Felipe Nunez

2papers

2 Papers

SYSep 4, 2012
Increasing sync rate of pulse-coupled oscillators via phase response function design: theory and application to wireless networks

Yongqiang Wang, Felipe Nunez, Francis J. Doyle · meta-ai

This paper addresses the synchronization rate of weakly connected pulse-coupled oscillators (PCOs). We prove that besides coupling strength, the phase response function is also a determinant of synchronization rate. Inspired by the result, we propose to increase the synchronization rate of PCOs by designing the phase response function. This has important significance in PCO-based clock synchronization of wireless networks. By designing the phase response function, synchronization rate is increased even under a fixed transmission power. Given that energy consumption in synchronization is determined by the product of synchronization time and transformation power, the new strategy reduces energy consumption in clock synchronization. QualNet experiments confirm the theoretical results.

CVDec 16, 2022
SceneGATE: Scene-Graph based co-Attention networks for TExt visual question answering

Feiqi Cao, Siwen Luo, Felipe Nunez et al.

Most TextVQA approaches focus on the integration of objects, scene texts and question words by a simple transformer encoder. But this fails to capture the semantic relations between different modalities. The paper proposes a Scene Graph based co-Attention Network (SceneGATE) for TextVQA, which reveals the semantic relations among the objects, Optical Character Recognition (OCR) tokens and the question words. It is achieved by a TextVQA-based scene graph that discovers the underlying semantics of an image. We created a guided-attention module to capture the intra-modal interplay between the language and the vision as a guidance for inter-modal interactions. To make explicit teaching of the relations between the two modalities, we proposed and integrated two attention modules, namely a scene graph-based semantic relation-aware attention and a positional relation-aware attention. We conducted extensive experiments on two benchmark datasets, Text-VQA and ST-VQA. It is shown that our SceneGATE method outperformed existing ones because of the scene graph and its attention modules.