Conceptos Plasticos

https://openscholarship.wustl.edu/bcs/1486/thumbnail.jp

Financial Crisis, Health Outcomes and Aging: Mexico in the 1980s and 1990s

We study the impact of economic crisis on health in Mexico. There have been four wide-scale economic crises in Mexico in the past two decades, the most recent in 1995-96. We find that mortality rates for the very young and the elderly increase or decline less rapidly in crisis years as compared with non-crisis years. In late 1995-96 crisis, mortality rates were about 5 to 7 percent higher in the crisis years compared to the years just prior to the crisis. This translates into a 0.4 percent increase in mortality for the elderly and a 0.06 percent increase in mortality for the very young. We find tentative evidence that economic crises affect mortality by reducing incomes and possibly by placing a greater burden on the medical sector, but not by forcing less healthy members of the population to work or by forcing primary caregivers to go to work.

AFT-VO: Asynchronous Fusion Transformers for Multi-View Visual Odometry Estimation

Motion estimation approaches typically employ sensor fusion techniques, such as the Kalman Filter, to handle individual sensor failures. More recently, deep learning-based fusion approaches have been proposed, increasing the performance and requiring less model-specific implementations. However, current deep fusion approaches often assume that sensors are synchronised, which is not always practical, especially for low-cost hardware. To address this limitation, in this work, we propose AFT-VO, a novel transformer-based sensor fusion architecture to estimate VO from multiple sensors. Our framework combines predictions from asynchronous multi-view cameras and accounts for the time discrepancies of measurements coming from different sources. Our approach first employs a Mixture Density Network (MDN) to estimate the probability distributions of the 6-DoF poses for every camera in the system. Then a novel transformer-based fusion module, AFT-VO, is introduced, which combines these asynchronous pose estimations, along with their confidences. More specifically, we introduce Discretiser and Source Encoding techniques which enable the fusion of multi-source asynchronous signals. We evaluate our approach on the popular nuScenes and KITTI datasets. Our experiments demonstrate that multi-view fusion for VO estimation provides robust and accurate trajectories, outperforming the state of the art in both challenging weather and lighting conditions

MDN-VO: Estimating Visual Odometry with Confidence

Visual Odometry (VO) is used in many applications including robotics and autonomous systems. However, traditional approaches based on feature matching are computationally expensive and do not directly address failure cases, instead relying on heuristic methods to detect failure. In this work, we propose a deep learning-based VO model to efficiently estimate 6-DoF poses, as well as a confidence model for these estimates. We utilise a CNN - RNN hybrid model to learn feature representations from image sequences. We then employ a Mixture Density Network (MDN) which estimates camera motion as a mixture of Gaussians, based on the extracted spatio-temporal representations. Our model uses pose labels as a source of supervision, but derives uncertainties in an unsupervised manner. We evaluate the proposed model on the KITTI and nuScenes datasets and report extensive quantitative and qualitative results to analyse the performance of both pose and uncertainty estimation. Our experiments show that the proposed model exceeds state-of-the-art performance in addition to detecting failure cases using the predicted pose uncertainty

Generalizing to New Tasks via One-Shot Compositional Subgoals

The ability to generalize to previously unseen tasks with little to no supervision is a key challenge in modern machine learning research. It is also a cornerstone of a future "General AI". Any artificially intelligent agent deployed in a real world application, must adapt on the fly to unknown environments. Researchers often rely on reinforcement and imitation learning to provide online adaptation to new tasks, through trial and error learning. However, this can be challenging for complex tasks which require many timesteps or large numbers of subtasks to complete. These "long horizon" tasks suffer from sample inefficiency and can require extremely long training times before the agent can learn to perform the necessary longterm planning. In this work, we introduce CASE which attempts to address these issues by training an Imitation Learning agent using adaptive "near future" subgoals. These subgoals are recalculated at each step using compositional arithmetic in a learned latent representation space. In addition to improving learning efficiency for standard long-term tasks, this approach also makes it possible to perform one-shot generalization to previously unseen tasks, given only a single reference trajectory for the task in a different environment. Our experiments show that the proposed approach consistently outperforms the previous state-of-the-art compositional Imitation Learning approach by 30%.Comment: Present at ICRA 2022 "Compositional Robotics: Mathematics and Tools

Rule of law versus soft rule of law

The power of the government rather than limiting it), human rights and democracy. Therefore, this paper will explore the following research questions: 1. Is the current macro legal framework in Peru and Ecuador consistent with what is generally defined as Rule of Law or is it more related to what is known as Soft Rule of Law? 2. To what extend does a theoretical analysis support the prevailing of a Soft Rule of Law in Ecuador and Peru? This paper has been divided into seven sections. Section one refers to a theoretical revision of the term Rule of Law, section two presents on the concept of soft rule of law, section three focuses on human rights as an essential element of the Rule of Law, section four explores the interaction between democracy and the Rule of Law, section five refers to the challenges faced by the Rule of Law in a consolidated democracy, section six presents, as an example, a brief reflection on the current problems of Peru and Ecuador and finally the paper’s conclusions are submitted.Campus Lima Centr

Rigorous diffraction of an electromagnetic beam by wavelength-size slits

A rigorous modal theory for the diffraction of Gaussian beams from N slits in an otherwise perfectly plane conducting screen (line finite-grating) is presented. The case of normal incidence and T.E.-polarization is considered, i.e. the electric field is parallel to the strips. The characteristics of the far-field radiation pattern as a function of the wavelength are analyzed, particularly within the vectorial region where the influence of polarization is more important. Furthermore, the influence of the beam width and beam alignment on the transmission coefficient, on the normally diffracted energy, and on the diffraction is studied

Teoría rigurosa de la dispersión de haces gaussianos por una rejilla con sustrato metálico

Se presenta una teoría rigurosa de la dispersión de haces Gaussianos a incidencia normal y oblicua por una rejilla finita en una pantalla conductora, de espesor cero y con sustrato metálico. El sustrato puede ser el vacío o un conductor. Se considera el caso de polarización T.E., es decir, el campo eléctrico es paralelo a las rendijas. Se analiza en la región vectorial de la difracción a patrones de dispersión en función de la longitud de onda, del hacho del haz y de la posición del haz. De estos resultados se ha encontrado que el ancho de los órdenes de dispersión aumenta con la relación λ/L, donde λ es la longitud de onda y L el ancho del haz incidente. Cuando el sustrato es un conductor la ecuación de dispersión por una rejilla en reflexión predice la posición angular de los órdenes dispersados de una rejilla finita con una buena aproximación. Además, la posición angular de estos órdenes es independiente del sustrato, del ancho y de la posición del haz

There and Back Again: Self-supervised Multispectral Correspondence Estimation

Across a wide range of applications, from autonomous vehicles to medical imaging, multi-spectral images provide an opportunity to extract additional information not present in color images. One of the most important steps in making this information readily available is the accurate estimation of dense correspondences between different spectra. Due to the nature of cross-spectral images, most correspondence solving techniques for the visual domain are simply not applicable. Furthermore, most cross-spectral techniques utilize spectra-specific characteristics to perform the alignment. In this work, we aim to address the dense correspondence estimation problem in a way that generalizes to more than one spectrum. We do this by introducing a novel cycle-consistency metric that allows us to self-supervise. This, combined with our spectra-agnostic loss functions, allows us to train the same network across multiple spectra. We demonstrate our approach on the challenging task of dense RGB-FIR correspondence estimation. We also show the performance of our unmodified network on the cases of RGB-NIR and RGB-RGB, where we achieve higher accuracy than similar self-supervised approaches. Our work shows that cross-spectral correspondence estimation can be solved in a common framework that learns to generalize alignment across spectra

Learning adaptive neighborhoods for graph neural networks

Graph convolutional networks (GCNs) enable end-to-end learning on graph structured data. However, many works assume a given graph structure. When the input graph is noisy or unavailable, one approach is to construct or learn a latent graph structure. These methods typically fix the choice of node degree for the entire graph, which is suboptimal. Instead, we propose a novel end-to-end differentiable graph generator which builds graph topologies where each node selects both its neighborhood and its size. Our module can be readily integrated into existing pipelines involving graph convolution operations, replacing the predetermined or existing adjacency matrix with one that is learned, and optimized, as part of the general objective. As such it is applicable to any GCN. We integrate our module into trajectory prediction, point cloud classification and node classification pipelines resulting in improved accuracy over other structure-learning methods across a wide range of datasets and GCN backbones. We will release the code