2,567 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Fictocritical Cyberfeminism: A Paralogical Model for Post-Internet Communication
This dissertation positions the understudied and experimental writing practice of fictocriticism as an analog for the convergent and indeterminate nature of “post-Internet” communication as well a cyberfeminist technology for interfering and in-tervening in metanarratives of technoscience and technocapitalism that structure contemporary media. Significant theoretical valences are established between twen-tieth century literary works of fictocriticism and the hybrid and ephemeral modes of writing endemic to emergent, twenty-first century forms of networked communica-tion such as social media. Through a critical theoretical understanding of paralogy, or that countercultural logic of deploying language outside legitimate discourses, in-volving various tactics of multivocity, mimesis and metagraphy, fictocriticism is ex-plored as a self-referencing linguistic machine which exists intentionally to occupy those liminal territories “somewhere in among/between criticism, autobiography and fiction” (Hunter qtd. in Kerr 1996). Additionally, as a writing practice that orig-inated in Canada and yet remains marginal to national and international literary scholarship, this dissertation elevates the origins and ongoing relevance of fictocriti-cism by mapping its shared aims and concerns onto proximal discourses of post-structuralism, cyberfeminism, network ecology, media art, the avant-garde, glitch feminism, and radical self-authorship in online environments. Theorized in such a matrix, I argue that fictocriticism represents a capacious framework for writing and reading media that embodies the self-reflexive politics of second-order cybernetic theory while disrupting the rhetoric of technoscientific and neoliberal economic forc-es with speech acts of calculated incoherence. Additionally, through the inclusion of my own fictocritical writing as works of research-creation that interpolate the more traditional chapters and subchapters, I theorize and demonstrate praxis of this dis-tinctively indeterminate form of criticism to empirically and meaningfully juxtapose different modes of knowing and speaking about entangled matters of language, bod-ies, and technologies. In its conclusion, this dissertation contends that the “creative paranoia” engendered by fictocritical cyberfeminism in both print and digital media environments offers a pathway towards a more paralogical media literacy that can transform the terms and expectations of our future media ecology
La traduzione specializzata all’opera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.
Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The “Language Toolkit – Le lingue straniere al servizio dell’internazionalizzazione dell’impresa” project, promoted by the Department of Interpreting and Translation (Forlì Campus) in collaboration with the Romagna Chamber of Commerce (Forlì-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices
Sensitivity of NEXT-100 detector to neutrinoless double beta decay
Nesta tese estúdiase a sensibilidade do detector NEXT-100 á desintegración dobre
beta sen neutrinos. Existe un gran interese na busca desta desintegración xa que
podería respostar preguntas fundamentais en física de neutrinos. O detector constitúe
a terceira fase do experimento NEXT, colaboración na que se desenrolou esta tese.
A continuación inclúese un resumo de cada un dos capítulos nos que se divide a
tese. Comézase introducindo o marco teórico e experimental nas seccións Física de
neutrinos, A busca da desintegración dobre beta sen neutrinos e O experimento
NEXT. Posteriormente descríbense a parte principal do análise da tese en Simulación
do detector, Procesamento de datos e Sensibilidade do detector NEXT-100
Unveiling the frontiers of deep learning: innovations shaping diverse domains
Deep learning (DL) enables the development of computer models that are
capable of learning, visualizing, optimizing, refining, and predicting data. In
recent years, DL has been applied in a range of fields, including audio-visual
data processing, agriculture, transportation prediction, natural language,
biomedicine, disaster management, bioinformatics, drug design, genomics, face
recognition, and ecology. To explore the current state of deep learning, it is
necessary to investigate the latest developments and applications of deep
learning in these disciplines. However, the literature is lacking in exploring
the applications of deep learning in all potential sectors. This paper thus
extensively investigates the potential applications of deep learning across all
major fields of study as well as the associated benefits and challenges. As
evidenced in the literature, DL exhibits accuracy in prediction and analysis,
makes it a powerful computational tool, and has the ability to articulate
itself and optimize, making it effective in processing data with no prior
training. Given its independence from training data, deep learning necessitates
massive amounts of data for effective analysis and processing, much like data
volume. To handle the challenge of compiling huge amounts of medical,
scientific, healthcare, and environmental data for use in deep learning, gated
architectures like LSTMs and GRUs can be utilized. For multimodal learning,
shared neurons in the neural network for all activities and specialized neurons
for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table
Viscous Thin-film Models of Nanoscale Self-organization Under Ion Bombardment
For decades, it has been observed that broad-beam irradiation of semiconductor surfaces can lead to spontaneous self-organization into highly regular patterns, sometimes at length scales of only a few nanometers. Initial theory was largely based on erosion and redistribution of material occurring on fast time scales, which are able to produce good agreement with certain aspects of surface evolution. However, further experimental and theoretical work eventually led to the realization that numerous effects are active in the irradiated target, including stresses associated with ion-implantation and the accumulation of damage leading to the development of a disordered, amorphous layer atop the substrate. It was also shown that relaxation of this amorphous layer proceeds in a manner closer to viscous flow ratherthan surface diffusion on a crystal lattice.
Observing the viscous character of the amorphous layer, it is natural to consider whether stress-based continuum models might help explain pattern formation under ion bombardment and the observations described above. Indeed, there are early indications from the experimental literature that this may be the case, and, at low energies (∼ 1keV), at least one experimental-theoretical study has shown that they may even dominate erosive and redistributive effects in their contribution to surface evolution.
In this thesis, we develop a continuum model based on viscous thin-film flow and ion-induced stresses within the amorphous layer. This model is a composite of, and significant generalization of, a previously-studied “anisotropic plastic flow” (APF) mechanism and a previously-studied “ion-induced isotropic swelling” (IIS) mechanism. Previous work has shown that, with certain simplifying assumptions about the amorphous-crystalline interface and spatial homogeneity of anisotropic plastic flow, this mechanism produces an instability capable of predicting pattern formation beginning at 45◦ angle of incidence against the macroscopically-flat substrate, consistent with some experimental systems. Under similar simplifying assumptions, ion-induced swelling has been shown to be capable of suppressing pattern formation. Our generalizations allow the use of simulation data to inform both linear and nonlinear surface evolution due to the spatial localization of APF and IIS to certain regions of the bulk, improved treatment of the amorphous-crystalline geometry, andboundary conditions suitable to the physical systems of interest. We are then able to provide insight into several phenomena that have previously been difficult to explain, but seem to emerge naturally from a more detailed treatment of the physical system
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
RingMo-lite: A Remote Sensing Multi-task Lightweight Network with CNN-Transformer Hybrid Framework
In recent years, remote sensing (RS) vision foundation models such as RingMo
have emerged and achieved excellent performance in various downstream tasks.
However, the high demand for computing resources limits the application of
these models on edge devices. It is necessary to design a more lightweight
foundation model to support on-orbit RS image interpretation. Existing methods
face challenges in achieving lightweight solutions while retaining
generalization in RS image interpretation. This is due to the complex high and
low-frequency spectral components in RS images, which make traditional single
CNN or Vision Transformer methods unsuitable for the task. Therefore, this
paper proposes RingMo-lite, an RS multi-task lightweight network with a
CNN-Transformer hybrid framework, which effectively exploits the
frequency-domain properties of RS to optimize the interpretation process. It is
combined by the Transformer module as a low-pass filter to extract global
features of RS images through a dual-branch structure, and the CNN module as a
stacked high-pass filter to extract fine-grained details effectively.
Furthermore, in the pretraining stage, the designed frequency-domain masked
image modeling (FD-MIM) combines each image patch's high-frequency and
low-frequency characteristics, effectively capturing the latent feature
representation in RS data. As shown in Fig. 1, compared with RingMo, the
proposed RingMo-lite reduces the parameters over 60% in various RS image
interpretation tasks, the average accuracy drops by less than 2% in most of the
scenes and achieves SOTA performance compared to models of the similar size. In
addition, our work will be integrated into the MindSpore computing platform in
the near future
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