Zobrazeno 1 - 10
of 6 987
pro vyhledávání: '"Ergun AŞ"'
We suggest a new theoretical method to describe the baryon clusterization of nuclei in hadron-nucleus reactions. As an example we have explored the nuclei production in $\pi^-+C$ and $\pi^-+W$ collisions at p$_{lab}$=1.7 GeV by using the hybrid appro
Externí odkaz:
http://arxiv.org/abs/2409.19290
This paper addresses the challenges of embedding common droop control characteristics in ac-dc power system steady-state simulation and optimization problems. We propose a smooth approximation methodology to construct differentiable functions that en
Externí odkaz:
http://arxiv.org/abs/2409.18376
Autor:
Stawarz, J. E., Muñoz, P. A., Bessho, N., Bandyopadhyay, R., Nakamura, T. K. M., Eriksson, S., Graham, D., Büchner, J., Chasapis, A., Drake, J. F., Shay, M. A., Ergun, R. E., Hasegawa, H., Khotyaintsev, Yu. V., Swisdak, M., Wilder, F.
Alongside magnetic reconnection, turbulence is another fundamental nonlinear plasma phenomenon that plays a key role in energy transport and conversion in space and astrophysical plasmas. From a numerical, theoretical, and observational point of view
Externí odkaz:
http://arxiv.org/abs/2407.20787
Autor:
Biçici, Ergun
We identify the similarity between two words in English by casting the task as machine translation performance prediction (MTPP) between the words given the context and the distance between their similarities. We use referential translation machines
Externí odkaz:
http://arxiv.org/abs/2407.06230
Autor:
Biçici, Ergun
We use referential translation machines (RTMs) to identify the similarity between an attribute and two words in English by casting the task as machine translation performance prediction (MTPP) between the words and the attribute word and the distance
Externí odkaz:
http://arxiv.org/abs/2407.05154
Autor:
Biçici, Ergun
We present a new parser performance prediction (PPP) model using machine translation performance prediction system (MTPPS), statistically independent of any language or parser, relying only on extrinsic and novel features based on textual, link struc
Externí odkaz:
http://arxiv.org/abs/2407.05116
Autor:
Biçici, Ergun
We use transductive regression techniques to learn mappings between source and target features of given parallel corpora and use these mappings to generate machine translation outputs. We show the effectiveness of $L_1$ regularized regression (\texti
Externí odkaz:
http://arxiv.org/abs/2406.19478
Autor:
Biçici, Ergun
Extreme Learning Machines (ELM) provide a fast alternative to traditional gradient-based learning in neural networks, offering rapid training and robust generalization capabilities. Its theoretical basis shows its universal approximation capability.
Externí odkaz:
http://arxiv.org/abs/2406.17828
Frequency Dispersed Ion Acoustic Waves in the Near Sun Solar Wind: Signatures of Impulsive Ion Beams
Autor:
Malaspina, David M., Ergun, Robert E., Cairns, Iver H., Short, Benjamin, Verniero, Jaye L., Cattell, Cynthia, Livi, Roberto
This work reports a novel plasma wave observation in the near-Sun solar wind: frequency-dispersed ion acoustic waves. Similar waves were previously reported in association with interplanetary shocks or planetary bow shocks, but the waves reported her
Externí odkaz:
http://arxiv.org/abs/2404.14559
Autor:
Krishnamurthy, Vinayak, Akleman, Ergun
We present medial parametrization, a new approach to parameterizing any compact planar domain bounded by simple closed curves. The basic premise behind our proposed approach is to use two close Voronoi sites, which we call dipoles, to construct and r
Externí odkaz:
http://arxiv.org/abs/2403.03622