Clustered Moore automata (CMA) are subsequen-tial Moore transducers whose states can contain smaller CMA that operate on a faster timescale, subject to an Artinian limitation.

Fully autonomous vehicles (AVs) continue to spark immense global interest, yet predictions on when they will operate safely and broadly remain heavily debated. This paper synthesizes two distinct research traditions: computational complexity and algorithmic constraints versus reliability growth modeling and real-world testing to form an integrated, quantitative timeline for future AV deployment. We propose a mathematical framework that unifies NP-hard multi-agent path planning analyses, high-performance computing (HPC) projections, and extensive Crow-AMSAA reliability growth calculations, factoring in operational design domain (ODD) variations, severity, and partial vs. full domain restrictions. Through category-specific case studies (e.g., consumer automotive, robo-taxis, highway trucking, industrial and defense applications), we show how combining HPC limitations, safety demonstration requirements, production/regulatory hurdles, and parallel/serial test strategies can push out the horizon for universal Level 5 deployment by up to several decades. Conversely, more constrained ODDs; like fenced industrial sites or specialized defense operations; may see autonomy reach commercial viability in the near-to-medium term. Our findings illustrate that while targeted domains can achieve automated service sooner, widespread driverless vehicles handling every environment remain far from realized. This paper thus offers a unique and rigorous perspective on why AV timelines extend well beyond short-term optimism, underscoring how each dimension of complexity and reliability imposes its own multi-year delays. By quantifying these constraints and exploring potential accelerators (e.g., advanced AI hardware, infrastructure up-grades), we provide a structured baseline for researchers, policymakers, and industry stakeholders to more accurately map their expectations and investments in AV technology.

Recent studies reveal that Large Language Models (LLMs) are susceptible to backdoor attacks, where adversaries embed hidden triggers that manipulate model responses. Existing backdoor defense methods are primarily designed for vision or classification tasks, and are thus ineffective for text generation tasks, leaving LLMs vulnerable. We introduce Internal Consistency Regularization (CROW), a novel defense using consistency regularization finetuning to address layer-wise inconsistencies caused by backdoor triggers. CROW leverages the intuition that clean models exhibit smooth, consistent transitions in hidden representations across layers, whereas backdoored models show noticeable fluctuation when triggered. By enforcing internal consistency through adversarial perturbations and regularization, CROW neutralizes backdoor effects without requiring clean reference models or prior trigger knowledge, relying only on a small set of clean data. This makes it practical for deployment across various LLM architectures. Experimental results demonstrate that CROW consistently achieves a significant reductions in attack success rates across diverse backdoor strategies and tasks, including negative sentiment, targeted refusal, and code injection, on models such as Llama-2 (7B, 13B), CodeLlama (7B, 13B) and Mistral-7B, while preserving the model's generative capabilities.

Safe motion planning algorithms are necessary for deploying autonomous robots in unstructured environments. Motion plans must be safe to ensure that the robot does not harm humans or damage any nearby objects. Generating these motion plans in real-time is also important to ensure that the robot can adapt to sudden changes in its environment. Many trajectory optimization methods introduce heuristics that balance safety and real-time performance, potentially increasing the risk of the robot colliding with its environment. This paper addresses this challenge by proposing Conformalized Reachable Sets for Obstacle Avoidance With Spheres (CROWS). CROWS is a novel real-time, receding-horizon trajectory planner that generates probalistically-safe motion plans. Offline, CROWS learns a novel neural network-based representation of a spherebased reachable set that overapproximates the swept volume of the robot's motion. CROWS then uses conformal prediction to compute a confidence bound that provides a probabilistic safety guarantee on the learned reachable set. At runtime, CROWS performs trajectory optimization to select a trajectory that is probabilstically-guaranteed to be collision-free. We demonstrate that CROWS outperforms a variety of state-of-the-art methods in solving challenging motion planning tasks in cluttered environments while remaining collision-free. Code, data, and video demonstrations can be found at https://roahmlab.github.io/crows/

Clever Hans, a performing horse, drew amazed crowds wherever he went. With his owner Wilhelm, a maths teacher, he put on incredible displays of arithmetic, beating out the answer to sums with his hooves. Hans even appeared to be able to read, though sceptics insisted the horse was merely responding to signals given by Wilhelm, touring Germany before the First World War. However the trick was done, neither the animal nor the teacher would have been surprised by news this month that horses are more intelligent than previously guessed. Researchers at Nottingham Trent University taught 20 horses to touch cards with their noses in return for treats.

For one to guarantee higher-quality software development processes, risk management is essential. Furthermore, risks are those that could negatively impact an organization's operations or a project's progress. The appropriate prioritisation of software project risks is a crucial factor in ascertaining the software project's performance features and eventual success. They can be used harmoniously with the same training samples and have good complement and compatibility. We carried out in-depth tests on four benchmark datasets to confirm the efficacy of our CIA approach in closed-world and open-world scenarios, with and without defence. We also present a sequential augmentation parameter optimisation technique that captures the interdependencies of the latest deep learning state-of-the-art WF attack models. To achieve precise software risk assessment, the enhanced crow search algorithm (ECSA) is used to modify the ANFIS settings. Solutions that very slightly alter the local optimum and stay inside it are extracted using the ECSA. ANFIS variable when utilising the ANFIS technique. An experimental validation with NASA 93 dataset and 93 software project values was performed. This method's output presents a clear image of the software risk elements that are essential to achieving project performance. The results of our experiments show that, when compared to other current methods, our integrative fuzzy techniques may perform more accurately and effectively in the evaluation of software project risks.

In many real-world applications, test data may commonly exhibit categorical shifts, characterized by the emergence of novel classes, as well as distribution shifts arising from feature distributions different from the ones the model was trained on. However, existing methods either discover novel classes in the open-world setting or assume domain shifts without the ability to discover novel classes. In this work, we consider a cross-domain open-world discovery setting, where the goal is to assign samples to seen classes and discover unseen classes under a domain shift. To address this challenging problem, we present CROW, a prototype-based approach that introduces a cluster-then-match strategy enabled by a well-structured representation space of foundation models. In this way, CROW discovers novel classes by robustly matching clusters with previously seen classes, followed by fine-tuning the representation space using an objective designed for cross-domain open-world discovery. Extensive experimental results on image classification benchmark datasets demonstrate that CROW outperforms alternative baselines, achieving an 8% average performance improvement across 75 experimental settings.

The sound is a warning to every other crow: Frédéric Jiguet, a tall ornithologist whose dark hair is graying around the ears, has shown up for work. As Jiguet walks to his office at the French National Museum of Natural History, which is on the garden's grounds, dozens of the black vandals take to the trees and rain abuse on him, as though he were a condemned man. "I think I'm the best friend of French crows," Jiguet told me. "But I am probably the man they hate most." Crows are famous for holding grudges.

It sounds like the plot of a new Disney movie, but experts predict AI will allow people to communicate with household pets and even wild animals. Researchers around the world are using'digital bioacoustics' - tiny, portable, digital recorders - to capture the sounds, tics and behaviors of animals that are too quiet or nuanced for humans to pick up on. These databases will be used train artificial intelligence to decipher these miniature communications and translate them into something more comprehendible to us, almost like a'ChatGPT for animals'. Projects such as the Earth Species Project expect a breakthrough in the next 12 to 36 months. Founded in 2017, the AI non-profit aims to record, understand and'talk back' to animals - from cats and dogs to more unusual species such as whales and crows.

At her Rock & Roll Hall of Fame induction interview backstage, Sheryl Crow told reporters that AI inspired her to write a song to deal with her fear of the technology. Sheryl Crow found inspiration for her new album from artificial intelligence, though she said the technology left her "terrified." At her induction to the Rock & Roll Hall of Fame earlier this month, Crow said she hadn't intended to do another album, planning instead to just release songs. But then "when the whole AI thing started coming out, particularly with the Beatles thing, and also having witnessed how AI is being used in my art form, I wrote a song about it." She continued, "I was terrified, and where do I go when I'm terrified? I go to my studio," adding, "And I found myself writing just one thing after another, and lo and behold I had 10 songs."