Distribution Constraints Research Articles

Monogamy relations within quadripartite Einstein-Podolsky-Rosen steering based on cascaded four-wave mixing processes

Multipartite Einstein-Podolsky-Rosen (EPR) steering has been recognized as an essential resource for secure quantum communication tasks composed of several spatially separated parties who cannot be fully trusted. Nevertheless, this resource cannot be distributed arbitrarily over many parties; for instance, two independent players cannot simultaneously steer the third party by two-setting measurements. This feature is referred to as monogamy of steering, which ensures the security of quantum cryptographic protocols and is thus a very desired property to investigate for multipartite steering. Here, we propose symmetric and asymmetric structures of cascaded four-wave mixing of rubidium atoms to generate versatile quadripartite EPR steering and investigate four distinct types of monogamy relations of Gaussian steering. We find that the distribution constraint described by one of the monogamy relations can be lifted only for the quadripartite steering created by the symmetric setup. This result paves the way for a better understanding of the distribution rules of multipartite EPR steering and its potential applications for secure quantum communication.

Bernstein copula-based spatial cosimulation for petrophysical property prediction conditioned to elastic attributes

A new methodology for the simulation of spatially distributed petrophysical properties conditioned by elastic attributes as secondary variables is presented. The method, namely Bernstein copula-based spatial cosimulation (BCSCS), is based on Bernstein copula for the estimation of the joint probability function and simulated annealing for the spatial simulation. The proposed method can model complex non-linear relationships between variables in a fully nonparametric approach. The main advantage is that it does not require linear dependence between variables nor any distribution constraint. This method is first validated in a 1-dimensional case at the well-log scale and it is applied in a 2-dimensional case at the seismic scale to predict effective porosity conditioned to P-impedance in a marine hydrocarbon reservoir located offshore Mexico. The uncertainty quantification analysis shows that the BCSCS method significantly reduces the uncertainty compared to the traditional sequential Gaussian cosimulation method.

A tracking error–based fully probabilistic control for stochastic discrete-time systems with multiplicative noise

This article proposes the exploitation of the Kullback–Leibler divergence to characterise the uncertainty of the tracking error for general stochastic systems without constraints of certain distributions. The general solution to the fully probabilistic design of the tracking error control problem is first stated. Further development then focuses on the derivation of a randomised controller for a class of linear stochastic Gaussian systems that are affected by multiplicative noise. The derived control solution takes the multiplicative noise of the controlled system into consideration in the derivation of the randomised controller. The proposed fully probabilistic design of the tracking error of the system dynamics is a more legitimate approach than the conventional fully probabilistic design method. It directly characterises the main objective of system control. The efficiency of the proposed method is then demonstrated on a flexible beam example where the vibration quenching in flexible beams is shown to be effectively suppressed.

A Particle Swarm Based Algorithm for Functional Distributed Constraint Optimization Problems

Distributed Constraint Optimization Problems (DCOPs) are a widely studied constraint handling framework. The objective of a DCOP algorithm is to optimize a global objective function that can be described as the aggregation of several distributed constraint cost functions. In a DCOP, each of these functions is defined by a set of discrete variables. However, in many applications, such as target tracking or sleep scheduling in sensor networks, continuous valued variables are more suited than the discrete ones. Considering this, Functional DCOPs (F-DCOPs) have been proposed that can explicitly model a problem containing continuous variables. Nevertheless, state-of-the-art F-DCOPs approaches experience onerous memory or computation overhead. To address this issue, we propose a new F-DCOP algorithm, namely Particle Swarm based F-DCOP (PFD), which is inspired by a meta-heuristic, Particle Swarm Optimization (PSO). Although it has been successfully applied to many continuous optimization problems, the potential of PSO has not been utilized in F-DCOPs. To be exact, PFD devises a distributed method of solution construction while significantly reducing the computation and memory requirements. Moreover, we theoretically prove that PFD is an anytime algorithm. Finally, our empirical results indicate that PFD outperforms the state-of-the-art approaches in terms of solution quality and computation overhead.

Growth, Distribution, and External Constraints: A Post-Kaleckian Model Applied to Brazil

ABSTRACT The purpose of this research is to analyze whether the Brazilian economy behaved under a wage-led or profit-led regime between 1960 and 2011, considering a Post-Kaleckian model in a context of external constraints. The time span is limited by data availability (i.e., 2011). To answer the question of whether the Brazilian economy works under a wage-led or profit-led regime, we propose a simple Post-Kaleckian model. The model suggests that a profit-led regime is more probable for Brazil. Moreover, a wage-led regime occurs when a balance of payments constrained growth model is taken into consideration. Likewise, the real exchange rate has a positive impact on economic growth through the export channel. This result is a novelty in the recent literature about the relationship between real exchange rate and economic growth within a Post-Kaleckian model. The Brazilian economy was chosen as it is one of the biggest economies in Latin America.

On the emergence of non‐profit orchestras

AbstractIn the market for live classical music, a symphony orchestra typically has fixed costs that are high relative to demand, so that the demand curve lies entirely below the average cost curve (Baumol & Bowen, 1966)—a situation in which a for‐profit enterprise cannot survive. The present study aims to explain how a non‐profit orchestra can survive in such an adverse market environment by relying on private donations, even in the absence of altruism. We argue that a financially distressed orchestra may employ a strategy to impose a non‐profit distribution constraint on itself—thus becoming a non‐profit, tax‐deductible organization—and use its member musicians' worker surplus to produce donor privileges, which enables the orchestra to induce donations from an individually rational audience by taking advantage of a tax deduction scheme. With this strategy, the orchestra can successfully extract consumer surplus from the audience to offset its loss.

Network reconfiguration and optimal allocation of distributed generations for Mayangone distribution system

This paper focuses on reconfiguration of radial distribution system incorporated with optimal allocation of Distributed Generation units (DGs). The network reconfiguration is to optimize the structure of radial feeders by changing the status of normally closed sectionalizing and normally open tie switches. The optimal allocation of DGs in distribution system has to determine the DGs location and their correspondent sizing for enhancing the system effectively. There are many investigations with both two researches either separately or incorporated simultaneously. But network reconfiguration by using Particle Swarm Optimization (PSO) incorporated with optimal allocation of DGs based on Voltage Stability Index (VSI) and analytical approach of Forward Backward Sweep (FBS) method in radial power flow is the novelty of this research. The main objective is to achieve the minimum losses and improvement of voltage profile while all distribution constraints are satisfied. In proposed algorithm, optimal allocation is determined by finding the best sizing and location of DGs based on VSI and FBS load flow, and then PSO reconfiguration finds out the optimal location of tie and sectionalizing switches simultaneously. The study of test system is carried out on Mayangone Distribution System (MDS) and the obtained results are inefficient for this research.

Assignment Mechanisms Under Distributional Constraints

Assigning refugee families to different locations in a host country is a timely challenge due to the global refugee crisis. This paper studies how to assign refugees to different locations while ac...

Describing macroecological patterns in microbes: Approaches for comparative analyses of operational taxonomic unit read number distribution with a case study of global oceanic bacteria

AbstractDescribing the variation in commonness and rarity in a community is a fundamental method of evaluating biodiversity. Such patterns have been studied in the context of species abundance distributions (SADs) among macroscopic organisms in numerous communities. Recently, models for analyzing variation in local SAD shapes along environmental gradients have been constructed. The recent development of high‐throughput sequencing enables evaluation of commonness and rarity in local communities of microbes using operational taxonomic unit (OTU) read number distributions (ORDs), which are conceptually similar to SADs. However, few studies have explored the variation in local microbial ORD shapes along environmental gradients. Therefore, the similarities and differences between SADs and ORDs are unclear, clouding any universal rules of global biodiversity patterns. We investigated the similarities and differences in ORD shapes versus SADs, and how well environmental variables explain the variation in ORDs along latitudinal and depth gradients. Herein, we integrate ORDs into recent comparative analysis methods for SAD shape using datasets generated on the Tara Oceans expedition. About 56% of the variance in skewness of ORDs among global oceanic bacterial communities was explained with this method. Moreover, we confirmed that the parameter combination constraints of Weibull distributions were shared by ORDs of bacterial communities and SADs of tree communities, suggesting common long‐term limitation processes such as adaptation and community persistence acting on current abundance variation. On the other hand, skewness was significantly greater for bacterial communities than tree communities, and many ecological predictions did not apply to bacterial communities, suggesting differences in the community assembly rules for microbes and macroscopic organisms. Approaches based on ORDs provide opportunities to quantify macroecological patterns of microbes under the same framework as macroscopic organisms.

Strategyproof and fair matching mechanism for ratio constraints

We introduce a new type of distributional constraints called ratio constraints, which explicitly specify the required balance among schools in two-sided matching. Since ratio constraints do not belong to the known well-behaved class of constraints called M-convex set, developing a fair and strategyproof mechanism that can handle them is challenging. We develop a novel mechanism called quota reduction deferred acceptance (QRDA), which repeatedly applies the standard DA by sequentially reducing artificially introduced maximum quotas. As well as being fair and strategyproof, QRDA always yields a weakly better matching for students compared to a baseline mechanism called artificial cap deferred acceptance (ACDA), which uses predetermined artificial maximum quotas. Finally, we experimentally show that, in terms of student welfare and nonwastefulness, QRDA outperforms ACDA and another fair and strategyproof mechanism called Extended Seat Deferred Acceptance (ESDA), in which ratio constraints are transformed into minimum and maximum quotas.

Matching through institutions

We analyze a three-sided matching market where institutions own objects and individuals belong to institutions. Institutions pool their objects to enlarge the choice set of individuals. For any institution, the number of individuals who receive an object must be equal to the number of objects initially owned. Under this distributional constraint, individually rational and fair assignments may fail to exist. However, when the number of individuals is sufficiently large, fair assignments exist and can be found using a new algorithm, called the Nested Deferred Acceptance algorithm with interrupters (NDAI). This procedure nests a one-to-one matching between agents and objects and a one-to-many matching between objects and institutions. We show that it outputs a matching which is Pareto optimal among fair matchings and strategy-proof for individuals. When agents belong to several institutions, the NDAI results in assignments which are fair for agents of the same institution.

An Image Clustering Auto-Encoder Based on Predefined Evenly-Distributed Class Centroids and MMD Distance

In this paper, we propose a novel, effective and simpler end-to-end image clustering auto-encoder algorithm: ICAE. The algorithm uses PEDCC (Predefined Evenly-Distributed Class Centroids) as the clustering centers, which ensures the inter-class distance of latent features is maximal, and adds data distribution constraint, data augmentation constraint, auto-encoder reconstruction constraint and Sobel smooth constraint to improve the clustering performance. Specifically, we perform one-to-one data augmentation to learn the more effective features. The data and the augmented data are simultaneously input into the autoencoder to obtain latent features and the augmented latent features whose similarity are constrained by an augmentation loss. Then, making use of the maximum mean discrepancy distance (MMD), we combine the latent features and augmented latent features to make their distribution close to the PEDCC distribution (uniform distribution between classes, Dirac distribution within the class) to further learn clustering-oriented features. At the same time, the MSE of the original input image and reconstructed image is used as reconstruction constraint, and the Sobel smooth loss to build generalization constraint to improve the generalization ability. Finally, extensive experiments on three common datasets MNIST, Fashion-MNIST, COIL20 are conducted. The experimental results show that the algorithm has achieved the best clustering results so far. In addition, we can use the predefined PEDCC class centers, and the decoder to clearly generate the samples of each class. The code can be downloaded at https://github.com/zyWang-Power/Clustering!

Coordinated Dynamic Use of Dispersed Flexibility to Maximize the Time-variant Aggregated Potential for Redispatch

During recent years, the European interconnected transmission system has been affected by a significant increase of grid congestion. Nowadays, large-scaled power plants are providing redispatch power, but will be phased out in coming decades. Besides this development, new technologies like renewable energies or battery storage systems emerged in power systems. These technologies are typically small-scaled, dispersed and connected to distribution systems. Dispersed generation units, storage systems, electric vehicles, and controllable loads may be capable of providing flexible power. Coordinated dynamic use of flexible power from large numbers of small units could become a key contribution to cope with grid congestion in the future. The paper presents a methodology to determine the maximum, time-variant potential for redispatch of distributed units aggregated in a virtual power plant. It is based on parameter-based optimization and takes into account dynamic and energy capacity related constraints. Moreover, the constraints of distribution and transmission systems as well as the impact on power flows is considered and investigated for multiple scenarios by using linearized power flow sensitivities.

Effective, Fair and Equitable Pandemic Rationing

We study the problem of allocating scarce and heterogeneous medical resources to patients. Our solutions optimize for aggregate match-effectiveness subject to fairness and distributional constraints. The main solution uses a novel cutoff characterization result for fair and equitable allocations and a minimum-cost flow formulation. Match-effectiveness gains from optimization are substantial. Even when there are only two types of vaccines, in equal quantities, our algorithm results in more than 33% larger aggregate match-effectiveness compared to the random allocation benchmark.

Large-Scale Crop Mapping From Multisource Remote Sensing Images in Google Earth Engine

Large-scale crop mapping is vitally important to agriculrural monitoring and management. However, traditional methods cannot well meet the needs of large-scale applications. Therefore, this study proposed a method for large-scale crop mapping based on multisource remote sensing images. To be specific, 1) harmonic analysis was conducted on normalized difference vegetation index time-series derived from moderate resolution imaging spectroradiometer images and synthetic aperture radar backscattering coefficient time-series derived from Sentinel-1 data, respectively, extracting harmonic-derived phenological features and harmonic-derived backscattering features, and then combined with spectral features from Landsat-8 and Sentinel-2 images to construct the final multisource feature set for crop classification; 2) it employed prior constraints of crop dominance and cropland distribution to reduce misclassifications in large scale crop mapping; and 3) the whole process was conducted on the Google Earth Engine online platform, which can reduce the computational burdens caused by the spatiotemporal data. In the experimental study, we evaluated three crops, including wheat, rapeseed, and corn in Qinhai in 2018, based on the classification and regression tree classifier. The results show that the Jeffries–Matusita distances between crop samples are close to 2, and the overall accuracy is 84.25%. Furthermore, this study found that the distribution of the crops in Qinghai is associated with climate, topography, and cultivation habits.

AUTOCON-IoT: Automated and Scalable Online Conformance Testing for IoT Applications

As the Internet of Things (IoT) is evolving rapidly in various verticals, such as smart cities and smart industries, the number of IoT applications is growing exponentially. Standardized conformance testing mechanisms are widely used to test software applications, including IoT applications. However, current conformance testing mechanisms require continuous human intervention and additional software to be embedded into a target device being tested, which is time consuming, costly, and requires additional memory on the target device. Therefore, there is a considerable need to enhance and optimize existing conformance testing so that it is adequate for automatically testing highly distributed constraint IoT applications. In this paper, we propose a novel mechanism for automated and scalable conformance testing for IoT applications by introducing a test triggering mechanism based on a standardized test interface. This triggering mechanism is based on several new logical components in a testing environment that exchange messages between a testing system and a target IoT device. This technique of automatic conformance testing can lessen the cost and human intercession to decrease the number of missteps and accelerate the IoT market by certifying IoT applications.

Applying Max-sum to asymmetric distributed constraint optimization problems

We study the adjustment and use of the Max-sum algorithm for solving Asymmetric Distributed Constraint Optimization Problems (ADCOPs). First, we formalize asymmetric factor-graphs and apply the different versions of Max-sum to them. Apparently, in contrast to local search algorithms, most Max-sum versions perform similarly when solving symmetric and asymmetric problems and some even perform better on asymmetric problems. Second, we prove that the convergence properties of Max-sum_ADVP (an algorithm that was previously found to outperform standard Max-sum and Bounded Max-sum) and the quality of the solutions it produces, are dependent on the order between nodes involved in each constraint, i.e., the inner constraint order (ICO). A standard ICO allows to reproduce the properties achieved for symmetric problems. Third, we demonstrate that a non-standard ICO can be used to balance exploration and exploitation. Our results indicate that Max-sum_ADVP with non-standard ICO and Damped Max-sum, when solving asymmetric problems, both outperform other versions of Max-sum, as well as local search algorithms specifically designed for solving ADCOPs.

Homophily as a Process Generating Social Networks: Insights from Social Distance Attachment Model

Real-world social networks often exhibit high levels of clustering, positive degree assortativity, short average path lengths (small-world property) and right-skewed but rarely power law degree distributions. On the other hand homophily, defined as the propensity of similar agents to connect to each other, is one of the most fundamental social processes observed in many human and animal societies. In this paper we examine the extent to which homophily is sufficient to produce the typical structural properties of social networks. To do so, we conduct a simulation study based on the Social Distance Attachment (SDA) model, a particular kind of Random Geometric Graph (RGG), in which nodes are embedded in a social space and connection probabilities depend functionally on distances between nodes. We derive the form of the model from first principles based on existing analytical results and argue that the mathematical construction of RGGs corresponds directly to the homophily principle, so they provide a good model for it. We find that homophily, especially when combined with a random edge rewiring, is sufficient to reproduce many of the characteristic features of social networks. Additionally, we devise a hybrid model combining SDA with the configuration model that allows generating homophilic networks with arbitrary degree sequences and we use it to study interactions of homophily with processes imposing constraints on degree distributions. We show that the effects of homophily on clustering are robust with respect to distribution constraints, while degree assortativity can be highly dependent on the particular kind of enforced degree sequence.

Non-Parallel Voice Conversion System With WaveNet Vocoder and Collapsed Speech Suppression

In this paper, we integrate a simple non-parallel voice conversion (VC) system with a WaveNet (WN) vocoder and a proposed collapsed speech suppression technique. The effectiveness of WN as a vocoder for generating high-fidelity speech waveforms on the basis of acoustic features has been confirmed in recent works. However, when combining the WN vocoder with a VC system, the distorted acoustic features, acoustic and temporal mismatches, and exposure bias usually lead to significant speech quality degradation, making WN generate some very noisy speech segments called collapsed speech. To tackle the problem, we take conventional-vocoder-generated speech as the reference speech to derive a linear predictive coding distribution constraint (LPCDC) to avoid the collapsed speech problem. Furthermore, to mitigate the negative effects introduced by the LPCDC, we propose a collapsed speech segment detector (CSSD) to ensure that the LPCDC is only applied to the problematic segments to limit the loss of quality to short periods. Objective and subjective evaluations are conducted, and the experimental results confirm the effectiveness of the proposed method, which further improves the speech quality of our previous non-parallel VC system submitted to Voice Conversion Challenge 2018.

STRUCTURE OF RICE DEMAND AND CONSUMER LEXICOGRAPHIC PREFERENCES IN INDONESIA

This research aims to identify the constraints in the distribution of rice before the enactment and after the implementation of HET in the demand structure of the three types of rice, head rice, medium rice and premium rice associated with changes in consumer lexicographic preferences. The study was conducted in urban and rural areas in Indonesia with cases in South Sumatra Province. 120 samples of rice consumers were selected in 60 rural and 60 urban consumers. The method of research is in depth study by observing and interviewing to exploring the variety of reasons for choosing lexicographic preferences in consuming all three types of rice, both in urban and rural areas. Susenas data is used to assess the structure of rice demand. Data analyzed by using quantitative and qualitative descriptive methods. The research results indicated that: (1) there was an increase in demand for medium and premium rice in rural and urban areas in the period 2013-2017, but decreased in the 2018-2019 period. This condition is due to the imposition of the Ceiling Price (HET) and inflation in 2018; (2) along with the factors affecting rice demand between rice times in rural and urban areas there are differences; (3) there are differences in the factors affecting the demand for rice between rural and urban rice. The structure of urban consumer demand remains influenced by quality factors, while rural is not; (4) based on a lexicographic analysis of rice demand in rural consumers more on the preference of physical attributes of rice and urban consumers to quality attributes, so that urban consumers of rice quality attributes continue to influence hedonic prices; (5) Based on the lexicograpphic function of household level demand for rice by the chow test, medium rice consumption in urban areas does not change the structure of consumption, but changes in rural areas. However, medium rice has been mixed with head rice, so it can be strongly suspected that urban consumers are the same as rural, that is, they do not yet have a proper understanding of good quality attributes for rice consumption. Unlike the urban consumers who still survive the consumption of premium rice that already has sufficient awareness and knowledge about the ins and outs of rice.