arXiv subjects

Luis Alvarez

Publications and source records attributed to Luis Alvarez.

At least 19 recordsLinked to original sources

arXiv2025

On the relationship between prediction intervals, tests of sharp nulls and inference on realized treatment effects in settings with few treated units

We study how inference methods for settings with few treated units that rely on treatment effect homogeneity extend to alternative inferential targets when treatment effects are heterogeneous -- namely, tests of sharp null hypotheses, inference on realized treatment effects, and prediction intervals. We show that inference methods for these alternative targets are deeply interconnected: they are either equivalent or become equivalent under additional assumptions. Our results show that methods designed under treatment effect homogeneity can remain valid for these alternative targets when treatment effects are stochastic, offering new theoretical justifications and insights on their applicability.

econ.EM↗

arXiv2025

An Active Contour Model for Silhouette Vectorization using B\'ezier Curves

In this paper, we propose an active contour model for silhouette vectorization using cubic B\'ezier curves. Among the end points of the B\'ezier curves, we distinguish between corner and regular points where the orientation of the tangent vector is prescribed. By minimizing the distance of the B\'ezier curves to the silhouette boundary, the active contour model optimizes the location of the B\'ezier curves end points, the orientation of the tangent vectors in the regular points, and the estimation of the B\'ezier curve parameters. This active contour model can use the silhouette vectorization obtained by any method as an initial guess. The proposed method significantly reduces the average distance between the silhouette boundary and its vectorization obtained by the world-class graphic software Inkscape, Adobe Illustrator, and a curvature-based vectorization method, which we introduce for comparison. Our method also allows us to impose additional regularity on the B\'ezier curves by reducing their lengths.

cs.GR↗

arXiv2025

Inference with few treated units

In many causal inference applications, only one or a few units (or clusters of units) are treated. An important challenge in such settings is that standard inference methods relying on asymptotic theory may be unreliable, even with large total sample sizes. This survey reviews and categorizes inference methods designed to accommodate few treated units, considering cross-sectional and panel data methods. We discuss trade-offs and connections between different approaches. In doing so, we propose slight modifications to improve the finite-sample performance of some methods, and we also provide theoretical justifications for existing heuristic approaches that have been proposed in the literature.

econ.EM↗

arXiv2023

The learning effects of subsidies to bundled goods: a semiparametric approach

Can temporary subsidies to bundles induce long-run changes in demand due to learning about the quality of one of the constituent goods? This paper provides theoretical support and empirical evidence on this mechanism. Theoretically, we introduce a model where an agent learns about the quality of an innovation through repeated consumption. We then assess the predictions of our theory in a randomised experiment in a ridesharing platform. The experiment subsidised car trips integrating with a train or metro station, which we interpret as a bundle. Given the heavy-tailed nature of our data, we propose a semiparametric specification for treatment effects that enables the construction of more efficient estimators. We then introduce an efficient estimator for our specification by relying on L-moments. Our results indicate that a ten-weekday 50\% discount on integrated trips leads to a large contemporaneous increase in the demand for integration, and, consistent with our model, persistent changes in the mean and dispersion of nonintegrated app rides. These effects last for over four months. A calibration of our theoretical model suggests that around 40\% of the contemporaneous increase in integrated rides may be attributable to increased incentives to learning. Our results have nontrivial policy implications for the design of public transit systems.

econ.EM↗

arXiv2023

A maximal inequality for local empirical processes under weak dependence

We introduce a maximal inequality for a local empirical process under strongly mixing data. Local empirical processes are defined as the (local) averages $\frac{1}{nh}\sum_{i=1}^n \mathbf{1}\{x - h \leq X_i \leq x+h\}f(Z_i)$, where $f$ belongs to a class of functions, $x \in \mathbb{R}$ and $h > 0$ is a bandwidth. Our nonasymptotic bounds control estimation error uniformly over the function class, evaluation point $x$ and bandwidth $h$. They are also general enough to accomodate function classes whose complexity increases with $n$. As an application, we apply our bounds to function classes that exhibit polynomial decay in their uniform covering numbers. When specialized to the problem of kernel density estimation, our bounds reveal that, under weak dependence with exponential decay, these estimators achieve the same (up to a logarithmic factor) sharp uniform-in-bandwidth rates derived in the iid setting by \cite{Einmahl2005}.

econ.EM↗

arXiv2023

Extensions for Inference in Difference-in-Differences with Few Treated Clusters

In settings with few treated units, Difference-in-Differences (DID) estimators are not consistent, and are not generally asymptotically normal. This poses relevant challenges for inference. While there are inference methods that are valid in these settings, some of these alternatives are not readily available when there is variation in treatment timing and heterogeneous treatment effects; or for deriving uniform confidence bands for event-study plots. We present alternatives in settings with few treated units that are valid with variation in treatment timing and/or that allow for uniform confidence bands.

econ.EM↗

arXiv2022

Reward Function Optimization of a Deep Reinforcement Learning Collision Avoidance System

The proliferation of unmanned aircraft systems (UAS) has caused airspace regulation authorities to examine the interoperability of these aircraft with collision avoidance systems initially designed for large transport category aircraft. Limitations in the currently mandated TCAS led the Federal Aviation Administration to commission the development of a new solution, the Airborne Collision Avoidance System X (ACAS X), designed to enable a collision avoidance capability for multiple aircraft platforms, including UAS. While prior research explored using deep reinforcement learning algorithms (DRL) for collision avoidance, DRL did not perform as well as existing solutions. This work explores the benefits of using a DRL collision avoidance system whose parameters are tuned using a surrogate optimizer. We show the use of a surrogate optimizer leads to DRL approach that can increase safety and operational viability and support future capability development for UAS collision avoidance.

cs.AI↗

arXiv2022

Developing Modular Autonomous Capabilities for sUAS Operations

Small teams in the field can benefit from the capabilities provided by small Uncrewed Aerial Systems (sUAS) for missions such as reconnaissance, hostile attribution, remote emplacement, and search and rescue. The mobility, communications, and flexible payload capacity of sUAS can offer teams new levels of situational awareness and enable more highly coordinated missions than previously possible. However, piloting such aircraft for specific missions draws personnel away from other mission-critical tasks, increasing the load on remaining personnel while also increasing complexity of operations. For wider adoption and use of sUAS for security and humanitarian missions, safe and robust autonomy must be employed to reduce this burden on small teams. In this paper, we present the development of the Collaborative-UAS for Hostile Attribution, Surveillance, Emplacement, and Reconnaissance (CHASER) testbed, for rapidly prototyping capabilities that will reduce strain on small teams through sensor-guided autonomous control. We attempt to address autonomy needs unfilled by commercial sUAS platforms by creating and testing a series of composable modules that can be configured to support multiple missions. Methods implemented and presented here include radar track correlation, on-board computer vision target detection, target position estimation, closed-loop relative position control, and efficient search of a 3D volume for target acquisition. We configure and test a series of these modules in an example mission, executing a fully autonomous chase of an intruding sUAS in live flight, and demonstrating the success of the modularized autonomy approach. We present performance results from simulation or live flight tests for each module. Lastly, we describe the software architecture that we have developed for flexible controls and comment on how the capabilities presented may enable additional missions.

cs.RO↗

arXiv2022

Inference on model parameters with many L-moments

This paper studies parameter estimation using L-moments, an alternative to traditional moments with attractive statistical properties. The estimation of model parameters by matching sample L-moments is known to outperform maximum likelihood estimation (MLE) in small samples from popular distributions. The choice of the number of L-moments used in estimation remains ad-hoc, though: researchers typically set the number of L-moments equal to the number of parameters, which is inefficient in larger samples. In this paper, we show that, by properly choosing the number of L-moments and weighting these accordingly, one is able to construct an estimator that outperforms MLE in finite samples, and yet retains asymptotic efficiency. We do so by introducing a generalised method of L-moments estimator and deriving its properties in an asymptotic framework where the number of L-moments varies with sample size. We then propose methods to automatically select the number of L-moments in a sample. Monte Carlo evidence shows our approach can provide mean-squared-error improvements over MLE in smaller samples, whilst working as well as it in larger samples. We consider extensions of our approach to the estimation of conditional models and a class semiparametric models. We apply the latter to study expenditure patterns in a ridesharing platform in Brazil.

stat.ME↗

arXiv2022

Randomization Inference Tests for Shift-Share Designs

We consider the problem of inference in shift-share research designs. The choice between existing approaches that allow for unrestricted spatial correlation involves tradeoffs, varying in terms of their validity when there are relatively few or concentrated shocks, and in terms of the assumptions on the shock assignment process and treatment effects heterogeneity. We propose alternative randomization inference methods that combine the advantages of different approaches. These methods are valid in finite samples under relatively stronger assumptions, while asymptotically valid under weaker assumptions.

econ.EM↗

arXiv2022

Homophily in preferences or meetings? Identifying and estimating an iterative network formation model

Is homophily in social and economic networks driven by a taste for homogeneity (preferences) or by a higher probability of meeting individuals with similar attributes (opportunity)? This paper studies identification and estimation of an iterative network game that distinguishes between these two mechanisms. Our approach enables us to assess the counterfactual effects of changing the meeting protocol between agents. As an application, we study the role of preferences and meetings in shaping classroom friendship networks in Brazil. In a network structure in which homophily due to preferences is stronger than homophily due to meeting opportunities, tracking students may improve welfare. Still, the relative benefit of this policy diminishes over the school semester.

econ.EM↗

arXiv2020

Inference in Difference-in-Differences with Few Treated Units and Spatial Correlation

We consider the problem of inference in Difference-in-Differences (DID) when there are few treated units and errors are spatially correlated. We first show that, when there is a single treated unit, some existing inference methods designed for settings with few treated and many control units remain asymptotically valid when errors are weakly dependent. However, these methods may be invalid with more than one treated unit. We propose a menu of alternatives that are asymptotically valid in this setting, even when the relevant distance metric across units is unavailable. These alternatives vary in terms of the length of the resulting confidence intervals and the strength of the required assumptions. Our methods are also valid for comparison-of-means estimators and for construction of prediction intervals for counterfactual imputation methods.

econ.EM↗

arXiv2020

An empirical algorithm to forecast the evolution of the number of COVID-19 symptomatic patients after social distancing interventions

We present an empirical algorithm to forecast the evolution of the number of COVID-19 symptomatic patients in the early stages of the pandemic spread and after strict social distancing interventions. The algorithm is based on a low dimensional model for the variation of the exponential growth rate that decreases after the implementation of strict social distancing measures. From the observable data given by the number of tested positive, our model estimates the number of infected hindcast introducing in the model formulation the incubation time. We also use the model to follow the number of infected patients who later die using the registered number of deaths and the distribution time from infection to death. The relationship of the proposed model with the SIR models is studied. Model parameters fitting is done by minimizing a quadratic error between the data and the model forecast. An extended model is also proposed that allows a longer term forecast. An online implementation of the model is avalaible at www.ctim.es/covid19

q-bio.PE↗

arXiv2019

The 2019 Motile Active Matter Roadmap

Activity and autonomous motion are fundamental in living and engineering systems. This has stimulated the new field of active matter in recent years, which focuses on the physical aspects of propulsion mechanisms, and on motility-induced emergent collective behavior of a larger number of identical agents. The scale of agents ranges from nanomotors and microswimmers, to cells, fish, birds, and people. Inspired by biological microswimmers, various designs of autonomous synthetic nano- and micromachines have been proposed. Such machines provide the basis for multifunctional, highly responsive, intelligent (artificial) active materials, which exhibit emergent behavior and the ability to perform tasks in response to external stimuli. A major challenge for understanding and designing active matter is their inherent nonequilibrium nature due to persistent energy consumption, which invalidates equilibrium concepts such as free energy, detailed balance, and time-reversal symmetry. Unraveling, predicting, and controlling the behavior of active matter is a truly interdisciplinary endeavor at the interface of biology, chemistry, ecology, engineering, mathematics, and physics. The vast complexity of phenomena and mechanisms involved in the self-organization and dynamics of motile active matter comprises a major challenge. Hence, to advance, and eventually reach a comprehensive understanding, this important research area requires a concerted, synergetic approach of the various disciplines.

cond-mat.soft↗

arXiv2017

Human sperm steer with second harmonics of the flagellar beat

Sperm are propelled by bending waves travelling along the flagellum. During steering in gradients of sensory cues, sperm adjust the flagellar beat waveform. Symmetric and asymmetric beat waveforms produce straight and curved swimming paths, respectively. Two different mechanisms controlling the flagellar beat have been proposed: average intrinsic curvature and dynamic buckling instability. Both mechanisms create spatially asymmetric waveforms that could be modulated for steering. Using video microscopy, we image the flagellar waveform of human sperm tethered with the head to a glass surface. The waveform is characterized by a fundamental beat frequency and its second harmonic. We show that superposition of first and second harmonics breaks the beat symmetry temporally rather than spatially. As a result, sperm rotate around the tethering point. The rotation velocity is determined by the amplitude and phase of the second harmonic. Sperm stimulation with the female sex hormone progesterone enhances the second-harmonic contribution, modulates the flagellar beat, and ultimately sperm rotation. The temporal breaking of beat symmetry represents a new mechanism of sperm steering. Higher-frequency components were also reported for the flagellar beat of other cells; therefore, this steering mechanism might by quite general and could inspire the design of synthetic microswimmers.

physics.bio-ph↗

arXiv2017

Con los juegos también se educa: un enfoque educativo de los juegos de la Oca y el Parchís

In this work, we have designed and implemented, based on traditional board games such as the game of the Goose or Parchis, an educational software that aim to reinforce the learning of children. The idea that we are going to develop to do this is very simple: the children play with the same rules as in the traditional game but we add the functionality that after throwing the dice the system asks a question randomly chosen from a predefined database that can be easily modified, and the game piece only moves in the case the question is answered correctly.

cs.CY↗

arXiv2017

Adding educational funcionalities to classic board games

In this paper we revisit some classic board games like Pachisi or the Game of Gosse. The main contribution of the paper is to design and add some functionalities to the games in order to transform them in serious games, that is, in games with learning and educational purposes. To do that, at the beginning of the game, players choose one or several topics and during the game, players have to anwers questions on these topics in order to move their markers. We choose classic board games because a lot of people are familiar with them so it is very easy to start to play without wasting time learning game rules and, we think that this is an important element to make the game more attractive to people. To enlarge the number of potential users we have implement the games just using html and javascript and the games can be used in any web browser, in any computer (including tablets) , in any computer arquitecture (Windows, Mac, Linux) and no internet/server conexion is required. Associated software is distributed under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 licence and can be obtained at http://www.ctim.es/SeriousGames

cs.CY↗

arXiv2015

Classification of Complex Wishart Matrices with a Diffusion-Reaction System guided by Stochastic Distances

We propose a new method for PolSAR (Polarimetric Synthetic Aperture Radar) imagery classification based on stochastic distances in the space of random matrices obeying complex Wishart distributions. Given a collection of prototypes $\{Z_m\}_{m=1}^M$ and a stochastic distance $d(.,.)$, we classify any random matrix $X$ using two criteria in an iterative setup. Firstly, we associate $X$ to the class which minimizes the weighted stochastic distance $w_md(X,Z_m)$, where the positive weights $w_m$ are computed to maximize the class discrimination power. Secondly, we improve the result by embedding the classification problem into a diffusion-reaction partial differential system where the diffusion term smooths the patches within the image, and the reaction term tends to move the pixel values towards the closest class prototype. In particular, the method inherits the benefits of speckle reduction by diffusion-like methods. Results on synthetic and real PolSAR data show the performance of the method.

cs.CV↗