Miracle-dz/UniFAR-FTUs huggingface dataset:
A systematic approach to improving the reliability and scale of evidence from health care data

Concerns over reproducibility in science extend to research using existing healthcare data; many observational studies investigating the same topic produce conflicting results, even when using the same data. To address this problem, we propose a paradigm shift. The current paradigm centers on generating one estimate at a time using a unique study design with unknown reliability and publishing (or not) one estimate at a time. The new paradigm advocates for high-throughput observational studies using consistent and standardized methods, allowing evaluation, calibration, and unbiased dissemination to generate a more reliable and complete evidence base. We demonstrate this new paradigm by comparing all depression treatments for a set of outcomes, producing 17,718 hazard ratios, each using methodology on par with state-of-the-art studies. We furthermore include control hypotheses to evaluate and calibrate our evidence generation process. Results show good transitivity and consistency between databases, and agree with four out of the five findings from clinical trials. The distribution of effect size estimates reported in literature reveals an absence of small or null effects, with a sharp cutoff at p = 0.05. No such phenomena were observed in our results, suggesting more complete and more reliable evidence.
Towards Brain-inspired System: Deep Recurrent Reinforcement Learning for Simulated Self-driving Agent

An effective way to achieve intelligence is to simulate various intelligent behaviors in the human brain. In recent years, bio-inspired learning methods have emerged, and they are different from the classical mathematical programming principle. In the perspective of brain inspiration, reinforcement learning has gained additional interest in solving decision-making tasks as increasing neuroscientific research demonstrates that significant links exist between reinforcement learning and specific neural substrates. Because of the tremendous research that focuses on human brains and reinforcement learning, scientists have investigated how robots can autonomously tackle complex tasks in the form of a self-driving agent control in a human-like way. In this study, we propose an end-to-end architecture using novel deep-Q-network architecture in conjunction with a recurrence to resolve the problem in the field of simulated self-driving. The main contribution of this study is that we trained the driving agent using a brain-inspired trial-and-error technique, which was in line with the real world situation. Besides, there are three innovations in the proposed learning network: raw screen outputs are the only information which the driving agent can rely on, a weighted layer that enhances the differences of the lengthy episode, and a modified replay mechanism that overcomes the problem of sparsity and accelerates learning. The proposed network was trained and tested under a third-partied OpenAI Gym environment. After training for several episodes, the resulting driving agent performed advanced behaviors in the given scene. We hope that in the future, the proposed brain-inspired learning system would inspire practicable self-driving control solutions.
Penalized Partial Least Squares Based on B-Splines Transformations

We propose a novel method to model nonlinear regression problems by adapting the principle of penalization to Partial Least Squares (PLS). Starting with a generalized additive model, we expand the additive component of each variable in terms of a generous amount of B-Splines basis functions. In order to prevent overfitting and to obtain smooth functions, we estimate the regression model by applying a penalized version of PLS. Although our motivation for penalized PLS stems from its use for B-Splines transformed data, the proposed approach is very general and can be applied to other penalty terms or to other dimension reduction techniques. It turns out that penalized PLS can be computed virtually as fast as PLS. We prove a close connection of penalized PLS to the solutions of preconditioned linear systems. In the case of high-dimensional data, the new method is shown to be an attractive competitor to other techniques for estimating generalized additive models. If the number of predictor variables is high compared to the number of examples, traditional techniques often suffer from overfitting. We illustrate that penalized PLS performs well in these situations.
Simulation study of estimating between-study variance and overall effect in meta-analysis of odds-ratios

Random-effects meta-analysis requires an estimate of the between-study variance, $\tau^2$. We study methods of estimation of $\tau^2$ and its confidence interval in meta-analysis of odds ratio, and also the performance of related estimators of the overall effect. We provide results of extensive simulations on five point estimators of $\tau^2$ (the popular methods of DerSimonian-Laird, restricted maximum likelihood, and Mandel and Paule; the less-familiar method of Jackson; and the new method (KD) based on the improved approximation to the distribution of the Q statistic by Kulinskaya and Dollinger (2015)); five interval estimators for $\tau^2$ (profile likelihood, Q-profile, Biggerstaff and Jackson, Jackson, and KD), six point estimators of the overall effect (the five inverse-variance estimators related to the point estimators of $\tau^2$ and an estimator (SSW) whose weights use only study-level sample sizes), and eight interval estimators for the overall effect (five based on the point estimators for $\tau^2$; the Hartung-Knapp-Sidik-Jonkman (HKSJ) interval; a KD-based modification of HKSJ; and an interval based on the sample-size-weighted estimator). Results of our simulations show that none of the point estimators of $\tau^2$ can be recommended, however the new KD estimator provides a reliable coverage of $\tau^2$. Inverse-variance estimators of the overall effect are substantially biased. The SSW estimator of the overall effect and the related confidence interval provide the reliable point and interval estimation of log-odds-ratio.
Bayesian and Spline based Approaches for (EM based) Graphon Estimation.

The paper proposes the estimation of a graphon function for network data using principles of the EM algorithm. The approach considers both, variability with respect to ordering the nodes of a network and estimation of the unique representation of a graphon. To do so (linear) B-splines are used, which allows to easily accommodate constraints in the estimation routine so that the estimated graphon fulfills the canonical representation, meaning its univariate margin is monotonic. The graphon estimate itself allows to apply Bayesian ideas to explore both, the degree distribution and the ordering of the nodes with respect to their degree. Variability and uncertainty is taken into account using MCMC techniques. Combining both steps gives an EM based approach for graphon estimation.
Exact factorization-based density functional theory of electron-phonon systems

Density functional theory is generalized to incorporate electron-phonon coupling. Kohn-Sham equations yielding the electronic density $n_U(\mathbf{r})$, a conditional probability density depending parametrically on the set of phonon normal mode amplitudes $U=\{U_{\mathbf{q}\lambda}\}$, are coupled to the nuclear Schr\"odinger equation of the exact factorization scheme. It is shown that a nonzero Berry curvature on nuclear configuration space affects the phonon modes. An orbital-dependent functional approximation for the nonadiabatic exchange-correlation energy reproduces the leading-order nonadiabatic electron-phonon-induced band structure renormalization in the Fr\"ohlich model.
New statistical methodology for second level global sensitivity analysis

Global sensitivity analysis (GSA) of numerical simulators aims at studying the global impact of the input uncertainties on the output. To perform the GSA, statistical tools based on inputs/output dependence measures are commonly used. We focus here on dependence measures based on reproducing kernel Hilbert spaces: the Hilbert-Schmidt Independence Criterion denoted HSIC. Sometimes, the probability distributions modeling the uncertainty of inputs may be themselves uncertain and it is important to quantify the global impact of this uncertainty on GSA results. We call it here the second-level global sensitivity analysis (GSA2). However, GSA2, when performed with a double Monte Carlo loop, requires a large number of model evaluations which is intractable with CPU time expensive simulators. To cope with this limitation, we propose a new statistical methodology based on a single Monte Carlo loop with a limited calculation budget. Firstly, we build a unique sample of inputs from a well chosen probability distribution and the associated code outputs are computed. From this inputs/output sample, we perform GSA for various assumed probability distributions of inputs by using weighted HSIC measures estimators. Statistical properties of these weighted esti-mators are demonstrated. Finally, we define 2 nd-level HSIC-based measures between the probability distributions of inputs and GSA results, which constitute GSA2 indices. The efficiency of our GSA2 methodology is illustrated on an analytical example, thereby comparing several technical options. Finally, an application to a test case simulating a severe accidental scenario on nuclear reactor is provided.
Personalizing Path-Specific Effects

Unlike classical causal inference, which often has an average causal effect of a treatment within a population as a target, in settings such as personalized medicine, the goal is to map a given unit's characteristics to a treatment tailored to maximize the expected outcome for that unit. Obtaining high-quality mappings of this type is the goal of the dynamic regime literature (Chakraborty and Moodie 2013), with connections to reinforcement learning and experimental design. Aside from the average treatment effects, mechanisms behind causal relationships are also of interest. A well-studied approach to mechanism analysis is establishing average effects along with a particular set of causal pathways, in the simplest case the direct and indirect effects. Estimating such effects is the subject of the mediation analysis literature (Robins and Greenland 1992; Pearl 2001). In this paper, we consider how unit characteristics may be used to tailor a treatment assignment strategy that maximizes a particular path-specific effect. In healthcare applications, finding such a policy is of interest if, for instance, we are interested in maximizing the chemical effect of a drug on an outcome (corresponding to the direct effect), while assuming drug adherence (corresponding to the indirect effect) is set to some reference level. To solve our problem, we define counterfactuals associated with path-specific effects of a policy, give a general identification algorithm for these counterfactuals, give a proof of completeness, and show how classification algorithms in machine learning (Chen, Zeng, and Kosorok 2016) may be used to find a high-quality policy. We validate our approach via a simulation study.
When Locally Linear Embedding Hits Boundary

Based on the Riemannian manifold model, we study the asymptotic behavior of a widely applied unsupervised learning algorithm, locally linear embedding (LLE), when the point cloud is sampled from a compact, smooth manifold with boundary. We show several peculiar behaviors of LLE near the boundary that are different from those diffusion-based algorithms. Particularly, LLE converges to a mixed-type differential operator with degeneracy. This study leads to an alternative boundary detection algorithm and two potential approaches to recover the Dirichlet Laplace-Beltrami operator.
Variable Selection in Restricted Linear Regression Models

The use of prior information in the linear regression is well known to provide more efficient estimators of regression coefficients. The methods of non-stochastic restricted regression estimation proposed by Theil and Goldberger (1961) are preferred when prior information is available. In this study, we will consider parameter estimation and the variable selection in non-stochastic restricted linear regression model, using least absolute shrinkage and selection operator (LASSO) method introduced by Tibshirani (1996). A small simulation study and real data example are provided to illustrate the performance of the proposed method for dealing with the variable selection and the parameter estimation in restricted linear regression models.
The ALMA-PILS survey: gas dynamics in IRAS 16293−2422 and the connection between its two protostars

Context. The majority of stars form in binary or higher order systems. The evolution of each protostar in a multiple system may start at different times and may progress differently. The Class 0 protostellar system IRAS 16293–2422 contains two protostars, “A” and “B”, separated by ~600 au and embedded in a single, 104 au scale envelope. Their relative evolutionary stages have been debated. Aims. We aim to study the relation and interplay between the two protostars A and B at spatial scales of 60 au up to ~103 au. Methods. We selected molecular gas line transitions of the species CO, H2CO, HCN, CS, SiO, and C2H from the ALMA-PILS spectral imaging survey (329–363 GHz) and used them as tracers of kinematics, density, and temperature in the IRAS 16293–2422 system. The angular resolution of the PILS data set allows us to study these quantities at a resolution of 0.5′′ (60 au at the distance of the source). Results. Line-of-sight velocity maps of both optically thick and optically thin molecular lines reveal: (i) new manifestations of previously known outflows emanating from protostar A; (ii) a kinematically quiescent bridge of dust and gas spanning between the two protostars, with an inferred density between 4 × 104 cm−3 and ~3 × 107 cm−3; and (iii) a separate, straight filament seemingly connected to protostar B seen only in C2H, with a flat kinematic signature. Signs of various outflows, all emanating from source A, are evidence of high-density and warmer gas; none of them coincide spatially and kinematically with the bridge. Conclusions. We hypothesize that the bridge arc is a remnant of filamentary substructure in the protostellar envelope material from which protostellar sources A and B have formed. One particular morphological structure appears to be due to outflowing gas impacting the quiescent bridge material. The continuing lack of clear outflow signatures unambiguously associated to protostar B and the vertically extended shape derived for its disk-like structure lead us to conclude that source B may be in an earlier evolutionary stage than source A.
Estimating atmospheric motion winds from satellite image data using space‐time drift models

Geostationary weather satellites collect high‐resolution data comprising a series of images. The Derived Motion Winds (DMW) Algorithm is commonly used to process these data and estimate atmospheric winds by tracking features in the images. However, the wind estimates from the DMW Algorithm are often missing and do not come with uncertainty measures. Also, the DMW Algorithm estimates can only be half‐integers, since the algorithm requires the original and shifted data to be at the same locations, in order to calculate the displacement vector between them. This motivates us to statistically model wind motions as a spatial process drifting in time. Using a covariance function that depends on spatial and temporal lags and a drift parameter to capture the wind speed and wind direction, we estimate the parameters by local maximum likelihood. Our method allows us to compute standard errors of the local estimates, enabling spatial smoothing of the estimates using a Gaussian kernel weighted by the inverses of the estimated variances. We conduct extensive simulation studies to determine the situations where our method performs well. The proposed method is applied to the GOES‐15 brightness temperature data over Colorado and reduces prediction error of brightness temperature compared to the DMW Algorithm.
Construction and assessment of prediction rules for binary outcome in the presence of missing predictor data using multiple imputation: theoretical perspective and data-based evaluation

We investigate the problem of calibration and assessment of predictive rules in prognostic designs when missing values are present in the predictors. Our paper has two key objectives which are entwined. The first is to investigate how the calibration of the prediction rule can be combined with the use of multiple imputation to account for missing predictor observations. The second objective is to propose such methods that can be implemented with current multiple imputation software, while allowing for unbiased predictive assessment through validation on new observations for which outcome is not yet available. To inform the definition of methodology, we commence with a review of the theoretical background of multiple imputation as a model estimation approach as opposed to a purely algorithmic description. We specifically contrast application of multiple imputation for parameter (effect) estimation with predictive calibration. Based on this review, two approaches are formulated, of which the second utilizes application of the classical Rubin's rules for parameter estimation, while the first approach averages probabilities from models fitted on single imputations to directly approximate the predictive density for future observations. We present implementations using current software which allow for validatory or cross-validatory estimation of performance measures, as well as imputation of missing data in predictors on the future data where outcome is by definition as yet unobserved. We restrict discussion to binary outcome and logistic regression throughout, though the principles discussed are generally applicable. We present two data sets as examples from our regular consultative practice. Results show little difference between methods for accuracy but substantial reductions in variation of calibrated probabilities when using the first approach.
A Model-Centric Analysis of Openness, Replication, and Reproducibility

The literature on the reproducibility crisis presents several putative causes for the proliferation of irreproducible results, including HARKing, p-hacking and publication bias. Without a theory of reproducibility, however, it is difficult to determine whether these putative causes can explain most irreproducible results. Drawing from an historically informed conception of science that is open and collaborative, we identify the components of an idealized experiment and analyze these components as a precursor to develop such a theory. Openness, we suggest, has long been intuitively proposed as a solution to irreproducibility. However, this intuition has not been validated in a theoretical framework. Our concern is that the under-theorizing of these concepts can lead to flawed inferences about the (in)validity of experimental results or integrity of individual scientists. We use probabilistic arguments and examine how openness of experimental components relates to reproducibility of results. We show that there are some impediments to obtaining reproducible results that precede many of the causes often cited in literature on the reproducibility crisis. For example, even if erroneous practices such as HARKing, p-hacking, and publication bias were absent at the individual and system level, reproducibility may still not be guaranteed.
Quantum non demolition measurements: parameter estimation for mixtures of multinomials

In Quantum Non Demolition measurements, the sequence of observations is distributed as a mixture of multinomial random variables. Parameters of the dynamics are naturally encoded into this family of distributions. We show the local asymptotic mixed normality of the underlying statistical model and the consistency of the maximum likelihood estimator. Furthermore, we prove the asymptotic optimality of this estimator as it saturates the usual Cramer Rao bound.
Some Computational Aspects to Find Accurate Estimates for the Parameters of the Generalized Gamma distribution

In this paper, we discuss computational aspects to obtain accurate inferences for the parameters of the generalized gamma (GG) distribution. Usually, the solution of the maximum likelihood estimators (MLE) for the GG distribution have no stable behavior depending on large sample sizes and good initial values to be used in the iterative numerical algorithms. From a Bayesian approach, this problem remains, but now related to the choice of prior distributions for the parameters of this model. We presented some exploratory techniques to obtain good initial values to be used in the iterative procedures and also to elicited appropriate informative priors. Finally, our proposed methodology is also considered for data sets in the presence of censorship.
A robust method for cluster analysis

Let there be given a contaminated list of n R d -valued observations coming from g different, normally distributed populations with a common covariance matrix. We compute the ML-estimator with respect to a certain statistical model with n - r outliers for the parameters of the g populations; it detects outliers and simultaneously partitions their complement into g clusters. It turns out that the estimator unites both the minimum-covariance-determinant rejection method and the well-known pooled determinant criterion of cluster analysis. We also propose an efficient algorithm for approximating this estimator and study its breakdown points for mean values and pooled SSP matrix.
Bayesian regression with spatio-temporal varying coefficients

To study the impact of climate variables on morbidity of some diseases in Mexico, we propose a spatio-temporal varying coefficients regression model. For that we introduce a new spatio-temporal dependent process prior, in a Bayesian context, with identically distributed normal marginal distributions and joint multivariate normal distribution. We study its properties and characterise the dependence induced. Our results show that the effect of climate variables, on the incidence of specific diseases, is not constant across space and time and our proposed model is able to capture and quantify those changes.
Organic Fiducial Inference

A substantial generalisation is put forward of the theory of subjective fiducial inference as it was outlined in earlier papers. In particular, this theory is extended to deal with cases where the data are discrete or categorical rather than continuous, and cases where there was important pre-data knowledge about some or all of the model parameters. The system for directly expressing and then handling this pre-data knowledge, which is via what are referred to as global and local pre-data functions for the parameters concerned, is distinct from that which involves attempting to directly represent this knowledge in the form of a prior distribution function over these parameters, and then using Bayes' theorem. In this regard, the individual attributes of what are identified as three separate types of fiducial argument, namely the strong, moderate and weak fiducial arguments, form an integral part of the theory that is developed. Various practical examples of the application of this theory are presented, including examples involving binomial, Poisson and multinomial data. The fiducial distribution functions for the parameters of the models in these examples are interpreted in terms of a generalised definition of subjective probability that was set out previously.