Protests across Minnesota--and around the country--are ongoing, as residents demonstrate against their federal government. It's been one week since a US Immigration and Customs Enforcement (ICE) agent shot and killed Renee Nicole Good, a resident of Minneapolis, Minnesota . Since then, the city has been in tumult. Thousands of protestors--from young students to elderly residents--have taken to the streets, setting up memorials for Good and facing off with ICE agents. More than 2,000 ICE agents have been deployed to Minneapolis, with another 1,000 on the way.

A convent turned hotel, Caribou Coffee, and progressive coworking space called The Coven--plus more things to see and do while on a business trip to Minneapolis and St. Paul. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. Minnesota is the birthplace of the supercomputer, developed for code cracking during World War II. Tech giants of their day, including Cray Research and Control Data Corporation, were based in the Twin Cities.

More'Frankenstein' rabbits are appearing across the US, sparking fears of a wider outbreak. Originally spotted in Colorado, these bizarre rabbits, with tentacle-like growths sprouting from their faces, have now been reported in Minnesota, Nebraska, and South Dakota. The animals are infected with cottontail rabbit papilloma virus (CRPV), also known as Shope papilloma virus, which can be spread through mosquito and tick bites. While humans are unlikely to contract CRPV, Dr Omer Awan of the University of Maryland School of Medicine cautioned that people could still face risks from other diseases carried by ticks or mosquitoes that have fed on infected rabbits. 'You're not going to get CRPV, and you likely won't show symptoms of it,' Dr Awan told the Daily Mail.

We investigate the challenge of establishing stochastic-like guarantees when sequentially learning from a stream of i.i.d. data that includes an unknown quantity of clean-label adversarial samples. We permit the learner to abstain from making predictions when uncertain. The regret of the learner is measured in terms of misclassification and abstention error, where we allow the learner to abstain for free on adversarial injected samples. This approach is based on the work of Goel, Hanneke, Moran, and Shetty from arXiv:2306.13119. We explore the methods they present and manage to correct inaccuracies in their argumentation. However, this approach is limited to the realizable setting, where labels are assigned according to some function $f^*$ from the hypothesis space $\mathcal{F}$. Based on similar arguments, we explore methods to make adaptations for the agnostic setting where labels are random. Introducing the notion of a clean-label adversary in the agnostic context, we are the first to give a theoretical analysis of a disagreement-based learner for thresholds, subject to a clean-label adversary with noise.

Motion planning and trajectory generation are crucial technologies in various domains including the control of Unmanned Aerial Vehicles (UAV), manipulators, and rockets. However, optimization-based real-time motion planning becomes increasingly challenging due to the problem's probable non-convexity and the inherent limitations of Non-Linear Programming algorithms. Highly nonlinear dynamics, obstacle avoidance constraints, and non-convex inputs can exacerbate these difficulties. To address these hurdles, this paper proposes a two-layer optimization algorithm for 2D vehicles by dynamically reformulating small time horizon convex programming subproblems, aiming to provide real-time guarantees for trajectory optimization. Our approach involves breaking down the original problem into small horizon-based planning cycles with fixed final times, referred to as planning cycles. Each planning cycle is then solved within a series of restricted convex sets identified by our customized search algorithms incrementally. The key benefits of our proposed algorithm include fast computation speeds and lower task time. We demonstrate these advantages through mathematical proofs under some moderate preconditions and experimental results.

Designing robots capable of traversing uneven terrain and overcoming physical obstacles has been a longstanding challenge in the field of robotics. Walking robots show promise in this regard due to their agility, redundant DOFs and intermittent ground contact of locomoting appendages. However, the complexity of walking robots and their numerous DOFs make controlling them extremely difficult and computation heavy. Linear policies trained with reinforcement learning have been shown to perform adequately to enable quadrupedal walking, while being computationally light weight. The goal of this research is to study the effect of augmentation of observation space of a linear policy with newer state variables on performance of the policy. Since ground contact and reaction forces are the primary means of robot-environment interaction, they are essential state variables on which the linear policy must be informed. Experimental results show that augmenting the observation space with ground contact and reaction force data trains policies with better survivability, better stability against external disturbances and higher adaptability to untrained conditions.

Target tracking entails the estimation of the evolution of the target state over time, namely the target trajectory. Different from the classical state space model, our series of studies, including this paper, model the collection of the target state as a stochastic process (SP) that is further decomposed into a deterministic part which represents the trend of the trajectory and a residual SP representing the residual fitting error. Subsequently, the tracking problem is formulated as a learning problem regarding the trajectory SP for which a key part is to estimate a trajectory FoT (T-FoT) best fitting the measurements in time series. For this purpose, we consider the polynomial T-FoT and address the regularized polynomial T-FoT optimization employing two distinct regularization strategies seeking trade-off between the accuracy and simplicity. One limits the order of the polynomial and then the best choice is determined by grid searching in a narrow, bounded range while the other adopts $\ell_0$ norm regularization for which the hybrid Newton solver is employed. Simulation results obtained in both single and multiple maneuvering target scenarios demonstrate the effectiveness of our approaches.

--In the realm of target tracking, performance evaluation plays a pivotal role in the design, comparison, and analytics of trackers. Compared with the traditional trajectory composed of a set of point-estimates obtained by a tracker in the measurement time-series, the trajectory that our series of studies including this paper pursued is given by a curve function of time (FoT). The trajectory FoT provides complete information of the movement of the target over time and can be used to infer the state corresponding to arbitrary time, not only at the measurement time. However, there are no metrics available for comparing and evaluating the trajectory FoT . T o address this lacuna, we propose a metric denominated as the spatiotemporal-aligned trajectory integral distance (Star-ID). The Star-ID associates and aligns the estimated and actual trajectories in the spatio-temporal domain and distinguishes between the time-aligned and unaligned segments in calculating the spatial divergence including false alarm, miss-detection and localization errors. The effectiveness of the proposed distance metric and the time-averaged version is validated through theoretical analysis and numerical examples of a single target or multiple targets. UL TI-target tracking (MTT) is an intricate process that entails the sequential estimation of both the cardinality (number of targets) and the kinematic states of multiple targets, where both parameters are potentially time-variant [1], [2], [3]. It has been a key technology in the applications of autonomous driving, guidance and defense systems, traffic control, and robotics.

Our ability to continuously acquire, organize, and leverage knowledge is a key feature of human intelligence that AI systems must approximate to unlock their full potential. Given the challenges in continual learning with large language models (LLMs), retrieval-augmented generation (RAG) has become the dominant way to introduce new information. However, its reliance on vector retrieval hinders its ability to mimic the dynamic and interconnected nature of human long-term memory. Recent RAG approaches augment vector embeddings with various structures like knowledge graphs to address some of these gaps, namely sense-making and associativity. However, their performance on more basic factual memory tasks drops considerably below standard RAG. We address this unintended deterioration and propose HippoRAG 2, a framework that outperforms standard RAG comprehensively on factual, sense-making, and associative memory tasks. HippoRAG 2 builds upon the Personalized PageRank algorithm used in HippoRAG and enhances it with deeper passage integration and more effective online use of an LLM. This combination pushes this RAG system closer to the effectiveness of human long-term memory, achieving a 7% improvement in associative memory tasks over the state-of-the-art embedding model while also exhibiting superior factual knowledge and sense-making memory capabilities. This work paves the way for non-parametric continual learning for LLMs. Our code and data will be released at https://github.com/OSU-NLP-Group/HippoRAG.

Then, it proposes a hybrid AI system, integrating deep reinforcement learning in a multi-agent centralized swarm architecture. The proposed system is tailored to perform surveillance of a specific area, searching and tracking ground targets, for security and law enforcement applications. The swarm is governed by a central swarm controller responsible for distributing different search and tracking tasks among the cooperating UAVs. Each UAV agent is then controlled by a collection of cooperative sub-agents, whose behaviors have been trained using different deep reinforcement learning models, tailored for the different task types proposed by the swarm controller. More specifically, proximal policy optimization (PPO) algorithms were used to train the agents' behavior. In addition, several metrics to assess the performance of the swarm in this application were defined. The results obtained through simulation show that our system searches the operation area effectively, acquires the targets in a reasonable time, and is capable of tracking them continuously and consistently.