Officials are still investigating unusual drone activity reported in recent weeks in New Jersey. The FAA set temporary restrictions over Trump National Golf Club in Bedminster. Authorities are still investigating mysterious drone activity observed several times in northern New Jersey in recent days, including the vicinity of President-elect Trump's Bedminster golf club. Multiple videos show drones flying in Somerset and Morris counties over the past few weeks, including Dec. 1 and Dec. 3. In a video from Nov. 25, a Morris County resident named Mike Walsh spotted drones flying over Black River Middle School in Chester.

Three milestones are for England - more housebuilding and planning reform, speeding up how long it takes to get a routine NHS operation, and improving pre-school education. There will also be a greener energy target for the UK and for putting more police on the beat in England and Wales. "My government was elected to deliver change, and today marks the next step. People are tired of being promised the world, but short-term sticking plaster politics letting them down," Sir Keir will say. "My mission-led government will deliver." Insiders I talk too are pretty sanguine about the prospect of this being seen by critics as a re-launch.

Whoopi Goldberg and'View' guest Charlamagne Tha God argued over President Biden's pardon of Hunter and whether he was a liar. "The View" co-host Ana Navarro admitted that she relied on information given by ChatGPT after she was mocked for sharing false information about presidential pardons in her defense of President Biden. Biden came under fire this week for issuing a sweeping pardon to his son Hunter Biden on Sunday after repeatedly insisting he would not do so. Navarro, who identifies as a Republican but is an ardent supporter of Democrats who reliably offers liberal commentary on "The View" and on CNN, came out swinging against Biden's critics. She wrote on X, "Woodrow Wilson pardoned his brother-in-law, Hunter deButts. Bill Clinton pardoned his brother, Roger. Donald Trump pardoned his daughter's father-in-law, Charlie Kushner. And just appointed him Ambassador to France. But tell me again how Joe Biden'is setting precedent'?" Navarro's bizarre claim about Woodrow Wilson's pardon of a fictional brother-in-law named "Hunter deButts" instantly raised eyebrows.

In Laser Powder Bed Fusion (LPBF), the applied laser energy produces high thermal gradients that lead to unacceptable final part distortion. Accurate distortion prediction is essential for optimizing the 3D printing process and manufacturing a part that meets geometric accuracy requirements. This study introduces data-driven parameterized reduced-order models (ROMs) to predict distortion in LPBF across various machine process settings. We propose a ROM framework that combines Proper Orthogonal Decomposition (POD) with Gaussian Process Regression (GPR) and compare its performance against a deep-learning based parameterized graph convolutional autoencoder (GCA). The POD-GPR model demonstrates high accuracy, predicting distortions within $\pm0.001mm$, and delivers a computational speed-up of approximately 1800x.

Accurate short-term streamflow and flood forecasting are critical for mitigating river flood impacts, especially given the increasing climate variability. Machine learning-based streamflow forecasting relies on large streamflow datasets derived from rating curves. Uncertainties in rating curve modeling could introduce errors to the streamflow data and affect the forecasting accuracy. This study proposes a streamflow forecasting method that addresses these data errors, enhancing the accuracy of river flood forecasting and flood modeling, thereby reducing flood-related risk. A convolutional recurrent neural network is used to capture spatiotemporal patterns, coupled with residual error learning and forecasting. The neural network outperforms commonly used forecasting models over 1-6 hours of forecasting horizons, and the residual error learners can further correct the residual errors. This provides a more reliable tool for river flood forecasting and climate adaptation in this critical 1-6 hour time window for flood risk mitigation efforts.

Machine learning models today remain vulnerable to adversarial examples [11, 27, 1, 2, 9, 10, 29], where perturbed inputs lead to unexpected model outputs. Such adversarial examples take a variety of forms, including digital attacks [11, 27] and physical [9, 2, 10] attacks, where the attack can be physically-realized in the real-world in the form of printed stickers [9, 10] or 3D objects [2]. Thus, the patch attack has been of increasing interest due to its ability to practically inject an attack via the insertion of a printed physical patch into the scene. A variety of patch attacks defenses have thus been proposed, including several certified [15, 5, 33, 32, 34, 19] and empirical [35, 20, 16, 37, 28, 4] defenses, with many of these defenses designed around the operation of identifying and then removing the patch. Such defenses rely on being able to accurately identify the patch attack without false positives and remove the effects of identified patches with a variety of techniques, including blacking them out [20] or setting it to the image's mean color [35]. Our first key contribution is that we unify these types of defenses under a general framework called OODSmoother (Section 3), as shown in Figure 1.

In the rapidly evolving domain of Artificial Intelligence (AI), the complex interaction between innovation and regulation has become an emerging focus of our society. Despite tremendous advancements in AI's capabilities to excel in specific tasks and contribute to diverse sectors, establishing a high degree of trust in AI-generated outputs and decisions necessitates meticulous caution and continuous oversight. A broad spectrum of stakeholders, including governmental bodies, private sector corporations, academic institutions, and individuals, have launched significant initiatives. These efforts include developing ethical guidelines for AI and engaging in vibrant discussions on AI ethics, both among AI practitioners and within the broader society. This article thoroughly analyzes the ground-breaking AI regulatory framework proposed by the European Union. It delves into the fundamental ethical principles of safety, transparency, non-discrimination, traceability, and environmental sustainability for AI developments and deployments. Considering the technical efforts and strategies undertaken by academics and industry to uphold these principles, we explore the synergies and conflicts among the five ethical principles. Through this lens, work presents a forward-looking perspective on the future of AI regulations, advocating for a harmonized approach that safeguards societal values while encouraging technological advancement.

The increasing frequency and sophistication of cybersecurity vulnerabilities in software systems underscore the urgent need for robust and effective methods of vulnerability assessment. However, existing approaches often rely on highly technical and abstract frameworks, which hinders understanding and increases the likelihood of exploitation, resulting in severe cyberattacks. Given the growing adoption of Large Language Models (LLMs) across diverse domains, this paper explores their potential application in cybersecurity, specifically for enhancing the assessment of software vulnerabilities. We propose ChatNVD, an LLM-based cybersecurity vulnerability assessment tool leveraging the National Vulnerability Database (NVD) to provide context-rich insights and streamline vulnerability analysis for cybersecurity professionals, developers, and non-technical users. We develop three variants of ChatNVD, utilizing three prominent LLMs: GPT-4o mini by OpenAI, Llama 3 by Meta, and Gemini 1.5 Pro by Google. To evaluate their efficacy, we conduct a comparative analysis of these models using a comprehensive questionnaire comprising common security vulnerability questions, assessing their accuracy in identifying and analyzing software vulnerabilities. This study provides valuable insights into the potential of LLMs to address critical challenges in understanding and mitigation of software vulnerabilities.

With the approach of the High Luminosity Large Hadron Collider (HL-LHC) era set to begin particle collisions by the end of this decade, it is evident that the computational demands of traditional collision simulation methods are becoming increasingly unsustainable. Existing approaches, which rely heavily on first-principles Monte Carlo simulations for modeling event showers in calorimeters, are projected to require millions of CPU-years annually -- far exceeding current computational capacities. This bottleneck presents an exciting opportunity for advancements in computational physics by integrating deep generative models with quantum simulations. We propose a quantum-assisted hierarchical deep generative surrogate founded on a variational autoencoder (VAE) in combination with an energy conditioned restricted Boltzmann machine (RBM) embedded in the model's latent space as a prior. By mapping the topology of D-Wave's Zephyr quantum annealer (QA) into the nodes and couplings of a 4-partite RBM, we leverage quantum simulation to accelerate our shower generation times significantly. To evaluate our framework, we use Dataset 2 of the CaloChallenge 2022. Through the integration of classical computation and quantum simulation, this hybrid framework paves way for utilizing large-scale quantum simulations as priors in deep generative models.

The popularisation of applying AI in businesses poses significant challenges relating to ethical principles, governance, and legal compliance. Although businesses have embedded AI into their day-to-day processes, they lack a unified approach for mitigating its potential risks. This paper introduces a framework ensuring that AI must be ethical, controllable, viable, and desirable. Balancing these factors ensures the design of a framework that addresses its trade-offs, such as balancing performance against explainability. A successful framework provides practical advice for businesses to meet regulatory requirements in sectors such as finance and healthcare, where it is critical to comply with standards like GPDR and the EU AI Act. Different case studies validate this framework by integrating AI in both academic and practical environments. For instance, large language models are cost-effective alternatives for generating synthetic opinions that emulate attitudes to environmental issues. These case studies demonstrate how having a structured framework could enhance transparency and maintain performance levels as shown from the alignment between synthetic and expected distributions. This alignment is quantified using metrics like Chi-test scores, normalized mutual information, and Jaccard indexes. Future research should explore the framework's empirical validation in diverse industrial settings further, ensuring the model's scalability and adaptability.