Anomalous diffusion, as it has come to be called, extends the concept of Brownian motion and is connected to disordered systems, non-equilibrium phenomena, flows of energy and information, and transport in living systems[3]. Anomalous diffusion is "non-universal" in the sense that physically very different systems share the same power-law form of the mean squared displacement x

A key promise of machine learning is the ability to assist users with personal tasks. Because the personal context required to make accurate predictions is often sensitive, we require systems that protect privacy. A gold standard privacy-preserving system will satisfy perfect secrecy, meaning that interactions with the system provably reveal no private information. However, privacy and quality appear to be in tension in existing systems for personal tasks. Neural models typically require copious amounts of training to perform well, while individual users typically hold a limited scale of data, so federated learning (FL) systems propose to learn from the aggregate data of multiple users. FL does not provide perfect secrecy, but rather practitioners apply statistical notions of privacy -- i.e., the probability of learning private information about a user should be reasonably low. The strength of the privacy guarantee is governed by privacy parameters. Numerous privacy attacks have been demonstrated on FL systems and it can be challenging to reason about the appropriate privacy parameters for a privacy-sensitive use case. Therefore our work proposes a simple baseline for FL, which both provides the stronger perfect secrecy guarantee and does not require setting any privacy parameters. We initiate the study of when and where an emerging tool in ML -- the in-context learning abilities of recent pretrained models -- can be an effective baseline alongside FL. We find in-context learning is competitive with strong FL baselines on 6 of 7 popular benchmarks from the privacy literature and a real-world case study, which is disjoint from the pretraining data. We release our code here: https://github.com/simran-arora/focus

Many real-world reinforcement learning (RL) problems necessitate learning complex, temporally extended behavior that may only receive reward signal when the behavior is completed. If the reward-worthy behavior is known, it can be specified in terms of a non-Markovian reward function - a function that depends on aspects of the state-action history, rather than just the current state and action. Such reward functions yield sparse rewards, necessitating an inordinate number of experiences to find a policy that captures the reward-worthy pattern of behavior. Recent work has leveraged Knowledge Representation (KR) to provide a symbolic abstraction of aspects of the state that summarize reward-relevant properties of the state-action history and support learning a Markovian decomposition of the problem in terms of an automaton over the KR. Providing such a decomposition has been shown to vastly improve learning rates, especially when coupled with algorithms that exploit automaton structure. Nevertheless, such techniques rely on a priori knowledge of the KR. In this work, we explore how to automatically discover useful state abstractions that support learning automata over the state-action history. The result is an end-to-end algorithm that can learn optimal policies with significantly fewer environment samples than state-of-the-art RL on simple non-Markovian domains.

The woman at the New Year address has been named in Russian media as Anna Sergeevna Sidorenko, a captain and military doctor. Comparing her face at the event with an image taken from a video interview posted online by the Russian Isvestia newspaper gave a 99.5% match. Her name also appears on a members list of a Russian military regiment published by the Ukrainian intelligence services.

After canceling our CES plans in 2022 (and not even having the option of attending in person in 2021), the Engadget team sent a dozen staffers to CES 2023 this week, including reporters, editors and videographers. It's too soon to say how many stories and videos we've published -- in fact, we have more good stuff coming -- but suffice to say, it was a lot. Though our team swears the show still wasn't as busy as pre-pandemic years, they were kept busy enough that it felt like a true return to form, not just for us, but for the tech industry at large. One thing that never stopped was Engadget's annual Best of CES Awards program, although this year marks the first time in three years we've been able to base our judgments off of a full slate of in-person hands-on experiences. All told, we're handing out a dozen awards this year, including the most prestigious: Best of the Best.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The Department of Homeland Security said its Science and Technology Directorate has developed a portable gunshot detection system in collaboration with the Massachusetts-based Shooter Detection Systems company. The department said that the system, known as SDS Outdoor, could provide "critical information about outdoor shooting incidents almost instantaneously to first responders." The new system is reportedly an enhancement to the commercial, off-the-shelf Guardian Indoor Active Shooter Detection System.

Fox News senior foreign affairs correspondent Greg Palkot provides details on the August 2021 U.S. drone strike that mistakenly killed 10 civilians. A New York Times report on the investigation into how the U.S. military conducted a drone strike that killed several civilians, including children, in Afghanistan last year, characterized the attack as a "deadly blunder" that was motivated by the "assumptions and biases" of those conducting the strike. The report also claimed that the U.S. military was aware that innocent children had been killed in the attack only hours after the strike, and it made "misleading" statements to the public about that reality. The Times report noted that through a FOIA request, it obtained internal documents from a U.S. Central Command investigation into the August 2021 U.S. drone strike that killed 10 civilians in Kabul, Afghanistan. GENERAL SAYS IT IS UNLIKELY ISIS-K MEMBERS KILLED IN AUGUST KABUL DRONE STRIKE: 'A TRAGIC MISTAKE' Photo taken on Sept. 2, 2021 shows damaged vehicles at the site of the U.S. airstrike in Kabul, capital of Afghanistan.

The United States has announced that it is sanctioning Iranian industries that produce ballistic missiles and drones, also known as unmanned aerial vehicles (UAVs), which the US says have been used to facilitate Russia's war in Ukraine. In a news release on Friday, US Secretary of State Antony Blinken said the sanctions would target seven people in leadership positions at Qods Aviation Industries -- an Iranian UAV manufacturer -- and Iran's Aerospace Industries Organization (AIO), which manages the country's ballistic missile programme. "Iran has now become Russia's top military backer," Blinken said in the statement. "Iran must cease its support for Russia's unprovoked war of aggression in Ukraine, and we will continue to use every tool at our disposal to disrupt and delay these transfers and impose costs on actors engaged in this activity." Iran is fueling Russia's war in Ukraine with its provision of UAV technology. Today, the United States sanctioned seven people involved in Iran's UAV and ballistic missile programs – programs Moscow is using to target Ukraine's critical infrastructure.

I started working for e.Republic (Government Technology's parent company) as a writer almost 12 years ago. I was doing a lot of interviews for case study-style stories on technology deployments. I would later transcribe those interviews word for word to maximize my understanding and faithfully reproduce any direct quotes I ended up using. At the time, AI tools to automate the transcription process fell short -- comically short, actually. Artificial intelligence, in the ensuing years, has advanced exponentially in its capabilities and ease of use.

Rogan questioned what was preventing someone from tracking others if Musk gave in to the account holder's demand for money or a job at Tesla. Elon Musk responded to an AI-generated Harry Potter story about "Wokey the house elf," joking that Twitter has its fair share of woke users. Mosaic web browser co-author Marc Andreessen fed ChatGPT -- a popular new artificial intelligence service -- a prompt to write a "Broadway stage play" set in the Harry Potter universe. The cast included the wizard Harry and his friends Ron and Hermione. But the fourth character, "Wokey the house elf," attracted the most attention on Twitter.