10,317 research outputs found
Convergence of Dynamics on Inductive Systems of Banach Spaces
Many features of physical systems, both qualitative and quantitative, become
sharply defined or tractable only in some limiting situation. Examples are
phase transitions in the thermodynamic limit, the emergence of classical
mechanics from quantum theory at large action, and continuum quantum field
theory arising from renormalization group fixed points. It would seem that few
methods can be useful in such diverse applications. However, we here present a
flexible modeling tool for the limit of theories: soft inductive limits
constituting a generalization of inductive limits of Banach spaces. In this
context, general criteria for the convergence of dynamics will be formulated,
and these criteria will be shown to apply in the situations mentioned and more.Comment: Comments welcom
Evaluation of different segmentation-based approaches for skin disorders from dermoscopic images
Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2022-2023. Tutor/Director: Sala Llonch, Roser, Mata Miquel, Christian, Munuera, JosepSkin disorders are the most common type of cancer in the world and the incident has been lately increasing over the past decades. Even with the most complex and advanced technologies, current image acquisition systems do not permit a reliable identification of the skin lesion by visual examination due to the challenging structure of the malignancy. This promotes the need for the implementation of automatic skin lesion segmentation methods in order to assist in physicians’ diagnostic when determining the lesion's region and to serve as a preliminary step for the classification of the skin lesion. Accurate and precise segmentation is crucial for a rigorous screening and monitoring of the disease's progression.
For the purpose of the commented concern, the present project aims to accomplish a state-of-the-art review about the most predominant conventional segmentation models for skin lesion segmentation, alongside with a market analysis examination. With the rise of automatic segmentation tools, a wide number of algorithms are currently being used, but many are the drawbacks when employing them for dermatological disorders due to the high-level presence of artefacts in the image acquired.
In light of the above, three segmentation techniques have been selected for the completion of the work: level set method, an algorithm combining GrabCut and k-means methods and an intensity automatic algorithm developed by Hospital Sant Joan de DĂ©u de Barcelona research group. In addition, a validation of their performance is conducted for a further implementation of them in clinical training. The proposals, together with the got outcomes, have been accomplished by means of a publicly available skin lesion image database
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Multidimensional adaptive order GP-WENO via kernel-based reconstruction
This paper presents a fully multidimensional kernel-based reconstruction
scheme for finite volume methods applied to systems of hyperbolic conservation
laws, with a particular emphasis on the compressible Euler equations.
Non-oscillatory reconstruction is achieved through an adaptive order weighted
essentially non-oscillatory (WENO-AO) method cast into a form suited to
multidimensional stencils and reconstruction. A kernel-based approach inspired
by Gaussian process (GP) modeling is presented here. This approach allows the
creation of a scheme of arbitrary order with simply defined multidimensional
stencils and substencils. Furthermore, the fully multidimensional nature of the
reconstruction allows a more straightforward extension to higher spatial
dimensions and removes the need for complicated boundary conditions on
intermediate quantities in modified dimension-by-dimension methods. In
addition, a new simple-yet-effective set of reconstruction variables is
introduced, as well as an easy-to-implement effective limiter for positivity
preservation, both of which could be useful in existing schemes with little
modification. The proposed scheme is applied to a suite of stringent and
informative benchmark problems to demonstrate its efficacy and utility.Comment: Submitted to Journal of Computational Physics April 202
Deep Transfer Learning Applications in Intrusion Detection Systems: A Comprehensive Review
Globally, the external Internet is increasingly being connected to the
contemporary industrial control system. As a result, there is an immediate need
to protect the network from several threats. The key infrastructure of
industrial activity may be protected from harm by using an intrusion detection
system (IDS), a preventive measure mechanism, to recognize new kinds of
dangerous threats and hostile activities. The most recent artificial
intelligence (AI) techniques used to create IDS in many kinds of industrial
control networks are examined in this study, with a particular emphasis on
IDS-based deep transfer learning (DTL). This latter can be seen as a type of
information fusion that merge, and/or adapt knowledge from multiple domains to
enhance the performance of the target task, particularly when the labeled data
in the target domain is scarce. Publications issued after 2015 were taken into
account. These selected publications were divided into three categories:
DTL-only and IDS-only are involved in the introduction and background, and
DTL-based IDS papers are involved in the core papers of this review.
Researchers will be able to have a better grasp of the current state of DTL
approaches used in IDS in many different types of networks by reading this
review paper. Other useful information, such as the datasets used, the sort of
DTL employed, the pre-trained network, IDS techniques, the evaluation metrics
including accuracy/F-score and false alarm rate (FAR), and the improvement
gained, were also covered. The algorithms, and methods used in several studies,
or illustrate deeply and clearly the principle in any DTL-based IDS subcategory
are presented to the reader
Saliency-aware Stereoscopic Video Retargeting
Stereo video retargeting aims to resize an image to a desired aspect ratio.
The quality of retargeted videos can be significantly impacted by the stereo
videos spatial, temporal, and disparity coherence, all of which can be impacted
by the retargeting process. Due to the lack of a publicly accessible annotated
dataset, there is little research on deep learning-based methods for stereo
video retargeting. This paper proposes an unsupervised deep learning-based
stereo video retargeting network. Our model first detects the salient objects
and shifts and warps all objects such that it minimizes the distortion of the
salient parts of the stereo frames. We use 1D convolution for shifting the
salient objects and design a stereo video Transformer to assist the retargeting
process. To train the network, we use the parallax attention mechanism to fuse
the left and right views and feed the retargeted frames to a reconstruction
module that reverses the retargeted frames to the input frames. Therefore, the
network is trained in an unsupervised manner. Extensive qualitative and
quantitative experiments and ablation studies on KITTI stereo 2012 and 2015
datasets demonstrate the efficiency of the proposed method over the existing
state-of-the-art methods. The code is available at
https://github.com/z65451/SVR/.Comment: 8 pages excluding references. CVPRW conferenc
The Disputation: The Enduring Representations in William Holman Hunt's “The Finding of the Saviour in the Temple,” 1860
This interdisciplinary thesis problematizes the Jewish presence in the painting The Finding of the Saviour in the Temple (1860) by William Holman Hunt. This “Jewish presence” refers to characters within the painting, Jews who posed for the picture and the painting’s portrayal of Judaism. The thesis takes a phenomenological and hermeneutical approach to The Finding providing careful description and interpretation of what appears in the painting. It situates the painting within a newly configured genre of disputation paintings depicting the Temple scene from the Gospel of Luke (2:47 – 52). It asks two questions. Why does The Finding look the way it does? And how did Holman Hunt know how to create the picture? Under the rubric of the first question, it explores and challenges customary accounts of the painting, explicitly challenging the over reliance upon F.G. Stephens’s pamphlet. Additionally, it examines Pre-Raphaelite and Victorian religious contexts and bringing hitherto unacknowledged artistic contexts to the fore. The second question examines less apparent influences through an analysis of the originary Lukan narrative in conjunction with the under-examined genre of Temple “disputation” paintings, and a legacy of scholarly and religious disputation. This demonstrates a discourse of disputation informing The Finding over and above the biblical narrative. In showing that this discourse strongly correlates with the painting’s objectifying and spectacular properties, this thesis provides a new way to understand The Finding’s orientalism which is further revealed in its typological critical reworking of two Christian medieval and renaissance paintings. As a demonstration of the discourse, the thesis includes an examination of Jewish artists who addressed the theme of disputation overtly or obliquely thereby engaging with and challenging the assumptions upon which the disputation rests
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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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