arXiv:2602.16867v1 Announce Type: new Abstract: In this paper, we study the {em green bin packing} (GBP) problem where $beta ge 0$ and $G in [0, 1]$ are two given values as part of the input. The energy consumed by a bin is $max {0, beta (x-G) }$ where $x$ is the total size of the items packed into the bin. The GBP aims to pack all items into a set of unit-capacity bins so that the number of […]
arXiv:2410.04264v3 Announce Type: replace Abstract: Dynamic feature transformation (the rich regime) does not always align with predictive performance (better representation), yet accuracy is often used as a proxy for richness, limiting analysis of their relationship. We propose a computationally efficient, performance-independent metric of richness grounded in the low-rank bias of rich dynamics, which recovers neural collapse as a special case. The metric is empirically more stable than existing alternatives and captures known lazy-torich transitions (e.g., grokking) without relying […]
Enterprise leaders must progress past generative applications and scale “autonomous intelligence” to capture real growth. Generating text or summarising internal communications offers localised productivity improvements, yet these abilities rarely alter the core cost or revenue structure of a large organisation. Enterprises are now focused on deploying systems capable of independent execution. Leaders are demanding applications that can traverse internal networks, execute multi-step logic, and finalise transactions without constant human prompting. Prakul Sharma, principal and AI & Insights Practice […]
This is our fourth work in the series on machine learning (ML) moment closure models for the radiative transfer equation (RTE). In the first three papers of this series, we considered the RTE in slab geometry in 1D1V (i.e. one dimension in physical space and one dimension in angular space), and introduced a gradient-based ML moment closure [1], then enforced the hyperbolicity through a symmetrizer [2], or together with physical characteristic speeds by learning the eigenvalues of the […]
arXiv:2604.15323v1 Announce Type: new Abstract: Deceptive/Manipulative Patterns (DMP) are interface designs, also known as “dark patterns,” that manipulate user behavior. While considerable attention has been paid to their ethical and legal implications, empirical evidence about their real-world effects remains diffuse. This review synthesizes up-to-date experimental studies, focusing on works that quantify how (or whether) DMPs influence users. We also aggregate findings on interventions aimed at reducing DMP effects. Our synthesis highlights the experimental agreement that DMPs do significantly […]
arXiv:2602.18443v1 Announce Type: new Abstract: Psychosocial online counselling frequently encounters generic subject lines that impede efficient case prioritisation. This study evaluates eleven large language models generating six-word subject lines for German counselling emails through hierarchical assessment – first categorising outputs, then ranking within categories to enable manageable evaluation. Nine assessors (counselling professionals and AI systems) enable analysis via Krippendorff’s $alpha$, Spearman’s $rho$, Pearson’s $r$ and Kendall’s $tau$. Results reveal performance trade-offs between proprietary services and privacy-preserving open-source alternatives, […]
La noticia de que Meta va a instalar en los ordenadores de sus empleados estadounidenses una herramienta capaz de registrar movimientos de ratón, clics, pulsaciones de teclado y capturas ocasionales de pantalla para entrenar sus modelos de inteligencia artificial no es simplemente una anécdota más sobre la obsesión de Mark Zuckerberg con la inteligencia artificial. Es, más bien, una magnífica radiografía del momento en que nos encontramos: la empresa ya no solo quiere medir lo que hacen sus […]
arXiv:2603.06872v1 Announce Type: cross Abstract: We present a unified theoretical and computational framework for constructing reproducing kernels tailored to transport equations and adapted to Koopman eigenfunctions of nonlinear dynamical systems. These eigenfunctions satisfy a transport-type partial differential equation (PDE) that we invert using three analytically grounded methods: (i) A Lions-type variational principle in a reproducing kernel Hilbert space (RKHS), (ii) convolution with a Green’s function, and (iii) a resolvent operator constructed via Laplace transforms along characteristic flows. We […]
arXiv:2602.20271v1 Announce Type: new Abstract: Accurate delivery delay prediction is critical for maintaining operational efficiency and customer satisfaction across modern supply chains. Yet the increasing complexity of logistics networks, spanning multimodal transportation, cross-country routing, and pronounced regional variability, makes this prediction task inherently challenging. This paper introduces a multi-task deep learning model for delivery delay duration prediction in the presence of significant imbalanced data, where delayed shipments are rare but operationally consequential. The model embeds high-dimensional shipment features […]
Accurate prediction of structural dynamics is imperative for preserving digital twin fidelity throughout operational lifetimes. Parametric models with fixed nominal parameters often omit critical physical effects due to simplifications in geometry, material behavior, damping, or boundary conditions, resulting in model form errors (MFEs) that impair predictive accuracy. This work introduces a comprehensive framework for MFE estimation and correction in high-dimensional finite element (FE) based structural dynamical systems. The Gaussian Process Latent Force Model (GPLFM) represents discrepancies non-parametrically in […]