Quantum geometric tensor and the topological characterization of the extended Su-Schrieffer-Heeger model

We investigate the quantum metric and topological Euler number in a cyclically modulated Su-Schrieffer-Heeger(SSH)model with long-range hopping terms.By computing the quantum geometry tensor,we derive exact expressions for the quantum metric and Berry curvature of the energy band electrons,and we obtain the phase diagram of the model marked by the first Chern number.Furthermore,we also obtain the topological Euler number of the energy band based on the Gauss-Bonnet theorem on the topological characterization of the closed Bloch states manifold in the first Brillouin zone.However,some regions where the Berry curvature is identically zero in the first Brillouin zone result in the degeneracy of the quantum metric,which leads to ill-defined non-integer topological Euler numbers.Nevertheless,the non-integer"Euler number"provides valuable insights and an upper bound for the absolute values of the Chern numbers.

Extracting Vegetation Phenology Metrics in Changbai Mountains Using an Improved Logistic Model

Remotely sensing images are now available for monitoring vegetation dynamics over large areas.In this paper,an improved logistic model that combines double logistic model and global function was developed.Using this model with SPOT/NDVI data,three key vegetation phenology metrics,the start of growing season (SOS),the end of growing season (EOS) and the length of growing season (LOS),were extracted and mapped in the Changbai Mountains,and the relationship between the key phenology metrics and elevation were established.Results show that average SOS of forest,cropland and grassland in the Changbai Mountains are on the 119th,145th,and 133rd day of year,respectively.The EOS of forest and grassland are similar,with the average on the 280th and 278th,respectively.In comparison,average EOS of the cropland is relatively earlier.The LOS of forest is mainly from the 160th to 180th,that of the grassland extends from the 140th to the 160th,and that of cropland stretches from the 110th to the 130th.As the latitude increases for the same land cover in the study area,the SOS significantly delays and the EOS becomes earlier.The SOS delays approximately three days as the elevation increases 100 m in the areas with elevation higher than 900 m above sea level (a.s.l.).The EOS is slightly earlier as the elevation increases especially in the areas with elevation below 1200 m a.s.l.The LOS shortens approximately four days as the elevation increases 100 m in the areas with elevation higher than 900 m a.s.l.The relationships between vegetation phenology metrics and elevation may be greatly influenced by the land covers.Validation by comparing with the field data and previous research results indicates that the improved logistic model is reliable and effective for extracting vegetation phenology metrics.

Estimation of land surface evapotranspiration using the METRIC model in Nepal

Climatic and atmospheric properties vary significantly within a small area for a topographically diverse region like Nepal.Remote sensing can be used for large-scale monitoring of atmospheric parameters in such diverse terrains.This work evaluates the Landsat-based METRIC(Mapping Evapotranspiration at High Resolution with Internalized Calibration)model for estimating Evapotranspiration(ET)in Nepal.The slope and aspect of terrain are accounted for in our implementation,making the model suitable for regions with topographical variations.The estimations obtained from the model were compared with ground-based measurements.The root-meansquare error for hourly ET(daily ET)was 0.06 mm h-1(1.24 mm d-1),while the mean bias error was0.03 mm h-1(0.29 mm d-1).These results are comparable with results from other studies in the literature that have used the METRIC model for different regions of the world.Thus,this work validates the applicability of the METRIC model for ET estimation in a mountainous area like Nepal.Further,this implementation provides ET estimation at a very high resolution of 30 m compared to the best available resolution of 5 km in earlier works,without compromising on the accuracy.ET estimation with high resolution over a large region in Nepal has applications in agricultural planning and monitoring,among others.

Online RGB-D person re-identification based on metric model update

Person re-identification （re-id） on robot platform is an important application for human-robot- interaction （HRI）, which aims at making the robot recognize the around persons in varying scenes. Although many effective methods have been proposed for surveillance re-id in recent years, re-id on robot platform is still a novel unsolved problem. Most existing methods adapt the supervised metric learning offline to improve the accuracy. However, these methods can not adapt to unknown scenes. To solve this problem, an online re-id framework is proposed. Considering that robotics can afford to use high-resolution RGB-D sensors and clear human face may be captured, face information is used to update the metric model. Firstly, the metric model is pre-trained offline using labeled data. Then during the online stage, we use face information to mine incorrect body matching pairs which are collected to update the metric model online. In addition, to make full use of both appearance and skeleton information provided by RGB-D sensors, a novel feature funnel model （FFM） is proposed. Comparison studies show our approach is more effective and adaptable to varying environments.

The Digital Fingerprinting Method for Static Images Based on Weighted Hamming Metric and on Weighted Container Model

The algorithm of fingerprint constructing for still images based on weighted image structure model is proposed. The error correcting codes that are perfect in weighted Hamming metric are used as a base for fingerprint constructing.

基于METRIC模型的巴基斯坦农业区蒸散量估算

蒸散量是水分循环和能量循环的重要载体,精确估算农田蒸散量对农业水资源管理具有重要意义。巴基斯坦农业区是世界上重要的灌溉农区之一,如何基于遥感技术估算区域实际蒸散量成为农业水资源精细化管理的基础和前提。本文利用MODIS数据、气象数据以及DEM数据,采用METRIC模型,估算了2019—2020年巴基斯坦农业区的实际蒸散量,并分析了不同作物生育期蒸散量的时空分布特征,以期为巴基斯坦农业水资源合理利用提供科学依据。研究结果表明:1)比较基于METRIC模型在日尺度和月尺度的蒸散估算结果与农业站点蒸渗仪的实际观测数据发现,二者的均方根误差分别为1.2 mm∙d^(−1)和25 mm∙month^(−1),相关系数分别为0.65和0.84;在空间上,与ETMonitor产品比较,METRIC模型估算结果的空间分布和量级更为合理。2)巴基斯坦农业区蒸散量的空间分布与种植结构密切相关,蒸散量自北向南总体呈阶梯递减格局,小麦、棉花、水稻和甘蔗生育期累积蒸散量分别为392 mm、652 mm、745 mm和1224 mm;就同一种作物来说,旁遮普省作物生育期累积蒸散量高于信德省。3)小麦生育期内月蒸散量呈先下降再上升后下降的变化特征;旁遮普省棉花生育期内月蒸散量呈“单峰”变化特征,信德省棉花生育期内月蒸散量呈“双峰”变化特征;水稻和甘蔗生育期内月蒸散量呈“单峰”变化特征。本研究实现了METRIC模型在巴基斯坦农业区的参数本地化应用和适用性分析,为基于遥感手段估算区域或农作物尺度蒸散量提供了方法借鉴,对揭示不同作物蒸散耗水的时空特征和区域农业水资源管理具有重要意义。

MultiDMet: Designing a Hybrid Multidimensional Metrics Framework to Predictive Modeling for Performance Evaluation and Feature Selection

In a competitive digital age where data volumes are increasing with time, the ability to extract meaningful knowledge from high-dimensional data using machine learning (ML) and data mining (DM) techniques and making decisions based on the extracted knowledge is becoming increasingly important in all business domains. Nevertheless, high-dimensional data remains a major challenge for classification algorithms due to its high computational cost and storage requirements. The 2016 Demographic and Health Survey of Ethiopia (EDHS 2016) used as the data source for this study which is publicly available contains several features that may not be relevant to the prediction task. In this paper, we developed a hybrid multidimensional metrics framework for predictive modeling for both model performance evaluation and feature selection to overcome the feature selection challenges and select the best model among the available models in DM and ML. The proposed hybrid metrics were used to measure the efficiency of the predictive models. Experimental results show that the decision tree algorithm is the most efficient model. The higher score of HMM (m, r) = 0.47 illustrates the overall significant model that encompasses almost all the user’s requirements, unlike the classical metrics that use a criterion to select the most appropriate model. On the other hand, the ANNs were found to be the most computationally intensive for our prediction task. Moreover, the type of data and the class size of the dataset (unbalanced data) have a significant impact on the efficiency of the model, especially on the computational cost, and the interpretability of the parameters of the model would be hampered. And the efficiency of the predictive model could be improved with other feature selection algorithms (especially hybrid metrics) considering the experts of the knowledge domain, as the understanding of the business domain has a significant impact.

Review of Recent Trends in the Hybridisation of Preprocessing-Based and Parameter Optimisation-Based Hybrid Models to Forecast Univariate Streamflow

Forecasting river flow is crucial for optimal planning,management,and sustainability using freshwater resources.Many machine learning(ML)approaches have been enhanced to improve streamflow prediction.Hybrid techniques have been viewed as a viable method for enhancing the accuracy of univariate streamflow estimation when compared to standalone approaches.Current researchers have also emphasised using hybrid models to improve forecast accuracy.Accordingly,this paper conducts an updated literature review of applications of hybrid models in estimating streamflow over the last five years,summarising data preprocessing,univariate machine learning modelling strategy,advantages and disadvantages of standalone ML techniques,hybrid models,and performance metrics.This study focuses on two types of hybrid models:parameter optimisation-based hybrid models(OBH)and hybridisation of parameter optimisation-based and preprocessing-based hybridmodels(HOPH).Overall,this research supports the idea thatmeta-heuristic approaches precisely improveML techniques.It’s also one of the first efforts to comprehensively examine the efficiency of various meta-heuristic approaches(classified into four primary classes)hybridised with ML techniques.This study revealed that previous research applied swarm,evolutionary,physics,and hybrid metaheuristics with 77%,61%,12%,and 12%,respectively.Finally,there is still room for improving OBH and HOPH models by examining different data pre-processing techniques and metaheuristic algorithms.

Mesh representation matters:investigating the influence of different mesh features on perceptual and spatial fidelity of deep 3D morphable models

Background Deep 3D morphable models(deep 3DMMs)play an essential role in computer vision.They are used in facial synthesis,compression,reconstruction and animation,avatar creation,virtual try-on,facial recognition systems and medical imaging.These applications require high spatial and perceptual quality of synthesised meshes.Despite their significance,these models have not been compared with different mesh representations and evaluated jointly with point-wise distance and perceptual metrics.Methods We compare the influence of different mesh representation features to various deep 3DMMs on spatial and perceptual fidelity of the reconstructed meshes.This paper proves the hypothesis that building deep 3DMMs from meshes represented with global representations leads to lower spatial reconstruction error measured with L_(1) and L_(2) norm metrics and underperforms on perceptual metrics.In contrast,using differential mesh representations which describe differential surface properties yields lower perceptual FMPD and DAME and higher spatial fidelity error.The influence of mesh feature normalisation and standardisation is also compared and analysed from perceptual and spatial fidelity perspectives.Results The results presented in this paper provide guidance in selecting mesh representations to build deep 3DMMs accordingly to spatial and perceptual quality objectives and propose combinations of mesh representations and deep 3DMMs which improve either perceptual or spatial fidelity of existing methods.

Security Vulnerability Analyses of Large Language Models (LLMs) through Extension of the Common Vulnerability Scoring System (CVSS) Framework

Large Language Models (LLMs) have revolutionized Generative Artificial Intelligence (GenAI) tasks, becoming an integral part of various applications in society, including text generation, translation, summarization, and more. However, their widespread usage emphasizes the critical need to enhance their security posture to ensure the integrity and reliability of their outputs and minimize harmful effects. Prompt injections and training data poisoning attacks are two of the most prominent vulnerabilities in LLMs, which could potentially lead to unpredictable and undesirable behaviors, such as biased outputs, misinformation propagation, and even malicious content generation. The Common Vulnerability Scoring System (CVSS) framework provides a standardized approach to capturing the principal characteristics of vulnerabilities, facilitating a deeper understanding of their severity within the security and AI communities. By extending the current CVSS framework, we generate scores for these vulnerabilities such that organizations can prioritize mitigation efforts, allocate resources effectively, and implement targeted security measures to defend against potential risks.

基于CreditMetrics模型评估银行信贷的信用风险

随着市场竞争日趋激烈,金融风险管理显得越来越重要。文章首先论述CreditMetrics模型的建模逻辑过程及其特点;基于风险价值(var)概念进行蒙特卡罗模拟,计算得出某商业银行信贷数据的核心参数:信用风险转移矩阵、门槛率、违约回复率以及最终的风险价值,进而利用这些参数测算出该商业银行贷款的风险等级及其分布。

基于Vari-Metric的舰载机可修复件库存配置模型

针对舰载机可修复备件配置问题,提出了基于Vari-Metric的多等级、多层级库存优化配置模型。首先,对舰载机保障过程进行分析;其次,进行可修复备件配置优化分析,根据舰载机保障特点,给出了备件短缺数、备件需求率和备件供应渠道模型;然后,给出基于边际优化算法的配置优化模型;最后,进行了实例分析,证明了模型的有效性,能够为保障人员备件配置优化提供一定的决策依据。

Credit Metrics模型计算信用风险的实例分析

CreditMetrics作为计算资产组合信用风险的模型,是一个联系信用和证券市场的简单、动态的架构。本文从此模型出发,分别讨论了单个贷款和资产组合基于违约率,信用迁移概率的计算原理和实例,并对违约率的测算作了进一步的分析和讨论。

中文学术成果评价的Altmetrics发展及实践路径

为有效解决中文学术成果评价的Altmetrics应用问题,针对中文环境下Altmetrics评价数据基础设施缺失现状,在综述Altmetrics思想、方法、技术发展历程及中文学术成果应用实践的基础上,提出中文学术成果Altmetrics评价需要解决三方面的问题:利益相关者的Altmetrics应用诉求识别,中文Altmetrics工具开发,以引文为基础的传统计量与以网络空间数据为基础的Altmetrics计量的融合模型。只有解决中文环境下Altmetrics评价的这些基础性问题,中文学术成果评价才会具有坚实的数据基础设施。

程序复杂度度量系统pgrmetrics的设计与实现

介绍一种源程序度量系统的实现方法,它以可扩展程序对象模型作为其构建基础,将对源程序的 信息抽取变为对抽象对象模型的信息抽取,故而无论从设计和实现系统的角度看都显得较为简洁易行。

基于METRIC模型对可用度与费用关系曲线的研究

通过对可修备件管理多级法的分析研究,提出基于优化可修航空器材备件计划库存的方法,针对装备上的各项备件计算各个航空部门的最优库存量,航空备件需求量和其他参数,以此来寻求仓库短缺数最低,相当于寻求装备可用度最高。最终以可用度与费用最优关系曲线的形式推荐给航空部门备件主管人员。

Impact of ionospheric irregularity on SBAS integrity:spatial threat modeling and improvement

The ionosphere, as the largest and least predictable error source, its behavior cannot be observed at all places simultaneously. The confidence bound, called the grid ionospheric vertical error(GIVE), can only be determined with the aid of a threat model which is used to restrict the expected ionospheric behavior. However, the spatial threat model at present widespread used, which is based on fit radius and relative centroid metric(RCM), is too conservative or the resulting GIVEs will be too large and will reduce the availability of satellite-based augmentation system(SBAS). In this paper, layered two-dimensional parameters, the vertical direction double RCMs, are introduced based on the spatial variability of the ionosphere. Comparing with the traditional threat model, the experimental results show that the user ionospheric vertical error(UIVE) average reduction rate reaches 16%. And the 95% protection level of conterminous United States(CONUS) is 28%, even under disturbed days, which reaches about 5% reduction rates.The results show that the system service performance has been improved better.

A Review on Evaluation Methods of Climate Modeling

There is scientific progress in the evaluation methods of recent Earth system models(ESMs).Methods range from single variable to multi-variables,multi-processes,multi-phenomena quantitative evaluations in five layers(spheres)of the Earth system,from climatic mean assessment to climate change(such as trends,periodicity,interdecadal variability),extreme values,abnormal characters and quantitative evaluations of phenomena,from qualitative assessment to quantitative calculation of reliability and uncertainty for model simulations.Researchers started considering independence and similarity between models in multi-model use,as well as the quantitative evaluation of climate prediction and projection efect and the quantitative uncertainty contribution analysis.In this manuscript,the simulations and projections by both CMIP5 and CMIP3 that have been published after 2007 are reviewed and summarized.

A LINEAR APPROACH TO METRIC CIRCUMFERENCE COMPUTATIO FOR DIGITIZED CONVEX SHAPES

Metric measurement of digitized shapes is commonly applied in optical measuring systems.In this letter,three shape-related factors defined by the authors are used in the construction of amultiple linear regression model which is utilized to compute the circumference of the convex shapes inmillimeter unit.The model is first built upon the relationship hypothesis and then its adequacy ismathematically validated.The results of applying the developed model to the given number of convexshapes in a finite circumferential length range suggest that,in terms of percent error,the model pre-cision is to satisfaction by being within±4%.The test also shows the model’s robustness against theshape’s orientation anisotropy.

Application of the Credit Metrics in the Credit Risk Management of Commercial Banks

Credit risk is one of the main risks the commercial banks faces all over the world,especially in the risk structure of the banks of China.In order to control credit risk more scientifically,we shall connect the qualitative analysis and the quantitative analysis.Put forward by J.P.Morgan Credit Metrics model is the application of the VaR in the field of credit risk,showing great advantage in quantitative bonds and credit risk of loan.This paper studies the Credit Metrics model and analyzes the hypothesis and framework of this model,attempting to explore the application of the model in China in order to promote the realization of the risk quantification of the commercial banks of China.