Zobrazeno 1 - 10
of 58 112
pro vyhledávání: '"An, Yunus"'
Brain-Computer Interfaces (BCIs) help people with severe speech and motor disabilities communicate and interact with their environment using neural activity. This work focuses on the Rapid Serial Visual Presentation (RSVP) paradigm of BCIs using noni
Externí odkaz:
http://arxiv.org/abs/2412.15862
Autor:
Waheed, Yunus, Shit, Sumitra, Surendran, Jithin T, Prasad, Indrajeet D, Watanabe, Kenji, Taniguchi, Takashi, Kumar, Santosh
Transition metal dichalcogenides and related layered materials in their monolayer and a few layers thicknesses regime provide a promising optoelectronic platform for exploring the excitonic- and many-body physics. Strain engineering has emerged as a
Externí odkaz:
http://arxiv.org/abs/2412.10114
Autor:
Prasad, Indrajeet Dhananjay, Shit, Sumitra, Waheed, Yunus, Surendran, Jithin Thoppil, Watanabe, Kenji, Taniguchi, Takashi, Kumar, Santosh
Bilayers of transition-metal dichalcogenides show many exciting features, including long-lived interlayer excitons and wide bandgap tunability using strain. Not many investigations on experimental determinations of deformation potentials relating cha
Externí odkaz:
http://arxiv.org/abs/2412.00453
The ability of groups to make accurate collective decisions depends on a complex interplay of various factors, such as prior information, biases, social influence, and the structure of the interaction network. Here, we investigate a spin model that a
Externí odkaz:
http://arxiv.org/abs/2411.19829
Autor:
Celik, Mehmet, Duguin, Mathis, Guo, Jia, Luo, Dianlun, Spinelli, Kamryn, Zeytuncu, Yunus E., Zhu, Zhuoyu
In 2021, Dan Reznik made a YouTube video demonstrating that power circles of Poncelet triangles have an invariant total area. He made a simulation based on this observation and put forward a few conjectures. One of these conjectures suggests that the
Externí odkaz:
http://arxiv.org/abs/2410.18863
Autor:
Yuan, Yuncheng, Scheepers, Péter, Tasiou, Lydia, Gültekin, Yunus Can, Corradi, Federico, Alvarado, Alex
This paper analyzes the design and competitiveness of four neural network (NN) architectures recently proposed as decoders for forward error correction (FEC) codes. We first consider the so-called single-label neural network (SLNN) and the multi-labe
Externí odkaz:
http://arxiv.org/abs/2410.15899
Autor:
Yunus, Fajrian, Abdessalem, Talel
A lot of effort in recent years have been expended to explain machine learning systems. However, some machine learning methods are inherently explainable, and thus are not completely black box. This enables the developers to make sense of the output
Externí odkaz:
http://arxiv.org/abs/2410.03841
Autor:
Kartal, Enise, Selcuk, Yunus, Kaynak, Batuhan E., Yildiz, M. Taha, Yanik, Cenk, Hanay, M. Selim
Reservoir computing offers an energy-efficient alternative to deep neural networks (DNNs) by replacing complex hidden layers with a fixed nonlinear system and training only the final layer. This work investigates nanoelectromechanical system (NEMS) r
Externí odkaz:
http://arxiv.org/abs/2409.16805
Autor:
Gümüş, Kadir, Frazão, João dos Reis, Albores-Mejia, Aaron, Škorić, Boris, Liga, Gabriele, Gültekin, Yunus Can, Bradley, Thomas, Alvarado, Alex, Okonkwo, Chigo
In this paper we introduce a reconciliation protocol with a two-step error correction scheme using a short blocklength low rate code and a long blocklength high rate code. We show that by using this two-step decoding method it is possible to achieve
Externí odkaz:
http://arxiv.org/abs/2409.13667
In recent years, transformer-based architectures become the de facto standard for sequence modeling in deep learning frameworks. Inspired by the successful examples, we propose a causal visual-inertial fusion transformer (VIFT) for pose estimation in
Externí odkaz:
http://arxiv.org/abs/2409.08769