7,294 research outputs found
Improving diagnostic procedures for epilepsy through automated recording and analysis of patientsâ history
Transient loss of consciousness (TLOC) is a time-limited state of profound cognitive impairment characterised by amnesia, abnormal motor control, loss of responsiveness, a short duration and complete recovery. Most instances of TLOC are caused by one of three health conditions: epilepsy, functional (dissociative) seizures (FDS), or syncope. There is often a delay before the correct diagnosis is made and 10-20% of individuals initially receive an incorrect diagnosis. Clinical decision tools based on the endorsement of TLOC symptom lists have been limited to distinguishing between two causes of TLOC. The Initial Paroxysmal Event Profile (iPEP) has shown promise but was demonstrated to have greater accuracy in distinguishing between syncope and epilepsy or FDS than between epilepsy and FDS. The objective of this thesis was to investigate whether interactional, linguistic, and communicative differences in how people with epilepsy and people with FDS describe their experiences of TLOC can improve the predictive performance of the iPEP. An online web application was designed that collected information about TLOC symptoms and medical history from patients and witnesses using a binary questionnaire and verbal interaction with a virtual agent. We explored potential methods of automatically detecting these communicative differences, whether the differences were present during an interaction with a VA, to what extent these automatically detectable communicative differences improve the performance of the iPEP, and the acceptability of the application from the perspective of patients and witnesses. The two feature sets that were applied to previous doctor-patient interactions, features designed to measure formulation effort or detect semantic differences between the two groups, were able to predict the diagnosis with an accuracy of 71% and 81%, respectively. Individuals with epilepsy or FDS provided descriptions of TLOC to the VA that were qualitatively like those observed in previous research. Both feature sets were effective predictors of the diagnosis when applied to the web application recordings (85.7% and 85.7%). Overall, the accuracy of machine learning models trained for the threeway classification between epilepsy, FDS, and syncope using the iPEP responses from patients that were collected through the web application was worse than the performance observed in previous research (65.8% vs 78.3%), but the performance was increased by the inclusion of features extracted from the spoken descriptions on TLOC (85.5%). Finally, most participants who provided feedback reported that the online application was acceptable. These findings suggest that it is feasible to differentiate between people with epilepsy and people with FDS using an automated analysis of spoken seizure descriptions. Furthermore, incorporating these features into a clinical decision tool for TLOC can improve the predictive performance by improving the differential diagnosis between these two health conditions. Future research should use the feedback to improve the design of the application and increase perceived acceptability of the approach
EnTri: Ensemble Learning with Tri-level Representations for Explainable Scene Recognition
Scene recognition based on deep-learning has made significant progress, but
there are still limitations in its performance due to challenges posed by
inter-class similarities and intra-class dissimilarities. Furthermore, prior
research has primarily focused on improving classification accuracy, yet it has
given less attention to achieving interpretable, precise scene classification.
Therefore, we are motivated to propose EnTri, an ensemble scene recognition
framework that employs ensemble learning using a hierarchy of visual features.
EnTri represents features at three distinct levels of detail: pixel-level,
semantic segmentation-level, and object class and frequency level. By
incorporating distinct feature encoding schemes of differing complexity and
leveraging ensemble strategies, our approach aims to improve classification
accuracy while enhancing transparency and interpretability via visual and
textual explanations. To achieve interpretability, we devised an extension
algorithm that generates both visual and textual explanations highlighting
various properties of a given scene that contribute to the final prediction of
its category. This includes information about objects, statistics, spatial
layout, and textural details. Through experiments on benchmark scene
classification datasets, EnTri has demonstrated superiority in terms of
recognition accuracy, achieving competitive performance compared to
state-of-the-art approaches, with an accuracy of 87.69%, 75.56%, and 99.17% on
the MIT67, SUN397, and UIUC8 datasets, respectively.Comment: Submitted to Pattern Recognition journa
Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
Deep learning has demonstrated remarkable performance across various tasks in
medical imaging. However, these approaches primarily focus on supervised
learning, assuming that the training and testing data are drawn from the same
distribution. Unfortunately, this assumption may not always hold true in
practice. To address these issues, unsupervised domain adaptation (UDA)
techniques have been developed to transfer knowledge from a labeled domain to a
related but unlabeled domain. In recent years, significant advancements have
been made in UDA, resulting in a wide range of methodologies, including feature
alignment, image translation, self-supervision, and disentangled representation
methods, among others. In this paper, we provide a comprehensive literature
review of recent deep UDA approaches in medical imaging from a technical
perspective. Specifically, we categorize current UDA research in medical
imaging into six groups and further divide them into finer subcategories based
on the different tasks they perform. We also discuss the respective datasets
used in the studies to assess the divergence between the different domains.
Finally, we discuss emerging areas and provide insights and discussions on
future research directions to conclude this survey.Comment: Under Revie
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Production networks in the cultural and creative sector: case studies from the publishing industry
The CICERONE project investigates cultural and creative industries through case study research, with a focus on production networks. This report, part of WP2, examines the publishing industry within this framework. It aims to understand the industryâs hidden aspects, address statistical issues in measurement, and explore the industryâs transformation and integration of cultural and economic values. The report provides an overview of the production network, explores statistical challenges, and presents qualitative analyses of two case studies. It concludes by highlighting the potential of the Global Production Network (GPN) approach for analyzing, researching, policymaking, and intervening in the European publishing network.
The CICERONE projectâs case study research delves into the publishing industry, investigating its production networks and examining key aspects often unseen by the public. The report addresses statistical challenges in measuring the industry and sheds light on its ongoing transformations and integration of cultural and economic values. It presents an overview of the production network, explores statistical issues, and provides qualitative analyses of two case studies. The report emphasizes the potential of the GPN approach for analyzing and intervening in the European publishing network, ultimately contributing to research, policymaking, and understanding within the industry
DASS Good: Explainable Data Mining of Spatial Cohort Data
Developing applicable clinical machine learning models is a difficult task
when the data includes spatial information, for example, radiation dose
distributions across adjacent organs at risk. We describe the co-design of a
modeling system, DASS, to support the hybrid human-machine development and
validation of predictive models for estimating long-term toxicities related to
radiotherapy doses in head and neck cancer patients. Developed in collaboration
with domain experts in oncology and data mining, DASS incorporates
human-in-the-loop visual steering, spatial data, and explainable AI to augment
domain knowledge with automatic data mining. We demonstrate DASS with the
development of two practical clinical stratification models and report feedback
from domain experts. Finally, we describe the design lessons learned from this
collaborative experience.Comment: 10 pages, 9 figure
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Central-provincial Politics and Industrial Policy-making in the Electric Power Sector in China
In addition to the studies that provide meaningful insights into the complexity of technical and economic issues, increasing studies have focused on the political process of market transition in network industries such as the electric power sector. This dissertation studies the centralâprovincial interactions in industrial policy-making and implementation, and attempts to evaluate the roles of Chinese provinces in the market reform process of the electric power sector. Market reforms of this sector are used as an illustrative case because the new round of market reforms had achieved some significant breakthroughs in areas such as pricing reform and wholesale market trading. Other policy measures, such as the liberalization of the distribution market and cross-regional market-building, are still at a nascent stage and have only scored moderate progress. It is important to investigate why some policy areas make greater progress in market reforms than others. It is also interesting to examine the impacts of Chinese central-provincial politics on producing the different market reform outcomes. Guangdong and Xinjiang are two provinces being analyzed in this dissertation. The progress of market reforms in these two provinces showed similarities although the provinces are very different in terms of local conditions such as the stages of their economic development and energy structures. The actual reform can be understood as the outcomes of certain modes of interactions between the central and provincial actors in the context of their particular capabilities and preferences in different policy areas. This dissertation argues that market reform is more successful in policy areas where the central and provincial authorities are able to engage mainly in integrative negotiations than in areas where they engage mainly in distributive negotiations
Exploring Potential Domains of Agroecological Transformation in the United States
There is now substantial evidence that agroecology constitutes a necessary pathway towards socially just and ecologically resilient agrifood systems. In the United States, however, agroecology remains relegated to the margins of research and policy spaces. This dissertation explores three potential domains of agroecological transformation in the US. Domains of transformation are sites of contestation in which agroecology interfaces with the industrial agrifood system; these material and conceptual spaces may point to important pathways for scaling agroecology. To explore this concept, I examine formal agroecology education (Chapter 1), extension services and statewide discourses around soil health (Chapter 2), and models of farmland access not based on private property (Chapter 3). While these constitute three distinct topics, I seek to demonstrate that they are linked by similar forces that enable and constrain the extent to which these domains can be sites of agroecological transformation.
First, I use case study methodology to explore the evolution of an advanced undergraduate agroecology course at the University of Vermont. I examine how course content and pedagogy align with a transformative framing of agroecology as inherently transdisciplinary, participatory, action-oriented, and political. I find that student-centered pedagogies and experiential education on farms successfully promote transformative learning whereby students shift their understanding of agrifood systems and their role(s) within them. In my second chapter, I zoom out to consider soil health discourses amongst farmers and extension professionals in Vermont. Using co-created mental models and participatory analysis, I find that a singular notion of soil health based on biological, chemical, and physical properties fails to capture the diverse ways in which farmers and extension professionals understand soil health. I advocate for a principles-based approach to soil health that includes social factors and may provide a valuable heuristic for mobilizing knowledge towards agroecology transition pathways. My third chapter, conducted in collaboration with the national non-profit organization Agrarian Trust, considers equitable farmland access. Through semi-structured interviews with 13 farmers and growers across the US, I explore both farmer motivations for engaging with alternative land access models (ALAMs) and the potential role(s) these models may play within broader transformation processes. I argue that ALAMs constitute material and conceptual âthird spacesâ within which the private property regime is challenged and new identities and language around land ownership can emerge; as such, ALAMs may facilitate a (re)imagining of land-based social-ecological relationships.
I conclude the dissertation by identifying conceptual and practical linkages across the domains explored in Chapters 1-3. I pay particular attention to processes that challenge neoliberal logics, enact plural ways of knowing, and prefigure just futures. In considering these concepts, I apply an expansive notion of pedagogy to explore how processes of teaching and (un)learning can contribute to cultivating foundational capacities for transition processes
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Into the Multiverse: Methods for Studying Developmental Neuroscience
One major challenge in developmental neuroscience research is the sheer number of choices researchers face when addressing even a single research question. Even once data collection is complete, the journey from raw data to interpretation of findings may depend on numerous decisions. To address this issue, this dissertation explores âmultiverseâ analysis techniques for following many analytical paths at once in the same dataset.
In chapter 1, multiverses are used to examine which analyses of age-related change in amygdala-medial prefrontal cortex circuitry are robust versus sensitive to researcher decisions. Chapter 2 uses multiverse analysis to identify optimal solutions for mitigating breathing-induced artifacts in resting-state functional magnetic resonance imaging data. Chapter 3 uses a variety of model specifications to characterize simultaneous reward learning strategies in youth contingent on both visual task cues and spatial-motor information.
Despite varied approaches and goals, each of the three studies highlight the benefits of conducting multiple parallel analyses for both addressing questions in developmental neuroscience and deepening understanding of the methods used to address them
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An Agile Musicology: Improvisation in Corporate Management and Lean Startups
The last decade of the twentieth century saw a proliferation of publications that use jazz as a metaphor for corporate management, arguing that in the contemporary knowledge economy, jazz is superior to the symphonic model that governed mid-century factory floors. As the literature on the jazz metaphor, and organizational improvisation more broadly, continued to develop into the twenty-first century, another managerial methodology became widely adopted by entrepreneurs: agile. While agile is yet to be fully theorized as an improvisatory practice, agile shares several core tenets with the models promoted by organizational improvisation scholars, including the use of small teams, an emphasis on feedback, and an openness to change. In this dissertation, I argue that agile methods, and the adjacent lean methodology, are inherently improvisatory and that understanding them as improvisatory offers opportunities not only for their deployment within growing businesses, but also for adoption at-scale in large corporations.
I draw on an array of disciplinary perspectives, including management science, organizational studies, musicology, and critical improvisation studies, as well as a wide range of sources, from peer-reviewed journal publications to trade manuals. Each chapter builds upon the former: a substantial and critical review of the jazz metaphor literature is followed by a dissection of its main themes under a musicological lens; after securing the foundations of organizational improvisation, the next chapter reveals the improvisatory nature of agile and lean startup practices and links them to concepts discussed within the jazz metaphor literature. Drawing on insights from large-scale improvisatory musical practices, the final chapter reveals how improvisation, as a set of practices shared between corporate management and agile methodologies, provides avenues for agile to be scaled up as startups grow or for its widespread adoption within established companies
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