Identifying vessels that do not show up in conventional monitoring systems--known as "dark vessels"--is key to managing and securing the health

For many graph-related problems, it can be essential to have a set of structurally diverse graphs. For instance, such graphs can be used for testing graph algorithms or their neural approximations. However, to the best of our knowledge, the problem of generating structurally diverse graphs has not been explored in the literature. In this paper, we fill this gap. First, we discuss how to define diversity for a set of graphs, why this task is non-trivial, and how one can choose a proper diversity measure.

Tuning hyperparameters is a crucial but arduous part of the machine learning pipeline. Hyperparameter optimization is even more challenging in federated learning, where models are learned over a distributed network of heterogeneous devices; here, the need to keep data on device and perform local training makes it difficult to efficiently train and evaluate configurations. In this work, we investigate the problem of federated hyperparameter tuning. We first identify key challenges and show how standard approaches may be adapted to form baselines for the federated setting. Then, by making a novel connection to the neural architecture search technique of weight-sharing, we introduce a new method, FedEx, to accelerate federated hyperparameter tuning that is applicable to widely-used federated optimization methods such as FedAvg and recent variants.

The Neural Tangent Kernel (NTK) viewpoint is widely employed to analyze the training dynamics of overparameterized Physics-Informed Neural Networks (PINNs). However, unlike the case of linear Partial Differential Equations (PDEs), we show how the NTK perspective falls short in the nonlinear scenario. Specifically, we establish that the NTK yields a random matrix at initialization that is not constant during training, contrary to conventional belief. Another significant difference from the linear regime is that, even in the idealistic infinite-width limit, the Hessian does not vanish and hence it cannot be disregarded during training. We explore the convergence guarantees of such methods in both linear and nonlinear cases, addressing challenges such as spectral bias and slow convergence. Every theoretical result is supported by numerical examples with both linear and nonlinear PDEs, and we highlight the benefits of second-order methods in benchmark test cases.

Human-Machine Teaming (HMT) is revolutionizing collaboration across domains such as defense, healthcare, and autonomous systems by integrating AI-driven decision-making, trust calibration, and adaptive teaming. This survey presents a comprehensive taxonomy of HMT, analyzing theoretical models, including reinforcement learning, instance-based learning, and interdependence theory, alongside interdisciplinary methodologies. Unlike prior reviews, we examine team cognition, ethical AI, multi-modal interactions, and real-world evaluation frameworks. Key challenges include explainability, role allocation, and scalable benchmarking. We propose future research in cross-domain adaptation, trust-aware AI, and standardized testbeds. By bridging computational and social sciences, this work lays a foundation for resilient, ethical, and scalable HMT systems.

This evolution indicates an expansion from industrial uses into diverse fields, including healthcare [61], [59]. The core functionalities of digital twins include an accurate mirroring of their physical counterparts, capturing all associated processes in a data-driven manner, maintaining a continuous connection that synchronizes with the real-time state of their physical twins, and simulating physical behavior for predictive analysis [85]. In the context of healthcare, a novel extension of this technology manifests in the form of Human Digital Twins (HDTs), designed to provide a comprehensive digital mirror of individual patients. HDTs not only represent physical attributes but also integrate dynamic changes across molecular, physiological, and behavioral dimensions. This advancement is aligned with a shift toward personalized healthcare (PH) paradigms, enabling tailored treatment strategies based on a patient's unique health profile, thereby enhancing preventive, diagnostic, and therapeutic processes in clinical settings [44], [50]. The personalization aspect of HDTs underscores their potential to revolutionize healthcare by facilitating precise and individualized treatment plans that optimize patient outcomes [72]. Although the potential of digital twins in healthcare has garnered much attention, practical applications remain newly developing, with critical literature highlighting that many implementations are still in exploratory stages [59]. Notably, institutions like the IEEE Computer Society and Gartner recognize this technology as a pivotal component in the ongoing evolution of healthcare systems that emphasize both precision and personalization [31], [89].

The stability of language model pre-training and its effects on downstream performance are still understudied. Prior work shows that the training process can yield significantly different results in response to slight variations in initial conditions, e.g., the random seed. Crucially, the research community still lacks sufficient resources and tools to systematically investigate pre-training stability, particularly for decoder-only language models. We introduce the PolyPythias, a set of 45 new training runs for the Pythia model suite: 9 new seeds across 5 model sizes, from 14M to 410M parameters, resulting in about 7k new checkpoints that we release. Using these new 45 training runs, in addition to the 5 already available, we study the effects of different initial conditions determined by the seed -- i.e., parameters' initialisation and data order -- on (i) downstream performance, (ii) learned linguistic representations, and (iii) emergence of training phases. In addition to common scaling behaviours, our analyses generally reveal highly consistent training dynamics across both model sizes and initial conditions. Further, the new seeds for each model allow us to identify outlier training runs and delineate their characteristics. Our findings show the potential of using these methods to predict training stability.

Large language models (LLMs) like OpenAI ChatGPT, Google Gemini, and GitHub Copilot are rapidly gaining traction in the software industry, but their full impact on software engineering remains insufficiently explored. Despite their growing adoption, there is a notable lack of formal, qualitative assessments of how LLMs are applied in real-world software development contexts. To fill this gap, we conducted semi-structured interviews with sixteen early-adopter professional developers to explore their use of LLMs throughout various stages of the software development life cycle. Our investigation examines four dimensions: people - how LLMs affect individual developers and teams; process - how LLMs alter software engineering workflows; product - LLM impact on software quality and innovation; and society - the broader socioeconomic and ethical implications of LLM adoption. Thematic analysis of our data reveals that while LLMs have not fundamentally revolutionized the development process, they have substantially enhanced routine coding tasks, including code generation, refactoring, and debugging. Developers reported the most effective outcomes when providing LLMs with clear, well-defined problem statements, indicating that LLMs excel with decomposed problems and specific requirements. Furthermore, these early-adopters identified that LLMs offer significant value for personal and professional development, aiding in learning new languages and concepts. Early-adopters, highly skilled in software engineering and how LLMs work, identified early and persisting challenges for software engineering, such as inaccuracies in generated content and the need for careful manual review before integrating LLM outputs into production environments. Our study provides a nuanced understanding of how LLMs are shaping the landscape of software development, with their benefits, limitations, and ongoing implications.

In this work, we explain our approach employed in the BabyLM Challenge, which uses various methods of training language models (LMs) with significantly less data compared to traditional large language models (LLMs) and are inspired by how human children learn. While a human child is exposed to far less linguistic input than an LLM, they still achieve remarkable language understanding and generation abilities. To this end, we develop a model trained on a curated dataset consisting of 10 million words, primarily sourced from child-directed transcripts. The 2024 BabyLM Challenge initial dataset of 10M words is filtered to 8.5M. Next, it is supplemented with a randomly selected subset of TVR dataset consisting of 1.5M words of television dialogues. The latter dataset ensures that similar to children, the model is also exposed to language through media. Furthermore, we reduce the vocabulary size to 32,000 tokens, aligning it with the limited vocabulary of children in the early stages of language acquisition. We use curriculum learning and is able to match the baseline on certain benchmarks while surpassing the baseline on others. Additionally, incorporating common LLM training datasets, such as MADLAD-400, degrades performance. These findings underscore the importance of dataset selection, vocabulary scaling, and curriculum learning in creating more data-efficient language models that better mimic human learning processes.

As technology has become more embedded into our society, the security of modern-day systems is paramount. One topic which is constantly under discussion is that of patching, or more specifically, the installation of updates that remediate security vulnerabilities in software or hardware systems. This continued deliberation is motivated by complexities involved with patching; in particular, the various incentives and disincentives for organizations and their cybersecurity teams when deciding whether to patch. In this paper, we take a fresh look at the question of patching and critically explore why organizations and IT/security teams choose to patch or decide against it (either explicitly or due to inaction). We tackle this question by aggregating and synthesizing prominent research and industry literature on the incentives and disincentives for patching, specifically considering the human aspects in the context of these motives. Through this research, this study identifies key motivators such as organizational needs, the IT/security team's relationship with vendors, and legal and regulatory requirements placed on the business and its staff. There are also numerous significant reasons discovered for why the decision is taken not to patch, including limited resources (e.g., person-power), challenges with manual patch management tasks, human error, bad patches, unreliable patch management tools, and the perception that related vulnerabilities would not be exploited. These disincentives, in combination with the motivators above, highlight the difficult balance that organizations and their security teams need to maintain on a daily basis. Finally, we conclude by discussing implications of these findings and important future considerations.