CybAI news

Payroll systems have made remarkable progress from their inception to the digital era. In this article, we explore the evolution and revolutionary impact of technology on payroll management, helping readers better understand this critical business operation's future. Over the years, payroll systems have evolved tremendously. Payroll management has evolved into a sophisticated digital system that automates what used to be a manual process. To understand how emerging trends will shape the future of payroll systems, we'll look at their past, current state in the digital era, and potential. Taking a look back Starting at the beginning, let's look back at…

arXiv:2405.18458v1 Announce Type: new Abstract: Physical neural networks (PNNs) are emerging paradigms for neural network acceleration due to their high-bandwidth, in-propagation analogue processing. Despite the advantages of PNN for inference, training remains a challenge. The imperfect information of the physical transformation means the failure of conventional gradient-based updates from backpropagation (BP). Here, we present the asymmetrical training (AT) method, which treats the PNN structure as a grey box. AT performs training while only knowing the last layer output and neuron topological connectivity of a deep neural network structure, not requiring information about the physical control-transformation mapping. We experimentally demonstrated the…

[Submitted on 8 Apr 2024] View a PDF of the paper titled GHOST: Grounded Human Motion Generation with Open Vocabulary Scene-and-Text Contexts, by Zolt'an 'A. Milacski and 4 other authors View PDF Abstract:The connection between our 3D surroundings and the descriptive language that characterizes them would be well-suited for localizing and generating human motion in context but for one problem. The complexity introduced by multiple modalities makes capturing this connection challenging with a fixed set of descriptors. Specifically, closed vocabulary scene encoders, which require learning text-scene associations from scratch, have been favored in the literature, often resulting in inaccurate motion…

arXiv:2405.18448v1 Announce Type: new Abstract: This research aims to classify numerical values extracted from medical documents across seven distinct physiological categories, employing CamemBERT-bio. Previous studies suggested that transformer-based models might not perform as well as traditional NLP models in such tasks. To enhance CamemBERT-bio's performances, we introduce two main innovations: integrating keyword embeddings into the model and adopting a number-agnostic strategy by excluding all numerical data from the text. The implementation of label embedding techniques refines the attention mechanisms, while the technique of using a `numerical-blind' dataset aims to bolster context-centric learning. Another key component of our research is determining…