Japan may effectively shut off China from supplying drones to its government to protect sensitive information, according to six people in government and the ruling party familiar with the matter, as part of a broad effort to bolster national security. The primary concerns, those people said, centered on information technology, supply chains, cybersecurity and intellectual property -- worries that have been rising outside Japan as well. But Japan must balance such fears -- particularly Beijing's growing push to export sensitive technologies such as commercial drones and security cameras -- against deep economic dependence on China. It must also navigate increasingly choppy waters between China and Japan's closest ally, the United States, which is at odds with Beijing over many things, including technology. "China is a big market and it is important for Japan," one of the senior government officials said. "On the other hand, there are worries that advanced technologies and information could leak to China and could be diverted for military use."

Globally, researchers are working to discover how artificial intelligence (AI) can be usefully applied across a range of commercial and public-sector situations. Much of the most exciting work is taking place in tech clusters, particularly in China and the United States, which are attracting some of the brightest global talent. Europe is also seeing rising levels of engagement, but not on the same scale. The onus is now on European companies and policy makers to ensure that the continent keeps pace with its peers and realizes AI's benefits across societies. If Europe is to make the most of AI, it must find a way to bring together stakeholders from across the continent.

We study the fundamental limits for reconstruction in weighted graph (or matrix) database alignment. We consider a model of two graphs where $\pi^*$ is a planted uniform permutation and all pairs of edge weights $(A_{i,j}, B_{\pi^*(i),\pi^*(j)})_{1 \leq i0$, there is an estimator $\hat{\pi}$ -- namely the MAP estimator -- based on the observation of databases $A,B$ that achieves exact reconstruction with high probability. Conversely, if $n \rho^2 \leq 4 \log n - \log \log n - \omega(1)$, then any estimator $\hat{\pi}$ verifies $\hat{\pi}=\pi$ with probability $o(1)$. This result shows that the information-theoretic threshold for exact recovery is the same as the one obtained for detection in a recent work by Y. Wu, J. Xu and S. Yu: in other words, for Gaussian weighted graph alignment, the problem of reconstruction is not more difficult than that of detection. Though the reconstruction task was already well understood for vector-shaped database alignment (that is taking signal of the form $(u_i, v_{\pi^*(i)})_{1 \leq i\leq n}$ where $(u_i, v_{\pi^*(i)})$ are i.i.d. pairs in $\mathbb{R}^{d_u} \times \mathbb{R}^{d_v}$), its formulation for graph (or matrix) databases brings a drastically different problem for which the hard phase is conjectured to be huge. The study is based on the analysis of the MAP estimator, and proofs rely on proper use of the correlation structure of energies of permutations.

The purpose of this tutorial is to help individuals use the \underline{FireCommander} game environment for research applications. The FireCommander is an interactive, probabilistic joint perception-action reconnaissance environment in which a composite team of agents (e.g., robots) cooperate to fight dynamic, propagating firespots (e.g., targets). In FireCommander game, a team of agents must be tasked to optimally deal with a wildfire situation in an environment with propagating fire areas and some facilities such as houses, hospitals, power stations, etc. The team of agents can accomplish their mission by first sensing (e.g., estimating fire states), communicating the sensed fire-information among each other and then taking action to put the firespots out based on the sensed information (e.g., dropping water on estimated fire locations). The FireCommander environment can be useful for research topics spanning a wide range of applications from Reinforcement Learning (RL) and Learning from Demonstration (LfD), to Coordination, Psychology, Human-Robot Interaction (HRI) and Teaming. There are four important facets of the FireCommander environment that overall, create a non-trivial game: (1) Complex Objectives: Multi-objective Stochastic Environment, (2)Probabilistic Environment: Agents' actions result in probabilistic performance, (3) Hidden Targets: Partially Observable Environment and, (4) Uni-task Robots: Perception-only and Action-only agents. The FireCommander environment is first-of-its-kind in terms of including Perception-only and Action-only agents for coordination. It is a general multi-purpose game that can be useful in a variety of combinatorial optimization problems and stochastic games, such as applications of Reinforcement Learning (RL), Learning from Demonstration (LfD) and Inverse RL (iRL).

Explicit and implicit bias clouds human judgement, leading to discriminatory treatment of minority groups. A fundamental goal of algorithmic fairness is to avoid the pitfalls in human judgement by learning policies that improve the overall outcomes while providing fair treatment to protected classes. In this paper, we propose a causal framework that learns optimal intervention policies from data subject to fairness constraints. We define two measures of treatment bias and infer best treatment assignment that minimizes the bias while optimizing overall outcome. We demonstrate that there is a dilemma of balancing fairness and overall benefit; however, allowing preferential treatment to protected classes in certain circumstances (affirmative action) can dramatically improve the overall benefit while also preserving fairness. We apply our framework to data containing student outcomes on standardized tests and show how it can be used to design real-world policies that fairly improve student test scores. Our framework provides a principled way to learn fair treatment policies in real-world settings.

In an ideal world, the Supreme Court would provide stability in the run-up to a presidential election, imposing uniform rules based on long-accepted principles of election law. We do not live in that world. One week out from the 2020 election, four Supreme Court justices have launched a scorched-earth mission against voting rights. They teed up a Bush v. Gore reprise that could hand Donald Trump an unearned victory. These justices are in open revolt against voting rights, abandoning the pretense of "voter fraud" and embracing state legislatures' right to disenfranchise their constituents.

Apple may be stealthily developing its own search engine, as Google faces a lawsuit from the U.S. antitrust authorities regarding the search engine giant's agreements with companies to be the default search tool. In the newest operating system update for the iPhone, the iOS 14, Apple has started showing its own search results and direct links to websites when users search from their home screen. In its updated version, iOS 14 does not use Google for many of its search functions, as it previously used to. The search window that appears in iPhones when users swipe right now compiles Apple-generated search suggestions rather than Google results. Earlier this week, the U.S. Department of Justice, in a landmark lawsuit said, Google is monopolizing the search space by entering into multi-billion dollar deals with mobile companies like Apple, Motorola, and network carriers like AT&T and Verizon, to be the default search engine on devices.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. It may have the same basic external configuration, weight and 120mm cannon, but today's Abrams tank is, simply put, far more lethal than ever before due to the addition of sensors, ammunition, armor, EW (Electronic Warfare) and new weapons. The battle-tested platform has over the years, continued to incorporate cutting-edge innovations. For example, the Army is now testing and preparing a new Advanced.

Google has had an eventful couple of weeks, announcing enhancements to its search and map capabilities at its virtual "Search On" event on Oct. 15, and on Oct. 20 being accused by the US Justice Department of engaging in anti-competitive practices in order to preserve its search engine business. At the Search On event, Google detailed how it has tapped AI and machine learning techniques to make improvements to Google Maps as well as Search. In an expansion of its search "busyness metrics," users will be able to see how busy locations are without identifying the specific beach, grocery store, pharmacy or other location. COVID-19 safety information will also be added to business profiles across Search and Maps, indicating whether the business is using safety precautions such as temperature checks or plexiglass shields, according to an account in VentureBeat. An improvement to the algorithm beneath the "Did you mean?" features of search, will enable more accurate and precise spelling suggestions.