Filming his World War I epic "1917" like it was one unbroken shot, Sam Mendes has dazzled audiences and critics. Is a best picture Oscar next?

Predicting the four acting races for the 2020 Oscars is easy this year, but there's still drama in the best picture race and others

¿Hispano, latino o ambos? ¿Blanco o de color? El debate sobre la identidad se desató después de que algunos declararan a Banderas como "persona de color" cuando se anunciaron las nominaciones de los Oscars de actuación abrumadoramente blanca.

"1917," Sam Mendes' World War I adventure tops fellow best-picture nominee "The Irishman" along with three flashier contenders: "The Lion King," "Star Wars: The Rise of Skywalker," "Avengers: Endgame."

Oscar's big winner, 'Parasite,' and war tale '1917' show how interactive entertainment is shaping linear storytelling — often for the better.

Elon Musk tiene un gran incentivo financiero para activar la conducción de servicio completo. ¿Pero está Tesla cerca de estar listo?

¿Cómo pueden los artistas responder a la pandemia de COVID-19? El muy ridiculizado discurso de aceptación de Oscar de Joaquín Phoenix en realidad sugiere una respuesta.

PENSION scam warnings have been issued by The Financial Conduct Authority and The Pensions Regulator after victims were scammed out of an average of £91,000. But how have people been duped by pension fraud?

Local officials say the community is shocked that racism could have fueled the shooting of Ahmaud Arbery.

A Victory arch greeted the troops as an official welcome home

سنہ 1918 سے 1920 کے دوران پوری دنیا کو فلو (زکام) کی ایسی وبا نے اپنی لپیٹ میں لیا جس سے دنیا کی ایک تہائی سے زیادہ آبادی متاثر ہوئی۔ اس کے خاتمے تک دو سے پانچ کروڑ افراد ہلاک ہوئے۔ اس وبائی حالت کے بعد دنیا کس روپ میں سامنے آئی، اور کیا ایسے اشارے ہیں جو ہمیں کوروناوائرس کے بعد کی دنیا کے خدوخال کا پتہ دے سکیں؟

velocityconf: New #velocityconf CA program preview is up: http://t.co/rKjf91RXdD @ariyahidayat on End-to-End JS Quality Analysis.

Rudy Maxa - Savvy Traveler

The Amateur Traveler talks to Diana Higuera who is a native of Venezuela who worked for a number of years as a tour guide on adventure tours to see Venezuela's wildlife and natural beauty. Diana takes us on a virtual tour starting in Margarita island, then heading to the Los Roques archipelago north of Venezuela, then in an indian canoe up the Orinoco river. From there we will head to the tepuis (table top mountains) in Canaima National Park in Venezuela's Amazon basin. There we will see the highest waterfall in the world at Angel Falls. We will also go behind Sapo falls. Along the way we will see birds, monkeys and anacondas. We will fish for piranas, pet crocodiles and eat street food in Venezuela.

The Amateur Traveler talks to Matt Wall about traveling to and living in Honduras. Matt recommends the Bay Islands in the Caribbean for a beach resort vacation, scuba diving and snorkeling. The main island of Roatan have everything from all-inclusive resorts to backpacking accommodations in West End. Matt lived in the town of Copán Ruinas near the Guatemala border named after the "pretty incredible" Mayan ruins there. For the very adventurous who are willing to rough it Matt also suggests the Mosquito Coast at the far Western edge of Honduras which is a rain forest area sometimes called the "little Amazon". "You go there if you want to have a jungle experience".

Hear about travel to Baton Rouge as the Amateur Traveler talks to Lindsay Thomas from TheTraveluster.com about her hometown. Baton Rouge is the capital of Louisiana. It can be a great gateway to Cajun food and culture.

Hear about travel to El Salvador as the Amateur Traveler talks to Joe Baur of joebaur.com about his recent visit to this Central American country.

Hear about travel to Rwanda as the Amateur Traveler talks to VJ from imvoyager.com about his trip to that country.

Hear about travel to Bulgaria as the Amateur Traveler talks to Tom and Patty Burkett about their road trip from Sofia to the Black Sea.

We're back this week to bring you all the hot news from E3 and some cooler takes on everything Apple announced at its WWDC developer conference a few weeks back.

To help us do that Macworld UK editor Karen Khan runs through all the iOS and macOS Mojave updates Apple users can look forward to later this year.

Then Tech Advisor staff writer Sean Bradley is on hand for a whistlestop tour of all of the big gaming announcements from E3 in LA so far, including Gears of War, Death Stranding and The Last Of Us 2.

See you next week!

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GPR120 is implicated as a lipid receptor in the oro-sensory detection of dietary fatty acids. However, the effects of GPR120 activation on dietary fat intake or obesity are not clearly understood. We investigated to determine whether the binding of TUG891, a novel GPR120 agonist, to lingual GPR120 modulates fat preference in mice. We explored the effects of TUG891 on obesity-related hormones and conducted behavioral choice tests on mice to better understand the physiologic relevance of the action of TUG891. In cultured mouse and human taste bud cells (TBCs), TUG891 induced a rapid increase in Ca²⁺ by acting on GPR120. A long-chain dietary fatty acid, linoleic acid (LA), also recruited Ca²⁺ via GPR120 in human and mouse TBCs. Both TUG891 and LA induced ERK1/2 phosphorylation and enhanced in vitro release of glucagon-like peptide-1 from cultured human and mouse TBCs. In situ application of TUG891 onto the tongue of anesthetized mice triggered the secretion of pancreatobiliary juice, probably via the tongue-brain-gut axis. Furthermore, lingual application of TUG891 altered circulating concentrations of cholecystokinin and adipokines, associated with decreased circulating LDL, in conscious mice. In behavioral tests, mice exhibited a spontaneous preference for solutions containing either TUG891 or LA instead of a control. However, addition of TUG891 to a solution containing LA significantly curtailed fatty acid preference. Our study demonstrates that TUG891 binds to lingual GPR120 receptors, activates the tongue-brain-gut axis, and modulates fat preference. These findings may support the development of new fat taste analogs that can change the approach to obesity prevention and treatment.

When the problem occurs, you are unable to perform any workflow actions in a SAS solution that uses SAS Workflow Services.

When you issue a LIBNAME statement for a Google BigQuery database without including the SCHEMA= option, all tables in the project are shown when the libref is expanded. However, if you try to acces

You can create create a Databricks table by using PROC SQL, but you cannot insert data into the table. PROC APPEND cannot create a new table or append data to an existing table.

A "Shoeless" Joe Jackson baseball card from 1910 sold at auction for $492,000 this week.

MPI llevó a cabo un seminario en línea para marcar el lanzamiento de: Un portal sobre Migración en América Latina y el Caribe; y un informe que examina los efectos de las políticas migratorias y de integración en 11 países en América Latina y el Caribe ante el aumento de la migración venezolana y nicaragüense.

MPI llevó a cabo un seminario en línea para marcar el lanzamiento de: Un portal sobre Migración en América Latina y el Caribe; y un informe que examina los efectos de las políticas migratorias y de integración en 11 países en América Latina y el Caribe ante el aumento de la migración venezolana y nicaragüense.

World War, 1914-1918 -- Causes.

The South Eastern Gazette (1852-1912), formerly the Maidstone Gazette, was a major regional title for the south-east of England. Available free in the UK thanks to the 'Your Heritage Lottery Fund' and the Kent Messenger, the title covers much of the South-East of England from East Hampshire through to Surrey and Sussex to South Essex and parts of London.

London : Macmillan, 1891.

[London?] : [Endell Street Military Hospital?], [1920] (Harlesden, London N.W. 10 : Leveridge & Co.)

In this paper we propose a bimodal gamma distribution using a quadratic transformation based on the alpha-skew-normal model. We discuss several properties of this distribution such as mean, variance, moments, hazard rate and entropy measures. Further, we propose a new regression model with censored data based on the bimodal gamma distribution. This regression model can be very useful to the analysis of real data and could give more realistic fits than other special regression models. Monte Carlo simulations were performed to check the bias in the maximum likelihood estimation. The proposed models are applied to two real data sets found in literature.

We focus on estimating emph{a priori} generalization error of two-layer ReLU neural networks (NNs) trained by mean squared error, which only depends on initial parameters and the target function, through the following research line. We first estimate emph{a priori} generalization error of finite-width two-layer ReLU NN with constraint of minimal norm solution, which is proved by cite{zhang2019type} to be an equivalent solution of a linearized (w.r.t. parameter) finite-width two-layer NN. As the width goes to infinity, the linearized NN converges to the NN in Neural Tangent Kernel (NTK) regime citep{jacot2018neural}. Thus, we can derive the emph{a priori} generalization error of two-layer ReLU NN in NTK regime. The distance between NN in a NTK regime and a finite-width NN with gradient training is estimated by cite{arora2019exact}. Based on the results in cite{arora2019exact}, our work proves an emph{a priori} generalization error bound of two-layer ReLU NNs. This estimate uses the intrinsic implicit bias of the minimum norm solution without requiring extra regularity in the loss function. This emph{a priori} estimate also implies that NN does not suffer from curse of dimensionality, and a small generalization error can be achieved without requiring exponentially large number of neurons. In addition the research line proposed in this paper can also be used to study other properties of the finite-width network, such as the posterior generalization error.

We focus on the challenge of finding a diverse collection of quality solutions on complex continuous domains. While quality diver-sity (QD) algorithms like Novelty Search with Local Competition (NSLC) and MAP-Elites are designed to generate a diverse range of solutions, these algorithms require a large number of evaluations for exploration of continuous spaces. Meanwhile, variants of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) are among the best-performing derivative-free optimizers in single-objective continuous domains. This paper proposes a new QD algorithm called Covariance Matrix Adaptation MAP-Elites (CMA-ME). Our new algorithm combines the self-adaptation techniques of CMA-ES with archiving and mapping techniques for maintaining diversity in QD. Results from experiments based on standard continuous optimization benchmarks show that CMA-ME finds better-quality solutions than MAP-Elites; similarly, results on the strategic game Hearthstone show that CMA-ME finds both a higher overall quality and broader diversity of strategies than both CMA-ES and MAP-Elites. Overall, CMA-ME more than doubles the performance of MAP-Elites using standard QD performance metrics. These results suggest that QD algorithms augmented by operators from state-of-the-art optimization algorithms can yield high-performing methods for simultaneously exploring and optimizing continuous search spaces, with significant applications to design, testing, and reinforcement learning among other domains.

This paper develops a general framework for analyzing asymptotics of $V$-statistics. Previous literature on limiting distribution mainly focuses on the cases when $n o infty$ with fixed kernel size $k$. Under some regularity conditions, we demonstrate asymptotic normality when $k$ grows with $n$ by utilizing existing results for $U$-statistics. The key in our approach lies in a mathematical reduction to $U$-statistics by designing an equivalent kernel for $V$-statistics. We also provide a unified treatment on variance estimation for both $U$- and $V$-statistics by observing connections to existing methods and proposing an empirically more accurate estimator. Ensemble methods such as random forests, where multiple base learners are trained and aggregated for prediction purposes, serve as a running example throughout the paper because they are a natural and flexible application of $V$-statistics.

A graph homomorphism is a map between two graphs that preserves adjacency relations. We consider the problem of sampling a random graph homomorphism from a graph $F$ into a large network $mathcal{G}$. We propose two complementary MCMC algorithms for sampling a random graph homomorphisms and establish bounds on their mixing times and concentration of their time averages. Based on our sampling algorithms, we propose a novel framework for network data analysis that circumvents some of the drawbacks in methods based on independent and neigborhood sampling. Various time averages of the MCMC trajectory give us various computable observables, including well-known ones such as homomorphism density and average clustering coefficient and their generalizations. Furthermore, we show that these network observables are stable with respect to a suitably renormalized cut distance between networks. We provide various examples and simulations demonstrating our framework through synthetic networks. We also apply our framework for network clustering and classification problems using the Facebook100 dataset and Word Adjacency Networks of a set of classic novels.

Factor analysis is a flexible technique for assessment of multivariate dependence and codependence. Besides being an exploratory tool used to reduce the dimensionality of multivariate data, it allows estimation of common factors that often have an interesting theoretical interpretation in real problems. However, standard factor analysis is only applicable when the variables are scaled, which is often inappropriate, for example, in data obtained from questionnaires in the field of psychology,where the variables are often categorical. In this framework, we propose a factor model for the analysis of multivariate ordered and non-ordered polychotomous data. The inference procedure is done under the Bayesian approach via Markov chain Monte Carlo methods. Two Monte-Carlo simulation studies are presented to investigate the performance of this approach in terms of estimation bias, precision and assessment of the number of factors. We also illustrate the proposed method to analyze participants' responses to the Motivational State Questionnaire dataset, developed to study emotions in laboratory and field settings.

A deep neural network has relieved the burden of feature engineering by human experts, but comparable efforts are instead required to determine an effective architecture. On the other hands, as the size of a network has over-grown, a lot of resources are also invested to reduce its size. These problems can be addressed by sparsification of an over-complete model, which removes redundant parameters or connections by pruning them away after training or encouraging them to become zero during training. In general, however, these approaches are not fully differentiable and interrupt an end-to-end training process with the stochastic gradient descent in that they require either a parameter selection or a soft-thresholding step. In this paper, we propose a fully differentiable sparsification method for deep neural networks, which allows parameters to be exactly zero during training, and thus can learn the sparsified structure and the weights of networks simultaneously using the stochastic gradient descent. We apply the proposed method to various popular models in order to show its effectiveness.