ICE Agent's'Dragging' Case May Help Expose Evidence in Renee Good Shooting The government has withheld details of the investigation of Renee Good's killing--but an unrelated case involving the ICE agent who shot her could force new revelations. Defense attorneys for a Minnesota man convicted in December of assaulting Immigration and Customs Enforcement officer Jonathan Ross are seeking access to investigative files related to the killing of Renee Nicole Good, after learning Ross was the same officer who shot and killed her during a targeted operation in Minneapolis last month. Attorneys for Roberto Carlos Muñoz-Guatemala asked a federal judge on Friday to order prosecutors to turn over training records as well as investigative files related to Ross, the ICE agent who killed Good on January 7 during Operation Metro Surge and was also injured in a June 2025 incident in which Muñoz-Guatemala dragged him with his car. A separate post-trial motion by the defense, filed in the US District Court in Minnesota, asks the judge to pause deadlines for a new-trial motion until the discovery motion is resolved. Muñoz-Guatemala's attorneys argue that even if the court ultimately decides that any newly discovered evidence doesn't entitle their client to a new trial, he's entitled to explore whether there are mitigating factors that could impact the length of his sentence, such as whether Ross' injuries could have been, to some degree, brought upon him by his own behavior.

A contract justification published in a federal register on Tuesday says that 31 ICE vehicles operating in the Twin Cities area "lack the necessary emergency lights and sirens" to be "compliant." Paul area "currently lack the necessary emergency lights and sirens" required to be "compliant with law enforcement requirements," according to a contract justification published in a federal register on Tuesday. The document justifies ICE paying Whelen Engineering Company, a Connecticut-based firm specializing in "emergency warning and lighting technology," $47,330.49 The document explains that the ATLAS kits would "allow vehicles to be immediately operational and compliant with law enforcement requirements to support the current surge operation" out of Homeland Security Investigations (HSI)'s St. Paul office, which conducts operations in Minnesota, North Dakota, and South Dakota. "These vehicles were deployed prior to being permanently retrofitted and currently lack the necessary emergency lights and sirens required for operational use," the document says.

FBI Agent's Sworn Testimony Contradicts Claims ICE's Jonathan Ross Made Under Oath The testimony also calls into question whether Ross failed to follow his training during the incident in which he reportedly shot and killed Minnesota citizen Renee Good. In testimony last month in federal court in Minnesota, FBI special agent Bernardo Medellin appeared to directly contradict a claim that ICE agent Jonathan Ross made under oath about whether a man they were trying to detain had asked to speak to his attorney. Medellin's testimony, which details federal training for interactions with drivers, also calls into question whether Ross followed his training during the interaction that led to the shooting and killing of Renee Nicole Good, a 37-year-old mother, last week. Ross has been identified by multiple media outlets as the shooter; while the Trump administration has declined to confirm those reports, details about the shooter shared by Vice President JD Vance match details of Ross' biography. As WIRED previously reported, in December Ross testified that last June he led a team seeking to apprehend a man named Roberto Carlos Muñoz-Guatemala, who had an administrative warrant out for being in the US without authorization.

The home space for optimal control is a Sobolev space. The home space for pseudospectral theory is also a Sobolev space. It thus seems natural to combine pseudospectral theory with optimal control theory and construct ``pseudospectral optimal control theory,'' a term coined by Ross. In this paper, we review key theoretical results in pseudospectral optimal control that have proven to be critical for a successful flight. Implementation details of flight demonstrations onboard NASA spacecraft are discussed along with emerging trends and techniques in both theory and practice. The 2011 launch of pseudospectral optimal control in embedded platforms is changing the way in which we see solutions to challenging control problems in aerospace and autonomous systems.

This paper derives various Hessians associated with Birkhoff-theoretic methods for trajectory optimization. According to a theorem proved in this paper, approximately 80% of the eigenvalues are contained in the narrow interval [-2, 4] for all Birkhoff-discretized optimal control problems. A preliminary analysis of computational complexity is also presented with further discussions on the grand challenge of solving a million point trajectory optimization problem.

Every fingerprint is believed to be unique, making it possible to identify an individual by matching a new fingerprint with an image on file, whether to unlock a mobile phone, access a bank account, or solve a murder. Fingerprint examiners, however, do not always agree on whether two print images match and, asked to recheck their work after several months, they sometimes do not even agree with themselves. That is leading to increased use of neural networks, powerhouses for identifying and matching patterns of all sorts, to automate and improve decisions about whether two fingerprints come from the same person. A group of computer scientists decided to use neural networks to test the assumption that no two fingerprints are the same. Using twin neural networks, researchers from Columbia University, Tufts University, and the State University of New York (SUNY) University at Buffalo looked for similarities between different fingerprints in a database from the National Institute of Standards and Technology (NIST).

Before bashing out emails and text messages by thumb became an accepted form of communication, typing was a fully manual skill. In the 80s, "the office" was an exclusive preserve for freaks who could type 40 words per minute at least. Those too modest or miserly to sign up for brick-and-mortar classes could pick up a software program called Mavis Beacon Teaches Typing for 50. At my Catholic high school, the application was the typing class. The priests just switched on the computers.

Recent advancements in Large Language Models (LLMs) have significantly enhanced the capabilities of conversational agents, making them applicable to various fields (e.g., education). Despite their progress, the evaluation of the agents often overlooks the complexities of real-world conversations, such as real-time interactions, multi-party dialogues, and extended contextual dependencies. To bridge this gap, we introduce DialSim, a real-time dialogue simulator. In this simulator, an agent is assigned the role of a character from popular TV shows, requiring it to respond to spontaneous questions using past dialogue information and to distinguish between known and unknown information. Key features of DialSim include evaluating the agent's ability to respond within a reasonable time limit, handling long-term multi-party dialogues, and managing adversarial settings (e.g., swap character names) to challenge the agent's reliance on pre-trained knowledge. We utilized this simulator to evaluate the latest conversational agents and analyze their limitations. Our experiments highlight both the strengths and weaknesses of these agents, providing valuable insights for future improvements in the field of conversational AI.

The brain that wrote your favorite novel consumed Dickens and Austen, Pynchon and Didion. The brain that wrote this article devoured Bradbury and Orwell, Ishiguro and Octavia Butler. But the "brain" that powers that chatbot you played around with over the weekend ingested 170,000 books, all so it can spit out language that sounds smart, colorful, or helpful--even if it's really not. But language-guzzling artificial intelligence models, which need to "train" on existing works, present a bigger challenge. In July, a group of writers including comedian Sarah Silverman and novelist Michael Chabon filed suits against OpenAI and Meta, alleging that the companies improperly trained their models on the authors' books.

This is part II of a two-part paper. Part I presented a universal Birkhoff theory for fast and accurate trajectory optimization. The theory rested on two main hypotheses. In this paper, it is shown that if the computational grid is selected from any one of the Legendre and Chebyshev family of node points, be it Lobatto, Radau or Gauss, then, the resulting collection of trajectory optimization methods satisfy the hypotheses required for the universal Birkhoff theory to hold. All of these grid points can be generated at an $\mathcal{O}(1)$ computational speed. Furthermore, all Birkhoff-generated solutions can be tested for optimality by a joint application of Pontryagin's- and Covector-Mapping Principles, where the latter was developed in Part~I. More importantly, the optimality checks can be performed without resorting to an indirect method or even explicitly producing the full differential-algebraic boundary value problem that results from an application of Pontryagin's Principle. Numerical problems are solved to illustrate all these ideas. The examples are chosen to particularly highlight three practically useful features of Birkhoff methods: (1) bang-bang optimal controls can be produced without suffering any Gibbs phenomenon, (2) discontinuous and even Dirac delta covector trajectories can be well approximated, and (3) extremal solutions over dense grids can be computed in a stable and efficient manner.