Not long ago, “life hacks” meant simple tricks—like using keyboard shortcuts, keeping a to-do list, or waking up earlier to get more done. But today, the concept of life hacking is evolving. Artificial intelligence and smart digital tools are changing how we manage work, time, and information. Instead of trying to do everything ourselves, we can now use technology to handle repetitive tasks and speed up everyday processes. The result isn’t just higher productivity—it’s more time to focus […]

Ver mais

Machine learning models are increasingly used in high-stakes domains where their predictions can actively shape the environments in which they operate, a phenomenon known as performative prediction. This dynamic, in which the deployment of the model influences the very outcome it seeks to predict, can lead to unintended consequences, including feedback loops, performance issues, and significant societal risks. While the literature in the field has grown rapidly in recent years, a socio-technical synthesis that systemises the phenomenon concepts […]

Ver mais

Solving massive-scale optimization problems requires scalable first-order methods with low per-iteration cost. This tutorial highlights a shift in optimization: using differentiable programming not only to execute algorithms but to learn how to design them. Modern frameworks such as PyTorch, TensorFlow, and JAX enable this paradigm through efficient automatic differentiation. Embedding first-order methods within these systems allows end-to-end training that improves convergence and solution quality. Guided by Fenchel-Rockafellar duality, the tutorial demonstrates how duality-informed iterative schemes such as ADMM […]

Ver mais

People can use generative AI to create art, text, and music from datasets of previous art, which is significantly impacting the current creative economy. The debate of what makes an artist and the lack of clear compensation are growing concerns, prompting the evolving issue of royalty battles over AI-generated work. The Evolution of Royalty Battles AI is changing royalty battles to a debate about what makes art original. The traditional notions of authorship and ownership are being abandoned, […]

Ver mais

This paper addresses the problem of training a reinforcement learning (RL) policy under partial observability by exploiting a privileged, anytime-feasible planner agent available exclusively during training. We formalize this as a Partially Observable Markov Decision Process (POMDP) in which a planner agent with access to an approximate dynamical model and privileged state information guides a learning agent that observes only a lossy projection of the true state. To realize this framework, we introduce an anytime-feasible Model Predictive Control […]

Ver mais

arXiv:2603.25904v1 Announce Type: new Abstract: Semiconductor intellectual property (IP) theft incurs hundreds of billions in annual losses, driven by advanced reverse engineering (RE) techniques. Traditional “cryptic” IC camouflaging methods typically focus on hiding localized gate functionality but remain vulnerable to system-level structural analysis. This paper explores “mimetic deception,” where a functional IP (F) is designed to structurally and visually masquerade as a completely different appearance IP (A). We provide a comprehensive evaluation of three deceptive methodologies: IP Camouflage, […]

Ver mais

Training deep neural networks with noisy labels remains a significant challenge, often leading to degraded performance. Existing methods for handling label noise typically rely on either transition matrix, noise detection, or meta-learning techniques, but they often exhibit low utilization efficiency of noisy samples and incur high computational costs. In this paper, we propose a self-training label correction framework using decoupled bilevel optimization, where a classifier and neural correction function co-evolve. Leveraging a small clean dataset, our method employs […]

Ver mais

Federated learning on connected electric vehicles (BEVs) faces severe instability due to intermittent connectivity, time-varying client participation, and pronounced client-to-client variation induced by diverse operating conditions. Conventional FedAvg and many advanced methods can suffer from excessive drift and degraded convergence under these realistic constraints. This work introduces Fractional-Order Roughness-Informed Federated Averaging (FO-RI-FedAvg), a lightweight and modular extension of FedAvg that improves stability through two complementary client-side mechanisms: (i) adaptive roughness-informed proximal regularization, which dynamically tunes the pull toward […]

Ver mais

arXiv:2603.03409v1 Announce Type: new Abstract: This short note describes a simple variant of the Squint algorithm of Koolen and Van Erven [2015] for the classic expert problem. Via an equally simple modification of their proof, we prove that this variant ensures a regret bound that resembles the one shown in a recent work by Freund et al. [2026] for a variant of the NormalHedge algorithm [Chaudhuri et al., 2009].

Ver mais

Two months ago, I was looking for a way to program an AI for a video game and I stumbled upon Behavior Trees. Today I’ll be talking about what they are, why they are used and how. Behavior Trees Originally, behavior trees were used in the game industry to model the behavior of NPC characters, it then started getting used in other domains, such as robotics.

Ver mais