-TEDA(Neighbor Search Based Typicality and Eccentricity Data Analysis)algorithm by incorporating a KD-Tree(K-Dimensional Tree)algorithm integrated with the Scapegoat Tree.Upon arrival,this ensures that new data points interact solely with clusters in very close proximity.This significantly enhances algorithm efficiency while preventing a single data point from joining too many clusters and mitigating the merging of clusters with high overlap to some extent.We apply the NS-TEDA algorithm to several well-known datasets,comparing its performance with other data stream clustering algorithms and the original TEDA algorithm.The results demonstrate that the proposed algorithm achieves higher accuracy,and its runtime exhibits almost linear dependence on the volume of data,making it more suitable for large-scale data stream analysis research.

Characteristics of the microbial communities regulate soil multi-functionality under different cover crop amendments in Ultisol

The use of cover crops is a promising strategy for influencing the soil microbial consortium,which is essential for the delivery of multiple soil functions(i.e.,soil multifunctionality).Nonetheless,relatively little is known about the role of the soil microbial consortium in mediating soil multifunctionality under different cover crop amendments in dryland Ultisols.Here,we assessed the multifunctionality of soils subjected to four cover crop amendments(control,non-amended treatment;RD,radish monoculture;HV,hairy vetch monoculture;and RDHV,radish-hairy vetch mixture),and we investigated the contributions of soil microbial richness,network complexity,and ecological clusters to soil multifunctionality.Our results demonstrated that cover crops whose chemical composition differed from that of the main plant crop promoted higher multifunctionality,and the radish-hairy vetch mixture rendered the highest enhancement.We obtained evidence that changes in soil microbial richness and network complexity triggered by the cover crops were associated with higher soil multifunctionality.Specifically,specialized microbes in a key ecological cluster(ecological cluster 2)of the soil microbial network were particularly important for maintaining soil multifunctionality.Our results highlight the importance of cover crop-induced variations in functionally important taxa for promoting the soil multifunctionality of dryland Ultisols.

On the evolutionary trail of MagRs

Magnetic sense,or termed magnetoreception,has evolved in a broad range of taxa within the animal kingdom to facilitate orientation and navigation.MagRs,highly conserved A-type iron-sulfur proteins,are widely distributed across all phyla and play essential roles in both magnetoreception and iron-sulfur cluster biogenesis.However,the evolutionary origins and functional diversification of MagRs from their prokaryotic ancestor remain unclear.In this study,MagR sequences from 131 species,ranging from bacteria to humans,were selected for analysis,with 23 representative sequences covering species from prokaryotes to Mollusca,Arthropoda,Osteichthyes,Reptilia,Aves,and mammals chosen for protein expression and purification.Biochemical studies revealed a gradual increase in total iron content in MagRs during evolution.Three types of MagRs were identified,each with distinct iron and/or iron-sulfur cluster binding capacity and protein stability,indicating continuous expansion of the functional roles of MagRs during speciation and evolution.This evolutionary biochemical study provides valuable insights into how evolution shapes the physical and chemical properties of biological molecules such as MagRs and how these properties influence the evolutionary trajectories of MagRs.