Spaces of phylogenetic networks from generalized nearest-neighbor interchange operations

Phylogenetic networks are a generalization of evolutionary or phylogenetic trees that are used to represent the evolution of species which have undergone reticulate evolution. In this paper we consider spaces of such networks defined by some novel local operations that we introduce for converting one phylogenetic network into another. These operations are modeled on the well-studied nearest-neighbor interchange (NNI) operations on phylogenetic trees, and lead to natural generalizations of the tree spaces that have been previously associated to such operations. We present several results on spaces of some relatively simple networks, called level-1 networks, including the size of the neighborhood of a fixed network, and bounds on the diameter of the metric defined by taking the smallest number of operations required to convert one network into another.We expect that our results will be useful in the development of methods for systematically searching for optimal phylogenetic networks using, for example, likelihood and Bayesian approaches

Safe use of jet pull

Jet pull is an observable designed to probe colour flow between jets. Thus far, a particular projection of the pull vector, the pull angle, has been employed to distinguish colour flow between jets produced by a colour singlet or an octet decay. This is of particular importance in order to separate the decay of a Higgs boson to a pair of bottom quarks from the QCD background. However, the pull angle is not infra-red and collinear (IRC) safe. In this paper we introduce IRC safe projections of the pull vector that exhibit good sensitivity to colour flow, while maintaining calculability. We calculate these distributions to next-to-leading logarithmic accuracy, in the context of the hadronic decay of a Higgs boson, and compare these results to Monte Carlo simulations. This study allows us to define an IRC safe version of the pull angle in terms of asymmetry distributions. Furthermore, because of their sensitivity to wide-angle soft radiation, we anticipate that these asymmetries can play an important role in assessing subleading colour correlations and their modelling in general-purpose Monte Carlo parton showers.Comment: 21 pages, 6 figures. Version accepted for publicatio

On assumptions in optimisation of warranty policies

Optimisation of warranty policy has been a hot research topic in both operations research and statistics communities since warranty providers hope to balance cost-benefit analysis in the nowadays competitive market. Some assumptions are inevitably needed for such research. Most of the existing publications, however, make assumptions that may not be true in practice, based on which biased decision may be made. This paper discusses pitfalls in the assumptions, which include causes of warranty claims, pattern of warranty claims, warranty claim models, field reliability vs product reliability, the relationship between usage and age in 2-dimensional warranty. A real-world example is used to elaborate the arguments

A rescaled method for RBF approximation

A new method to compute stable kernel-based interpolants has been presented by the second and third authors. This rescaled interpolation method combines the standard kernel interpolation with a properly defined rescaling operation, which smooths the oscillations of the interpolant. Although promising, this procedure lacks a systematic theoretical investigation. Through our analysis, this novel method can be understood as standard kernel interpolation by means of a properly rescaled kernel. This point of view allow us to consider its error and stability properties. First, we prove that the method is an instance of the Shepard\u2019s method, when certain weight functions are used. In particular, the method can reproduce constant functions. Second, it is possible to define a modified set of cardinal functions strictly related to the ones of the not-rescaled kernel. Through these functions, we define a Lebesgue function for the rescaled interpolation process, and study its maximum - the Lebesgue constant - in different settings. Also, a preliminary theoretical result on the estimation of the interpolation error is presented. As an application, we couple our method with a partition of unity algorithm. This setting seems to be the most promising, and we illustrate its behavior with some experiments

A rescaled method for RBF approximation

In the recent paper [8], a new method to compute stable kernel-based interpolants has been presented. This \textit{rescaled interpolation} method combines the standard kernel interpolation with a properly defined rescaling operation, which smooths the oscillations of the interpolant. Although promising, this procedure lacks a systematic theoretical investigation. Through our analysis, this novel method can be understood as standard kernel interpolation by means of a properly rescaled kernel. This point of view allow us to consider its error and stability properties

Theory predictions for the pull angle

Pull is a jet observable that is sensitive to color flow between dipoles. It has seen wide use for discrimination of particles with similar decay topologies but carrying different color representations and has been measured on W bosons from top quark decays by the D0 and ATLAS experiments. In this paper, we present the first theoretical predictions of pull, focusing on a color-singlet decaying in two jets. The pull angle observable is particularly sensitive to color flow, but is not infrared and collinear safe and so cannot be calculated in fixed-order perturbation theory. Nevertheless, all-orders resummation renders its distribution finite, a property referred to as Sudakov safety. In our prediction of the pull angle, we also include an estimation of the effects from hadronization and directly compare our results to simulation and experimental data

Model Cards for Model Reporting

Trained machine learning models are increasingly used to perform high-impact tasks in areas such as law enforcement, medicine, education, and employment. In order to clarify the intended use cases of machine learning models and minimize their usage in contexts for which they are not well suited, we recommend that released models be accompanied by documentation detailing their performance characteristics. In this paper, we propose a framework that we call model cards, to encourage such transparent model reporting. Model cards are short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions, such as across different cultural, demographic, or phenotypic groups (e.g., race, geographic location, sex, Fitzpatrick skin type) and intersectional groups (e.g., age and race, or sex and Fitzpatrick skin type) that are relevant to the intended application domains. Model cards also disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information. While we focus primarily on human-centered machine learning models in the application fields of computer vision and natural language processing, this framework can be used to document any trained machine learning model. To solidify the concept, we provide cards for two supervised models: One trained to detect smiling faces in images, and one trained to detect toxic comments in text. We propose model cards as a step towards the responsible democratization of machine learning and related AI technology, increasing transparency into how well AI technology works. We hope this work encourages those releasing trained machine learning models to accompany model releases with similar detailed evaluation numbers and other relevant documentation