We are excited to share our latest work on joint time-vertex signal processing published in IEEE Signal Processing Letters! In this paper, we explore the complexities of time-varying graph signals and how they can be more efficiently processed using the joint time-vertex framework. Traditionally, separating signal from noise in these structures has posed significant challenges. […]
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Dr. Deniz Aybas Tumturk Bilkent University Date/Time: Thursday, 18 April 2024, 16:30-17:30 Location: SC-106 Abstract: We use optically pumped alkali atoms near room-temperature to measure perturbations in the local magnetic fields. These perturbations can be sourced from astrophysical particles that have magnetic signatures, or from nuclear magnetic resonance (NMR) signals of liquid or solid samples. Optically pumped magnetometers […]
Read More Dr. Mehmet Akcakaya University of Minnesota, USA Date/Time: Tuesday, 26 March 2024, 17:30-18:30 (Time in Türkiye) Zoom Link: https://zoom.us/j/94655579979 Abstract: Lengthy data acquisition remains a major bottleneck in magnetic resonance imaging (MRI), often necessitating tradeoffs in resolution and signal-to-noise ratio. Thus, reconstruction and acquisition techniques for rapid imaging, noise reduction and improved data acquisition have received great interest. Each of […]
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Happy to share our latest paper titled “Graph Receptive Transformer Encoder (GRTE) for Text Classification” published in IEEE Transactions on Signal and Information Processing over Networks! Our novel approach combines graph neural networks (GNNs) with large-scale pre-trained models to address limitations in attention mechanisms of transformers for text classification. GRTE retrieves global and contextual information […]
Read More Dr. Siyuan Zhao Date: February 8, 2024 Time: 16.30 Place: Zoom https://zoom.us/j/3750698016?pwd=YXhhNWNsUHBCdXJvMWI0R201ZGo0QT09&omn=92639551011 Zoom ID: 375 069 8016 Pass: 505709 Abstract: Stably recording the electrical activity of the same neurons across all relevant timescales is important for understanding the neural activity associated with memory and aging, enhancing brain-computer interface performance, and investigating neurological disorders. However, current implantable devices cannot provide stable recordings over long […]
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