6,317 research outputs found

    Segmentation of Pathology Images: A Deep Learning Strategy with Annotated Data

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    Cancer has significantly threatened human life and health for many years. In the clinic, histopathology image segmentation is the golden stand for evaluating the prediction of patient prognosis and treatment outcome. Generally, manually labelling tumour regions in hundreds of high-resolution histopathological images is time-consuming and expensive for pathologists. Recently, the advancements in hardware and computer vision have allowed deep-learning-based methods to become mainstream to segment tumours automatically, significantly reducing the workload of pathologists. However, most current methods rely on large-scale labelled histopathological images. Therefore, this research studies label-effective tumour segmentation methods using deep-learning paradigms to relieve the annotation limitations. Chapter 3 proposes an ensemble framework for fully-supervised tumour segmentation. Usually, the performance of an individual-trained network is limited by significant morphological variances in histopathological images. We propose a fully-supervised learning ensemble fusion model that uses both shallow and deep U-Nets, trained with images of different resolutions and subsets of images, for robust predictions of tumour regions. Noise elimination is achieved with Convolutional Conditional Random Fields. Two open datasets are used to evaluate the proposed method: the ACDC@LungHP challenge at ISBI2019 and the DigestPath challenge at MICCAI2019. With a dice coefficient of 79.7 %, the proposed method takes third place in ACDC@LungHP. In DigestPath 2019, the proposed method achieves a dice coefficient 77.3 %. Well-annotated images are an indispensable part of training fully-supervised segmentation strategies. However, large-scale histopathology images are hardly annotated finely in clinical practice. It is common for labels to be of poor quality or for only a few images to be manually marked by experts. Consequently, fully-supervised methods cannot perform well in these cases. Chapter 4 proposes a self-supervised contrast learning for tumour segmentation. A self-supervised cancer segmentation framework is proposed to reduce label dependency. An innovative contrastive learning scheme is developed to represent tumour features based on unlabelled images. Unlike a normal U-Net, the backbone is a patch-based segmentation network. Additionally, data augmentation and contrastive losses are applied to improve the discriminability of tumour features. A convolutional Conditional Random Field is used to smooth and eliminate noise. Three labelled, and fourteen unlabelled images are collected from a private skin cancer dataset called BSS. Experimental results show that the proposed method achieves better tumour segmentation performance than other popular self-supervised methods. However, by evaluated on the same public dataset as chapter 3, the proposed self-supervised method is hard to handle fine-grained segmentation around tumour boundaries compared to the supervised method we proposed. Chapter 5 proposes a sketch-based weakly-supervised tumour segmentation method. To segment tumour regions precisely with coarse annotations, a sketch-supervised method is proposed, containing a dual CNN-Transformer network and a global normalised class activation map. CNN-Transformer networks simultaneously model global and local tumour features. With the global normalised class activation map, a gradient-based tumour representation can be obtained from the dual network predictions. We invited experts to mark fine and coarse annotations in the private BSS and the public PAIP2019 datasets to facilitate reproducible performance comparisons. Using the BSS dataset, the proposed method achieves 76.686 % IOU and 86.6 % Dice scores, outperforming state-of-the-art methods. Additionally, the proposed method achieves a Dice gain of 8.372 % compared with U-Net on the PAIP2019 dataset. The thesis presents three approaches to segmenting cancers from histology images: fully-supervised, unsupervised, and weakly supervised methods. This research effectively segments tumour regions based on histopathological annotations and well-designed modules. Our studies comprehensively demonstrate label-effective automatic histopathological image segmentation. Experimental results prove that our works achieve state-of-the-art segmentation performances on private and public datasets. In the future, we plan to integrate more tumour feature representation technologies with other medical modalities and apply them to clinical research

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    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

    Complexity Science in Human Change

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    This reprint encompasses fourteen contributions that offer avenues towards a better understanding of complex systems in human behavior. The phenomena studied here are generally pattern formation processes that originate in social interaction and psychotherapy. Several accounts are also given of the coordination in body movements and in physiological, neuronal and linguistic processes. A common denominator of such pattern formation is that complexity and entropy of the respective systems become reduced spontaneously, which is the hallmark of self-organization. The various methodological approaches of how to model such processes are presented in some detail. Results from the various methods are systematically compared and discussed. Among these approaches are algorithms for the quantification of synchrony by cross-correlational statistics, surrogate control procedures, recurrence mapping and network models.This volume offers an informative and sophisticated resource for scholars of human change, and as well for students at advanced levels, from graduate to post-doctoral. The reprint is multidisciplinary in nature, binding together the fields of medicine, psychology, physics, and neuroscience

    The Forward Physics Facility at the High-Luminosity LHC

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    Vitalism and Its Legacy in Twentieth Century Life Sciences and Philosophy

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    This Open Access book combines philosophical and historical analysis of various forms of alternatives to mechanism and mechanistic explanation, focusing on the 19th century to the present. It addresses vitalism, organicism and responses to materialism and its relevance to current biological science. In doing so, it promotes dialogue and discussion about the historical and philosophical importance of vitalism and other non-mechanistic conceptions of life. It points towards the integration of genomic science into the broader history of biology. It details a broad engagement with a variety of nineteenth, twentieth and twenty-first century vitalisms and conceptions of life. In addition, it discusses important threads in the history of concepts in the United States and Europe, including charting new reception histories in eastern and south-eastern Europe. While vitalism, organicism and similar epistemologies are often the concern of specialists in the history and philosophy of biology and of historians of ideas, the range of the contributions as well as the geographical and temporal scope of the volume allows for it to appeal to the historian of science and the historian of biology generally

    Science and Innovations for Food Systems Transformation

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    This Open Access book compiles the findings of the Scientific Group of the United Nations Food Systems Summit 2021 and its research partners. The Scientific Group was an independent group of 28 food systems scientists from all over the world with a mandate from the Deputy Secretary-General of the United Nations. The chapters provide science- and research-based, state-of-the-art, solution-oriented knowledge and evidence to inform the transformation of contemporary food systems in order to achieve more sustainable, equitable and resilient systems

    Quantum coherent manipulation of spin information in molecular nanomagnets

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    Los sistemas cuánticos de dos niveles basados en estados de espín, conocidos como ``qubits de espín'', son bloques prometedores para el desarrollo de tecnologías cuánticas. Entre las distintas plataformas físicas, los qubits de espín definidos en imanes de molécula única (SMM) son candidatos prometedores porque su estructura electrónica puede ajustarse fácilmente mediante ingeniería química (es decir, el Hamiltoniano de espín molecular puede modificarse con facilidad). Sin embargo, los qubits moleculares de espín generados en SMM se enfrentan a varios retos: coherencia cuántica frágil, control coherente insuficiente de los estados de espín y generación de entrelazamiento entre los qubits de espín para aplicaciones de procesamiento de información cuántica. Para abordar estos retos y lograr la manipulación coherente de la información de espín, necesitamos comprender la relación entre los estados de espín, los movimientos moleculares (vibraciones o fonones) y la polarización de la carga (por ejemplo, la generada por un campo E externo). La presente Tesis explora la relación entre los estados de espín, las vibraciones y la polarización desde una perspectiva teórica. Inicialmente, estudiamos la interacción entre los estados de espín y las vibraciones (acoplamiento vibrónico) como una fuente importante de disipación de información de espín. En particular, se emplea un modelado detallado de los acoplamientos vibrónicos, apoyado por pruebas experimentales, para descifrar las vías de decoherencia en diferentes SMM. Nuestros resultados revelan que sólo algunas distorsiones moleculares asociadas a determinados modos vibracionales son capaces de acoplarse fuertemente a grados de libertad de espín y, por tanto, promover la decoherencia. Además, también identificamos que los espectros dispersos entre los estados de espín y fonón son cruciales para preservar las superposiciones cuánticas durante más tiempo. En segundo lugar, presentamos un estudio exhaustivo del control coherente de los estados de espín mediante campos eléctricos en un sistema qubit molecular que presenta transiciones de reloj (HoW10). Este control coherente se modela evaluando el acoplamiento espín-eléctrico (SEC); es decir, encontrando una relación entre los estados de espín, la polarización de la carga y las distorsiones moleculares. El fuerte SEC observado en HoW10 es suficiente para permitir el direccionamiento selectivo de los espines mediante un campo E local a nivel práctico. Por último, exploramos la posibilidad de construir una puerta de entrelazamiento de dos qubits en un par de dos reloj-qubit acoplados dipolarmente (HoW10--HoW10), donde el campo eléctrico se utiliza para controlar localmente los estados de los qubits. El trabajo presentado en esta Tesis avanza en la comprensión de los qubits de espín moleculares para su potencial aplicación en el procesamiento cuántico de la información.Quantum two-level systems based on spin states known as ``spin-qubits’’ are promising building blocks for the development of quantum technologies. Among different physical platforms, spin-qubits defined in single-molecule-magnets (SMMs) are promising candidates because their electronic structure can be easily tuned by chemical engineering (i.e., the molecular spin Hamiltonian can be easily modified). However, molecular spin qubits generated in SMMs faces several challenges: fragile quantum coherence, insufficient coherent control over spin states and generation of entanglement between the spin-qubits for quantum information processing applications. To address these challenges and achieve the coherent manipulation of spin information, we need to understand the relationship between spin states, molecular motions (vibrations or phonons) and charge polarization (e.g., that generated by an external E-field). The current Thesis explores the relationship between spin states, vibrations and polarization from a theoretical perspective. Initially, we study the interaction between spin states and vibrations (vibronic coupling) as an important source of spin information dissipation. In particular, a detailed modelling of vibronic couplings, supported by experimental evidence, is employed to decipher the decoherence pathways in different SMMs. Our outcomes reveal that only some molecular distortions associated to certain vibrational modes are able to strongly couple to spin degrees of freedom and, thus, promoting decoherence. Additionally, we also identified that sparse spectra between spin and phonon states are crucial to preserve quantum superpositions longer times. Secondly, we present a comprehensive study of coherent control over spin states using electrical fields in a molecular qubit system that exhibits clock transitions (HoW10). This coherent control is modelled by evaluating the spin-electric coupling (SEC); that is, finding a relation between spin states, charge polarization, and molecular distortions. The strong SEC observed in HoW10 is sufficient to allow selective addressing of the spins using a local E-field at practical level. Finally, we explore the possibility of constructing two-qubit entanglement gate in a pair of two dipolar-coupled clock-qubit (HoW10--HoW10), where electrical field is used to locally control the qubit states. The work presented in this Thesis advances the understanding of molecular spin qubits for their potential application in quantum information processing

    Cyberbullying in educational context

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    Kustenmacher and Seiwert (2004) explain a man’s inclination to resort to technology in his interaction with the environment and society. Thus, the solution to the negative consequences of Cyberbullying in a technologically dominated society is represented by technology as part of the technological paradox (Tugui, 2009), in which man has a dual role, both slave and master, in the interaction with it. In this respect, it is noted that, notably after 2010, there have been many attempts to involve artificial intelligence (AI) to recognize, identify, limit or avoid the manifestation of aggressive behaviours of the CBB type. For an overview of the use of artificial intelligence in solving various problems related to CBB, we extracted works from the Scopus database that respond to the criterion of the existence of the words “cyberbullying” and “artificial intelligence” in the Title, Keywords and Abstract. These articles were the subject of the content analysis of the title and, subsequently, only those that are identified as a solution in the process of recognizing, identifying, limiting or avoiding the manifestation of CBB were kept in the following Table where we have these data synthesized and organized by years

    FeIDo: Recoverable FIDO2 Tokens Using Electronic IDs (Extended Version)

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    Two-factor authentication (2FA) mitigates the security risks of passwords as sole authentication factor. FIDO2---the de facto standard for interoperable web authentication---leverages strong, hardware-backed second factors. However, practical challenges hinder wider FIDO2 user adoption for 2FA tokens, such as the extra costs (2020-30 per token) or the risk of inaccessible accounts upon token loss/theft. To tackle the above challenges, we propose FeIDo, a virtual FIDO2 token that combines the security and interoperability of FIDO2 2FA authentication with the prevalence of existing eIDs (e.g., electronic passports). Our core idea is to derive FIDO2 credentials based on personally-identifying and verifiable attributes---name, date of birth, and place of birth---that we obtain from the user's eID. As these attributes do not change even for refreshed eID documents, the credentials "survive" token loss. Even though FeIDo operates on privacy-critical data, all personal data and resulting FIDO2 credentials stay unlinkable, are never leaked to third parties, and are securely managed in attestable hardware containers (e.g., SGX enclaves). In contrast to existing FIDO2 tokens, FeIDo can also derive and share verifiable meta attributes (anonymous credentials) with web services. These enable verified but pseudonymous user checks, e.g., for age verification (e.g., "is adult")
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