Recent strides in pretrained transformer-based language models have propelled state-of-the-art performance in numerous NLP tasks. Yet, as these models grow in size and deployment, their robustness under input perturbations becomes an increasingly urgent question. Existing robustness methods often diverge between small-parameter and large-scale models (LLMs), and they typically rely on labor-intensive, sample-specific adversarial designs. In this paper, we propose a unified, local (sample-level) robustness framework (SALMAN) that evaluates model stability without modifying internal parameters or resorting to complex perturbation heuristics. Central to our approach is a novel Distance Mapping Distortion (DMD) measure, which ranks each sample's susceptibility by comparing input-to-output distance mappings in a near-linear complexity manner. By demonstrating significant gains in attack efficiency and robust training, we position our framework as a practical, model-agnostic tool for advancing the reliability of transformer-based NLP systems.

The president's comments on the oil price came after he spoke to Saudi Crown Prince Mohammed bin Salman on Wednesday. According to Saudi State media Bin Salman pledged to invest as much as 600bn in the US over the next four years, however this figure was not mentioned in the White House statement after the call. Despite the cordial exchange, Trump said he would be asking "the Crown Prince, who's a fantastic guy, to round it out to around 1tn". The price of crude fell by 1% following Trump's comments. According to David Oxley, Chief Climate and Commodities Economist at Capital Economics these comments are in keeping with Trump's desire for lower gasoline prices.

The tool, called PhotoGuard, works like a protective shield by altering photos in tiny ways that are invisible to the human eye but prevent them from being manipulated. If someone tries to use an editing app based on a generative AI model such as Stable Diffusion to manipulate an image that has been "immunized" by PhotoGuard, the result will look unrealistic or warped. Right now, "anyone can take our image, modify it however they want, put us in very bad-looking situations, and blackmail us," says Hadi Salman, a PhD researcher at MIT who contributed to the research. It was presented at the International Conference on Machine Learning this week. PhotoGuard is "an attempt to solve the problem of our images being manipulated maliciously by these models," says Salman.

Dr Abdulsalam Salman, the Librarian, Kwara State University (KWASU) Malete, said the library will introduce Artificial Intelligence and Robotics to address the challenges of staff shortage in the Dept. Salman Friday said that the introduction of robots was one of the ways of digitalising the services in the library. "One of my missions is to see how we can fully digitalise the services in the library. "We are trying to introduce Artificial Intelligence and Robotics where we can use robots in providing library services. "Instead of you interfacing with humans, robots will take over, so, the issue of understaffing that we have as a challenge will be taken care of," the librarian said.

The Precedence Constrained Generalized Traveling Salesman Problem (PCGTSP) combines the Generalized Traveling Salesman Problem (GTSP) and the Sequential Ordering Problem (SOP). We present a novel branching technique for the GTSP which enables the extension of a powerful pruning technique. This is combined with some modifications of known bounding methods for related problems. The algorithm manages to solve problem instances with 12-26 groups within a minute, and instances with around 50 groups which are denser with precedence constraints within 24 hours.

Autonomous exploration and search have important applications in robotics. One interesting application is cooperative control of mobile robotic/sensor networks to achieve uniform coverage of a domain. Ergodic coverage is one solution for this problem in which control laws for the agents are derived so that the agents uniformly cover a target area while maintaining coordination with each other. Prior approaches have assumed the target regions contain no obstacles. In this work, we tackle the problem of static and dynamic obstacle avoidance while maintaining an ergodic coverage goal. We pursue a vector-field-based obstacle avoidance approach and define control laws for idealized kinematic and dynamic systems that avoid static and dynamic obstacles while maintaining ergodicity. We demonstrate this obstacle avoidance methodology via numerical simulation and show how ergodicity is maintained.