The chart of the nuclides is limited by particle drip lines beyond which nuclear stability to proton or neutron emission is lost. Predicting the range of particle-bound isotopes poses an appreciable challenge for nuclear theory as it involves extreme extrapolations of nuclear masses beyond the regions where experimental information is available. Still, quantified extrapolations are crucial for a variety of applications, including the modeling of stellar nucleosynthesis. We use microscopic nuclear mass models and Bayesian methodology to provide quantified predictions of proton and neutron separation energies as well as Bayesian probabilities of existence throughout the nuclear landscape all the way to the particle drip lines. We apply nuclear density functional theory with several energy density functionals. To account for uncertainties, Bayesian Gaussian processes are trained on the separation-energy residuals for each individual model, and the resulting predictions are combined via Bayesian model averaging. This framework allows to account for systematic and statistical uncertainties and propagate them to extrapolative predictions. We characterize the drip-line regions where the probability that the nucleus is particle-bound decreases from $1$ to $0$. In these regions, we provide quantified predictions for one- and two-nucleon separation energies. According to our Bayesian model averaging analysis, 7759 nuclei with $Z\leq 119$ have a probability of existence $\geq 0.5$. The extrapolations obtained in this study will be put through stringent tests when new experimental information on exotic nuclei becomes available. In this respect, the quantified landscape of nuclear existence obtained in this study should be viewed as a dynamical prediction that will be fine-tuned when new experimental information and improved global mass models become available.

Efficient human resource management needs accurate assessment and representation of available competences as well as effective mapping of required competences for specific jobs and positions. In this regard, appropriate definition and identification of competence gaps express differences between acquired and required competences. Using a detailed quantification scheme together with a mathematical approach is a way to support accurate competence analytics, which can be applied in a wide variety of sectors and fields. This article describes the combined use of software technologies and mathematical and statistical methods for assessing and analyzing competences in human resource information systems. Based on a standard competence model, which is called a Professional, Innovative and Social competence tree, the proposed framework offers flexible tools to experts in real enterprise environments, either for evaluation of employees towards an optimal job assignment and vocational training or for recruitment processes. The system has been tested with real human resource data sets in the frame of the European project called ComProFITS.

Whether enjoying the lucid prose of a favorite author or slogging through some other writer's cumbersome, heavy-set prattle (full of parentheses, em dashes, compound adjectives, and Oxford commas), readers will notice stylistic signatures not only in word choice and grammar, but also in punctuation itself. Indeed, visual sequences of punctuation from different authors produce marvelously different (and visually striking) sequences. Punctuation is a largely overlooked stylistic feature in "stylometry", the quantitative analysis of written text. In this paper, we examine punctuation sequences in a corpus of literary documents and ask the following questions: Are the properties of such sequences a distinctive feature of different authors? Is it possible to distinguish literary genres based on their punctuation sequences? Do the punctuation styles of authors evolve over time? Are we on to something interesting in trying to do stylometry without words, or are we full of sound and fury (signifying nothing)?

However, the process is not yet foolproof and many such false images leave visible signs they are fake. Blurring or flickering around a person's face is a common indicator an image has been doctored, especially when the face changes angles rapidly. Lt Gen Shanahan warns it is increasingly likely society will be influenced by false imagery, as hackers' expertise and the sophistication of AI technology increases. "Within probably 30 minutes you can get online and start developing fairly high fidelity deep fakes," he said. The General also sought to provide reassure that as AI capability developed, the US military would not seek to develop autonomous "killer robots".

Exploratory data analysis is one of the most important parts of any machine learning workflow and Natural Language Processing is no different. But which tools you should choose to explore and visualize text data efficiently? In this article, we will discuss and implement nearly all the major techniques that you can use to understand your text data and give you a complete(ish) tour into Python tools that get the job done. In this article, we will use a million news headlines dataset from Kaggle. Now, we can take a look at the data. The dataset contains only two columns, the published date, and the news heading. For simplicity, I will be exploring the first 10000 rows from this dataset.

About the Technology TensorFlow, Google's library for large-scale machine learning, makes powerful ML techniques easily accessible. It simplifies often-complex computations by representing them as graphs that are mapped to machines in a cluster or to the processors of a single machine. Offering a complete ecosystem for all stages and types of machine learning, TensorFlow's end-to-end functionality empowers machine learning engineers of all skill levels to solve their problems with ML.

The term Artificial Intelligence (AI) was coined by John McCarthy in 1956. AI is defined in the Oxford English Dictionary as "the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages." Artificial Intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. AI is increasingly being used in the defence sector to boost the military capabilities in many developing nations of the world. In a December 2019 strategic research paper entitled, "A Candle in the Dark: US National Security Strategy for Artificial Intelligence", Stephen Rodriguez and Tate Nurkin shed more light on this aspect.

China is on its way to becoming the first global superpower for Artificial Intelligence. The People's Republic of China has the most ambitious AI strategy of all nations and provides the most resources worldwide for its implementation. China combines a gigantic amount of data with talent, companies, research and capital to build the world's leading AI ecosystem. In 2017, the State Council of the People's Republic of China (also known as the Central People's Government) published the Artificial Intelligence Development Plan (here you can find the original document in English). This strategy is part of the even bigger national "Made in China 2025" plan and will also be linked to the new (digital) Silk Road.

Facial recognition technology has run amok across the globe. In the US it continues to perpetuate at an alarming rate despite bipartisan push-back from politicians and several geographical bans. Even China's government has begun to question whether there's enough benefit to the use of ubiquitous surveillance tech to justify the utter destruction of public privacy. The truth of the matter is that facial recognition technology serves only two legitimate purposes: access control and surveillance. And, far too often, the people developing the technology aren't the ones who ultimately determine how it's used. Most decent, law-abiding citizens don't mind being filmed in public and, to a certain degree, would tend to take no exception to the use of facial recognition technology in places where it makes sense.

SAN FRANCISCO – The top diplomats of Japan, the United States and South Korea on Tuesday urged North Korea to refrain from military provocation and continue denuclearization talks, but ruled out any easing of crushing economic sanctions without progress in the stalled negotiations. Foreign Minister Toshimitsu Motegi held discussions with his U.S. and South Korean counterparts, Mike Pompeo and Kang Kyung-wha, in East Palo Alto, just outside San Francisco, two weeks after a deadline set by Pyongyang for progress by the end of 2019 passed. "We agreed on the importance of North Korea making positive efforts in talks with the United States rather than going through with provocative moves," Motegi told reporters. The statement appeared to contradict remarks in a New Year speech by South Korean President Moon Jae-in a day earlier in Seoul, where he said that he could seek exemptions of U.N. sanctions to bring about improved inter-Korean relations that he believes would help restart the deadlocked nuclear negotiations between Pyongyang and Washington. Moon has previously made similar comments, despite outside worries that any lifting of sanctions could undermine U.S.-led efforts to eliminate North Korea's nuclear arsenal.