Miracle-dz/UniFAR-FTUs huggingface dataset:
A primer on statistically validated networks

In this contribution we discuss some approaches of network analysis providing information about single links or single nodes with respect to a null hypothesis taking into account the heterogeneity of the system empirically observed. With this approach, a selection of nodes and links is feasible when the null hypothesis is statistically rejected. We focus our discussion on approaches using (i) the so-called disparity filter and (ii) statistically validated network in bipartite networks. For both methods we discuss the importance of using multiple hypothesis test correction. Specific applications of statistically validated networks are discussed. We also discuss how statistically validated networks can be used to (i) pre-process large sets of data and (ii) detect cores of communities that are forming the most close-knit and stable subsets of clusters of nodes present in a complex system.
Shape theory via polar decomposition

This work proposes a new model in the context of statistical theory of shape, based on the polar decomposition. The non isotropic noncentral elliptical shape distributions via polar decomposition is derived in the context of zonal polynomials, avoiding the invariant polynomials and the open problems for their computation. The new polar shape distributions are easily computable and then the inference procedure can be studied under exact densities. As an example of the technique, a classical application in Biology is studied under three models, the usual Gaussian and two non normal Kotz models; the best model is selected by a modified BIC criterion, then a test for equality in polar shapes is performed.
Privacy Preserving Integrative Regression Analysis of High-dimensional Heterogeneous Data

Meta-analyzing multiple studies, enabling more precise estimation and investigation of generalizability, is important for evidence based decision making. Integrative analysis of multiple heterogeneous studies is, however, highly challenging in the high dimensional setting. The challenge is even more pronounced when the individual level data cannot be shared across studies due to privacy concerns. Under ultra high dimensional sparse regression models, we propose in this paper a novel integrative estimation procedure by aggregating and debiasing local estimators (ADeLE), which allows us to base solely on the derived data to perform estimation with general loss functions. The ADeLE procedure accommodates between study heterogeneity in both the covariate distribution and model parameters, and attains consistent variable selection. Furthermore, the prediction and estimation errors incurred by aggregating derived data is negligible compared to the statistical minimax rate. In addition, the ADeLE estimator is shown to be asymptotically equivalent in prediction and estimation to the ideal estimator obtained by sharing all data. The finite-sample performance of the ADeLE procedure is studied via extensive simulations. We further illustrate the utility of the ADeLE procedure to derive phenotyping algorithms for coronary artery disease using electronic health records data from multiple disease cohorts.
Unbiased Estimation and Sensitivity Analysis for Network-Specific Spillover Effects: Application to An Online Network Experiment

Online network experiments have become widely-used methods to estimate spillover effects and study online social influence. In such experiments, the spillover effects often arise not only through an online network of interest but also through a face-to-face network. Social scientists are therefore often interested in measuring the importance of online human interactions by estimating the spillover effects specific to the online network, separately from spillover effects attributable to the offline network. However, the unbiased estimation of these network-specific spillover effects requires an often-violated assumption that researchers observe all relevant networks; they cannot observe the offline network in many applications. In this paper, we derive an exact expression of bias due to unobserved networks. Unlike the conventional omitted variable bias, this bias for the network-specific spillover effect remains even when treatment assignment is randomized and when unobserved networks and the network of interest are independently generated. By incorporating this bias into the estimation, we also develop parametric and nonparametric sensitivity analysis methods, with which researchers can examine the robustness of empirical findings to the possible existence of unmeasured networks. We analyze a political mobilization experiment on the Twitter network and find that an estimate of the Twitter-specific spillover effect is sensitive to assumptions about an unobserved face-to-face network.
Attention-augmented End-to-end Multi-task Learning for Emotion Prediction from Speech

Despite the increasing research interest in end-to-end learning systems for speech emotion recognition, conventional systems either suffer from the overfitting due in part to the limited training data, or do not explicitly consider the different contributions of automatically learnt representations for a specific task. In this contribution, we propose a novel end-to-end framework which is enhanced by learning other auxiliary tasks and an attention mechanism. That is, we jointly train an end-to-end network with several different but related emotion prediction tasks, i. e. , arousal, valence, and dominance predictions, to extract more robust representations shared among various tasks than traditional systems with the hope that it is able to relieve the overfitting problem. Meanwhile, an attention layer is implemented on top of the layers for each task, with the aim to capture the contribution distribution of different segment parts for each individual task. To evaluate the effectiveness of the proposed system, we conducted a set of experiments on the widely used database IEMOCAP. The empirical results show that the proposed systems significantly outperform corresponding baseline systems.
On the Well-posedness of Bayesian Inverse Problems

The subject of this article is the introduction of a weaker concept of well-posedness of Bayesian inverse problems. The conventional concept of (`Lipschitz') well-posedness in [Stuart 2010, Acta Numerica 19, pp. 451-559] is difficult to verify in practice, especially when considering blackbox models, and probably too strong in many contexts. Our concept replaces the Lipschitz continuity of the posterior measure in the Hellinger distance by just continuity. This weakening is tolerable, since the continuity is in general only used as a stability criterion. The main result of this article is a proof of well-posedness for a large class of Bayesian inverse problems, where very little or no information about the underlying model is available. It includes any Bayesian inverse problem arising when observing finite-dimensional data perturbed by additive, non-degenerate Gaussian noise. Moreover, well-posedness with respect to other probability metrics is investigated, including weak convergence, total variation, Wasserstein, and also the Kullback-Leibler divergence.
A Record Linkage Model Incorporating Relational Data

In this paper we introduce a novel Bayesian approach for linking multiple social networks in order to discover the same real world person having different accounts across networks. In particular, we develop a latent model that allow us to jointly characterize the network and linkage structures relying in both relational and profile data. In contrast to other existing approaches in the machine learning literature, our Bayesian implementation naturally provides uncertainty quantification via posterior probabilities for the linkage structure itself or any function of it. Our findings clearly suggest that our methodology can produce accurate point estimates of the linkage structure even in the absence of profile information, and also, in an identity resolution setting, our results confirm that including relational data into the matching process improves the linkage accuracy. We illustrate our methodology using real data from popular social networks such as Twitter, Facebook, and YouTube.
One optional observation inflates $\alpha$ by $100/\sqrt{n}$ per cent

For one-sample level $\alpha$ tests $\psi_m$ based on independent observations $X_1,. ,X_m$, we prove an asymptotic formula for the actual level of the test rejecting if at least one of the tests $\psi_{n},. ,\psi_{n+k}$ would reject. For $k=1$ and usual tests at usual levels $\alpha$, the result is approximately summarized by the title of this paper. Our method of proof, relying on some second order asymptotic statistics as developed by Pfanzagl and Wefelmeyer, might also be useful for proper sequential analysis. A simple and elementary alternative proof is given for $k=1$ in the special case of the Gauss test.
Propensity Score Matching underestimates Real Treatment Effect, in a simulated theoretical multivariate model.

Propensity Score Matching (PSM) is an useful method to reduce the impact ofTreatment - Selection Bias in the estimation of causal effects in observational studies. After matching, the PSM significantly reduces the sample under investigation, which may lead to other possible biases. In this sense, we want to analyse the behaviour of this PSM compared with other widely used method to deal with non-comparable groups, as is the Multivariate Regression Model (MRM). Monte Carlo Simulations are made to construct groups with different effects in order to compare the behaviour of PSM and MRM estimating this effects. Also the Treatment - Selection Bias reduction for the PSM is calculated. With the PSM a reduction in the Treatment - Selection Bias is achieved, with a reduction in the Relative Real Treatment Effect Estimation Error, but despite of this bias reduction and estimation error reduction, the MRM significantly reduces more this estimation error compared with the PSM. Also the PSM leads to a not insignificant reduction of the sample. This loss of information derived from the matching process may lead to another not known bias, and thus, to the inaccurate of the effect estimation compared with the MRM.
Kernel Smoothing of the Treatment Effect CDF

The strata-specific treatment effect or so-called blip for a randomly drawn strata of confounders defines a random variable and a corresponding cumulative distribution function. However, the CDF is not pathwise differentiable, necessitating a kernel smoothing approach to estimate it at a given point or perhaps many points. Assuming the CDF is continuous, we derive the efficient influence curve of the kernel smoothed version of the blip CDF and a CV-TMLE estimator. The estimator is asymptotically efficient under two conditions, one of which involves a second order remainder term which, in this case, shows us that knowledge of the treatment mechanism does not guarantee a consistent estimate. The remainder term also teaches us exactly how well we need to estimate the nuisance parameters (outcome model and treatment mechanism) to guarantee asymptotic efficiency. Through simulations we verify theoretical properties of the estimator and show the importance of machine learning over conventional regression approaches to fitting the nuisance parameters. We also derive the bias and variance of the estimator, the orders of which are analogous to a kernel density estimator. This estimator opens up the possibility of developing methodology for optimal choice of the kernel and bandwidth to form confidence bounds for the CDF itself.
Change Point Estimation in a Dynamic Stochastic Block Model

We consider the problem of estimating the location of a single change point in a dynamic stochastic block model. We propose two methods of estimating the change point, together with the model parameters. The first employs a least squares criterion function and takes into consideration the full structure of the stochastic block model and is evaluated at each point in time. Hence, as an intermediate step, it requires estimating the community structure based on a clustering algorithm at every time point. The second method comprises of the following two steps: in the first one, a least squares function is used and evaluated at each time point, but ignores the community structures and just considers a random graph generating mechanism exhibiting a change point. Once the change point is identified, in the second step, all network data before and after it are used together with a clustering algorithm to obtain the corresponding community structures and subsequently estimate the generating stochastic block model parameters. A comparison between these two methods is illustrated. Further, for both methods under their respective identifiability and certain additional regularity conditions, we establish rates of convergence and derive the asymptotic distributions of the change point estimators. The results are illustrated on synthetic data.
Multi-set Canonical Correlation Analysis simply explained.

There are a multitude of methods to perform multi-set correlated component analysis (MCCA), including some that require iterative solutions. The methods differ on the criterion they optimize and the constraints placed on the solutions. This note focuses perhaps on the simplest version, which can be solved in a single step as the eigenvectors of matrix ${\bf D}^{-1} {\bf R}$. Here ${\bf R}$ is the covariance matrix of the concatenated data, and ${\bf D}$ is its block-diagonal. This note shows that this solution maximizes inter-set correlation (ISC) without further constraints. It also relates the solution to a two step procedure, which first whitens each dataset using PCA, and then performs an additional PCA on the concatenated and whitened data. Both these solutions are known, although a clear derivation and simple implementation are hard to find. This short note aims to remedy this.
A flexible parametric modelling framework for survival analysis

We introduce a general, flexible, parametric survival modelling framework which encompasses key shapes of hazard functions (constant; increasing; decreasing; up then down; down then up) and various common survival distributions (log‐logistic; Burr type XII; Weibull; Gompertz) and includes defective distributions (cure models). This generality is achieved by using four distributional parameters: two scale‐type parameters—one of which relates to accelerated failure time (AFT) modelling; the other to proportional hazards (PH) modelling—and two shape parameters. Furthermore, we advocate ‘multiparameter regression’ whereby more than one distributional parameter depends on covariates—rather than the usual convention of having a single covariate‐dependent (scale) parameter. This general formulation unifies the most popular survival models, enabling us to consider the practical value of possible modelling choices. In particular, we suggest introducing covariates through just one or other of the two scale parameters (covering AFT and PH models), and through a ‘power’ shape parameter (covering more complex non‐AFT or non‐PH effects); the other shape parameter remains covariate independent and handles automatic selection of the baseline distribution. We explore inferential issues and compare with alternative models through various simulation studies, with particular focus on evidence concerning the need, or otherwise, to include both AFT and PH parameters. We illustrate the efficacy of our modelling framework by using data from lung cancer, melanoma and kidney function studies. Censoring is accommodated throughout.
Invited Article: Swept-Wavelength Mid-Infrared Fiber Laser for Real-Time Ammonia Gas Sensing

The mid-infrared (mid-IR) spectral region holds great promise for new laser-based sensing technologies, based on measuring strong mid-IR molecular absorption features. Practical applications have been limited to date, however, by current low-brightness broadband mid-IR light sources and slow acquisition-time detection systems. Here, we report a new approach by developing a swept-wavelength mid-infrared fiber laser, exploiting the broad emission of dysprosium and using an acousto-optic tunable filter to achieve electronically controlled swept-wavelength operation from 2.89 to 3.25 μm (3070–3460 cm−1). Ammonia (NH3) absorption spectroscopy is demonstrated using this swept source with a simple room-temperature single-pixel detector, with 0.3 nm resolution and 40 ms acquisition time. This creates new opportunities for real-time high-sensitivity remote sensing using simple, compact mid-IR fiber-based technologies.
CID Models on Real-world Social Networks and Goodness of Fit Measurements

Assessing the model fit quality of statistical models for network data is an ongoing and under-examined topic in statistical network analysis. Traditional metrics for evaluating model fit on tabular data such as the Bayesian Information Criterion are not suitable for models specialized for network data. We propose a novel self-developed goodness of fit (GOF) measure, the `stratified-sampling cross-validation' (SCV) metric, that uses a procedure similar to traditional cross-validation via stratified-sampling to select dyads in the network's adjacency matrix to be removed. SCV is capable of intuitively expressing different models' ability to predict on missing dyads. Using SCV on real-world social networks, we identify the appropriate statistical models for different network structures and generalize such patterns. In particular, we focus on conditionally independent dyad (CID) models such as the Erdos Renyi model, the stochastic block model, the sender-receiver model, and the latent space model.
Transporting stochastic direct and indirect effects to new populations

Transported mediation effects may contribute to understanding how interventions work differently when applied to new populations. However, we are not aware of any estimators for such effects. Thus, we propose two doubly robust, efficient estimators of transported stochastic (also called randomized interventional) direct and indirect effects. We demonstrate their finite sample properties in a simulation study. We then apply the preferred substitution estimator to longitudinal data from the Moving to Opportunity Study, a large‐scale housing voucher experiment, to transport stochastic indirect effect estimates of voucher receipt in childhood on subsequent risk of mental health or substance use disorder mediated through parental employment across sites, thereby gaining understanding of drivers of the site differences.
Bayesian Calibration and Sensitivity Analysis for a Karst Aquifer Model Using Active Subspaces

In this article, we perform a parameter study for a recently developed karst hydrological model. The study consists of a high‐dimensional Bayesian inverse problem and a global sensitivity analysis. For the first time in karst hydrology, we use the active subspace method to find directions in the parameter space that dominate the Bayesian update from the prior to the posterior distribution in order to effectively reduce the dimension of the problem and for computational efficiency. Additionally, the calculated active subspace can be exploited to construct sensitivity metrics on each of the individual parameters and be used to construct a natural model surrogate. The model consists of 21 parameters to reproduce the hydrological behavior of spring discharge in a karst aquifer located in the Kerschbaum spring recharge area at Waidhofen a.d. Ybbs in Austria. The experimental spatial and time series data for the inference process were collected by the water works in Waidhofen. We show that this case study has implicit low dimensionality, and we run an adjusted Markov chain Monte Carlo algorithm in a low‐dimensional subspace to construct samples of the posterior distribution. The results are visualized and verified by plots of the posterior's push‐forward distribution displaying the uncertainty in predicting discharge values due to the experimental noise in the data. Finally, a discussion provides hydrological interpretation of these results for the Kerschbaum area.
Bayesian Probabilistic Numerical Methods in Time-Dependent State Estimation for Industrial Hydrocyclone Equipment

Abstract The use of high-power industrial equipment, such as large-scale mixing equipment or a hydrocyclone for separation of particles in liquid suspension, demands careful monitoring to ensure correct operation. The fundamental task of state-estimation for the liquid suspension can be posed as a time-evolving inverse problem and solved with Bayesian statistical methods. In this article, we extend Bayesian methods to incorporate statistical models for the error that is incurred in the numerical solution of the physical governing equations. This enables full uncertainty quantification within a principled computation-precision trade-off, in contrast to the over-confident inferences that are obtained when all sources of numerical error are ignored. The method is cast within a sequential Monte Carlo framework and an optimized implementation is provided in Python.
Approximating multivariate posterior distribution functions from Monte Carlo samples for sequential Bayesian inference

An important feature of Bayesian statistics is the opportunity to do sequential inference: the posterior distribution obtained after seeing a dataset can be used as prior for a second inference. However, when Monte Carlo sampling methods are used for inference, we only have a set of samples from the posterior distribution. To do sequential inference, we then either have to evaluate the second posterior at only these locations and reweight the samples accordingly, or we can estimate a functional description of the posterior probability distribution from the samples and use that as prior for the second inference. Here, we investigated to what extent we can obtain an accurate joint posterior from two datasets if the inference is done sequentially rather than jointly, under the condition that each inference step is done using Monte Carlo sampling. To test this, we evaluated the accuracy of kernel density estimates, Gaussian mixtures, mixtures of factor analyzers, vine copulas and Gaussian processes in approximating posterior distributions, and then tested whether these approximations can be used in sequential inference. In low dimensionality, Gaussian processes are more accurate, whereas in higher dimensionality Gaussian mixtures, mixtures of factor analyzers or vine copulas perform better. In our test cases of sequential inference, using posterior approximations gives more accurate results than direct sample reweighting, but joint inference is still preferable over sequential inference whenever possible. Since the performance is case-specific, we provide an R package mvdens with a unified interface for the density approximation methods.
On the sure screening property of the iterative sure independence screening algorithm

The iterative version of the sure independence screening algorithm (ISIS) has been widely employed in various scientific fields since Fan and Lv [2008] proposed it. Despite the outstanding performance of ISIS in extensive applications, its sure screening property has not been theoretically verified during the past decade. To fill this gap, we adapt a technique of Wang [2009] in the context of forward regression (FR) to prove the sure screening property of ISIS, without relying on the marginal correlation assumption.
A note on Fibonacci Sequences of Random Variables

The focus of this paper is the random sequences in the form $\{X_{0},X_{1},$ $X_{n}=X_{n-2}+X_{n-1},n=2,3,. \dot{\}},$ referred to as Fibonacci Random Sequence (FRS). The initial random variables $X_{0}$ and $X_{1}$ are assumed to be absolutely continuous with joint probability density function (pdf) $f_{X_{0},X_{1}}. $ The FRS is completely determined by $X_{0}$ and $X_{1}$ and the members of Fibonacci sequence $\digamma \equiv\{0,1,1,2,3,5,8,13,21,34,55,89,144,. \}. $ We examine the distributional and limit properties of the random sequence $X_{n},n=0,1,2,. $ .