Numerical lattice quantum chromodynamics (QCD) is an integral part of the modern particle and nuclear theory toolkit [1-9]. In this framework, the discretized path integral is computed using Monte Carlo methods. Computationally, this is very expensive, and grows more so as physical limits of interest are approached [10-12]. Consequently, algorithmic developments are an important driver of progress. For example, resolving topological freezing [12-14]--an issue that arises in sampling gauge field configurations with state-of-the-art Markov chain Monte Carlo (MCMC) algorithms like heatbath [15-19] or Hybrid/Hamiltonian Monte Carlo (HMC) [20-22]--would provide access to finer lattice spacings than presently affordable. To such ends, recent work has explored how emerging machine learning (ML) techniques may be applied to lattice QCD [23, 24]. Of particular interest has been the possibility of accelerating gauge-field sampling [25-34] using normalizing flows [35-37], a class of generative statistical models with tractable density functions. In this framework, a flow is a learned, invertible (diffeomorphic) map between gauge fields. Abstractly, flows may be thought of as bridges between different distributions over gauge fields (or, equivalently, different theories or choices of action parameters).

Machine-learned normalizing flows can be used in the context of lattice quantum field theory to generate statistically correlated ensembles of lattice gauge fields at different action parameters. This work demonstrates how these correlations can be exploited for variance reduction in the computation of observables. Three different proof-of-concept applications are demonstrated using a novel residual flow architecture: continuum limits of gauge theories, the mass dependence of QCD observables, and hadronic matrix elements based on the Feynman-Hellmann approach. In all three cases, it is shown that statistical uncertainties are significantly reduced when machine-learned flows are incorporated as compared with the same calculations performed with uncorrelated ensembles or direct reweighting.

Consider a one-parameter family of Boltzmann distributions $p_t(x) = \tfrac{1}{Z_t}e^{-S_t(x)}$. This work studies the problem of sampling from $p_{t_0}$ by first sampling from $p_{t_1}$ and then applying a transformation $\Psi_{t_1}^{t_0}$ so that the transformed samples follow $p_{t_0}$. We derive an equation relating $\Psi$ and the corresponding family of unnormalized log-likelihoods $S_t$. The utility of this idea is demonstrated on the $\phi^4$ lattice field theory by extending its defining action $S_0$ to a family of actions $S_t$ and finding a $\tau$ such that normalizing flows perform better at learning the Boltzmann distribution $p_\tau$ than at learning $p_0$.

Self-driving vehicles require massive amounts of data, which can be difficult to obtain without vehicles equipped with cameras and LiDAR and miles and miles of testing. To help advance autonomous vehicle research, Ford is releasing a comprehensive self-driving dataset to academics and researchers. "There's no better way of promoting research and development than ensuring the academic community has the data it needs to create effective self-driving vehicle algorithms," the company wrote in a Medium post. The data was collected over the span of one year, and it comes from multiple self-driving research vehicles. It includes LiDAR and camera sensor data, GPS and trajectory information, as well as 3D point cloud and ground reflectivity maps.

In the future, cars will be electric. People who use them might never own them, but share them instead. The auto industry is going through massive change -- and Tesla and its outspoken CEO, Elon Musk, are seen as the great disruptors. But as Ford CEO Jim Hackett pointed out during a recent interview for CNN Business' The Table with Poppy Harlow in Detroit, his company caused, arguably, the biggest industrial disruption of the entire 20th century. And he's not about to let anyone else take away that legacy.

In the future, cars will be electric. People who use them might never own them, but share them instead. The auto industry is going through massive change -- and Tesla and its outspoken CEO, Elon Musk, are seen as the great disruptors. But as Ford CEO Jim Hackett pointed out during a recent interview for CNN Business' The Table with Poppy Harlow in Detroit, his company caused, arguably, the biggest industrial disruption of the entire 20th century. And he's not about to let anyone else take away that legacy.

DETROIT - Ford revealed details of its long-awaited restructuring plan Monday as it prepared for a future of electric and autonomous vehicles by parting ways with 7,000 white-collar workers worldwide, about 10 percent of its global salaried workforce. The major revamp, which had been underway since last year, will save about $600 million per year by eliminating bureaucracy and increasing the number of workers reporting to each manager. In the U.S. about 2,300 jobs will be cut through buyouts and layoffs. About 1,500 have left voluntarily or with buyouts, while another 300 have already been laid off. About 500 workers will be let go starting this week, largely in and around the company's headquarters in Dearborn, Michigan, just outside Detroit.

Uber, once the enfant terrible of the tech industry, put on its big kid pants and publicly filed for IPO this week, attempting to prove, once and for all, that it's got its crap together. Its filing reveals a sprawling company that's made strides since ex-CEO Travis Kalanick was dropping Boober jokes back in 2014--but one that also has a few big, hulking problems on the horizon, like fighting drivers on employee classification issues and, you know, achieving profitability. Also in transpo people and companies trying to prove themselves: Tesla goes off-menu for the $35,000 Model 3, ostensibly to shore up cash and streamline production; another industry insider says, yes, self-driving car hype got ahead of reality; and Audi argues its slightly dispiriting E-tron range numbers matter little compared to its luxury features. Let's get you caught up. Why do new premium electric vehicles keep coming up short on range?

Ford CEO Jim Hackett Tuesday joined the growing ranks of vehicle and tech execs willing to say publicly that self-driving cars won't arrive as soon as some had hoped. The industry "overestimated the arrival of autonomous vehicles," Hackett told the Detroit Economic Club. Though Ford is not wavering from its self-imposed due date of 2021 for its first purpose-built driverless car, Hackett acknowledged that the vehicle's "applications will be narrow, what we call geo-fenced, because the problem is so complex." Bloomberg earlier reported the comments. Hackett is the latest high-ranking industry insider to engage in public real talk about the prospects for self-driving cars, which back in 2016 seemed just around the corner.

Ford is expanding its line-up of electric vehicles, starting with its beloved F-Series pickup trucks. At the Deutsche Bank Global Auto Industry Conference, company execs revealed a plan for an all-electric version of the F-150 – which was initially pegged to be hybridized by 2020. Now, it appears Ford will be churning out two new models, though the firm has not revealed any additional details as of yet. At the Deutsche Bank Global Auto Industry Conference, company execs revealed a plan for an all-electric version of the F-150 – which was initially pegged to be hybridized by 2020. The announcement follows the news that Volkswagen and Ford are forming a global alliance to develop commercial vans and medium-sized pickups together while exploring broader cooperation on future battery-powered and autonomous vehicles and services.