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Gerardo Flores

Publications and source records attributed to Gerardo Flores.

At least 19 recordsLinked to original sources
arXiv2026

The QuadSoft: Design, Construction, and Experimental Validation of a Soft and Actuated Quadrotor

This paper presents QuadSoft, a novel fully actuated quadrotor equipped with continuous-curvature, tendon-driven soft robotic arms. The design combines a semi-rigid central frame with flexible arms, enabling controlled structural reconfiguration during flight without altering the propeller layout. Unlike existing soft aerial platforms that rely on discrete bending joints, QuadSoft utilizes a continuum deformation approach to modulate arm curvature, actively adjusting its thrust vector and aerodynamic characteristics. We characterize the geometric mapping between servomotor input and the resulting constant curvature, validating it experimentally. Outdoor flight tests demonstrate stable take-off, hover, directional maneuvers, and landing, confirming that controlled arm bending can generate horizontal displacement while preserving altitude. Measurements of pitch, roll, and curvature angles show that the platform follows intended actuation patterns with minimal attitude deviations. These results demonstrate that QuadSoft preserves the baseline stability of rigid quadrotors while enabling morphology-driven maneuverability, all under the standard PX4 autopilot without retuning. Beyond a proof of concept, this work establishes a distinctive outdoor validation of a tendon-driven continuum morphing quadrotor, opening a new research avenue toward adaptive aerial systems that combine the safety and versatility of soft robotics with the performance of conventional UAVs.

eess.SY↗
arXiv2025

A Consequentialist Critique of Binary Classification Evaluation: Theory, Practice, and Tools

Machine learning-supported decisions, such as ordering diagnostic tests or determining preventive custody, often require converting probabilistic forecasts into binary classifications. We adopt a consequentialist perspective from decision theory to argue that evaluation methods should prioritize forecast quality across thresholds and base rates. This motivates the use of proper scoring rules such as the Brier score and log loss. However, our empirical review of practices at major ML venues (ICML, FAccT, CHIL) reveals a dominant reliance on top-K metrics or fixed-threshold evaluations. To bridge this disconnect, we introduce a decision-theoretic framework that maps evaluation metrics to their appropriate use cases, accompanied by a practical Python package, \texttt{briertools}, which lowers the barrier to applying proper scoring rules in practice. Methodologically, we derive and implement a clipped Brier score variant that avoids full integration and better reflects bounded, interpretable threshold ranges. Theoretically, we reconcile the Brier score with decision curve analysis, directly addressing the critique of (Assel, et al. 2017) regarding the clinical utility of proper scoring rules.

cs.LG↗
arXiv2024

The Soft-PVTOL: modeling and control

This paper presents, for the first time, the soft planar vertical take-off and landing (Soft-PVTOL) aircraft. This concept captures the soft aerial vehicle's fundamental dynamics with a minimum number of states and inputs but retains the main features to consider when designing control laws. Unlike conventional PVTOL and multi-rotors, where altering position inevitably impacts orientation due to their underactuated design, the Soft-PVTOL offers the unique advantage of separating these dynamics, opening doors to unparalleled maneuverability and precision. We demonstrate that the Soft-PVTOL can be modeled using the Euler-Lagrange equations by assuming a constant curvature model in the aerial robot's arms. Such a mathematical model is presented in detail and can be extended to several constant curvature segments in each Soft-PVTOL arm. Moreover, we design a passivity-based control law that exploits the flexibility of the robot's arms. We solve the tracking control problem, proving that the error equilibrium globally exponentially converges to zero. The controller is tested in numerical simulations, demonstrating robust performance and ensuring the efficacy of the closed-loop system.

eess.SY↗
arXiv2024

Nilpotent orbit theorem in $p$-adic Hodge theory

We state and prove three orbit theorems on the period domains for the $p$-adic Hodge structure analogous to the complex case. We shall consider the variation of de Rham (resp. étale) cohomology in a family of projective varieties $f:\mathfrak{X} \to S$ defined over a p-adic field. First, we show that any nilpotent orbit in the period domain of p-adic Hodge structures converges to a semistable point (filtration) in the period domain of the p-adic Hodge structure. Furthermore, the nilpotent orbits of the limit point are asymptotic to the twisted period map [Theorem \ref{thm:nilpotent-orbit}]. The orbit theorems come with some estimates of the distance between the nilpotent orbit and the twisted period map. The distance estimate in the p-adic nilpotent orbit theorem is given concerning the non-archimedean metric and is based on the p-adic Fourier analysis of Amice-Schneider. The result is analogous to the orbit theorems of W. Schmid [\cite{Sch}-1973] on complex Hodge structures. Our proof is based on a \textit{Geometric Invariant Theory} (GIT) criterion for semi-stability (Kempf-Ness theorem) and estimates from the (Amice-Schneider) p-adic Fourier theory. We also state the $SL_2$-orbit theorem in the p-adic case, [Theorem \ref{th:homomorphism}]. Finally, we explain how the nilpotent orbit theorem should be modified and stated for a variation of the mixed Hodge structure [Theorem \ref{thm:mixed-orbit}].}

math.NT↗
arXiv2022

Fock Space of Level infinity and Characters of Vertex Operators

We present an extension of the trace of a vertex operator and explain a representation-theoretic interpretation of the trace. Specifically, we consider a twist of the vertex operator with infinitely many Casimir operators and compute its trace as a character formula. To do this, we define the Fock space of infinite level $\mathfrak{F}^{\infty}$. Then, we prove a duality between $\mathfrak{gl}_{\infty}$ and $\mathfrak{a}_{\infty}=\widehat{\mathfrak{gl}}_{\infty}$ of Howe type, which provides a decomposition of $\mathfrak{F}^{\infty}$ into irreducible representations with joint highest weight vector for $\mathfrak{gl}_{\infty}$ and $\mathfrak{a}_{\infty}$. The decomposition of the Fock space $\mathfrak{F}^{\infty}$ into highest weight representations provides a method to calculate and interpret the extended trace.

math.RT↗
arXiv2021

Graded Linearity of Stanley-Reisner Ring of Broken Circuit Complexes

This paper introduces two new notions of graded linear resolution and graded linear quotients, which generalize the concepts of linear resolution property and linear quotient for modules over the polynomial ring $A=k[x_1, \dots ,x_n]$. Besides, we compare graded linearity with componentwise linearity in general. For modules minimally generated by a regular sequence in a maximal ideal of $A$, we show that graded linear quotients imply graded linear resolution property for the colon ideals. On the other hand, we provide specific characterizations of graded linear resolution property for the Stanley-Reisner ring of broken circuit complexes and generalize several results of \cite{RV} on the decomposition of matroids into the direct sum of uniform matroids. Specifically, we show that the matroid can be stratified such that each strata has a decomposition into uniform matroids. We also present analogs of our results for the Orlik-Terao ideal of hyperplane arrangements which are translations of the corresponding results on matroids.

math.AC↗
arXiv2021

Geometry of Weighted Homogeneous Spaces

In this paper, we define the weighted homogeneous space (WHS), denoted by $\frac{G}{P}[ψ_H]$ where $ψ_H$ is weight function defined on the set of simple roots of $G$, by an element $H$ in the highest Weyl chamber. The weight function $ψ_H$ describes the action of the maximal torus $T$ on different Bruhat cells and is well behaved via the change of coordinates defined by the action of the Weyl group $W$. The major effort in this text is to prove basic algebraic and geometric properties of a weighted homogeneous space. The definition can be compared with an existing version given by Reid-Corti \cite{CR}. Additionally, we express $\frac{G}{P}[ψ_H]$ as a whole compact quotient of $G/P$ by a certain action of a finite abelian group. Besides, it is presented a criterion when two WHS with possibly different weight systems are isomorphic. The criteria give a simple method to understand the regular maps between two WHS's, defined by matrices with specific polynomial entries. We also explain invariant Kähler differentials on WHS by using certain potential functions on $G$. Our contribution is a generalization of the results presented in \cite{Al, AL, AKQ}. For that, we explain how the weights affect different computations of chern classes of line bundles given in \cite{AL, AKQ}. Finally, we provide a result on the coordinate ring of a WHS by cluster algebras associated to weighted quivers. Specifically, we show that the coordinate ring of a WHS is a weighted cluster algebra of finite type. In this case, the corresponding Dynkin quiver is equipped with a weight function defined on the vertices where the mutations also affect the weights. We present an embedding of a WHS in a product of weighted projective spaces, showing that the coordinate ring is a weighted graded algebra.

math.RT↗
arXiv2021

The STDyn-SLAM: A stereo vision and semantic segmentation approach for SLAM in dynamic outdoor environments

Commonly, SLAM algorithms are focused on a static environment, however, there are several scenes where dynamic objects are present. This work presents the STDyn-SLAM an image feature-based SLAM system working on dynamic environments using a series of sub-systems, like optic flow, orb features extraction, visual odometry, and convolutional neural networks to discern moving objects in the scene. The neural network is used to support object detection and segmentation to avoid erroneous maps and wrong system localization. The STDyn-SLAM employs a stereo pair and is developed for outdoor environments. Moreover, the processing time of the proposed system is fast enough to run in real-time as it was demonstrated through the experiments given in real dynamic outdoor environments. Further, we compare our SLAM with state-of-the-art methods achieving promising results.

cs.RO↗
arXiv2020

Teleoperated aerial manipulator and its avatar. Part 1: Communication, system's interconnection, control, and virtual world

The tasks that an aerial manipulator can perform are incredibly diverse. However, nowadays the technology is not completely developed to achieve complex tasks autonomously. That's why we propose a human-in-the-loop system that can control a semi-autonomous aerial manipulator to accomplish these kinds of tasks. Furthermore, motivated by the growing trend of virtual reality systems, together with teleoperation, we develop a system composed of: an aerial manipulator model programmed in PX4 and modeled in Gazebo, a virtual reality immersion with an interactive controller, and the interconnection between the aforementioned systems via the Internet. This research is the first part of a broader project. In this part, we present experiments in the software in the loop simulation. The code of this work is liberated on our GitHub page. Also, a video shows the conducted experiments.

cs.RO↗
arXiv2020

Google COVID-19 Search Trends Symptoms Dataset: Anonymization Process Description (version 1.0)

This report describes the aggregation and anonymization process applied to the initial version of COVID-19 Search Trends symptoms dataset (published at https://goo.gle/covid19symptomdataset on September 2, 2020), a publicly available dataset that shows aggregated, anonymized trends in Google searches for symptoms (and some related topics). The anonymization process is designed to protect the daily symptom search activity of every user with $\varepsilon$-differential privacy for $\varepsilon$ = 1.68.

cs.CR↗
arXiv2020

Transition control of a tail-sitter UAV using recurrent neural networks

This paper presents the implementation of a Recurrent Neural Network (RNN) based-controller for the stabilization of the flight transition maneuver (hover-cruise and vice versa) of a tail-sitter UAV. The control strategy is based on attitude and velocity stabilization. For that aim, the RNN is used for the estimation of high nonlinear aerodynamic terms during the transition stage. Then, this estimate is used together with a feedback linearization technique for stabilizing the entire system. Results show convergence of linear velocities and the pitch angle during the transition maneuver. To analyze the performance of our proposed control strategy, we present simulations for the transition from hover to cruise and vice versa.

eess.SY↗
arXiv2020

Unified controller for take-off and landing for a fixed-wing aircraft

Take-off and landing are the most important maneuvers for an aircraft's flight. Deployment for small fixed-wing aircraft is usually made by hand but when payload increases, take-off, and landing maneuvers are then performed on a runway making the procedures more complex. For that reason, we address the performance of the two maneuvers in order to develop a unique controller using the feedback control technique. We present the longitudinal aircraft dynamics to model the take-off and landing considering the rolling resistance forces during ground roll through a friction model. We also present the controller design for such a model. A stability proof is conducted to validate the stability of the system with the developed control law. Additionally, simulations are carried out to corroborate that the control law is effective applied to the dynamic model presented.

eess.SY↗
arXiv2020

Desmoking laparoscopy surgery images using an image-to-image translation guided by an embedded dark channel

In laparoscopic surgery, the visibility in the image can be severely degraded by the smoke caused by the $CO_2$ injection, and dissection tools, thus reducing the visibility of organs and tissues. This lack of visibility increases the surgery time and even the probability of mistakes conducted by the surgeon, then producing negative consequences on the patient's health. In this paper, a novel computational approach to remove the smoke effects is introduced. The proposed method is based on an image-to-image conditional generative adversarial network in which a dark channel is used as an embedded guide mask. Obtained experimental results are evaluated and compared quantitatively with other desmoking and dehazing state-of-art methods using the metrics of the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) index. Based on these metrics, it is found that the proposed method has improved performance compared to the state-of-the-art. Moreover, the processing time required by our method is 92 frames per second, and thus, it can be applied in a real-time medical system trough an embedded device.

eess.IV↗
arXiv2020

Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer

Effective modeling of electronic health records (EHR) is rapidly becoming an important topic in both academia and industry. A recent study showed that using the graphical structure underlying EHR data (e.g. relationship between diagnoses and treatments) improves the performance of prediction tasks such as heart failure prediction. However, EHR data do not always contain complete structure information. Moreover, when it comes to claims data, structure information is completely unavailable to begin with. Under such circumstances, can we still do better than just treating EHR data as a flat-structured bag-of-features? In this paper, we study the possibility of jointly learning the hidden structure of EHR while performing supervised prediction tasks on EHR data. Specifically, we discuss that Transformer is a suitable basis model to learn the hidden EHR structure, and propose Graph Convolutional Transformer, which uses data statistics to guide the structure learning process. The proposed model consistently outperformed previous approaches empirically, on both synthetic data and publicly available EHR data, for various prediction tasks such as graph reconstruction and readmission prediction, indicating that it can serve as an effective general-purpose representation learning algorithm for EHR data.

cs.LG↗
arXiv2019

Modelling EHR timeseries by restricting feature interaction

Time series data are prevalent in electronic health records, mostly in the form of physiological parameters such as vital signs and lab tests. The patterns of these values may be significant indicators of patients' clinical states and there might be patterns that are unknown to clinicians but are highly predictive of some outcomes. Many of these values are also missing which makes it difficult to apply existing methods like decision trees. We propose a recurrent neural network model that reduces overfitting to noisy observations by limiting interactions between features. We analyze its performance on mortality, ICD-9 and AKI prediction from observational values on the Medical Information Mart for Intensive Care III (MIMIC-III) dataset. Our models result in an improvement of 1.1% [p<0.01] in AU-ROC for mortality prediction under the MetaVision subset and 1.0% and 2.2% [p<0.01] respectively for mortality and AKI under the full MIMIC-III dataset compared to existing state-of-the-art interpolation, embedding and decay-based recurrent models.

cs.LG↗
arXiv2019

Laparoscopy Surgery CO2 Removal via Generative Adversary Network and Dark Channel Prior

Laparoscopic surgery uses a thin tube with a camera called a laparoscope, which is inserted into the abdomen through a small incision in the skin during surgery. This allows to a surgeon to see inside of the body without causing significant injury to the patient. These characteristics make laparoscopy a widely used technique. In laparoscopic surgery, image quality can be severely degraded by surgical smoke caused by the use of tissue dissection tools which reduces the visibility of the observed organs and tissues. This lack of visibility increases the possibility of errors and surgery time with the consequences that this may have on the patient's health. In this paper, we introduce a novel hybrid approach for computational smoke removal which is based on the combination of a widely dehazing method used: the dark channel prior (DCP) and a pixel-to-pixel neural network approach: Generative Adversary Network (GAN). The experimental results have proven that the proposed method achieves a better performance than the individual results of the DCP and GAN in terms of restoration quality, obtaining a PSNR value of 25 and SSIM index of 0.88 over a test set of synthetic images.

physics.med-ph↗
arXiv2019

Explaining an increase in predicted risk for clinical alerts

Much work aims to explain a model's prediction on a static input. We consider explanations in a temporal setting where a stateful dynamical model produces a sequence of risk estimates given an input at each time step. When the estimated risk increases, the goal of the explanation is to attribute the increase to a few relevant inputs from the past. While our formal setup and techniques are general, we carry out an in-depth case study in a clinical setting. The goal here is to alert a clinician when a patient's risk of deterioration rises. The clinician then has to decide whether to intervene and adjust the treatment. Given a potentially long sequence of new events since she last saw the patient, a concise explanation helps her to quickly triage the alert. We develop methods to lift static attribution techniques to the dynamical setting, where we identify and address challenges specific to dynamics. We then experimentally assess the utility of different explanations of clinical alerts through expert evaluation.

cs.LG↗