Effective approach for conformal subarray design based on maximum entropy of planar mappings

In this paper,an effective algorithm for optimizing the subarray of conformal arrays is proposed.The method first divides theconformal array into several first-level subarrays.It uses the X algorithm to solve the feasible solution of first-level subarray tiling and employs the particle swarm algorithm to optimize the conformal array subarray tiling scheme with the maximum entropy of the planar mapping as the fitness function.Subsequently,convex optimization is applied to optimize the subarray amplitude phase.Data results verify that the method can effectively find the optimal conformal array tiling scheme.

Predicting Potential Distribution of Tibetan Spruce (Picea smithiana) in Qomolangma (Mount Everest) National Nature Preserve Using Maximum Entropy Niche-based Model

Tibetan spruce (Picea smithiana) is an endemic species of the Himalayas,and it distributes only in a re-stricted area with very low number.To address the lack of detailed distributional information,we used maximum en-tropy (Maxent) niche-based model to predict the species' potential distribution from limited occurrence-only records.The location data of P.smithiana,relative bioclimatic variables,vegetation data,digital elevation model (DEM),and the derived data were analyzed in Maxent.The receiver operating characteristic (ROC) curve was applied to assess the prediction accuracy.The Maxent jackknife test was performed to quantify the training gains from data layers and the response of P.smithiana distribution to four typical environmental variables was analyzed.Results show that the model performs well at the regional scale.There is a potential for continued expansion of P.smithiana population numbers and distribution in China.P.smithiana potentially distributes in the lower reaches of Gyirong Zangbo and Poiqu rivers in Gyirong and Nyalam counties in Qomolangma (Mount Everest) National Nature Preserve (QNNP),China.The species prefers warm temperate climate in mountain area and mainly distributes in needle-leaved evergreen closed to open forest and mixed forest along the river valley at relatively low altitudes of about 2000-3000 m.Model simulations suggest that distribution patterns of rare species with few species numbers can be well predicted by Max-ent.

Spatial-temporal Evolution and Determinants of the Belt and Road Initiative: A Maximum Entropy Gravity Model Approach

The spatial interaction model is an effective way to explore the geographical disparities inherent in the Belt and Road Initiative(BRI) by simulating spatial flows. The traditional gravity model implies the hypothesis of equilibrium points without any reference to when or how to achieve it. In this paper, a dynamic gravity model was established based on the Maximum Entropy(MaxEnt) theory to estimate and monitor the interconnection intensity and dynamic characters of bilateral relations. In order to detect the determinants of interconnection intensity, a Geodetector method was applied to identify and evaluate the determinants of spatial networks in five dimensions. The empirical study clearly demonstrates a heterogeneous and non-circular spatial structure. The main driving forces of spatial-temporal evolution are foreign direct investment, tourism and railway infrastructure construction, while determinants in different sub-regions show obvious spatial differentiation. Southeast Asian countries are typically multi-island area where aviation infrastructure plays a more important role. North and Central Asian countries regard oil as a pillar industry where power and port facilities have a greater impact on the interconnection. While Western Asian countries are mostly influenced by the railway infrastructure, Eastern European countries already have relatively robust infrastructure where tariff policies provide a greater impetus.

A MAXIMUM ENTROPY CHUNKING MODEL WITH N-FOLD TEMPLATE CORRECTION

This letter presents a new chunking method based on Maximum Entropy (ME) model with N-fold template correction model.First two types of machine learning models are described.Based on the analysis of the two models,then the chunking model which combines the profits of conditional probability model and rule based model is proposed.The selection of features and rule templates in the chunking model is discussed.Experimental results for the CoNLL-2000 corpus show that this approach achieves impressive accuracy in terms of the F-score:92.93%.Compared with the ME model and ME Markov model,the new chunking model achieves better performance.

RESEARCH OF PINYIN-TO-CHARACTER CONVERSION BASED ON MAXIMUM ENTROPY MODEL

This paper applied Maximum Entropy (ME) model to Pinyin-To-Character (PTC) conversion in-stead of Hidden Markov Model (HMM) that could not include complicated and long-distance lexical informa-tion. Two ME models were built based on simple and complex templates respectively, and the complex one gave better conversion result. Furthermore, conversion trigger pair of y A → y B cBwas proposed to extract the long-distance constrain feature from the corpus; and then Average Mutual Information (AMI) was used to se-lect conversion trigger pair features which were added to the ME model. The experiment shows that conver-sion error of the ME with conversion trigger pairs is reduced by 4% on a small training corpus, comparing with HMM smoothed by absolute smoothing.

Prediction of the global potential geographical distribution of Hylurgus ligniperda using a maximum entropy model

Background: Hylurgus ligniperda(Fabricius) is native to Europe but has established populations in many countries and regions. H. ligniperda mainly infests Pinus species, and can cause severe weakness and even death of the host through its boring activity;it can also be a vector of various pathogenic fungi. This study was conducted to investigate the environmental variables limiting the distribution of H. ligniperda and the change trend of its suitable areas under climate change.Results: We used a maximum entropy model to predict the potential geographical distribution of H. ligniperda on a global scale under near current and future climatic scenarios using its occurrence data and environmental variables. The result shows that the areas surrounding the Mediterranean region, the eastern coastal areas of Asia, and the southeastern part of Oceania are highly suitable for H. ligniperda. The environmental variables with the greatest effect on the distribution of H. ligniperda were determined using the jackknife method and Pearson’s correlation analysis and included the monthly average maximum temperature in April, precipitation of driest quarter, the monthly average minimum temperature in December, precipitation of coldest quarter, mean temperature of driest quarter and mean diurnal range.Conclusions: Excessive precipitation in winter and low temperatures in spring had a great effect on the distribution of H. ligniperda. The potential geographical distribution of H. ligniperda was predicted to change under future climatic conditions compared with near current climate conditions. Highly suitable areas, moderately suitable areas and low suitable areas were predicted to increase by 59.99%, 44.43% and 22.92%, respectively, under the2081–2100 ssp245 scenario.

A New Detection Approach Based on the Maximum Entropy Model

The maximum entropy model was introduced and a new intrusion detection approach based on the maximum entropy model was proposed. The vector space model was adopted for data presentation. The minimal entropy partitioning method was utilized for attribute diseretization. Experiments on the KDD CUP 1999 standard data set were designed and the experimental results were shown. The receiver operating eharaeteristie（ROC） curve analysis approach was utilized to analyze the experimental results. The analysis results show that the proposed approach is comparable to those based on support vector maehine（SVM） and outperforms those based on C4.5 and Naive Bayes classifiers. According to the overall evaluation result, the proposed approach is a little better than those based on SVM.

Droplets diameter distribution using maximum entropy formulation combined with a new energy-based sub-model

The maximum entropy principle(MEP) is one of the first methods which have been used to predict droplet size and velocity distributions of liquid sprays. This method needs a mean droplets diameter as an input to predict the droplet size distribution. This paper presents a new sub-model based on the deterministic aspects of liquid atomization process independent of the experimental data to provide the mean droplets diameter for using in the maximum entropy formulation(MEF). For this purpose, a theoretical model based on the approach of energy conservation law entitled energy-based model(EBM) is presented. Based on this approach, atomization occurs due to the kinetic energy loss. Prediction of the combined model(MEF/EBM) is in good agreement with the available experimental data. The energy-based model can be used as a fast and reliable enough model to obtain a good estimation of the mean droplets diameter of a spray and the combined model(MEF/EBM) can be used to well predict the droplet size distribution at the primary breakup.

Video segmentation using Maximum Entropy Model

Detecting objects of interest from a video sequence is a fundamental and critical task in automated visual surveillance. Most current approaches only focus on discriminating moving objects by background subtraction whether or not the objects of interest can be moving or stationary. In this paper, we propose layers segmentation to detect both moving and stationary target objects from surveillance video. We extend the Maximum Entropy (ME) statistical model to segment layers with features, which are collected by constructing a codebook with a set of codewords for each pixel. We also indicate how the training models are used for the discrimination of target objects in surveillance video. Our experimental results are presented in terms of the success rate and the segmenting precision.

Predicting changes in the suitable habitats of six halophytic plant species in the arid areas of Northwest China

In the context of changes in global climate and land uses,biodiversity patterns and plant species distributions have been significantly affected.Soil salinization is a growing problem,particularly in the arid areas of Northwest China.Halophytes are ideal for restoring soil salinization because of their adaptability to salt stress.In this study,we collected the current and future bioclimatic data released by the WorldClim database,along with soil data from the Harmonized World Soil Database(v1.2)and A Big Earth Data Platform for Three Poles.Using the maximum entropy(MaxEnt)model,the potential suitable habitats of six halophytic plant species(Halostachys caspica(Bieb.)C.A.Mey.,Halogeton glomeratus(Bieb.)C.A.Mey.,Kalidium foliatum(Pall.)Moq.,Halocnemum strobilaceum(Pall.)Bieb.,Salicornia europaea L.,and Suaeda salsa(L.)Pall.)were assessed under the current climate conditions(average for 1970-2000)and future(2050s,2070s,and 2090s)climate scenarios(SSP245 and SSP585,where SSP is the Shared Socio-economic Pathway).The results revealed that all six halophytic plant species exhibited the area under the receiver operating characteristic curve values higher than 0.80 based on the MaxEnt model,indicating the excellent performance of the MaxEnt model.The suitability of the six halophytic plant species significantly varied across regions in the arid areas of Northwest China.Under different future climate change scenarios,the suitable habitat areas for the six halophytic plant species are expected to increase or decrease to varying degrees.As global warming progresses,the suitable habitat areas of K.foliatum,S.salsa,and H.strobilaceum exhibited an increasing trend.In contrast,the suitable habitat areas of H.glomeratus,S.europaea,and H.caspica showed an opposite trend.Furthermore,considering the ongoing global warming trend,the centroids of the suitable habitat areas for various halophytic plant species would migrate to different degrees,and four halophytic plant species,namely,S.salsa,H.strobilaceum,H.glomeratus,and H.capsica,would migrate to higher latitudes.Temperature,precipitation,and soil factors affected the possible distribution ranges of these six halophytic plant species.Among them,precipitation seasonality(coefficient of variation),precipitation of the warmest quarter,mean temperature of the warmest quarter,and exchangeable Na+significantly affected the distribution of halophytic plant species.Our findings are critical to comprehending and predicting the impact of climate change on ecosystems.The findings of this study hold significant theoretical and practical implications for the management of soil salinization and for the utilization,protection,and management of halophytes in the arid areas of Northwest China.

Potential distribution of Haloxylon ammodendron in Central Asia under climate change

Understanding the spatial distribution of plant species and their dynamic changes in arid areas is crucial for addressing the challenges posed by climate change.Haloxylon ammodendron shelterbelts are essential for the protection of plant resources and the control of desertification in Central Asia.Thus far,the potential suitable habitats of H.ammodendron in Central Asia are still uncertain in the future under global climate change conditions.This study utilised the maximum entropy(MaxEnt)model to combine the current distribution data of H.ammodendron with its growth-related data to analyze the potential distribution pattern of H.ammodendron across Central Asia.The results show that there are suitable habitats of H.ammodendron in the Aralkum Desert,northern slopes of the Tianshan Mountains,and the upstream of the Tarim River and western edge of the Taklimakan Desert in the Tarim Basin under the current climate conditions.The period from 2021 to 2040 is projected to undergo significant changes in the suitable habitat area of H.ammodendron in Central Asia,with a projected 15.0% decrease in the unsuitable habitat area.Inland areas farther from the ocean,such as the Caspian Sea and Aralkum Desert,will continue to experience a decrease in the suitable habitats of H.ammodendron.Regions exhibiting frequent fluctuations in the habitat suitability levels are primarily found along the axis stretching from Astana to Kazakhskiy Melkosopochnik in Kazakhstan.These regions can transition into suitable habitats under varying climate conditions,requiring the implementation of appropriate human intervention measures to prevent desertification.Future climate conditions are expected to cause an eastward shift in the geometric centre of the potential suitable habitats of H.ammodendron,with the extent of this shift amplifying alongside more greenhouse gas emissions.This study can provide theoretical support for the spatial configuration of H.ammodendron shelterbelts and desertification control in Central Asia,emphasising the importance of proactive measures to adapt to climate change in the future.

基于MaxEnt和GARP的阿蒙森海域南极磷虾(EUPHAUSIA SUPERBA)的分布区预测

南极磷虾是南大洋生态系统的关键物种,在南极碳汇过程中起到重要作用,近年来受到越来越多的关注。针对位于南大洋太平洋扇区的阿蒙森海域,运用最大熵模型(maximum entropy modeling,MaxEnt)和预设规则的遗传算法(genetic algorithm for rule-set production,GARP)两种生态位模型,基于已采集的南极磷虾分布点的数据,对其在阿蒙森海域的潜在分布区进行了预测和分析,并采用受试者工作特征曲线(receiver operating characteristic curve,ROC)下的面积(area under curve,AUC)和真实技巧统计法(true skill statistic,TSS)对模型结果进行评估。结果表明:MaxEnt模型中的高适生区刻画细致,GARP模型预测的高适生区分布范围更广。为克服单个模型的不确定性得到更佳结果,将两个模型的预测结果进行集合。集合后的结果模拟精度显著提高(AUC为0.946,TSS为0.78),达到了极好的预测效果。磷虾的高适生区集中分布在65°~73°S,占总面积的6.2%,中适生区占总面积的5.7%。海冰、平均海平面气压最小值和纬向流速最大值是MaxEnt中贡献最高的3个变量,3个变量贡献达81.3%。相较于MaxEnt模型,GARP模型中各个变量遗漏误差相对较平均。研究表明,集合的结果能够提高物种分布预测的准确性,阿蒙森海域南极磷虾的分布预测结果可以为磷虾保护、利用提供科学参考。

Resolution of overlapping ambiguity strings based on maximum entropy model

The resolution of overlapping ambiguity strings(OAS)is studied based on the maximum entropy model.There are two model outputs,where either the first two characters form a word or the last two characters form a word.The features of the model include one word in con-text of OAS,the current OAS and word probability relation of two kinds of segmentation results.OAS in training text is found by the combination of the FMM and BMM segmen-tation method.After feature tagging they are used to train the maximum entropy model.The People Daily corpus of January 1998 is used in training and testing.Experimental results show a closed test precision of 98.64%and an open test precision of 95.01%.The open test precision is 3.76%better compared with that of the precision of common word probability method.

Distribution Prediction of Suitable Growth Area for Eucommia ulmoides in China under Climatic Change Background

[ Objective] The research aimed to study distribution prediction of suitable growth area for Eucommia ulmoides in China under climatic change background. [ Method] By using the maximum entropy model and many kinds of climate change scenarios, we predicted current and future distribution pattems of suitable growth area for Eucommia ulmoides in China and its change process. [ Result ] At present, highly suitable growth area of E. ulmoides mainly distributed in Sichuan, Shaanxi and Chongqing, Under climate change background, total suitable growth areas in future three decades all drastically reduced when compared with that at present. It was noteworthy that moderately and highly suitable growth areas of wild E. ulmoides all disappeared, and junction between Shaanxi and Gansu and Taibai Mountain would be stable suitable growth area of wild E. ulmoides. [ Condusioa] The research could provide useful reference data for investigation, protection and sustainable development of the wild E. ulmoides resources.

SUBDIVIDING VERBS TO IMPROVE SYNTACTIC PARSING

This paper proposes a new way to improve the performance of dependency parser: subdividing verbs according to their grammatical functions and integrating the information of verb subclasses into lexicalized parsing model. Firstly,the scheme of verb subdivision is described. Secondly,a maximum entropy model is presented to distinguish verb subclasses. Finally,a statistical parser is developed to evaluate the verb subdivision. Experimental results indicate that the use of verb subclasses has a good influence on parsing performance.

Spatial Heterogeneity and Influencing Factors of HFRS Epidemics in Rural and Urban Areas:A Study in Guanzhong Plain of Shaanxi Province,China

Objective The Guanzhong Plain of Shaanxi Province is a severely afflicted hemorrhagic fever with renal syndrome(HFRS)epidemic area,while HFRS prevalence has decreased in most epidemic areas in China.Little information is available regarding the leading fine-scale influencing factors in this highly HFRSconcentrated area and the roles of natural environmental and socioeconomic factors.To investigate this,two regions in the Guanzhong Plain,that is,the Chang’an District and Hu County,with similar geographical environments,different levels of economic development,and high epidemic prevalence,were chosen as representative areas of the HFRS epidemic.Methods Maximum entropy models were constructed based on HFRS cases and fine-scale influencing factors,including meteorological,natural environmental,and socioeconomic factors,from 2014 to 2016.Results More than 95% of the HFRS cases in the study area were located in the northern plains,which has an altitude of less than 800 m,with topography contributed 84.1% of the impact on the spatial differentiation of the HFRS epidemic.In the northern plains,precipitation and population density jointly affected the spatial differentiation of the HFRS epidemic,with contribution rates of 60.7% and 28.0%,respectively.By comparing the influencing factors of the northern plains of Chang’an District and Hu County,we found that precipitation and the normalized difference vegetation index(NDVI)dominated the HFRS epidemic in the relatively developed Chang’an District,while land-use type,temperature,precipitation and population density dominated the HFRS epidemic in the relatively undeveloped Hu County.Conclusion Topography was the primary key factor for HFRS prevalence in the Chang’an District and Hu County,and the spatial differentiation of HFRS was dominated by precipitation and population density in the northern plains.Compared with the influencing factors of the relatively developed Chang’an District,the developing Hu County was more affected by socioeconomic factors.When formulating targeted HFRS epidemic prevention and control strategies in the targeted areas,it is crucial to consider the local economic development state and combine natural environmental factors,including the meteorological environment and vegetation coverage.

Evaluation and prediction of ecological suitability of medicinal plant American ginseng (panax quinquefolius)

Objective： American ginseng is a medicinal plant with large market demands,however,its producing areas are shrinking because of the continuous cropping obstacles in China.Therefore,it is urgent to establish a suitable model to determine the new producing areas.Here we evaluated and predicted the suitable areas of American ginseng using the maximum entropy model（Max Ent）.Methods： Based on the 37 environmental variables over thirty years from 1970 to 20 0 0 and 226 global distribution points of American ginseng,Max Ent was used to determine the global ecological suitable areas for American ginseng.The Receiver Operating Curve（ROC）was used to evaluate the model prediction accuracy.Meanwhile,an innovative ecological variable,the precipitation–temperature ratio,was established to indicate the climate characteristic in the American ginseng suitable areas based on the monthly precipitation and temperature.Results： The potential ecological suitable areas of American ginseng were primarily in Appalachian Mountain in America and Changbai Mountain in China,about in the range of 35 °N–50 °N,60 °W–120 °W and 35 °N–50 °N,110 °E–145 °E,respectively,including the United States,Canada,China,North Korea,South Korea,Russia and Japan.South Korea and Japan were the potential producing regions.The precipitation–temperature ratios were stable at（0.22,0.56）of the vigorous growth period（April–October）in the best suitable areas of American ginseng,serving as characteristic parameters to optimize the prediction model.The model showed that the common soil parameters were pH 4.5–7.2,Base Saturation（BS）above 80%,Cation Exchange Capacity（CEC）10–20 cmol/kg,organic carbon（OC）〈 1.4%,and the soil types were sandy loam or loam.Conclusion： An optimized Max Ent model was established to predict the producing area for American ginseng that needed to be validated by a field test.