906 research outputs found
Critical being: a philosophical approach to understanding and expanding the scope of critical thinking in UK higher education with attention to cross-cultural diversity
This thesis develops a philosophically informed understanding of critical thinking applicable
at an expansive scope in UK universities, with particular attention to cross-cultural diversity. I
begin from the assumptions, substantiated by existing literature, that 1) UK universities should
(and ostensibly do) aim to teach and practice critical thinking at an expansive scope 2) this aim
is often not being met, in part due to assumptions underpinning dominant understandings of
critical thinking 3) the increasingly international nature of UK higher education creates
additional challenges in the theory and practice of critical thinking. I argue that addressing
these issues requires renewed attention to critical thinking theory and that philosophy can
meaningfully contribute to this effort. I begin by justifying the above assumptions and
clarifying the aims of this project. I provide a terminological and conceptual model for
understanding the idea of critical scope. This includes building on the idea of critical being put
forward by Barnett (1997) as a manifestation of criticality at an expansive scope. I then show
how dominant conceptions of critical thinking unintentionally narrow critical scope in theory
by relying on context-specific educational aims and/or assumedly universal substantive values
as defining features of critical thinking. I argue these efforts constitute uncritical impositions
on critical thinking that are particularly problematic in cross-cultural contexts. I use exploration
of criticality in Chinese philosophical traditions to show how resources which are often
excluded from critical thinking theory can support a more expansive and inclusive
understanding. This includes attention to how non-critical modes of thinking and being – such
as those of wonder and wu-wei – can help expand critical thinking towards critical being
without need for the imposition of predetermined aims or assumedly universal values. This
leads me to argue that the context-specific and necessarily determinant aims of education
cannot define the entirety of critical thinking at an expansive scope, which is a context-reflexive
and indeterminant process. I contend that whatever features ‘define’ critical thinking must
themselves remain open to critique. This leads me to suggest pragmatic assumptions capable
of animating critical thinking at an expansive scope within and between diverse contexts while
avoiding dogmatism and relativism. I conclude by considering implications for practice,
including attention to how this approach to critical thinking – exemplified by critical being –
can help navigate perennial tensions within the purposes, aims, curricula, pedagogies, and
environments of UK universities. Ultimately, this thesis aims to support universities in
cultivating criticality that draws on diversity as a resource, helping people with divergent
perspectives think and speak with (instead of past) each other in constructive critical
endeavours
Displacement and the Humanities: Manifestos from the Ancient to the Present
This is the final version. Available on open access from MDPI via the DOI in this recordThis is a reprint of articles from the Special Issue published online in the open access journal Humanities (ISSN 2076-0787) (available at: https://www.mdpi.com/journal/humanities/special_issues/Manifestos Ancient Present)This volume brings together the work of practitioners, communities, artists and other researchers from multiple disciplines. Seeking to provoke a discourse around displacement within and beyond the field of Humanities, it positions historical cases and debates, some reaching into the ancient past, within diverse geo-chronological contexts and current world urgencies. In adopting an innovative dialogic structure, between practitioners on the ground - from architects and urban planners to artists - and academics working across subject areas, the volume is a proposition to: remap priorities for current research agendas; open up disciplines, critically analysing their approaches; address the socio-political responsibilities that we have as scholars and practitioners; and provide an alternative site of discourse for contemporary concerns about displacement. Ultimately, this volume aims to provoke future work and collaborations - hence, manifestos - not only in the historical and literary fields, but wider research concerned with human mobility and the challenges confronting people who are out of place of rights, protection and belonging
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges
In recent years, the development of robotics and artificial intelligence (AI)
systems has been nothing short of remarkable. As these systems continue to
evolve, they are being utilized in increasingly complex and unstructured
environments, such as autonomous driving, aerial robotics, and natural language
processing. As a consequence, programming their behaviors manually or defining
their behavior through reward functions (as done in reinforcement learning
(RL)) has become exceedingly difficult. This is because such environments
require a high degree of flexibility and adaptability, making it challenging to
specify an optimal set of rules or reward signals that can account for all
possible situations. In such environments, learning from an expert's behavior
through imitation is often more appealing. This is where imitation learning
(IL) comes into play - a process where desired behavior is learned by imitating
an expert's behavior, which is provided through demonstrations.
This paper aims to provide an introduction to IL and an overview of its
underlying assumptions and approaches. It also offers a detailed description of
recent advances and emerging areas of research in the field. Additionally, the
paper discusses how researchers have addressed common challenges associated
with IL and provides potential directions for future research. Overall, the
goal of the paper is to provide a comprehensive guide to the growing field of
IL in robotics and AI.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
Image Diversification via Deep Learning based Generative Models
Machine learning driven pattern recognition from imagery such as object detection has been prevalenting among society due to the high demand for autonomy and the recent remarkable advances in such technology. The machine learning technologies acquire the abstraction of the existing data and enable inference of the pattern of the future inputs. However, such technologies require a sheer amount of images as a training dataset which well covers the distribution of the future inputs in order to predict the proper patterns whereas it is impracticable to prepare enough variety of images in many cases.
To address this problem, this thesis pursues to discover the method to diversify image datasets for fully enabling the capability of machine learning driven applications.
Focusing on the plausible image synthesis ability of generative models, we investigate a number of approaches to expand the variety of the output images using image-to-image translation, mixup and diffusion models along with the technique to enable a computation and training dataset efficient diffusion approach. First, we propose the combined use of unpaired image-to-image translation and mixup for data augmentation on limited non-visible imagery. Second, we propose diffusion image-to-image translation that generates greater quality images than other previous adversarial training based translation methods. Third, we propose a patch-wise and discrete conditional training of diffusion method enabling the reduction of the computation and the robustness on small training datasets.
Subsequently, we discuss a remaining open challenge about evaluation and the direction of future work. Lastly, we make an overall conclusion after stating social impact of this research field
Transition 2.0: Re-establishing Constitutional Democracy in EU Member States
The central question of Transition 2.0 is this: what (and how) may a new government do to re-establish constitutional democracy, as well as repair membership within the European Union, without breaching the European rule of law? This volume demonstrates that EU law and international commitments impose constraints but also offer tools and assistance for facilitating the way back after rule of law and democratic backsliding. The various contributions explore the constitutional, legal, and social framework of 'Transition 2.0'.Dieser Band zeigt, dass das EU-Recht und die internationalen Verpflichtungen zwar Zwänge auferlegen, aber auch Instrumente und Hilfestellungen bieten, um den Weg zurück in die Europäische Union nach Rechtsstaatlichkeitsdefiziten und demokratischen Rückschritten zu erleichtern. Die verschiedenen Beiträge untersuchen den verfassungsrechtlichen, rechtlichen und sozialen Rahmen des "Übergangs 2.0"
Fast and Data-Efficient Image Segmentation
Abundance and affordability of cameras has enabled scalable and affordable collection
of image data. This has led to many research opportunities both in robot-assisted
surgery and general computer vision domain related to image segmentation. In this
thesis, we focus on image segmentation problem as it is a fundamental task which
has many applications including pose estimation of surgical tools in robotic surgery
and eye tracking in head mounted displays. As a result of our work we present a
data-efficient method that does not require human annotation of data and exhibits
real-time inference.
First, we introduce the use of residual neural networks for surgical instrument
segmentation for robotic surgery. We show state of the art results on multiple instrument
segmentation datasets. Second, we introduce a neural architecture search
method that is able to find a very efficient image segmentation model capable of realtime
inference. Real-time inference is a crucial requirement for image segmentation
methods for robotic surgery. Third, to reduce the amount of annotation required
for our method, we introduce a semi-supervised approach which leverages unlabeled images and synthetic training data. Finally, we introduce the use of generative adversarial
networks for unsupervised discovery of segmentation classes from unlabeled
image data. Here, we show for this first time that this task is possible without any
annotated data. Data annotation for image segmentation is a very time consuming
procedure as it requires every pixel of an image to be classified into one of the classes.
We study the ability of recently introduced multimodal approaches like CLIP to assign
text labels to our discovered segmentation regions. At the end, we present a
model that is able to not only discover segmentation regions automatically but also
assigns text labels them
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