Explicit Distribution Research Articles

Distribution of scattering resonances for generic Schrödinger operators

Let −Δ+V be the Schrödinger operator acting on L2(Rd,C) with d≥3 odd. Here V is a bounded real- or complex-valued function vanishing outside the closed ball of center 0 and radius a. If V belongs to the class Ma of potentials introduced by Christiansen, we show that when r→∞, the resonances of −Δ+V, scaled down by the factor r, are asymptotically distributed, with respect to an explicit probability distribution on the lower unit half-disc of the complex plane. The rate of convergence is also considered for subclasses of potentials.

Autocorrelation and Lower Bound on the 2-Adic Complexity of LSB Sequence of p-Ary m-Sequence

LSB (Least Significant Bit) sequences are widely used as the initial inputs in some modern stream ciphers, such as the ZUC algorithm-the core of the 3GPP LTE International Encryption Standard. Therefore, analyzing the statistical properties (for example, autocorrelation, linear complexity and 2-adic complexity) of these sequences becomes an important research topic. In this article, we first reduce the autocorrelation distribution of the LSB sequence of a p-ary m-sequence with period p n -1 for any order n≥2 to the autocorrelation distribution of a corresponding Costas sequence with period p-1, and from the computing of which by computer, we obtain the explicit autocorrelation distribution of the LSB sequence for each prime p<; 100. In addition, we give a lower bound on the 2-adic complexity of each of these LSB sequences for all primes p<; 20, which proves to be large enough to resist the analysis of RAA (Rational Approximation Algorithm) for FCSRs (Feedback with Carry Shift Registers). In particular, for a Mersenne prime p=2 k -1 (i.e., k is a prime such that p is also a prime), our results hold for all its bit-component sequences since they are shift equivalent to the LSB sequence.

Distributed Key-Value Storage for Edge Computing and Its Explicit Data Distribution Method

In this article, we provide a data framework for edge computing where developers can easily attain efficient data transfer between devices or users. We propose a distributed key-value storage platform for edge computing and its explicit data distribution management method. In this platform, edge servers organize the distributed key-value storage in a uniform namespace. To enable fast data access to a record in edge computing, the allocation strategy of the record and its cache on the edge servers is important. Our platform offers a distributed objects that can dynamically change home server and allocate cache objects following user defined rules. A rule is defined in a declarative manner and specifies where to place cache objects depending on the status of the target record and its associated records. We also integrate a push notification system using WebSocket to immediately notify events on a specified table. We evaluate the performance of our system using a messaging service application between mobile appliances.

A Semantic Framework for the Design of Distributed Reactive Real-Time Languages and Applications

The proliferation of on-demand internet services delivered over a network of a heterogeneous set of computing devices has created the need for high-performing dynamic systems in real-time. Services such as audio and video streaming, self-driving cars, the Internet of things (IoT), or instant communication on social networks have forced system designers to rethink the architectures and tools for implementing computer systems. Reactive programming has been advocated as a programming paradigm suitable for implementing dynamic applications with complex and heterogeneous architectural needs. However, there is no consensus on the core set of features that a reactive framework must-have. Furthermore, the current set of features proposed in reactive tools seems very restricted to cope with the actual needs for concurrency and distribution in modern systems. In this paper, several alternative semantics for distributed reactive languages are investigated, addressing complex open issues such as glitch avoidance, explicit distribution support, and constructs for explicit time management. First, we propose a reactive event-based programming language with explicit support for distribution, concurrency, and explicit time manipulation (ReactiveXD). Second, we present a reactive event-based semantic framework called Distributed Reactive Rewriting Framework (DRRF). The framework uses rewriting logic to model the components of a distributed base application, observables, and observers, and predicates supporting explicit time manipulation. Finally, to validate the proposal, the paper discusses the specification of the semantics of ReactiveXD and a scenario describing a case of intrusion detection on IoT networks.

Bayesian Estimation of Bivariate Weighted Exponential Distribution

In this paper, we consider Bayesian estimation for the parameters of bivariate weighted exponential Distribution based on generalized exponential distribution (BWEGE). The BWEGE has four parameters, two scale parameters and two shape parameters, which makes it more flexible to describe different types of real data than its sub model, the three-parameter bivariate weighted exponential distribution with weighted exponential marginal (BWE distribution). This four-parameter BWEGE has explicit joint probability density and distribution function. The Bayes estimators of the parameters under squared error loss function are obtained using the Lindley’s approximation method due to the lack of explicit forms of estimators.

Modeling soil organic carbon spatial distribution for a complex terrain based on geographically weighted regression in the eastern Qinghai-Tibetan Plateau

Permafrost regions store a large amount soil organic carbon (SOC), and the decomposition of these carbon pools can release greenhouse gases and further strength climate warming. An explicit spatial distribution of SOC is one of the basic databases for Earth System Models. However, efficient approaches for obtaining the spatial distribution of SOC remain challenging, especially in mountainous areas which are characterized by complex terrains. Here, we modeled the spatial SOC distribution using the geographically weighted regression (GWR) approach in an area on the eastern part of the Qinghai-Tibetan Plateau (QTP). We analyzed multiple environmental variables and soil profile data (n = 73) to find the best prediction models for the SOC density (SOCD) for the 0–50 cm layers. The results showed that normalized difference vegetation index (NDVI), elevation, and slope gradient are the significant predictors for the SOCD. For the upper 50 cm soil layers, the SOCD ranged from 1.08 to 18.32 kg·m−2, with higher values in mountain slopes but lower values in mountain valleys and basins. The GWR model had a higher prediction accuracy in the modeling SOCD in comparison with other models such as ordinary kriging (OK) interpolation, multiple linear regression (MLR) model. Our results showed that GWR model is a useful tool for modeling of SOC distribution and potentially can be integrated into Earth system models in areas of complex terrains.

Inference in MCMC step selection models.

Habitat selection models are used in ecology to link the spatial distribution of animals to environmental covariates and identify preferred habitats. The most widely used models of this type, resource selection functions, aim to capture the steady-state distribution of space use of the animal, but they assume independence between the observed locations of an animal. This is unrealistic when location data display temporal autocorrelation. The alternative approach of step selection functions embed habitat selection in a model of animal movement, to account for the autocorrelation. However, inferences from step selection functions depend on the underlying movement model, and they do not readily predict steady-state space use. We suggest an analogy between parameter updates and target distributions in Markov chain Monte Carlo (MCMC) algorithms, and step selection and steady-state distributions in movement ecology, leading to a step selection model with an explicit steady-state distribution. In this framework, we explain how maximum likelihood estimation can be used for simultaneous inference about movement and habitat selection. We describe the local Gibbs sampler, a novel rejection-free MCMC scheme, use it as the basis of a flexible class of animal movement models, and derive its likelihood function for several important special cases. In a simulation study, we verify that maximum likelihood estimation can recover all model parameters. We illustrate the application of the method with data from azebra.

An innovative modeling approach of linking land use patterns with soil antibiotic contamination in peri-urban areas

Due to the intensive use and continuous release, high and persistent concentrations of antibiotics are found in soils worldwide. This severe contamination elevates the risks associated with antibiotic exposure and resistance for soil ecosystems and human health. Estimating antibiotic concentrations in soils is a complex and important challenge because the limited information is available on antibiotic use and emission and the high exposure risk to human health occurred in peri-urban areas. In this study, soil antibiotic contamination was linked with land use patterns in a data-scarce peri-urban area in four different seasons, and we established a modeling framework based on land use to estimate spatially explicit distribution of antibiotics in soils. The soil antibiotic concentration was found to be substantially affected by surrounding land use patterns in buffer zones with a radius of 350 m. Agricultural land was the main source of antibiotics entering the soil. Notably, road networks also had considerable impacts on antibiotic residues in soils. Then, a statistical model was developed in describing the linkage between land use patterns and soil antibiotic concentration. Model evaluation suggested that the proposed model successfully simulated the variation of antibiotics in soil with good statistical performance (R2 > 0.7). Finally, the model was extrapolated to investigate detailed distribution of antibiotics in soils. Clear spatial and seasonal dynamics can be found in soil antibiotic concentration. To our knowledge, this was the first attempt to adopt a model focusing on land use pattern to estimate the spatially explicit distribution of antibiotics in soils. Despite of some uncertainties, the research provides a land-use-based modeling approach as a reference for preventing and controlling soil antibiotic contamination in the future.

Fragmented tropical forests lose mutualistic plant–animal interactions

AbstractAimForest fragmentation is among the principal causes of global biodiversity loss, yet how it affects mutualistic interactions between plants and animals at large spatial scale is poorly understood. In particular, tropical forest regeneration depends on animal‐mediated seed dispersal, but the seed‐dispersing animals face rapid decline due to forest fragmentation and defaunation. Here, we assess how fragmentation influences the pairwise interactions between 407 seed disperser and 1,424 tree species in a highly fragmented biodiversity hotspot.LocationAtlantic Forest, South America.MethodsWe predicted interaction networks in 912 sites covering the entire biome by combining verified interaction data with co‐occurrence probabilities obtained from a spatially explicit joint species distribution model. We identified keystone seed dispersers by computing a species‐specific keystone index and by selecting those species belonging to the top 5% quantile.ResultsWe show that forest fragmentation affects seed dispersal interactions negatively, and the decreased area of functionally connected forest, rather than increased edge effects, is the main driver behind the loss of interactions. Both the seed disperser availability for the local tree communities and in particular the proportion of interactions provided by keystone seed dispersers decline with increasing degree of fragmentation. Importantly, just 21 keystone species provided &gt;40% of all interactions. The numbers of interactions provided by keystone and non‐keystone species, however, were equally negatively affected by fragmentation, suggesting that seed dispersal interactions may not be rewired under strong fragmentation effects.ConclusionsWe highlight the importance of understanding the fragmentation‐induced compositional shifts in seed disperser communities as they may lead to lagged and multiplicative effects on tree communities. Our results illustrate the utility of model‐based prediction of interaction networks as well as model‐based identification of keystone species as a tool for prioritizing conservation efforts. Similar modelling approaches could be applied to other threatened ecosystems and interaction types globally.

Risk analysis: Survival data analysis vs. machine learning. Application to Alzheimer prediction

We present here the statistical models that are most in use in survival data analysis. The parametric ones are based on explicit distributions, depending only on real unknown parameters, while the preferred models are semi-parametric, like Cox model, which imply unknown functions to be estimated. Now, as big data sets are available, two types of methods are needed to deal with the resulting curse of dimensionality including non informative factors which spoil the informative part relative to the target: on one hand, methods that reduce the dimension while maximizing the information left in the reduced data, and then applying classical stochastic models; on the other hand algorithms that apply directly to big data, i.e. artificial intelligence (AI or machine learning). Actually, those algorithms have a probabilistic interpretation. We present here several of the former methods. As for the latter methods, which comprise neural networks, support vector machines, random forests and more (see second edition, January 2017 of Hastie, Tibshirani et al. (2005) [1]), we present the neural networks approach. Neural networks are known to be efficient for prediction on big data. As we analyzed, using a classical stochastic model, risk factors for Alzheimer on a data set of around 5000 patients and p=17 factors, we were interested in comparing its prediction performance with the one of a neural network on this relatively small sample size data.

Exploring group interactions in synchronous mobile computer-supported learning activities

This paper presents the results of a study of synchronous mobile computer-supported collaborative learning (mCSCL) that emphasized levels of pre-structuring in the context of primary school participants who need more guidance to benefit from the collaborative work. The male and female participants, who were, on average, 8-year-old primary school students, used Internet-connected mobile devices to work synchronously in small groups on mathematics tasks. The study examined two mCSCL interaction modes. In the first mode, each group member is assigned a role, and should have completed one part of the task before the group members negotiated their solutions. In the second mode, all group members completed parts of the task individually, and then negotiated their solutions to progress through the activity. The two interaction modes were compared in terms of student task completion attempts and incorrect completion attempts. The study results are of medium to large effect size and indicated that for tasks of lower difficulty, task distribution using roles led to statistically significantly more incorrect task completion attempts compared to the design without roles. For tasks of greater difficulty, there were more incorrect task completion attempts in groups with no explicit distribution of work compared to the groups with roles. The design of synchronous mCSCL technology emphasizes the importance of state preservation mechanisms, synchronization mechanisms, and immediate feedback messages. Practical implications of this study are that teachers must actively consider the type of mCSCL design they choose when preparing their mobile collaborative lessons, and choose an adequate design for the planned task difficulty level.

ClimCKmap, a spatially, temporally and climatically explicit distribution database for the Italian fauna

Understanding and counteracting biodiversity losses requires quantitative knowledge on species distribution and abundance across space and time, as well as integrated and interoperable information on climate conditions and climatic changes. In this paper we developed a new biodiversity-climate database for Italy, ClimCKmap, based on the critical analysis, quality estimation and subsequent integration of the CKmap database with several high-resolution climate datasets. The original database was quality-checked for errors in toponym, species name and dating; the retained records were georeferenced and their distribution polygonised via Voronoi tessellation. We then integrated the species distribution information with several high-resolution climatic datasets: average monthly minimum and maximum temperature and total monthly precipitation were reconstructed for each Voronoi cell and year. The resulting database contains 268,977 occurrence records from 8,445 binomials and 16,332 localities, dating between 1680 and 2006 CE. This dataset, fully available at https://doi.org/10.6084/m9.figshare.7906739.v4 and http://hdl.handle.net/21.11125/a91f85cb-befd-4e14-8e83-24f17c4a0491, represents the largest, fully quality-checked, spatially, temporally and climatically explicit distribution database ever assembled for the Italian fauna, now ready for scientific exploitation.

Orthogonal and symplectic Harish-Chandra integrals and matrix product ensembles

In this paper, we highlight the role played by orthogonal and symplectic Harish-Chandra integrals in the study of real-valued matrix product ensembles. By making use of these integrals and the matrix-valued Fourier-Laplace transform, we find the explicit eigenvalue distributions for particular Hermitian anti-symmetric matrices and Hermitian anti-self dual matrices, involving both sums and products. As a consequence of these results, the eigenvalue probability density function of the random product structure [Formula: see text], where each [Formula: see text] is a standard real Gaussian matrix, and [Formula: see text] is a real anti-symmetric matrix can be determined. For [Formula: see text] and [Formula: see text] the bidiagonal anti-symmetric matrix with 1’s above the diagonal, this reclaims results of Defosseux. For general [Formula: see text], and this choice of [Formula: see text], or [Formula: see text] itself a standard Gaussian anti-symmetric matrix, the eigenvalue distribution is shown to coincide with that of the squared singular values for the product of certain complex Gaussian matrices first studied by Akemann et al. As a point of independent interest, we also include a self-contained diffusion equation derivation of the orthogonal and symplectic Harish-Chandra integrals.

Metal distribution in iron-nickel sulfide mineral pentlandite: First-principles study

Pentlandite (Fe,Ni)9S8 has been recently reported in experiments to show a good performance in hydrogen evolution reaction (HER) whose atomic scale mechanism necessarily requires explicit metal distribution. However, the knowledge of its explicit Fe/Ni distribution is lacking, impeding further HER research. In this work, we employed first-principles method to study metal distribution in pentlandite. The results demonstrated that the metal distribution at tetrahedral sites plays a major role in structural stability, where FeNi contact can significantly increase it. Additionally, it is importantly revealed that the metal atoms at octahedral sites can be tailored easily compared with those at tetrahedral sites.

Explicit Residence Time Distribution of a Generalised Cascade of Continuous Stirred Tank Reactors for a Description of Short Recirculation Time (Bypassing)

The tanks-in-series model (TIS) is a popular model to describe the residence time distribution (RTD) of non-ideal continuously stirred tank reactors (CSTRs) with limited back-mixing. In this work, the TIS model was generalised to a cascade of n CSTRs with non-integer non-negative n. The resulting model describes non-ideal back-mixing with n &gt; 1. However, the most interesting feature of the n-CSTR model is the ability to describe short recirculation times (bypassing) with n &lt; 1 without the need of complex reactor networks. The n-CSTR model is the only model that connects the three fundamental RTDs occurring in reactor modelling by variation of a single shape parameter n: The unit impulse at n→0, the exponential RTD of an ideal CSTR at n = 1, and the delayed impulse of an ideal plug flow reactor at n→∞. The n-CSTR model can be used as a stand-alone model or as part of a reactor network. The bypassing material fraction for the regime n &lt; 1 was analysed. Finally, a Fourier analysis of the n-CSTR was performed to predict the ability of a unit operation to filter out upstream fluctuations and to model the response to upstream set point changes.

The Langevin diffusion as a continuous‐time model of animal movement and habitat selection

Abstract The utilization distribution of an animal describes the relative probability of space use. It is natural to think of it as the long‐term consequence of the animal's short‐term movement decisions: it is the accumulation of small displacements which, over time, gives rise to global patterns of space use. However, many estimation methods for the utilization distribution either assume the independence of observed locations and ignore the underlying movement (e.g. kernel density estimation), or are based on simple Brownian motion movement rules (e.g. Brownian bridges). We introduce a new continuous‐time model of animal movement, based on the Langevin diffusion. This stochastic process has an explicit stationary distribution, conceptually analogous to the idea of the utilization distribution, and thus provides an intuitive framework to integrate movement and space use. We model the stationary (utilization) distribution with a resource selection function to link the movement to spatial covariates, and allow inference about habitat preferences of animals. Standard approximation techniques can be used to derive the pseudo‐likelihood of the Langevin diffusion movement model, and to estimate habitat preference and movement parameters from tracking data. We investigate the performance of the method on simulated data, and discuss its sensitivity to the time scale of the sampling. We present an example of its application to tracking data of Steller sea lions Eumetopias jubatus. Due to its continuous‐time formulation, this method can be applied to irregular telemetry data. The movement model is specified using a habitat‐dependent utilization distribution, and it provides a rigorous framework to estimate long‐term habitat selection from correlated movement data. The Langevin movement model can be written as a linear model, which allows for very fast inference. Standard tools such as residuals can be used for model checking.

Stacked Wasserstein Autoencoder

Approximating distributions over complicated manifolds, such as natural images, are conceptually attractive. The deep latent variable model, trained using variational autoencoders and generative adversarial networks, is now a key technique for representation learning. However, it is difficult to unify these two models for exact latent-variable inference and parallelize both reconstruction and sampling, partly due to the regularization under the latent variables, to match a simple explicit prior distribution. These approaches are prone to be oversimplified, and can only characterize a few modes of the true distribution. Based on the recently proposed Wasserstein autoencoder (WAE) with a new regularization as an optimal transport. The paper proposes a stacked Wasserstein autoencoder (SWAE) to learn a deep latent variable model. SWAE is a hierarchical model, which relaxes the optimal transport constraints at two stages. At the first stage, the SWAE flexibly learns a representation distribution, i.e., the encoded prior; and at the second stage, the encoded representation distribution is approximated with a latent variable model under the regularization encouraging the latent distribution to match the explicit prior. This model allows us to generate natural textual outputs as well as perform manipulations in the latent space to induce changes in the output space. Both quantitative and qualitative results demonstrate the superior performance of SWAE compared with the state-of-the-art approaches in terms of faithful reconstruction and generation quality.

Remote Detection of Large-Area Crop Types: The Role of Plant Phenology and Topography

Sustainable agricultural practices necessitate accurate baseline data of crop types and their detailed spatial distribution. Compared with field surveys, remote sensing has demonstrated superior performance, offering spatially explicit crop distribution in a timely manner. Recent studies have taken advantage of remote sensing time series to capture the variation in plant phenology, inferring major crop types. However, such an approach was rarely used to extract detailed, multiple crop types spanning a large area, and the impact of topography has yet to be well analyzed in mountainous regions. This study aims to answer two questions in crop type extraction: (i) Is it feasible to accurately map multiple crop types over a large mountainous area with phenology-based modeling? (ii) What are the effects of topography in such modeling? To answer the questions, phenological metrics were extracted from MODIS (Moderate Resolution Imaging Spectroradiometer) satellite time series, and the random forests classifier was used to map 12 crop types in South China (236,700 km2), featuring a subtropical monsoon climate and high topographic variation. Our study revealed promising results using MODIS EVI (Enhanced Vegetation Index) and NDVI (Normalized Difference Vegetation Index) time series, although EVI outperformed NDVI (overall accuracy: 85% versus 81%). The spectral and temporal metrics of plant phenology significantly contributed to crop identification, where the spectral information exhibited greater importance. The increase of slope led to a decrease in model accuracy in general. However, uniformly distributed tree plantations (e.g., tea-oil camellia, gum, and tea trees) being cultivated on large slopes (&gt;15 degrees) achieved accuracies greater than 80%.

Implications of Spatial Habitat Diversity on Diet Selection of European Bison and Przewalski’s Horses in a Rewilding Area

In Europe, the interest in introducing megaherbivores to achieve ambitious habitat restoration goals is increasing. In this study, we present the results of a one-year monitoring program in a rewilding project in Germany (Doeberitzer Heide), where European bison (Bison bonasus) and Przewalski’s horses (Equus ferus przewalskii) were introduced for ecological restoration purposes. Our objectives were to investigate diet and habitat preferences of Przewalski’s horses and European bison under free-choice conditions without fodder supplementation. In a random forest classification approach, we used multitemporal RapidEye time series imagery to map the diversity of available habitats within the study area. This spatially explicit habitat distribution from satellite imagery was combined with direct field observations of seasonal diet preferences of both species. In line with the availability of preferred forage plants, European bison and Przewalski’s horses both showed seasonal habitat preferences. Because of their different preferences for forage plants, they did not overlap in habitat use except for a short time in the colder season. European bison used open habitats and especially wet open habitats more than expected based on available habitats in the study area. Comparative foraging and feeding niches should be considered in the establishment of multispecies projects to maximize the outcome of restoration processes.

Magneto-induced surface morphologies in magnetorheological elastomer films: an analytical study

In this paper, we develop a three-dimension semi-infinite mesoscopic model to analyze the surface micro-deformation of the MRE films under external magnetic fields. By considering the matrix of MRE films as an infinite continuous solid with absolutely smooth surfaces, we firstly analyze the surface morphologies induced by a magnetized particle and a magnetized particle pair. Following the superposition principle, we then derive an analytical solution based on an explicit discrete particle distribution to reveal the surface morphology changes resulting from many-magnetized-particle interactions. The theoretical analysis demonstrates that the particle distributions and concentrations play a key role in the formation of surface microstructures. The magneto-induced morphologies such as bump-like, concave-like and the mountain-like microstructures are predicted by the model which are closely related with the manners of particle arrangement inside the matrix. However, the particle magnetization intensity and the matrix modulus only have an influence on the amplitudes of the surface displacement. Several magneto-induced surface configurations are presented and discussed to show how we can control the surface of MRE films to make them having tunable properties in friction, wettability and adhesion. In addition, by averaging the magnetic induced surface displacement, the proposed model gives an excellent agreement with the dipole magnetostrictive theory. Moreover, we found that for an inherent hydrophobic material (contact angle >90°), the anisotropic MRE films with column-structured particles could become super-hydrophobic (contact angle >160°) when a moderate magnetic field is applied.