Learning how to be robust: Deep polynomial regression

Polynomial regression is a recurrent problem with a large number of applications. In computer vision it often appears in motion analysis. Whatever the application, standard methods for regression of polynomial models tend to deliver biased results when the input data is heavily contaminated by outliers. Moreover, the problem is even harder when outliers have strong structure. Departing from problem-tailored heuristics for robust estimation of parametric models, we explore deep convolutional neural networks. Our work aims to find a generic approach for training deep regression models without the explicit need of supervised annotation. We bypass the need for a tailored loss function on the regression parameters by attaching to our model a differentiable hard-wired decoder corresponding to the polynomial operation at hand. We demonstrate the value of our findings by comparing with standard robust regression methods. Furthermore, we demonstrate how to use such models for a real computer vision problem, i.e., video stabilization. The qualitative and quantitative experiments show that neural networks are able to learn robustness for general polynomial regression, with results that well overpass scores of traditional robust estimation methods.Comment: 18 pages, conferenc

Agricultura de terrazas en el cerro Tenismo, Toluca. México

Los campesinos mexicanos practican diversos agroecosistemas tradicionales, caso particular, las terrazas.Los campesinos mexicanos practican diversos agroecosistemas tradicionales, caso particular, las terrazas. Este sistema es uno de los más antiguos en el Valle de Toluca y actualmente se encuentra en proceso de abandono. El objetivo fue caracterizar el sistema de terrazas del cerro Tenismo en Calixtlahuaca, Toluca, México, destacando sus componentes principales: muros de contención, zanja, vegetación, cultivos. A partir de la investigación cualitativa, observación directa y la descripción del agroecosistema, se analizan los componentes de quince terrazas de la ladera media y ladera alta del cerro Tenismo. Los resultados indican que las terrazas de Calixtlahuaca con muro de roca y metepantles (semiterraza), son un sistema importante que contribuye a la conservación de las laderas y de donde se obtienen cultivos de autoconsumo. Se concluye que el manejo que realizan los campesinos por medio de las terrazas (muros, zanjas y vegetación) tiene características agroecológicas que contribuyen a la preservación del ambiente.Universidad Autónoma del Estado de México Colegio de Ciencias Geográficas del Estado de México AC

The catalytic Ornstein-Uhlenbeck process with superprocess catalyst

The main objective of this work is to study a natural class of catalytic Ornstein-Uhlenbeck (O-U) processes with a measure-valued random catalyst, for example, super-Brownian motion. We relate this to the class of affine processes that provides a unified setting in which to view Ornstein-Uhlenbeck processes, superprocesses, and Ornstein-Uhlenbeck processes with superprocess catalyst. We then review some basic properties of super-Brownian motion which we need and introduce the Ornstein-Uhlenbeck process with catalyst given by a superprocess. The main results are the affine characterization of the characteristic functional-Laplace transform of the joint catalytic O-U process and catalyst process and the identification of basic properties of the quenched and annealed versions of these processes.Comment: 23 page

Adaptive Resolution Loss: An Efficient and Effective Loss for Time Series Self-Supervised Learning Framework

Time series data is a crucial form of information that has vast opportunities. With the widespread use of sensor networks, largescale time series data has become ubiquitous. One of the most prominent problems in time series data mining is representation learning. Recently, with the introduction of self-supervised learning frameworks (SSL), numerous amounts of research have focused on designing an effective SSL for time series data. One of the current state-of-the-art SSL frameworks in time series is called TS2Vec. TS2Vec specially designs a hierarchical contrastive learning framework that uses loss-based training, which performs outstandingly against benchmark testing. However, the computational cost for TS2Vec is often significantly greater than other SSL frameworks. In this paper, we present a new self-supervised learning loss named, adaptive resolution loss. The proposed solution reduces the number of resolutions used for training the model via score functions, leading to an efficient adaptive resolution learning algorithm. The proposed method preserves the original model’s integrity while significantly enhancing its training time

Macropoéticas y Micropoéticas de la representación del cuerpo en la iconósfera contemporánea

This essay retrieves the notion of territoriality to lay out the possibility of the construction of a comparative poetic about the representations of the contemporary body within the panorama of the Visual Arts in Latin America and its devices of production, representation, presentation and circulation.Este ensayo recupera la noción de territorialidad para plantear la posibilidad de construir una poética comparada de las figuraciones y representaciones del cuerpo contemporáneo dentro del panorama de las artes visuales en América Latina y sus dispositivos de producción, representación, presentación y circulación.Este ensaio recupera a noção de territorialidade para propor a possibilidade de construir uma poética comparativa de figurações e representações do corpo contemporâneo dentro do panorama das artes visuais na América Latina e seus dispositivos de produção, representação, apresentação e circulação

Collection and processing of data from wrist wearable devices in heterogeneous and multiple-user scenarios

Over recent years, we have witnessed the development of mobile and wearable technologies to collect data from human vital signs and activities. Nowadays, wrist wearables including sensors (e.g., heart rate, accelerometer, pedometer) that provide valuable data are common in market. We are working on the analytic exploitation of this kind of data towards the support of learners and teachers in educational contexts. More precisely, sleep and stress indicators are defined to assist teachers and learners on the regulation of their activities. During this development, we have identified interoperability challenges related to the collection and processing of data from wearable devices. Different vendors adopt specific approaches about the way data can be collected from wearables into third-party systems. This hinders such developments as the one that we are carrying out. This paper contributes to identifying key interoperability issues in this kind of scenario and proposes guidelines to solve them. Taking into account these topics, this work is situated in the context of the standardization activities being carried out in the Internet of Things and Machine to Machine domains.Xunta de Galicia | Ref. GRC2013-00

Calculation of sleep indicators in students using smartphones and wearables

Data produced by the use of mobile devices (smartphones and wearables) can be used to obtain patterns and indicators of user behavior. This paper focuses on obtaining sleep-related indicators to apply them in educational settings. Initially the most relevant indicators defined in the literature and available in existing mobile platforms are studied. Based on them, we propose new indicators that can be calculated automatically and transparently analyzing the data generated by mobile device sensors. The ultimate goal of these indicators is to facilitate the construction of software services (recommenders and detectors of risk situations) to improve the learning processes of students. Keywords Learning analytics Sleep pattern Wearables SmartphonesXunta de Galici

How do you sleep? Using off the shelf wrist wearables to estimate sleep quality, sleepiness level, chronotype and sleep regularity indicators

This piece of research is situated in the domain of multi-modal analytics. New commercial off the shelf wearables, such as smartwatches or wristbands, are becoming popular and increasingly used for fitness and wellness in a new trend known as the quantified-self movement. The sensors included in these devices (e.g. accelerometer, heart rate) in conjunction with data analytics algorithms are used to provide information such as steps walked, calories consumed, etc. The main goal of this piece of research is to check if new wearable technologies could be used to estimate sleep indicators in an automatic way. The available medical literature proposes several sleep-related features and methods to calculate them involving direct user observation, interviews or specific medical instrumentation. Off the shelf wearable vendors also provide some sleep indicators, such as the sleep duration, the number of awakes or the time to fall asleep. Taking as a reference the results and methods described in the medical literature and the data available in commercial off the shelf wearables, we propose new sleep indicators offering a greater interpretative value: sleep quality, sleepiness level, chronotype. The results obtained after initial experiments demonstrate the feasibility of this approach to be applied in real contexts. Eventually, we plan to apply these solutions to support educational scenarios related to self-regulated learning and teaching support.Agencia Estatal de Investigación | Ref. TIN2016-80515-RXunta de Galicia | Ref. GRC2013-006Universidade de Vig