Miracle-dz/UniFAR-FTUs huggingface dataset:
A circular nonhomogeneous hidden Markov field for the spatial segmentation of wildfire occurrences

Motivated by studies of wildfire seasonality, we propose a nonhomogeneous hidden Markov random field to model the spatial distribution of georeferenced fire occurrences during the year, by representing occurrence times as circular data. The model is based on a mixture of Kato–Jones circular densities, whose parameters vary across space according to a latent nonhomogeneous Potts model, modulated by georeferenced covariates. It allows us to segment fire occurrences according to a finite number of latent classes that represent the conditional distributions of the data under specific periods of the year, simultaneously accounting for unobserved heterogeneity and spatial autocorrelation. Further, it parsimoniously accommodates specific features of wildfire occurrence data such as multimodality, skewness, and kurtosis. Due to the numerical intractability of the likelihood function, estimation of the parameters is based on composite likelihood methods. It reduces to a computationally efficient expectation–maximization algorithm that iteratively alternates the maximization of a weighted composite likelihood function with weights updating. The proposal is illustrated in a study of wildfire occurrences in the Iberian Peninsula during a decade.
Implicit Langevin Algorithms for Sampling From Log-concave Densities

For sampling from a log-concave density, we study implicit integrators resulting from $\theta$-method discretization of the overdamped Langevin diffusion stochastic differential equation. Theoretical and algorithmic properties of the resulting sampling methods for $ \theta \in [0,1] $ and a range of step sizes are established. Our results generalize and extend prior works in several directions. In particular, for $\theta\ge1/2$, we prove geometric ergodicity and stability of the resulting methods for all step sizes. We show that obtaining subsequent samples amounts to solving a strongly-convex optimization problem, which is readily achievable using one of numerous existing methods. Numerical examples supporting our theoretical analysis are also presented.
Parametrization and generation of geological models with generative adversarial networks

One of the main challenges in the parametrization of geological models is the ability to capture complex geological structures often observed in the subsurface. In recent years, generative adversarial networks (GAN) were proposed as an efficient method for the generation and parametrization of complex data, showing state-of-the-art performances in challenging computer vision tasks such as reproducing natural images (handwritten digits, human faces, etc.). In this work, we study the application of Wasserstein GAN for the parametrization of geological models. The effectiveness of the method is assessed for uncertainty propagation tasks using several test cases involving different permeability patterns and subsurface flow problems. Results show that GANs are able to generate samples that preserve the multipoint statistical features of the geological models both visually and quantitatively. The generated samples reproduce both the geological structures and the flow statistics of the reference geology.
On minimax optimality of sparse Bayes predictive density estimates

We study predictive density estimation under Kullback-Leibler loss in $\ell_0$-sparse Gaussian sequence models. We propose proper Bayes predictive density estimates and establish asymptotic minimaxity in sparse models. A surprise is the existence of a phase transition in the future-to-past variance ratio $r$. For $r < r_0 = (\surd 5 - 1)/4$, the natural discrete prior ceases to be asymptotically optimal. Instead, for subcritical $r$, a `bi-grid' prior with a central region of reduced grid spacing recovers asymptotic minimaxity. This phenomenon seems to have no analog in the otherwise parallel theory of point estimation of a multivariate normal mean under quadratic loss. For spike-and-slab priors to have any prospect of minimaxity, we show that the sparse parameter space needs also to be magnitude constrained. Within a substantial range of magnitudes, spike-and-slab priors can attain asymptotic minimaxity.
Empirical Priors for Prediction in Sparse High-dimensional Linear Regression

In this paper we adopt the familiar sparse, high-dimensional linear regression model and focus on the important but often overlooked task of prediction. In particular, we consider a new empirical Bayes framework that incorporates data in the prior in two ways: one is to center the prior for the non-zero regression coefficients and the other is to provide some additional regularization. We show that, in certain settings, the asymptotic concentration of the proposed empirical Bayes posterior predictive distribution is very fast, and we establish a Bernstein--von Mises theorem which ensures that the derived empirical Bayes prediction intervals achieve the targeted frequentist coverage probability. The empirical prior has a convenient conjugate form, so posterior computations are relatively simple and fast. Finally, our numerical results demonstrate the proposed method's strong finite-sample performance in terms of prediction accuracy, uncertainty quantification, and computation time compared to existing Bayesian methods.
Child mortality estimation incorporating summary birth history data

The United Nations' Sustainable Development Goal 3.2 aims to reduce under‐five child mortality to 25 deaths per 1000 live births by 2030. Child mortality tends to be concentrated in developing regions where information needed to assess achievement of this goal often comes from surveys and censuses. In both, women are asked about their birth histories, but with varying degrees of detail. Full birth history (FBH) data contain the reported dates of births and deaths of every surveyed mother's children. In contrast, summary birth history (SBH) data contain only the total number of children born and total number of children who died for each mother. Specialized methods are needed to accommodate this type of data into analyses of child mortality trends. We develop a data augmentation scheme within a Bayesian framework where for SBH data, birth and death dates are introduced as auxiliary variables. Since we specify a full probability model for the data, many of the well‐known biases that exist in this data can be accommodated, along with space‐time smoothing on the underlying mortality rates. We illustrate our approach in a simulation, showing robustness to model misspecification and that uncertainty is reduced when incorporating SBH data over simply analyzing all available FBH data. We also apply our approach to data from the Central region of Malawi and compare with the well‐known Brass method.
ESFNet: Efficient Network for Building Extraction From High-Resolution Aerial Images

Building footprint extraction from high-resolution aerial images is always an essential part of urban dynamic monitoring, planning, and management. It has also been a challenging task in remote sensing research. In recent years, deep neural networks have made great achievement in improving the accuracy of building extraction from remote sensing imagery. However, most of the existing approaches usually require a large amount of parameters and floating point operations for high accuracy, it leads to high memory consumption and low inference speed which are harmful to research. In this paper, we proposed a novel efficient network named ESFNet which employs separable factorized residual block and utilizes the dilated convolutions, aiming to preserve slight accuracy loss with low computational cost and memory consumption. Our ESFNet obtains a better trade-off between accuracy and efficiency, it can run at over 100 FPS on single Tesla V100, requires 6x fewer FLOPs and has 18x fewer parameters than state-of-the-art real-time architecture ERFNet while preserving similar accuracy without any additional context module, post-processing and pre-trained scheme. We evaluated our networks on WHU building dataset and compared it with other state-of-the-art architectures. The result and comprehensive analysis show that our networks are benefit for efficient remote sensing researches, and the idea can be further extended to other areas. The code is publicly available at: https://github.com/mrluin/ESFNet-Pytorch
Rapid-cycling synchrotron for multi-megawatt proton facility at Fermilab

The Fermilab accelerator complex delivers intense high-energy proton beams to a variety of fixed-target scientific programs, including a flagship long-baseline neutrino program. With the advent of the Deep Underground Neutrino Experiment (DUNE) and Long Baseline Neutrino Facility (LBNF) program there is strong motivation for a 2.4 MW beam power upgrade of the Fermilab proton facility. We show the Fermilab proton facility can achieve 2.4 MW with a new rapid-cycling synchrotron (RCS) to replace the Fermilab Booster and we provide a comprehensive technical analysis of the RCS-based facility design. Past design efforts and operational experience at the Fermilab Booster, J-PARC RCS, and Oak Ridge SNS are leveraged to provide strong empirical precedent for the design. We provide a parametric study of slip-stacking accumulation, RCS extraction energy, space-charge limits, beampipe aperture, eddy current heating, injection foil heating, and lattice optics. The 2.4 MW benchmark for the long baseline neutrino program is achieved independently of a previously proposed multi-GeV linac program, but we assess the impact the linac upgrade would have on RCS performance.
Optimal FDR control in the two-group model

The highly influential two group model in testing a large number of statistical hypothesis assumes that the test statistics come from a mixture of a high probability null distribution and a low probability alternative. Optimal control of the marginal false discovery rate (mFDR), in the sense that it provides maximal power (expected true discoveries) subject to mFDR control, is achieved by thresholding the local false discovery rate (locFDR) with a fixed threshold. In this paper we address the challenge of controlling the popular false discovery rate (FDR) rather than mFDR in the two group model. Since FDR is less conservative, this results in more rejections. We derive the optimal multiple testing (OMT) policy for this task, which turns out to be thresholding the locFDR with a threshold that is a function of the entire set of statistics. We show how to evaluate this threshold in time that is linear in the number of hypotheses, leading to an efficient algorithm for finding this policy. Thus, we can easily derive and apply the optimal procedure for problems with thousands of hypotheses. We show that for K=5000 hypotheses there can be significant power gain in OMT with FDR versus mFDR control.
Towards A Better Understanding Of The Leading Digits Phenomena

That the logarithmic distribution manifests itself in the random as well as in the deterministic (multiplication processes) has long intrigued researchers in Benford's Law. In this article it is argued that it springs from one common intrinsic feature of their density curves. On the other hand, the profound dichotomy between the random and the deterministic in the context of Benford's Law is noted here, acknowledging the need to distinguish between them. From its very inception, the field has been suffering from a profound confusion and mixing of these two very different logarithmic flavors, causing mistaken conclusions. One example is Allaart's proof of equality of sums along digital lines, which can only be applied to deterministic processes. Random data lack this equality and consistently show significantly larger sums for lower digits, thus rendering any attempt at test of summation equality irrelevant and futile in the context of forensic analysis regarding accounting and financial fraud detection. Another digital regularity is suggested here, one that is found in logarithmic as well as non-logarithmic random data sets. In addition, chains of distributions that are linked via parameter selection are found to be logarithmic, either in the limit where the number of the sequences in the chain approaches infinity, or where the distributions generating the parameters are themselves logarithmic. A new forensic data analysis method in the context of fraud detection is suggested here even for data types that do not obey Benford's Law, and in particularly regarding tax evasion applications. This can also serve as a robust forensic tool to investigate fraudulent fake data provided by the sophisticated cheater already aware of Benford's Law, a challenge that would become increasing problematic to tax authorities in the future as Benford's Law becomes almost common knowledge.
Local calibration of verbal autopsy algorithms

Computer-coded verbal autopsy (CCVA) algorithms predict cause of death from high-dimensional family questionnaire data (verbal autopsies) of a deceased individual. CCVA algorithms are typically trained on non-local data, then used to generate national and regional estimates of cause-specific mortality fractions. These estimates may be inaccurate if the non-local training data is different from the local population of interest. This problem is a special case of transfer learning. However, most transfer learning classification approaches are concerned with individual (e.g. a person's) classification within a target domain (e.g. a particular population) with training performed in data from a source domain. Epidemiologists are often more interested in estimating population-level etiological distributions, using datasets much smaller than those used in common transfer learning applications. We present a parsimonious hierarchical Bayesian transfer learning framework to directly estimate population-level class probabilities in a target domain. To address small sample sizes, we introduce a novel shrinkage prior for the transfer error rates guaranteeing that, in absence of any labeled target domain data or when the baseline classifier has zero transfer error, the calibrated estimate of class probabilities coincides with the naive estimates from the baseline classifier, thereby subsuming the default practice as a special case. A novel Gibbs sampler using data-augmentation enables fast implementation. We extend our approach to use not one, but an ensemble of baseline classifiers. Theoretical and empirical results demonstrate how the ensemble model favors the most accurate baseline classifier. We present extensions allowing class probabilities to vary with covariates, and an EM-algorithm-based MAP estimation. An R-package implementing this method is developed.
Human Papillomavirus Antibody Levels and Quadrivalent Vaccine Clinical Effectiveness in Perinatally Human Immunodeficiency Virus-infected and Exposed, Uninfected Youth.

BACKGROUND Persons who are infected with human immunodeficiency virus (HIV) are at high risk of human papillomavirus (HPV)-associated cancers. The objectives are to compare antibody titers to HPV 6, 11, 16, and 18 and rate of abnormal cytology between perinatally HIV-infected (PHIV) and perinatally HIV-exposed, uninfected (PHEU) youth. METHODS This is a prospective observational cohort study of HPV4 vaccinated youth performed as part of the multicenter Pediatric HIV/AIDS Cohort Study Adolescent Master Protocol. Seroconversion and geometric mean titer (GMT) against HPV types 6, 11, 16, and 18 were calculated. Vaccine effectiveness included rates of abnormal cervical cytology and genital warts. RESULTS Seroconversion to HPV 6, 11, 16, and 18 occurred in 83%, 84%, 90%, and 62% of 310 vaccinated PHIV youth compared to 94%, 96%, 99%, and 87% of 148 vaccinated PHEU youth, respectively (P < .05 for all comparisons). GMTs were lower in the PHIV vs PHEU within each category of HPV4 doses received. Higher GMTs were associated with younger age, lower HIV type 1 RNA viral load, and higher CD4% at first HPV4 vaccination, as well as shorter duration between last vaccine dose and antibody specimen. Abnormal cytology occurred in 33 of 56 PHIV and 1 of 7 PHEU sexually active vaccinated females, yielding incidence rates per 100 person-years of 15.0 (10.9 to 20.6) and 2.9 (0.4 to 22.3), respectively. CONCLUSION Antibody titers to HPV4 were lower for all serotypes in PHIV compared to PHEU youth. Protection against abnormal cytology was also diminished in sexually active PHIV females.
Evaluation of Automatic Blood Analyzer as Screening Method in Fetomaternal Hemorrhage

Screening of fetomaternal hemorrhage (FMH) is essential in management of fetomaternal antigen incompatibilities of blood. The objective in this study was to evaluate the ability of automatic blood analyzer (ABA) to screen FMH, also comparing this method with flow cytometry (FCM). The contents of fetal red blood cells and fetal hemoglobin were evaluated by FCM and ABA, respectively, using both blood samples of male adults laced with umbilical cord blood diluted at 1/10, 1/100, 1/1,000, and 1/10,000, or blood from puerperal women collected within 48 hours following delivery. FCM had better performance (area under curve, AUC = 0.8723) than ABA (AUC = 0.6569) in detecting fetal blood laced with blood from male adults. At a critical level of 0.5%, ABA indicated that 27.5% of puerperal women would have FMH while FCM did not detect FMH. Our results showed that ABA overestimates FMH and disagrees with FCM on indicating puerperal women with FMH. ABA is inadequate for being used to screen for or to measure FMH.
Assessing the reliability of ensemble forecasting systems under serial dependence

The problem of testing the reliability of ensemble forecasting systems is revisited. A popular tool to assess the reliability of ensemble forecasting systems (for scalar verifications) is the rank histogram; this histogram is expected to be more or less flat, since for a reliable ensemble, the ranks are uniformly distributed among their possible outcomes. Quantitative tests for flatness (e.g. Pearson’s goodness–of–fit test) have been suggested; without exception though, these tests assume the ranks to be a sequence of independent random variables, which is not the case in general as can be demonstrated with simple toy examples. In this paper, tests are developed that take the temporal correlations between the ranks into account. A refined analysis shows that exploiting the reliability property, the ranks still exhibit strong decay of correlations. This property is key to the analysis, and the proposed tests are valid for general ensemble forecasting systems with minimal extraneous assumptions.
orthoDr: Semiparametric Dimension Reduction via Orthogonality Constrained Optimization

orthoDr is a package in R that solves dimension reduction problems using orthogonality constrained optimization approach. The package serves as a unified framework for many regression and survival analysis dimension reduction models that utilize semiparametric estimating equations. The main computational machinery of orthoDr is a first-order algorithm developed by \cite{wen2013feasible} for optimization within the Stiefel manifold. We implement the algorithm through Rcpp and OpenMP for fast computation. In addition, we developed a general-purpose solver for such constrained problems with user-specified objective functions, which works as a drop-in version of optim(). The package also serves as a platform for future methodology developments along this line of work.
Sequentially additive nonignorable missing data modelling using auxiliary marginal information

We study a class of missingness mechanisms, referred to as sequentially additive nonignorable, for modelling multivariate data with item nonresponse. These mechanisms explicitly allow the probability of nonresponse for each variable to depend on the value of that variable, thereby representing nonignorable missingness mechanisms. These missing data models are identified by making use of auxiliary information on marginal distributions, such as marginal probabilities for multivariate categorical variables or moments for numeric variables. We prove identification results and illustrate the use of these mechanisms in an application.
Hanson–Wright inequality in Hilbert spaces with application to $K$-means clustering for non-Euclidean data

We derive a dimensional-free Hanson-Wright inequality for quadratic forms of independent sub-gaussian random variables in a separable Hilbert space. Our inequality is an infinite-dimensional generalization of the classical Hanson-Wright inequality for finite-dimensional Euclidean random vectors. We illustrate an application to the generalized $K$-means clustering problem for non-Euclidean data. Specifically, we establish the exponential rate of convergence for a semidefinite relaxation of the generalized $K$-means, which together with a simple rounding algorithm imply the exact recovery of the true clustering structure.
Hieroglyph: Hierarchical Glia Graph Skeletonization and Matching

Automatic 3D reconstruction of glia morphology is a powerful tool necessary for investigating the role of microglia in neurological disorders in the central nervous system. Current glia skeleton reconstruction techniques fail to capture an accurate tracing of the processes over time, useful for the study of the microglia motility and morphology in the brain during healthy and diseased states. We propose Hieroglyph, a fully automatic temporal 3D skeleton reconstruction algorithm for glia imaged via 3D multiphoton microscopy. Hieroglyph yielded a 21% performance increase compared to state of the art automatic skeleton reconstruction methods and outperforms the state of the art in different measures of consistency on datasets of 3D images of microglia. The results from this method provide a 3D graph and digital reconstruction of glia useful for a myriad of morphological analyses that could impact studies in brain immunology and disease.
Robust nonparametric inference for the median

We consider the problem of constructing robust nonparametric confidence intervals and tests of hypothesis for the median when the data distribution is unknown and the data may contain a small fraction of contamination. We propose a modification of the sign test (and its associated confidence interval) which attains the nominal significance level (probability coverage) for any distribution in the contamination neighborhood of a continuous distribution. We also define some measures of robustness and efficiency under contamination for confidence intervals and tests. These measures are computed for the proposed procedures.
A scalable estimate of the extra-sample prediction error via approximate leave-one-out

The paper considers the problem of out-of-sample risk estimation under the high dimensional settings where standard techniques such as $K$-fold cross validation suffer from large biases. Motivated by the low bias of the leave-one-out cross validation (LO) method, we propose a computationally efficient closed-form approximate leave-one-out formula (ALO) for a large class of regularized estimators. Given the regularized estimate, calculating ALO requires minor computational overhead. With minor assumptions about the data generating process, we obtain a finite-sample upper bound for $|\text{LO} - \text{ALO}|$. Our theoretical analysis illustrates that $|\text{LO} - \text{ALO}| \rightarrow 0$ with overwhelming probability, when $n,p \rightarrow \infty$, where the dimension $p$ of the feature vectors may be comparable with or even greater than the number of observations, $n$. Despite the high-dimensionality of the problem, our theoretical results do not require any sparsity assumption on the vector of regression coefficients. Our extensive numerical experiments show that $|\text{LO} - \text{ALO}|$ decreases as $n,p$ increase, revealing the excellent finite sample performance of ALO. We further illustrate the usefulness of our proposed out-of-sample risk estimation method by an example of real recordings from spatially sensitive neurons (grid cells) in the medial entorhinal cortex of a rat.