Research in trajectory forecasting (i.e., predicting where an agent will be in the future) has grown

In this paper, we consider a different data format for images: vector graphics. In contrast to raster graphics which are widely used in image recognition, vector graphics can be scaled up or down into any resolution without aliasing or information loss, due to the analytic representation of the primitives in the document. Furthermore, vector graphics are able to give extra structural information on how low-level elements group together to form high level shapes or structures. These merits of graphic vectors have not been fully leveraged in existing methods. To explore this data format, we target on the fundamental recognition tasks: object localization and classification. We propose an efficient CNN-free pipeline that does not render the graphic into pixels (i.e.

Despite advances in limited precision inference in recent years, training of neural networks in low bit-width remains a challenging problem.

Abstract--Current products, especially in the automotive sector, pose complex technical systems having a multi-disciplinary mechatronic nature. Industrial standards supporting system engineering and production typically (i) address the production phase only, but do not cover the complete product life cycle, and (ii) focus on production processes and resources rather than the products themselves. The presented approach is motivated by incorporating the impacts of the end-of-life phase of the product life cycle into the engineering phase. This paper proposes a modeling approach coming up from the Product-Process-Resource (PPR) modeling paradigm. It combines requirements on (i) respecting the product structure as a basis for the model, and (ii) incorporates repairing, remanufacturing, or upcycling within cyber-physical production systems. The proposed model called PoPAN should accompany the product during the entire life cycle as a digital shadow encapsulated within the Asset Administration Shell of a product. T o facilitate the adoption of the proposed paradigm, the paper also proposes serialization of the model in the AutomationML data format. The model is demonstrated on a use-case for disassembling electric vehicle batteries to support their remanufacturing for stationary battery applications.

Research in trajectory forecasting (i.e., predicting where an agent will be in the future) has grown

This paper leverages microscaling floating-point formats, a novel technique designed to address these challenges by reducing the storage and computational overhead associated with numerical representations in LLMs. Unlike traditional floating-point representations that allocate a dedicated scale for each value, microscaling employs a shared scale across a block of values, enabling compact one-byte floating-point representations while maintaining an extended dynamic range. We explore the application of microscaling in the context of 8-bit floating-point formats to significantly reduce memory footprint and computational costs. We tested several configurations of microscaling floats within the GPT -2 LLM architecture, demonstrating that microscaling data formats can achieve competitive accuracy during training and inference, proving its efficacy as a resource-efficient alternative for deploying LLMs at scale. The source code is publicly available at: https://github.com/