2,987 research outputs found

    Multi-modal and multi-dimensional biomedical image data analysis using deep learning

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    There is a growing need for the development of computational methods and tools for automated, objective, and quantitative analysis of biomedical signal and image data to facilitate disease and treatment monitoring, early diagnosis, and scientific discovery. Recent advances in artificial intelligence and machine learning, particularly in deep learning, have revolutionized computer vision and image analysis for many application areas. While processing of non-biomedical signal, image, and video data using deep learning methods has been very successful, high-stakes biomedical applications present unique challenges such as different image modalities, limited training data, need for explainability and interpretability etc. that need to be addressed. In this dissertation, we developed novel, explainable, and attention-based deep learning frameworks for objective, automated, and quantitative analysis of biomedical signal, image, and video data. The proposed solutions involve multi-scale signal analysis for oraldiadochokinesis studies; ensemble of deep learning cascades using global soft attention mechanisms for segmentation of meningeal vascular networks in confocal microscopy; spatial attention and spatio-temporal data fusion for detection of rare and short-term video events in laryngeal endoscopy videos; and a novel discrete Fourier transform driven class activation map for explainable-AI and weakly-supervised object localization and segmentation for detailed vocal fold motion analysis using laryngeal endoscopy videos. Experiments conducted on the proposed methods showed robust and promising results towards automated, objective, and quantitative analysis of biomedical data, that is of great value for potential early diagnosis and effective disease progress or treatment monitoring.Includes bibliographical references

    From Elastic Deformation to Terminal Flow of a Monodisperse Entangled Melt in Uniaxial Extension

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    Using a well-entangled monodisperse styrene-butadiene random-copolymer (SBR) melt as a model system, we illustrate generic features of uniaxial extension behavior that may be shared by all well-entangled thermoplastic and elastomeric materials. Depending on the imposed extensional rate, the same sample may behave like a viscous liquid or an elastic solid. Analogous to the recently revealed shear inhomogeneity, the SBR melt inevitably undergoes cohesive failure in the form of sample breakage whenever the Weissenberg number is much greater than unity, making it challenging to reach steady state. In the elastic deformation regime where the external deformation rate is faster than Rouse relaxation rate, the sample undergoes a finite amount of uniform stretching before yielding occurs in a period much shorter than the terminal relaxation time. Steady flow can be achieved only in the terminal regime where entangled chains utilize directed molecular diffusion to achieve rearrangement and enable uniform flow

    Integer colorings with forbidden rainbow sums

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    For a set of positive integers A⊆[n]A \subseteq [n], an rr-coloring of AA is rainbow sum-free if it contains no rainbow Schur triple. In this paper we initiate the study of the rainbow Erd\H{o}s-Rothchild problem in the context of sum-free sets, which asks for the subsets of [n][n] with the maximum number of rainbow sum-free rr-colorings. We show that for r=3r=3, the interval [n][n] is optimal, while for r≥8r\geq8, the set [⌊n/2⌋,n][\lfloor n/2 \rfloor, n] is optimal. We also prove a stability theorem for r≥4r\geq4. The proofs rely on the hypergraph container method, and some ad-hoc stability analysis.Comment: 20 page

    Exploring Stress Overshoot Phenomenon Upon Startup Deformation of Entangled Linear Polymeric Liquids

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    This work explores the picture associated with stress overshoot during sudden continual (i.e., startup) external deformation of entangled polymeric liquids and proposes a specific scaling form to depict the intermolecular interactions responsible for chain deformation. Following a previously proposed idea that the stress overshoot in startup deformation is a signature of yielding, we search for ingredients that should go into the description of the force imbalance at the yield point and show that the expression for the intermolecular locking force f(iml), derived from the characteristics associated with the yield point, can be tested against experiment. New rate-switching experiments support the proposed formula for fiml. (C) 2009 The Society of Rheology. [DOI: 10.1122/1.3208063

    Mixed mode oscillations in three-timescale coupled Morris-Lecar neurons

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