LA CRÓNICA SIMULTÁNEA: CONSTRUIR EL ACONTECIMIENTO DESDE UNA PERSPECTIVA TEMPORAL

Las crónicas simultáneas, o live blogs, quebraron una limitación histórica de la prensa escrita al permitir informar sobre un evento mientras sucede. El presente trabajo se propone mostrar que este tipo de crónicas presenta importantes diferencias respecto de las noticias ordinarias, o crónicas retrospectivas, incluso cuando se producen luego del suceso que cubren. La extrema inmediatez respecto de los hechos y la capacidad de publicar actualizaciones periódicas confieren al texto una perspectiva temporal definida por la acumulación de múltiples observaciones parciales sobre su objeto. Esa situación enunciativa inhibe la adopción de una perspectiva de punto final y contribuye a generar un efecto de sentido de noticia en desarrollo, lo que redunda en un modo de construir el acontecimiento más fragmentario en comparación con las noticias tradicionales, y que la asemeja a la cobertura de los medios que emplean la toma directa como la radio y la televisión. Abstract: Simultaneous reports, or live blogs, overcame a barrier that existed in the written press since its beginnings: they made it possible to inform about an event as it happens. This article postulates that this kind of reports differs significantly from ordinary news pieces, or retrospective reports, even when they are produced after the event they cover. The extreme immediacy of the facts and the capability to post periodic updates provide the text with a temporal perspective defined by the accumulation of multiple partial observations of its object. This enunciative situation inhibits assuming an endpoint perspective and helps generating a strong sense of developing news, which results in a fragmentary construction of the event in comparison with ordinary news, and that is closer to the live coverage of radio and television.

Estimate the spectrum of affine dynamical systems from partial observations of a single trajectory data

In this paper, we study the nonlinear inverse problem of estimating the spectrum of a system matrix, that drives a finite-dimensional affine dynamical system, from partial observations of a single trajectory data. In the noiseless case, we prove an annihilating polynomial of the system matrix, whose roots are a subset of the spectrum, can be uniquely determined from data. We then study which eigenvalues of the system matrix can be recovered and derive various sufficient and necessary conditions to characterize the relationship between the recoverability of each eigenvalue and the observation locations. We propose various reconstruction algorithms 1with theoretical guarantees, generalizing the classical Prony method, ESPRIT, and matrix pencil method. We test the algorithms over a variety of examples with applications to graph signal processing, disease modeling and a real-human motion dataset. The numerical results validate our theoretical results and demonstrate the effectiveness of the proposed algorithms, even when the data did not follow an exact linear dynamical system.

Agent-Based Markov Modeling for Improved COVID-19 Mitigation Policies

The year 2020 saw the covid-19 virus lead to one of the worst global pandemics in history. As a result, governments around the world have been faced with the challenge of protecting public health while keeping the economy running to the greatest extent possible. Epidemiological models provide insight into the spread of these types of diseases and predict the effects of possible intervention policies. However, to date, even the most data-driven intervention policies rely on heuristics. In this paper, we study how reinforcement learning (RL) and Bayesian inference can be used to optimize mitigation policies that minimize economic impact without overwhelming hospital capacity. Our main contributions are (1) a novel agent-based pandemic simulator which, unlike traditional models, is able to model fine-grained interactions among people at specific locations in a community; (2) an RLbased methodology for optimizing fine-grained mitigation policies within this simulator; and (3) a Hidden Markov Model for predicting infected individuals based on partial observations regarding test results, presence of symptoms, and past physical contacts. This article is part of the special track on AI and COVID-19.

Parameter Estimation of Partially Observed Turbulent Systems Using Conditional Gaussian Path-Wise Sampler

Parameter estimation of complex nonlinear turbulent dynamical systems using only partially observed time series is a challenging topic. The nonlinearity and partial observations often impede using closed analytic formulae to recover the model parameters. In this paper, an exact path-wise sampling method is developed, which is incorporated into a Bayesian Markov chain Monte Carlo (MCMC) algorithm in light of data augmentation to efficiently estimate the parameters in a rich class of nonlinear and non-Gaussian turbulent systems using partial observations. This path-wise sampling method exploits closed analytic formulae to sample the trajectories of the unobserved variables, which avoid the numerical errors in the general sampling approaches and significantly increase the overall parameter estimation efficiency. The unknown parameters and the missing trajectories are estimated in an alternating fashion in an adaptive MCMC iteration algorithm with rapid convergence. It is shown based on the noisy Lorenz 63 model and a stochastically coupled FitzHugh–Nagumo model that the new algorithm is very skillful in estimating the parameters in highly nonlinear turbulent models. The model with the estimated parameters succeeds in recovering the nonlinear and non-Gaussian features of the truth, including capturing the intermittency and extreme events, in both test examples.

Simultaneous completion and spatiotemporal grouping of corrupted motion tracks

Given an unordered list of 2D or 3D point trajectories corrupted by noise and partial observations, in this paper we introduce a framework to simultaneously recover the incomplete motion tracks and group the points into spatially and temporally coherent clusters. This advances existing work, which only addresses partial problems and without considering a unified and unsupervised solution. We cast this problem as a matrix completion one, in which point tracks are arranged into a matrix with the missing entries set as zeros. In order to perform the double clustering, the measurement matrix is assumed to be drawn from a dual union of spatiotemporal subspaces. The bases and the dimensionality for these subspaces, the affinity matrices used to encode the temporal and spatial clusters to which each point belongs, and the non-visible tracks, are then jointly estimated via augmented Lagrange multipliers in polynomial time. A thorough evaluation on incomplete motion tracks for multiple-object typologies shows that the accuracy of the matrix we recover compares favorably to that obtained with existing low-rank matrix completion methods, specially under noisy measurements. In addition, besides recovering the incomplete tracks, the point trajectories are directly grouped into different object instances, and a number of semantically meaningful temporal primitive actions are automatically discovered.

Numerical method for parameter inference of systems of nonlinear ordinary differential equations with partial observations

Parameter inference of dynamical systems is a challenging task faced by many researchers and practitioners across various fields. In many applications, it is common that only limited variables are observable. In this paper, we propose a method for parameter inference of a system of nonlinear coupled ordinary differential equations with partial observations. Our method combines fast Gaussian process-based gradient matching and deterministic optimization algorithms. By using initial values obtained by Bayesian steps with low sampling numbers, our deterministic optimization algorithm is both accurate, robust and efficient with partial observations and large noise.