7,707 research outputs found
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Exploring cognitive mechanisms involved in self-face recognition
Due to the own face being a significant stimulus that is critical to oneâs identity, the own face is suggested to be processed in a quantitatively different (i.e., faster and better recognition) and qualitatively different (i.e., processed in a more featural manner) manner compared to other faces. This thesis further explored the cognitive mechanisms (perceptual and attentional systems) involved in the processing of the own face.
Chapter 2 explored the role of holistic and featural processing involved in the processing of self-face (and other faces) with eye-tracking measures in a passive-viewing paradigm and a face identification task. In the passive-viewing paradigm, the own face was sampled in a more featural manner compared to other faces whereas when asked to identify faces, all faces were sampled in a more holistic manner. Chapter 3 further explored the role of holistic and featural processing in the identification of the own face using the three standard measures of holistic face processing: The face inversion task, the composite face task, and the part-whole task. Compared to other faces, individuals showed a smaller âholistic interferenceâ by a task irrelevant bottom half for the own face in the composite face task and a stronger feature advantage for the own face, but inversion impaired the identification of all faces. These findings suggest that self-face is processed in a more featural manner, but the findings do not deny the role of holistic processing.
The final experimental chapter, Chapter 4, explored the modulation effects of cultural differences in oneâs self-concept (i.e., independent vs. interdependent self-concept) and a negative self-concept (i.e., depressive traits) on the attentional prioritization for the own face with a visual search paradigm. Findings showed that the attentional prioritization for the own face over an unfamiliar face is not modulated by cultural differences of oneâs self-concept nor oneâs level of depressive traits, and individuals showed no difference in the attentional prioritization for both the own face and friendâs face, demonstrating no processing advantage for the own face over a personally familiar face. These findings suggests that the attentional prioritization for the own face is better explained by a familiar face advantage.
Altogether, the findings of this thesis suggest that the own face is processed qualitatively different compared to both personally familiar and unfamiliar face, with the own face being processed in a more featural manner. However, in terms of quantitative differences, the self-face is processed differently compared to an unfamiliar face, but not to a familiar face. Although the specific face processing strategies for the own face may be due to the distinct visual experience that one has with their face, the attentional prioritization of the own face is however, better explained by a familiar face advantage rather than a self-specificity effect
Bildung in der digitalen Transformation
Die Coronapandemie und der durch sie erzwungene zeitweise Ăbergang von PrĂ€senz- zu Distanzlehre haben die Digitalisierung des Bildungswesens enorm vorangetrieben. Noch deutlicher als vorher traten dabei positive wie negative Aspekte dieser Entwicklung zum Vorschein. WĂ€hrend den Hochschulen der Wechsel mit vergleichsweise geringen Reibungsverlusten gelang, offenbarten sich diese an Schulen weitaus deutlicher. Trotz aller Widrigkeiten erscheint eines klar: Die zeitweisen VerĂ€nderungen werden Nachwirkungen zeigen. Eine völlige RĂŒckkehr zum Status quo ante ist kaum noch vorstellbar. Zwei Fragen bestimmen vor diesem Hintergrund die Doppelgesichtigkeit des Themas der 29. Jahrestagung der Gesellschaft fĂŒr Medien in der Wissenschaft (GMW). Erstens: Wie âfunktioniertâ Bildung in der sich derzeit ereignenden digitalen Transformation und welche Herausforderungen gibt es? Und zweitens: Befindet sich möglicherweise Bildung selbst in der Transformation? BeitrĂ€ge zu diesen und weiteren Fragen vereint der vorliegende Tagungsband
A New Paradigm for Knowledge Discovery and Design in Nanophotonics Based on Artificial Intelligence
The design of photonic devices in the nanoscale regime outperforming the bulky optical components has been a long-lasting challenge in state-of-the-art applications. Accordingly, devising a comprehensive model to understand and explain the physics and dynamics of light-matter interaction in these nanostructures is a substantial step toward realizing novel photonic devices. This thesis presents a new paradigm based on leveraging the intelligent aspect of artificial intelligence (AI) to design nanostructure and understand the underlying physics of light-matter interactions. Considering a large number of design parameters and the complex and non-unique nature of the input-output relations in nanophotonic structures, conventional approaches cannot be used for their design and analysis. The dimensionality reduction (DR) techniques in this research considerably reduce the computing requirements. This thesis also focuses on developing a reliable inverse design approach by overcoming the non-uniqueness challenge. This thesis presents a double-step DR technique to reduce the complexity of the inverse design problem while preserving the necessary information for finding the optimum nanostructure for the desired functionality. I established an approach based on defining physics-driven metrics to explore the low-dimensional manifold of design-response space and provide a sweet region in the reduced design space for the desired functionality. In the later part of the thesis, we have shown that we achieved the optimum nanostructure for a particular desired response by employing manifold learning while minimizing the geometrical complexity. Also, in this thesis, we have developed a manifold learning-based technique for accelerating the design of nanostructures focusing on selecting the optimum material and geometric parameters.Ph.D
Molecular Research in Rice: Agronomically Important Traits 2.0
This volume presents recent research achievements concerning the molecular genetic basis of agronomic traits in rice. Rice (Oryza sativa L.) is the most important food crop in the world, being a staple food for more than half of the worldâs population. Recent improvements in living standards have increased the worldwide demand for high-yielding and high-quality rice cultivars. To develop novel cultivars with superior agronomic performance, we need to understand the molecular basis of agronomically important traits related to grain yield, grain quality, disease resistance, and abiotic stress tolerance. Decoding the whole rice genome sequence revealed that ,while there are more than 37,000 genes in the ~400 Mbp rice genome, there are only about 3000 genes whose molecular functions are characterized in detail. We collected in this volume the continued research efforts of scholars that elucidate genetic networks and the molecular mechanisms controlling agronomically important traits in rice
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Learning disentangled speech representations
A variety of informational factors are contained within the speech signal and a single short recording of speech reveals much more than the spoken words. The best method to extract and represent informational factors from the speech signal ultimately depends on which informational factors are desired and how they will be used. In addition, sometimes methods will capture more than one informational factor at the same time such as speaker identity, spoken content, and speaker prosody.
The goal of this dissertation is to explore different ways to deconstruct the speech signal into abstract representations that can be learned and later reused in various speech technology tasks. This task of deconstructing, also known as disentanglement, is a form of distributed representation learning. As a general approach to disentanglement, there are some guiding principles that elaborate what a learned representation should contain as well as how it should function. In particular, learned representations should contain all of the requisite information in a more compact manner, be interpretable, remove nuisance factors of irrelevant information, be useful in downstream tasks, and independent of the task at hand. The learned representations should also be able to answer counter-factual questions.
In some cases, learned speech representations can be re-assembled in different ways according to the requirements of downstream applications. For example, in a voice conversion task, the speech content is retained while the speaker identity is changed. And in a content-privacy task, some targeted content may be concealed without affecting how surrounding words sound. While there is no single-best method to disentangle all types of factors, some end-to-end approaches demonstrate a promising degree of generalization to diverse speech tasks.
This thesis explores a variety of use-cases for disentangled representations including phone recognition, speaker diarization, linguistic code-switching, voice conversion, and content-based privacy masking. Speech representations can also be utilised for automatically assessing the quality and authenticity of speech, such as automatic MOS ratings or detecting deep fakes. The meaning of the term "disentanglement" is not well defined in previous work, and it has acquired several meanings depending on the domain (e.g. image vs. speech). Sometimes the term "disentanglement" is used interchangeably with the term "factorization". This thesis proposes that disentanglement of speech is distinct, and offers a viewpoint of disentanglement that can be considered both theoretically and practically
Unsupervised clustering of MDS data using federated learning
In this master thesis we developed a model for unsupervised clustering on a data set of biomedical data. This data has been collected by GenoMed4All consortium from patients affected by Myelodysplastic Syndrome (MDS), that is an haematological disease. The main focus is put on the genetic mutations collected that are used as features of the patients in order to cluster them. Clustering approaches have been used in several studies concerning haematological diseases such MDS. A neural network-based model was used to solve the task. The results of the clustering have been compared with labels from a "gold standard'' technique, i.e. hierarchical Dirichlet processes (HDP). Our model was designed to be also implemented in the context of federated learning (FL). This innovative technique is able to achieve machine learning objective without the necessity of collecting all the data in one single center, allowing strict privacy policies to be respected. Federated learning was used because of its properties, and because of the sensitivity of data. Several recent studies regarding clinical problems addressed with machine learning endorse the development of federated learning settings in such context, because its privacy preserving properties could represent a cornerstone for applying machine learning techniques to medical data. In this work will be then discussed the clustering performance of the model, and also its generative capabilities
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