The task of link prediction for knowledge graphs is to predict missing relationships between entities. Knowledge graph embedding, which aims to represent entities and relations of a knowledge graph as low dimensional vectors in a continuous vector space, has achieved promising predictive performance. If an embedding model can cover different types of connectivity patterns and mapping properties of relations as many as possible, it will potentially bring more benefits for link prediction tasks. In this paper, we propose a novel embedding model, namely LineaRE, which is capable of modeling four connectivity patterns (i.e., symmetry, antisymmetry, inversion, and composition) and four mapping properties (i.e., one-to-one, one-to-many, many-to-one, and many-to-many) of relations. Specifically, we regard knowledge graph embedding as a simple linear regression task, where a relation is modeled as a linear function of two low-dimensional vector-presented entities with two weight vectors and a bias vector. Since the vectors are defined in a real number space and the scoring function of the model is linear, our model is simple and scalable to large knowledge graphs. Experimental results on multiple widely used real-world datasets show that the proposed LineaRE model significantly outperforms existing state-of-the-art models for link prediction tasks.

My brother-in-law Don Hunter, who has died aged 93, was a physicist who worked on some of the first electronic computers in the Rutherford Laboratory at Cambridge University and later helped set up one of the first major computer software companies in the UK. Don worked as a research assistant in the maths department of the Rutherford Laboratory from 1949 until 1952. There he was involved in pioneering work on the electronic delay storage automatic calculator (Edsac 1) computer. In 1955 he took up a research post at the Standard Telecommunication Laboratories (STL) in Harlow, Essex, where he was part of the design team for a computer called Step 1. Later, in a collaboration between STL and a Dutch subsidiary, he was involved in the development of Zebra, another early computer. During this time, Don worked in Paris, New York and Italy; he spoke fluent French and Italian.

For this week's ML practitioner's series, Analytics India Magazine(AIM) got in touch with Théo Viel, a Kaggle GM and a deep learning researcher at a French Startup called Damae Medical. In this interview, Théo shares his rich experiences from an impressive career in the world of algorithms. Théo: I recently graduated from a French Engineering school at which I studied applied Mathematics and Computer Science. I did the Ecole des Ponts ParisTech which is a Grande Ecole: it is a great school to do if you aspire to be a Data Scientist in France. I was introduced to Machine Learning at school, but mostly got into it during my internships, and doing Kaggle competitions.

We are introducing a monthly digest to keep you up-to-date with the latest happenings in the AI world. You can catch up with any AIhub stories you may have missed, get the low-down on recent conferences, and generally immerse yourself in all things AI. The big news from AIhub is that we recently launched our first ever focus series: "AI for good: UN sustainable development goals". Each month we will be concentrating on a different sustainable development goal (SDG) and bringing you a collection of work from people in the field. Our first topic was SDG number 3: good health and well-being.

SYDNEY – BGI Group, the world's largest genomics company, has worked with China's military on research that ranges from mass testing for respiratory pathogens to brain science, a review of research, patent filings and other documents has found. The review, of more than 40 publicly available documents and research papers in Chinese and English, shows BGI's links to the People's Liberation Army (PLA) include research with China's top military supercomputing experts. The extent of those links has not previously been reported. BGI has sold millions of COVID-19 test kits outside China since the outbreak of the new coronavirus pandemic, including to Europe, Australia and the United States. Shares of BGI Genomics Co., the company's subsidiary listed on the Shenzhen stock exchange, have doubled in price over the past 12 months, giving it a market value of about $9 billion.

Autonomous Vehicles (AVs) raise important social and ethical concerns, especially about accountability, dignity, and justice. We focus on the specific concerns arising from how AV technology will affect the lives and livelihoods of professional and semi-professional drivers. Whereas previous studies of such concerns have focused on the opinions of experts, we seek to understand these ethical and societal challenges from the perspectives of the drivers themselves. To this end, we adopted a qualitative research methodology based on semi-structured interviews. This is an established social science methodology that helps understand the core concerns of stakeholders in depth by avoiding the biases of superficial methods such as surveys. We find that whereas drivers agree with the experts that AVs will significantly impact transportation systems, they are apprehensive about the prospects for their livelihoods and dismiss the suggestions that driving jobs are unsatisfying and their profession does not merit protection. By showing how drivers differ from the experts, our study has ramifications beyond AVs to AI and other advanced technologies. Our findings suggest that qualitative research applied to the relevant, especially disempowered, stakeholders is essential to ensuring that new technologies are introduced ethically.

Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. Cloris Leachman, known for her decades-long career in film, television and beyond, has died at the age of 94. She died on Wednesday of natural causes, her rep told Fox News. Leachman was a history-making actress, having racked up more Emmy award wins than any other performer in the business with eight awards for primetime programming and an additional Daytime Emmy for appearing in "ABC Afterschool Specials."

Care and Feeding is Slate's parenting advice column. Have a question for Care and Feeding? Submit it here or post it in the Slate Parenting Facebook group. My husband and I both have full-time jobs and an 18-month-old son. I am pregnant with our second child, due in February. Since our son was born, my husband seems to have regressed.

My father, Bill Broderick, who has died aged 80 of Covid-19, was an educationist ahead of his time in the field of computing. His vision and enthusiasm led to the first computer being installed in a British secondary school, the Royal Liberty school in Romford, Essex, where he was a maths teacher, in 1965. In a broadcast by the BBC programme Tomorrow's World from the school, Bill said: "Computers are as radical and important a keystone to our standard of living and industrial wellbeing as was the steam engine." Born in Farnborough, Kent, Bill was the only son of Ralph Broderick, an engineer, and Ida (nee Massey). He was educated at Lord Wandsworth college in Long Sutton, Hampshire, then went to Hull University to study mathematics.

Although deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples, affording only a shrinking segment of the AI community access to their development. Resolution of these limitations requires new, sample-efficient methods. To facilitate research in this direction, we propose this second iteration of the MineRL Competition. The primary goal of the competition is to foster the development of algorithms which can efficiently leverage human demonstrations to drastically reduce the number of samples needed to solve complex, hierarchical, and sparse environments. To that end, participants compete under a limited environment sample-complexity budget to develop systems which solve the MineRL ObtainDiamond task in Minecraft, a sequential decision making environment requiring long-term planning, hierarchical control, and efficient exploration methods. The competition is structured into two rounds in which competitors are provided several paired versions of the dataset and environment with different game textures and shaders. At the end of each round, competitors submit containerized versions of their learning algorithms to the AIcrowd platform where they are trained from scratch on a hold-out dataset-environment pair for a total of 4-days on a pre-specified hardware platform. In this follow-up iteration to the NeurIPS 2019 MineRL Competition, we implement new features to expand the scale and reach of the competition. In response to the feedback of the previous participants, we introduce a second minor track focusing on solutions without access to environment interactions of any kind except during test-time. Further we aim to prompt domain agnostic submissions by implementing several novel competition mechanics including action-space randomization and desemantization of observations and actions.