Networks, which represent agents and interactions between them, arise in myriad applications throughout the sciences, engineering, and even the humanities. To understand large-scale structure in a network, a common task is to cluster a network's nodes into sets called "communities" such that there are dense connections within communities but sparse connections between them. A popular and statistically principled method to perform such clustering is to use a family of generative models known as stochastic block models (SBMs). In this paper, we show that maximum likelihood estimation in an SBM is a network analog of a well-known continuum surface-tension problem that arises from an application in metallurgy. To illustrate the utility of this bridge, we implement network analogs of three surface-tension algorithms, with which we successfully recover planted community structure in synthetic networks and which yield fascinating insights on empirical networks from the field of hyperspectral video segmentation.

Embeddings of medical concepts such as medication, procedure and diagnosis codes in Electronic Medical Records (EMRs) are central to healthcare analytics. Previous work on medical concept embedding takes medical concepts and EMRs as words and documents respectively. Nevertheless, such models miss out the temporal nature of EMR data. On the one hand, two consecutive medical concepts do not indicate they are temporally close, but the correlations between them can be revealed by the time gap. On the other hand, the temporal scopes of medical concepts often vary greatly (e.g., \textit{common cold} and \textit{diabetes}). In this paper, we propose to incorporate the temporal information to embed medical codes. Based on the Continuous Bag-of-Words model, we employ the attention mechanism to learn a "soft" time-aware context window for each medical concept. Experiments on public and proprietary datasets through clustering and nearest neighbour search tasks demonstrate the effectiveness of our model, showing that it outperforms five state-of-the-art baselines.

As spacecraft send back increasing amounts of telemetry data, improved anomaly detection systems are needed to lessen the monitoring burden placed on operations engineers and reduce operational risk. Current spacecraft monitoring systems only target a subset of anomaly types and often require costly expert knowledge to develop and maintain due to challenges involving scale and complexity. We demonstrate the effectiveness of Long Short-Term Memory (LSTMs) networks, a type of Recurrent Neural Network (RNN), in overcoming these issues using expert-labeled telemetry anomaly data from the Soil Moisture Active Passive (SMAP) satellite and the Mars Science Laboratory (MSL) rover, Curiosity. We also propose a complementary unsupervised and nonparametric anomaly thresholding approach developed during a pilot implementation of an anomaly detection system for SMAP, and offer false positive mitigation strategies along with other key improvements and lessons learned during development.

The robots are coming but while they might steal many of our jobs and cause significant social upheaval, it isn't all bad, delegates at the FutureScope conference in Dublin were told on Thursday. Speakers at the one-day event said there is no doubt that automation is changing the way we work and that more must be done to ensure we manage the changes effectively. Siobhan O'Shea, client services director at listed technology recruitment firm CPL, said the impact of robots in the workplace was already beginning to be felt. The rise of automation will lead "not to mass unemployment, but mass redeployment" of workers," she said. Ms O'Shea said education is a key area in which preparation for future changes needs to be made. She noted that the number of teachers in Ireland with qualifications in biology currently outnumbers those with qualifications in physics by three to one and that this needs to change. "Ireland has one of the lowest rates in Europe for lifelong learning so it presents a systemic challenge for us," she said. "With advances in technology growing what people learn at college or university now will be out of date within two years," Ms O'Shea added. Anthony Behan, industry lead in IBM's Watson IoT division said while there will be job losses, there will also be opportunities, many of which we've yet to imagine. "We have enormous amounts of jobs being created in new areas due to technological innovation, it isn't just about losing them, Mr Behan said.

Experts have raised ethical issues surrounding this technology before. The Malicious AI report focused on fake videos to make people believe false information, and could jeopardize political security. The paper doesn't address these concerns too much. But it did say that pushing the limits of this technology and democratising "calls for additional care in ensuring verifiable video authenticity, e.g., through invisible watermarking." Justus Thies, a coauthor of the paper and a postdoctoral researcher at the Technical University of Munich, in Germany, told The Register that he recognized the potential dangers of using AI to manipulate fake videos.

The US government is deploying a new facial recognition system at the southern border that would record images of people inside vehicles entering and leaving the country. The pilot program, scheduled to begin in August, will build on secretive tests conducted in Arizona and Texas during which authorities collected a "massive amount of data", including images captured "as people were leaving work, picking up children from school, and carrying out other daily routines", according to government records. The project, which US Customs and Border Protection (CBP) confirmed to the Guardian on Tuesday, sparked immediate criticisms from civil liberties advocates who said there were a host of privacy and constitutional concerns with an overly broad surveillance system relying on questionable technology. Already the largest and most funded federal law enforcement agency in its own right, the border patrol is part of the umbrella agency US Customs and Border Protection (CBP). CBP's approximately 60,000 employees are split in four major divisions: officers who inspect imports; an air and marine division; agents who staff ports of entry – international airports, seaports and land crossings; and the approximately 20,000 agents of the border patrol, who are concentrated in the south-west, but stationed nationwide.

The Department of Homeland Security (DHS) is trialing a new facial recognition technology at US borders aimed at keeping track of people as the enter and exit the country. Called the Vehicle Face System, the project is being spearheaded by Customs and Border Protection at the Anzalduas Border Crossing, located at the southern tip of Texas, in August, according to the Verge. Sophisticated cameras will take photos of people arriving and departing the US and match them with government documents like visas and passports. The cameras are expected to remain in operation at the crossing for a full year. A customs spokesperson told the Verge that the purpose of the project will be to'evaluate capturing facial biometrics of travelers entering and departing the US and compare those images to photos on file in government holdings'. In the past, facial recognition technology struggled to correctly identify individuals behind a windshield due to glares and other obstructions.

As announced by the International Telecommunication Union (ITU), the United Nations specialised agency for information and communication technology (ICT), its annual AI for Good Global Summit has successfully generated thirty-five innovative project proposals leveraging the power of artificial intelligence (AI) for good. "Leveraging the power of ICTs, including artificial intelligence, is imperative if we are to improve the livelihoods of all people, everywhere, through achievement of the United Nations Sustainable Development Goals," said ITU Secretary-General Mr Houlin Zhao. "This year, we hope to spur action to ensure that artificial intelligence accelerates progress towards the Sustainable Development Goals (SDGs)," Mr Zhao said in his welcoming remarks. "Already, AI solutions are being developed to help increase crop yields, manage natural disasters, reduce road congestion, or diagnose heart, eye, and blood disorders." The summit gathered AI innovators with public and private-sector decision-makers, creating collaboration opportunities to execute the AI for Good project proposals in the near and medium terms.

Researchers have taken another step towards realistic, synthesized video. The team, made up of scientists in Germany, France, the UK and the US, used AI to transfer the head poses, facial expressions, eye motions and blinks of a person in one video onto another entirely different person in a separate video. The researchers say it's the first time a method has transferred these types of movements between videos and the result is a series of clips that look incredibly realistic. The neural network created by the researchers only needs a few minutes of the target video for training and it can then translate the head, facial and eye movements of the source to the target. It can even manipulate some background shadows when they're present.

Last year, the Department of Homeland Security (DHS) put out a notice, saying it was looking for a facial recognition system that could work with images taken of people inside their cars. The idea was that such a system could be used to scan people entering and leaving the country through the US/Mexico border and match them to government documents like passports and visas. Now, The Verge reports that DHS will be launching a test of a system aiming to do just that. The Vehicle Face System, as it's called, is scheduled for an initial deployment in August and it will be installed at the Anzalduas border crossing. The test will take place over one year and will aim to take images of passengers in every car that enters or leaves the US through the crossing.