Web Page Recommendation Using Distributional Recurrent Neural Network

In the data retrieval process of the Data recommendation system,the matching prediction and similarity identification take place a major role in the ontology.In that,there are several methods to improve the retrieving process with improved accuracy and to reduce the searching time.Since,in the data recommendation system,this type of data searching becomes complex to search for the best matching for given query data and fails in the accuracy of the query recommendation process.To improve the performance of data validation,this paper proposed a novel model of data similarity estimation and clustering method to retrieve the relevant data with the best matching in the big data processing.In this paper advanced model of the Logarithmic Directionality Texture Pattern(LDTP)method with a Metaheuristic Pattern Searching(MPS)system was used to estimate the similarity between the query data in the entire database.The overall work was implemented for the application of the data recommendation process.These are all indexed and grouped as a cluster to form a paged format of database structure which can reduce the computation time while at the searching period.Also,with the help of a neural network,the relevancies of feature attributes in the database are predicted,and the matching index was sorted to provide the recommended data for given query data.This was achieved by using the Distributional Recurrent Neural Network(DRNN).This is an enhanced model of Neural Network technology to find the relevancy based on the correlation factor of the feature set.The training process of the DRNN classifier was carried out by estimating the correlation factor of the attributes of the dataset.These are formed as clusters and paged with proper indexing based on the MPS parameter of similarity metric.The overall performance of the proposed work can be evaluated by varying the size of the training database by 60%,70%,and 80%.The parameters that are considered for performance analysis are Precision,Recall,F1-score and the accuracy of data retrieval,the query recommendation output,and comparison with other state-of-art methods.

Distributional dynamic and trend evolution of China's agricultural carbon emissions-an analysis on panel data of 31 provinces from 2002 to 2011

Researching the dynamic distribution characteristics and trend evolution of agricultural carbon emissions is of considerable significance in formulating an effective agricultural carbon reduction policy.Based on measurement of agricultural carbon emissions of 31 provinces over the period 2002-2011,the study observed regional differences and the dynamic evolution of distribution of agricultural carbon emissions using agricultural carbon intensity as the indicator,accompanied by Gini coefficients and the kernel density estimation method.The results demonstrate first that agricultural carbon emissions for China show an obvious nonequilibrium nature in regard to spatial distribution.According to the differences in agricultural carbon emissions dynamic trends,we divided the 31 regions into four types- continuous decline,fluctuating decline,continuous increase,and fluctuating increase.Further,agricultural carbon emissions intensity showed a downward trend with significant differences in the research areas.Second,the gap in spatial distribution of national agricultural carbon emissions is gradually expanding based on the results calculated by Gini coefficient.From the perception of regional differences in agricultural carbon emissions,the eastern region showed an average level,the gap was more obvious in the central region,while western region showed a trend of fluctuating downward.Third,according to estimation by kernel density,the regional disparity in agricultural carbon emissions had a downward,but limited,trend.In regard to agricultural carbon emissions over the three areas,the regional gap not only tended to decrease but also showed a "four way" differentiation phenomenon in the eastern region.The difference in the central region difference was narrower.On the whole,the gap for the western region reduced steadily over a small range.

Second Order Periodic Boundary Value Problems Involving the Distributional Henstock-Kurzweil Integral

We apply the distributional derivative to study the existence of solutions of the second order periodic boundary value problems involving the distributional Henstock-Kurzweil integral. The distributional Henstock-Kurzweil integral is a general intergral, which contains the Lebesgue and Henstock-Kurzweil integrals. And the distributional derivative includes ordinary derivatives and approximate derivatives. By using the method of upper and lower solutions and a fixed point theorem, we achieve some results which are the generalizations of some previous results in the literatures.

Distributional features of temperature and salinity in the southern Taiwan Strait and its adjacent sea areas in late summer, 1994

Based on the CTD data obtained in the southern Taiwan Strait and its adjacent areas in August and September of 1994, the distributional features of the temperature and salinity in the studied area have been analyzed in detail. The results are as follows: (1) There are two low temperature and high salinity regions in the nearshore area between Dongshan and Shantou and in the southeastern Taiwan Shoal, respectively, which may be caused by upwellings. (2) There exists a cold eddy in the northwestern sea area and a warm eddy with two high temperature cores in the eastern sea area of the Dongsha Islands, which are related to the anti-cyclonic turning of the seawater near the Dongsha Islands. (3) A westward high temperature and high salinity water tongue extends through the northern Luzon Strait and reaches the sea areas near the Dengsha Islands and southern Taiwan Strait.

Distributional characteristics of planktonic Amphipoda（Hyperiidea） in the South Huanghai Sea and East China Sea

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Distributional Reinforcement Learning with Quantum Neural Networks

Traditional reinforcement learning (RL) uses the return, also known as the expected value of cumulative random rewards, for training an agent to learn an optimal policy. However, recent research indicates that learning the distribution over returns has distinct advantages over learning their expected value as seen in different RL tasks. The shift from using the expectation of returns in traditional RL to the distribution over returns in distributional RL has provided new insights into the dynamics of RL. This paper builds on our recent work investigating the quantum approach towards RL. Our work implements the quantile regression (QR) distributional Q learning with a quantum neural network. This quantum network is evaluated in a grid world environment with a different number of quantiles, illustrating its detailed influence on the learning of the algorithm. It is also compared to the standard quantum Q learning in a Markov Decision Process (MDP) chain, which demonstrates that the quantum QR distributional Q learning can explore the environment more efficiently than the standard quantum Q learning. Efficient exploration and balancing of exploitation and exploration are major challenges in RL. Previous work has shown that more informative actions can be taken with a distributional perspective. Our findings suggest another cause for its success: the enhanced performance of distributional RL can be partially attributed to its superior ability to efficiently explore the environment.

Was Polchinski Wrong? Colombeau Distributional Rindler Space-Time with Distributional Levi-CivitàConnection Induced Vacuum Dominance. Unruh Effect Revisited

The vacuum energy density of free scalar quantum field in a Rindler distributional space-time with distributional Levi-Cività connection is considered. It has been widely believed that, except in very extreme situations, the influence of acceleration on quantum fields should amount to just small, sub-dominant contributions. Here we argue that this belief is wrong by showing that in a Rindler distributional background space-time with distributional Levi-Cività connection the vacuum energy of free quantum fields is forced, by the very same background distributional space-time such a Rindler distributional background space-time, to become dominant over any classical energy density component. This semiclassical gravity effect finds its roots in the singular behavior of quantum fields on a Rindler distributional space-times with distributional Levi-Cività connection. In particular we obtain that the vacuum fluctuations have a singular behavior at a Rindler horizon . Therefore sufficiently strongly accelerated observer burns up near the Rindler horizon. Thus Polchinski’s account doesn’t violate the Einstein equivalence principle.

Potential distributional shifts in North America of allelopathic invasive plant species under climate change models

Predictive studies play a crucial role in the study of biological invasions of terrestrial plants under possible climate change scenarios.Invasive species are recognized for their ability to modify soil microbial communities and influence ecosystem dynamics.Here,we focused on six species of allelopathic flowering plants-Ailanthus altissima,Casuarina equisetifolia,Centaurea stoebe ssp.micranthos,Dioscorea bulbifera,Lantana camara,and Schinus terebinthifolia-Xhat are invasive in North America and examined their potential to spread further during projected climate change.We used Species Distribution Models(SDMs)to predict future suitable areas for these species in North America under several proposed future climate models.ENMEval and Maxent were used to develop SDMs,estimate current distributions,and predict future areas of suitable climate for each species.Areas with the greatest predicted suitable climate in the future include the northeastern and the coastal northwestern regions of North America.Range size estimations demonstrate the possibility of extreme range loss for these invasives in the southeastern United States,while new areas may become suitable in the northeastern United States and southeastern Canada.These findings show an overall northward shift of suitable climate during the next few decades,given projected changes in temperature and precipitation.Our results can be utilized to analyze potential shifts in the distribution of these invasive species and may aid in the development of conservation and management plans to target and control dissemination in areas at higher risk for potential future invasion by these allelopathic species.

The Homogeneity of Distributional Patterns of the Global Terrestrial Animal,Plant and Microorganism under the Influence of Ecological Conditions

The world animal geographical regionalization scheme and the plant geographical regionalization scheme have been formulated by zoologists and botanists respectively since the biogeography has been established.This research team initially confirmed the homogeneity of Chinese animal and plant geography.To explore the relationship between the distribution pattern of global animals,plants,and microorganisms,global 141,814 genera of terrestrial animals,17,526 genera of plants,21,321 genera of microorganisms,and their major taxa were analyzed using their proposed SGF(Similarity General Formula)and a new multivariate similarity clustering analysis method.Almost identical analytical results were obtained,meeting the requirements of statistics,geography,ecology and biology respectively.The expected consistency of their distribution pattern was achieved for the first time.We prove that the earth’s ecological conditions affect the homogeneity and accumulation of the distribution of animals,plants and microorganisms.Homogeneity determines the distribution pattern of global kinds of biological consistency,accumulation determines the impact of the evolutionary period on the breadth of distribution,microorganisms appear earliest,plants second,animals later,and their average distribution domain decreases in turn,reflecting these differences.Therefore,this study not only provides a theoretical basis and quantitative basis for the establishment of geographical regionalization scheme but also advances the development of biogeography to a new stage and raises the theory of biogeographic analysis to a new height.

Description of a new species and new distributional data for two species of Aphytis (Hymenoptera: Aphelinidae) from China

Aphytis tridentatus Gao and Li sp. nov. is described and illustrated from southwest China, and A.bangalorensis Rosen and De Bach is newly recorded from China. New distributional data for A. chionaspis Ren are also provided.

Distributional features of δ^(18) O in precipitation in China

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A Distributional Representation of Gamma Function with Generalized Complex Domian

In this paper, we present a new representation of gamma function as a series of complex delta functions. We establish the convergence of this representation in the sense of distributions. It turns out that the gamma function can be defined over a space of complex test functions of slow growth denoted by Z. Some properties of gamma function are discussed by using the properties of delta function.

Clustering Analysis of Distributional Patterns of Global Terrestrial Mammal

In this study,the world’s land(except Antarctica)is divided into 67 basic geographical units according to ecological types.Using our newly proposed MSCA(Multivariate Similarity Clustering Analysis)method,7,591 species of modern terrestrial mammals belonging to 1,374 genera in 162 families and 2,378 species of mammals in the Wallace era before 1876 are quantitatively analyzed,and almost the same clustering results are obtained,with clear levels and reasonable clustering,which conform to the principles of geography,statistics,ecology and biology.It not only affirms and supports the reasonable kernel of Wallace’s scheme,but also puts forward suggestions that should be revised and improved.The large or small differences between the clustering results and the mammalian geographical zoning schemes of contemporary scholars are caused by different analysis methods,and they are highly consistent with the analysis results of chordates,angiosperms and insects in the world analyzed by the same method.Once again,it confirms the homogeneity of the global biological distribution pattern of major groups,and the possibility of building a unified biogeographic zoning system in the world.

Data-driven Distributionally Adjustable Robust Chance-constrained DG Capacity Assessment

Moving away from fossil fuels towards renewable sources requires system operators to determine the capacity of distribution systems to safely accommodate green and distributed generation(DG).However,the DG capacity of a distribution system is often underestimated due to either overly conservative electrical demand and DG output uncertainty modelling or neglecting the recourse capability of the available components.To improve the accuracy of DG capacity assessment,this paper proposes a distributionally adjustable robust chance-constrained approach that utilises uncertainty information to reduce the conservativeness of conventional robust approaches.The proposed approach also enables fast-acting devices such as inverters to adjust to the real-time realisation of uncertainty using the adjustable robust counterpart methodology.To achieve a tractable formulation,we first define uncertain chance constraints through distributionally robust conditional value-at-risk(CVaR),which is then reformulated into convex quadratic constraints.We subsequently solve the resulting large-scale,yet convex,model in a distributed fashion using the alternating direction method of multipliers(ADMM).Through numerical simulations,we demonstrate that the proposed approach outperforms the adjustable robust and conventional distributionally robust approaches by up to 15%and 40%,respectively,in terms of total installed DG capacity.

Distributional Chaoticity of the Minimal Subshift of Shift Operators

This paper focus on the chaotic properties of minimal subshift of shift operators. It is proved that the minimal subshift of shift operators is uniformly distributional chaotic, distributional chaotic in a sequence, distributional chaotic of type k ( k∈{ 1,2,2 1 2 ,3 } ), and ( 0,1 ) -distribution.

Day-ahead Network-constrained Unit Commitment Considering Distributional Robustness and Intraday Discreteness:A Sparse Solution Approach

Quick-start generation units are critical devices and flexible resources to ensure a high penetration level of renewable energy in power systems.By considering the wind uncertainty and both binary and continuous decisions of quickstart generation units within the intraday dispatch,we develop a Wasserstein-metric-based distributionally robust optimization model for the day-ahead network-constrained unit commitment(NCUC)problem with mixed-integer recourse.We propose two feasible frameworks for solving the optimization problem.One approximates the continuous support of random wind power with a finite number of events,and the other leverages the extremal distributions instead.Both solution frameworks rely on the classic nested column-and-constraint generation(C&CG)method.It is shown that due to the sparsity of L_(1)-norm Wasserstein metric,the continuous support of wind power generation could be represented by a discrete one with a small number of events,and the rendered extremal distributions are sparse as well.With this reduction,the distributionally robust NCUC model with complicated mixed-integer recourse problems can be efficiently handled by both solution frameworks.Numerical studies are carried out,demonstrating that the model considering quick-start generation units ensures unit commitment(UC)schedules to be more robust and cost-effective,and the distributionally robust optimization method captures the wind uncertainty well in terms of out-of-sample tests.

Unsupervised image translation with distributional semantics awareness

Unsupervised image translation(UIT)studies the mapping between two image domains.Since such mappings are under-constrained,existing research has pursued various desirable properties such as distributional matching or two-way consistency.In this paper,we re-examine UIT from a new perspective:distributional semantics consistency,based on the observation that data variations contain semantics,e.g.,shoes varying in colors.Further,the semantics can be multi-dimensional,e.g.,shoes also varying in style,functionality,etc.Given two image domains,matching these semantic dimensions during UIT will produce mappings with explicable correspondences,which has not been investigated previously.We propose distributional semantics mapping(DSM),the first UIT method which explicitly matches semantics between two domains.We show that distributional semantics has been rarely considered within and beyond UIT,even though it is a common problem in deep learning.We evaluate DSM on several benchmark datasets,demonstrating its general ability to capture distributional semantics.Extensive comparisons show that DSM not only produces explicable mappings,but also improves image quality in general.

Spectral and biodistributional engineering of deep near-infrared chromophore

Fluorescence-guided surgery calls for development of near-infrared fluorophores.Despite the wide-spread application and a safe clinical record of Indocyanine Green(ICG),its maximal absorption wavelength at780 nm is rather short and longer-wavelength dyes are desired to exploit such benefits as low phototoxicity and deep penetration depth.Here,we report ECY,a stable deep near-infrared(NIR)fluorochromic scaffold absorbing/emitting at 836/871 nm with a fluorescence quantum yield of 16%in CH_(2)Cl_(2).ECY was further rationally engineered for biological distribution specificity.Analogous bearing different numbers of sulfonate group or a polyethylene glycol chain were synthesized.By screening this focused library upon intravenous injection to BALB/c mice,ECYS2 was identified to be a suitable candidate for bioimaging of organs involved in hepatobiliary excretion,and ECYPEG was found to be a superior candidate for vasculature imaging.They have potentials in intraoperative imaging.

The Probability Density Function Related to Shallow Cumulus Entrainment Rate and Its Influencing Factors in a Large-Eddy Simulation

The process of entrainment-mixing between cumulus clouds and the ambient air is important for the development of cumulus clouds.Accurately obtaining the entrainment rate(λ)is particularly important for its parameterization within the overall cumulus parameterization scheme.In this study,an improved bulk-plume method is proposed by solving the equations of two conserved variables simultaneously to calculateλof cumulus clouds in a large-eddy simulation.The results demonstrate that the improved bulk-plume method is more reliable than the traditional bulk-plume method,becauseλ,as calculated from the improved method,falls within the range ofλvalues obtained from the traditional method using different conserved variables.The probability density functions ofλfor all data,different times,and different heights can be well-fitted by a log-normal distribution,which supports the assumed stochastic entrainment process in previous studies.Further analysis demonstrate that the relationship betweenλand the vertical velocity is better than other thermodynamic/dynamical properties;thus,the vertical velocity is recommended as the primary influencing factor for the parameterization ofλin the future.The results of this study enhance the theoretical understanding ofλand its influencing factors and shed new light on the development ofλparameterization.