Miracle-dz/UniFAR-FTUs huggingface dataset:
Promyelocytic leukemia protein (PML) controls breast cancer cell proliferation by modulating Forkhead transcription factors

The multitasking promyelocytic leukemia (PML) protein was originally recognized as a tumor‐suppressive factor, but more recent evidence has implicated PML in tumor cell prosurvival actions and poor patient prognosis in specific cancer settings. Here, we report that inducible PMLIV expression inhibits cell proliferation as well as self‐renewal and impairs cell cycle progression of breast cancer cell lines in a reversible manner. Transcriptomic profiling identified a large number of PML‐deregulated genes associated with various cell processes. Among them, cell cycle‐ and division‐related genes and their cognitive regulators are highly ranked. In this study, we focused on previously unknown PML targets, namely the Forkhead transcription factors. PML suppresses the Forkhead box subclass M1 (FOXM1) transcription factor at both the RNA and protein levels, along with many of its gene targets. We show that FOXM1 interacts with PMLIV primarily via its DNA‐binding domain and dynamically colocalizes in PML nuclear bodies. In parallel, PML modulates the activity of Forkhead box O3 (FOXO3), a factor opposing certain FOXM1 activities, to promote cell survival and stress resistance. Thus, PMLIV affects the balance of FOXO3 and FOXM1 transcriptional programs by acting on discrete gene subsets to favor both growth inhibition and survival. Interestingly, PMLIV‐specific knockdown mimicked ectopic expression vis‐à‐vis loss of proliferative ability and self‐renewal, but also led to loss of survival ability as shown by increased apoptosis. We propose that divergent or similar effects on cell physiology may be elicited by high or low PMLIV levels dictated by other concurrent genetic or epigenetic cancer cell states that may additionally account for its disparate effects in various cancer types.
Optimal cleaning for singular values of cross-covariance matrices

We give a new algorithm for the estimation of the cross-covariance matrix $\mathbb{E} XY'$ of two large dimensional signals $X\in\mathbb{R}^n$, $Y\in\mathbb{R}^p$ in the context where the number $T$ of observations of the pair $(X,Y)$ is itself large, but with $n,p$ non negligible with respect to $T$. This algorithm is optimal among rotationally invariant estimators, i.e. estimators derived from the empirical estimator by cleaning the singular values, while letting singular vectors unchanged. We give an interpretation of the singular value cleaning in terms of overfitting ratios.
Ranking and empirical minimization of U-statistics

The problem of ranking/ordering instances, instead of simply classifying them, has recently gained much attention in machine learning. In this paper we formulate the ranking problem in a rigorous statistical framework. The goal is to learn a ranking rule for deciding, among two instances, which one is "better," with minimum ranking risk. Since the natural estimates of the risk are of the form of a U-statistic, results of the theory of U-processes are required for investigating the consistency of empirical risk minimizers. We establish in particular a tail inequality for degenerate U-processes, and apply it for showing that fast rates of convergence may be achieved under specific noise assumptions, just like in classification. Convex risk minimization methods are also studied.
Lending Orientation to Neural Networks for Cross-View Geo-Localization

This paper studies image-based geo-localization (IBL) problem using ground-to-aerial cross-view matching. The goal is to predict the spatial location of a ground-level query image by matching it to a large geotagged aerial image database (e.g., satellite imagery). This is a challenging task due to the drastic differences in their viewpoints and visual appearances. Existing deep learning methods for this problem have been focused on maximizing feature similarity between spatially close-by image pairs, while minimizing other images pairs which are far apart. They do so by deep feature embedding based on visual appearance in those ground-and-aerial images. However, in everyday life, humans commonly use orientation information as an important cue for the task of spatial localization. Inspired by this insight, this paper proposes a novel method which endows deep neural networks with the `commonsense' of orientation. Given a ground-level spherical panoramic image as query input (and a large georeferenced satellite image database), we design a Siamese network which explicitly encodes the orientation (i.e., spherical directions) of each pixel of the images. Our method significantly boosts the discriminative power of the learned deep features, leading to a much higher recall and precision outperforming all previous methods. Our network is also more compact using only 1/5th number of parameters than a previously best-performing network. To evaluate the generalization of our method, we also created a large-scale cross-view localization benchmark containing 100K geotagged ground-aerial pairs covering a city. Our codes and datasets are available at https://github.com/Liumouliu/OriCNN.
A Gravity Model Analysis of Irish Merchandise Goods Exports under Brexit

We examine the effect of a Hard Brexit on Irish Exports using the PPML Gravity Model and Irish Exports data at the micro level. We find a hard Brexit could reduce Irish national income by over 9 billion euro annually and the effect would be sustained mst in th etraditional sectors of Agriculture and Food production of the Irish economy
Inference for extreme values under threshold‐based stopping rules

There is a propensity for an extreme value analysis to be conducted as a consequence of a large flooding event. This timing of the analysis introduces bias and poor coverage probabilities into the associated risk assessments and leads subsequently to inefficient flood protection schemes. We explore these problems through studying stochastic stopping criteria and propose new likelihood‐based inferences that mitigate against these difficulties. Our methods are illustrated through the analysis of the river Lune, following its experiencing the UK's largest ever measured flow event in 2015. We show that without accounting for this stopping feature there would be substantial overdesign in response to the event.
Approach to learning and educational environment: time to rethink measurement tools in postgraduate medical training?

Objectives To assess the correlation between perception of the learning environment and the approach to learning adopted by anesthesiology residents throughout training in an academic institution in the United States. Methods This is a cross-sectional study involving forty-one anesthesiology residents who completed electronic forms of the Revised Two-Factor Study Process Questionnaire to assess learning approaches, and the Dundee Ready Educational Environment Measure questionnaire to assess learning environment. Convenience sampling was used with the current anesthesiology residents. Learning approaches were analyzed with a multiple regression model for correlation between total score, domains, and training level. Analysis of variance was used to assess differences in perception of the learning environment based on training level. Multivariate logistic regression was used to assess the correlation between domains of learning the environment and approaches questionnaires. Cronbach α was used to evaluate the internal consistency of responses within each domain of both questionnaires. Results Forty-one residents completed the questionnaires. Cronbach α varied between 0.604 and 0.76 among the domains in the Study Process Questionnaire and was greater than 0.60 for the Dundee questionnaire. There was a moderate correlation between total deep approach scores and the total subjective perception of teachers scores (R2= - 0.507, p <0.01). There was no significant association between specific domains of Dundee and study process questionnaires and resident year of training. Conclusions The learning approaches adopted by anesthesiology residents and the perception of the educational environment are not correlated with years of training. The DREEM and R-SPQ-2F questionnaires should not be recommended for evaluation of anesthesiology residents.
A Robust Learning Algorithm for Regression Models Using Distributionally Robust Optimization under the Wasserstein Metric

We present a Distributionally Robust Optimization (DRO) approach to estimate a robustified regression plane in a linear regression setting, when the observed samples are potentially contaminated with adversarially corrupted outliers. Our approach mitigates the impact of outliers through hedging against a family of distributions on the observed data, some of which assign very low probabilities to the outliers. The set of distributions under consideration are close to the empirical distribution in the sense of the Wasserstein metric. We show that this DRO formulation can be relaxed to a convex optimization problem which encompasses a class of models. By selecting proper norm spaces for the Wasserstein metric, we are able to recover several commonly used regularized regression models. We provide new insights into the regularization term and give guidance on the selection of the regularization coefficient from the standpoint of a confidence region. We establish two types of performance guarantees for the solution to our formulation under mild conditions. One is related to its out-of-sample behavior (prediction bias), and the other concerns the discrepancy between the estimated and true regression planes (estimation bias). Extensive numerical results demonstrate the superiority of our approach to a host of regression models, in terms of the prediction and estimation accuracies. We also consider the application of our robust learning procedure to outlier detection, and show that our approach achieves a much higher AUC (Area Under the ROC Curve) than M-estimation.
Solving the Empirical Bayes Normal Means Problem with Correlated Noise

The Normal Means problem plays a fundamental role in many areas of modern high-dimensional statistics, both in theory and practice. And the Empirical Bayes (EB) approach to solving this problem has been shown to be highly effective, again both in theory and practice. However, almost all EB treatments of the Normal Means problem assume that the observations are independent. In practice correlations are ubiquitous in real-world applications, and these correlations can grossly distort EB estimates. Here, exploiting theory from Schwartzman (2010), we develop new EB methods for solving the Normal Means problem that take account of unknown correlations among observations. We provide practical software implementations of these methods, and illustrate them in the context of large-scale multiple testing problems and False Discovery Rate (FDR) control. In realistic numerical experiments our methods compare favorably with other commonly-used multiple testing methods.
Star formation rates for photometric samples of galaxies using machine learning methods

Star Formation Rates or SFRs are crucial to constrain theories of galaxy formation and evolution. SFRs are usually estimated via spectroscopic observations requiring large amounts of telescope time. We explore an alternative approach based on the photometric estimation of global SFRs for large samples of galaxies, by using methods such as automatic parameter space optimisation, and supervised Machine Learning models. We demonstrate that, with such approach, accurate multi-band photometry allows to estimate reliable SFRs. We also investigate how the use of photometric rather than spectroscopic redshifts, affects the accuracy of derived global SFRs. Finally, we provide a publicly available catalogue of SFRs for more than 27 million galaxies extracted from the Sloan Digital Sky survey Data Release 7. The catalogue is available through the Vizier facility at the following link this ftp URL
A Generalization of Stationary AR(1) Schemes

Here we develop a first order autoregressive model {Xn} that is marginally stationary where Xn is the sum/ extreme of k i.i. d observations. We prove that stationary solutions to these models are either semi-selfdecomposable/ extreme-semi-selfdecomposable or, sum/ extreme stable with respect to Harris distribution.
The False Positive Control Lasso

In high dimensional settings where a small number of regressors are expected to be important, the Lasso estimator can be used to obtain a sparse solution vector with the expectation that most of the non-zero coefficients are associated with true signals. While several approaches have been developed to control the inclusion of false predictors with the Lasso, these approaches are limited by relying on asymptotic theory, having to empirically estimate terms based on theoretical quantities, assuming a continuous response class with Gaussian noise and design matrices, or high computation costs. In this paper we show how: (1) an existing model (the SQRT-Lasso) can be recast as a method of controlling the number of expected false positives, (2) how a similar estimator can used for all other generalized linear model classes, and (3) this approach can be fit with existing fast Lasso optimization solvers. Our justification for false positive control using randomly weighted self-normalized sum theory is to our knowledge novel. Moreover, our estimator's properties hold in finite samples up to some approximation error which we find in practical settings to be negligible under a strict mutual incoherence condition.
Bayesian Model Selection with Graph Structured Sparsity

We propose a general algorithmic framework for Bayesian model selection. A spike-and-slab Laplacian prior is introduced to model the underlying structural assumption. Using the notion of effective resistance, we derive an EM-type algorithm with closed-form iterations to efficiently explore possible candidates for Bayesian model selection. The deterministic nature of the proposed algorithm makes it more scalable to large-scale and high-dimensional data sets compared with existing stochastic search algorithms. When applied to sparse linear regression, our framework recovers the EMVS algorithm [Rockova and George, 2014] as a special case. We also discuss extensions of our framework using tools from graph algebra to incorporate complex Bayesian models such as biclustering and submatrix localization. Extensive simulation studies and real data applications are conducted to demonstrate the superior performance of our methods over its frequentist competitors such as $\ell_0$ or $\ell_1$ penalization.
Bootstrapping Generalization Error Bounds for Time Series

We consider the problem of finding confidence intervals for the risk of forecasting the future of a stationary, ergodic stochastic process, using a model estimated from the past of the process. We show that a bootstrap procedure provides valid confidence intervals for the risk, when the data source is sufficiently mixing, and the loss function and the estimator are suitably smooth. Autoregressive (AR(d)) models estimated by least squares obey the necessary regularity conditions, even when mis-specified, and simulations show that the finite- sample coverage of our bounds quickly converges to the theoretical, asymptotic level. As an intermediate step, we derive sufficient conditions for asymptotic independence between empirical distribution functions formed by splitting a realization of a stochastic process, of independent interest.
Estimation of the asymptotic variance of univariate and multivariate random fields and statistical inference

Correlated random fields are a common way to model dependence struc- tures in high-dimensional data, especially for data collected in imaging. One important parameter characterizing the degree of dependence is the asymp- totic variance which adds up all autocovariances in the temporal and spatial domain. Especially, it arises in the standardization of test statistics based on partial sums of random fields and thus the construction of tests requires its estimation. In this paper we propose consistent estimators for this parameter for strictly stationary {\phi}-mixing random fields with arbitrary dimension of the domain and taking values in a Euclidean space of arbitrary dimension, thus allowing for multivariate random fields. We establish consistency, provide cen- tral limit theorems and show that distributional approximations of related test statistics based on sample autocovariances of random fields can be obtained by the subsampling approach. As in applications the spatial-temporal correlations are often quite local, such that a large number of autocovariances vanish or are negligible, we also investigate a thresholding approach where sample autocovariances of small magnitude are omitted. Extensive simulation studies show that the proposed estimators work well in practice and, when used to standardize image test statistics, can provide highly accurate image testing procedures.
Simultaneous Prediction Intervals for Small Area Parameter

In this paper we address the construction of simultaneous prediction intervals for small area parameters in linear mixed models. Simultaneous intervals are necessary to compare areas, or to look at several areas at a time, as the presently available intervals are not statistically valid for these scenarios. We consider two frameworks to develop simultaneous intervals: the Monte Carlo approximation of the volume of a tube based intervals and bootstrap bands. Proofs of the consistency as well as the asymptotic coverage probability of the bootstrap intervals are provided. Our proposal is accompanied by simulation experiments and a data example. The simulations show which method works best under a particular scenario. We illustrate the utility of simultaneous intervals for the analysis of small area parameters. When comparing the areas, the classical methods lead to erroneous conclusions, visible in the study of the household income distribution in Galicia in Northern Spain.
Aggregated test of independence based on HSIC measures

Dependence measures based on reproducing kernel Hilbert spaces, also known as Hilbert-Schmidt Independence Criterion and denoted HSIC, are widely used to statistically decide whether or not two random vectors are dependent. Recently, non-parametric HSIC-based statistical tests of independence have been performed. However, these tests lead to the question of prior choice of the kernels associated to HSIC, there is as yet no method to objectively select specific kernels. In order to avoid a particular kernel choice, we propose a new HSIC-based aggregated procedure allowing to take into account several Gaus-sian kernels. To achieve this, we first propose non-asymptotic single tests of level $\alpha$ $\in$ (0, 1) and second type error controlled by $\beta$ $\in$ (0, 1). We also provide a sharp upper bound of the uniform seperation rate of the proposed tests. Thereafter, we introduce a multiple testing procedure in the case of Gaussian kernels, considering a set of various parameters. These agregated tests are shown to be of level $\alpha$ and to overperform single tests in terms of uniform separation rates.
On the sign recovery given by the thresholded LASSO and thresholded Basis Pursuit

We consider the regression model, when the number of observations is smaller than the number of explicative variables. It is well known that the popular Least Absolute Shrinkage and Selection Operator (LASSO) can recover the sign of regression coefficients only if a very stringent irrepresentable condition is satisfied. In this article, in a first step, we provide a new result about the irrepresentable condition: the probability to recover the sign with LASSO is smaller than 1/2 once the irrepresentable condition does not hold. Next, we revisit properties of thresholded LASSO and provide new theoretical results in the asymptotic setup under which the design matrix is fixed and the magnitudes of nonzero regression coefficients tend to infinity. Apart from LASSO, our results cover also basis pursuit, which can be thought of as a limiting case of LASSO when the tuning parameter tends to 0. Compared to the classical asymptotics, our approach allows for reduction of the technical burden. We formulate an easy identifiability condition which turns out to be sufficient and necessary for thresholded LASSO to recover the sign of the sufficiently large signal. Our simulation study illustrates the difference between the irrepresentable and the identifiability condition. Interestingly, while irrepresentable condition becomes more difficult to be satisfied for strongly correlated designs, it does not seem to be true for identifiability condition. Actually, when the correlations are positive and the nonzero coefficients are of the same sign, the identifiability condition allows the number of nonzero coefficients to be larger than in case where the regressors are independent.
Equi-energy sampler with applications in statistical inference and statistical mechanics

We introduce a new sampling algorithm, the equi-energy sampler, for efficient statistical sampling and estimation. Complementary to the widely used temperature-domain methods, the equi-energy sampler, utilizing the temperature-energy duality, targets the energy directly. The focus on the energy function not only facilitates efficient sampling, but also provides a powerful means for statistical estimation, for example, the calculation of the density of states and microcanonical averages in statistical mechanics. The equi-energy sampler is applied to a variety of problems, including exponential regression in statistics, motif sampling in computational biology and protein folding in biophysics. We congratulate Samuel Kou, Qing Zhou and Wing Wong (referred to subsequently as KZW) for this beautifully written paper, which opens a new direction in Monte Carlo computation. This discussion has two parts. First, we describe a very closely related method, multicanonical sampling (MCS), and report a simulation example that compares the equi-energy (EE) sampler with MCS. Overall, we found the two algorithms to be of comparable efficiency for the simulation problem considered. In the second part, we develop some additional convergence results for the EE sampler. Novel sampling algorithms can significantly impact open questions in computational biology, most notably the in silico protein folding problem. By using computational methods, protein folding aims to find the three-dimensional structure of a protein chain given the sequence of its amino acid building blocks. The complexity of the problem strongly depends on the protein representation and its energy function. The more detailed the model, the more complex its corresponding energy function and the more challenge it sets for sampling algorithms. Kou, Zhou and Wong have introduced a novel sampling method, which could contribute significantly to the field of structural prediction.