The primary focus of multi-agent reinforcement learning (MARL) has been to study interactions among a fixed number of agents embedded in an environment. However, in the real world, the number of agents is neither fixed nor known a priori. Moreover, an agent can decide to create other agents (for example, a cell may divide, or a company may spin off a division). In this paper, we propose a framework that allows agents to create other agents; we call […]

Ver mais

Below is my annual summary of some of the best books I read in the past year. There are a few themes that weave their way throughout: the brain, running, history, and biographies. Here I also try to rank them loosely – the first ones are my top choices. Enjoy!  The Strange Order of Things by Antonio Damasio I heard Damasio talk a few times and was unimpressed, feeling that his arrogance to substance ratio was too high […]

Ver mais

arXiv:2602.20193v1 Announce Type: new Abstract: Standard evaluations of backdoor attacks on text-to-image (T2I) models primarily measure trigger activation and visual fidelity. We challenge this paradigm, demonstrating that encoder-side poisoning induces persistent, trigger-free semantic corruption that fundamentally reshapes the representation manifold. We trace this vulnerability to a geometric mechanism: a Jacobian-based analysis reveals that backdoors act as low-rank, target-centered deformations that amplify local sensitivity, causing distortion to propagate coherently across semantic neighborhoods. To rigorously quantify this structural degradation, we […]

Ver mais

arXiv:2506.18124v3 Announce Type: replace-cross Abstract: Multiobject tracking (MOT) is an important task in applications including autonomous driving, ocean sciences, and aerospace surveillance. Traditional MOT methods are model-based and combine sequential Bayesian estimation with data association and an object birth model. More recent methods are fully data-driven and rely on the training of neural networks. Both approaches offer distinct advantages in specific settings. In particular, model-based methods are generally applicable across a wide range of scenarios, whereas data-driven MOT […]

Ver mais

arXiv:2601.03304v1 Announce Type: new Abstract: Artificial Intelligence’s dual-use nature is revolutionizing the cybersecurity landscape, introducing new threats across four main categories: deepfakes and synthetic media, adversarial AI attacks, automated malware, and AI-powered social engineering. This paper aims to analyze emerging risks, attack mechanisms, and defense shortcomings related to AI in cybersecurity. We introduce a comparative taxonomy connecting AI capabilities with threat modalities and defenses, review over 70 academic and industry references, and identify impactful opportunities for research, such […]

Ver mais

Home Table of Contents DeepSeek-V3 Model: Theory, Config, and Rotary Positional Embeddings Introduction to the DeepSeek-V3 Model The Four Pillars of DeepSeek-V3 What You Will Build Prerequisites and Setup for Building the DeepSeek-V3 Model Implementing DeepSeek-V3 Model Configuration and RoPE DeepSeek-V3 Model Parameters and Configuration Rotary Positional Embeddings: Geometric Position Encoding Implementation: Configuration and Rotary Positional Embeddings Summary Citation Information Introduction to the DeepSeek-V3 Model The landscape of large language models has been rapidly evolving, with innovations in […]

Ver mais

Bayesian optimisation is a sample efficient method for finding a global optimum of expensive black-box objective functions. Historic datasets from related problems can be exploited to help improve performance of Bayesian optimisation by adapting transfer learning methods to various components of the Bayesian optimisation pipeline. In this study we perform an empirical analysis of various ensemble-based transfer learning Bayesian optimisation methods and pipeline components. We expand on previous work in the literature by contributing some specific pipeline components, […]

Ver mais

arXiv:2602.17818v1 Announce Type: new Abstract: Speech enhancement performance degrades significantly in noisy environments, limiting the deployment of speech-controlled technologies in industrial settings, such as manufacturing plants. Existing speech enhancement solutions primarly rely on advanced digital signal processing techniques, deep learning methods, or complex software optimization techniques. This paper introduces a novel enhancement strategy that incorporates a physical optimization stage by dynamically modifying the geometry of a microphone array to adapt to changing acoustic conditions. A sixteen-microphone array is […]

Ver mais

arXiv:2503.19190v3 Announce Type: replace Abstract: This paper addresses the task of learning convex regularizers to guide the reconstruction of images from limited data. By imposing that the reconstruction be amplitude-equivariant, we narrow down the class of admissible functionals to those that can be expressed as a power of a seminorm. We then show that such functionals can be approximated to arbitrary precision with the help of polyhedral norms. In particular, we identify two dual parameterizations of such systems: […]

Ver mais