Vegetation development in coastal foredunes -179

Abstract. In coastal foredunes marram grass (Ammophila arenaria) is used to stabilize windblown sand. The development of traditionally planted Ammophila into a more natural foredune vegetation may take 5 -10 yr. For economic reasons, traditional planting may be replaced by alternative techniques such as planting seeds or disk-harrowing rhizome fragments. In this paper, we compare the initial vegetation development of traditionally planted stands with stands established from seeds and from rhizomes. The experiments were conducted on an artificial foredune originating from dredged sea sand. The total experimental area covered more than 100 ha and the vegetation development was studied for 6 yr. The data were analysed by a priori grouping of plant species according to their ecology, as well as by Principal Components Analysis (PCA) and Redundancy Analysis (RA) of the percentage ground cover per plant species. Comparing ecological groups of plants showed that all planting methods delivered equal numbers of plant species that are indicative for coastal dunes. PCA and RA showed that methods based on the use of rhizome material resulted in a higher percentage cover of clonal perennials (Calammophila baltica, Festuca rubra ssp. arenaria, Carex arenaria and Cirsium arvense) than the traditionally planted stands and the stands obtained from seeds. The latter two were characterized by the dominance of annuals, bi-annuals and (mostly nonrhizomatous) perennials. Initially, the rates of succession were highest in the stands obtained from rhizomes. However, after 3 -6 yr there were no differences between the various stands. During the first four years, the percentage cover by rhizomatous foredune plants developed faster than that of annuals, bi-annuals and perennials. After 6 yr, the latter contributed almost as much to the percentage cover as the clonal species. Keywords: Clonal plant; Dune management; Dune reinforcement; Sand dune; Sand stabilization; Succession. Nomenclature

The plastic number and its generalized polynomial

The polynomial $X^{3}-X-1$ has a unique positive root known as plastic number, which is denoted by $\rho$ and is approximately equal to $1.32471795$. In this note we study the zeroes of the generalized polynomial $X^{k}-\sum_{j=0}^{k-2}X^{j}$ for $k\geq 3$ and prove that its unique positive root $\lambda_{k}$ tends to the golden ratio $\phi=\frac{1+\sqrt{5}}{2}$ as $k \to \infty$. We also derive bounds on $\lambda_{k}$ in terms of Fibonacci numbers.Comment: Publisher's pdf versio

Expert-Augmented Machine Learning

Machine Learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption by the level of trust that models afford users. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of man and machine. Here we present Expert-Augmented Machine Learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We use a large dataset of intensive care patient data to predict mortality and show that we can extract expert knowledge using an online platform, help reveal hidden confounders, improve generalizability on a different population and learn using less data. EAML presents a novel framework for high performance and dependable machine learning in critical applications

Estimation of Recurrence of Colorectal Adenomas with Dependent Censoring Using Weighted Logistic Regression

In colorectal polyp prevention trials, estimation of the rate of recurrence of adenomas at the end of the trial may be complicated by dependent censoring, that is, time to follow-up colonoscopy and dropout may be dependent on time to recurrence. Assuming that the auxiliary variables capture the dependence between recurrence and censoring times, we propose to fit two working models with the auxiliary variables as covariates to define risk groups and then extend an existing weighted logistic regression method for independent censoring to each risk group to accommodate potential dependent censoring. In a simulation study, we show that the proposed method results in both a gain in efficiency and reduction in bias for estimating the recurrence rate. We illustrate the methodology by analyzing a recurrent adenoma dataset from a colorectal polyp prevention trial

Long range antiferromagnetic order of formally nonmagnetic Eu3 Van Vleck ions observed in multiferroic Eu1 xYxMnO3

We report on resonant magnetic x ray scattering and absorption spectroscopy studies of exchange coupled antiferromagnetic ordering of Eu3 magnetic moments in multiferroic Eu1 amp; 8722;xYxMnO3 in the absence of an external magnetic field. The observed resonant spectrum is characteristic of a magnetically ordered 7F1 state that mirrors the Mn magnetic ordering, due to exchange coupling between the Eu 4f and Mn 3d spins. Here, we observe long range magnetic order generated by exchange coupling of magnetic moments of formally nonmagnetic Van Vleck ions, which is a step further towards the realization of exotic phases induced by exchange coupling in systems entirely composed of non magnetic ion

Combinatorial integer labeling theorems on finite sets with applications

Tucker’s well-known combinatorial lemma states that, for any given symmetric triangulation of the n-dimensional unit cube and for any integer labeling that assigns to each vertex of the triangulation a label from the set {±1, ±2, · · · , ±n} with the property that antipodal vertices on the boundary of the cube are assigned opposite labels, the triangulation admits a 1-dimensional simplex whose two vertices have opposite labels. In this paper, we are concerned with an arbitrary finite set D of integral vectors in the n-dimensional Euclidean space and an integer labeling that assigns to each element of D a label from the set {±1, ±2, · · · , ±n}. Using a constructive approach, we prove two combinatorial theorems of Tucker type. The theorems state that, under some mild conditions, there exists two integral vectors in D having opposite labels and being cell-connected in the sense that both belong to the set {0, 1} n +q for some integral vector q. These theorems are used to show in a constructive way the existence of an integral solution to a system of nonlinear equations under certain natural conditions. An economic application is provided

Expert-augmented machine learning.

Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of humans and machines. Here, we present expert-augmented machine learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We used a large dataset of intensive-care patient data to derive 126 decision rules that predict hospital mortality. Using an online platform, we asked 15 clinicians to assess the relative risk of the subpopulation defined by each rule compared to the total sample. We compared the clinician-assessed risk to the empirical risk and found that, while clinicians agreed with the data in most cases, there were notable exceptions where they overestimated or underestimated the true risk. Studying the rules with greatest disagreement, we identified problems with the training data, including one miscoded variable and one hidden confounder. Filtering the rules based on the extent of disagreement between clinician-assessed risk and empirical risk, we improved performance on out-of-sample data and were able to train with less data. EAML provides a platform for automated creation of problem-specific priors, which help build robust and dependable machine-learning models in critical applications

Model selection in High-Dimensions: A Quadratic-risk based approach

In this article we propose a general class of risk measures which can be used for data based evaluation of parametric models. The loss function is defined as generalized quadratic distance between the true density and the proposed model. These distances are characterized by a simple quadratic form structure that is adaptable through the choice of a nonnegative definite kernel and a bandwidth parameter. Using asymptotic results for the quadratic distances we build a quick-to-compute approximation for the risk function. Its derivation is analogous to the Akaike Information Criterion (AIC), but unlike AIC, the quadratic risk is a global comparison tool. The method does not require resampling, a great advantage when point estimators are expensive to compute. The method is illustrated using the problem of selecting the number of components in a mixture model, where it is shown that, by using an appropriate kernel, the method is computationally straightforward in arbitrarily high data dimensions. In this same context it is shown that the method has some clear advantages over AIC and BIC.Comment: Updated with reviewer suggestion