Small Uncrewed Aerial Systems (sUAS) are increasingly deployed as autonomous swarms in search-and-rescue and other disaster-response scenarios. In these settings, they use computer vision (CV) to detect objects of interest and autonomously adapt their missions. However, traditional CV systems often struggle to recognize unfamiliar objects in open-world environments or to infer their relevance for mission planning. To address this, we incorporate large language models (LLMs) to reason about detected objects and their implications. While LLMs can offer valuable insights, they are also prone to hallucinations and may produce incorrect, misleading, or unsafe recommendations. To ensure safe and sensible decision-making under uncertainty, high-level decisions must be governed by cognitive guardrails. This article presents the design, simulation, and real-world integration of these guardrails for sUAS swarms in search-and-rescue missions.

We consider the problem of mean estimation under user-level local differential privacy, where $n$ users are contributing through their local pool of data samples. Previous work assume that the number of data samples is the same across users. In contrast, we consider a more general and realistic scenario where each user $u \in [n]$ owns $m_u$ data samples drawn from some generative distribution $\mu$; $m_u$ being unknown to the statistician but drawn from a known distribution $M$ over $\mathbb{N}^\star$. Based on a distribution-aware mean estimation algorithm, we establish an $M$-dependent upper bounds on the worst-case risk over $\mu$ for the task of mean estimation. We then derive a lower bound. The two bounds are asymptotically matching up to logarithmic factors and reduce to known bounds when $m_u = m$ for any user $u$.

Training social event detection models through federated learning (FedSED) aims to improve participants' performance on the task. However, existing federated learning paradigms are inadequate for achieving FedSED's objective and exhibit limitations in handling the inherent heterogeneity in social data. This paper proposes a personalized federated learning framework with a dual aggregation mechanism for social event detection, namely DAMe. We present a novel local aggregation strategy utilizing Bayesian optimization to incorporate global knowledge while retaining local characteristics. Moreover, we introduce a global aggregation strategy to provide clients with maximum external knowledge of their preferences. In addition, we incorporate a global-local event-centric constraint to prevent local overfitting and ``client-drift''. Experiments within a realistic simulation of a natural federated setting, utilizing six social event datasets spanning six languages and two social media platforms, along with an ablation study, have demonstrated the effectiveness of the proposed framework. Further robustness analyses have shown that DAMe is resistant to injection attacks.

The dame-flame Python package is the first major implementation of two algorithms, the dynamic almost matching exactly (DAME) algorithm (Dieng, Liu, Roy, Rudin, and Volfovsky 2019, published in AISTATS'19), and the fast, large-scale almost matching exactly (FLAME) algorithm (Wang, Morucci, Awan, Liu, Roy, Rudin, and Volfovsky 2019, published in JMLR'21), which provide almost exact matching of treatment and control units in discrete observational data for causal analysis. As discussed in Dieng et al. (2019), and Wang et al. (2019), the two algorithms produce high-quality interpretable matched groups, by using machine learning on a holdout training set to learn distance metrics. DAME solves an optimization problem that matches units on as many covariates as possible, prioritizing matches on important covariates. FLAME approximates the solution found by DAME via a much faster backward feature selection procedure. The DAME and FLAME algorithms are discussed in the remainder of this section. We also provide testing and installation details. In Section 2, we discuss the class structure in the dame-flame package, detail special features of dame-flame, and compare dame-flame to other matching packages. In Section 3, we offer examples and a user guide.

The University of Florida, a proponent for ethics in artificial intelligence, is part of a new global agreement with seven other worldwide universities that are committed to the development of human-centered approaches to artificial intelligence (AI) that will impact people everywhere. During the Global University Summit at Notre Dame University, Joseph Glover, UF provost and senior vice president of academic affairs, signed The Rome Call for AI Ethics on October 27 on behalf of the University of Florida and served as a panelist for the two-day summit attended by 36 universities invited from around the world. The event was held in Notre Dame, IN. The signing indicates a commitment to the principles of the Rome Call for AI Ethics: to ensure artificial intelligence serves the interests of humanity and to support regulations and principles to deliver emerging technologies that are ethically centered. UF joins a network of universities that will share best practices, tools, and educational content, as well as meet regularly to share updates and discuss innovative ideas.

Mike Chapple is Teaching Professor of IT, Analytics, and Operations at the University of Notre Dame's Mendoza College of Business where he teaches graduate and undergraduate courses in cybersecurity and business analytics. Prior to joining Notre Dame's faculty, Mike served as Senior Director for IT Service Delivery at the University. In this role, he oversaw the information security, IT compliance, cloud computing, data governance, IT architecture, learning platforms, project management, strategic planning and product management functions for the Office of Information Technologies. Mike led Notre Dame's Cloud First strategy which moved 80% of the institution's IT services into the cloud over three years. Mike previously served as Senior Advisor to the Executive Vice President at Notre Dame for two years.

The Abbey Library of St. Gall in Switzerland is home to approximately 160,000 volumes of literary and historical manuscripts dating back to the eighth century--all of which are written by hand, on parchment, in languages rarely spoken in modern times. To preserve these historical accounts of humanity, such texts, numbering in the millions, have been kept safely stored away in libraries and monasteries all over the world. A significant portion of these collections are available to the general public through digital imagery, but experts say there is an extraordinary amount of material that has never been read--a treasure trove of insight into the world's history hidden within. Now, researchers at University of Notre Dame are developing an artificial neural network to read complex ancient handwriting based on human perception to improve capabilities of deep learning transcription. "We're dealing with historical documents written in styles that have long fallen out of fashion, going back many centuries, and in languages like Latin, which are rarely ever used anymore," said Walter Scheirer, the Dennis O. Doughty Collegiate Associate Professor in the Department of Computer Science and Engineering at Notre Dame.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The NCAA's proposal to permit athletes to earn money from endorsements would stand in the way of players' names, images and likenesses being used in EA Sports' new college football video game. Until that changes, Notre Dame doesn't want to be in the game. The Fighting Irish are not alone among major college football programs passing on inclusion in the rebooted game until players can get paid to be in it, too.

Extracting large amounts of data from biological samples is not feasible due to radiation issues, and image processing in the small-data regime is one of the critical challenges when working with a limited amount of data. In this work, we applied an existing algorithm named Variational Auto Encoder (VAE) that pre-trains a latent space representation of the data to capture the features in a lower-dimension for the small-data regime input. The fine-tuned latent space provides constant weights that are useful for classification. Here we will present the performance analysis of the VAE algorithm with different latent space sizes in the semi-supervised learning using the CIFAR-10 dataset.

An 1,100-pound emergency robot helped to save a piece of human history during a blaze at Paris' Notre Dame cathedral that threatened to burn the historic monument to the ground. The formidable device, dubbed Colossus, a remote-controlled drone equipped with hoses and cameras, was able to roll its way into the cathedral to help fight the fire -- which burned through the structure's old wooden roof -- from within. Colossus, which is both fire-resistant, water-proof, and capable of carrying up to 1,200 pounds not only helped to stop the fire before it completely razed the structure, but reduced the need for fire fighters to enter the church where they would be in danger from falling debris. At the time, the cathedral was only 15 to 30 minutes away from being completely burned to the ground, reports say. Weighing in at 1,100 pounds, Colossus is a firefighting robot that can be controlled remotely.