Response of exact solutions of the nonlinear Schrodinger equation to small perturbations in a class of complex external potentials having supersymmetry and parity-time symmetry

We discuss the effect of small perturbation on nodeless solutions of the nonlinear \Schrodinger\ equation in 1+1 dimensions in an external complex potential derivable from a parity-time symmetric superpotential that was considered earlier [Phys.~Rev.~E 92, 042901 (2015)]. In particular we consider the nonlinear partial differential equation \{ \, \rmi \, \partial_t + \partial_x^2 + g |\psi(x,t)|^2 - V^{+}(x) \, \} \, \psi(x,t) = 0, where V^{+}(x) = \qty( -b^2 - m^2 + 1/4 ) \, \sech^2(x) - 2 i \, m \, b \, \sech(x) \, \tanh(x) represents the complex potential. Here we study the perturbations as a function of $b$ and $m$ using a variational approximation based on a dissipation functional formalism. We compare the result of this variational approach with direct numerical simulation of the equations. We find that the variational approximation works quite well at small and moderate values of the parameter $b m$ which controls the strength of the imaginary part of the potential. We also show that the dissipation functional formalism is equivalent to the generalized traveling wave method for this type of dissipation.Comment: 18 pages, 6 figure

Multimodal representation learning with neural networks

Abstract: Representation learning methods have received a lot of attention by researchers and practitioners because of their successful application to complex problems in areas such as computer vision, speech recognition and text processing [1]. Many of these promising results are due to the development of methods to automatically learn the representation of complex objects directly from large amounts of sample data [2]. These efforts have concentrated on data involving one type of information (images, text, speech, etc.), despite data being naturally multimodal. Multimodality refers to the fact that the same real-world concept can be described by different views or data types. Addressing multimodal automatic analysis faces three main challenges: feature learning and extraction, modeling of relationships between data modalities and scalability to large multimodal collections [3, 4]. This research considers the problem of leveraging multiple sources of information or data modalities in neural networks. It defines a novel model called gated multimodal unit (GMU), designed as an internal unit in a neural network architecture whose purpose is to find an intermediate representation based on a combination of data from different modalities. The GMU learns to decide how modalities influence the activation of the unit using multiplicative gates. The GMU can be used as a building block for different kinds of neural networks and can be seen as a form of intermediate fusion. The model was evaluated on four supervised learning tasks in conjunction with fully-connected and convolutional neural networks. We compare the GMU with other early and late fusion methods, outperforming classification scores in the evaluated datasets. Strategies to understand how the model gives importance to each input were also explored. By measuring correlation between gate activations and predictions, we were able to associate modalities with classes. It was found that some classes were more correlated with some particular modality. Interesting findings in genre prediction show, for instance, that the model associates the visual information with animation movies while textual information is more associated with drama or romance movies. During the development of this project, three new benchmark datasets were built and publicly released. The BCDR-F03 dataset which contains 736 mammography images and serves as benchmark for mass lesion classification. The MM-IMDb dataset containing around 27000 movie plots, poster along with 50 metadata annotations and that motivates new research in multimodal analysis. And the Goodreads dataset, a collection of 1000 books that encourages the research on success prediction based on the book content. This research also facilitates reproducibility of the present work by releasing source code implementation of the proposed methods.Doctorad

Representation learning for histopathology image analysis

Abstract. Nowadays, automatic methods for image representation and analysis have been successfully applied in several medical imaging problems leading to the emergence of novel research areas like digital pathology and bioimage informatics. The main challenge of these methods is to deal with the high visual variability of biological structures present in the images, which increases the semantic gap between their visual appearance and their high level meaning. Particularly, the visual variability in histopathology images is also related to the noise added by acquisition stages such as magnification, sectioning and staining, among others. Many efforts have focused on the careful selection of the image representations to capture such variability. This approach requires expert knowledge as well as hand-engineered design to build good feature detectors that represent the relevant visual information. Current approaches in classical computer vision tasks have replaced such design by the inclusion of the image representation as a new learning stage called representation learning. This paradigm has outperformed the state-of-the-art results in many pattern recognition tasks like speech recognition, object detection, and image scene classification. The aim of this research was to explore and define a learning-based histopathology image representation strategy with interpretative capabilities. The main contribution was a novel approach to learn the image representation for cancer detection. The proposed approach learns the representation directly from a Basal-cell carcinoma image collection in an unsupervised way and was extended to extract more complex features from low-level representations. Additionally, this research proposed the digital staining module, a complementary interpretability stage to support diagnosis through a visual identification of discriminant and semantic features. Experimental results showed a performance of 92% in F-Score, improving the state-of-the-art representation by 7%. This research concluded that representation learning improves the feature detectors generalization as well as the performance for the basal cell carcinoma detection task. As additional contributions, a bag of features image representation was extended and evaluated for Alzheimer detection, obtaining 95% in terms of equal error classification rate. Also, a novel perspective to learn morphometric measures in cervical cells based on bag of features was presented and evaluated obtaining promising results to predict nuclei and cytoplasm areas.Los métodos automáticos para la representación y análisis de imágenes se han aplicado con éxito en varios problemas de imagen médica que conducen a la aparición de nuevas áreas de investigación como la patología digital. El principal desafío de estos métodos es hacer frente a la alta variabilidad visual de las estructuras biológicas presentes en las imágenes, lo que aumenta el vacío semántico entre su apariencia visual y su significado de alto nivel. Particularmente, la variabilidad visual en imágenes de histopatología también está relacionada con el ruido añadido por etapas de adquisición tales como magnificación, corte y tinción entre otros. Muchos esfuerzos se han centrado en la selección de la representacion de las imágenes para capturar dicha variabilidad. Este enfoque requiere el conocimiento de expertos y el diseño de ingeniería para construir buenos detectores de características que representen la información visual relevante. Los enfoques actuales en tareas de visión por computador han reemplazado ese diseño por la inclusión de la representación en la etapa de aprendizaje. Este paradigma ha superado los resultados del estado del arte en muchas de las tareas de reconocimiento de patrones tales como el reconocimiento de voz, la detección de objetos y la clasificación de imágenes. El objetivo de esta investigación es explorar y definir una estrategia basada en el aprendizaje de la representación para imágenes histopatológicas con capacidades interpretativas. La contribución principal de este trabajo es un enfoque novedoso para aprender la representación de la imagen para la detección de cáncer. El enfoque propuesto aprende la representación directamente de una colección de imágenes de carcinoma basocelular en forma no supervisada que permite extraer características más complejas a partir de las representaciones de bajo nivel. También se propone el módulo de tinción digital, una nueva etapa de interpretabilidad para apoyar el diagnóstico a través de una identificación visual de las funciones discriminantes y semánticas. Los resultados experimentales mostraron un rendimiento del 92% en términos de F-Score, mejorando la representación del estado del arte en un 7%. Esta investigación concluye que el aprendizaje de la representación mejora la generalización de los detectores de características así como el desempeño en la detección de carcinoma basocelular. Como contribuciones adicionales, una representación de bolsa de caracteristicas (BdC) fue ampliado y evaluado para la detección de la enfermedad de Alzheimer, obteniendo un 95% en términos de EER. Además, una nueva perspectiva para aprender medidas morfométricas en las células del cuello uterino basado en BdC fue presentada y evaluada obteniendo resultados prometedores para predecir las areás del nucleo y el citoplasma.Maestrí

A Novel Machine Learning Model Based on Exudate Localization to Detect Diabetic Macular Edema

Diabetic macular edema is one of the leading causes of legal blindness worldwide. Early, and accessible, detection of ophthalmological diseases is especially important in developing countries, where there are major limitations to access to specialized medical diagnosis and treatment. Deep learning models, such as deep convolutional neural networks have shown great success in different computer vision tasks. In medical images they have been also applied with great success. The present paper presents a novel strategy based on convolutional neural networks to combine exudates localization and eye fundus images for automatic classification of diabetic macular edema as a support for diabetic retinopathy diagnosis

A Scoping Review of Injuries in Amateur and Professional Men\u27s Ice Hockey.

Background: Orthopaedic injuries are common in ice hockey at all levels and can result in physical and psychological adverse effects on these athletes. Purpose: Primarily, to summarize published data on orthopaedic hockey injuries at the junior through professional level. Secondarily, to characterize the literature based on anatomic site injured, return-to-play rates, cause/mechanism of injury, time lost, and treatments used. Study Design: Scoping review; Level of evidence, 4. Methods: PubMed, EMBASE, Cochrane library, and SCOPUS were searched using the terms hockey and injuries using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, and 4163 studies involving orthopaedic injuries were identified. Our inclusion criteria consisted of accessible full-text articles that evaluated orthopaedic injuries in men\u27s ice hockey athletes of all levels. We excluded case reports and articles evaluating women\u27s ice hockey injuries, as well as those evaluating nonorthopaedic injuries, such as concussions; traumatic brain injuries; and facial, dental, and vascular injuries, among others. Studies were divided based on level of play and anatomic site of injury. Level of evidence, year published, country of corresponding author, method of data collection, incidence of injury per athlete-exposure, and time lost were extracted from each article. Results: A total of 92 articles met the inclusion criteria and were performed between 1975 and 2020, with the majority published between 2015 and 2020. These were divided into 8 anatomic sites: nonanatomic-specific (37%), intra-articular hip (20.7%), shoulder (9.8%), knee (8.7%), trunk/pelvis (7.6%), spine (7.6%), foot/ankle (6.5%), and hand/wrist (2.2%). Of these studies, 71% were level 4 evidence. Data were obtained mostly via surveillance programs and searches of publicly available information (eg, injury reports, player profiles, and press releases). Conclusion: This scoping review provides men\u27s hockey players and physicians taking care of elite ice hockey athletes of all levels with a single source of the most current literature regarding orthopaedic injuries. Most research focused on nonanatomic-specific injuries, intra-articular hip injuries, knee injuries, and shoulder injuries, with the majority having level 4 evidence

Sulfur and Metal Fertilization of the Lower Continental Crust

Mantle-derived melts and metasomatic fluids are considered to be important in the transport and distribution of trace elements in the subcontinental lithospheric mantle. However, the mechanisms that facilitate sulfur and metal transfer from the upper mantle into the lower continental crust are poorly constrained. This study addresses this knowledge gap by examining a series of sulfide- and hydrous mineral-rich alkaline mafic-ultramafic pipes that intruded the lower continental crust of the Ivrea-Verbano Zone in the Italian Western Alps. The pipes are relatively small (<300 m diameter) and primarily composed of a matrix of subhedral to anhedral amphibole (pargasite), phlogopite and orthopyroxene that enclose sub-centimeter-sized grains of olivine. The 1 to 5 m wide rim portions of the pipes locally contain significant blebby and disseminated Fe-Ni-Cu-PGE sulfide mineralization.Stratigraphic relationships, mineral chemistry, geochemical modeling and phase equilibria suggest that the pipes represent open-ended conduits within a large magmatic plumbing system. The earliest formed pipe rocks were olivine-rich cumulates that reacted with hydrous melts to produce orthopyroxene, amphibole and phlogopite.Sulfides precipitated as immiscible liquid droplets that were retained within a matrix of silicate crystals and scavenged metals from the percolating hydrous melt. New high-precision chemical abrasion TIMS-UPb dating of zircons from one of the pipes indicates that these pipes were emplaced at 249.1+/-0.2 Ma, following partial melting of lithospheric mantle pods that were metasomatized during the Eo-Variscan oceanic to continental subduction (approx. 420-310 Ma). The thermal energy required to generate partial melting of the metasomatized mantle was most likely derived from crustal extension, lithospheric decompression and subsequent asthenospheric rise during the orogenic collapse of the Variscan belt (<300 Ma). Unlike previous models, outcomes from this study suggest a significant temporal gap between the occurrence of mantle metasomatism, subsequent partial melting and emplacement of the pipes.We argue that this multi-stage process is a very effective mechanism to fertilize the commonly dry and refractory lower continental crust in metals and volatiles. During the four-dimensional evolution of the thermo-tectonic architecture of any given terrain, metals and volatiles stored in the lower continental crust may become available as sources for subsequent ore-forming processes, thus enhancing the prospectivity of continental block margins for a wide range of mineral systems

El sistema de pago de obligaciones tributarias con el gobierno central (spot) en la empresa PET CT Perú S. A., Miraflores - Lima, 2020

El objetivo de la investigación fue analizar de qué manera el sistema de pago de obligaciones tributarias beneficia a la empresa PET CT Perú S.A., Miraflores – Lima 2020. La metodología aplicada para realizar la investigación se enmarca en una investigación cualitativa, con un tipo de diseño no experimental, con alcance descriptivo. La presente investigación se realizó a través de una entrevista, análisis documental y adicionalmente la técnica de observación. De su empleo, se pudo conocer el ámbito de aplicación y el cumplimiento oportuno del sistema, obteniendo como resultado, que se realiza una correcta aplicación del sistema por parte de la empresa y de sus clientes; se pudo conocer el destino de los depósitos de la cuenta de detracciones, determinándose que la empresa cumple casi con la totalidad de sus obligaciones tributarias con esta cuenta; asimismo, se identificó que la empresa no ha solicitado la liberación de fondos en el periodo investigado. Se llego a la conclusión, para que el sistema beneficie a la empresa, este se debe aplicar correctamente, tanto por parte del cliente como del propio contribuyente, de esta manera se contará con el respaldo para hacer frente a las obligaciones tributarias de la empresa

Social regulation of gene expression in human leukocytes

Analysis of differentially expressed in circulating leukocytes from people who chronically experienced high versus low levels of subjective social isolation (loneliness) revealed over-expression of some anti-inflammatory genes and under-expression of some pro-inflammatory genes

Representación de imágenes de histopatología utilizada en tareas de análisis automático: estado del arte

This paper presents a review of the state-of-the-art in histopathology image representation used in automatic image analysis tasks. Automatic analysis of histopathology images is important for building computer-assisted diagnosis tools, automatic image enhancing systems and virtual microscopy systems, among other applications. Histopathology images have a rich mix of visual patterns with particularities that make them difficult to analyze. The paper discusses these particularities, the acquisition process and the challenges found when doing automatic analysis. Second an overview of recent works and methods addressed to deal with visual content representation in different automatic image analysis tasks is presented. Third an overview of applications of image representation methods in several medical domains and tasks is presented. Finally, the paper concludes with current trends of automatic analysis of histopathology images like digital pathology