Manipulating training data leads to robust neural models for MT.

High-resolution nowcasting is an essential tool needed for effective adaptation to climate change, particularly for extreme weather. As Deep Learning (DL) techniques have shown dramatic promise in many domains, including the geosciences, we present an application of DL to the problem of precipitation nowcasting, i.e., high-resolution (1 km x 1 km) short-term (1 hour) predictions of precipitation. We treat forecasting as an image-to-image translation problem and leverage the power of the ubiquitous UNET convolutional neural network. We find this performs favorably when compared to three commonly used models: optical flow, persistence and NOAA's numerical one-hour HRRR nowcasting prediction.

Various researchers have studied posterior inference of parameters in Bayesian mixture models [24, 42, 23], so that the statistical behavior of such models is relatively well-understood. In contrast, much less is known about the efficiency of different algorithms for sampling from the posterior distributions that arise from Bayesian mixture models. A standard approach for doing so is via some form of Markov Chain Monte Carlo (MCMC). Many different types of MCMC algorithms have been introduced for various types of Bayesian mixture models, including finite Bayesian mixture models [21, 49, 50, 26, 40], Dirichlet process mixture models [37, 41, 25, 28], and hierarchical and nested Dirichlet process models [52, 47]. Despite the plethora of possible MCMC methods, upper bounds on their mixing times are often challenging to establish. We refer the reader to the papers [27, 3, 55, 48, 57] for non-asymptotic upper bounds on mixing times for certain types of Bayesian models, different from those studied in this paper. In recent years, it has been increasingly common in the Bayesian literature to make use of a fractional likelihood--meaning an ordinary likelihood raised to some fractional power. Combining such a fractional likelihood with a prior distribution in the usual way leads to a class of posteriors known as power posterior or fractional posterior distributions. The power posterior distributions have been shown to have attractive properties in terms of robustness to mis-specification in Bayesian mixture models [39], and have been used in various applications 1 arXiv:1912.05153v1

Commonly-used clustering algorithms usually find ellipsoidal, spherical or other regular-structured clusters, but are more challenged when the underlying groups lack formal structure or definition. Syncytial clustering is the name that we introduce for methods that merge groups obtained from standard clustering algorithms in order to reveal complex group structure in the data. Here, we develop a distribution-free fully-automated syncytial clustering algorithm that can be used with $k$-means and other algorithms. Our approach computes the cumulative distribution function of the normed residuals from an appropriately fit $k$-groups model and calculates the nonparametric overlap between each pair of clusters. Groups with high pairwise overlap are merged as long as the generalized overlap decreases. Our methodology is always a top performer in identifying groups with regular and irregular structures in several datasets and can be applied to datasets with scatter or incomplete records. The approach is also used to identify the distinct kinds of gamma ray bursts in the Burst and Transient Source Experiment 4Br catalog and the distinct kinds of activation in a functional Magnetic Resonance Imaging study.

Allowing machines to choose whether to kill humans would be devastating for world peace and security. But how do we equip machines with the ability to learn ethical or even moral choices? Jentzsch et al.(2019) showed that applying machine learning to human texts can extract deontological ethical reasoning about "right" and "wrong" conduct by calculating a moral bias score on a sentence level using sentence embeddings. The machine learned that it is objectionable to kill living beings, but it is fine to kill time; It is essential to eat, yet one might not eat dirt; it is important to spread information, yet one should not spread misinformation. However, the evaluated moral bias was restricted to simple actions -- one verb -- and a ranking of actions with surrounding context. Recently BERT ---and variants such as RoBERTa and SBERT--- has set a new state-of-the-art performance for a wide range of NLP tasks. But has BERT also a better moral compass? In this paper, we discuss and show that this is indeed the case. Thus, recent improvements of language representations also improve the representation of the underlying ethical and moral values of the machine. We argue that through an advanced semantic representation of text, BERT allows one to get better insights of moral and ethical values implicitly represented in text. This enables the Moral Choice Machine (MCM) to extract more accurate imprints of moral choices and ethical values.

Flight records and related materials from police drone programs have been uncovered following a security breach at DroneSense, which provides services to a number of private corporations and government agencies. The records included flight paths, pilot names and email addresses, and operation names from more than 200 different drone flights, offering insight into how police use drones in day to day law enforcement. The records come from drone operations at the Atlanta Police Department, Nassau County Police Department, and others. The files also included information from other DroneSense clients, including Boise Fire Department, City of Coral Springs, and the US Army Corps of Engineers. According to a report in Vice, the records show a number of different police drone operations, including the Atlanta police using a drone to surveil an apartment complex and nearby parking lot.

TALLINN, Dec 10 (Reuters) - Finland, which holds the rotating EU presidency until the end of the year, said on Tuesday it aims to teach 1% of all Europeans basic skills in artificial intelligence through a free online course it will now translate into all official EU languages. The European Union is pushing for wide deployment of artificial intelligence across the bloc, to help European companies catch up with rivals in Asia and the United States. "Our investment has three goals: we want to equip EU citizens with digital skills for the future, we wish to increase practical understanding of what artificial intelligence is, and by doing so, we want to give a boost to the digital leadership of Europe," said Finnish Minister of Employment Timo Harakka. "As our Presidency ends, we want to offer something concrete. It's about one of the most pressing challenges facing Europe and Finland today: how to develop our digital literacy," Harakka said in a statement. The course, conducted by the University of Helsinki and originally launched in 2018, already has enrolled more than 220,000 students from more than 110 countries.

WASHINGTON – U.S. President Donald Trump has days to decide whether to impose tariffs on nearly $160 billion in Chinese consumer goods just weeks before Christmas, a move that could be unwelcome in both the United States and China. The White House's top economic and trade advisers, including Trade Representative Robert Lighthizer, Larry Kudlow, Peter Navarro, and Treasury Secretary Steven Mnuchin are expected to meet in coming days with Trump over that decision, one person briefed on the situation said. There is still no clarity on what the decision will be. "They may very well go into effect. They may very well hold back. And the president's going to decide," the source said Monday afternoon.

Smart home devices are designed to make our lives easier, but they also make it easier for hackers to infiltrate our lives. The FBI has sent out a warning that'hackers can use those innocent devices to do a virtual drive-by of your digital life.' The US intelligence agency urges users to regularly change passwords, check for firmware updates and never have two devices on the same network. Digital assistants, smart watches, fitness trackers, home security devices, thermostats, refrigerators, and even light bulbs are all on the list of devices that can be infiltrated by cybercriminals. And if these devices, among other smart home technology, are not properly protected, they can be used by hackers to'do a virtual drive-by of your digital life.' Samsung are developing an interactive kitchen that includes a fridge, oven and TV.

Fox News Flash top headlines for Dec. 10 are here. Check out what's clicking on Foxnews.com A Wisconsin state trooper used a drone to find a dog and reunite it with its injured owner following a vehicle crash last week, officials said. Trooper John Jones used his drone to locate River, a 3-year-old Australian Shepherd, in a wooded area in Brown County. The dog and a driver were traveling on State Highway 57 on Friday when a deer darted onto the road, causing the unidentified driver to swerve and crash into a median, the Wisconsin Department of Transportation (DOT) said.