New Zealand drew global attention for its unity and support for the Muslim community targeted during the horrific Christchurch attacks. Yet the country's road to inclusion has been far from straightforward, and amid rising diversity it is grappling with the best way to achieve inclusion for its multiethnic population, including indigenous Māori peoples and migrants. This article outlines the opportunities and challenges to fostering multiculturalism against a backdrop of bicultural policies.

Where did the new coronavirus originate, how did it spread so fast -- and what's next? Sharing insights from the outbreak, global health expert and TED Fellow Alanna Shaikh traces the spread of COVID-19, discusses why travel restrictions aren't effective and highlights the medical changes needed worldwide to prepare for the next pandemic. "We need to make sure that every country in the world has the capacity to identify new diseases and treat them," she says. (Recorded March 5, 2020. Update: the CDC is now calling for everyone to wear face coverings in public.)

Montana Superintendent Denise Juneau said it would be "in the best interest of our students" to let districts cancel Smarter Balanced testing if necessary.

In addition to a delay, Wisconsin had to eliminate certain questions from its Smarter Balanced exam, after opting not to use the adaptive testing feature of the test.

The state decided that the Smarter Balanced Assessment Consortium offers it a chance to share assessment goals with neighboring states.

The state requested a waiver from the federal requirement in January. Failure to meet the 95-percent requirement can lead to funding penalties for states.

North Dakota and Wyoming state superintendents said this week that they will soon hire new testing vendors.

Autism spectrum disorders in children.

Keyboard instruments -- History -- 20th century.

Braunschweig : Druck und Verlag, 1896.

Rockville, Maryland : National Institute on Drug Abuse, 1988.

Rockville, Maryland : National Institute on Drug Abuse, 1979.

Mahalanobis distance is a classical tool in multivariate analysis. We suggest here an extension of this concept to the case of functional data. More precisely, the proposed definition concerns those statistical problems where the sample data are real functions defined on a compact interval of the real line. The obvious difficulty for such a functional extension is the non-invertibility of the covariance operator in infinite-dimensional cases. Unlike other recent proposals, our definition is suggested and motivated in terms of the Reproducing Kernel Hilbert Space (RKHS) associated with the stochastic process that generates the data. The proposed distance is a true metric; it depends on a unique real smoothing parameter which is fully motivated in RKHS terms. Moreover, it shares some properties of its finite dimensional counterpart: it is invariant under isometries, it can be consistently estimated from the data and its sampling distribution is known under Gaussian models. An empirical study for two statistical applications, outliers detection and binary classification, is included. The results are quite competitive when compared to other recent proposals in the literature.

Anomaly detection is not an easy problem since distribution of anomalous samples is unknown a priori. We explore a novel method that gives a trade-off possibility between one-class and two-class approaches, and leads to a better performance on anomaly detection problems with small or non-representative anomalous samples. The method is evaluated using several data sets and compared to a set of conventional one-class and two-class approaches.

Sylvia Frühwirth-Schnatter.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 4, 706--733.

Abstract:
Finite mixture models and their extensions to Markov mixture and mixture of experts models are very popular in analysing data of various kind. A challenge for these models is choosing the number of components based on marginal likelihoods. The present paper suggests two innovative, generic bridge sampling estimators of the marginal likelihood that are based on constructing balanced importance densities from the conditional densities arising during Gibbs sampling. The full permutation bridge sampling estimator is derived from considering all possible permutations of the mixture labels for a subset of these densities. For the double random permutation bridge sampling estimator, two levels of random permutations are applied, first to permute the labels of the MCMC draws and second to randomly permute the labels of the conditional densities arising during Gibbs sampling. Various applications show very good performance of these estimators in comparison to importance and to reciprocal importance sampling estimators derived from the same importance densities.

In many Machine Learning domains, datasets are characterized by highly imbalanced and overlapping classes. Particularly in the medical domain, a specific list of symptoms can be labeled as one of various different conditions. Some of these conditions may be more prevalent than others by several orders of magnitude. Here we present a novel unsupervised Domain Adaptation scheme for such datasets. The scheme, based on a specific type of Quantification, is designed to work under both label and conditional shifts. It is demonstrated on datasets generated from Electronic Health Records and provides high quality results for both Quantification and Domain Adaptation in very challenging scenarios. Potential benefits of using this scheme in the current COVID-19 outbreak, for estimation of prevalence and probability of infection, are discussed.

Early detection of patients vulnerable to infections acquired in the hospital environment is a challenge in current health systems given the impact that such infections have on patient mortality and healthcare costs. This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-care units by means of machine-learning methods. The aim is to support decision making addressed at reducing the incidence rate of infections. In this field, it is necessary to deal with the problem of building reliable classifiers from imbalanced datasets. We propose a clustering-based undersampling strategy to be used in combination with ensemble classifiers. A comparative study with data from 4616 patients was conducted in order to validate our proposal. We applied several single and ensemble classifiers both to the original dataset and to data preprocessed by means of different resampling methods. The results were analyzed by means of classic and recent metrics specifically designed for imbalanced data classification. They revealed that the proposal is more efficient in comparison with other approaches.

Introducing common shocks is a popular dependence modelling approach, with some recent applications in loss reserving. The main advantage of this approach is the ability to capture structural dependence coming from known relationships. In addition, it helps with the parsimonious construction of correlation matrices of large dimensions. However, complications arise in the presence of "unbalanced data", that is, when (expected) magnitude of observations over a single triangle, or between triangles, can vary substantially. Specifically, if a single common shock is applied to all of these cells, it can contribute insignificantly to the larger values and/or swamp the smaller ones, unless careful adjustments are made. This problem is further complicated in applications involving negative claim amounts. In this paper, we address this problem in the loss reserving context using a common shock Tweedie approach for unbalanced data. We show that the solution not only provides a much better balance of the common shock proportions relative to the unbalanced data, but it is also parsimonious. Finally, the common shock Tweedie model also provides distributional tractability.

Class imbalance is an inherent characteristic of multi-label data that hinders most multi-label learning methods. One efficient and flexible strategy to deal with this problem is to employ sampling techniques before training a multi-label learning model. Although existing multi-label sampling approaches alleviate the global imbalance of multi-label datasets, it is actually the imbalance level within the local neighbourhood of minority class examples that plays a key role in performance degradation. To address this issue, we propose a novel measure to assess the local label imbalance of multi-label datasets, as well as two multi-label sampling approaches based on the local label imbalance, namely MLSOL and MLUL. By considering all informative labels, MLSOL creates more diverse and better labeled synthetic instances for difficult examples, while MLUL eliminates instances that are harmful to their local region. Experimental results on 13 multi-label datasets demonstrate the effectiveness of the proposed measure and sampling approaches for a variety of evaluation metrics, particularly in the case of an ensemble of classifiers trained on repeated samples of the original data.

Johann Gagnon-Bartsch, Yotam Shem-Tov.

Source: The Annals of Applied Statistics, Volume 13, Number 3, 1464--1483.

Abstract:
The gold standard for identifying causal relationships is a randomized controlled experiment. In many applications in the social sciences and medicine, the researcher does not control the assignment mechanism and instead may rely upon natural experiments or matching methods as a substitute to experimental randomization. The standard testable implication of random assignment is covariate balance between the treated and control units. Covariate balance is commonly used to validate the claim of as good as random assignment. We propose a new nonparametric test of covariate balance. Our Classification Permutation Test (CPT) is based on a combination of classification methods (e.g., random forests) with Fisherian permutation inference. We revisit four real data examples and present Monte Carlo power simulations to demonstrate the applicability of the CPT relative to other nonparametric tests of equality of multivariate distributions.

London : Cleanair (33 Stillness Rd, London, SE23 1NG), [198-?]

Sebastian Onasch
Apr 15, 2020; 40:3186-3202
Systems/Circuits

Spanish translation of press release - the Basel Committee on Banking Supervision is finalising stress-testing principles, reviews ways to stop regulatory arbitrage behaviour, agrees on annual G-SIB list, discusses leverage ratio, crypto-assets, market risk framework and implementation, 20 September 2018.

Neurons and circuits each with a distinct balance of intrinsic and synaptic conductances can generate similar behavior but sometimes respond very differently to perturbation. Examining a large family of circuit models with non-identical neurons and synapses underlying rhythmic behavior, we analyzed the circuits' response to modifications in single and multiple intrinsic conductances in the individual neurons. To summarize these changes over the entire range of perturbed parameters, we quantified circuit output by defining a global stability measure. Using this measure, we identified specific subsets of conductances that when perturbed generate similar behavior in diverse individuals of the population. Our unbiased clustering analysis enabled us to quantify circuit stability when simultaneously perturbing multiple conductances as a nonlinear combination of single conductance perturbations. This revealed surprising conductance combinations that can predict the response to specific perturbations, even when the remaining intrinsic and synaptic conductances are unknown. Therefore, our approach can expose hidden variability in the balance of intrinsic and synaptic conductances of the same neurons across different versions of the same circuit solely from the circuit response to perturbations. Developed for a specific family of model circuits, our quantitative approach to characterizing high-dimensional degenerate systems provides a conceptual and analytic framework to guide future theoretical and experimental studies on degeneracy and robustness.

SIGNIFICANCE STATEMENT Neural circuits can generate nearly identical behavior despite neuronal and synaptic parameters varying several-fold between individual instantiations. Yet, when these parameters are perturbed through channel deletions and mutations or environmental disturbances, seemingly identical circuits can respond very differently. What distinguishes inconsequential perturbations that barely alter circuit behavior from disruptive perturbations that drastically disturb circuit output remains unclear. Focusing on a family of rhythmic circuits, we propose a computational approach to reveal hidden variability in the intrinsic and synaptic conductances in seemingly identical circuits based solely on circuit output to different perturbations. We uncover specific conductance combinations that work similarly to maintain stability and predict the effect of changing multiple conductances simultaneously, which often results from neuromodulation or injury.

Imagine living in one of the driest areas on the planet. What little rain there is falls over the space of a few months, yielding around 700 mm in total each year. A population of 1.2 million has to survive on 65 percent less water than the rest of their compatriots, on a traditional staple diet of corn and beans. [...]

Robbyn Plumb tried to balance a career in the public service with looking after two kids, one with special needs. When the pandemic hit she finally hit a wall and decided to stop working. She writes about how hard it is for parents trying to do it all during COVID-19.

A fine and delicate balance in contemporary jazz  Telegraph India

The African Union Development Agency says countries should think about how they choose the lesser of the two evils because strict lockdowns have severe consequences.

On 1 December, 22 women hiked Tongariro Alpine Crossing to raise awareness and funds for women and children trafficked in France and India.

About 110 men, women and children climbed five volcanoes in Auckland in the Freedom Climb New Zealand on Saturday, 16 August.

THE first cap is the sweetest. For hockey player Alan Forsyth it came on October 19, 2015: Great Britain versus Argentina at Bisham Abbey. He scored on his debut, too, teed up by fellow Scot Chris Grassick after 28 minutes.

Alan Gray – An Appreciation

Oil headed for its first back-to-back weekly gain since February as output cuts from the biggest producers and a nascent recovery in demand began to rebalance a market awash with crude.

Valencia will need to must a remarkable home recovery to turn round their tie against Atalanta after a 4-1 first-leg defeat.

Having student board members with voting clout on school boards poses a number of logistical challenges, readers say in response to a recent Education Week feature.

Robert Guthrie
Sep 1, 1963; 32:338-343
ARTICLES

Economic growth prospects are better and Finance Minister Malusi Gigaba has made the right choices about where to spend the money, says ANC national chair Gwede Mantashe.