Fox News correspondent Gillian Turner has the latest on the president's focus amid calls for an impeachment inquiry on "Special Report." The National Security Agency (NSA) will launch a new artificial intelligence security center to both protect U.S. AI systems and defend against external threats. The new security center launches as the U.S. government has increased its use of algorithms and AI systems in defense and intelligence and is seeking to safeguard systems from theft or sabotage. Army Gen. Paul Nakasone, NSA director, told the Associated Press that the new center could be incorporated into the NSA's existing Cybersecurity Collaboration Center, which works with the private sector and internal partners to strengthen U.S. defenses from near-peer rivals such as China and Russia. Christopher Alexander, the chief analytics officer of Pioneer Development Group, told Fox News Digital such a center is "desperately needed for intelligence analysis and is crucial for national security."

Financial markets play a pivotal role in global economic activities, and their operations and dynamic evolutions are intricately linked to a myriad of chaotic and complex factors, including economic configurations, seasonal components, and the international milieu [1] [2]. As the economy progresses and financial markets expand continuously, time series analysis in finance has become indispensable [3]. This analytical approach has significantly advanced the understanding of market dynamics, refined intelligent decision-making processes, and bolstered developments in forecasting investment returns [4][2]. Consequently, it has garnered immense scholarly attention, leading to abundant research contributions in this domain. In stark contrast to conventional time series prediction endeavors characterizing various scientific domains--such as the temporal allocation mechanisms associated with wind energy integration [5], the granular analysis of protracted energy consumption patterns in architectural structures [6], or the intricate forecasting of load dynamics within thermal frameworks [7]--the sphere of financial time series forecasting is imbued with an elevated level of complexity and unpredictability.

Spherical robots have garnered increasing interest 1 INTRODUCTION for their applications in exploration, tunnel inspection, Spherical rolling robots (SRRs) are a fascinating category and extraterrestrial missions. Diverse designs of robots characterized by their ability to move by have emerged, including barycentric configurations, rolling on themselves, owing to their unique spherical pendulum-based mechanisms, etc. However, beneath this seemingly simple concept wide spectrum of control strategies has been proposed, lies a plethora of sophisticated mechanisms and control ranging from traditional PID approaches to cutting-edge strategies that enable such motion. Our systematic review aims to comprehensively ago, NASA introduced the idea of "Beach-Ball" Robotic identify and categorize locomotion systems and Rovers Notably, the Rollo, designed the years 1996 to 2023. A meticulous search across five in 1996 at Finland's Helsinki University of Technology databases yielded a dataset of 3189 records. As a result [1], stands as one of the pioneering spherical of our exhaustive analysis, we identified a collection robots aimed at operating in hostile environments. Leveraging the inherent protective nature of their spherical shell renders insights garnered, we provide valuable recommendations them well-suited for challenging terrains, safeguarding for optimizing the design and control aspects of spherical sensitive mechatronics, including sensors and actuators. Furthermore, we illuminate [2], surveys of dusty construction sites, tracking key research directions that hold the potential to crop yields in muddy fields, and even missions in extreme unlock the full capabilities of spherical robots. Barycentric spherical robots (BSRs) manipulate reviews exist in the literature, they fail to encapsulate the the center of mass to achieve desired motion, exemplified latest advancements in this field. For instance, a comprehensive by wheeled mechanisms within a spherical shell examination of rolling in robotics [6] delves into or popular pendulum-driven spherical robots.

Large language models (LLMs) are demonstrating significant promise as an alternate strategy to facilitate analyses and optimizations of high-performance computing programs, circumventing the need for resource-intensive manual tool creation. In this paper, we explore a novel LLM-based data race detection approach combining prompting engineering and fine-tuning techniques. We create a dedicated dataset named DRB-ML, which is derived from DataRaceBench, with fine-grain labels showing the presence of data race pairs and their associated variables, line numbers, and read/write information. DRB-ML is then used to evaluate representative LLMs and fine-tune open-source ones. Our experiment shows that LLMs can be a viable approach to data race detection. However, they still cannot compete with traditional data race detection tools when we need detailed information about variable pairs causing data races.

A tactical military unit is a complex system composed of many agents such as infantry, robots, or drones. Given a mission, an automated planner can find an optimal plan. Therefore, the mission itself must be modeled. The problem is that languages like PDDL are too low-level to be usable by the end-user: an officer in the field. We present ORTAC+, a language and a planning tool designed for this end-user. Its main objective is to allow a natural modeling of the mission, to minimize the risk of bad modeling, and thus obtain reliable plans. The language offers high-level constructs specifically designed to describe tactical missions, but at the same time has clear semantics allowing a translation to PDDL, to take advantage of state-of-the-art planners.

An overview of the process to develop a safety case for an autonomous robot deployment on a nuclear site in the UK is described and a safety case for a hypothetical robot incorporating AI is presented. This forms a first step towards a deployment, showing what is possible now and what may be possible with development of tools. It forms the basis for further discussion between nuclear site licensees, the Office for Nuclear Regulation (ONR), industry and academia.

Whittle index policy is a heuristic to the intractable restless multi-armed bandits (RMAB) problem. Although it is provably asymptotically optimal, finding Whittle indices remains difficult. In this paper, we present Neural-Q-Whittle, a Whittle index based Q-learning algorithm for RMAB with neural network function approximation, which is an example of nonlinear two-timescale stochastic approximation with Q-function values updated on a faster timescale and Whittle indices on a slower timescale. Despite the empirical success of deep Q-learning, the non-asymptotic convergence rate of Neural-Q-Whittle, which couples neural networks with two-timescale Q-learning largely remains unclear. This paper provides a finite-time analysis of Neural-Q-Whittle, where data are generated from a Markov chain, and Q-function is approximated by a ReLU neural network. Our analysis leverages a Lyapunov drift approach to capture the evolution of two coupled parameters, and the nonlinearity in value function approximation further requires us to characterize the approximation error. Combing these provide Neural-Q-Whittle with $\mathcal{O}(1/k^{2/3})$ convergence rate, where $k$ is the number of iterations.

Federated learning (FL) is a distributed learning process that uses a trusted aggregation server to allow multiple parties (or clients) to collaboratively train a machine learning model without having them share their private data. Recent research, however, has demonstrated the effectiveness of inference and poisoning attacks on FL. Mitigating both attacks simultaneously is very challenging. State-of-the-art solutions have proposed the use of poisoning defenses with Secure Multi-Party Computation (SMPC) and/or Differential Privacy (DP). However, these techniques are not efficient and fail to address the malicious intent behind the attacks, i.e., adversaries (curious servers and/or compromised clients) seek to exploit a system for monetization purposes. To overcome these limitations, we present a ledger-based FL framework known as FLEDGE that allows making parties accountable for their behavior and achieve reasonable efficiency for mitigating inference and poisoning attacks. Our solution leverages crypto-currency to increase party accountability by penalizing malicious behavior and rewarding benign conduct. We conduct an extensive evaluation on four public datasets: Reddit, MNIST, Fashion-MNIST, and CIFAR-10. Our experimental results demonstrate that (1) FLEDGE provides strong privacy guarantees for model updates without sacrificing model utility; (2) FLEDGE can successfully mitigate different poisoning attacks without degrading the performance of the global model; and (3) FLEDGE offers unique reward mechanisms to promote benign behavior during model training and/or model aggregation.

This paper explores the robustness of LLMs' preference to their internal memory or the given prompt, which may contain contrasting information in real-world applications due to noise or task settings. To this end, we establish a quantitative benchmarking framework and conduct the role playing intervention to control LLMs' preference. In specific, we define two types of robustness, factual robustness targeting the ability to identify the correct fact from prompts or memory, and decision style to categorize LLMs' behavior in making consistent choices -- assuming there is no definitive "right" answer -- intuitive, dependent, or rational based on cognitive theory. Our findings, derived from extensive experiments on seven open-source and closed-source LLMs, reveal that these models are highly susceptible to misleading prompts, especially for instructing commonsense knowledge. While detailed instructions can mitigate the selection of misleading answers, they also increase the incidence of invalid responses. After Unraveling the preference, we intervene different sized LLMs through specific style of role instruction, showing their varying upper bound of robustness and adaptivity.

How to make artificial intelligence (AI) systems safe and aligned with human values is an open research question. Proposed solutions tend toward relying on human intervention in uncertain situations, learning human values and intentions through training or observation, providing off-switches, implementing isolation or simulation environments, or extrapolating what people would want if they had more knowledge and more time to think. Law-based approaches--such as inspired by Isaac Asimov--have not been well regarded. This paper makes a case that effective legal systems are the best way to address AI safety. Law is defined as any rules that codify prohibitions and prescriptions applicable to particular agents in specified domains/contexts and includes processes for enacting, managing, enforcing, and litigating such rules.