Thesis advisor: Ellen Winner / Public polling and anecdotal evidence suggests that the general public greatly values music education. I argue that this is not because of content, discipline-specific skills like reading music notation or playing the trumpet, but because of the generalizable habits of mind, or broad thinking dispositions, that teachers teach in ensembles. Through analysis of systematic observation and interview data from multiple rehearsals of six band, choir and orchestra ensembles, eight Ensemble Habits of Mind emerged: Evaluate, Express, Imagine, Listen, Notice, Participate in Community, Persist, and Set Goals & Be Prepared. Using methodology similar to that of parallel work identifying Studio Habits of Mind in visual arts education (Hetland et al., 2013), this study shows many similarities between habits of mind in the two disciplines. However, two habits of mind that were specifically sought out in observations because they are frequently reported in advocacy arguments, Use Creativity and Recognize More than One Correct Answer, were not observed even under broad inclusion criteria. Suggestions are given for the practical application of these findings and discussion of how this framework can simultaneously support the good thinking happening in traditional large ensembles while bolstering rationale that informal music learning and other means of student-centered music making should be included in music programs in order to advance students’ creative thinking and tolerance for ambiguity. / Thesis (PhD) — Boston College, 2021. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Psychology.

The troubling negative effects of music performance anxiety (MPA) have remained less investigated under ensemble settings and with undergraduate non-music majors than under solo settings with music majors and professional musicians. This study examined the experience and prevalence of music performance anxiety in ensemble rehearsal and concert settings in 166 undergraduate non-music majors, 108 undergraduate music majors, 4 undeclared undergraduates, 9 graduate non-music majors, and 14 graduate music majors. The participants (instrumentalists and vocalists) were drawn from 10 Mid-Atlantic colleges and universities. The Kenny Music Performance Anxiety Inventory Revised (K-MPAI-R; Kenny, 2009) was administered within the last six weeks of the semester. Students reported the full range of cognitive, somatic, affective, and behavioral symptoms of MPA, with cognitive symptoms most frequently reported. The factor structure of the K-MPAI-R was found to be stable with those factor structures previously established by Kenny using elite professional musicians and tertiary-level music students. The results of a standard multiple regression conducted to identify unique predictors of MPA indicated that although depression, being an instrumentalist, being female, and having had a music performance breakdown, all made significant contributions to K-MPAI-R scores, and depression made the strongest unique contribution (beta = .42, p < 0.001). Greater self-efficacy (as indicated by higher scores on confidence statements in the K-MPAI-R) was correlated with lower MPA under both concert (r = .49, p < 0.0005) and rehearsal settings (r = .52, p < 0.0005). Students with higher depression indicator scores exhibited higher MPA than students with lower depression indicator scores (WT = 31.40, p < 0.001). Music performance breakdowns occurred more often during solos than ensemble performances and memory lapse (16.3% of all students) was cited as the leading cause of music performance breakdowns. Overall, MPA and depression indicator scores for the sample were high compared with other groups that had been previously evaluated with the K-MPAI-R and the same depression screen. Instructors should therefore be sensitive to the possibility that ensemble musicians may experience significant negative effects of MPA during both concerts and rehearsals, and apprise themselves of evidence-based treatment options and coping strategies available for MPA.

Climate change is one of the most challenging and defining issues that has resulted in substantial societal, economic, and environmental impacts across the world. To assess the potential climate change impact, climate projections are generated with General Circulation Models (GCMs). However, the climate change signals remain uncertain and GCMs have difficulty in representing regional climate features. Therefore, comprehensive knowledge of climate change signals and reliable high-resolution climate projections are highly desired. This dissertation aims to address such challenges by developing climate projections with multi-model ensembles for climate impact assessment. This includes: i) developing multi-model ensembles to analyze global changes in all water components within the hydrological cycle and quantify the uncertainties with GCM projections; ii) development of bias correction models for generating high-resolution daily maximum and minimum temperature projections with individual GCMs and multi-model ensemble means over Canada; iii) proposing bias correction models with individual GCMs and multi-model ensemble means for high-resolution daily precipitation projections for Canada. The proposed models are capable of developing high-resolution climate projections at a regional scale and exploring the climate change signals. The reliable climate projections generated could provide valuable information for formulating appropriate climate change mitigation and adaptation strategies across the world. / Thesis / Doctor of Philosophy (PhD)

Epitaxially grown quantum dots (QDs) make up a significant portion of nanoscale semiconductor research, yet precise solutions for their eigenstates in complex geometries are often unknown. Eigenstates are extremely relevant as they impact the emission wavelength, performance, and stability of many optoelectronic devices. In this thesis, atomic force microscopy, transmission electron microscopy, and atom probe tomography (APT) are used to assess and compare QD size and core concentration. APT by means of isosurface reconstruction provides the most accurate ensemble averaged quantum dot size and core concentration. High-angle annular dark-field imaging quantifies core concentration very well, but fails in comparison to precisely quantify QD size. Ensemble averaging is discarded in favour of using the raw APT data to devise a model that can solve the Schrödinger equation in 3-dimensional space and can be expanded upon to include non-trivial quantum dot geometries of any kind. The electron and hole eigenstates for an entire quantum dot ensemble are solved using this model. Hybridized eigenstates between neighbouring quantum dots are realized and found to experience both bonding and anti-bonding of the charge carriers. The existence of a degenerate state is also discovered. The simulated eigenenergies are compared to the photoluminescence emission spectrum and found to accurately represent the exciton recombination energy. This makes it possible to obtain very realistic 3-D eigenstate representations for a variety of complex structures. The modelling technique outlined in this thesis is not constrained to just QDs, but can also be applied to an array of many other nanoscale structures. / Thesis / Master of Applied Science (MASc)

I det här arbetet har jag undersökt och prövat tillämpningen av en lektionsserie innehållande ensembleövningar bestående av rytmiska permutationer, dynamik, samspel och ensembleledning i undervisningen för kursen Ensemble 2 på gymnasienivå. Syftet har varit att undersöka om ensembleövningar kan fungera som komplement till undervisningen för ensemble, där fokus vanligtvis till ligger på arbete med repertoar. I genomförandet av lektionsserien har jag för insamlandet av data låtit de berörda eleverna fylla i en kvalitativ enkät vid lektionsseriens början och slut där eleverna värderat och beskrivit sin förståelse för samspel i ensemble, samt genomfört en fokusgruppsdiskussion med de berörda eleverna. Resultatet visade att den här typen av ensembleövningar möjligtvis kan ha en plats i undervisningen. Resultatet av arbetet hoppas jag kan inspirera till nytänkande och utvecklande förhållningssätt till ensembleundervisning för musiklärare inom alla skolformer.

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Interplay has always been a key characteristic of jazz and improvised music. The ability to communicate in real-time and to build on each other's ideas is essential in this genre, where the very essence of the music lies in the musical conversation between musicians. The purpose of this essay is to investigate different methods for developing a beneficial foundation for collective creativity, and to discern how the various qualities of the guitar affect the resulting interplay within the group - in this case a guitar trio with bass and drums. In addition to this, I have also worked with a specific method for composing, with the aim to write new music for the specific musicians in this group. Through the usage of ensemble exercises we investigated parameters such as dynamics, contrasts, and motifs. We noticed that it wasn’t mainly the exercises that led to progress, but rather the conversations about the music we had just played. The composing method was a highly tangible and creative way of capturing the trio’s musical nature. Since the ideas for the compositions came straight from the musicians themselves, the music I wrote came to properly reflect the group and our particular sound. / <p>Medverkande musiker: Agnes Persson; elgitarr, Jakob Ulmestrand; kontrabas, Ludwig Aronsson; trumset, Maja Svantesson; sång. Repertoar:"Låt 1", "Låt 4/Kvarnen", "Låt 5", "Låt 3", "Jordens Hjärta" (Dikt av Peter Curman). Samtliga kompositioner skrivna av Agnes Persson med band.</p>

Traditional clustering is typically based on a single feature set. In some domains, several feature sets may be available to represent the same objects, but it may not be easy to compute a useful and effective integrated feature set. We hypothesize that clustering individual datasets and then combining them using a suitable ensemble algorithm will yield better quality clusters compared to the individual clustering or clustering based on an integrated feature set. We present two classes of algorithms to address the problem of combining the results of clustering obtained from multiple related datasets where the datasets represent identical or overlapping sets of objects but use different feature sets. One class of algorithms was developed for combining hierarchical clustering generated from multiple datasets and another class of algorithms was developed for combining partitional clustering generated from multiple datasets. The first class of algorithms, called EPaCH, are based on graph-theoretic principles and use the association strengths of objects in the individual cluster hierarchies. The second class of algorithms, called CEMENT, use an EM (Expectation Maximization) approach to progressively refine the individual clusterings until the mutual entropy between them converges toward a maximum. We have applied our methods to the problem of clustering a document collection consisting of journal abstracts from ten different Library of Congress categories. After several natural language preprocessing steps, both syntactic and semantic feature sets were extracted. We present empirical results that include the comparison of our algorithms with several baseline clustering schemes using different cluster validation indices. We also present the results of one-tailed paired emph{T}-tests performed on cluster qualities. Our methods are shown to yield higher quality clusters than the baseline clustering schemes that include the clustering based on individual feature sets and clustering based on concatenated feature sets. When the sets of objects represented in two datasets are overlapping but not identical, our algorithms outperform all baseline methods for all indices.

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