Two agents involved in the shooting deaths of US citizens in Minneapolis are reportedly part of highly militarized DHS units whose extreme tactics are generally reserved for war zones. An officer with a Department of Homeland Security Special Response Team stands against a protester in Portland, Oregon. As Minneapolis continues to reel from the fatal shooting of 37-year-old intensive care nurse Alex Pretti by federal agents on the morning of January 24, the international spotlight is firmly fixed on the heavily armed and masked operatives who have spearheaded the Trump administration's violent immigration sweeps. At the heart of the deployment in Minnesota, as well as the chaotic clashes with communities in Southern California and Illinois, are hundreds of agents that operate within Immigration and Customs Enforcement (ICE) and Customs and Border Protection: ICE's two Special Response Teams (SRT), CBP's one SRT, and the Border Patrol Tactical Unit (BORTAC). These paramilitary tactical units behave not like local police, but instead like special forces in Iraq, Afghanistan, or other far-flung battlefields from the Forever Wars of the past quarter century.

We thank the reviewers for their thoughtful feedback and helpful suggestions. We address specific points below. Dwork (2012) defines an algorithm to be fair if it gives similar predictions to similar individuals. The formalization of this definition was extended into Counterfactual Fairness (Kusner, 2017). XLNet, which are consistent with the results from GPT -2.

Three residents filed a lawsuit this week against Sonoma County seeking to block code enforcement from using drones to take aerial images of their homes in what the American Civil Liberties Union is calling a "runaway spying operation." The lawsuit, filed by the ACLU Wednesday on behalf of the three residents, alleges that the county began using drones with high-powered cameras and zoom lenses in 2019 to track illegal cannabis cultivation, but in the years since, officials have used the devices more than 700 times to find other code violations on private property without first seeking a warrant. "For too long, Sonoma County code enforcement has used high-powered drones to warrantlessly sift through people's private affairs and initiate charges that upend lives and livelihoods. All the while, the county has hidden these unlawful searches from the people they have spied on, the community, and the media," Matt Cagle, a senior staff attorney with the ACLU Foundation of Northern California, said in a statement. A spokesperson for Sonoma County said the county is reviewing the complaint and takes "the allegations very seriously."

Quantifying uncertainties for machine learning (ML) models is a foundational challenge in modern data analysis. This challenge is compounded by at least two key aspects of the field: (a) inconsistent terminology surrounding uncertainty and estimation across disciplines, and (b) the varying technical requirements for establishing trustworthy uncertainties in diverse problem contexts. In this position paper, we aim to clarify the depth of these challenges by identifying these inconsistencies and articulating how different contexts impose distinct epistemic demands. We examine the current landscape of estimation targets (e.g., prediction, inference, simulation-based inference), uncertainty constructs (e.g., frequentist, Bayesian, fiducial), and the approaches used to map between them. Drawing on the literature, we highlight and explain examples of problematic mappings. To help address these issues, we advocate for standards that promote alignment between the \textit{intent} and \textit{implementation} of uncertainty quantification (UQ) approaches. We discuss several axes of trustworthiness that are necessary (if not sufficient) for reliable UQ in ML models, and show how these axes can inform the design and evaluation of uncertainty-aware ML systems. Our practical recommendations focus on scientific ML, offering illustrative cases and use scenarios, particularly in the context of simulation-based inference (SBI).

We thank the reviewers for their thoughtful feedback and helpful suggestions. We address specific points below. Dwork (2012) defines an algorithm to be fair if it gives similar predictions to similar individuals. The formalization of this definition was extended into Counterfactual Fairness (Kusner, 2017). XLNet, which are consistent with the results from GPT-2.

Lawmakers in New York have proposed a new bill that would make it illegal to mount weapons to robots or drones. That is of course, unless the people operating those weaponized machines happen to work with law enforcement or the military. If passed, the Responsible Robotics Act would make New York the first state to ban weaponized robots for civilians, at a time when government spending on autonomous and semi-autonomous police technology is on the rise. The bill specifically bans the sale, transfer, modification, operation, or equipping of robots or drones with mounted weapons. Prohibited weapons include firearms, stun guns, chemical agents, lasers, and explosives.

Let me begin by stating that my judgement is based on assuming the the derivations are mathematically correct - though I attempted at verifying, I am not certain if everything is free of error. With that in mind: Originality: I believe the central idea of the work is novel, and the derivation is unseen, at least to my limited knowledge of related work. However, the generative model involved (AIR) is borrowed directly from related work, and there have definitely been prior work attempting to regularize the posterior. Though the idea of using structural knowledge to directly shape the distribution of interpretable posteriors such as size and number of objects is novel, I am not so sure if this idea is generalizable enough (see quality and significance section) to warrant a major original contribution. Quality: I believe the derivation is technically sound, though I have not verified the mathematical details and cannot be sure if it is free of errors.

The paper is generally well written and easy to understand. I quite like the proposed model: kU-net provides an answer to the ability to capture multi-scale features within a medical image, and the bi-directional LSTM scheme is an elegant way to account for broader context from the z-dimention. However, I offer a few reservations to the paper as it currently stands. Standard ways of dealing with anisotropy include resampling (e.g. For datasets in which the across-plane resolution is reasonably close to the within-plane one (e.g.

EXCLUSIVE: Sen. Lindsey Graham is demanding answers on reporting that British International Criminal Court (ICC) prosecutor Karim Khan was accused of sexual misconduct at the same time he was pursuing criminal charges against Israeli officials. "Public reports indicate that allegations of harassment surfaced in early May--just a few days before Prosecutor Khan applied for arrest warrants against the Prime Minister and Minister of Defense of Israel for alleged violations of law during the defensive Israeli-Hamas War," Graham wrote in a letter obtained by Fox News Digital. "The timing of the allegations is troubling, and only compounds the other strong legal, jurisdictional, and prudential objections I have expressed regarding the Prosecutor's decision to seek arrest warrants." On May 20, Khan requested arrest warrants for Israeli President Benjamin Netanyahu and Defence Minister Yoav Gallant, as well as Hamas leaders Yahya Sinwar, Ismail Haniyeh and Mohammed Deif. All three Hamas leaders have been killed in the past year.

We propose misogyny detection as an Argumentative Reasoning task and we investigate the capacity of large language models (LLMs) to understand the implicit reasoning used to convey misogyny in both Italian and English. The central aim is to generate the missing reasoning link between a message and the implied meanings encoding the misogyny. Our study uses argumentation theory as a foundation to form a collection of prompts in both zero-shot and few-shot settings. These prompts integrate different techniques, including chain-of-thought reasoning and augmented knowledge. Our findings show that LLMs fall short on reasoning capabilities about misogynistic comments and that they mostly rely on their implicit knowledge derived from internalized common stereotypes about women to generate implied assumptions, rather than on inductive reasoning.