Miracle-dz/UniFAR-FTUs huggingface dataset:
PyTorch-BigGraph: A Large-scale Graph Embedding System

Graph embedding methods produce unsupervised node features from graphs that can then be used for a variety of machine learning tasks. Modern graphs, particularly in industrial applications, contain billions of nodes and trillions of edges, which exceeds the capability of existing embedding systems. We present PyTorch-BigGraph (PBG), an embedding system that incorporates several modifications to traditional multi-relation embedding systems that allow it to scale to graphs with billions of nodes and trillions of edges. PBG uses graph partitioning to train arbitrarily large embeddings on either a single machine or in a distributed environment. We demonstrate comparable performance with existing embedding systems on common benchmarks, while allowing for scaling to arbitrarily large graphs and parallelization on multiple machines. We train and evaluate embeddings on several large social network graphs as well as the full Freebase dataset, which contains over 100 million nodes and 2 billion edges.
A cautionary tale on using imputation methods for inference in matched-pairs design

MOTIVATION Imputation procedures in biomedical fields have turned into statistical practice, since further analyses can be conducted ignoring the former presence of missing values. In particular, non-parametric imputation schemes like the Random Forest have shown favorable imputation performance compared to the more traditionally used MICE procedure. However, their effect on valid statistical inference has not been analyzed so far. This paper closes this gap by investigating their validity for inferring mean differences in incompletely observed pairs while opposing them to a recent approach that only works with the given observations at hand. RESULTS Our findings indicate that machine learning schemes for (multiply) imputing missing values may inflate type-I-error or result in comparably low power in small to moderate matched pairs, even after modifying the test statistics using Rubin's multiple imputation rule. In addition to an extensive simulation study, an illustrative data example from a breast cancer gene study has been considered. AVAILABILITY The corresponding R-code can be accessed through the authors and the gene expression data can be downloaded at www.gdac. broadinstitute.org. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Automatic detection of tidal disruption events and other long‐duration transients in XMM‐Newton data

XMM-Newton's large field of view and excellent sensitivity have resulted in hundreds of thousands of serendipitous X-ray detections. Whilst their spectra have been widely exploited, their variable nature has been little studied. Part of this is due to the way XMM-Newton currently operates, where observations generally have a 12 month proprietary period. It is often too late to follow-up a serendipitous transient a year after detection. New robust software could be introduced into the pipeline to automatically identify bright transients that are not the target of the observation. Statistically, hundreds of tidal disruption events (TDEs) have been detected serendipitously by XMM-Newton. With prior consent from the PI of the observation, an automatic alert to a new transient could be set up, allowing it to be followed-up within weeks, ideal for TDEs that are bright for about a year. Over the next decade, hundreds more TDEs should be detected. Following-up the brightest in quasi-real time would allow constraints to be made on the black hole mass, spin and accretion regime and identify intermediate-mass black holes that are expected to be hidden in faint, low-mass galaxies. This article discusses the advantages that such changes would have on the follow-up of transients and TDEs.
Using an online sample to learn about an offline population

Online data sources offer tremendous promise to demography and other social sciences, but researchers worry that the group of people who are represented in online datasets can be different from the general population. We show that by sampling and anonymously interviewing people who are online, researchers can learn about both people who are online and people who are offline. Our approach is based on the insight that people everywhere are connected through in-person social networks, such as kin, friendship, and contact networks. We illustrate how this insight can be used to derive an estimator for tracking the *digital divide* in access to the internet, an increasingly important dimension of population inequality in the modern world. We conducted a large-scale empirical test of our approach, using an online sample to estimate internet adoption in five countries ($n \approx 15,000$). Our test embedded a randomized experiment whose results can help design future studies. Our approach could be adapted to many other settings, offering one way to overcome some of the major challenges facing demographers in the information age.
Rapid Prediction of Electron–Ionization Mass Spectrometry Using Neural Networks

When confronted with a substance of unknown identity, researchers often perform mass spectrometry on the sample and compare the observed spectrum to a library of previously collected spectra to identify the molecule. While popular, this approach will fail to identify molecules that are not in the existing library. In response, we propose to improve the library’s coverage by augmenting it with synthetic spectra that are predicted from candidate molecules using machine learning. We contribute a lightweight neural network model that quickly predicts mass spectra for small molecules, averaging 5 ms per molecule with a recall-at-10 accuracy of 91.8%. Achieving high-accuracy predictions requires a novel neural network architecture that is designed to capture typical fragmentation patterns from electron ionization. We analyze the effects of our modeling innovations on library matching performance and compare our models to prior machine-learning-based work on spectrum prediction.
Invariance, Causality and Robustness

We discuss recent work for causal inference and predictive robustness in a unifying way. The key idea relies on a notion of probabilistic invariance or stability: it opens up new insights for formulating causality as a certain risk minimization problem with a corresponding notion of robustness. The invariance itself can be estimated from general heterogeneous or perturbation data which frequently occur with nowadays data collection. The novel methodology is potentially useful in many applications, offering more robustness and better `causal-oriented' interpretation than machine learning or estimation in standard regression or classification frameworks.
Statistical modelling of conidial discharge of entomophthoralean fungi using a newly discovered Pandora species

Entomophthoralean fungi are insect pathogenic fungi and are characterized by their active discharge of infective conidia that infect insects. Our aim was to study the effects of temperature on the discharge and to characterize the variation in the associated temporal pattern of a newly discovered Pandora species with focus on peak location and shape of the discharge. Mycelia were incubated at various temperatures in darkness, and conidial discharge was measured over time. We used a novel modification of a statistical model (pavpop), that simultaneously estimates phase and amplitude effects, into a setting of generalized linear models. This model is used to test hypotheses of peak location and discharge of conidia. The statistical analysis showed that high temperature leads to an early and fast decreasing peak, whereas there were no significant differences in total number of discharged conidia. Using the proposed model we also quantified the biological variation in the timing of the peak location at a fixed temperature.
Segmented correspondence curve regression model for quantifying reproducibility of high-throughput experiments

The reliability of a high-throughput biological experiment relies highly on the settings of the operational factors in its experimental and data-analytic procedures. Understanding how operational factors influence the reproducibility of the experimental outcome is critical for constructing robust workflows and obtaining reliable results. One challenge in this area is that candidates at different levels of significance may respond to the operational factors differently. To model this heterogeneity, we develop a novel segmented regression model, based on the rank concordance between candidates from different replicate samples, to characterize the varying effects of operational factors for candidates at different levels of significance. A grid search method is developed to identify the change point in response to the operational factors and estimate the covariate effects accounting for the change. A sup-likelihood-ratio-type test is proposed to test the existence of a change point. Simulation studies show that our method yields a well-calibrated type I error, is powerful in detecting the difference in reproducibility, and achieves a better model fitting than the existing method. An application on a ChIP-seq dataset reveals interesting insights on how sequencing depth affects the reproducibility of experimental results, demonstrating the usefulness of our method in designing cost-effective and reliable high-throughput workflows.
A deterministic balancing score algorithm to avoid common pitfalls of propensity score matching

Propensity score matching (PSM) is the de-facto standard for estimating causal effects in observational studies. We show that PSM and its implementations are susceptible to several major drawbacks and illustrate these findings using a case study with $17,427$ patients. We derive four formal properties an optimal statistical matching algorithm should meet, and propose Deterministic Balancing Score exact Matching (DBSeM) which meets the aforementioned properties for an exact matching. Furthermore, we investigate one of the main problems of PSM, that is that common PSM results in one valid set of matched pairs or a bootstrapped PSM in a selection of possible valid sets of matched pairs. For exact matchings we provide the mathematical proof, that DBSeM, as a result, delivers the expected value of all valid sets of matched pairs for the investigated dataset.
Inverse Problems and Data Assimilation

This concise introduction provides an entry point to the world of inverse problems and data assimilation for advanced undergraduates and beginning graduate students in the mathematical sciences. It will also appeal to researchers in science and engineering who are interested in the systematic underpinnings of methodologies widely used in their disciplines. The authors examine inverse problems and data assimilation in turn, before exploring the use of data assimilation methods to solve generic inverse problems by introducing an artificial algorithmic time. Topics covered include maximum a posteriori estimation, (stochastic) gradient descent, variational Bayes, Monte Carlo, importance sampling and Markov chain Monte Carlo for inverse problems; and 3DVAR, 4DVAR, extended and ensemble Kalman filters, and particle filters for data assimilation. The book contains a wealth of examples and exercises, and can be used to accompany courses as well as for self-study.
Subsampling to Enhance Efficiency in Input Uncertainty Quantification

Quantifying the impact of input estimation errors in data-driven stochastic simulation often encounters substantial computational challenges due to the entanglement of Monte Carlo and input data noises. In this paper, we propose a subsampling framework to bypass this computational bottleneck, by leveraging the form of the output variance and its estimation error in terms of data size and sampling effort. Compared with standard subsampling in the literature, our motivation is distinctly to reduce the sampling complexity of the two-layer bootstrap required in simulation uncertainty quantification. Compared with standard bootstraps, our subsampling approach provably and experimentally leads to more accurate variance and confidence interval estimations under the same amount of simulation budget.
Smooth Adjustment for Correlated Effects

This paper considers a high dimensional linear regression model with corrected variables. A variety of methods have been developed in recent years, yet it is still challenging to keep accurate estimation when there are complex correlation structures among predictors and the response. We propose an adaptive and "reversed" penalty for regularization to solve this problem. This penalty doesn't shrink variables but focuses on removing the shrinkage bias and encouraging grouping effect. Combining the l_1 penalty and the Minimax Concave Penalty (MCP), we propose two methods called Smooth Adjustment for Correlated Effects (SACE) and Generalized Smooth Adjustment for Correlated Effects (GSACE). Compared with the traditional adaptive estimator, the proposed methods have less influence from the initial estimator and can reduce the false negatives of the initial estimation. The proposed methods can be seen as linear functions of the new penalty's tuning parameter, and are shown to estimate the coefficients accurately in both extremely highly correlated variables situation and weakly correlated variables situation. Under mild regularity conditions we prove that the methods satisfy certain oracle property. We show by simulations and applications that the proposed methods often outperforms other methods.
Testing for high-dimensional network parameters in auto-regressive models

High-dimensional auto-regressive models provide a natural way to model influence between $M$ actors given multi-variate time series data for $T$ time intervals. While there has been considerable work on network estimation, there is limited work in the context of inference and hypothesis testing. In particular, prior work on hypothesis testing in time series has been restricted to linear Gaussian auto-regressive models. From a practical perspective, it is important to determine suitable statistical tests for connections between actors that go beyond the Gaussian assumption. In the context of \emph{high-dimensional} time series models, confidence intervals present additional estimators since most estimators such as the Lasso and Dantzig selectors are biased which has led to \emph{de-biased} estimators. In this paper we address these challenges and provide convergence in distribution results and confidence intervals for the multi-variate AR(p) model with sub-Gaussian noise, a generalization of Gaussian noise that broadens applicability and presents numerous technical challenges. The main technical challenge lies in the fact that unlike Gaussian random vectors, for sub-Gaussian vectors zero correlation does not imply independence. The proof relies on using an intricate truncation argument to develop novel concentration bounds for quadratic forms of dependent sub-Gaussian random variables. Our convergence in distribution results hold provided $T = \Omega((s \vee \rho)^2 \log^2 M)$, where $s$ and $\rho$ refer to sparsity parameters which matches existed results for hypothesis testing with i.i. d. samples. We validate our theoretical results with simulation results for both block-structured and chain-structured networks.
Privacy of trajectory micro-data : a survey

We survey the literature on the privacy of trajectory micro-data, i.e., spatiotemporal information about the mobility of individuals, whose collection is becoming increasingly simple and frequent thanks to emerging information and communication technologies. The focus of our review is on privacy-preserving data publishing (PPDP), i.e., the publication of databases of trajectory micro-data that preserve the privacy of the monitored individuals. We classify and present the literature of attacks against trajectory micro-data, as well as solutions proposed to date for protecting databases from such attacks. This paper serves as an introductory reading on a critical subject in an era of growing awareness about privacy risks connected to digital services, and provides insights into open problems and future directions for research.
Matching on Generalized Propensity Scores with Continuous Exposures.

In the context of a binary treatment, matching is a well-established approach in causal inference. However, in the context of a continuous treatment or exposure, matching is still underdeveloped. We propose an innovative matching approach to estimate an average causal exposure-response function under the setting of continuous exposures that relies on the generalized propensity score (GPS). Our approach maintains the following attractive features of matching: a) clear separation between the design and the analysis; b) robustness to model misspecification or to the presence of extreme values of the estimated GPS; c) straightforward assessments of covariate balance. We first introduce an assumption of identifiability, called local weak unconfoundedness. Under this assumption and mild smoothness conditions, we provide theoretical guarantees that our proposed matching estimator attains point-wise consistency and asymptotic normality. In simulations, our proposed matching approach outperforms existing methods under settings with model misspecification or in the presence of extreme values of the estimated GPS. We apply our proposed method to estimate the average causal exposure-response function between long-term PM2.5 exposure and all-cause mortality among 68.5 million Medicare enrollees, 2000-2016. We found strong evidence of a harmful effect of long-term PM2.5 exposure on mortality. Code for the proposed matching approach is provided in the CausalGPS R package, which is available on CRAN and provides a computationally efficient implementation.
Companion-driven evolution of massive stellar binaries

At least $70\, {\rm per\, cent}$ of massive OBA-type stars reside in binary or higher order systems. The dynamical evolution of these systems can lend insight into the origins of extreme phenomena such as X-ray binaries and gravitational wave sources. In one such dynamical process, the Eccentric Kozai–Lidov (EKL) mechanism, a third companion star alters the secular evolution of a binary system. For dynamical stability, these triple systems must have a hierarchical configuration. We explore the effects of a distant third companion’s gravitational perturbations on a massive binary’s orbital configuration before significant stellar evolution has taken place (≤10 Myr). We include tidal dissipation and general relativistic precession. With large (38 000 total) Monte Carlo realizations of massive hierarchical triples, we characterize imprints of the birth conditions on the final orbital distributions. Specifically, we find that the final eccentricity distribution over the range of 0.1–0.7 is an excellent indicator of its birth distribution. Furthermore, we find that the period distributions have a similar mapping for wide orbits. Finally, we demonstrate that the observed period distribution for approximately 10-Myr-old massive stars is consistent with EKL evolution.
Probabilistic Forecasting of Sensory Data With Generative Adversarial Networks – ForGAN

Time series forecasting is one of the challenging problems for humankind. The traditional forecasting methods using mean regression models have severe shortcomings in reflecting real-world fluctuations. While new probabilistic methods rush to rescue, they fight with technical difficulties like quantile crossing or selecting a prior distribution. To meld the different strengths of these fields while avoiding their weaknesses, as well as, to push the boundary of the state-of-the-art, we introduce ForGAN – one step ahead probabilistic forecasting with generative adversarial networks. ForGAN utilizes the power of the conditional generative adversarial network to learn the data generating distribution and compute probabilistic forecasts from it. We argue how to evaluate ForGAN in opposition to regression methods. To investigate probabilistic forecasting of ForGAN, we create a new dataset and demonstrate our method abilities on it. This dataset will be made publicly available for comparison. Furthermore, we test ForGAN on two publicly available datasets, namely Mackey-Glass dataset and Internet traffic dataset (A5M), where the impressive performance of ForGAN demonstrate its high capability in forecasting future values.
Trends in the Extremes of Environments Associated with Severe U.S. Thunderstorms

Severe thunderstorms can have devastating impacts. Concurrently high values of convective available potential energy (CAPE) and storm relative helicity (SRH) are known to be conducive to severe weather, so high values of PROD = (CAPE)1/2 × SRH have been used to indicate high risk of severe thunderstorms. We consider the extreme values of these three variables for a large area of the contiguous United States over the period 1979–2015 and use extreme-value theory and a multiple testing procedure to show that there is a significant time trend in the extremes for PROD maxima in April, May, and August, for CAPE maxima in April, May, and June, and for maxima of SRH in April and May. These observed increases in CAPE are also relevant for rainfall extremes and are expected in a warmer climate but have not previously been reported. Moreover, we show that El Niño–Southern Oscillation explains variation in the extremes of PROD and SRH in February. Our results suggest that the risk from severe thunderstorms in April and May is increasing in parts of the United States where it was already high and that the risk from storms in February is increased over the main part of the region during La Niña years.
The distribution of the Lasso: Uniform control over sparse balls and adaptive parameter tuning

The Lasso is a popular regression method for high-dimensional problems in which the number of parameters $\theta_1,\dots,\theta_N$, is larger than the number $n$ of samples: $N>n$. A useful heuristics relates the statistical properties of the Lasso estimator to that of a simple soft-thresholding denoiser,in a denoising problem in which the parameters $(\theta_i)_{i\le N}$ are observed in Gaussian noise, with a carefully tuned variance. Earlier work confirmed this picture in the limit $n,N\to\infty$, pointwise in the parameters $\theta$, and in the value of the regularization parameter. Here, we consider a standard random design model and prove exponential concentration of its empirical distribution around the prediction provided by the Gaussian denoising model. Crucially, our results are uniform with respect to $\theta$ belonging to $\ell_q$ balls, $q\in [0,1]$, and with respect to the regularization parameter. This allows to derive sharp results for the performances of various data-driven procedures to tune the regularization. Our proofs make use of Gaussian comparison inequalities, and in particular of a version of Gordon's minimax theorem developed by Thrampoulidis, Oymak, and Hassibi, which controls the optimum value of the Lasso optimization problem. Crucially, we prove a stability property of the minimizer in Wasserstein distance, that allows to characterize properties of the minimizer itself.