Hierarchical Joint Research Articles

Deterministic hierarchical joint remote state preparation with six-particle partially entangled state**Project supported by the National Natural Science Foundation of China (Grant Nos. 61701285 and 61701284), the Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents, China (Grant No. 2017RCJJ070), and China Postdoctoral Science Foundation Funded Project (Grant No. 2017M622233).

In this paper, we present a novel scheme for hierarchical joint remote state preparation (HJRSP) in a deterministic manner, where two senders can jointly and remotely prepare an arbitrary single-qubit at three receivers’ port. A six-particle partially entangled state is pre-shared as the quantum channel. There is a hierarchy among the receivers concerning their powers to reconstruct the target state. Due to various unitary operations and projective measurements, the unit success probability can always be achieved irrespective of the parameters of the pre-shared partially entangled state.

Exploring the Effect of Different Neighboring Structures on Spatial Hierarchical Joint Crash Frequency Models

Corridor safety analysis is a primary interest of many road safety studies. Corridors typically contain intersections and roadway segments. Having both components while analyzing corridors in addition to corridor-level variables in a hierarchical joint model would provide a comprehensive understanding of the existing corridor safety problems. There will probably be spatial correlation among road entities along a corridor, especially if the distance between the road entities is not large. Therefore, it is crucial to consider spatial effects in the model. However, this data structure is relatively new, and the best spatial weight matrix for this hierarchical spatial joint model has yet to be investigated. Therefore, this study estimates a hierarchical Poisson-lognormal (HPLN) joint model with spatial effects and explores the effect of different neighboring structures. A total of thirteen HPLN joint models are estimated: one HPLN joint model with corridor random effect and twelve HPLN joint models with spatial effects. Four types of conceptualization of spatial relationships were considered: (a) adjacency-based, (b) adjacency-route, (c) distance-order, and (d) distance-based spatial weight features. The results show the importance of incorporating spatial effects in the model. It was found that having a joint model is important since one of the best models is the adjacency-based first-order model, where the feeding road entities in addition to the directly adjacent road entity of the same type as the road entity of interest are considered. The results confirm the importance of spatial autocorrelation between road entities along the same corridor.

Do priority effects outweigh environmental filtering in a guild of dominant freshwater macroinvertebrates?

Abiotic conditions have long been considered essential in structuring freshwater macroinvertebrate communities. Ecological drift, dispersal and biotic interactions also structure communities, and although these mechanisms are more difficult to detect, they may be of equal importance in natural communities. Here, we hypothesized that in 10 naturally replicated headwater streams in eastern Switzerland, locally dominant amphipod species would be associated with differences in environmental conditions. We conducted repeated surveys of amphipods and used a hierarchical joint species distribution model to assess the influence of different drivers on species co-occurrences. The species had unique environmental requirements, but a distinct spatial structure in their distributions was unrelated to habitat. Species co-occurred much less frequently than predicted by the model, which was surprising because laboratory and field evidence suggests they are capable of coexisting in equal densities. We suggest that niche preemption may limit their distribution and that a blocking effect related to the specific linear configuration of streams determines which species colonizes and dominates a given stream catchment, thus suggesting a new solution a long-standing conundrum in freshwater ecology.

Abstract P4-09-01: A multi-study correlative analysis of change in Ki67 in the neoadjuvant setting and disease free survival improvement in the adjuvant setting

Abstract Background: Based on results from the ATAC and IMPACT trials, Dowsett et al.1 hypothesized that “short-term changes in proliferation in the neoadjuvant setting may be able to predict outcome during adjuvant use of the same treatments.” Recent neoadjuvant studies2,3,4 of novel agents for HR+ breast cancer used Ki67, a biomarker for cell proliferation, as an endpoint for evaluating biological activity. However, the potential of this endpoint for predicting the adjuvant efficacy of therapeutic agents remains highly uncertain, motivating continued study based on clinical data. After curating relevant data from multiple trials (in both the neoadjuvant and adjuvant settings), a statistical model was constructed to evaluate the current evidence regarding this hypothesis. Methods: Data were collated from randomized trials of systemic therapies in post-menopausal women with HR+ breast cancer reporting Ki67 reduction in the neoadjuvant setting (7 studies) or disease-free survival (DFS) in the adjuvant setting (5 studies). A Bayesian hierarchical joint network meta-analysis model was constructed and fit to the data to evaluate: (1) if there is an association between study-level neoadjuvant Ki67 reduction for a given regimen and corresponding adjuvant DFS hazard ratio, and (2) if the relationship does exist, what DFS effect does it predict for various regimens for which only neoadjuvant data are currently available. Results: The results of the statistical model indicate that effect-size based on neoadjuvant Ki67 correlates with adjuvant (DFS) hazard ratio. Using neoadjuvant Ki67 data for several therapies for which adjuvant data are presently unavailable, we demonstrated how forecasts (and associated confidence intervals) of adjuvant effect-size may be produced using the model. Conclusions: While randomized trials yielding paired data from both clinical settings are sparse, this assessment using a statistically rigorous approach sheds additional light on the hypothesis of a correlation between neoadjuvant Ki67 changes and adjuvant DFS improvements. This analysis suggests the potential utility of Ki67 as a surrogate endpoint to screen/prioritize experimental regimens for development in the adjuvant setting.

Enhanced effects of biotic interactions on predicting multispecies spatial distribution of submerged macrophytes after eutrophication.

Water eutrophication creates unfavorable environmental conditions for submerged macrophytes. In these situations, biotic interactions may be particularly important for explaining and predicting the submerged macrophytes occurrence. Here, we evaluate the roles of biotic interactions in predicting spatial occurrence of submerged macrophytes in 1959 and 2009 for Dianshan Lake in eastern China, which became eutrophic since the 1980s. For the four common species occurred in 1959 and 2009, null species distribution models based on abiotic variables and full models based on both abiotic and biotic variables were developed using generalized linear model (GLM) and boosted regression trees (BRT) to determine whether the biotic variables improved the model performance. Hierarchical Bayesian‐based joint species distribution models capable of detecting paired biotic interactions were established for each species in both periods to evaluate the changes in the biotic interactions. In most of the GLM and BRT models, the full models showed better performance than the null models in predicting the species presence/absence, and the relative importance of the biotic variables in the full models increased from less than 50% in 1959 to more than 50% in 2009 for each species. Moreover, co‐occurrence correlation of each paired species interaction was higher in 2009 than that in 1959. The findings suggest biotic interactions that tend to be positive play more important roles in the spatial distribution of multispecies assemblages of macrophytes and should be included in prediction models to improve prediction accuracy when forecasting macrophytes’ distribution under eutrophication stress.

Bayesian hierarchical joint modeling of repeatedly measured continuous and ordinal markers of disease severity: Application to Ugandan diabetes data.

Modeling of correlated biomarkers jointly has been shown to improve the efficiency of parameter estimates, leading to better clinical decisions. In this paper, we employ a joint modeling approach to a unique diabetes dataset, where blood glucose (continuous) and urine glucose (ordinal) measures of disease severity for diabetes are known to be correlated. The postulated joint model assumes that the outcomes are from distributions that are in the exponential family and hence modeled as multivariate generalized linear mixed effects model associated through correlated and/or shared random effects. The Markov chain Monte Carlo Bayesian approach is used to approximate posterior distribution and draw inference on the parameters. This proposed methodology provides a flexible framework to account for the hierarchical structure of the highly unbalanced data as well as the association between the 2 outcomes. The results indicate improved efficiency of parameter estimates when blood glucose and urine glucose are modeled jointly. Moreover, the simulation studies show that estimates obtained from the joint model are consistently less biased and more efficient than those in the separate models.

Hierarchical joint remote state preparation in noisy environment

A novel scheme for quantum communication having substantial applications in practical life is designed and analyzed. Specifically, we have proposed a hierarchical counterpart of the joint remote state preparation (JRSP) protocol, where two senders can jointly and remotely prepare a quantum state. One sender has the information regarding amplitude, while the other one has the phase information of a quantum state to be jointly prepared at the receiver's port. However, there exists a hierarchy among the receivers, as far as powers to reconstruct the quantum state is concerned. A 5-qubit cluster state has been used here to perform the task. Further, it is established that the proposed scheme for hierarchical JRSP (HJRSP) is of enormous practical importance in critical situations involving defense and other sectors, where it is essential to ensure that an important decision/order that can severely affect a society or an organization is not taken by a single person, and once the order is issued all the receivers don't possess an equal right to implement it. Further, the effect of different noise models (e.g., amplitude damping (AD), phase damping (PD), collective noise and Pauli noise models) on the HJRSP protocol proposed here is investigated. It is found that in AD and PD noise models a higher power agent can reconstruct the quantum state to be remotely prepared with higher fidelity than that done by the lower power agent(s). In contrast, the opposite may happen in the presence of collective noise models. We have also proposed a scheme for probabilistic HJRSP using a non-maximally entangled 5-qubit cluster state.

Hierarchical game joint optimization for product family-driven modular design

Product family design takes advantage of modularity to enable product variety while maintaining mass production efficiency. Focusing on a set of similar product variants, product family modularity (PFM) is achieved by reusing common components and minimizing fulfillment costs throughout the product realization process. On the other hand, traditional modular design emphasizes technical system modularity (TSM) that focuses on a single product and is geared towards product decomposition in light of technical feasibility. While it is appealing to incorporate product family considerations into the prevailing modularization theories and methods, the key challenge lies in that TSM and PFM are essentially associated with different goals and decision criteria. This leads to a dilemma that TSM and PFM are competing in decision making for identification of modules by grouping similar components. Realizing the importance of game-theoretic decision making underlying product family-driven modular design, this paper proposes to leverage TSM and PFM within a coherent framework of joint optimization. A hierarchical game joint optimization model is developed in line with bilevel programming. A two-dimension evaluation criteria taxonomy is presented for TSM and PFM criteria measure. A bilevel nested genetic algorithm is put forward for efficient solution of the non-linear hierarchical joint optimization model. A case study of robotic vacuum cleaner modular design is reported to gain insight into joint optimization of TSM and PFM. Results and analyses demonstrate that the proposed hierarchical joint optimization model is robust and can empower modular design in cohesion with product family concerns.

Hierarchical-game-based algorithm for downlink joint subchannel and power allocation in OFDMA femtocell networks

Heterogeneous wireless networks are promising technologies to provide high-data rate services. Our study proposes a hierarchical joint subchannel and power allocation algorithm in orthogonal frequency division multiple access femtocell networks. FBSs and MBSs in the network maximize their capacity and compete with each other through resource allocation strategy. This competition is captured in the framework of a Stackelberg game, and the Stackelberg equilibrium is introduced as the solution to the problem. An iterative method is proposed for MBSs and FBSs to achieve the hierarchical Stackelberg equilibrium. We specifically recommend a new power allocation function for MBS. The best response strategy of MBS can be directly obtained by using the proposed power-allocation function. As a result, costs are decreased. We also divide the strategy of MBSs into two substrategies, namely, subchannel selection strategy and power strategy. The two substrategies interact with each other in the game process to improve the flexibility of the MBS strategy. A dynamic step-size mechanism is proposed to accelerate the algorithm. Simulation results are presented to show the effectiveness of the proposed algorithm under high-interference conditions.

A New Cascade Model for the Hierarchical Joint Classification of Multitemporal and Multiresolution Remote Sensing Data

In this paper, we propose a novel method for the joint classification of both multidate and multiresolution remote sensing imagery, which represents an important and relatively unexplored classification problem. The proposed classifier is based on an explicit hierarchical graph-based model that is sufficiently flexible to address a coregistered time series of images collected at different spatial resolutions. Within this framework, a novel element of the proposed approach is the use of multiple quadtrees in cascade, each associated with the images available at each observation date in the considered time series. For each date, the input images are inserted in a hierarchical structure on the basis of their resolutions, whereas missing levels are filled in with wavelet transforms of the images embedded in finer-resolution levels. This approach is aimed at both exploiting multiscale information, which is known to play a crucial role in high-resolution image analysis, and supporting input images acquired at different resolutions in the input time series. The experimental results are shown for multitemporal and multiresolution optical data.

Compressive multi-spectral imaging using self-correlations of images based on hierarchical joint sparsity models

We propose a novel multi-spectral imaging method based on compressive sensing (CS). In CS theory, the enhancement of signal sparsity is important for accurate signal reconstruction. The main novelty of the proposed method is the employment of a self-correlation of an image, that is a local intensity similarity and multi-spectral correlation, to enhance the sparsity of the multi-spectral image to be recovered. Local intensity similarity, which is based on the concept that spatial changes in intensity are likely to be similar within local regions, contributes to sparsity enhancement. Furthermore, we exploit multi-spectral correlation to improve the sparsity of the multi-spectral components to be recovered. In order to simultaneously exploit different types of characteristics (i.e., local intensity similarity and multi-spectral correlation) for representing a signal as sufficiently sparse, we introduce a hierarchical joint sparsity model in the CS image recovery process. Our experiments show that the use of a self-correlation significantly improves the performance of multi-spectral image reconstruction.

Mixture Joint Models for Event Time and Longitudinal Data With Multiple Features

ABSTRACTIt often happens in longitudinal studies that repeated measurements of markers are observed with various data features of a heterogeneous population comprising of several subclasses, left-censoring due to a limit of detection (LOD) and covariates measured with error. Moreover, repeatedly measured markers in time may be associated with a time-to-event of interest. Inferential procedures may become very complicated when one analyzes data with these features together. This article explores a finite mixture of hierarchical joint models of event times and longitudinal measures with an attempt to alleviate departures from homogeneous characteristics, tailor observations below LOD as missing values, mediate accuracy from measurement error in covariate and overcome shortages of confidence in specifying a parametric time-to-event model with a nonparametric distribution. The Bayesian joint modeling is employed to not only estimate all parameters in mixture of joint models, but also evaluate probabilities of class membership. A real data example is analyzed to demonstrate the methodology by jointly modeling the viral dynamics and the time to decrease in CD4/CD8 ratio in the presence of CD4 cell counts with measurement error and the analytic results are reported by comparing potential models for various scenarios. Supplementary materials for this article are available online.

Road network safety evaluation using Bayesian hierarchical joint model

Safety and efficiency are commonly regarded as two significant performance indicators of transportation systems. In practice, road network planning has focused on road capacity and transport efficiency whereas the safety level of a road network has received little attention in the planning stage. This study develops a Bayesian hierarchical joint model for road network safety evaluation to help planners take traffic safety into account when planning a road network. The proposed model establishes relationships between road network risk and micro-level variables related to road entities and traffic volume, as well as socioeconomic, trip generation and network density variables at macro level which are generally used for long term transportation plans. In addition, network spatial correlation between intersections and their connected road segments is also considered in the model.A road network is elaborately selected in order to compare the proposed hierarchical joint model with a previous joint model and a negative binomial model. According to the results of the model comparison, the hierarchical joint model outperforms the joint model and negative binomial model in terms of the goodness-of-fit and predictive performance, which indicates the reasonableness of considering the hierarchical data structure in crash prediction and analysis. Moreover, both random effects at the TAZ level and the spatial correlation between intersections and their adjacent segments are found to be significant, supporting the employment of the hierarchical joint model as an alternative in road-network-level safety modeling as well.

Haptic Rendering of Compliant Shapes

Dynamically controlling the shape of an object by means of a 3-D shape interface offers many new opportunities in the fields of virtual reality and design as direct exploration of objects becomes feasible. Using a parallel kinematics as 3-D shape interface comes with features such as high stiffness and speed, but requires a series of points to be controlled simultaneously and loop constraints to be met. In this paper we present an approach of controlling the shape of a 3-D parallel kinematics. Shape, loop constraints, and constraints resulting from the user interaction are considered in the form of a hierarchical framework and joint limitations are taken into account. Two alternative control modes for stiff and compliant objects are presented. Implicit surfaces as well as polygon sets represent the input for the virtual shape to be rendered. Results show that the calculation of the inverse kinematics can be achieved with a sampling rate of 1 kHz, independent of the representation of the shape as implicit surface or polygonal set, guaranteeing a sufficiently high sampling rate typical for haptic devices.

Erratum to: Image Fusion by Hierarchical Joint Sparse Representation

Erratum to: Image Fusion by Hierarchical Joint Sparse Representation

A mixture of hierarchical joint models for longitudinal data with heterogeneity, non-normality, missingness, and covariate measurement error

ABSTRACTLongitudinal data arise frequently in medical studies and it is a common practice to analyze such complex data with nonlinear mixed-effects (NLME) models. However, the following four issues may be critical in longitudinal data analysis. (i) A homogeneous population assumption for models may be unrealistically obscuring important features of between-subject and within-subject variations; (ii) normality assumption for model errors may not always give robust and reliable results, in particular, if the data exhibit skewness; (iii) the responses may be missing and the missingness may be nonignorable; and (iv) some covariates of interest may often be measured with substantial errors. When carrying out statistical inference in such settings, it is important to account for the effects of these data features; otherwise, erroneous or even misleading results may be produced. Inferential procedures can be complicated dramatically when these four data features arise. In this article, the Bayesian joint modeling approach based on a finite mixture of NLME joint models with skew distributions is developed to study simultaneous impact of these four data features, allowing estimates of both model parameters and class membership probabilities at population and individual levels. A real data example is analyzed to demonstrate the proposed methodologies, and to compare various scenarios-based potential models with different specifications of distributions.

Bio-inspired hierarchical design of composite T-joints with improved structural properties

The biological principle of hierarchical (multi-scale level) design was used at the structural and laminate levels to design a novel carbon/epoxy T-joint with improved structural properties for potential use in light-weight aircraft structures. The bio-inspired structural modification mimics tree branch–trunk joints by embedding the stiffener flange into skin plies. This design concept results in increased fracture toughness due to crack branching and deflection. Simultaneously, bio-inspired ply angle optimisation was used to mimic the tailored arrangement of cellulose micro-fibrils observed in the wood cells contained within tree branch joints. The optimisation procedure minimises the interlaminar stress concentration in the T-joint radius bend and increases strength while maintaining similar global laminate stiffness properties. The hierarchical joint resulted in a significantly improved tensile strength compared to a conventionally designed T-joint. The new design additionally exhibited higher absorbed strain energy to failure load for bending and tension loading. Additionally, the hierarchical T-joint had a significantly reduced critical joint cross-sectional area (weight) due to the embedded design.

Bayesian hierarchical model for multiple repeated measures and survival data: an application to Parkinson's disease.

Multilevel item response theory models have been increasingly used to analyze the multivariate longitudinal data of mixed types (e.g., continuous and categorical) in clinical studies. To address the possible correlation between multivariate longitudinal measures and time to terminal events (e.g., death and dropout), joint models that consist of a multilevel item response theory submodel and a survival submodel have been previously developed. However, in multisite studies, multiple patients are recruited and treated by the same clinical site. There can be a significant site correlation because of common environmental and socioeconomic status, and similar quality of care within site. In this article, we develop and study several hierarchical joint models with the hazard of terminal events dependent on shared random effects from various levels. We conduct extensive simulation study to evaluate the performance of various models under different scenarios. The proposed hierarchical joint models are applied to the motivating deprenyl and tocopherol antioxidative therapy of Parkinsonism study to investigate the effect of tocopherol in slowing Parkinson's disease progression.

Image Fusion by Hierarchical Joint Sparse Representation

Joint sparse representation (JSR) based image fusion, as one of competitive sparse representation based fusion methods, has been widely studied recently. In this kind of methods, image features are represented as sparse coefficients. They are typically calculated with two decomposition algorithms, namely orthogonal matching pursuit and basis pursuit. In both of them, an error tolerance parameter is specified to control the fineness of a fused image. Intuitively, the more detailed an image fineness is, the more micro-information is presented; the more rough it is, the more macro-information is summarized. Therefore, it is reasonable to assume that complementary information exists among the images generated by different error tolerance parameters. Motivated by this, in this paper, we have tried to combine the features in these images and verify the above assumption. Specifically, we have proposed a two-layer hierarchical framework based on JSR. Extensive experiments demonstrate that effectively combining features in images of different fineness does improve the quality of the fused image significantly. The proposed framework outperforms previous methods according to many objective evaluation criteria.

Intervehicular Sensor Fusion for Situation Awareness

The intelligent vehicles equipped with sensors may have an ability to recognize the environment and make a safe and efficient decision. This paper proposes the intervehicular sensor fusion system for tracking the nearby objects. The proposed system gathers information from nearby vehicles and returns more exact and precise information about the environment. The system is implemented by hierarchical joint probabilistic data association (JPDA) filter. In the first layer, the hierarchical JPDA filter tracks vehicles first. In the second layer, the obstacles are tracked using the measurements from the vehicles. This system improves the tracking performance by tracking targets which are not seen by an ego vehicle but seen by another vehicle. To verify the effect of the system, the simulation results are given.