A dozen Republican U.S. lawmakers urged the administration of President Joe Biden on Friday to address the use of Chinese-manufactured agriculture drones, saying their use on American farms poses national security risks. Elise Stefanik, Ashley Hinson and John Moolenaar, who chairs a select committee on China, asked the Agriculture Department and Cybersecurity and Infrastructure Security Agency to detail the administration's efforts to address risks posed by aerosol-dispensing drones. The lawmakers asked for a briefing by Sept. 30, citing the large number of drones produced by Chinese drone manufacturer DJI as a security concern.

Modern machine-learning techniques are generally considered data-hungry. However, this may not be the case for turbulence as each of its snapshots can hold more information than a single data file in general machine-learning applications. This study asks the question of whether nonlinear machine-learning techniques can effectively extract physical insights even from as little as a single snapshot of a turbulent vortical flow. As an example, we consider machine-learning-based super-resolution analysis that reconstructs a high-resolution field from low-resolution data for two-dimensional decaying turbulence. We reveal that a carefully designed machine-learning model trained with flow tiles sampled from only a single snapshot can reconstruct vortical structures across a range of Reynolds numbers. Successful flow reconstruction indicates that nonlinear machine-learning techniques can leverage scale-invariance properties to learn turbulent flows. We further show that training data of turbulent flows can be cleverly collected from a single snapshot by considering characteristics of rotation and shear tensors. The present findings suggest that embedding prior knowledge in designing a model and collecting data is important for a range of data-driven analyses for turbulent flows. More broadly, this work hopes to stop machine-learning practitioners from being wasteful with turbulent flow data.

Large language models (LLMs) are increasingly used in business dialogue systems but they pose security and ethical risks. Multiturn conversations, where context influences the model's behavior, can be exploited to produce undesired responses. In this paper, we examine the effectiveness of utilizing off-theshelf LLMs in straightforward red-teaming approaches, where an attacker LLM aims to elicit undesired output from a target LLM, comparing both single-turn and conversational redteaming tactics. Our experiments offer insights into various usage strategies that significantly affect their performance as red teamers. They suggest that off-the-shelf models can act as effective red teamers and even adjust their attack strategy based on past attempts, although their effectiveness decreases with greater alignment. Figure 1: An example dialogue between a red-teaming Warning: This paper contains examples and model (red) and the target model (blue) in a conversational model-generated content that may be considered setting, with a judge LLM (grey) scoring the offensive.

Relation extraction is a Natural Language Processing task aiming to extract relationships from textual data. It is a critical step for information extraction. Due to its wide-scale applicability, research in relation extraction has rapidly scaled to using highly advanced neural networks. Despite their computational superiority, modern relation extractors fail to handle complicated extraction scenarios. However, a comprehensive performance analysis of the state-of-the-art relation extractors that compile these challenges has been missing from the literature, and this paper aims to bridge this gap. The goal has been to investigate the possible data-centric characteristics that impede neural relation extraction. Based on extensive experiments conducted using 15 state-of-the-art relation extraction algorithms ranging from recurrent architectures to large language models and seven large-scale datasets, this research suggests that modern relation extractors are not robust to complex data and relation characteristics. It emphasizes pivotal issues, such as contextual ambiguity, correlating relations, long-tail data, and fine-grained relation distributions. In addition, it sets a marker for future directions to alleviate these issues, thereby proving to be a critical resource for novice and advanced researchers. Efficient handling of the challenges described can have significant implications for the field of information extraction, which is a critical part of popular systems such as search engines and chatbots. Data and relevant code can be found at https://github.com/anushkasw/MaxRE.

Automatic Essay Scoring (AES) is widely used to evaluate candidates for educational purposes. However, due to the lack of representative data, most existing AES systems are not robust, and their scoring predictions are biased towards the most represented data samples. In this study, we propose a model-agnostic phrase-level method to generate an adversarial essay set to address the biases and robustness of AES models. Specifically, we construct an attack test set comprising samples from the original test set and adversarially generated samples using our proposed method. To evaluate the effectiveness of the attack strategy and data augmentation, we conducted a comprehensive analysis utilizing various neural network scoring models. Experimental results show that the proposed approach significantly improves AES model performance in the presence of adversarial examples and scenarios without such attacks.

Prognostic and diagnostic AI-based medical devices hold immense promise for advancing healthcare, yet their rapid development has outpaced the establishment of appropriate validation methods. Existing approaches often fall short in addressing the complexity of practically deploying these devices and ensuring their effective, continued operation in real-world settings. Building on recent discussions around the validation of AI models in medicine and drawing from validation practices in other fields, a framework to address this gap is presented. It offers a structured, robust approach to validation that helps ensure device reliability across differing clinical environments. The primary challenges to device performance upon deployment are discussed while highlighting the impact of changes related to individual healthcare institutions and operational processes. The presented framework emphasizes the importance of repeating validation and fine-tuning during deployment, aiming to mitigate these issues while being adaptable to challenges unforeseen during device development. The framework is also positioned within the current US and EU regulatory landscapes, underscoring its practical viability and relevance considering regulatory requirements. Additionally, a practical example demonstrating potential benefits of the framework is presented. Lastly, guidance on assessing model performance is offered and the importance of involving clinical stakeholders in the validation and fine-tuning process is discussed.

Cyber-physical control systems are critical infrastructures designed around highly responsive feedback loops that are measured and manipulated by hundreds of sensors and controllers. Anomalous data, such as from cyber-attacks, greatly risk the safety of the infrastructure and human operators. With recent advances in the quantum computing paradigm, the application of quantum in anomaly detection can greatly improve identification of cyber-attacks in physical sensor data. In this paper, we explore the use of strong pre-processing methods and a quantum-hybrid Support Vector Machine (SVM) that takes advantage of fidelity in parameterized quantum circuits to efficiently and effectively flatten extremely high dimensional data. Our results show an F-1 Score of 0.86 and accuracy of 87% on the HAI CPS dataset using an 8-qubit, 16-feature quantum kernel, performing equally to existing work and 14% better than its classical counterpart.

The utility of Synthetic Aperture Radar (SAR) imagery in remote sensing and satellite image analysis is well established, offering robustness under various weather and lighting conditions. However, SAR images, characterized by their unique structural and texture characteristics, often pose interpretability challenges for analysts accustomed to electrooptical (EO) imagery. This application compares state-of-the-art Generative Adversarial Networks (GANs) including Pix2Pix, CycleGan, S-CycleGan, and a novel dualgenerator GAN utilizing partial convolutions and a novel dual-generator architecture utilizing transformers. These models are designed to progressively refine the realism in the translated optical images, thereby enhancing the visual interpretability of SAR data. We demonstrate the efficacy of our approach through qualitative and quantitative evaluations, comparing the synthesized EO images with actual EO images in terms of visual fidelity and feature preservation. The results show significant improvements in interpretability, making SAR data more accessible for analysts familiar with EO imagery. Furthermore, we explore the potential of this technology in various applications, including environmental monitoring, urban planning, and military reconnaissance, where rapid, accurate interpretation of SAR data is crucial. Our research contributes to the field of remote sensing by bridging the gap between SAR and EO imagery, offering a novel tool for enhanced data interpretation and broader application of SAR technology in various domains. NTRODUCTION Synthetic Aperture Radar (SAR) systems are capable of creating high-resolution remote sensing images of the earths surface from satellite and aircraft. These images offer several key advantages over standard electro-optical (EO) images, most significantly, the ability to penetrate clouds and operate independently of daylight, which has led to SAR systems being deployed extensively in various fields, including environmental monitoring, natural disaster assessment, military reconnaissance, and geological mapping [1]. Figure 1 shows the benefit of a SAR image when cloud coverage is present. Despite these advantages, SAR images poses significant challenges and still has drawbacks compared to EO images, specifically regarding human interpretability.

In hundreds of videos since taken down by YouTube, right-wing influencers working for Tenet Media--a company the US Department of Justice alleges was financed and guided by a state-backed Russian news network--showed interest in a highly specific set of topics, according to a WIRED analysis. Using closed captioning of the videos we downloaded before the videos were removed, we've compiled lists of terms frequently mentioned in them, along with a searchable database: The content of these videos was described by prosecutors as "consistent" with Russia's aim of sowing political discord in the US. Among the areas covered: free speech, illegal immigrants, diversity in video games, supposed racism toward white people, and Elon Musk. While an indictment unsealed earlier this week does not name Tenet, WIRED and other outlets were able to identify it because prosecutors gave its motto as that of a business identified as "U.S. Company-1." Prosecutors allege that two employees of the state-backed Russian network RT, Kostiantyn Kalashnikov and Elena Afanasyeva, who are charged with conspiracy to commit money laundering and to violate the Foreign Agents Registration Act, paid Tenet and its parent company 9.7 million to produce and distribute videos supporting Russian aims.

In August, X, the social media company once known as Twitter, publicly released Grok 2, the latest iteration of its AI chatbot. With limited guardrails, Grok has been responsible for pushing misinformation about elections and allowing users to make life-like artificial intelligence-generated images – otherwise known as deepfakes – of elected officials in ethically questionable positions. The social media giant has started to rectify some of its problems. After election officials in Michigan, Minnesota, New Mexico, Pennsylvania and Washington wrote to X head Elon Musk alleging that the chatbot produced false information about state ballot deadlines, X now points users to Vote.gov for election-related questions. But when it comes to deepfakes, that's a different story.