Trees are a low-tech, high-efficiency way to offset much of humankind's negative impact on the climate. What's even better, we have plenty of room for a lot more of them. A new study conducted by researchers at Switzerland's ETH-Zürich, published in Science, details how Earth could support almost an additional billion hectares of trees without the new forests pushing into existing urban or agricultural areas. Once the trees grow to maturity, they could store more than 200 billion metric tons of carbon. Great news indeed, but it still leaves us with some huge unanswered questions. Where and how are we going to plant all the new trees?

Discovering content and stories in movies is one of the most important concepts in multimedia content research studies. Network models have proven to be an efficient choice for this purpose. When an audience watches a movie, they usually compare the characters and the relationships between them. For this reason, most of the models developed so far are based on social networks analysis. They focus essentially on the characters at play. By analyzing characters' interactions, we can obtain a broad picture of the narration's content. Other works have proposed to exploit semantic elements such as scenes, dialogues, etc. However, they are always captured from a single facet. Motivated by these limitations, we introduce in this work a multilayer network model to capture the narration of a movie based on its script, its subtitles, and the movie content. After introducing the model and the extraction process from the raw data, we perform a comparative analysis of the whole 6-movie cycle of the Star Wars saga. Results demonstrate the effectiveness of the proposed framework for video content representation and analysis.

The results of data mining endeavors are majorly driven by data quality. Throughout these deployments, serious show-stopper problems are still unresolved, such as: data collection ambiguities, data imbalance, hidden biases in data, the lack of domain information, and data incompleteness. This paper is based on the premise that context can aid in mitigating these issues. In a traditional data science lifecycle, context is not considered. Context-driven Data Science Lifecycle (C-DSL); the main contribution of this paper, is developed to address these challenges. Two case studies (using data-sets from sports events) are developed to test C-DSL. Results from both case studies are evaluated using common data mining metrics such as: coefficient of determination (R2 value) and confusion matrices. The work presented in this paper aims to re-define the lifecycle and introduce tangible improvements to its outcomes.

Temporal networks representing a stream of timestamped edges are seemingly ubiquitous in the real-world. However, the massive size and continuous nature of these networks make them fundamentally challenging to analyze and leverage for descriptive and predictive modeling tasks. In this work, we propose a general framework for temporal network sampling with unbiased estimation. We develop online, single-pass sampling algorithms and unbiased estimators for temporal network sampling. The proposed algorithms enable fast, accurate, and memory-efficient statistical estimation of temporal network patterns and properties. In addition, we propose a temporally decaying sampling algorithm with unbiased estimators for studying networks that evolve in continuous time, where the strength of links is a function of time, and the motif patterns are temporally-weighted. In contrast to the prior notion of a $\bigtriangleup t$-temporal motif, the proposed formulation and algorithms for counting temporally weighted motifs are useful for forecasting tasks in networks such as predicting future links, or a future time-series variable of nodes and links. Finally, extensive experiments on a variety of temporal networks from different domains demonstrate the effectiveness of the proposed algorithms.

Scheduling of personnel in a hospital environment is vital to improving the service provided to patients and balancing the workload assigned to clinicians. Many approaches have been tried and successfully applied to generate efficient schedules in such settings. However, due to the computational complexity of the scheduling problem in general, most approaches resort to heuristics to find a non-optimal solution in a reasonable amount of time. We designed an integer linear programming formulation to find an optimal schedule in a clinical division of a hospital. Our formulation mitigates issues related to computational complexity by minimizing the set of constraints, yet retains sufficient flexibility so that it can be adapted to a variety of clinical divisions. We then conducted a case study for our approach using data from the Infectious Diseases division at St. Michael's Hospital in Toronto, Canada. We analyzed and compared the results of our approach to manually-created schedules at the hospital, and found improved adherence to departmental constraints and clinician preferences. We used simulated data to examine the sensitivity of the runtime of our linear program for various parameters and observed reassuring results, signifying the practicality and generalizability of our approach in different real-world scenarios.

ABU DHABI, 16th October, 2019 (WAM) -- Abu Dhabi today announced the establishment of the Mohamed bin Zayed University of Artificial Intelligence, MBZUAI, the first graduate level, research-based AI university in the world. MBZUAI will enable graduate students, businesses, and governments to advance the artificial intelligence field. The University is named after His Highness Sheikh Mohamed bin Zayed Al Nahyan, Crown Prince of Abu Dhabi and Deputy Supreme Commander of the UAE Armed Forces, who has long advocated for the UAE's development of human capital through knowledge and scientific thinking to take the nation into the future. MBZUAI will introduce a new model of academia and research to the field of AI, providing students and faculty access to some of the world's most advanced AI systems to unleash its potential for economic and societal development. The announcement was made at a press conference at the University campus in Masdar City and was immediately followed by the first meeting of the MBZUAI Board of Trustees.

You probably picture robots as clodhoppers: ponderous, clunky, even doddery droids that need caffeine, badly. But robots are on the brink of making giant strides. Just ask Columbia University engineering professor Hod Lipson, who writes in Nature that "young animals gallop across fields, climb trees, and immediately find their feet with grace after they fall"--and robots are set to follow suit. A new breed of speedy robots promises to eventually outdo the runners at the 2020 Tokyo Olympics. Notable cybernetic contenders include MIT's dominant Cheetah, Boston Dynamics' Petman and Handle, Michigan Robotics' MABEL, and--further afield in South Africa--the University of Cape Town's Baleka. Plus, that efficiency-geared Florida University powerhouse, the Institute for Human & Machine Cognition (IHMC), fields a smart, sensor-free biped plainly called Planar Elliptical Runner (PER).

We are co-organising the 2nd annual Deep Learning Indaba which will be held at Stellenbosch University from 9-14 September 2018. The Deep Learning Indaba exists to celebrate and strengthen machine learning in Africa through state-of-the-art teaching, networking, policy debate, and through our support programmes, such as the IndabaX and the Kambule and Maathai awards. The Indaba works towards the vision of Africans becoming critical contributors, owners, and shapers of the coming advances in artificial intelligence and machine learning. The report on the outcomes of the first Indaba 2017 can be read here.

BELGRADE – When hundreds of video cameras with the power to identify and track individuals started appearing in the streets of Belgrade as part of a major surveillance project, some protesters began having second thoughts about joining anti-government demonstrations in the Serbian capital. Local authorities assert the system, created by Chinese telecommunications company Huawei, helps reduce crime in the city of 2 million. Critics contend it erodes personal freedoms, makes political opponents vulnerable to retribution and even exposes the country's citizens to snooping by the Chinese government. The cameras, equipped with facial recognition technology, are being rolled out across hundreds of cities around the world, particularly in poorer countries with weak track records on human rights where Beijing has increased its influence through big business deals. With the United States claiming that Chinese state authorities can get backdoor access to Huawei data, the aggressive rollout is raising concerns about the privacy of millions of people in countries with little power to stand up to China.

Longtime Beth Israel Deaconess Medical Chief Information Officer Dr. John Halamka left that role six months ago after more than two decades – during which time he also became one of the most vocal health information technology champions and visible thought leaders during a pivotal time of IT uptake. Halamka has been traveling the world recently – more than 400,000 miles this year, he says – from Europe to Israel to Africa to China, back to his Sherborn, Massachusetts-based Unity Farm Sanctuary for a quick visit with Dudley, his shaggy Scottish Highland Bull, and then out again to explore the newest global trends in leading-edge digital health. Halamka will keynote the ConVerge2Xcelerate event in Boston on October 15, hosted by Blockchain in Healthcare Today – where he is editor-in-chief – and co-presented as part of the preconference activities of the Connected Health Conference. We caught up with him recently at another Boston event and asked him about what he's seeing on his travels. You were CIO of Beth Israel Deaconess for so long – twenty two years. How has your newish gig, as International Healthcare Innovation Professor, been going? A. That's my academic title, my Harvard Medical School title, and that was three years ago. But the official transition from CIO, a hospital based-title, was March 1, 2019. That's when the merger of Beth Israel and Lahey came together, and the CEO and I talked about: How do you create innovation? So what is next with healthcare innovation?