Construction of cyclic quadrilateral: A pre-service teacher’s journey from subsets to the generic concept through instrumentation of a dynamic technology

This case study examines instrumented techniques of a pre-service mathematics teacher when utilising a dynamic geometry system (DGS) for the construction of cyclic quadrilaterals. Equally, it investigates her understanding of the hierarchical relations between the constructed cyclic quadrilaterals. For this aim, the hierarchical classification and instrumental genesis constitute the theoretical framework of our study. The data was collected through task-based interviews in which the participant was asked to construct the cyclic quadrilateral without using the circle tool in DGS. Data analysis was carried out to examine the participant’s development of construction strategies related to her instrumented techniques and her reasoning regarding the properties of different cyclic quadrilaterals. The analysis showed that the participant’s initial instrumented techniques enabled her to construct subsets of the cyclic quadrilaterals. Also, the process of reaching the most inclusive cyclic quadrilateral affected both her instrumented techniques and her understanding about the hierarchical relations between the constructed quadrilaterals.     

Enhanced formulations and branch-and-cut for the two level network design problem with transition facilities

We consider a new combinatorial optimization problem that combines network design and facility location aspects. Given a graph with two types of customers and two technologies that can be installed on the edges, the objective is to find a minimum cost subtree connecting all customers while the primary customers are served by a primary subtree that is embedded into the secondary subtree. In addition, besides fixed link installation costs, facility opening costs, associated to each node where primary and secondary subtree connect, have to be paid. The problem is called the Two Level Network Design Problem with Transition Facilities (TLNDF).     

A method for the construction of hierarchical generalized space shift keying (GSSK) modulation for unequal error protection

In this paper, we propose a systematic method to achieve two-level unequal error protection (UEP) with generalized space shift keying (GSSK) modulation for multiple-input multiple-output (MIMO) systems. GSSK is a modulation scheme that encodes the source information in the antenna indices. To enable the nonuniform arrangement of the spatial constellation of GSSK modulation, new techniques are needed as new features of GSSK modulation are observed. The proposed method is based on first partitioning the GSSK constellation into subsets and then choosing constellation points inside a subset. The high-priority (HP) bits select the subset and the low-priority (LP) bits select constellation points inside a subset. The proposed subset partitioning procedure developed based on a theoretical study on set partitioning in the multidimensional space guarantees optimal or near-optimal HP protection, enables simple and systematic designs for LP protection, and increases the LP capacity. The proposed systematic design approach applies to systems with any numbers of transmit antennas and can create various hierarchical GSSK schemes with different HP/LP protection capabilities.     

二项分布可靠度E-Bayes估计的性质

作者以前提出了一种新的参数估计方法——E-Bayes估计法,对二项分布的可靠度,给出了E-Bayes估计的定义、E-Bayes估计和多层Bayes估计公式,但没有给出E-Bayes估计的性质.该文给出了二项分布可靠度F-Bayes估计的性质.     

Imputation of Mixed Data With Multilevel Singular Value Decomposition

Statistical analysis of large datasets offers new opportunities to better understand underlying processes. Yet, data accumulation often implies relaxing acquisition procedures or compounding diverse sources. As a consequence, datasets often contain mixed data, that is, both quantitative and qualitative, and many missing values. Furthermore, aggregated data present a natural multilevel structure, where individuals or samples are nested within different sites, such as countries or hospitals. Imputation of multilevel data has therefore drawn some attention recently, but current solutions are not designed to handle mixed data, and suffer from important drawbacks, such as their computational cost. In this article, we propose a single imputation method for multilevel data, which can be used to complete either quantitative, categorical, or mixed data. The method is based on multilevel singular value decomposition (SVD), which consists in decomposing the variability of the data into two components, the between and within groups variability, and performing an SVD on both parts. We show on a simulation study that in comparison to competitors, the method has the advantages of handling datasets of various size, and being computationally faster. Furthermore, it is the first so far to handle mixed data. We apply the method to impute a medical dataset resulting from the aggregation of several hospitals datasets. This application falls in the framework of a larger project on Trauma patients. To overcome obstacles associated to the aggregation of medical data, we turn to distributed computation. The method is implemented in the R package missMDA. Supplementary materials for this article are available online.     

Variational Approximation for Mixtures of Linear Mixed Models

Mixtures of linear mixed models (MLMMs) are useful for clustering grouped data and can be estimated by likelihood maximization through the Expectation–Maximization algorithm. A suitable number of components is then determined conventionally by comparing different mixture models using penalized log-likelihood criteria such as Bayesian information criterion. We propose fitting MLMMs with variational methods, which can perform parameter estimation and model selection simultaneously. We describe a variational approximation for MLMMs where the variational lower bound is in closed form, allowing for fast evaluation and develop a novel variational greedy algorithm for model selection and learning of the mixture components. This approach handles algorithm initialization and returns a plausible number of mixture components automatically. In cases of weak identifiability of certain model parameters, we use hierarchical centering to reparameterize the model and show empirically that there is a gain in efficiency in variational algorithms similar to that in Markov chain Monte Carlo (MCMC) algorithms. Related to this, we prove that the approximate rate of convergence of variational algorithms by Gaussian approximation is equal to that of the corresponding Gibbs sampler, which suggests that reparameterizations can lead to improved convergence in variational algorithms just as in MCMC algorithms. Supplementary materials for the article are available online.     

Approximate Predetermined Convergence Properties of the Gibbs Sampler

This article aims to provide a method for approximately predetermining convergence properties of the Gibbs sampler. This is to be done by first finding an approximate rate of convergence for a normal approximation of the target distribution. The rates of convergence for different implementation strategies of the Gibbs sampler are compared to find the best one. In general, the limiting convergence properties of the Gibbs sampler on a sequence of target distributions (approaching a limit) are not the same as the convergence properties of the Gibbs sampler on the limiting target distribution. Theoretical results are given in this article to justify that under conditions, the convergence properties of the Gibbs sampler can be approximated as well. A number of practical examples are given for illustration.     

A Model for Multi-timescaled Sequential Decision-making Processes with Adversary

Extending the multi-timescale model proposed by the author et al. in the context of Markov decision processes, this paper proposes a simple analytical model called M timescale two-person zero-sum Markov Games (MMGs) for hierarchically structured sequential decision-making processes in two players' competitive situations where one player (the minimizer) wishes to minimize their cost that will be paid to the adversary (the maximizer). In this hierarchical model, for each player, decisions in each level in the M-level hierarchy are made in M different discrete timescales and the state space and the control space of each level in the hierarchy are non-overlapping with those of the other levels, respectively, and the hierarchy is structured in a "pyramid" sense such that a decision made at level m (slower timescale) state and/or the state will affect the evolutionary decision making process of the lower-level m+1 (faster timescale) until a new decision is made at the higher level but the lower-level decisions themselves do not affect the transition dynamics of higher levels. The performance produced by the lower-level decisions will affect the higher level decisions for each player. A hierarchical objective function for the minimizer and the maximizer is defined, and from this we define "multi-level equilibrium value function" and derive a "multi-level equilibrium equation". We also discuss how to solve hierarchical games exactly.     

Synthesis, structural analysis, and visualization of a library of dendronized polyphenylacetylenes

A library of eleven high cis-content cis-transoidal polyphenylacetylenes (PPAs) dendronized with self-assembling dendrons was prepared from a library of fifteen convergently synthesized macromonomers. Using [Rh(C triple bond CPh)(nbd)(PPh(3))(2)] (nbd=2,5-norbornadiene) in the presence of 10 equiv of N,N-dimethylaminopyridine, predictive control over molecular weight and narrow molecular weight distribution are obtained. The PPA backbone serves as a helical scaffold for the self-assembling dendrons. The dendron primary structure dictates the diameter of the cylindrical PPAs in bulk, both in the self-organized hexagonal columnar (Phi(h)) lattice determined by X-ray diffraction (XRD) and in monolayers on highly ordered pyrolytic graphite (HOPG) and mica visualized by atomic force microscopy (AFM). Thermal and bulk phase characteristics of the cylindrical PPAs reinforces the generality that flexible polymer backbones adopt a helical conformation within the cylindrical macromolecules generated by polymers jacketed with self-assembling dendrons.