Ketel Marte does not lack for confidence when it comes to switching from second base/shortstop to center field, which is where he's expected to see a lot of time this season.

The world’s first international agreement on migration was approved by 164 countries in December 2018, but not without turbulence. U.S. withdrawal from the nonbinding Global Compact on Safe, Orderly, and Regular Migration, on grounds it could impinge on sovereignty, triggered similar actions by others, particularly in Eastern Europe. Amid ongoing political ripple effects, attention now turns to implementation of the deal's goals.

The last supermoon of 2020, May's so-called "Flower Moon," will be visible in the night skies this week, and its brightness will likely obscure the yearly Eta Aquarids meteor shower, according to NASA.

New research in mice suggests that a pregnancy hormone contributes to brain and behavioral changes caused by childhood adversity

-- Read more on ScientificAmerican.com

Forget about buying low and selling high. If you are worried about the recent volatility in the stock market, perhaps you should let the weather be your guide.

Information technology -- Political aspects.

Menopause.

Edinburgh : Young J. Pentland, 1889.

An Alaskan high school exchange program works to promote understanding between the state's urban centers and its remote Native Villages and communities.

Stuttgart : In Kommission: Wissenschaftliche Verlagsgesellschaft, 2019.

Consider an unknown smooth function $f: [0,1]^d ightarrow mathbb{R}$, and assume we are given $n$ noisy mod 1 samples of $f$, i.e., $y_i = (f(x_i) + eta_i) mod 1$, for $x_i in [0,1]^d$, where $eta_i$ denotes the noise. Given the samples $(x_i,y_i)_{i=1}^{n}$, our goal is to recover smooth, robust estimates of the clean samples $f(x_i) mod 1$. We formulate a natural approach for solving this problem, which works with angular embeddings of the noisy mod 1 samples over the unit circle, inspired by the angular synchronization framework. This amounts to solving a smoothness regularized least-squares problem -- a quadratically constrained quadratic program (QCQP) -- where the variables are constrained to lie on the unit circle. Our proposed approach is based on solving its relaxation, which is a trust-region sub-problem and hence solvable efficiently. We provide theoretical guarantees demonstrating its robustness to noise for adversarial, as well as random Gaussian and Bernoulli noise models. To the best of our knowledge, these are the first such theoretical results for this problem. We demonstrate the robustness and efficiency of our proposed approach via extensive numerical simulations on synthetic data, along with a simple least-squares based solution for the unwrapping stage, that recovers the original samples of $f$ (up to a global shift). It is shown to perform well at high levels of noise, when taking as input the denoised modulo $1$ samples. Finally, we also consider two other approaches for denoising the modulo 1 samples that leverage tools from Riemannian optimization on manifolds, including a Burer-Monteiro approach for a semidefinite programming relaxation of our formulation. For the two-dimensional version of the problem, which has applications in synthetic aperture radar interferometry (InSAR), we are able to solve instances of real-world data with a million sample points in under 10 seconds, on a personal laptop.

Derivatives play an important role in bandwidth selection methods (e.g., plug-ins), data analysis and bias-corrected confidence intervals. Therefore, obtaining accurate derivative information is crucial. Although many derivative estimation methods exist, the majority require a fixed design assumption. In this paper, we propose an effective and fully data-driven framework to estimate the first and second order derivative in random design. We establish the asymptotic properties of the proposed derivative estimator, and also propose a fast selection method for the tuning parameters. The performance and flexibility of the method is illustrated via an extensive simulation study.

Umberto Amato, Anestis Antoniadis, Italia De Feis.

Source: Statistics Surveys, Volume 14, 32--70.

Abstract:
We present and compare some nonparametric estimation methods (wavelet and/or spline-based) designed to recover a one-dimensional piecewise-smooth regression function in both a fixed equidistant or not equidistant design regression model and a random design model. Wavelet methods are known to be very competitive in terms of denoising and compression, due to the simultaneous localization property of a function in time and frequency. However, boundary assumptions, such as periodicity or symmetry, generate bias and artificial wiggles which degrade overall accuracy. Simple methods have been proposed in the literature for reducing the bias at the boundaries. We introduce new ones based on adaptive combinations of two estimators. The underlying idea is to combine a highly accurate method for non-regular functions, e.g., wavelets, with one well behaved at boundaries, e.g., Splines or Local Polynomial. We provide some asymptotic optimal results supporting our approach. All the methods can handle data with a random design. We also sketch some generalization to the multidimensional setting. To study the performance of the proposed approaches we have conducted an extensive set of simulations on synthetic data. An interesting regression analysis of two real data applications using these procedures unambiguously demonstrates their effectiveness.

Jennifer E. Starling, Jared S. Murray, Carlos M. Carvalho, Radek K. Bukowski, James G. Scott.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 28--50.

Abstract:
This article introduces BART with Targeted Smoothing, or tsBART, a new Bayesian tree-based model for nonparametric regression. The goal of tsBART is to introduce smoothness over a single target covariate $t$ while not necessarily requiring smoothness over other covariates $x$. tsBART is based on the Bayesian Additive Regression Trees (BART) model, an ensemble of regression trees. tsBART extends BART by parameterizing each tree’s terminal nodes with smooth functions of $t$ rather than independent scalars. Like BART, tsBART captures complex nonlinear relationships and interactions among the predictors. But unlike BART, tsBART guarantees that the response surface will be smooth in the target covariate. This improves interpretability and helps to regularize the estimate. After introducing and benchmarking the tsBART model, we apply it to our motivating example—pregnancy outcomes data from the National Center for Health Statistics. Our aim is to provide patient-specific estimates of stillbirth risk across gestational age $(t)$ and based on maternal and fetal risk factors $(x)$. Obstetricians expect stillbirth risk to vary smoothly over gestational age but not necessarily over other covariates, and tsBART has been designed precisely to reflect this structural knowledge. The results of our analysis show the clear superiority of the tsBART model for quantifying stillbirth risk, thereby providing patients and doctors with better information for managing the risk of fetal mortality. All methods described here are implemented in the R package tsbart .

O. Collier, L. Comminges, A.B. Tsybakov.

Source: Bernoulli, Volume 26, Number 3, 1989--2020.

Abstract:
We study the problem of estimation of $N_{gamma }( heta )=sum_{i=1}^{d}| heta _{i}|^{gamma }$ for $gamma >0$ and of the $ell _{gamma }$-norm of $ heta $ for $gamma ge 1$ based on the observations $y_{i}= heta _{i}+varepsilon xi _{i}$, $i=1,ldots,d$, where $ heta =( heta _{1},dots , heta _{d})$ are unknown parameters, $varepsilon >0$ is known, and $xi _{i}$ are i.i.d. standard normal random variables. We find the non-asymptotic minimax rate for estimation of these functionals on the class of $s$-sparse vectors $ heta $ and we propose estimators achieving this rate.

Arun K. Kuchibhotla, Rohit K. Patra.

Source: Bernoulli, Volume 26, Number 2, 1587--1618.

Abstract:
We consider estimation and inference in a single index regression model with an unknown but smooth link function. In contrast to the standard approach of using kernels or regression splines, we use smoothing splines to estimate the smooth link function. We develop a method to compute the penalized least squares estimators (PLSEs) of the parametric and the nonparametric components given independent and identically distributed (i.i.d.) data. We prove the consistency and find the rates of convergence of the estimators. We establish asymptotic normality under mild assumption and prove asymptotic efficiency of the parametric component under homoscedastic errors. A finite sample simulation corroborates our asymptotic theory. We also analyze a car mileage data set and a Ozone concentration data set. The identifiability and existence of the PLSEs are also investigated.

Moore, Amy, 1908-2005.

Moore, Amy, 1908-2005 -- Family.

Nadja Klein, Michael Stanley Smith.

Source: Bayesian Analysis, Volume 14, Number 4, 1143--1171.

Abstract:
We show how to extract the implicit copula of a response vector from a Bayesian regularized regression smoother with Gaussian disturbances. The copula can be used to compare smoothers that employ different shrinkage priors and function bases. We illustrate with three popular choices of shrinkage priors—a pairwise prior, the horseshoe prior and a g prior augmented with a point mass as employed for Bayesian variable selection—and both univariate and multivariate function bases. The implicit copulas are high-dimensional, have flexible dependence structures that are far from that of a Gaussian copula, and are unavailable in closed form. However, we show how they can be evaluated by first constructing a Gaussian copula conditional on the regularization parameters, and then integrating over these. Combined with non-parametric margins the regularized smoothers can be used to model the distribution of non-Gaussian univariate responses conditional on the covariates. Efficient Markov chain Monte Carlo schemes for evaluating the copula are given for this case. Using both simulated and real data, we show how such copula smoothing models can improve the quality of resulting function estimates and predictive distributions.

A new simulation points to a previously untold chapter in Titan’s history

Similarities between lunar samples and Earth's makeup were throwing off a leading theory of the moon's origin

Despite the name, moon won’t have a rosy hue. The name alludes to flowers that bloom in April

New calculations show that the ring of light surrounding a black hole is actually made up of infinite subrings that can’t be seen with current technology

The project hasn’t yet been greenlit, but a proposal just got major funding to explore the potential for the lunar observatory

The rendering builds on decades of data that dates back to the Apollo missions, which happened some fifty years ago

The rock crash-landed in the Sahara Desert after a presumed collision chipped it off the lunar surface

Yamagata February, the winter was approaching the end in the flatland. I was aiming for the summit of "Mt.zao" from the night. There is a beautiful crater lake which is still active as a volcano, called "Goshikinuma", and in the summer it is a beautiful lake of emerald green. It was covered with strong wind and fog, but it showed its appearance in a momentary fine weather. The crater lake where the accumulated snow began to melt was like a blue moon.

Some farmers across the province are worried about getting their fuel in time for spring seeding. The Agricultural Producers Association of Saskatchewan says it has been fielding complaints this week about delays at the Co-op cardlock near Moose Jaw.

Canadians Kirsten Moore-Towers and Michael Marinaro won the bronze medal in pairs on Saturday at the ISU Four Continents figure skating competition in Seoul, South Korea.

See the Full Flower Moon, last supermoon of 2020, bloom in these stunning photos  Space.comIn Pictures: 'Full-flower supermoon' amid coronavirus lockdowns  Aljazeera.comIn pics | Last supermoon in 2020: Stunning views from around th...

OM Hong Kong celebrates the annual Mid-Autumn Festival with South Asian friend and meets other families to learn about their needs.

Atlantic Crossing :: Making Logos II shipshape for a longer voyage

A Thousand Moons

A GROUP of Glasgow-based students are working on an international project to design the building blocks of a Moon base.

Moonlight, Film 4, Wednesday, 9pm

Developed by physicians and neuroscientists, Apollo isn't the sleekiest of wearables—you might mistake it for a court-ordered ankle bracelet—but it put a little pep in my step, and is an interesting talking point, if nothing else.

Motion smoothing is horrible for most of what you watch, but it can make live sports look much better. Here's why you should turn on motion smoothing for watching games.

Analysts across the board have been certain about the heavy economic toll that the pandemic will take on the country.

If you are finding those toasts, butter and half fried eggs a tad too monotonous, you can think of adding this delicious smoothie to the fare.

"Love it," commented Karisma Kapoor

Action will create benchmark budgeting mechanism and promote fiscal sustainability DOVER, Del. – Governor John Carney on Saturday signed Executive Order 21 to implement recommendations of the advisory panel to DEFAC to study potential fiscal controls and budget smoothing mechanisms. The Order will create a benchmark budgeting mechanism, and a Budget Stabilization Fund for budget […]

Bernice and Douglas Moore were honored as Delaware Tree Farmers of the Year at the annual meeting of the Delaware Forestry Association on March 12 in Felton. The Moores were recognized for their continuous commitment to sustainable forest management and stewardship planning, as well as extensive engagement in forestry education and outreach. Their 102-acre property near Georgetown has been a certified Tree Farm since 1993. On hand to present a proclamation from the Delaware General Assembly were Reps. David L. Wilson (R-35) and Harvey R. Kenton (R-36). Governor Markell also sent a video message to congratulate the Moores for their accomplishment.