Now showing items 1-20 of 538

    • Ensemble Kalman filtering with colored observation noise

      Raboudi, Naila Mohammed Fathi; Ait-El-Fquih, Boujemaa; Ombao, Hernando; Hoteit, Ibrahim (Quarterly Journal of the Royal Meteorological Society, Wiley, 2021-10-15) [Article]
      The Kalman filter (KF) is derived under the assumption of time-independent (white) observation noise. Although this assumption can be reasonable in many ocean and atmospheric applications, the recent increase in sensors coverage such as the launching of new constellations of satellites with global spatio-temporal coverage will provide high density of oceanic and atmospheric observations that are expected to have time-dependent (colored) error statistics. In this situation, the KF update has been shown to generally provide overconfident probability estimates, which may degrade the filter performance. Different KF-based schemes accounting for time-correlated observation noise were proposed for small systems by modeling the colored noise as a first-order autoregressive model driven by white Gaussian noise. This work introduces new ensemble Kalman filters (EnKFs) that account for colored observational noises for efficient data assimilation into large-scale oceanic and atmospheric applications. More specifically, we follow the standard and the one-step-ahead smoothing formulations of the Bayesian filtering problem with colored observational noise, modeled as an autoregressive model, to derive two (deterministic) EnKFs. We demonstrate the relevance of the colored observational noise-aware EnKFs and analyze their performances through extensive numerical experiments conducted with the Lorenz-96 model.
    • A stacked deep learning approach to cyber-attacks detection in industrial systems: application to power system and gas pipeline systems

      Wang, Wu; Harrou, Fouzi; Bouyeddou, Benamar; Senouci, Sidi-Mohammed; Sun, Ying (Cluster Computing, Springer Science and Business Media LLC, 2021-10-05) [Article]
      Presently, Supervisory Control and Data Acquisition (SCADA) systems are broadly adopted in remote monitoring large-scale production systems and modern power grids. However, SCADA systems are continuously exposed to various heterogeneous cyberattacks, making the detection task using the conventional intrusion detection systems (IDSs) very challenging. Furthermore, conventional security solutions, such as firewalls, and antivirus software, are not appropriate for fully protecting SCADA systems because they have distinct specifications. Thus, accurately detecting cyber-attacks in critical SCADA systems is undoubtedly indispensable to enhance their resilience, ensure safe operations, and avoid costly maintenance. The overarching goal of this paper is to detect malicious intrusions that already detoured traditional IDS and firewalls. In this paper, a stacked deep learning method is introduced to identify malicious attacks targeting SCADA systems. Specifically, we investigate the feasibility of a deep learning approach for intrusion detection in SCADA systems. Real data sets from two laboratory-scale SCADA systems, a two-line three-bus power transmission system and a gas pipeline are used to evaluate the proposed method’s performance. The results of this investigation show the satisfying detection performance of the proposed stacked deep learning approach. This study also showed that the proposed approach outperformed the standalone deep learning models and the state-of-the-art algorithms, including Nearest neighbor, Random forests, Naive Bayes, Adaboost, Support Vector Machine, and oneR. Besides detecting the malicious attacks, we also investigate the feature importance of the cyber-attacks detection process using the Random Forest procedure, which helps design more parsimonious models.
    • Variance partitioning in spatio-temporal disease mapping models

      Franco-Villoria, M.; Ventrucci, M.; Rue, Haavard (arXiv, 2021-09-27) [Preprint]
      Bayesian disease mapping, yet if undeniably useful to describe variation in risk over time and space, comes with the hurdle of prior elicitation on hard-to-interpret precision parameters. We introduce a reparametrized version of the popular spatio-temporal interaction models, based on Kronecker product intrinsic Gaussian Markov Random Fields, that we name variance partitioning (VP) model. The VP model includes a mixing parameter that balances the contribution of the main and interaction effects to the total (generalized) variance and enhances interpretability. The use of a penalized complexity prior on the mixing parameter aids in coding any prior information in a intuitive way. We illustrate the advantages of the VP model on two case studies.
    • Integer-valued autoregressive processes with prespecified marginal and innovation distributions: a novel perspective

      Guerrero, Matheus B.; Barreto-Souza, Wagner; Ombao, Hernando (Stochastic Models, Informa UK Limited, 2021-09-26) [Article]
      Integer-valued autoregressive (INAR) processes are generally defined by specifying the thinning operator and either the innovations or the marginal distributions. The major limitations of such processes include difficulties in deriving the marginal properties and justifying the choice of the thinning operator. To overcome these drawbacks, we propose a novel approach for building an INAR model that offers the flexibility to prespecify both marginal and innovation distributions. Thus, the thinning operator is no longer subjectively selected but is rather a direct consequence of the marginal and innovation distributions specified by the modeler. Novel INAR processes are introduced following this perspective; these processes include a model with geometric marginal and innovation distributions (Geo-INAR) and models with bounded innovations. We explore the Geo-INAR model, which is a natural alternative to the classical Poisson INAR model. The Geo-INAR process has interesting stochastic properties, such as MA(∞) representation, time reversibility, and closed forms for the hth-order transition probabilities, which enables a natural framework to perform coherent forecasting. To demonstrate the real-world application of the Geo-INAR model, we analyze a count time series of criminal records in sex offenses using the proposed methodology and compare it with existing INAR and integer-valued generalized autoregressive conditional heteroscedastic models.
    • Quantification of empirical determinacy: the impact of likelihood weighting on posterior location and spread in Bayesian meta-analysis estimated with JAGS and INLA

      Hunanyan, Sona; Rue, Haavard; Plummer, Martyn; Roos, Małgorzata (arXiv, 2021-09-24) [Preprint]
      The popular Bayesian meta-analysis expressed by Bayesian normal-normal hierarchical model (NNHM) synthesizes knowledge from several studies and is highly relevant in practice. Moreover, NNHM is the simplest Bayesian hierarchical model (BHM), which illustrates problems typical in more complex BHMs. Until now, it has been unclear to what extent the data determines the marginal posterior distributions of the parameters in NNHM. To address this issue we computed the second derivative of the Bhattacharyya coefficient with respect to the weighted likelihood, defined the total empirical determinacy (TED), the proportion of the empirical determinacy of location to TED (pEDL), and the proportion of the empirical determinacy of spread to TED (pEDS). We implemented this method in the R package \texttt{ed4bhm} and considered two case studies and one simulation study. We quantified TED, pEDL and pEDS under different modeling conditions such as model parametrization, the primary outcome, and the prior. This clarified to what extent the location and spread of the marginal posterior distributions of the parameters are determined by the data. Although these investigations focused on Bayesian NNHM, the method proposed is applicable more generally to complex BHMs.
    • Lattice Paths for Persistent Diagrams

      Chung, Moo K.; Ombao, Hernando (Springer International Publishing, 2021-09-21) [Conference Paper]
      Persistent homology has undergone significant development in recent years. However, one outstanding challenge is to build a coherent statistical inference procedure on persistent diagrams. In this paper, we first present a new lattice path representation for persistent diagrams. We then develop a new exact statistical inference procedure for lattice paths via combinatorial enumerations. The lattice path method is applied to the topological characterization of the protein structures of the COVID-19 virus. We demonstrate that there are topological changes during the conformational change of spike proteins.
    • Efficient land desertification detection using a deep learning-driven generative adversarial network approach: A case study

      Zerrouki, Nabil; Dairi, Abdelkader; Harrou, Fouzi; Zerrouki, Yacine; Sun, Ying (Concurrency and Computation: Practice and Experience, Wiley, 2021-09-12) [Article]
      Precisely detecting land cover changes aids in improving the analysis of the dynamics of the landscape and plays an essential role in mitigating the effects of desertification. Mainly, sensing desertification is challenging due to the high correlation between desertification and like-desertification events (e.g., deforestation). An efficient and flexible deep learning approach is introduced to address desertification detection through Landsat imagery. Essentially, a generative adversarial network (GAN)-based desertification detector is designed and for uncovering the pixels influenced by land cover changes. In this study, the adopted features have been derived from multi-temporal images and incorporate multispectral information without considering image segmentation preprocessing. Furthermore, to address desertification detection challenges, the GAN-based detector is constructed based on desertification-free features and then employed to identify atypical events associated with desertification changes. The GAN-detection algorithm flexibly learns relevant information from linear and nonlinear processes without prior assumption on data distribution and significantly enhances the detection's accuracy. The GAN-based desertification detector's performance has been assessed via multi-temporal Landsat optical images from the arid area nearby Biskra in Algeria. This region is selected in this work because desertification phenomena heavily impact it. Compared to some state-of-the-art methods, including deep Boltzmann machine (DBM), deep belief network (DBN), convolutional neural network (CNN), as well as two ensemble models, namely, random forests and AdaBoost, the proposed GAN-based detector offers superior discrimination performance of deserted regions. Results show the promising potential of the proposed GAN-based method for the analysis and detection of desertification changes. Results also revealed that the GAN-driven desertification detection approach outperforms the state-of-the-art methods.
    • Latent group detection in functional partially linear regression models

      Wang, Huixia Judy; Sun, Ying; Wang, Huixia Judy (Biometrics, Wiley, 2021-09-05) [Article]
      In this paper, we propose a functional partially linear regression model with latent group structures to accommodate the heterogeneous relationship between a scalar response and functional covariates. The proposed model is motivated by a salinity tolerance study of barley families, whose main objective is to detect salinity tolerant barley plants. Our model is flexible, allowing for heterogeneous functional coefficients while being efficient by pooling information within a group for estimation. We develop an algorithm in the spirit of the K-means clustering to identify latent groups of the subjects under study. We establish the consistency of the proposed estimator, derive the convergence rate and the asymptotic distribution, and develop inference procedures. We show by simulation studies that the proposed method has higher accuracy for recovering latent groups and for estimating the functional coefficients than existing methods. The analysis of the barley data shows that the proposed method can help identify groups of barley families with different salinity tolerant abilities.
    • An O(N) algorithm for computing expectation of N-dimensional truncated multi-variate normal distribution I: fundamentals

      Huang, Jingfang; Cao, Jian; Fang, Fuhui; Genton, Marc G.; Keyes, David E.; Turkiyyah, George (Advances in Computational Mathematics, Springer Science and Business Media LLC, 2021-09-01) [Article]
      In this paper, we present the fundamentals of a hierarchical algorithm for computing the N-dimensional integral ϕ(a,b;A)=∫abH(x)f(x|A)dx representing the expectation of a function H(X) where f(x|A) is the truncated multi-variate normal (TMVN) distribution with zero mean, x is the vector of integration variables for the N-dimensional random vector X, A is the inverse of the covariance matrix Σ, and a and b are constant vectors. The algorithm assumes that H(x) is “low-rank” and is designed for properly clustered X so that the matrix A has “low-rank” blocks and “low-dimensional” features. We demonstrate the divide-and-conquer idea when A is a symmetric positive definite tridiagonal matrix and present the necessary building blocks and rigorous potential theory–based algorithm analysis when A is given by the exponential covariance model. The algorithm overall complexity is O(N) for N-dimensional problems, with a prefactor determined by the rank of the off-diagonal matrix blocks and number of effective variables. Very high accuracy results for N as large as 2048 are obtained on a desktop computer with 16G memory using the fast Fourier transform (FFT) and non-uniform FFT to validate the analysis. The current paper focuses on the ideas using the simple yet representative examples where the off-diagonal matrix blocks are rank 1 and the number of effective variables is bounded by 2, to allow concise notations and easier explanation. In a subsequent paper, we discuss the generalization of current scheme using the sparse grid technique for higher rank problems and demonstrate how all the moments of kth order or less (a total of O(Nk) integrals) can be computed using O(Nk) operations for k ≥ 2 and O(NlogN) operations for k = 1.
    • Long-term outcome in patients with takotsubo syndrome : A single center study from Vienna.

      Pogran, Edita; Abd El-Razek, Ahmed; Gargiulo, Laura; Weihs, Valerie; Kaufmann, Christoph; Horvath, Samuel; Geppert, Alexander; Nürnberg, Michael; Wessely, Emil; Smetana, Peter; Huber, Kurt (Wiener klinische Wochenschrift, Springer Science and Business Media LLC, 2021-08-20) [Article]
      BackgroundThere is an increasing amount of evidence suggesting multiple fatal complications in takotsubo syndrome; however, findings on the long-term outcome are scarce and show inconsistent evidence.MethodsThis is a single center study of long-term prognosis in takotsubo patients admitted to the Klinik Ottakring, Vienna, Austria, from September 2006 to August 2019. We investigated the clinical features, prognostic factors and outcome of patients with takotsubo syndrome. Furthermore, survivors and non-survivors and patients with a different cause of death were compared.ResultsOverall, 147 patients were included in the study and 49 takotsubo patients (33.3%) died during the follow-up, with a median of 126 months. The most common cause of death was a non-cardiac cause (71.4% of all deaths), especially malignancies (26.5% of all deaths). Moreover, non-survivors were older and more often men with more comorbidities (chronic kidney disease, malignancy). Patients who died because of cardiovascular disease were older and more often women than patients who died due to non-cardiovascular cause. Adjusted analysis showed no feature of an independent predictor of cardiovascular mortality for takotsubo patients. Female gender (HR = 0.32, CI: 0.16-0.64, p 
    • A cyclostationary model for temporal forecasting and simulation of solar global horizontal irradiance

      Das, Soumya; Genton, Marc G.; Alshehri, Yasser Mohammed; Stenchikov, Georgiy L. (Environmetrics, Wiley, 2021-08-04) [Article]
      As part of Saudi Vision 2030, a major strategic framework developed by the Council of Economic and Development Affairs of Saudi Arabia, the country aims to reduce its dependency on oil and promote renewable energy for domestic power generation. Among the sustainable energy resources, solar energy is one of the leading resources because of the endowment of Saudi Arabia with plentiful sunlight exposure and year-round clear skies. This essentializes to forecast and simulate solar irradiance, in particular global horizontal irradiance (GHI), as accurately as possible, mainly to be utilized by the power system operators among many others. Motivated by a dataset of hourly solar GHIs, this article proposes a model for short-term point forecast and simulation of GHIs. Two key points, that make our model competent, are: (1) the consideration of the strong dependency of GHIs on aerosol optical depths and (2) the identification of the periodic correlation structure or cyclostationarity of GHIs. The proposed model is shown to produce better forecasts and more realistic simulations than a classical model, which fails to recognize the GHI data as cyclostationary. Further, simulated samples from both the models as well as the original GHI data are used to calculate the corresponding photovoltaic power outputs to provide a comprehensive comparison among them.
    • An Effective Wind Power Prediction using Latent Regression Models

      Bouyeddou, Benamar; Harrou, Fouzi; Saidi, Ahmed; Sun, Ying (IEEE, 2021-08-02) [Conference Paper]
      Wind power is considered one of the most promising renewable energies. Efficient prediction of wind power will support in efficiently integrating wind power in the power grid. However, the major challenge in wind power is its high fluctuation and intermittent nature, making it challenging to predict. This paper investigated and compared the performance of two commonly latent variable regression methods, namely principal component regression (PCR) and partial least squares regression (PLSR), for predicting wind power. Actual measurements recorded every 10 minutes from an actual wind turbine are used to demonstrate the prediction precision of the investigated techniques. The result showed that the prediction performances of PCR and PLSR are relatively comparable. The investigated models in this study can represent a helpful tool for model-based anomaly detection in wind turbines.
    • A temporal model for vertical extrapolation of wind speed and wind energy assessment

      Crippa, Paola; Alifa, Mariana; Bolster, Diogo; Genton, Marc G.; Castruccio, Stefano (Applied Energy, Elsevier BV, 2021-07-28) [Article]
      Accurate wind speed estimates at turbine hub height are critical for wind farm operational purposes, such as forecasting and grid operation, but also for wind energy assessments at regional scales. Power law models have widely been used for vertical wind speed profiles due to their simplicity and suitability for many applications over diverse geographic regions. The power law requires estimation of a wind shear coefficient, α, linking the surface wind speed to winds at higher altitudes. Prior studies have mostly adopted simplified models for α, ranging from a single constant, to a site-specific constant in time value. In this work we (i) develop a new model for α which is able to capture hourly variability across a range of geographic/topographic features; (ii) quantify its improved skill compared to prior studies; and (iii) demonstrate implications for wind energy estimates over a large geographical area. To achieve this we use long-term high-resolution simulations by the Weather Research and Forecasting model, as well as met-mast and radiosonde observations of vertical profiles of wind speed and other atmospheric properties. The study focuses on Saudi Arabia, an emerging country with ambitious renewable energy plans, and is part of a bigger effort supported by the Saudi Arabian government to characterize wind energy resources over the country. Results from this study indicate that the proposed model outperforms prior formulations of α, with a domain average reduction of the wind speed RMSE of 23–33%. Further, we show how these improved estimates impact assessments of wind energy potential and associated wind farm siting.
    • Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification

      Noman, Fuad; Ting, Chee-Ming; Kang, Hakmook; Phan, Raphael C. -W.; Boyd, Brian D.; Taylor, Warren D.; Ombao, Hernando (arXiv, 2021-07-27) [Preprint]
      Brain functional connectivity (FC) reveals biomarkers for identification of various neuropsychiatric disorders. Recent application of deep neural networks (DNNs) to connectome-based classification mostly relies on traditional convolutional neural networks using input connectivity matrices on a regular Euclidean grid. We propose a graph deep learning framework to incorporate the non-Euclidean information about graph structure for classifying functional magnetic resonance imaging (fMRI)- derived brain networks in major depressive disorder (MDD). We design a novel graph autoencoder (GAE) architecture based on the graph convolutional networks (GCNs) to embed the topological structure and node content of large-sized fMRI networks into low-dimensional latent representations. In network construction, we employ the Ledoit-Wolf (LDW) shrinkage method to estimate the high-dimensional FC metrics efficiently from fMRI data. We consider both supervised and unsupervised approaches for the graph embedded learning. The learned embeddings are then used as feature inputs for a deep fully-connected neural network (FCNN) to discriminate MDD from healthy controls. Evaluated on a resting-state fMRI MDD dataset with 43 subjects, results show that the proposed GAE-FCNN model significantly outperforms several state-of-the-art DNN methods for brain connectome classification, achieving accuracy of 72.50% using the LDW-FC metrics as node features. The graph embeddings of fMRI FC networks learned by the GAE also reveal apparent group differences between MDD and HC. Our new framework demonstrates feasibility of learning graph embeddings on brain networks to provide discriminative information for diagnosis of brain disorders.
    • Landslide size matters: A new data-driven, spatial prototype

      Lombardo, Luigi; Tanyas, Hakan; Huser, Raphaël; Guzzetti, Fausto; Castro-Camilo, Daniela (Engineering Geology, Elsevier BV, 2021-07-24) [Article]
      The standard definition of landslide hazard requires the estimation of where, when (or how frequently) and how large a given landslide event may be. The geoscientific community involved in statistical models has addressed the component pertaining to how large a landslide event may be by introducing the concept of landslide-event magnitude scale. This scale, which depends on the planimetric area of the given population of landslides, in analogy to the earthquake magnitude, has been expressed with a single value per landslide event. As a result, the geographic or spatially-distributed estimation of how large a population of landslide may be when considered at the slope scale, has been disregarded in statistically-based landslide hazard studies. Conversely, the estimation of the landslide extent has been commonly part of physically-based applications, though their implementation is often limited to very small regions. In this work, we initially present a review of methods developed for landslide hazard assessment since its first conception decades ago. Subsequently, we introduce for the first time a statistically-based model able to estimate the planimetric area of landslides aggregated per slope units. More specifically, we implemented a Bayesian version of a Generalized Additive Model where the maximum landslide size per slope unit and the sum of all landslide sizes per slope unit are predicted via a Log-Gaussian model. These “max” and “sum” models capture the spatial distribution of (aggregated) landslide sizes. We tested these models on a global dataset expressing the distribution of co-seismic landslides due to 24 earthquakes across the globe. The two models we present are both evaluated on a suite of performance diagnostics that suggest our models suitably predict the aggregated landslide extent per slope unit. In addition to a complex procedure involving variable selection and a spatial uncertainty estimation, we built our model over slopes where landslides triggered in response to seismic shaking, and simulated the expected failing surface over slopes where the landslides did not occur in the past. What we achieved is the first statistically-based model in the literature able to provide information about the extent of the failed surface across a given landscape. This information is vital in landslide hazard studies and should be combined with the estimation of landslide occurrence locations. This could ensure that governmental and territorial agencies have a complete probabilistic overview of how a population of landslides could behave in response to a specific trigger. The predictive models we present are currently valid only for the 25 cases we tested. Statistically estimating landslide extents is still at its infancy stage. Many more applications should be successfully validated before considering such models in an operational way. For instance, the validity of our models should still be verified at the regional or catchment scale, as much as it needs to be tested for different landslide types and triggers. However, we envision that this new spatial predictive paradigm could be a breakthrough in the literature and, in time, could even become part of official landslide risk assessment protocols.
    • Fault Detection in Solar PV Systems Using Hypothesis Testing

      Harrou, Fouzi; Taghezouit, Bilal; Bouyeddou, Benamar; Sun, Ying; Arab, Amar Hadj (IEEE, 2021-07-21) [Conference Paper]
      The demand for solar energy has rapidly increased throughout the world in recent years. However, anomalies in photovoltaic (PV) plants can reduce performances and result in serious consequences. Developing reliable statistical approaches able to detect anomalies in PV plants is vital to improving the management of these plants. Here, we present a statistical approach for detecting anomalies in the DC part of PV plants and partial shading. Firstly, we model the monitored PV plant. Then, we employ a generalized likelihood ratio test, which is a powerful anomaly detection tool, to check the residuals from the model and reveal anomalies in the supervised PV array. The proposed strategy is illustrated via actual measurements from a 9.54 PV plant.
    • BICNet: A Bayesian Approach for Estimating Task Effects on Intrinsic Connectivity Networks in fMRI Data

      Tang, Meini; Ting, Chee-Ming; Ombao, Hernando (arXiv, 2021-07-19) [Preprint]
      Intrinsic connectivity networks (ICNs) are specific dynamic functional brain networks that are consistently found under various conditions including rest and task. Studies have shown that some stimuli actually activate intrinsic connectivity through either suppression, excitation, moderation or modification. Nevertheless, the structure of ICNs and task-related effects on ICNs are not yet fully understood. In this paper, we propose a Bayesian Intrinsic Connectivity Network (BICNet) model to identify the ICNs and quantify the task-related effects on the ICN dynamics. Using an extended Bayesian dynamic sparse latent factor model, the proposed BICNet has the following advantages: (1) it simultaneously identifies the individual ICNs and group-level ICN spatial maps; (2) it robustly identifies ICNs by jointly modeling resting-state functional magnetic resonance imaging (rfMRI) and task-related functional magnetic resonance imaging (tfMRI); (3) compared to independent component analysis (ICA)-based methods, it can quantify the difference of ICNs amplitudes across different states; (4) it automatically performs feature selection through the sparsity of the ICNs rather than ad-hoc thresholding. The proposed BICNet was applied to the rfMRI and language tfMRI data from the Human Connectome Project (HCP) and the analysis identified several ICNs related to distinct language processing functions.
    • Multivariate Conway-Maxwell-Poisson Distribution: Sarmanov Method and Doubly-Intractable Bayesian Inference

      Piancastelli, Luiza S. C.; Friel, Nial; Barreto-Souza, Wagner; Ombao, Hernando (arXiv, 2021-07-15) [Preprint]
      In this paper, a multivariate count distribution with Conway-Maxwell (COM)-Poisson marginals is proposed. To do this, we develop a modification of the Sarmanov method for constructing multivariate distributions. Our multivariate COM-Poisson (MultCOMP) model has desirable features such as (i) it admits a flexible covariance matrix allowing for both negative and positive non-diagonal entries; (ii) it overcomes the limitation of the existing bivariate COM-Poisson distributions in the literature that do not have COM-Poisson marginals; (iii) it allows for the analysis of multivariate counts and is not just limited to bivariate counts. Inferential challenges are presented by the likelihood specification as it depends on a number of intractable normalizing constants involving the model parameters. These obstacles motivate us to propose a Bayesian inferential approach where the resulting doubly-intractable posterior is dealt with via the exchange algorithm and the Grouped Independence Metropolis-Hastings algorithm. Numerical experiments based on simulations are presented to illustrate the proposed Bayesian approach. We analyze the potential of the MultCOMP model through a real data application on the numbers of goals scored by the home and away teams in the Premier League from 2018 to 2021. Here, our interest is to assess the effect of a lack of crowds during the COVID-19 pandemic on the well-known home team advantage. A MultCOMP model fit shows that there is evidence of a decreased number of goals scored by the home team, not accompanied by a reduced score from the opponent. Hence, our analysis suggests a smaller home team advantage in the absence of crowds, which agrees with the opinion of several football experts.
    • Sex ratio at birth in Vietnam among six subnational regions during 1980–2050, estimation and probabilistic projection using a Bayesian hierarchical time series model with 2.9 million birth records

      Chao, Fengqing; Guilmoto, Christophe Z.; Ombao, Hernando (PLOS ONE, Public Library of Science (PLoS), 2021-07-14) [Article]
      The sex ratio at birth (SRB, i.e., the ratio of male to female births) in Vietnam has been imbalanced since the 2000s. Previous studies have revealed a rapid increase in the SRB over the past 15 years and the presence of important variations across regions. More recent studies suggested that the nation’s SRB may have plateaued during the 2010s. Given the lack of exhaustive birth registration data in Vietnam, it is necessary to estimate and project levels and trends in the regional SRBs in Vietnam based on a reproducible statistical approach. We compiled an extensive database on regional Vietnam SRBs based on all publicly available surveys and censuses and used a Bayesian hierarchical time series mixture model to estimate and project SRB in Vietnam by region from 1980 to 2050. The Bayesian model incorporates the uncertainties from the observations and year-by-year natural fluctuation. It includes a binary parameter to detect the existence of sex ratio transitions among Vietnamese regions. Furthermore, we model the SRB imbalance using a trapezoid function to capture the increase, stagnation, and decrease of the sex ratio transition by Vietnamese regions. The model results show that four out of six Vietnamese regions, namely, Northern Midlands and Mountain Areas, Northern Central and Central Coastal Areas, Red River Delta, and South East, have existing sex imbalances at birth. The rise in SRB in the Red River Delta was the fastest, as it took only 12 years and was more pronounced, with the SRB reaching the local maximum of 1.146 with a 95% credible interval (1.129, 1.163) in 2013. The model projections suggest that the current decade will record a sustained decline in sex imbalances at birth, and the SRB should be back to the national SRB baseline level of 1.06 in all regions by the mid-2030s.
    • A Field Guide to Federated Optimization

      Wang, Jianyu; Charles, Zachary; Xu, Zheng; Joshi, Gauri; McMahan, H. Brendan; Arcas, Blaise Aguera y; Al-Shedivat, Maruan; Andrew, Galen; Avestimehr, Salman; Daly, Katharine; Data, Deepesh; Diggavi, Suhas; Eichner, Hubert; Gadhikar, Advait; Garrett, Zachary; Girgis, Antonious M.; Hanzely, Filip; Hard, Andrew; He, Chaoyang; Horvath, Samuel; Huo, Zhouyuan; Ingerman, Alex; Jaggi, Martin; Javidi, Tara; Kairouz, Peter; Kale, Satyen; Karimireddy, Sai Praneeth; Konecny, Jakub; Koyejo, Sanmi; Li, Tian; Liu, Luyang; Mohri, Mehryar; Qi, Hang; Reddi, Sashank J.; Richtarik, Peter; Singhal, Karan; Smith, Virginia; Soltanolkotabi, Mahdi; Song, Weikang; Suresh, Ananda Theertha; Stich, Sebastian U.; Talwalkar, Ameet; Wang, Hongyi; Woodworth, Blake; Wu, Shanshan; Yu, Felix X.; Yuan, Honglin; Zaheer, Manzil; Zhang, Mi; Zhang, Tong; Zheng, Chunxiang; Zhu, Chen; Zhu, Wennan (arXiv, 2021-07-14) [Preprint]
      Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection. The distributed learning process can be formulated as solving federated optimization problems, which emphasize communication efficiency, data heterogeneity, compatibility with privacy and system requirements, and other constraints that are not primary considerations in other problem settings. This paper provides recommendations and guidelines on formulating, designing, evaluating and analyzing federated optimization algorithms through concrete examples and practical implementation, with a focus on conducting effective simulations to infer real-world performance. The goal of this work is not to survey the current literature, but to inspire researchers and practitioners to design federated learning algorithms that can be used in various practical applications.