DTSC - GTSA - Artículos de Revistas

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Textile soft surface for back radiation reduction in bent wearable antennas
(IEEE, 2014-07-01) Rajo Iglesias, Eva; Gallego-Gallego, Iria; Inclán Sánchez, Luis Fernando de; Quevedo Teruel, Oscar
A textile soft surface is proposed to reduce back radiation of a textile patch antenna, and the performance is analyzed when the antenna is placed on a bent surface. This surface is assumed to be curved around cylinders with varying radii to emulate the real operation of the textile antenna when it is worn on the body, e. g., back, shoulders or arms. Two scenarios are considered for the evaluation of the performance of the antenna with the soft surface: a bent finite ground plane over an air cylinder and a more accurate model in which the electromagnetic properties of the body are included. In both situations the back radiation is reduced when compared to the same antenna without the soft surface. These results have been validated with experimental data which support this conclusion. This is the first textile implementation of a soft surface and the first demonstration that a soft surface can reduce the back radiation of a patch antenna in a conformal configuration.
A Bayesian inference and model selection algorithm with an optimization scheme to infer the model noise power
(Oxford Academic, 2021-11) López Santiago, Javier; Martino, Luca; Vázquez López, Manuel Alberto; Míguez Arenas, Joaquín; Comunidad de Madrid; Ministerio de Ciencia e Innovación (España)
Model fitting is possibly the most extended problem in science. Classical approaches include the use of least-squares fitting procedures and maximum likelihood methods to estimate the value of the parameters in the model. However, in recent years, Bayesian inference tools have gained traction. Usually, Markov chain Monte Carlo (MCMC) methods are applied to inference problems, but they present some disadvantages, particularly when comparing different models fitted to the same data set. Other Bayesian methods can deal with this issue in a natural and effective way. We have implemented an importance sampling (IS) algorithm adapted to Bayesian inference problems in which the power of the noise in the observations is not known a priori. The main advantage of IS is that the model evidence can be derived directly from the so-called importance weights - while MCMC methods demand considerable postprocessing. The use of our adaptive target adaptive importance sampling (ATAIS) method is shown by inferring, on the one hand, the parameters of a simulated flaring event that includes a damped oscillation and, on the other hand, real data from the Kepler mission. ATAIS includes a novel automatic adaptation of the target distribution. It automatically estimates the variance of the noise in the model. ATAIS admits parallelization, which decreases the computational run-times notably. We compare our method against a nested sampling method within a model selection problem.
Covariance matrix recovery from one-bit data with non-zero quantization thresholds: Algorithm and performance analysis
(IEEE, 2023-11-09) Xiao, Yu-Hang; Huang, Lei; Ramírez García, David; Qian, Cheng; So, Hing Cheung; Agencia Estatal de Investigación (España)
Covariance matrix recovery is a topic of great significance in the field of one-bit signal processing and has numerous practical applications. Despite its importance, the conventional arcsine law with zero threshold is incapable of recovering the diagonal elements of the covariance matrix. To address this limitation, recent studies have proposed the use of non-zero clipping thresholds. However, the relationship between the estimation error and the sampling threshold is not yet known. In this article, we undertake an analysis of the mean squared error by computing the Fisher information matrix for a given threshold. Our results reveal that the optimal threshold can vary considerably, depending on the variances and correlation coefficients. As a result, it is inappropriate to adopt a constant threshold to encompass parameters that vary widely. To mitigate this issue, we present a recovery scheme that incorporates time-varying thresholds. Our approach differs from existing methods in that it utilizes the exact values of the threshold, rather than its statistical properties, to increase the estimation accuracy. Simulation results, including those of the direction-of-arrival estimation problem, demonstrate the efficacy of the developed scheme, especially in complex scenarios where the covariance elements are widely separated.
Forecasting of Cairo Population using ARMA Model
(Faculty of Engineering, Zagazig University Egypt, 2016-07-10) Abdalmalak Dawoud, Kerlos Atia; González Serrano, Francisco Javier
The problem of large population is one of the most important factors influencingthe economy and social advancement of Egypt. Population forecasts, whencarefully and intelligently made, serves a valuable purpose in helping to direct theemployment of labor and capital to places or projects where they are most needed.Firstly, the paper focuses on studying the population of the capital of Egypt (Cairo).By large numbers of sampling to the population data sequence, the increasing trendis found. Then, a time series model is given which can accurately forecast thepopulation of Cairo. Multiple Autoregressive models AR (1), AR (2) are used theforecasting of the population in the next twenty years. The parameters of the modelare calculated using the famous two methods: Yule-Walker and Burg. Before usingthe model to make predictions, the test of model response is verified, and the MSEand MAPE are measured to verify the models. The result is a scary image of thepopulation in this city. Full descriptions for the steps of selecting the suitable modeland comprehensive MATLAB simulation are presented. Secondly, the totalpopulation density of Egypt is analyzing and forecasting with using the measureddata from 1970 to 2013. The same steps of the first part are done with thepopulation density and forecasting of the increasing of the population density ofEgypt in the 20 next years is presented. The main reasons for the populationproblem are discussed and solution of this problem is presented
Surface emitting ring quantum cascade lasers for chemical sensing
(2018-01-01) Szedlak, Rolf; Martín Mateos, Pedro; Acedo Gallardo, Pablo
We review recent advances in chemical sensing applications based on surface emitting ring quantum cascade lasers (QCLs). Such lasers can be implemented in monolithically integrated on-chip laser/detector devices forming compact gas sensors, which are based on direct absorption spectroscopy according to the Beer-Lambert law. Furthermore, we present experimental results on radio frequency modulation up to 150 MHz of surface emitting ring QCLs. This technique provides detailed insight into the modulation characteristics of such lasers. The gained knowledge facilitates the utilization of ring QCLs in combination with spectroscopic techniques, such as heterodyne phase-sensitive dispersion spectroscopy for gas detection and analysis.
Multiplex Decomposition of Non-Markovian Dynamics and the Hidden Layer Reconstruction Problem
(2018-08-07) Lacasa, Lucas; Nocosia, Vincenzo; Marino, Ines P; Míguez Arenas, Joaquín; Roldan, Edgar; Lisica, Ana; Grill, Stephan; Gomez Gardenes, Jesús; Ministerio de Economía y Competitividad (España)
Elements composing complex systems usually interact in several different ways, and as such, the interaction architecture is well modeled by a network with multiple layers-a multiplex network-where the system's complex dynamics is often the result of several intertwined processes taking place at different levels. However, only in a few cases can such multilayered architecture be empirically observed, as one usually only has experimental access to such structure from an aggregated projection. A fundamental challenge is thus to determine whether the hidden underlying architecture of complex systems is better modeled as a single interaction layer or if it results from the aggregation and interplay of multiple layers. Assuming a prior of intralayer Markovian diffusion, here we show that by using local information provided by a random walker navigating the aggregated network, it is possible to determine, in a robust manner, whether these dynamics can be more accurately represented by a single layer or if they are better explained by a (hidden) multiplex structure. In the latter case, we also provide Bayesian methods to estimate the most probable number of hidden layers and the model parameters, thereby fully reconstructing its architecture. The whole methodology enables us to decipher the underlying multiplex architecture of complex systems by exploiting the non-Markovian signatures on the statistics of a single random walk on the aggregated network. In fact, the mathematical formalism presented here extends above and beyond detection of physical layers in networked complex systems, as it provides a principled solution for the optimal decomposition and projection of complex, non-Markovian dynamics into a Markov switching combination of diffusive modes.
On the use of the channel second-order statistics in MMSE receivers for time- and frequency-selective MIMO transmission systems
(Springer, 2016-11-29) Vázquez López, Manuel Alberto; Míguez Arenas, Joaquín; Comunidad de Madrid; Ministerio de Economía y Competitividad (España)
Equalization of unknown frequency- and time-selective multiple input multiple output (MIMO) channels is often carried out by means of decision feedback receivers. These consist of a channel estimator and a linear filter (for the estimation of the transmitted symbols), interconnected by a feedback loop through a symbol-wise threshold detector. The linear filter is often a minimum mean square error (MMSE) filter, and its mathematical expression involves second-order statistics (SOS) of the channel, which are usually ignored by simply assuming that the channel is a known (deterministic) parameter given by an estimate thereof. This appears to be suboptimal and in this work we investigate the kind of performance gains that can be expected when the MMSE equalizer is obtained using SOS of the channel process. As a result, we demonstrate that improvements of several dBs in the signal-to-noise ratio needed to achieve a prescribed symbol error rate are possible.
Prior Design for Dependent Dirichlet Processes: An Application to Marathon Modeling
(PLOS, 2016-01-28) Fernández Pradier, Mélanie; Rodríguez Ruiz, Francisco Jesús; Pérez Cruz, Fernando; Comunidad de Madrid; European Commission; Ministerio de Economía (España); Ministerio de Educación (España)
This paper presents a novel application of Bayesian nonparametrics (BNP) for marathon data modeling. We make use of two well-known BNP priors, the single-p dependent Dirichlet process and the hierarchical Dirichlet process, in order to address two different problems. First, we study the impact of age, gender and environment on the runners' performance. We derive a fair grading method that allows direct comparison of runners regardless of their age and gender. Unlike current grading systems, our approach is based not only on top world records, but on the performances of all runners. The presented methodology for comparison of densities can be adopted in many other applications straightforwardly, providing an interesting perspective to build dependent Dirichlet processes. Second, we analyze the running patterns of the marathoners in time, obtaining information that can be valuable for training purposes. We also show that these running patterns can be used to predict finishing time given intermediate interval measurements. We apply our models to New York City, Boston and London marathons.
A comparison of Monte Carlo-based Bayesian parameter estimation methods for stochastic models of genetic networks
(Public Library of Science (PLoS), 2017-08-10) Marino, Ines P; Zaikin, Alexey; Míguez Arenas, Joaquín; Ministerio de Economía y Competitividad (España); Ministerio de Educación, Cultura y Deporte (España)
We compare three state-of-the-art Bayesian inference methods for the estimation of the unknown parameters in a stochastic model of a genetic network. In particular, we introduce a stochastic version of the paradigmatic synthetic multicellular clock model proposed by Ullner et al., 2007. By introducing dynamical noise in the model and assuming that the partial observations of the system are contaminated by additive noise, we enable a principled mechanism to represent experimental uncertainties in the synthesis of the multicellular system and pave the way for the design of probabilistic methods for the estimation of any unknowns in the model. Within this setup, we tackle the Bayesian estimation of a subset of the model parameters. Specifically, we compare three Monte Carlo based numerical methods for the approximation of the posterior probability density function of the unknown parameters given a set of partial and noisy observations of the system. The schemes we assess are the particle Metropolis-Hastings (PMH) algorithm, the nonlinear population Monte Carlo (NPMC) method and the approximate Bayesian computation sequential Monte Carlo (ABC-SMC) scheme. We present an extensive numerical simulation study, which shows that while the three techniques can effectively solve the problem there are significant differences both in estimation accuracy and computational efficiency.
Covariance determination for improving uncertainty realism in orbit determination and propagation
(Elsevier, 2023-10-01) Cano Sanchez, Alejandro; Pastor Rodríguez, Alejandro; Escobar, Diego; Míguez Arenas, Joaquín; Sanjurjo Rivo, Manuel; Comunidad de Madrid
The reliability of the uncertainty characterization, also known as uncertainty realism, is of the uttermost importance for Space Situational Awareness (SSA) services. Among the many sources of uncertainty in the space environment, the most relevant one is the inherent uncertainty of the dynamic models, which is generally not considered in the batch least-squares Orbit Determination (OD) processes in operational scenarios. A classical approach to account for these sources of uncertainty is the theory of consider parameters. In this approach, a set of uncertain parameters are included in the underlying dynamical model, in such a way that the model uncertainty is represented by the variances of these parameters. However, realistic variances of these consider parameters are not known a priori. This work introduces a methodology to infer the variance of consider parameters based on the observed distribution of the Mahalanobis distance of the orbital differences between predicted and estimated orbits, which theoretically should follow a chi-square distribution under Gaussian assumptions. Empirical Distribution Function statistics such as the Cramer-von-Mises and the Kolmogorov–Smirnov distances are used to determine optimum consider parameter variances. The methodology is presented in this paper and validated in a series of simulated scenarios emulating the complexity of operational applications.
Optimum Bayesian thresholds for rebalanced classification problems using class-switching ensembles
(Elsevier, 2023-03) Gutiérrez López, Aitor; González Serrano, Francisco Javier; Figueiras, Aníbal; Ministerio de Ciencia e Innovación (España)
Asymmetric label switching is an effective and principled method for creating a diverse ensemble of learners for imbalanced classification problems. This technique can be combined with other rebalancing mechanisms, such as those based on cost policies or class proportion modifications. In this study, and under the Bayesian theory framework, we specify the optimal decision thresholds for the combination of these mechanisms. In addition, we propose using a gating network to aggregate the learners contributions as an additional mechanism to improve the overall performance of the system.
Multinomial sampling of latent variables for hierarchical change-point detection
(Springer, 2021-10-08) Romero Medrano, Lorena; Moreno Múñoz, Pablo; Artés Rodríguez, Antonio; Agencia Estatal de Investigación (España); Comunidad de Madrid; European Commission
Bayesian change-point detection, with latent variable models, allows to perform segmentation of high-dimensional time-series with heterogeneous statistical nature. We assume that change-points lie on a lower-dimensional manifold where we aim to infer a discrete representation via subsets of latent variables. For this particular model, full inference is computationally unfeasible and pseudo-observations based on point-estimates of latent variables are used instead. However, if their estimation is not certain enough, change-point detection gets affected. To circumvent this problem, we propose a multinomial sampling methodology that improves the detection rate and reduces the delay while keeping complexity stable and inference analytically tractable. Our experiments show results that outperform the baseline method and we also provide an example oriented to a human behavioral study.
Efficient evaluation of the error probability for pilot-assisted URLLC with massive MIMO
(IEEE, 2023-07) Kislal, A. Oguz; Lancho Serrano, Alejandro; Giuseppe, Durisi; Ström, Erik G.; European Commission
We propose a numerically efficient method for evaluating the random-coding union bound with parameter s on the error probability achievable in the finite-blocklength regime by a pilot-assisted transmission scheme employing Gaussian codebooks and operating over a memoryless block-fading channel. Our method relies on the saddlepoint approximation, which, differently from previous results reported for similar scenarios, is performed with respect to the number of fading blocks (a.k.a. diversity branches) spanned by each codeword, instead of the number of channel uses per block. This different approach avoids a costly numerical averaging of the error probability over the realizations of the fading process and of its pilot-based estimate at the receiver and results in a significant reduction of the number of channel realizations required to estimate the error probability accurately. Our numerical experiments for both single-antenna communication links and massive multiple-input multiple-output (MIMO) networks show that, when two or more diversity branches are available, the error probability can be estimated accurately with the saddlepoint approximation with respect to the number of fading blocks using a numerical method that requires about two orders of magnitude fewer Monte-Carlo samples than with the saddlepoint approximation with respect to the number of channel uses per block.
Unsourced multiple access with random user activity
(IEEE, 2023-07) Ngo, K. Hoang; Lancho Serrano, Alejandro; Giuseppe, Durisi; Graell I Amat, Alexandre; European Commission
To account for the massive uncoordinated random access scenario, which is relevant for the Internet of Things, Polyanskiy et al. (2017) proposed a novel formulation of the multiple-access problem, commonly referred to as unsourced multiple access, where all users employ a common codebook and the receiver decodes up to a permutation of the messages. In this paper, we extend this seminal work to the case where the number of active users is random and unknown a priori . We define a random-access code accounting for both misdetection (MD) and false alarm (FA), and derive a random-coding achievability bound for the Gaussian multiple access channel. Our bound captures the fundamental trade-off between MD and FA probabilities. It suggests that the lack of knowledge of the number of active users entails a small penalty in energy efficiency when the target MD and FA probabilities are high. However, as the target MD and FA probabilities decrease, the energy efficiency penalty becomes more significant. For example, in a typical IoT scenario with framelength 19200 complex channel uses and 25-300 active users in average, the required energy per bit to achieve both MD and FA probabilities below $10^{-1}$ , predicted by our bound, is only 0.5-;0.7 dB higher than that predicted by the bound in Polyanskiy et al. (2017) for a known number of active users. This gap increases to 3-4 dB when the target MD probability and/or FA probability is below $10^{-3}$ . Taking both MD and FA into account, we use our bound to benchmark the energy efficiency of slotted-ALOHA with multi-packet reception, of a decoder that simply treats interference as noise, and of some recently proposed unsourced multiple access schemes. Numerical results suggest that, when the target MD and FA probabilities are high, it is effective to estimate the number of active users, then treat this estimate as the true value, and use a coding scheme that performs well for the case of known number of active users. However, this approach becomes energy inefficient when the requirements on MD and FA probabilities are stringent.
Real time detection of malicious DoH traffic using statistical analysis
(Elsevier, 2023-10) Moure Garrido, Marta; Campo Vázquez, María Celeste; García Rubio, Carlos; Comunidad de Madrid; Ministerio de Ciencia e Innovación (España)
The DNS protocol plays a fundamental role in the operation of ubiquitous networks. All devices connected to these networks need DNS to work, both for traditional domain name to IP address translation, and for more advanced services such as resource discovery. DNS over HTTPS (DoH) solves certain security problems present in the DNS protocol. However, malicious DNS tunnels, a covert way of encapsulating malicious traffic in a DNS connection, are difficult to detect because the encrypted data prevents performing an analysis of the content of the DNS traffic. In this study, we introduce a real-time system for detecting malicious DoH tunnels, which is based on analyzing DoH traffic using statistical methods. Our research demonstrates that it is feasible to identify in real-time malicious traffic by analyzing specific parameters extracted from DoH traffic. In addition, we conducted statistical analysis to identify the most significant features that distinguish malicious traffic from benign traffic. Using the selected features, we achieved satisfactory results in classifying DoH traffic as either benign or malicious.
Portable multispectral system based on color detector for the analysis of homogeneous surfaces
(Hindawi, 2019-01-01) Martínez Olmos, A; Martínez Olmos, Pablo; Erenas, M.M; Escobedo, P; Ministerio de Economía y Competitividad (España); Ministerio de Educación, Cultura y Deporte (España)
In this work, a compact affordable and portable spectral imaging system is presented. The system is intended to be employed in general applications, such as material classification or determination of the concentration of chemical species together with colorimetric sensors. The imaging device is reduced to a small digital color detector with an active area of 3x2mm2. This device provides a quantification of the incident emission in the form of four digital words corresponding to its averaged components blue, green, red, and near infrared. In this way, the size of the image is reduced to one pixel. The wavelength selection is carried out by means of a LED array disposed surrounding the color detector. The LEDs are selected to cover the wavelength range from 360 to 890nm. A sequential measurement protocol is followed, and the generated data is transmitted to an external portable device via a Bluetooth link where a classification protocol is implemented in a custom-developed Android application. The presented system has been applied in three different scenarios involving material classification, meat freshness monitoring, and chemical analysis. The analysis of the data using principal components shows that it is possible to find a set of wavelengths where the classification of the samples is optimal.
Optimizing HARQ and relay strategies in limited feedback communication systems
(MDPI, 2020-11-01) Zhang, Mai; Castillo, Andres; Peleato Iñarrea, Borja Manuel; European Commission
One of the key challenges for future communication systems is to deal with fast changing channels due to the mobility of users. Having a robust protocol capable of handling transmission failures in unfavorable channel conditions is crucial, but the feedback capacity may be greatly limited due to strict latency requirements. This paper studies the hybrid automatic repeat request (HARQ) techniques involved in re-transmissions when decoding failures occur at the receiver and proposes a scheme that relies on codeword bundling and adaptive incremental redundancy (IR) to maximize the overall throughput in a limited feedback system. In addition to the traditional codeword extension IR bits, this paper introduces a new type of IR, bundle parity bits, obtained from an erasure code across all the codewords in a bundle. The type and number of IR bits to be sent as a response to a decoding failure is optimized through a Markov Decision Process. In addition to the single link analysis, the paper studies how the same techniques generalize to relay and multi-user broadcast systems. Simulation results show that the proposed schemes can provide a significant increase in throughput over traditional HARQ techniques.
Universal mental health screening with a focus on suicidal behaviour using smartphones in a Mexican rural community: protocol for the SMART-SCREEN population-based survey
(BMJ, 2020-07-19) Arenas Castañeda, Pavel E.; Aroca Bisquert, Fuensanta; Martinez Nicolas, Ismael; Castillo Espindola, Luis A.; Barahona, Igor; Maya Hernandez, Cynthya; Lavana Hernandez, Martha Miriam; Manrique Miron, Paulo Cesar; Alvarado Barrera, Daniela Guadalupe; Treviño Aguilar, Erik; Barrios Nuñez, Axayacatl; De Jesus Carlos, Giovanna; Vildosola Garces, Anabel; Flores Mercado, Josselyne; Barrigón, María Luisa; Artés Rodríguez, Antonio; De Leon, Santiago; Molina Pizarro, Cristian Antonio; Rosado Franco, Arsenio; Perez-Rodriguez, Mercedes; Courtet, Philippe; Martínez-Alés, Gonzalo; Baca-García, Enrique
Introduction: Mental disorders represent the second cause of years lived with disability worldwide. Suicide mortality has been targeted as a key public health concern by the WHO. Smartphone technology provides a huge potential to develop massive and fast surveys. Given the vast cultural diversity of Mexico and its abrupt orography, smartphone-based resources are invaluable in order to adequately manage resources, services and preventive measures in the population. The objective of this study is to conduct a universal suicide risk screening in a rural area of Mexico, measuring also other mental health outcomes such as depression, anxiety and alcohol and substance use disorders. Methods and analysis: A population-based cross-sectional study with a temporary sampling space of 9 months will be performed between September 2019 and June 2020. We expect to recruit a large percentage of the target population (at least 70%) in a short-term survey of Milpa Alta Delegation, which accounts for 137 927 inhabitants in a territorial extension of 288 km2. They will be recruited via an institutional call and a massive public campaign to fill in an online questionnaire through mobile-assisted or computer-assisted web app. This questionnaire will include data on general health, validated questionnaires including Well-being Index 5, Patient Health Questionnaire-9, Generalized Anxiety Disorder Scale 2, Alcohol Use Disorders Identification Test, selected questions of the Drug Abuse Screening Test and Columbia-Suicide Severity Rating Scales and Diagnostic and statistical manual of mental disorders (DSM-5) questions about self-harm. We will take into account information regarding time to mobile app response and geo-spatial location, and aggregated data on social, demographical and environmental variables. Traditional regression modelling, multilevel mixed methods and data-driven machine learning approaches will be used to test hypotheses regarding suicide risk factors at the individual and the population level. Ethics and dissemination: Ethical approval (002/2019) was granted by the Ethics Review Board of the Hospital Psiquiátrico Yucatán, Yucatán (Mexico). This protocol has been registered in ClinicalTrials.gov. The starting date of the study is 3 September 2019. Results will serve for the planning and healthcare of groups with greater mental health needs and will be disseminated via publications in peer-reviewed journal and presented at relevant mental health conferences.
Adaptive quadrature schemes for bayesian inference via active learning
(IEEE, 2020-11-16) Llorente Fernández, Fernando; Martino, Luca; Elvira, Víctor; Delgado Gómez, David; López Santiago, Javier
We propose novel adaptive quadrature schemes based on an active learning procedure. We consider an interpolative approach for building a surrogate posterior density, combining it with Monte Carlo sampling methods and other quadrature rules. The nodes of the quadrature are sequentially chosen by maximizing a suitable acquisition function, which takes into account the current approximation of the posterior and the positions of the nodes. This maximization does not require additional evaluations of the true posterior. We introduce two specific schemes based on Gaussian and Nearest Neighbors bases. For the Gaussian case, we also provide a novel procedure for fitting the bandwidth parameter, in order to build a suitable emulator of a density function. With both techniques, we always obtain a positive estimation of the marginal likelihood (a.k.a., Bayesian evidence). An equivalent importance sampling interpretation is also described, which allows the design of extended schemes. Several theoretical results are provided and discussed. Numerical results show the advantage of the proposed approach, including a challenging inference problem in an astronomic dynamical model, with the goal of revealing the number of planets orbiting a star.
Parallel sequential Monte Carlo for stochastic gradient-free nonconvex optimization
(Springer, 2020-11) Akyildiz, Omer Deniz; Crisan, Dan; Míguez Arenas, Joaquín; Comunidad de Madrid; Agencia Estatal de Investigación (España)
We introduce and analyze a parallel sequential Monte Carlo methodology for the numerical solution of optimization problems that involve the minimization of a cost function that consists of the sum of many individual components. The proposed scheme is a stochastic zeroth-order optimization algorithm which demands only the capability to evaluate small subsets of components of the cost function. It can be depicted as a bank of samplers that generate particle approximations of several sequences of probability measures. These measures are constructed in such a way that they have associated probability density functions whose global maxima coincide with the global minima of the original cost function. The algorithm selects the best performing sampler and uses it to approximate a global minimum of the cost function. We prove analytically that the resulting estimator converges to a global minimum of the cost function almost surely and provide explicit convergence rates in terms of the number of generated Monte Carlo samples and the dimension of the search space. We show, by way of numerical examples, that the algorithm can tackle cost functions with multiple minima or with broad "flat" regions which are hard to minimize using gradient-based techniques.

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