Lattice Gluon and Ghost Propagators, and the Strong Coupling in Pure SU(3) Yang-Mills Theory: Finite Lattice Spacing and Volume Effects

The dependence of the Landau gauge two point gluon and ghost correlation functions on the lattice spacing and on the physical volume are investigated for pure SU(3) Yang-Mills theory in four dimensions using lattice simulations. We present data from very large lattices up to $128^4$ and for two lattice spacings $0.10$ fm and $0.06$ fm corresponding to volumes of $\sim$ (13 fm)$^4$ and $\sim$ (8 fm)$^4$, respectively. Our results show that, for sufficiently large physical volumes, both propagators have a mild dependence on the lattice volume. On the other hand, the gluon and ghost propagators change with the lattice spacing $a$ in the infrared region, with the gluon propagator having a stronger dependence on $a$ compared to the ghost propagator. In what concerns the strong coupling constant $\alpha_s (p^2)$, as defined from gluon and ghost two point functions, the simulations show a sizeable dependence on the lattice spacing for the infrared region and for momenta up to $\sim 1$ GeV

Using hierarchical information-theoretic criteria to optimize subsampling of extensive datasets

This paper addresses the challenge of subsampling large datasets, aiming to generate a smaller dataset that retains a significant portion of the original information. To achieve this objective, we present a subsampling algorithm that integrates hierarchical data partitioning with a specialized tool tailored to identify the most informative observations within a dataset for a specified underlying linear model, not necessarily first-order, relating responses and inputs. The hierarchical data partitioning procedure systematically and incrementally aggregates information from smaller-sized samples into new samples. Simultaneously, our selection tool employs Semidefinite Programming for numerical optimization to maximize the information content of the chosen observations. We validate the effectiveness of our algorithm through extensive testing, using both benchmark and real-world datasets. The real-world dataset is related to the physicochemical characterization of white variants of Portuguese Vinho Verde. Our results are highly promising, demonstrating the algorithm's capability to efficiently identify and select the most informative observations while keeping computational requirements at a manageable level

Randomizing a clinical trial in neuro-degenerative disease

The paper studies randomization rules for a sequential two-treatment, two-site clinical trial in Parkinson’s disease. An important feature is that we have values of responses and five potential prognostic factors from a sample of 144 patients similar to those to be enrolled in the trial. Analysis of this sample provides a model for trial analysis. The comparison of allocation rules is made by simulation yielding measures of loss due to imbalance and of potential bias. A major novelty of the paper is the use of this sample, via a two-stage algorithm, to provide an empirical distribution of covariates for the simulation; sampling of a correlated multivariate normal distribution is followed by transformation to variables following the empirical marginal distributions. Six allocation rules are evaluated. The paper concludes with some comments on general aspects of the evaluation of such rules and provides a recommendation for two allocation rules, one for each site, depending on the target number of patients to be enrolled

Non-linear, cata-Condensed, Polycyclic Aromatic Hydrocarbon Materials: A Generic Approach and Physical Properties

A generic approach to the regiospecific synthesis of halogenated polycyclic aromatics is made possible by the one- or two-directional benzannulation reactions of readily available (ortho-allylaryl)trichloroacetates (the “BHQ” reaction). Palladium-catalysed cross-coupling reactions of the so-formed haloaromatics enable the synthesis of functionalised polycyclic aromatic hydrocarbons (PAHs) with surgical precision. Overall, this new methodology enables the facile mining of chemical space in search of new electronic functional materials

A model-based framework assisting the design of vapor-liquid equilibrium experimental plans

In this paper we propose a framework for Model-based Sequential Optimal Design of Experiments to assist experimenters involved in Vapor-Liquid equilibrium characterization studies to systematically construct thermodynamically consistent models. The approach uses an initial continuous optimal design obtained via semidefinite programming, and then iterates between two stages (i) model fitting using the information available; and (ii) identification of the next experiment, so that the information content in data is maximized. The procedure stops when the number of experiments reaches the maximum for the experimental program or the dissimilarity between the parameter estimates during two consecutive iterations is below a given threshold. This methodology is exemplified with the D-optimal design of isobaric experiments, for characterizing binary mixtures using the NRTL and UNIQUAC thermodynamic models for liquid phase. Significant reductions of the confidence regions for the parameters are achieved compared with experimental plans where the observations are uniformly distributed over the domain

Impact of the Timing and Use of an Insecticide on Arthropods in Cover-Crop-Corn Systems

Cover crops provide a habitat for pests and beneficial arthropods. Unexpected pest pressure in a cover-crop-to-corn system can occur and result in increased use of insecticides. Eight site-years of on-farm field studies were conducted in 2019, 2020, and 2021. The objective of the study was to evaluate the impact of insecticide timing relative to cover-crop termination on arthropod activity in a cover-crop-to-corn system. The treatments consisted of (i) glyphosate to terminate the cover crop, (ii) glyphosate and pyrethroid tank mix to terminate the cover crop, and (iii) glyphosate to terminate the cover crop and pyrethroid application 25 days after the termination. Arthropod activity was measured with pitfall traps before and at each treatment application. A total of 33,316 arthropods were collected. Total arthropods, Collembola, and Aphididae were the only taxa reduced with an insecticide application. The other arthropod taxa were mainly influenced by the sampling period. No significant pest pressure occurred at any site-year. Insecticide applications are not generally needed in a cover-crop-to-corn system. Scouting for pests and applying strategies only when necessary is crucial to conserve potentially beneficial arthropods in the system

Object-Centric Learning with Capsule Networks : A Survey

The authors would like to thank all reviewers, and especially Professor Chris Williams from the School of Informatics of the University of Edinburgh, who provided constructive feedback and ideas on how to improve this work.Peer reviewe

Objects in contact with classical scrapie sheep act as a reservoir for scrapie transmission

Classical scrapie is an environmentally transmissible prion disease of sheep and goats. Prions can persist and remain potentially infectious in the environment for many years and thus pose a risk of infecting animals after re-stocking. In vitro studies using serial protein misfolding cyclic amplification (sPMCA) have suggested that objects on a scrapie- affected sheep farm could contribute to disease transmission. This in vivo study aimed to determine the role of field furniture (water troughs, feeding troughs, fencing, and other objects that sheep may rub against) used by a scrapie-infected sheep flock as a vector for disease transmission to scrapie-free lambs with the prion protein genotype VRQ/VRQ, which is associated with high susceptibility to classical scrapie. When the field furniture was placed in clean accommodation, sheep became infected when exposed to either a water trough (four out of five) or to objects used for rubbing (four out of seven). This field furniture had been used by the scrapie-infected flock 8 weeks earlier and had previously been shown to harbor scrapie prions by sPMCA. Sheep also became infected (20 out of 23) through exposure to contaminated field furniture placed within pasture not used by scrapie-infected sheep for 40 months, even though swabs from this furniture tested negative by PMCA. This infection rate decreased (1 out of 12) on the same paddock after replacement with clean field furniture. Twelve grazing sheep exposed to field furniture not in contact with scrapie-infected sheep for 18 months remained scrapie free. The findings of this study highlight the role of field furniture used by scrapie-infected sheep to act as a reservoir for disease re-introduction although infectivity declines considerably if the field furniture has not been in contact with scrapie-infected sheep for several months. PMCA may not be as sensitive as VRQ/VRQ sheep to test for environmental contamination

Classification of time series by shapelet transformation

Time-series classification (TSC) problems present a specific challenge for classification algorithms: how to measure similarity between series. A \emph{shapelet} is a time-series subsequence that allows for TSC based on local, phase-independent similarity in shape. Shapelet-based classification uses the similarity between a shapelet and a series as a discriminatory feature. One benefit of the shapelet approach is that shapelets are comprehensible, and can offer insight into the problem domain. The original shapelet-based classifier embeds the shapelet-discovery algorithm in a decision tree, and uses information gain to assess the quality of candidates, finding a new shapelet at each node of the tree through an enumerative search. Subsequent research has focused mainly on techniques to speed up the search. We examine how best to use the shapelet primitive to construct classifiers. We propose a single-scan shapelet algorithm that finds the best $k$ shapelets, which are used to produce a transformed dataset, where each of the $k$ features represent the distance between a time series and a shapelet. The primary advantages over the embedded approach are that the transformed data can be used in conjunction with any classifier, and that there is no recursive search for shapelets. We demonstrate that the transformed data, in conjunction with more complex classifiers, gives greater accuracy than the embedded shapelet tree. We also evaluate three similarity measures that produce equivalent results to information gain in less time. Finally, we show that by conducting post-transform clustering of shapelets, we can enhance the interpretability of the transformed data. We conduct our experiments on 29 datasets: 17 from the UCR repository, and 12 we provide ourselve