Differential privacy is a formal algorithmic guarantee that no single input has a large effect on the output of a computation. Since its introduction [13] over a decade ago, a rich line of work has made differential privacy a compelling privacy guarantee (see Dwork et al. [14] and Vadhan [26] for surveys), and deployments of differential privacy now exist at many organizations, including Apple [3], Google [6, 15], Microsoft [11], Mozilla [4], and the US Census Bureau [1, 22]. Much recent attention, including almost all industrial deployments, has focused on a stronger variant of differential privacy called local differential privacy [16, 21, 27]. In the local model private data is distributed across many users, and each user privatizes their data before the data is collected by an analyst. Thus, as any locally differentially private computation runs on already-privatized data, data contributors need not worry about compromised data analysts or insecure communication channels.In contrast, (global) differential privacy assumes that the data analyst has trusted access to the unprivatized data. As a result, under global differential privacy any violation of this trust may lead to serious privacy loss for the users contributing the data.

Abstract-- We use multiple measures of graph complexity to evaluate the realism of synthetically-generated networks of human activity, in comparison with several stylized network models as well as a collection of empirical networks from the literature. The synthetic networks are generated by integrating data about human populations from several sources, including the Census, transportation surveys, and geographical data. The resulting networks represent an approximation of daily or weekly human interaction. Our results indicate that the synthetically generated graphs according to our methodology are closer to the real world graphs, as measured across multiple structural measures, than a range of stylized graphs generated using common network models from the literature. I. INTRODUCTION Artificially generated graphs benefit from high demand in several application domains, wherever the phenomena of interest are driven by interactions between people, including health and medicine, communications, the economy, and national security. Lack of access to appropriate network data hampers the research community's ability to develop algorithms toanalyze and gain insight from these transactional graph datasets. Due to the access restrictions to real network data, there is value in crafting methods of synthetically generated data which faithfully represent behaviors of real world processes. As such, many stylized methods for creating graphs with rigorously understood structural properties have been established, making collective steady progress towards better approximating structures of real world processes. Despite this progress, these relatively simple stylized methods aren't universally applicable and suffer from lack of realism for some applications. We are particularly interested in creating realistic graphs which represent a complex set of interrelated processes involving a common subset of actors (i.e., the coherent alignment of disparate subgraphs which have many vertices in common and which represent different types of underlying activity).

Solving inverse problems continues to be a central challenge in computer vision. Existing techniques either explicitly construct an inverse mapping using prior knowledge about the corruption, or learn the inverse directly using a large collection of examples. However, in practice, the nature of corruption may be unknown, and thus it is challenging to regularize the problem of inferring a plausible solution. On the other hand, collecting task-specific training data is tedious for known corruptions and impossible for unknown ones. We present MimicGAN, an unsupervised technique to solve general inverse problems based on image priors in the form of generative adversarial networks (GANs). Using a GAN prior, we show that one can reliably recover solutions to underdetermined inverse problems through a surrogate network that learns to mimic the corruption at test time. Our system successively estimates the corruption and the clean image without the need for supervisory training, while outperforming existing baselines in blind image recovery. We also demonstrate that MimicGAN improves upon recent GAN-based defenses against adversarial attacks and represents one of the strongest test-time defenses available today.

A fascinating new video gives a unique look inside the Enigma cipher machine used by the Nazis during World War Two and famously cracked by a team of code breakers led by British mathematician Alan Turing. Scientists used state-of-the-art X-ray scans to peer inside the Enigma's metal casing, revealing the wiring and rotors that encrypted the messages sent using the machine. In total, more than 1,500 scans were taken of an Enigma machine built in Berlin in 1941 - one of just 274 known to have survived the war. Enigmas, which resembled large typewriters, were used by German air, naval and army forces to safely send messages throughout the Second World War. It used a complex series of rotors and lights to encrypt messages by swapping letters around via an ever-changing'enigma code'.

Huge solar storms could cause electricity blackouts, destroy computers and bring down the Internet and all communications, a study has found. The Met Office has told U.K. ministers a flare up of the kind that has hit Earth two or three times in 200 years would cost the country 16 billion pounds ($20.5 million) in catastrophic damage. Such flares generate intense magnetic fields over Earth, which in a flash could burn out delicate electronics and even set them on fire, The Sunday Times is reporting. Britain risks being crippled by huge electrical disturbances caused by storms in space, unless a satellite network is built that can detect them coming. The Met Office study said: "We find that for a one-in-100-year event, with no space weather forecasting capability, the gross domestic product loss to the United Kingdom could be as high as 15.9 billion pounds. "With existing satellites nearing the end of their life, forecasting capability will decrease in coming years, so if no further investment takes place, critical infrastructure will become more vulnerable to space weather."

This week, the Federal Communications Commission begins an auction for two chunks of radio waves that have long been considered useless. Thanks to new technologies and demand for wireless broadband, the market is ready to buy 24GHz and 28GHz spectrum. Meanwhile, in the unlicensed band, which anyone can use and isn't sold off by the FCC, another of the so-called millimeter wave spectrum bands is gaining interest. Roughly a decade ago, engineers had hoped to use the 60GHz band for sending fat files over short distances using a technology called ultra-wideband. At the time, the market didn't see a compelling need for the technology, so it was shelved.

The Library of Congress has restored the first film adaptation of Mary Shelley's Frankenstein, an Edison production from 1910 directed by J. Searle Dawley. We tend to think of effects-driven spectacles as a product of the modern era, but decades before that checkerboard floor in Terminator 2: Judgement Day started moving, studios were selling films on the basis of single FX shots. Here's how the Edison company described their Frankenstein: To those who are not familiar with the story, we can only say that the film tells an intensely dramatic story by the aid of some of the most remarkable photographic effects that have yet been attempted. The formation of the hideous monster from the blazing chemicals of a huge caldron in Frankenstein's laboratory is probably the most weird, mystifying, and fascinating scene ever shown on a film. Frankenstein's creation is no longer the most weird, mystifying, and fascinating scene ever shown on film, but it's a fun trick-shot, using reversed footage of a dummy that has been set on fire to give the impression of a body knitting itself together from nothing.

We present an experimental realization of a measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti cloud quantum computer. The experiment in this few-qubit superconducting chip faithfully reproduces the theoretical proposal, setting the first steps towards a semiautonomous quantum agent. This experiment paves the way towards quantum reinforcement learning with superconducting circuits.

As artificial intelligence (AI) advancements -- including cutting-edge robotics and silicon-based image recognition technology -- have now pushed the once-fantastic idea of'killer robots' onto the global stage, modern autonomous war machines that fire live ammo could soon seek and destroy battlefield combatants, leading many to wonder if there is an'off' switch. Among other nations, China and the US are working to make advancements in artificial intelligence, machine image recognition and semi-autonomous robotics to be used in combination with sensors and targeting computers, according to a New York Post published Thursday. Britain and Israel are currently using missiles and drones with autonomous features; such weapons can attack enemy radar, vehicles or ships without human commands. Technology for weapon systems to autonomously identify and destroy targets has existed for several decades. In the 1980s and 90s, Harpoon and Tomahawk missiles, which could identify targets autonomously, were developed by US war planners.

In a speech delivered at "Fintech and the New Financial Landscape" in Philadelphia on November 13, 2018, Federal Reserve Board Governor Lael Brainard discussed how technology is changing the financial landscape and the lessons being learned about artificial intelligence (AI) in financial services. According to Governor Brainard,"[a]lthough it is still in the early days, it is already evident that the application of artificial intelligence (AI) in financial services is potentially quite important and merits our attention." She noted that the Fintech working group is working across the Federal Reserve System "to take a deliberate approach to understanding the potential implications of AI for financial services, particularly as they relate to our responsibilities." The focus of Governor Brainard's speech was on the branch of AI known as machine learning – which applies and refines a series of algorithms on a large data set in order to identify patterns and make predictions for new data. Brainard highlighted how recent technological advances have made the three key components of AI – algorithms, processing power, and big data – all increasingly accessible.