Mission planning can often be formulated as a constrained control problem under multiple path constraints (i.e., safety constraints) and budget constraints (i.e., resource expenditure constraints). In a priori unknown environments, verifying that an offline solution will satisfy the constraints for all time can be difficult, if not impossible. We present ReRoot, a novel sampling-based framework that enforces safety and budget constraints for nonlinear systems in unknown environments. The main idea is that ReRoot grows multiple reverse RRT* trees online, starting from renewal sets, i.e., sets where the budget constraints are renewed. The dynamically feasible backup trajectories guarantee safety and reduce resource expenditure, which provides a principled backup policy when integrated into the gatekeeper safety verification architecture. We demonstrate our approach in simulation with a fixed-wing UAV in a GNSS-denied environment with a budget constraint on localization error that can be renewed at visual landmarks.

Artificial intelligence (AI) is reshaping higher education, yet current debates often feel tangled, mixing concerns about pedagogy, operations, curriculum, and the future of work without a shared framework. This paper offers a first attempt at a taxonomy to organize the diverse narratives of AI education and to inform discipline-based curricular discussions. We place these narratives within the enduring responsibility of higher education: the mission of knowledge. This mission includes not only the preservation and advancement of disciplinary expertise, but also the cultivation of skills and wisdom, i.e., forms of meta-knowledge that encompass judgment, ethics, and social responsibility. For the purpose of this paper's discussion, AI is defined as adaptive, data-driven systems that automate analysis, modeling, and decision-making, highlighting its dual role as enabler and disruptor across disciplines. We argue that the most consequential challenges lie at the level of curriculum and disciplinary purpose, where AI accelerates inquiry but also unsettles expertise and identity. We show how disciplines evolve through the interplay of research, curriculum, pedagogy, and faculty expertise, and why curricular reform is the central lever for meaningful change. Pedagogical innovation offers a strategic and accessible entry point, providing actionable steps that help faculty and students build the expertise needed to engage in deeper curricular rethinking and disciplinary renewal. Within this framing, we suggest that meaningful reform can move forward through structured faculty journeys: from AI literacy to pedagogy, curriculum design, and research integration. The key is to align these journeys with the mission of knowledge, turning the disruptive pressures of AI into opportunities for disciplines to sustain expertise, advance inquiry, and serve society.

'Fox & Friends First' host Carley Shimkus discusses the Fox Flight Team joining the search for UAPs in the Northeast and a classified briefing for lawmakers stating nothing'nefarious' is happening in New Jersey skies. Congress is set to pass legislation to avert a government shutdown that will reauthorize the government's ability to intercept and track unauthorized drones and crack down on U.S. investment in China. The 1,500 page continuing resolution (CR), which will fund the government until March 14, includes a provision reauthorizing a Department of Homeland Security program allowing agencies to coordinate and counter threats from drones. That authority, passed in 2018, was set to expire Friday – at a time when concerns about drone incursions are at an all-time high. However, it is a simple reauthorization of a program many drone experts say is outdated.

A quick look at census data (more than 11,000 people turn 65 each day in the U.S.), along with my own rough calculations, suggest that several hundred people are turning 70 each day in the great state of California, and every 10 minutes or so, one or more of them email me about their license renewal adventures with the DMV. I get the usual, always entertaining horror stories about testing: ("They put in ridiculous questions that do not pertain to driving," said 75-year-old Dahana Klerer of Newport Beach, who flunked twice and added, "I'm not a stupid person but they make you feel really stupid.") California is about to be hit by an aging population wave, and Steve Lopez is riding it. His column focuses on the blessings and burdens of advancing age -- and how some folks are challenging the stigma associated with older adults. "I had no problem," said 79-year-old Ruth Gleason of Ridgecrest, who added: "Thank you and Steve Gordon at the DMV for working to alleviate the test-taking fears for over-70 CA drivers."

This study aims to innovatively explore adaptive applications of large language models (LLM) in urban renewal. It also aims to improve its performance and text generation quality for knowledge question-answering (QA) tasks. Based on the ChatGLM, we automatically generate QA datasets using urban renewal scientific literature corpora in a self-instruct manner and then conduct joint fine-tuning training on the model using the Prefix and LoRA fine-tuning methods to create an LLM for urban renewal. By guiding the LLM to automatically generate QA data based on prompt words and given text, it is possible to quickly obtain datasets in the urban renewal field and provide data support for the fine-tuning training of LLMs. The experimental results show that the joint fine-tuning training method proposed in this study can significantly improve the performance of LLM on the QA tasks. Compared with LoRA fine-tuning, the method improves the Bleu and Rouge metrics on the test by about 5%; compared with the model before fine-tuning, the method improves the Bleu and Rouge metrics by about 15%-20%. This study demonstrates the effectiveness and superiority of the joint fine-tuning method using Prefix and LoRA for ChatGLM in the urban renewal knowledge QA tasks. It provides a new approach for fine-tuning LLMs on urban renewal-related tasks.

The application of reinforcement learning in credit scoring has created a unique setting for contextual logistic bandit that does not conform to the usual exploration-exploitation tradeoff but rather favors exploration-free algorithms. Through sufficient randomness in a pool of observable contexts, the reinforcement learning agent can simultaneously exploit an action with the highest reward while still learning more about the structure governing that environment. Thus, it is the case that greedy algorithms consistently outperform algorithms with efficient exploration, such as Thompson sampling. However, in a more pragmatic scenario in credit scoring, lenders can, to a degree, classify each borrower as a separate group, and learning about the characteristics of each group does not infer any information to another group. Through extensive simulations, we show that Thompson sampling dominates over greedy algorithms given enough timesteps which increase with the complexity of underlying features.

Risk management continues to dominate business conversations this year and with good reason.While risk is inherent in business, recent events revealed that many companies lacked the means to adequately manage third-party risk. Despite the digital transformation efforts companies have undertaken, many companies' obligations related to contractual, regulatory, financial reporting, and environmental requirements are still often managed manually. This creates informational silos that make it difficult for organizations to form a complete picture of potential risk and compliance exposures throughout their supply chains. As a result, companies lack the visibility to proactively manage these obligations and end up taking a reactive approach to identifying and addressing third-party risk. As industry and government regulation increases and pressure mounts to deliver quality goods at a competitive price, companies recognize the need to approach their risk management initiatives differently.

Expanded partnerships, access to clinical trials, and new medical and behavioral treatments and interventions reaching individuals more quickly will benefit communities in Pennsylvania and beyond thanks to the renewal of Penn State's Clinical and Translational Science Award (CTSA) funded by the National Institutes of Health (NIH). The NIH's National Center for Advancing Translational Sciences (NCATS) awarded Penn State more than $25 million to provide critical clinical and translational research infrastructure and continue building collaborations across the University's campuses and with communities around the state. NCATS' CTSA Program develops innovative solutions to improve processes for turning laboratory, clinical and community research into health knowledge, interventions and treatments. CTSA institutions partner to advance biomedical and health research and share best practices and tools. Penn State is one of 64 funded CTSA organizations nationally and is among the few that serve primarily rural communities.

Artificial intelligence is the buzzword in business today, much like digital marketing a decade ago. It is taking the business world by storm. With its ability to automate mundane tasks, and drive efficiency, every bit of attention it gets is well-deserved. Yes, the benefits are known to most. However, the question that lingers in the minds of most founders and CEOs is, "Well, how exactly AI is going to benefit our business?" Here, we unravel the mysteries that cloud your mind about AI and its relevance to business.

While most AI in use today can be classified as early-stage advanced analytics, some enterprises have built large data science teams to apply machine learning and deep learning algorithms to business processes. Many of these enterprises have built the necessary support infrastructure to train these algorithms on large data sets, deploying the resulting AI models to production to generate business insights. However, many consumer and business consumption patterns changed dramatically in 2020, causing these advanced AI models to fail or behave erratically. Many of these models that have been trained to make predictions based on historical data have not been able to deal with the data anomalies created by disruptive business conditions and changing preferences. Companies using AI for insights had a hard time making use of existing models in production.