Multi-agent systems play a central role in area coverage tasks across search-and-rescue, environmental monitoring, and precision agriculture. Achieving non-uniform coverage, where spatial priorities vary across the domain, requires coordinating agents while respecting dynamic and communication constraints. Density-driven approaches can distribute agents according to a prescribed reference density, but existing methods do not ensure connectivity. This limitation often leads to communication loss, reduced coordination, and degraded coverage performance. This letter introduces a connectivity-preserving extension of the Density-Driven Optimal Control (D2OC) framework. The coverage objective, defined using the Wasserstein distance between the agent distribution and the reference density, admits a convex quadratic program formulation. Communication constraints are incorporated through a smooth connectivity penalty, which maintains strict convexity, supports distributed implementation, and preserves inter-agent communication without imposing rigid formations. Simulation studies show that the proposed method consistently maintains connectivity, improves convergence speed, and enhances non-uniform coverage quality compared with density-driven schemes that do not incorporate explicit connectivity considerations.

As large language models (LLMs) continue to evolve, it is critical to assess the security threats and vulnerabilities that may arise both during their training phase and after models have been deployed. This survey seeks to define and categorize the various attacks targeting LLMs, distinguishing between those that occur during the training phase and those that affect already trained models. A thorough analysis of these attacks is presented, alongside an exploration of defense mechanisms designed to mitigate such threats. Defenses are classified into two primary categories: prevention-based and detection-based defenses. Furthermore, our survey summarizes possible attacks and their corresponding defense strategies. It also provides an evaluation of the effectiveness of the known defense mechanisms for the different security threats. Our survey aims to offer a structured framework for securing LLMs, while also identifying areas that require further research to improve and strengthen defenses against emerging security challenges.

A'flame in the sky,' eerie red glowing objects and swarms of UFOs over military bases are just some of the many sightings that have gravely concerned the US government. There are dozens of unsolved cases going back to the 1960s that occurred over nuclear missile installations, Navy ships and a desert in New Mexico. The FBI, CIA, and other government branches have spent years looking into these reports, but have yet to determine what the objects were and where they came from. One report in 2019 detailed how'drones' appeared over Colorado, Nebraska, Wyoming, and Kansas as locals reported spying a mothership hanging in the sky. In just the last few months, the skies over New Jersey were filled with unidentified aircraft and drones that required a formal response from both the Biden and Trump presidencies.

Inferring sky surface brightness distributions from noisy interferometric data in a principled statistical framework has been a key challenge in radio astronomy. In this work, we introduce Imaging for Radio Interferometry with Score-based models (IRIS). We use score-based models trained on optical images of galaxies as an expressive prior in combination with a Gaussian likelihood in the uv-space to infer images of protoplanetary disks from visibility data of the DSHARP survey conducted by ALMA. We demonstrate the advantages of this framework compared with traditional radio interferometry imaging algorithms, showing that it produces plausible posterior samples despite the use of a misspecified galaxy prior. Through coverage testing on simulations, we empirically evaluate the accuracy of this approach to generate calibrated posterior samples.

Anomaly-based cyber threat detection using deep learning is on a constant growth in popularity for novel cyber-attack detection and forensics. A robust, efficient, and real-time threat detector in a large-scale operational enterprise network requires high accuracy, high fidelity, and a high throughput model to detect malicious activities. Traditional anomaly-based detection models, however, suffer from high computational overhead and low detection accuracy, making them unsuitable for real-time threat detection. In this work, we propose LogSHIELD, a highly effective graph-based anomaly detection model in host data. We present a real-time threat detection approach using frequency-domain analysis of provenance graphs. To demonstrate the significance of graph-based frequency analysis we proposed two approaches. Approach-I uses a Graph Neural Network (GNN) LogGNN and approach-II performs frequency domain analysis on graph node samples for graph embedding. Both approaches use a statistical clustering algorithm for anomaly detection. The proposed models are evaluated using a large host log dataset consisting of 774M benign logs and 375K malware logs. LogSHIELD explores the provenance graph to extract contextual and causal relationships among logs, exposing abnormal activities. It can detect stealthy and sophisticated attacks with over 98% average AUC and F1 scores. It significantly improves throughput, achieves an average detection latency of 0.13 seconds, and outperforms state-of-the-art models in detection time.

Space surveillance depends on efficiently directing sensor resources to maintain custody of known catalog objects. However, it remains unclear how to best utilize these resources to rapidly search for and track newly detected space objects. Provided a novel measurement, a search set can be instantiated through admissible region constraints to inform follow-up observations. In lacking well-constrained bounds, this set rapidly spreads in the along-track direction, growing much larger than a follow-up sensor's finite field of view. Moreover, the number of novel objects may be uncertain, and follow-up observations are most commonly corrupted by false positives from known catalog objects and missed detections. In this work, we address these challenges through the introduction of a joint sensor control and multi-target tracking approach. The search set associated to a novel measurement is represented by a Cardinalized Probability Hypothesis Density (CPHD), which jointly tracks the state uncertainty associated to a set of objects and a probability mass function for the true target number. In follow-up sensor scans, the information contained in an empty measurement set, and returns from both novel objects and known catalog objects is succinctly captured through this paradigm. To maximize the utility of a follow-up sensor, we introduce an information-driven sensor control approach for steering the instrument. Our methods are tested on two relevant test cases and we provide a comparative analysis with current naive tasking strategies.

Varying dynamics pose a fundamental difficulty when deploying safe control laws in the real world. Safety Index Synthesis (SIS) deeply relies on the system dynamics and once the dynamics change, the previously synthesized safety index becomes invalid. In this work, we show the real-time efficacy of Safety Index Adaptation (SIA) in varying dynamics. SIA enables real-time adaptation to the changing dynamics so that the adapted safe control law can still guarantee 1) forward invariance within a safe region and 2) finite time convergence to that safe region. This work employs SIA on a package-carrying quadruped robot, where the payload weight changes in real-time. SIA updates the safety index when the dynamics change, e.g., a change in payload weight, so that the quadruped can avoid obstacles while achieving its performance objectives. Numerical study provides theoretical guarantees for SIA and a series of hardware experiments demonstrate the effectiveness of SIA in real-world deployment in avoiding obstacles under varying dynamics.

Data science and artificial intelligence are playing an increasingly important role in the physical sciences. Unfortunately, in the field of energetic materials data scarcity limits the accuracy and even applicability of ML tools. To address data limitations, we compiled multi-modal data: both experimental and computational results for several properties. We find that multi-task neural networks can learn from multi-modal data and outperform single-task models trained for specific properties. As expected, the improvement is more significant for data-scarce properties. These models are trained using descriptors built from simple molecular information and can be readily applied for large-scale materials screening to explore multiple properties simultaneously. This approach is widely applicable to fields outside energetic materials.

In the vast domain of cybersecurity, the transition from reactive defense to offensive has become critical in protecting digital infrastructures. This paper explores the integration of Artificial Intelligence (AI) into offensive cybersecurity, particularly through the development of an autonomous AI agent, ReaperAI, designed to simulate and execute cyberattacks. Leveraging the capabilities of Large Language Models (LLMs) such as GPT-4, ReaperAI demonstrates the potential to identify, exploit, and analyze security vulnerabilities autonomously. This research outlines the core methodologies that can be utilized to increase consistency and performance, including task-driven penetration testing frameworks, AI-driven command generation, and advanced prompting techniques. The AI agent operates within a structured environment using Python, enhanced by Retrieval Augmented Generation (RAG) for contextual understanding and memory retention. ReaperAI was tested on platforms including, Hack The Box, where it successfully exploited known vulnerabilities, demonstrating its potential power. However, the deployment of AI in offensive security presents significant ethical and operational challenges. The agent's development process revealed complexities in command execution, error handling, and maintaining ethical constraints, highlighting areas for future enhancement. This study contributes to the discussion on AI's role in cybersecurity by showcasing how AI can augment offensive security strategies. It also proposes future research directions, including the refinement of AI interactions with cybersecurity tools, enhancement of learning mechanisms, and the discussion of ethical guidelines for AI in offensive roles. The findings advocate for a unique approach to AI implementation in cybersecurity, emphasizing innovation.

We present source detection and catalogue construction pipelines to build the first catalogue of radio galaxies from the 270 $\rm deg^2$ pilot survey of the Evolutionary Map of the Universe (EMU-PS) conducted with the Australian Square Kilometre Array Pathfinder (ASKAP) telescope. The detection pipeline uses Gal-DINO computer-vision networks (Gupta et al., 2024) to predict the categories of radio morphology and bounding boxes for radio sources, as well as their potential infrared host positions. The Gal-DINO network is trained and evaluated on approximately 5,000 visually inspected radio galaxies and their infrared hosts, encompassing both compact and extended radio morphologies. We find that the Intersection over Union (IoU) for the predicted and ground truth bounding boxes is larger than 0.5 for 99% of the radio sources, and 98% of predicted host positions are within $3^{\prime \prime}$ of the ground truth infrared host in the evaluation set. The catalogue construction pipeline uses the predictions of the trained network on the radio and infrared image cutouts based on the catalogue of radio components identified using the Selavy source finder algorithm. Confidence scores of the predictions are then used to prioritize Selavy components with higher scores and incorporate them first into the catalogue. This results in identifications for a total of 211,625 radio sources, with 201,211 classified as compact and unresolved. The remaining 10,414 are categorized as extended radio morphologies, including 582 FR-I, 5,602 FR-II, 1,494 FR-x (uncertain whether FR-I or FR-II), 2,375 R (single-peak resolved) radio galaxies, and 361 with peculiar and other rare morphologies. We cross-match the radio sources in the catalogue with the infrared and optical catalogues, finding infrared cross-matches for 73% and photometric redshifts for 36% of the radio galaxies.