Comments and other data left on a PDF detailing Homeland Security's proposal to build "mega" detention and processing centers reveal the personnel involved in its creation. A PDF that Department of Homeland Security officials provided to New Hampshire governor Kelly Ayotte's office about a new effort to build "mega" detention and processing centers across the United States contains embedded comments and metadata identifying the people who worked on it. The seemingly accidental exposure of the identities of DHS personnel who crafted Immigration and Customs Enforcement's mega detention center plan lands amid widespread public pushback against the expansion of ICE detention centers and the department's brutal immigration enforcement tactics. Metadata in the document, which concerns ICE's "Detention Reengineering Initiative" (DRI), lists as its author Jonathan Florentino, the director of ICE's Newark, New Jersey, Field Office of Enforcement and Removal Operations. In a note embedded on top of an FAQ question, "What is the average length of stay for the aliens?"

How the US left Ukraine exposed to Russia's winter war Will Europe use frozen Russian assets to fund war? How can Ukraine rebuild China ties? Russian forces launched multiple attacks on Ukraine's Zaporizhia region, killing one person and injuring seven others over the past day, the region's military administration said on the Telegram messaging platform. The attacks involved 448 drones as well as 163 artillery strikes, causing damage to 136 homes, cars and other structures, the military administration said. Russian forces also continued shelling Ukraine's Donetsk region, forcing 173 people, including 135 children, to evacuate front-line areas over the past day, regional governor Vadym Filashkin said on Telegram.

We introduce the Blackwell discount factor for Markov Decision Processes (MDPs). Classical objectives for MDPs include discounted, average, and Blackwell opti-mality.

We introduce the Blackwell discount factor for Markov Decision Processes (MDPs). Classical objectives for MDPs include discounted, average, and Blackwell opti-mality.

Risk-averse reinforcement learning (RL) seeks to provide a risk-averse policy for high-stakes real-world decision problems. These high-stake domains include autonomous driving (Jin et al., 2019; Sharma et al., 2020), robot collision avoidance (Ahmadi et al., 2021; Hakobyan and Y ang, 2021),

'Trump will be gone in three years': Top Democrats try to reassure Europe US Secretary of State Marco Rubio was the centre of attention at the Munich Security Summit, as European leaders wondered apprehensively what tone he would strike in his remarks on Saturday. While his speech did not fully allay their concerns, it has been viewed as a reassurance to allies that while US relations may have frayed under Donald Trump, they will not break. Rubio's was not the only American political voice at the security summit, however. And even if the secretary of state's remarks had not been so well-received - if he had sharply criticised Europeans the way Vice-President JD Vance did at the conference last year - there were other American politicians doing their best impression of the Persian poet, counselling: This too shall pass. If there's nothing else I can communicate today, California Governor Gavin Newsom said at a conference event on Friday, Donald Trump is temporary.

Here, we propose a hybrid model, the Hyper-HMM, that simultaneously aligns both temporal and spatial features across brains.

More specifically, you will be given the following: 1. An image context: This will describe the contents of an image with sufficient detail to address the instruction.