arXiv:2507.09043v3 Announce Type: replace-cross Abstract: Gaussian Probability Path based Generative Models (GPPGMs) generate data by reversing a stochastic process that progressively corrupts samples with Gaussian noise. Despite state-of-the-art results in 3D molecular generation, their deployment is hindered by the high cost of long generative trajectories, often requiring hundreds to thousands of steps during training and sampling. In this work, we propose a principled method, named GAGA, to improve generation efficiency without sacrificing training granularity or inference fidelity of […]

Ver mais

Key Highlights: Europe is one of the markets across the globe where tech giants find themselves under heavy scrutiny from the European Commission. Meta, this time, has found itself in a spot of bother after introducing a WhatsApp policy that blocks third-party AI providers.  Italy’s antitrust regulator asks Meta to suspend its WhatsApp policy that blocks third-party AI providers Earlier this month, the European Commission launched a separate investigation into this matter. At the time, the regulator announced […]

Ver mais

arXiv:2601.09839v1 Announce Type: new Abstract: Lazy evaluation is a powerful technique that can optimize code execution by deferring evaluations until their results are required, thus enhancing efficiency. In most modern programming languages, like R, lazy evaluation is commonly applied to function arguments. However, the application of lazy evaluation in SAS has not been extensively explored. This paper focuses on the mechanisms of lazy evaluation in SAS MACROs and R functions, offering a comparative analysis of the underlying principles […]

Ver mais

This 2nd episode on a 2 part series analyzes how GenAI has evolved to deliver automation through AI Agents and Agentic AI. It highlights the differences between both of them citing many sources from IBM, Anthropic McKinsey and Gartner amongst others as well as their best use cases and differences and also mentioning some of the risks. 1. Generative AI (GenAI) Generative AI refers to reactive systems that wait for user input, typically a prompt, to generate content1. […]

Ver mais

arXiv:2601.14417v1 Announce Type: new Abstract: Many spoken languages, including English, exhibit wide variation in dialects and accents, making accent control an important capability for flexible text-to-speech (TTS) models. Current TTS systems typically generate accented speech by conditioning on speaker embeddings associated with specific accents. While effective, this approach offers limited interpretability and controllability, as embeddings also encode traits such as timbre and emotion. In this study, we analyze the interaction between speaker embeddings and linguistically motivated phonological rules […]

Ver mais

arXiv:2505.11323v2 Announce Type: replace Abstract: Constrained Bayesian optimization (CBO) methods have seen significant success in black-box optimization with constraints. One of the most commonly used CBO methods is the constrained expected improvement (CEI) algorithm. CEI is a natural extension of expected improvement (EI) when constraints are incorporated. However, the theoretical convergence rate of CEI has not been established. In this work, we study the convergence rate of CEI by analyzing its simple regret upper bound. First, we show […]

Ver mais

arXiv:2601.10868v1 Announce Type: new Abstract: This paper establishes convergence and steady-state properties for the signal bound disturbance attenuation regulator (SiDAR). Building on the finite horizon recursive solution developed in a companion paper, we introduce the steady-state SiDAR and derive its tractable linear matrix inequality (LMI) with $O(n^3)$ complexity. Systems are classified as degenerate or nondegenerate based on steady-state solution properties. For nondegenerate systems, the finite horizon solution converges to the steady-state solution for all states as the horizon […]

Ver mais

The energy management problem in the context of smart grids is inherently complex due to the interdependencies among diverse system components. Although Reinforcement Learning (RL) has been proposed for solving Optimal Power Flow (OPF) problems, the requirement for iterative interaction with an environment often necessitates computationally expensive simulators, leading to significant sample inefficiency. In this study, these challenges are addressed through the use of Physics-Informed Neural Networks (PINNs), which can replace conventional and costly smart grid simulators. The […]

Ver mais

arXiv:2602.05005v1 Announce Type: new Abstract: We propose and analyse a numerical method for time-harmonic acoustic scattering in $mathbb{R}^n$, $n=2,3$, by a class of inhomogeneities (penetrable scatterers) with fractal boundary. Our method is based on a Galerkin discretisation of the Lippmann-Schwinger volume integral equation, using a discontinuous piecewise-polynomial approximation space on a geometry-conforming mesh comprising elements which themselves have fractal boundary. We first provide a semi-discrete well-posedness and error analysis for both the $h$- and $p$-versions of our method […]

Ver mais