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pro vyhledávání: '"Bako AT"'
Autor:
Nagy, Dániel T. R., Czabán, Csaba, Bakó, Bence, Hága, Péter, Kallus, Zsófia, Zimborás, Zoltán
Recent progress in quantum machine learning has sparked interest in using quantum methods to tackle classical control problems via quantum reinforcement learning. However, the classical reinforcement learning environments often scale to high dimensio
Externí odkaz:
http://arxiv.org/abs/2410.18284
Autor:
Chen, Chen, Bako, Hannah K., Yu, Peihong, Hooker, John, Joyal, Jeffrey, Wang, Simon C., Kim, Samuel, Wu, Jessica, Ding, Aoxue, Sandeep, Lara, Chen, Alex, Sinha, Chayanika, Liu, Zhicheng
Chart corpora, which comprise data visualizations and their semantic labels, are crucial for advancing visualization research. However, the labels in most existing chart corpora are high-level (e.g., chart types), hindering their utility for broader
Externí odkaz:
http://arxiv.org/abs/2410.12268
In this work, we consider non-collocated sensors and actuators, and we address the problem of minimizing the number of sensor-to-actuator transmissions while ensuring that the L2 gain of the system remains under a threshold. By using causal factoriza
Externí odkaz:
http://arxiv.org/abs/2408.04012
Automatically generating data visualizations in response to human utterances on datasets necessitates a deep semantic understanding of the data utterance, including implicit and explicit references to data attributes, visualization tasks, and necessa
Externí odkaz:
http://arxiv.org/abs/2407.06129
Autor:
Tabi, Zsolt I., Bakó, Bence, Nagy, Dániel T. R., Vaderna, Péter, Kallus, Zsófia, Hága, Péter, Zimborás, Zoltán
This paper presents a comprehensive study on the possible hybrid quantum-classical autoencoder architectures for end-to-end radio communication against noisy channel conditions using standard encoded radio signals. The hybrid scenarios include single
Externí odkaz:
http://arxiv.org/abs/2405.18105
Autor:
Lin, Melissa, Patel, Heer, Lamkin, Medina, Tu, Tukey, Bako, Hannah, Raut, Soham, Battle, Leilani
Users often struggle to program visualizations using complex toolkits like D3. Before we can design effective code assistants to support them, we must first understand how D3 users reason about their code. In this work, we explore users' understandin
Externí odkaz:
http://arxiv.org/abs/2405.14341
Leveraging the intrinsic probabilistic nature of quantum systems, generative quantum machine learning (QML) offers the potential to outperform classical learning models. Current generative QML algorithms mostly rely on general-purpose models that, wh
Externí odkaz:
http://arxiv.org/abs/2405.14072
This paper discusses a general framework for designing robust state estimators for a class of discrete-time nonlinear systems. We consider systems that may be impacted by impulsive (sparse but otherwise arbitrary) measurement noise sequences. We show
Externí odkaz:
http://arxiv.org/abs/2401.06098
Autor:
Alina Bako
Publikováno v:
Incursiuni în imaginar, Vol 15, Iss 2, Pp 45-69 (2024)
The present discussion focuses on a reconfiguration influenced by regional space, incorporating concepts such as ‘magical realism.’ Various narrative forms that fulfill specific criteria have been analyzed to elucidate the framing of fiction unde
Externí odkaz:
https://doaj.org/article/d74c91d068394bf8bbbbd975add57580
Autor:
Friday Philip Tungchama, Simji Samuel Gomerep, Yusufu Taru Maigari, Margret Akogun, Amabu Enoch Bako, Charles Nnemeka Nwoga, Ebiloma Christiana Ajuma, Emmanuel Tope Amupitan, Nathan Yakubu Shehu
Publikováno v:
Nigerian Journal of Medicine, Vol 32, Iss 6, Pp 606-613 (2024)
Introduction: The COVID-19 pandemic brought about an unprecedented number of people to be isolated and this negatively impacted on manifestations of various anxiety and depressive symptoms. Our study determined the prevalence of anxiety and depressiv
Externí odkaz:
https://doaj.org/article/bda06115042c45c4b4825b2a602655ec