In many social situations, a discrepancy arises between an individual's private and expressed opinions on a given topic. Motivated by Solomon Asch's seminal experiments on social conformity and other related socio-psychological works, we propose a novel opinion dynamics model to study how such a discrepancy can arise in general social networks of interpersonal influence. Each individual in the network has both a private and an expressed opinion: an individual's private opinion evolves under social influence from the expressed opinions of the individual's neighbours, while the individual determines his or her expressed opinion under a pressure to conform to the average expressed opinion of his or her neighbours, termed the local public opinion. General conditions on the network that guarantee exponentially fast convergence of the opinions to a limit are obtained. Further analysis of the limit yields several semi-quantitative conclusions, which have insightful social interpretations, including the establishing of conditions that ensure every individual in the network has such a discrepancy. Last, we show the generality and validity of the model by using it to explain and predict the results of Solomon Asch's seminal experiments.

Deep learning models for graphs have advanced the state of the art on many tasks. Despite their recent success, little is known about their robustness. We investigate training time attacks on graph neural networks for node classification that perturb the discrete graph structure. Our core principle is to use meta-gradients to solve the bilevel problem underlying training-time attacks, essentially treating the graph as a hyperparameter to optimize. Our experiments show that small graph perturbations consistently lead to a strong decrease in performance for graph convolutional networks, and even transfer to unsupervised embeddings. Remarkably, the perturbations created by our algorithm can misguide the graph neural networks such that they perform worse than a simple baseline that ignores all relational information. Our attacks do not assume any knowledge about or access to the target classifiers.

Riga, Latvia – February 21, 2019 – The new automatic Facade Scan tool of UgCS for drone inspection mission planning is a time and cost saver for construction, engineering and mining industries. Various tools for surveying horizontal surfaces, even the uneven ones, have been developed to a high standard and are widely available on the market. Inspecting vertical surfaces is a completely different story -- previously it required a lot of manual work and so was a burden for professional drone users. But now, with the automatic Facade Scan tool from UgCS, this has changed. Making accurate digital models of buildings or cultural heritage objects, and finding heat leaks or damage to walls: these are some of the applications of the new Facade Scan tool for construction and architecture.

Taha Kass-Hout, MD, former and first-ever CIO for the FDA and a senior leader of artificial intelligence at Amazon, detailed how the tech giant is using AI for its healthcare services in a recent interview with STAT. A core component of Amazon's healthcare strategy is to support clinicians, according to Dr. Kass-Hout. "I hope we see that with AI we're finally getting to understand what patient has a disease, rather than what disease a patient has -- and truly start personalizing care to that level," Dr. Kass-Hout told STAT. "From a patient perspective and consumer perspective, AI is going to empower them, and for providers and healthcare systems, it's going to augment clinicians and bridge gaps." Dr. Kass-Hout also provided an update on how healthcare companies are using AWS' EHR-mining software Comprehend Medical, which launched in November 2018.

The dramatic success in machine learning has led to an explosion of artificial intelligence (AI) applications and increasing expectations for autonomous systems that exhibit human-level intelligence. These expectations have, however, met with fundamental obstacles that cut across many application areas. One such obstacle is adaptability, or robustness. Machine learning researchers have noted current systems lack the ability to recognize or react to new circumstances they have not been specifically programmed or trained for. Intensive theoretical and experimental efforts toward "transfer learning," "domain adaptation," and "lifelong learning"4 are reflective of this obstacle. Another obstacle is "explainability," or that "machine learning models remain mostly black boxes"26 unable to explain the reasons behind their predictions or recommendations, thus eroding users' trust and impeding diagnosis and repair; see Hutson8 and Marcus.11 A third obstacle concerns the lack of understanding of cause-effect connections.

There exists a solution universe of all the possible solutions to this set of constraints. If the solution universe contains a single possible solution, then the published statistics completely reveal the underlying confidential data--provided that noise was not added to either the microdata or the tabulations as a disclosure-avoidance mechanism. If there are multiple satisfying solutions, then any element (person) in common among all of the solutions is revealed. If the equations have no solution, either the set of published statistics is inconsistent with the fictional statistical agency's claim that it is tabulated from a real confidential database or an error was made in that tabulation. This doesn't mean that a high-quality reconstruction is not possible.

The EksoVest supports the wearer's arms during lifting. Millions of people Suffer from the effects of spinal cord injuries and strokes that have left them paralyzed. Millions more suffer from back pain, which makes movement painful. Exoskeletons are helping the paralyzed to walk again, enabling soldiers to carry heavy loads, and workers to lift heavy objects with greater ease. An exoskeleton is a mechanical device or soft material worn by a patient/operator, whose structure mirrors the skeletal structure of the operator's limbs (joints, muscles, etc.).

Anthony Levandowski is aware of his reputation. One of the most controversial figures in autonomous driving, his background has been well-covered, from building the Ghostrider motorcycle that competed in the DARPA Grand Challenge in 2004 and 2005, to his work on Google's self-driving car.

During the touchdown, which will last just a few seconds, the unmanned probe will use a projector device to shoot the "bullet" into the asteroid, blowing up material from beneath the surface. If all goes successfully, the craft will then collect samples that would eventually be sent back to Earth. Thursday's attempt is the first of three such touchdowns planned. The spacecraft is expected to touch down on the space rock around 6 p.m. ET. The brief landing will be challenging, given the uneven and boulder-covered surface.

After 15 years of diligently exploring the surface of Mars, the Opportunity rover finally succumbed to the elements and went offline Feb. 13. As obituaries and tributes to "Oppy" surfaced, fans caught a glimpse into the robot's final moments: the last picture it sent, its last words, the last-ditch attempts to revive it. Scientists wept as they said their final farewells. As employees swayed and embraced, mission control sent one final transmission to Oppy: Billie Holiday's 1944 recording of "I'll Be Seeing You." The muted, intimate timbre of Holiday's voice helped millions say goodbye to "the little robot who could": I'll find you in the morning sun, I'll be looking at the moon, But I'll be seeing you.