arXiv:2601.09755v1 Announce Type: new Abstract: After Industry 4.0 has embraced tight integration between machinery (OT), software (IT), and the Internet, creating a web of sensors, data, and algorithms in service of efficient and reliable production, a new concept of Society 5.0 is emerging, in which infrastructure of a city will be instrumented to increase reliability, efficiency, and safety. Robotics will play a pivotal role in enabling this vision that is pioneered by the NEOM initiative – a smart […]

Ver mais

On January 16, 2026, John Lee Dumas of Entrepreneurs on Fire hosted the 2026 Podcast Hall of Fame ceremony, which was a relatively intimate affair, unlike the elaborate red carpet events for movie stars and entertainers. A cash bar and appetizer station occupied one corner of the venue, nothing extravagant, but enough to make the space feel welcoming before the program began. The evening balanced live induction speeches with pre-recorded remarks from inductees who could not attend in person. The remote […]

Ver mais

Interventions implemented in geographic space (geo-interventions), have had success in reducing preventable deaths across the world. However, many studies supporting geo-interventions have focused on where to implement them rather than what they are. In this paper, we answer how to model and generate geo-interventions using spatial data, providing what these geo-interventions are and where to apply them. We defined geo-intervention modelling as a problem of optimizing actions and their locations, given the objective of maximizing predicted outcomes. To […]

Ver mais

arXiv:2604.07399v1 Announce Type: new Abstract: Continual learning (CL) on edge devices requires not only high accuracy but also training-time efficiency to support on-device adaptation under strict memory and computational constraints. While prompt-based continual learning (PCL) is parameter-efficient and achieves competitive accuracy, prior work has focused mainly on accuracy or inference-time performance, often overlooking the memory and computational costs of on-device training. In this paper, we propose CPS-Prompt, a critical patch-aware sparse prompting framework that explicitly targets training-time memory […]

Ver mais

Artificial Intelligence (AI) has been widely successful and effective in optimizing tasks across various fields such as smart agriculture, healthcare, and education, positioning it as a key enabler in advancing the Sustainable Development Goals (SDGs) set forth by the United Nations. Understanding the perspectives of industry and academic experts in domains such as AI, data science, biotechnology, natural and physical sciences, can provide valuable multidisciplinary insights into the responsible usage of AI. This study analyses 12 opinion essays […]

Ver mais

Accurate prediction of phase equilibria remains a central challenge in chemical engineering. Physics-consistent machine learning methods that incorporate thermodynamic structure into neural networks have recently shown strong performance for activity-coefficient modeling. However, extending such approaches to equilibrium data arising from an extremum principle, such as liquid-liquid equilibria, remains difficult. Here we present DISCOMAX, a differentiable algorithm for phase-equilibrium calculation that guarantees thermodynamic consistency at both training and inference, only subject to a user-specified discretization. The method is rooted […]

Ver mais

In this quiz, you’ll test your understanding of Python Application Layouts: A Reference. By working through this quiz, you’ll revisit how to structure one-off scripts, installable packages, larger applications with internal packages, and web projects built with Django or Flask. A dependable starting layout helps you avoid coder’s block so you can focus on writing code instead of figuring out where it should live. [ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & […]

Ver mais

Social learning highlights that learning agents improve not in isolation, but through interaction and structured knowledge exchange with others. When introduced into machine learning, this principle gives rise to social machine learning (SML), where multiple agents collaboratively learn by sharing abstracted knowledge. Federated learning (FL) provides a natural collaboration substrate for this paradigm, yet existing heterogeneous FL approaches often rely on sharing model parameters or intermediate representations, which may expose sensitive information and incur additional overhead. In this […]

Ver mais