Foreword

Foreword by the Provost and Senior Vice President for Academic Affairs, Cleveland State Universit

Foreword

Foreword by the Provost and Senior Vice President for Academic Affairs, Cleveland State Universit

Bounded-Distortion Metric Learning

Metric learning aims to embed one metric space into another to benefit tasks like classification and clustering. Although a greatly distorted metric space has a high degree of freedom to fit training data, it is prone to overfitting and numerical inaccuracy. This paper presents {\it bounded-distortion metric learning} (BDML), a new metric learning framework which amounts to finding an optimal Mahalanobis metric space with a bounded-distortion constraint. An efficient solver based on the multiplicative weights update method is proposed. Moreover, we generalize BDML to pseudo-metric learning and devise the semidefinite relaxation and a randomized algorithm to approximately solve it. We further provide theoretical analysis to show that distortion is a key ingredient for stability and generalization ability of our BDML algorithm. Extensive experiments on several benchmark datasets yield promising results

FAS-UNet: A Novel FAS-driven Unet to Learn Variational Image Segmentation

Solving variational image segmentation problems with hidden physics is often expensive and requires different algorithms and manually tunes model parameter. The deep learning methods based on the U-Net structure have obtained outstanding performances in many different medical image segmentation tasks, but designing such networks requires a lot of parameters and training data, not always available for practical problems. In this paper, inspired by traditional multi-phase convexity Mumford-Shah variational model and full approximation scheme (FAS) solving the nonlinear systems, we propose a novel variational-model-informed network (denoted as FAS-Unet) that exploits the model and algorithm priors to extract the multi-scale features. The proposed model-informed network integrates image data and mathematical models, and implements them through learning a few convolution kernels. Based on the variational theory and FAS algorithm, we first design a feature extraction sub-network (FAS-Solution module) to solve the model-driven nonlinear systems, where a skip-connection is employed to fuse the multi-scale features. Secondly, we further design a convolution block to fuse the extracted features from the previous stage, resulting in the final segmentation possibility. Experimental results on three different medical image segmentation tasks show that the proposed FAS-Unet is very competitive with other state-of-the-art methods in qualitative, quantitative and model complexity evaluations. Moreover, it may also be possible to train specialized network architectures that automatically satisfy some of the mathematical and physical laws in other image problems for better accuracy, faster training and improved generalization.The code is available at \url{https://github.com/zhuhui100/FASUNet}.Comment: 18 page

Coupled Multiple Kernel Learning for Supervised Classification

Multiple kernel learning (MKL) has recently received significant attention due to the fact that it is able to automatically fuse information embedded in multiple base kernels and then find a new kernel for classification or regression. In this paper, we propose a coupled multiple kernel learning method for supervised classification (CMKL-C), which comprehensively involves the intra-coupling within each kernel, inter-coupling among different kernels and coupling between target labels and real ones in MKL. Specifically, the intra-coupling controls the class distribution in a kernel space, the inter-coupling captures the co-information of base kernel matrices, and the last type of coupling determines whether the new learned kernel can make a correct decision. Furthermore, we deduce the analytical solutions to solve the CMKL-C optimization problem for highly efficient learning. Experimental results over eight UCI data sets and three bioinformatics data sets demonstrate the superior performance of CMKL-C in terms of the classification accuracy

Pediatric intestinal transplant: postoperative management and immunosuppressive agents

Trabajo fin de grado en EnfermeríaIntroducción: desde finales de los años 60 del siglo XX es posible cubrir la función intestinal a través de la nutrición parenteral (NP). Debido a las complicaciones asociadas a este tipo de nutrición, en ocasiones se hace necesaria la realización del trasplante intestinal (TI). Las innovaciones técnicas, los nuevos protocolos de inmunosupresión y un mejor manejo postoperatorio han permitido que el TI sea una opción terapéutica viable. Objetivo: estudiar la literatura publicada sobre los protocolos actuales de inmunosupresión utilizados en el trasplante intestinal pediátrico. Metodología: se realizó una revisión narrativa utilizando las bases de datos PubMed, Cuiden y Cinhal, además de consultar libros de ciencias de la salud y diversas asociaciones y entidades públicas. Se establecieron los criterios de inclusión y exclusión. Se realizaron las búsquedas bibliográficas utilizando lenguaje libre y controlado, combinando los términos mediante operadores booleanos. Resultados: se seleccionaron 21 artículos cuyo contenido se expuso en base a varios apartados, que comprenden (1) terapia inmunosupresora, (2) protocolos, (3) monitorización del rechazo, (4) complicaciones por infección y (5) enfermedad linfoproliferativa. Conclusiones: el TI solo ha sido posible gracias a la introducción de fármacos inmunosupresores. La atención a este tipo de pacientes, es un reto, que abre puertas a nuevos campos de actuación para la enfermería y permite un mayor desarrollo de nuestra profesión.Introduction: since the end of the years 60 of the twentieth century it is possible to cover the intestinal function through parenteral nutrition (NP). Due to the complications associated with this type of nutrition, it is sometimes necessary to carry out the intestinal transplant (TI). Technical innovations, new immunosuppression protocols, and improved postoperative management have allowed it to be a viable therapeutic option. Objective: to study the published literature on the current immunosuppression protocols used in pediatric intestinal transplantation. Methodology: a narrative review was carried out using the PubMed, Cuiden and Cinhal databases, in addition to consulting health Sciences books and various associations and public entities. Inclusion and exclusion criteria were established. Terms of free and controlled language were used for searches in combination with boolean operators. Results: 21 articles were chosen which content was detailed in four epigraphs comprising: (1) immunosuppressive therapy, (2) protocols, (3) Rejection monitoring, (4) Infection Complications and (5) Lymphoproliferative disease. Conclusions: TI alone has been made possible by the introduction of immunosuppressive drugs. The attention to this type of patients, is a challenge, that opens doors to new fields of action for the nursing and allows a further development of our profession

An Analysis of LED Light Distribution Based on Visual Spectral Characteristics

AbstractOn the analysis of the human visual structure characteristics and LED optical design principle, human visual color image model with background light was constructed in this paper, and the image sharpness function is defined. With high pressure sodium lamps, white light and green light LED as backlight, the model simulation of image sharpness is fulfilled. The results show that the green LED has better clarity and sensitivity with the same condition of radiation energy background light