Mario A. Quiroz-Juárez

Dr. Mario A. Quiroz-Juárez

Center for Applied Physics and Advanced Technology UNAM, Mexico City, Ciudad de México, Mexico
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Academic Career Length

16Years (2011-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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5 May 2020 in Applied Physics Reviews
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Journal Article
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CYChenglong You
Chenglong You
MQMario A. Quiroz-Juárez
Mario A. Quiroz-Juárez
ALAidan Lambert
Aidan Lambert
NBNarayan Bhusal
Narayan Bhusal
CDChao Dong
Chao Dong
OMOmar S. Magaña-Loaiza
Omar S. Magaña-Loaiza
The identification of light sources represents a task of utmost importance for the development of multiple photonic technologies. Over the last decades, the identification of light sources as diverse as sunlight, laser radiation, and molecule fluorescence has relied on the collection of photon statistics or the implementation of quantum state tomography. In general, this task requires an extensive number of measurements to unveil the characteristic statistical fluctuations and correlation properties of light, particularly in the low-photon flux regime. In this article, we exploit the self-learning features of artificial neural networks and the naive Bayes classifier to dramatically reduce the number of measurements required to discriminate thermal light from coherent light at the single-photon level. We demonstrate robust light identification with tens of measurements at mean photon numbers below one. In terms of accuracy and number of measurements, the methods described here dramatically outperform conventional schemes for characterization of light sources. Our work has important implications for multiple photonic technologies such as light detection and ranging, and microscopy.
18 July 2019 in Photonics Research
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Journal Article
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MQMario A. Quiroz-Juárez
Mario A. Quiroz-Juárez
APArmando Perez-Leija
Armando Perez-Leija
KTKonrad Tschernig
Konrad Tschernig
BRBlas Manuel Rodríguez-Lara
Blas Manuel Rodríguez-Lara
OMOmar S. Magaña-Loaiza
Omar S. Magaña-Loaiza
Roberto de J. León-MontielRoberto de J. León-Montiel
Roberto de J. León-Montiel
3 December 2018 in Communications Physics
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Roberto de J. León-MontielRoberto de J. León-Montiel
Roberto de J. León-Montiel
MQMario A. Quiroz-Juárez
Mario A. Quiroz-Juárez
JDJorge Luis Domínguez
Jorge Luis Domínguez
RQRafael Quintero-Torres
Rafael Quintero-Torres
JAJosé L. Aragón
José L. Aragón
Yogesh JoglekarYogesh Joglekar
Yogesh Joglekar
Abstrct: Passive parity-time symmetry breaking transitions, where long-lived eigenmodes emerge in a locally dissipative system, have been extensively studied in recent years. Conventional wisdom says that they occur at exceptional points. Here we report the observation of multiple transitions showing the emergence of slowly decaying eigenmodes in a dissipative, Floquet electronic system with synthetic components. Remarkably, in our system, the modes emerge without exceptional points. Our setup uses an electrical oscillator inductively coupled to a dissipative oscillator, where the time-periodic inductive coupling and resistive-heating losses are independently controlled. With a Floquet dissipation, slowly-decaying eigenmodes emerge at vanishingly small dissipation strength in the weak coupling limit. With a moderate Floquet coupling, multiple instances of their emergence and disappearance are observed. With an asymmetric dimer model, we show that these transitions, driven by avoided-level-crossing in purely dissipative systems, are generically present in static and Floquet domains.
17 September 2018 in IEEE Transactions on Nanobioscience
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Closed access
MQMario A. Quiroz-Juárez
Mario A. Quiroz-Juárez
OJOmar Jimenez-Ramirez
Omar Jimenez-Ramirez
RUBEN VAZQUEZ-MEDINARUBEN VAZQUEZ-MEDINA
RUBEN VAZQUEZ-MEDINA
ERElena Ryzhii
Elena Ryzhii
MRMaxim Ryzhii
Maxim Ryzhii
JAJosé L Aragón
José L Aragón
We present extended heterogeneous oscillator model of cardiac conduction system for generation of realistic 12 lead ECG waveforms. The model consists of main natural pacemakers represented by modified van der Pol equations, and atrial and ventricular muscles, in which the depolarization and repolarization processes are described by modified FitzHugh-Nagumo equations. We incorporate an artificial RR-tachogram with the specific statistics of a heart rate, the frequency-domain characteristics of heart rate variability produced by Mayer and respiratory sinus arrhythmia waves, normally distributed additive noise and a baseline wander that couple the respiratory frequency. The standard 12 lead ECG is calculated by means of a weighted linear combination of atria and ventricle signals and thus can be fitted to clinical ECG of real subject. The model is capable to simulate accurately realistic ECG characteristics including local pathological phenomena accounting for biophysical properties of the human heart. All these features provide significant advantages over existing nonlinear cardiac models. The proposed model constitutes a useful tool for medical education and for assessment and testing of ECG signal processing software and hardware systems.
20 September 2021 in PLOS ONE
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MQMario A. Quiroz-Juárez
Mario A. Quiroz-Juárez
ATArmando Torres-Gómez
Armando Torres-Gómez
IHIrma Hoyo-Ulloa
Irma Hoyo-Ulloa
RLRoberto de J. León-Montiel
Roberto de J. León-Montiel
AUAlfred B. U’rEn
Alfred B. U’rEn
The current COVID-19 public health crisis, caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), has produced a devastating toll both in terms of human life loss and economic disruption. In this paper we present a machine-learning algorithm capable of identifying whether a given patient (actually infected or suspected to be infected) is more likely to survive than to die, or vice-versa. We train this algorithm with historical data, including medical history, demographic data, as well as COVID-19-related information. This is extracted from a database of confirmed and suspected COVID-19 infections in Mexico, constituting the official COVID-19 data compiled and made publicly available by the Mexican Federal Government. We demonstrate that the proposed method can detect high-risk patients with high accuracy, in each of four identified clinical stages, thus improving hospital capacity planning and timely treatment. Furthermore, we show that our method can be extended to provide optimal estimators for hypothesis-testing techniques commonly-used in biological and medical statistics. We believe that our work could be of use in the context of the current pandemic in assisting medical professionals with real-time assessments so as to determine health care priorities.
27 November 2015 in Scientific Reports
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Open access
Roberto de J. León-MontielRoberto de J. León-Montiel
Roberto de J. León-Montiel
MQMario A. Quiroz-Juárez
Mario A. Quiroz-Juárez
RQRafael Quintero-Torres
Rafael Quintero-Torres
JDJorge Luis Domínguez
Jorge Luis Domínguez
Hector Manuel Moya-CessaHector Manuel Moya-Cessa
Hector Manuel Moya-Cessa
JAJosé L Aragón
José L Aragón
Noise is generally thought as detrimental for energy transport in coupled oscillator networks. However, it has been shown that for certain coherently evolving systems, the presence of noise can enhance, somehow unexpectedly, their transport efficiency; a phenomenon called environment-assisted quantum transport (ENAQT) or dephasing-assisted transport. Here, we report on the experimental observation of such effect in a network of coupled electrical oscillators. We demonstrate that by introducing stochastic fluctuations in one of the couplings of the network, a relative enhancement in the energy transport efficiency of 22.5 ± 3.6% can be observed.