stp2y

arXiv:2407.18402v1 Announce Type: new Abstract: In this paper, we develop an unsupervised learning approach to earthquake detection. We train a specific class of deep auto-encoders that learn to reproduce the input waveforms after a data-compressive bottleneck, and then use a simple triggering algorithm at the bottleneck to label waveforms as noise or signal. Our approach is motivated by the intuition that efficient compression of data should represent signals differently from noise, and is facilitated by a time-axis-preserving approach to auto-encoding and intuitively-motivated choices on the architecture and triggering. We demonstrate that the detection performance of the unsupervised approach is comparable…

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol

[Submitted on 26 Jul 2024 (v1), last revised 29 Jul 2024 (this version, v2)] View a PDF of the paper titled A Role-specific Guided Large Language Model for Ophthalmic Consultation Based on Stylistic Differentiation, by Laiyi Fu and 5 other authors View PDF Abstract:Ophthalmology consultations are crucial for diagnosing, treating, and preventing eye diseases. However, the growing demand for consultations exceeds the availability of ophthalmologists. By leveraging large pre-trained language models, we can design effective dialogues for specific scenarios, aiding in consultations. Traditional fine-tuning strategies for question-answering tasks are impractical due to increasing model size and often ignoring patient-doctor role…