Teaching through ensemble performance

Teaching ensemble performance in higher education tends to draw on staff members as conductors or mentors, approaches which can easily remain unexamined. This research investigates a third potential path, participatory ensemble teaching, which steps away from the conductor’s podium and plays less (obviously) important musical parts. Using alternating leadership in this way was designed to help students engage more critically than otherwise, creating a rehearsal environment where their musical decisions were meaningful and impactful. Through rehearsal observations and focus groups, the chapter assesses the effectiveness of this approach to small ensemble teaching across three years of an undergraduate music program. In addition to reflecting on the impact of participatory ensemble teaching, this case study explores issues around equality, power relationships, and the role of the lecturer within ensembles.

A New Scheme of Adaptive Covariance Inflation for Ensemble Filtering Data Assimilation

Due to the model and sampling errors of the finite ensemble, the background ensemble spread becomes small and the error covariance is underestimated during filtering for data assimilation. Because of the constraint of computational resources, it is difficult to use a large ensemble size to reduce sampling errors in high-dimensional real atmospheric and ocean models. Here, based on Bayesian theory, we explore a new spatially and temporally varying adaptive covariance inflation algorithm. To increase the statistical presentation of a finite background ensemble, the prior probability of inflation obeys the inverse chi-square distribution, and the likelihood function obeys the t distribution, which are used to obtain prior or posterior covariance inflation schemes. Different ensemble sizes are used to compare the assimilation quality with other inflation schemes within both the perfect and biased model frameworks. With two simple coupled models, we examined the performance of the new scheme. The results show that the new inflation scheme performed better than existing schemes in some cases, with more stability and fewer assimilation errors, especially when a small ensemble size was used in the biased model. Due to better computing performance and relaxed demand for computational resources, the new scheme has more potential applications in more comprehensive models for prediction initialization and reanalysis. In a word, the new inflation scheme performs well for a small ensemble size, and it may be more suitable for large-scale models.

Small music ensemble and empathy: A replication study in a South Korean music student sample

Small ensemble participation represents a unique form of human social activity involving a profound level of interpersonal and emotional communication. Previous researchers have suggested that engagement in group music making may have a positive influence on various social-emotional skills, including empathy. In line with this view, the initial study explored the relationship between small ensemble experience and empathy among college music students in the United States. The study results revealed a close association between the two, with students who participated in small ensembles more frequently showing a higher level of empathy. This study aimed to replicate the initial study using the identical survey questionnaire in a college music student population in South Korea ( N = 183). Overall, Korean students scored significantly lower in the empathy measure than the US student sample, which echoed relatively lower empathy among Asian American students in the initial study. Also, consistent with the previous finding, an association between the primary area of music study and empathy was found, with popular music majors showing a higher level of empathy than classical music major students. Finally, some of the small ensemble experience variables appeared to be significant predictors of students’ empathy skills, which partially replicated the initial study.

Approximating the Internal Variability of Bias-Corrected Global Temperature Projections with Spatial Stochastic Generators

Decision making under climate change, from vulnerability assessments to adaptation and mitigation, requires an accurate quantification of the uncertainty in the future climate. Physically constrained projections, in the presence of both observations and climate simulations, can be obtained by establishing an empirical relationship in the historical time period, and use it to correct the bias of future simulations. Traditional bias correction approaches do not account for the uncertainty in the climate simulation, and focus on regionally aggregated variables without spatial dependence, with loss of useful information such as the variability of gradients across regions. We propose a new statistical model for bias correction of monthly surface temperatures with sparse and interpretable spatial structure, and we use it to obtain future reanalysis projections with associated uncertainty, using only a small ensemble of global simulations.

A new correlation-based approach for ensemble selection in random forests

PurposeEnsemble methods have been widely used in the field of pattern recognition due to the difficulty of finding a single classifier that performs well on a wide variety of problems. Despite the effectiveness of these techniques, studies have shown that ensemble methods generate a large number of hypotheses and that contain redundant classifiers in most cases. Several works proposed in the state of the art attempt to reduce all hypotheses without affecting performance.Design/methodology/approachIn this work, the authors are proposing a pruning method that takes into consideration the correlation between classifiers/classes and each classifier with the rest of the set. The authors have used the random forest algorithm as trees-based ensemble classifiers and the pruning was made by a technique inspired by the CFS (correlation feature selection) algorithm.FindingsThe proposed method CES (correlation-based Ensemble Selection) was evaluated on ten datasets from the UCI machine learning repository, and the performances were compared to six ensemble pruning techniques. The results showed that our proposed pruning method selects a small ensemble in a smaller amount of time while improving classification rates compared to the state-of-the-art methods.Originality/valueCES is a new ordering-based method that uses the CFS algorithm. CES selects, in a short time, a small sub-ensemble that outperforms results obtained from the whole forest and the other state-of-the-art techniques used in this study.

Empirical determination of the covariance of forecast errors: an empirical justification and reformulation of Hybrid covariance models

<p>The replacement of climatological background error covariance models with Hybrid error covariance models that linearly combine a localized ensemble covariance matrix and a climatological error covariance matrix has led to significant forecast improvements at several forecasting centres. To deepen understanding of why the Hybrid’s superficially ad-hoc mix of ensemble based covariances and climatological covariances yielded such significant improvements, we derive the linear state estimation equations that minimize analysis error variance given an imperfect ensemble covariance. For high dimensional models, the computational cost of the very large sample sizes required to empirically estimate the terms in these equations is prohibitive. However, a reasonable and computationally feasible approximation to these equations can be obtained from empirical estimates of the true error covariance between two model variables given an imperfect ensemble covariance between the same two variables.   Here, using a Data Assimilation (DA) system featuring a simplified Global Circulation Model (SPEEDY), pseudo-observations of known error variance and an ensemble data assimilation scheme (LETKF),  we quantitatively demonstrate that the traditional Hybrid used by many operational centres is a much better approximation to the true covariance given the ensemble covariance than either the static climatological covariance or the localized ensemble covariance. These quantitative findings help explain why operational centres have found such large forecast improvements when switching from a static error covariance model to a Hybrid forecast error covariance model. Another fascinating finding of our empirical study is that the form of current Hybrid error covariance models is fundamentally incorrect in that the weight given to the static covariance matrix is independent of the separation distance of model variables. Our results show that this weight should be an increasing function of variable separation distance.  It is found that for ensemble covariances significantly different to zero, the true error covariance of spatially separated variables is an approximately linear function of the corresponding ensemble covariance, However, for small ensemble sizes and ensemble covariances near zero, the true covariance is an increasing function of the magnitude of the ensemble covariance and reaches a local minimum at the precise point where the ensemble covariance is equal to zero. It is hypothesized that this behaviour is a consequence of small ensemble size and, specifically, associated spurious fluctuations of the ensemble covariances and variances. Consistent with this hypothesis, this local minimum is almost eliminated by quadrupling the ensemble size.</p>

What ensemble size is required for accurate forecasts? Idealised model experiments with very large ensembles

<p>Ensembles of numerical weather prediction models are currently used to represent the forecast uncertainty of forecast variables. However due to the computationally expensive nature of these ensembles, these uncertainties are only known with a large sampling error, and often the underlying distributions are assumed to be gaussian for Data Assimilation purposes. Furthermore, it is unclear how many members are required in an ensemble to obtain a designated level of sampling error. This work endeavours to understand how this error decreases as ensembles become larger, and how the forecast uncertainty evolves over a 24 hour free forecast period, before answering the pressing question of: how many ensembles are required in an NWP ensemble in order to sufficiently resolve the uncertainty? To do this, a simple 1D modified shallow water model which replicates the main features of convection is employed in the form of a massive ensemble with over 100,000 members. The shape of the distributions from this ensemble, which develop significant non-gaussianity, resembles those of the operational NWP ensembles of SCALE-RM and ICON, indicating that this model is sufficiently realistic in representing the forecast uncertainty. The simple model will be used to determine the rate of convergence of different forecast variables as ensemble size increases, and to evaluate the errors resulting from using the small ensemble sizes that are typical in operational NWP.</p>

Rehearsal rooms in the context of norwegian standard ns 8178:2014

Music school students spend much more time rehearsing than performing in concert halls. Individual and small ensemble exercises are a major part of daily practice. The aim of the article is to verify whether the areas of rehearsal rooms given in functional programs attached to architectural contests for music schools, are sufficient to provide required acoustic conditions inside the chamber such as sound power level (SPL) and reverberation time (RT) which is preferred by musicians. The Norwegian Standard NS 8178:2014 was used to calculate the sound level generated by instruments. In this paper, the author will focus on small rehearsal rooms dedicated to individual practice and small practice groups of two or three members.

«Más allá está la luz»: comentario del poema «Un milagro de Buda» de Luis Alberto de Cuenca

Luis Alberto de Cuenca’s poetry seems to be like an open book, because of its powerful intertextuality, which has everything. In this sense, this work aims to look over a small ensemble of oriental poems, that adds another piece to the puzzle, and especially examines the poem “A Buda’s miracle”, which is interesting because of its curious textual history, as well as for its condition of buddhist poem that comes from a book about Buddha.