digitado

arXiv:2604.18636v1 Announce Type: new Abstract: This paper presents a virtual boundary integral neural network (VBINN) for exterior acoustic problems in three dimensions. The method introduces a virtual boundary inside the scatterer or vibrating body and represents the associated source density with a neural network. Coupled with the acoustic fundamental solution, this representation satisfies the Sommerfeld radiation condition by construction and enables direct evaluation of the acoustic pressure and its normal derivative at arbitrary field points. Because the integration […]

Ver mais

arXiv:2604.18635v1 Announce Type: new Abstract: In service to the mathematical underpinnings of the Information Integration Theory of Consciousness (IIT), we introduce four measures of integration based on the partial information decomposition framework. We compare our measures to current IIT practice in simple deterministic networks. We find synergy-based measures more suitable for IIT’s use-case than current practice. Outside IIT, these measures could also be useful as non-IIT-related measures of complexity within discrete dynamical systems.

Ver mais

arXiv:2604.18633v1 Announce Type: new Abstract: Web tracking by ad networks, social networks, and other third parties is privacy-invasive. To protect users’ privacy an increasing number of countries are adopting new privacy laws. However, a major reason why their application on the web is so challenging is that privacy laws are local while the web is global. To that end, we evaluate websites’ tracker connections for ten countries for two sets of sites — the global Common Top 525 […]

Ver mais

arXiv:2604.18632v1 Announce Type: new Abstract: Stomata play a crucial role in regulating plant physiological processes and reflecting environmental responses. However, accurate and high-throughput stomatal phenotyping remains challenging, as conventional approaches rely on destructive sampling and manual annotation, restricting large-scale and field deployment. To overcome these limitations, a noninvasive restoration-detection integrated framework, termed StomaD2, is developed to achieve accurate and fast stomatal phenotyping under complex imaging conditions. The framework incorporates a diffusion-based restoration module to recover degraded images and […]

Ver mais

arXiv:2604.18631v1 Announce Type: new Abstract: Historical metronome indications for Beethoven’s five piano and cello sonatas (as transmitted by Czerny, Moscheles, and Kolisch), have long been regarded as problematic by performers and scholars alike. This paper presents the first systematic empirical assessment of those indications against a corpus of over one hundred movement-level recordings spanning 1930–2012, encompassing first, second, and third movements across all five sonatas (Op.~5 Nos.~1 and~2; Op.~69; Op.~102 Nos.~1 and~2). The core findings are threefold. First, […]

Ver mais

arXiv:2604.18630v1 Announce Type: new Abstract: The choice of visualisation in empirical performance analysis is not a neutral presentation decision but an analytical one: different graphical forms reveal different features of the same dataset, and reliance on any single type systematically conceals what the others expose. This paper presents and argues for a suite of five complementary visualisation tools; tempographs, histograms with spline-smoothed probability density functions, ridgeline plots, stacked bar charts, and combination charts. These are applied to bar-level […]

Ver mais

arXiv:2604.18627v1 Announce Type: new Abstract: Ensuring human safety is of paramount importance in warehouse environments that feature mixed traffic of human workers and autonomous mobile robots (AMRs). Current approaches often treat humans as generic dynamic obstacles, leading to conservative AMR behaviors like slowing down or detouring, even when workers are fully aware and capable of safely sharing space. This paper presents a real-time vision-based method to estimate human awareness of an AMR using a single RGB camera. We […]

Ver mais

arXiv:2604.18623v1 Announce Type: new Abstract: Scene Graph Generation (SGG) unifies object localization and visual relationship reasoning by predicting boxes and subject-predicate-object triples. Yet most pipelines treat SGG as a one-shot, deterministic classification problem rather than a genuinely progressive, generative task. We propose FlowSG, which recasts SGG as continuous-time transport on a hybrid discrete-continuous state: starting from a noised graph, the model progressively grows an image-conditioned scene graph through constraint-aware refinements that jointly synthesize nodes (objects) and edges (predicates). […]

Ver mais

arXiv:2604.18620v1 Announce Type: new Abstract: Due to the increasing frequency and severity of storm events, driven by the escalation of anthropogenic climate change and urban expansion, there is a requirement for increasingly efficient flood risk management strategies. While Blue-Green Infrastructure (BGI) offers a sustainable solution for managing flood risk, optimal implementation is challenging. To help overcome this challenge, this study presents a novel multi-objective optimisation tool that couples a state-of-the-art hydrodynamic model with a bespoke evolutionary algorithm. The […]

Ver mais

arXiv:2604.18616v1 Announce Type: new Abstract: LLM-based coding agents can generate functionally correct GPU kernels, yet their performance remains far below hand-optimized libraries on critical computations such as matrix multiplication, attention, and Mixture-of-Experts (MoE). Peak GPU performance requires coordinated reasoning over tightly coupled optimizations, including tiling, shared-memory staging, software pipelining, and instruction scheduling, while existing agents rely on sparse pass/fail feedback, leaving them unable to diagnose global constraint violations. We present Argus, an agentic framework that addresses this through […]

Ver mais