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Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Sardinops melanostictus and Scomber japonicus, as the main associated species in the northwest Pacific Ocean, are of great scientific significance for the management and sustainable use of fishery resources to study their potential habitat distribution and differences in response to environmental changes in this area. The fishery data of S. melanostictus and S. japonicus in the northwest Pacific Ocean from May to November in 2017-2021 were used, combined with sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH ), chlorophyll-a (Chl. a), eddy kinetic energy (EKE ), and water temperature at different depths (50, 100, 150, 200 m) to construct a maximum entropy model. According to the response curves of the two to the key environmental factors of each month, the inter-month changes of the key factors on the potential habitats of S. melanostictus and S. japonicus were obtained, and the map of potential habitat distribution from May to November for S. melanostictus and S. japonicus was stimulated. The key factors affecting the change of suitable habitats from June and August to October were SSH, SST, and Chi. a, and their suitable ranges were not much different. The results show that the potential habitat changes of S. melanostictus and S. japonicus are consistent with the actual operating location, and with the increase of months, and they all show a trend of moving first to the northeast and turning back in the southwest direction, and their potential habitat positions overlap widely in each month. The key factors affecting the change of suitable habitat in May, July and November are different. The key factors affecting the change of suitable habitat in May, July and November are different, and the key factors of S. melanostictus are SSH, EKE, SST, the key factors in July are SSH, SST, Chl. a. The key factors in November are EKE, SST, Chl. a, while the key factors of S. japonicus are SSH, EKE, Chl. a in May, and the key factors in July are SST and Chl. a, SSS, while the key factors in November are SSH, SST, Chl. a, and the suitable range of environmental factors varies from month to month. In addition, as the number of months increases, the optimal habitat area of S. japonicus gradually decreases northward, with a minimum area in July and November, while the optimal habitat area of S. melanostictus tends to increase, extending to the area north of 37° north latitude in November. The study showed the changes of SSH, SST and Chi. a have an important impact on the distribution of potential habitats of S. japonicus and S. melanostictus in the northwest Pacific Ocean, and EKE and SSS are the main causes for the difference in the distribution of potential habitats. [ABSTRACT FROM AUTHOR]

The spectral matching algorithm based on the discrete particle swarm optimization algorithm (SMDPSO) sometimes overestimates extracted surface water areas. Here we constructed a new method (MEDPSO) by coupling discrete particle swarm optimization algorithm with maximum entropy model (MaxEnt) to extract water bodies using Landsat 8 Operational Land Imager (OLI) images. To compare the accuracy of the modified normalized difference water index (MNDWI), SMDPSO, and MEDPSO, we selected six areas , i.e. thermokarst lakes, Coongie Lakes National Park, the Amazon River, urban water bodies mixed with buildings, Erhai Lake that is surrounded by mountains, and high-altitude lakes. Our results show that the average overall accuracy of the MEDPSO for the six areas is 97.4%, which is higher than those of MNDWI and SMDPSO. The average commission errors and omission errors of MEDPSO (6.4% and 0.8%) are lower than those of MNDWI and SMDPSO. The MEDPSO has a higher accuracy because the maximum entropy model is a machine learning method that uses all the bands of Landsat imagery and four surface water indices in the calculation of the probability of surface water. Our study established a novel, high-precision water extraction method. [ABSTRACT FROM AUTHOR]

In this paper, the maximum entropy model (Maxent) and geographic information system ( ArcGIS 10.3) softwares were used to predict the potential distribution areas of four Litsea species under the current and future climate conditions of China ( 2061 - 2080), and to analyze and classify their suitable habitats. The results were as follows: Litsea cubeba was widely distributed in the south to the Yangtze Hiver in China, and under the future climate with two ( HCP2.6, HCP8.5) carhon dioxide concentrations, during 2061-2080 the suitable anca for its distribution was reduced by 4.9%and 0.5%, respectively; L. coreana was mainly distributed in central and northern subtropical regions of China, and under the climate scenarios with two carbon dioxide concentrations, in 2061-2080 the total suitable area would be increased by 5.6% and 4.5%, respectively; L. greennumiana was mainly distributed in the south subtropical regions of China; L. mollis was widely distributed in the subtropical region. Under the future climate HCP2. 6 scenarios, the suitable area of the two species would be shrinked by 1.0% and 3.3%, respectively, and under the climale HCP8.5, the suitable area of the two species would be shrinked by 5.6% and 8.3%, respectively. These results reveal that due lo different ecological requirements, Litsea species will have different distributional responses to the future climate. the introduction and cultivation of Litsea species should consider the influences of climale change. [ABSTRACT FROM AUTHOR]

Urban expansion is a hot topic in land use/land cover change (LUCC) researches. In this paper, maximum entropy model and cellular automata (CA) model are coupled into a new CA model (Maxent-CA) for urban expansion. This model can help to obtain transition rules from single-period dataset. Moreover, it can be constructed and calibrated easily with several steps. Firstly, Maxent-CA model was built by using remote sensing data of China in 2000 (basic data) and spatial variables (such as population density and Euclidean distance to cities). Secondly, the proposed model was calibrated by analyzing training samples, neighborhood structure and spatial scale. Finally, this model was verified by comparing logistic regression CA model and their simulation results. Experiments showed that suitable sampling ratio (sampling ratio equals the proportion of urban land in the whole region) and von Neumann neighborhood structure will help to yield better results. Spatial structure of simulation results becomes simple as spatial resolution decreases. Besides, simulation accuracy is significantly affected by spatial resolution. Compared to simulation results of logistic regression CA model, Maxent-CA model can avoid clusters phenomenon and obtain better results matching actual situation. It is found that the proposed model performs well in simulating urban expansion of China. It will be helpful for simulating even larger study area in the background of global environment changes. [ABSTRACT FROM AUTHOR]

Remotely sensed data and habitat model approach were employed to evaluate the present of oceanographic aspect in the Bigeye tuna's potential fishing zone (PFZ) at a profundity of 155 m. Vessel monitoring system was employed to acquire the angling vessels for Bigeye tuna from January through December, 2015-2016. Daily data of sub-surface temperature (Sub_ST), sub-surface chlorophyll-a (Sub_SC), and sub-surface salinity (Sub_SS) were downloaded from INDESO Project website. Vessel monitoring system and environmental data were employed for maximum entropy (maxent) model development. The model predictive achievement was then estimated applying the area under the curve (AUC) value. Maxent model results (AUC>0.745) exhibited its probable to understand the Bigeye tuna's spatial dispersion on the specific sub-surface. In addition, the results also showed Sub_ST (43,1%) was the most affective aspect in the Bigeye tuna dispersion, pursued by Sub_SC (35,2%) and Sub_SS (21,6%). [ABSTRACT FROM AUTHOR]

While the intelligent systems need parameter modeling, users often face the dilemma of scarce modeling samples.This paper proposed a BN parameter learning method-constrained data maximum entropy(CDME) algorithm for the modeling of BN parameters under the small data sets. In the case of estimating BN parameters by using small data sets, it transformed the qualitative expert knowledge into inequality constraints for the sake of generating candidate parameter sets by Bootstrap algorithm. Then it estimated the BN parameters in the light of the maximum entropy principle. The experimental results show that CDME algorithm learning effects are similar to the classical MLE algorithm when the modeling data size is sufficient. However, when the data size is limited, the parameters of BN can be modeled by using the CDME, and the learnt accuracy is superior to MLE or QMAP algorithm. It also applied CDME to a real fault diagnosis while the data set was relatively scarce. The results of the diagnosis reasoning demonstrate that the presented parameter learning approach is effective. The CDME parameter learning algorithm provides a new modeling way for BN parameter under the small data sets. [ABSTRACT FROM AUTHOR]

Abstract Visceral leishmaniasis (VL) is a fatal disease caused by sandfly-borne protozoa of the Leishmania genus. This study explored the influence of environmental factors on the distribution of VL in Sinkiang province, Mainland China, which is a known natural focus of leishmaniasis. Disease identification records were obtained from publicly available data, in which the existence of VL at each geographical location had been recorded as part of the surveillance of leishmaniasis in Sinkiang province. Maximum entropy modelling (Maxent) was used to predict the distribution of VL across Sinkiang province, and to match this distribution against environmental variables relating to elevation, climate and land cover, obtained from the WorldClim database, China Meteorological Data Sharing System and the National Geomatic Center of China dataset, respectively. Finally, a regional-scale map was developed to show the potential distribution of VL in the Sinkiang province. Receiver-Operating characteristic (ROC) analysis was used to evaluate the performance of the model. The daily average temperature, maximum temperature of the warmest quarter, daily precipitation and precipitation of the driest month were each found to be predictive of the distribution of VL in Sinkiang. Moreover, we found that presence of VL was significantly influenced by the distribution of grassland and shrubland. The results demonstrate that with proper construction and design, probability surfaces using Maxent can be used as an accurate method by which to predict the distribution of VL in Sinkiang province. The information generated by the model could be used to inform the design of detailed prevention and control strategies for leishmaniasis in this region of Mainland China. Graphical abstract Unlabelled Image Highlights • Meteorological factors and land cover may affect the distribution of VL. • Jackknife method was used to measure the importance of risk variables. • Temperature and precipitation affect the distribution of VL in Sinkiang province. • Distribution of VL has been found high in grassland and shrubland area. • A risk forecast map was built based on the results of MaxEnt model. [ABSTRACT FROM AUTHOR]

In a multithreaded execution environment, race condition leads to computational errors and system hazards. Up to date, a series of task scheduling strategies have been presented in the literature to reduce the risk of race condition. Because of the increasing complexity of this problem in multi-core systems, existing task scheduling approaches are not very efficient. To deal with this challenge, in this work, we develop a race-condition-aware and hardware-oriented task partitioning and scheduling algorithm using entropy maximization model. We model uncertainty as a probabilistic occurrence within a time interval, and hence the characteristics of event ordering are analyzed through an uncertainty matrix. Next, a metric is developed to measure the uncertainty of task execution in various execution environments. Finally, a maximum entropy model is generated to ensure the lowest probability of race condition during task execution. The smallest one among maximum entropy values is chosen and used in our proposed task scheduling algorithm. Experimental results show that the proposed task scheduling strategy based on our maximum entropy model outperforms existing state-of-the-art approaches. For example, in a 128-core computing system, the task execution time, CPU utilization ratio, and throughput of our proposed task scheduling is improved by [ABSTRACT FROM AUTHOR]

This study evaluates how a modelling approach to determine areas of suitable habitat for the Critically Endangered Albany cycad Encephalartos latifrons can assist in systematic conservation planning for this and other rare and threatened cycads. A map distinguishing suitable from unsuitable habitat for E. latifrons was produced and important environmental predictors (climate, geology, topography and vegetation) influencing the suitable habitat were estimated. The maximum entropy (MaxEnt) modelling technique was chosen for this study as it has consistently performed well compared with alternative modelling methods and is also an appropriate model choice when the sample size is small and locality records are relatively few. Predicted habitat suitability showed that some locations chosen for translocation and restoration of E. latifrons specimens are not suitable. This revealed that modelling suitable habitat can guide relocation and regeneration of E. latifrons and perhaps other threatened cycads with restricted distributions and few locality records. The species distribution model constructed for E. latifrons is the first reported habitat model for a Critically Endangered cycad in South Africa. The results may be incorporated into conservation planning and structured decision-making about translocations and restoration programmes involving vulnerable cycads, which are among the most threatened organisms globally. [ABSTRACT FROM AUTHOR]

One of the core content of the prediction is that, on the basis of text label attributes, we can use the algorithm and a heuristic approach to acquire the association of texts, and extract the available text for the user. Therefore, this paper proposes a new content. First, the multi-label attributes are chosen to be the feature structure of text, and it is given the classification and assignment according to the distinguish method of the statistical data. Second, considering the relation between texts, we improve the traditional maximum entropy method. We are able to control the number of multiple leading text and subsequent text at the same time. Our method makes stronger association of text, and it leads to a more unified direction and higher correlation of obtained text through the label attributes. Then we can predict the similar texts. Experiments show that with the consideration of multi-label attributes of text and the control of the number of leading text as well as the subsequent text, the recall rate and precision are definitely improved when compared to similar existing methods. [ABSTRACT FROM AUTHOR]

Fall webworm, is a quarantine pest, which has caused damage to agricultural and forestry production in China. In this paper, based on the use of maximum entropy method (MaxEnt), we constructed the geographic distribution prediction model of fall webworm. The results show that the accuracy of the prediction model is very high (AUC=0.921), and the distribution is mainly influenced by precipitation of warmest quarter (24.5%), altitude (24.4%), mean temperature of coldest quarter (20.5%), standard deviation of temperature seasonality (9.2%) and temperature annual range (3.7%). The current suitable areas of fall webworm in China are concentrated in the southern northeast China, Beijing-Tianjin-Hebei region and most parts of north China Plain. The common natural characteristics of these areas are: rain heat over the same period, higher relative humidity and no extreme low temperature. In the context of 3 representative concentration pathways (RCPs), the geographic distributions of fall webworm in 2050 share the same variation tendency, though the variation degree differs. There is a tendency of expansion towards the north, with distribution areas in parts of the northeast plain, parts of the Sichuan basin and the north China Plain. The results are conducive to a comprehensive understanding of the current and future distributions of fall webworm, and have theoretical significance for investigating and implementing scientific control tactics for the insect pest of fall webworm. [ABSTRACT FROM AUTHOR]

Human brain dynamics can be viewed through the lens of statistical mechanics, where neurophysiological activity evolves around and between local attractors representing mental states. Many physically-inspired models of these dynamics define brain states based on instantaneous measurements of regional activity. Yet, recent work in network neuroscience has provided evidence that the brain might also be well-characterized by time-varying states composed of locally coherent activity or functional modules. We study this network-based notion of brain state to understand how functional modules dynamically interact with one another to perform cognitive functions. We estimate the functional relationships between regions of interest (ROIs) by fitting a pair-wise maximum entropy model to each ROI's pattern of allegiance to functional modules. This process uses an information theoretic notion of energy (as opposed to a metabolic one) to produce an energy landscape in which local minima represent attractor states characterized by specific patterns of modular structure. The clustering of local minima highlights three classes of ROIs with similar patterns of allegiance to community states. Visual, attention, sensorimotor, and subcortical ROIs are well-characterized by a single functional community. The remaining ROIs affiliate with a putative executive control community or a putative default mode and salience community. We simulate the brain's dynamic transitions between these community states using a random walk process. We observe that simulated transition probabilities between basins are statistically consistent with empirically observed transitions in resting state fMRI data. These results offer a view of the brain as a dynamical system that transitions between basins of attraction characterized by coherent activity in groups of brain regions, and that the strength of these attractors depends on the ongoing cognitive computations. [ABSTRACT FROM AUTHOR]

Background: Leptospirosis is a water-borne and widespread spirochetal zoonosis caused by pathogenic bacteria called leptospires. Human leptospirosis is an important zoonotic infectious disease with frequent outbreaks in recent years in China. Leptospirosis's emergence has been linked to many environmental and ecological drivers of disease transmission. In this paper, we identified the environmental and socioeconomic factors associated with leptospirosis in China, and predict potential risk area of leptospirosis using predictive models.Methods: Leptospirosis incidence data were derived from the database of China's web-based infectious disease reporting system, a national surveillance network maintained by Chinese Center for Disease Control and Prevention. We built statistical relationship between occurrence of leptospirosis and nine environmental and socioeconomic risk factors using logistic regression model and Maxent model.Results: Both logistic regression model and Maxent model have high performance in predicting the occurrence of leptospirosis, with AUC value of 0.95 and 0.96, respectively. Annual mean temperature (Bio1) and annual total precipitation (Bio12) are two most important variables governing the geographic distribution of leptospirosis in China. The geographic distributions of areas at risk of leptospirosis predicted from both models show high agreement. The risk areas are located mainly in seven provinces of China: Sichuan Province, Chongqing Municipality, Hunan Province, Jiangxi Province, Guangdong Province, Guangxi Province, and Hainan Province, where surveillance and control programs are urgently needed. Logistic regression model and Maxent model predicted that 403 and 464 counties are at very high risk of leptospirosis, respectively.Conclusions: Our results highlight the importance of socioeconomic and environmental variables and predictive models in identifying risk areas for leptospirosis in China. The values of Geographic Information System and predictive models were demonstrated for investigating the geographic distribution, estimating socioeconomic and environmental risk factors, and enhancing our understanding of leptospirosis in China. [ABSTRACT FROM AUTHOR]

Copyright of Animal Biodiversity & Conservation is the property of Museu de Ciencies Naturals de Barcelona and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For conditional probabilistic knowledge bases with conditionals based on propositional logic, the principle of maximum entropy (ME) is well-established, determining a unique model inductively completing the explicitly given knowledge. On the other hand, there is no general agreement on how to extend the ME principle to relational conditionals containing free variables. In this paper, we focus on two approaches to ME semantics that have been developed for first-order knowledge bases: aggregating semantics and a grounding semantics. Since they use different variants of conditionals, we define the logic PCI, which covers both approaches as special cases and provides a framework where the effects of both approaches can be studied in detail. While the ME models under PCI-grounding and PCI-aggregating semantics are different in general, we point out that parametric uniformity of a knowledge base ensures that both semantics coincide. Using some concrete knowledge bases, we illustrate the differences and common features of both approaches, looking in particular at the ground instances of the given conditionals. [ABSTRACT FROM AUTHOR]

During rest, the human brain performs essential functions such as memory maintenance, which are associated with resting-state brain networks (RSNs) including the default-mode network (DMN) and frontoparietal network (FPN). Previous studies based on spiking-neuron network models and their reduced models, as well as those based on imaging data, suggest that resting-state network activity can be captured as attractor dynamics, i.e., dynamics of the brain state toward an attractive state and transitions between different attractors. Here, we analyze the energy landscapes of the RSNs by applying the maximum entropy model, or equivalently the Ising spin model, to human RSN data. We use the previously estimated parameter values to define the energy landscape, and the disconnectivity graph method to estimate the number of local energy minima (equivalent to attractors in attractor dynamics), the basin size, and hierarchical relationships among the different local minima. In both of the DMN and FPN, low-energy local minima tended to have large basins. A majority of the network states belonged to a basin of one of a few local minima. Therefore, a small number of local minima constituted the backbone of each RSN. In the DMN, the energy landscape consisted of two groups of low-energy local minima that are separated by a relatively high energy barrier. Within each group, the activity patterns of the local minima were similar, and different minima were connected by relatively low energy barriers. In the FPN, all dominant local minima were separated by relatively low energy barriers such that they formed a single coarse-grained global minimum. Our results indicate that multistable attractor dynamics may underlie the DMN, but not the FPN, and assist memory maintenance with different memory states. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of our study was to identify major hillslope sediment sources in a partially urbanized coastal watershed supporting salmonid habitat and to evaluate the use of physical and maximum entropy models in predicting sites of greatest concern. Questions include when and where increased runoff from trail and unpaved road surfaces has influenced patterns of landslides and gullies to a greater degree than what would be expected from background processes and controls, such as precipitation intensity, vegetation, soils, and slope characteristics. Materials and methods: San Pedro Creek Watershed, USA, provides habitat for Oncorhynchus mykiss despite 33% of the watershed being urbanized. The watershed drains steep hillslopes with a median slope of 21°, with the steepest slopes on the 578-m North Peak of Montara Mountain. We inventoried hillslope sediment sources based on field surveys and aerial photographic interpretation in 1941, 1955, 1975, 1983, and 1997. We interpreted causative factors using precipitation records, geologic and soil mapping, digital elevation derivatives, land cover, and road/trail network changes and applied a physical landslide susceptibility model (Stability Index Approach to Terrain Stability Hazard Mapping (SINMAP)) for hillslope stability and a maximum entropy model for assessing gully and landslide centroids. Results and discussion: Maps of landslide and gullies reveal an association with land use changes over time. Agricultural land uses led to the development of extensive gullies in parts of the watershed, and some of these continue to contribute significant sediment to the stream system; others were built-over in residential developments. The most significant remaining gullies result from impervious runoff from roads built into steep hillslopes. Although the best single predictor of landslide susceptibility is physically modelled hillslope stability (SINMAP), slope equally contributed to multivariate MAXENT models (area under the receiver operator characteristic curve (AUC) = 0.74 in 1941, 0.65 in 1975, and 0.79 in 1983). Other covariates in the maximum entropy models include plan curvature, trail distance in 1975, geology in 1983 (favoring colluvium), and vegetation. Conclusions: Combining physical hillslope stability with a maximum entropy model appears promising, although overall slope angle also contributed equally. Landslides are episodic and linked to major precipitation/runoff events, such as ENSO events in 1962, 1972, and 1982, but road and trail development from 1955 to 1975 also contributed equally. As by count most gullies relate to earlier agricultural practices, they represent ongoing sediment sources. [ABSTRACT FROM AUTHOR]

To solve the measurement of investment risk in stock portfolio, this paper provided a new optimization model with inflation rate. The intension and solution of maximum entropy principle were analyzed firstly, then an improved maximum entropy model was established in consideration of inflation rate, which can provide a reasonable optimization model for making some stock investment decisions so as to guarantee expected profits, meanwhile the improved model could also be objective for the profits or risk assessments of portfolio and be fit for current economic situations. Finally, the feasibility of this model was issued by seven stocks of SSE-180 index. [ABSTRACT FROM AUTHOR]

A new dynamic prediction model of load of urban traffic sites during snow disaster is presented. The model overcomes the shortcomings of traditional Markov Chain method. Considering the dynamic changes of passengers' preferences and the interaction of traffic sites, the author establishes the maximum entropy model of distribution of passengers during snow disaster, and on this basis a method of calculating the load rate of traffic sites is presented, with which the dynamic changes of the load of traffic sites can be deduced. This provides a reference for the relevant departments to make response measures. Finally, a ease is used to compare the method with the Markov chain method, and the result shows that the method the author presented is better. [ABSTRACT FROM AUTHOR]

Abstract: As a branch of classification, associative classification combines the basic ideas of association rule mining and general classification. Previous studies show that associative classification can achieve a higher classification accuracy comparing with traditional classification methods, such as C4.5. It is known that new frequent patterns may emerge from the classified resources during classification, and these newly emerging frequent patterns can be used to build new classification rules. However, this dynamic characteristics in associative classification has not been well reflected in traditional methods. In this paper, we propose an enhanced associative classification method by integrating the dynamic property in the process of associative classification. In the proposed method, we employ co-training to refine the discovered emerging frequent patterns for classification rule extension and utilize the maximum entropy model for class label prediction. The empirical study shows that our method can be used to classify increasing resources efficiently and effectively. [Copyright &y& Elsevier]

Interspecific competition in contact areas is a major topic in ecological studies. A number of studies were carried out on European vipers, focusing on contact areas between two or three species characterized as appropriate by ecological needs more or less similar. The aim of this study is to extend the analysis of this topic to the case of Vipera aspis and V. berus in an alpine area of northern Italy, by comparing suitability models to verify which ecological factors affect their occurrence and to assess a possible niche separation. Potential distribution was modelled using the maximum entropy method, using six non-correlated ecogeographical variables as predictors. The models fitted well for both species (mean AUC = 0.926; 87.4% of testing data correctly classified). The most informative variables were: habitat, altitude and solar radiation for the asp viper; altitude and habitat for the adder. Deciduous woods, meadows and urban areas had a positive effect on V. aspis distribution as wetlands, meadows and rocks vegetation did on V. berus. However, the variable best separating the species was the elevation, the adder occurring more frequently at higher altitude than the asp viper. Our data showed that the two vipers were mutually exclusive, as already observed by Saint Girons in 1975. Vipera aspis is more thermophilic and lives at low altitude, while Vipera berus lives under cool and humid areas typical of alpine pastures. A similar pattern were found in the contact areas between European vipers belonging to the V. aspis and Pelias group respectively. [ABSTRACT FROM AUTHOR]

Aim Species distribution models are invaluable tools in biogeographical, ecological and applied biological research, but specific concerns have been raised in relation to different modelling techniques in terms of their validity. Here we compare two fundamentally different approaches to species distribution modelling, one based on simple occurrence data where the lack of an ecological framework has been criticized, and the other firmly based in socio-ecological theory but requiring highly detailed behavioural information that is often limited in availability. Location (Sub-Saharan) Africa. Methods We used two distinct techniques to predict the realized distribution of a model species, the vervet monkey ( Cercopithecus aethiops Linnaeus, 1758). A maximum entropy model was produced taking 13 environmental variables and presence-only data from 174 sites throughout Africa as input, with an additional 58 sites retained to test the model. A time-budget model considering the same environmental variables was constructed from detailed behavioural data on 20 groups representing 14 populations, with presence-only data from the remaining 218 sites reserved to test model predictions on vervet monkey occurrence. Both models were further validated against a reference species distribution map as drawn up by the African Mammals Databank. Results Both models performed well, with the time budget and maximum entropy algorithms correctly predicting vervet monkey presence at 78.4% and 91.4% of their respective test sites. Similarly, the time-budget model correctly predicted presence and absence at 87.4% of map pixels against the reference distribution map, and the maximum entropy model achieved a success rate of 81.8%. Finally, there was a high level of agreement (81.6%) between the presence–absence maps produced by the two models, and the environmental variables identified as most strongly driving vervet monkey distribution were the same in both models. Main conclusions The time-budget and maximum entropy models produced accurate and remarkably similar species distribution maps, despite fundamental differences in their conceptual and methodological approaches. Such strong convergence not only provides support for the credibility of current results, but also relieves concerns about the validity of the two modelling approaches. [ABSTRACT FROM AUTHOR]

Abstract: We investigate the problem of using continuous features in the maximum entropy (MaxEnt) model. We explain why the MaxEnt model with the moment constraint (MaxEnt-MC) works well with binary features but not with the continuous features. We describe how to enhance constraints on the continuous features and show that the weights associated with the continuous features should be continuous functions instead of single values. We propose a spline-based solution to the MaxEnt model with non-linear continuous weighting functions and illustrate that the optimization problem can be converted into a standard log-linear model at a higher-dimensional space. The empirical results on two classification tasks that contain continuous features are reported. The results confirm our insight and show that our proposed solution consistently outperforms the MaxEnt-MC model and the bucketing approach with significant margins. [Copyright &y& Elsevier]

Aimed at solving the problem of single-sequence protein secondary structure prediction, a novel method based on Gaussian prior maximum entropy (GP-MaxEnt) model is proposed. In this method, the feature construction was firstly performed based on the conformational preference of amino acid residues, and the improved iterative scaling (IIS) method was used to train the GP-MaxEnt model. CB513 dataset was employed to test this model. The experimental results indicate that the proposed method is effective and can achieve better results in predictive accuracy. [ABSTRACT FROM AUTHOR]

Pine wilt disease (PWD) has caused a huge damage to pine forests. PWD is mainly transmitted by jumping diffusion, affected by insect vectors and human activities. Since the results of climate change, pine wood nematode (PWN—Bursaphelenchus xylophilus) has begun invading the temperate zones and higher elevation area. In this situation, predicting the distribution of PWD is an important part of the prevention and control of the epidemic situation. The research established the Maxent model to conduct a multi-angle, fine-scale prediction on the risk distribution of PWD. We adjusted two parameters, regularization multiplier (RM) and feature combination (FC), to optimize the model. Influence factors were selected and divided into natural, landscape, and human variables, according to the physical characteristics and spread rules of PWD. The middle-suitability regions and high-suitability regions are distributed in a Y-shape, and divided the study area into three parts. The high-suitability areas are concentrated in the region with high temperature, low elevation, and intensive precipitation. Among the selected variables, natural factors still play the most important role in the distribution of the disease, and human factors and landscape factors are also worked well. The permutation importance of factors is different due to differences in climate and other conditions in different regions. The multi-angle, fine-scale model can help provide useful information for effective control and tactical management of PWD. [ABSTRACT FROM AUTHOR]

This paper reports about the development of a Named Entity Recognition (NER) system in Indian languages, particularly for Bengali, Hindi, Telugu, Oriya and Urdu using the statistical Maximum Entropy (ME) framework. We have used the annotated corpora, obtained from the IJCNLP-08 NER Shared Task for South and South East Asian Languages (NERSSEAL) and tagged with the twelve NE tags. An appropriate tag conversion routine has been developed in order to convert these corpora to the forms, tagged with four NE tags, namely Person name, Location name, Organization name and Miscellaneous name. The system makes use of the different contextual information of the words along with the variety of orthographic word-level features that are helpful in predicting the four NE classes. In this work, we have considered language independent features as well as language specific features. Language independent features include the contextual words, prefixes and suffixes of all the words in the training corpus, several digit features depending upon the presence and/or the number of digits in a token, first word of the sentence and the frequency features of the words. The system considers linguistic features, particularly for Bengali and Hindi. Linguistic features of Bengali include the set of known suffixes that may appear with NEs, clue words that help in predicting the location and organization names, words that help to recognize measurement expressions, designation words that help to identify person names, various gazetteer lists like the first names, middle names, last names, location names, organization names, function words, month names, weekdays, etc. As part of linguistic features for Hindi, the system uses only the lists of first names, middle names, last names, function words, month names and weekdays along with the list of words that helps to recognize measurements. In addition to the other features, part of speech (POS) information of the word has been also considered for Bengali and Hindi. No linguistic features have been considered for Telugu, Oriya and Urdu. It has been observed from the evaluation results that the use of linguistic features improves the performance of the system. The system has been trained with 122,467 Bengali, 502,974 Hindi, 64,026 Telugu, 93,173 Oriya and 35,447 Urdu tokens. The system has demonstrated the highest overall average Recall, Precision, and F-Score values of 88.01%, 82.63%, and 85.22%, respectively, for Bengali with the 10-fold cross validation test. Experimental results of the 10-fold cross validation tests on the Hindi, Telugu, Oriya, and Urdu data have shown the overall average F-Score values of 82.66%, 70.11%, 70.13%, and 69.3%, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we describe a maximum entropy-based automatic prosody labeling framework that exploits both language and speech information. We apply the proposed framework to both prominence and phrase structure detection within the Tones and Break Indices (ToBI) annotation scheme. Our framework utilizes novel syntactic features in the form of supertags and a quantized acoustic-prosodic feature representation that is similar to linear parameterizations of the prosodic contour. The proposed model is trained discriminatively and is robust in the selection of appropriate features for the task of prosody detection. The proposed maximum entropy acoustic-syntactic model achieves pitch accent and boundary tone detection accuracies of 86.0% and 93.1% on the Boston University Radio News corpus, and, 79.8% and 90.3% on the Boston Directions corpus. The phrase structure detection through prosodic break index labeling provides accuracies of 84% and 87 % on the two corpora, respectively. The reported results are significantly better than previously reported results and demonstrate the strength of maximum entropy model in jointly modeling simple lexical, syntactic, and acoustic features for automatic prosody labeling. [ABSTRACT FROM AUTHOR]

The effective integration of geochemical data with multisource geoscience data is a necessary condition for mapping mineral prospects. In the present study, based on the maximum entropy principle, a maximum entropy model (MaxEnt model) was established to predict the potential distribution of copper deposits by integrating 43 ore-controlling factors from geological, geochemical and geophysical data. The MaxEnt model was used to screen the ore-controlling factors, and eight ore-controlling factors (i.e., stratigraphic combination entropy, structural iso-density, Cu, Hg, Li, La, U, Na2O) were selected to establish the MaxEnt model to determine the highest potential zone of copper deposits. The spatial correlation between each ore-controlling factor and the occurrence of a copper mine was studied using a response curve, and the relative importance of each ore-controlling factor was determined by jackknife analysis in the MaxEnt model. The results show that the occurrence of copper ore is positively correlated with the content of Cu, Hg, La, structural iso-density and stratigraphic combination entropy, and negatively correlated with the content of Na2O, Li and U. The model's performance was evaluated by the area under the receiver operating characteristic curve (AUC), Cohen's maximized Kappa and true skill statistic (TSS) (training AUC = 0.84, test AUC = 0.8, maximum Kappa = 0.5 and maximum TSS = 0.6). The results indicate that the model can effectively integrate multi-source geospatial data to map mineral prospectivity. [ABSTRACT FROM AUTHOR]