Zobrazeno 1 - 10
of 82 682
pro vyhledávání: '"Serdar, A."'
Autor:
Spor, Serdar, Gurkanli, Emre
In this study, the process $\gamma\gamma \to \gamma\gamma$ is investigated to establish constraints on anomalous Higgs boson couplings at $H\gamma\gamma$ vertice within the framework of the Standard Model Effective Field Theory (SMEFT). The study is
Externí odkaz:
http://arxiv.org/abs/2412.02346
Autor:
Rosenberg, Gili, Brubaker, J. Kyle, Schuetz, Martin J. A., Zhu, Elton Yechao, Kadıoğlu, Serdar, Borujeni, Sima E., Katzgraber, Helmut G.
Pruning neural networks, which involves removing a fraction of their weights, can often maintain high accuracy while significantly reducing model complexity, at least up to a certain limit. We present a neural network pruning technique that builds up
Externí odkaz:
http://arxiv.org/abs/2411.17796
The proton-rich nucleus $^{22}$Si is studied using Nuclear Lattice Effective Field Theory with high-fidelity chiral forces. Our results indicate that $^{22}$Si is more tightly bound than $^{20}$Mg, thereby excluding the possibility of two-proton emis
Externí odkaz:
http://arxiv.org/abs/2411.17462
Autor:
Çite, Serdar, Esen, Oğul
This work presents higher order Lagrangian dynamics possessing locally conformal character. More concretely, locally conformal higher order Euler-Lagrange equations are written with particular focus on the second- and the third-order cases.
Externí odkaz:
http://arxiv.org/abs/2411.17300
Autor:
Fareaa, Afrah, Celebi, Mustafa Serdar
We investigate the use of learnable activation functions in Physics-Informed Neural Networks (PINNs) for solving Partial Differential Equations (PDEs). Specifically, we compare the efficacy of traditional Multilayer Perceptrons (MLPs) with fixed and
Externí odkaz:
http://arxiv.org/abs/2411.15111
We present a systematic \textit{ab initio} study of the low-lying states in beryllium isotopes from $^7\text{Be}$ to $^{12}\text{Be}$ using nuclear lattice effective field theory with the N$^3$LO interaction. Our calculations achieve excellent agreem
Externí odkaz:
http://arxiv.org/abs/2411.14935
We develop rigorous approximation and near optimality results for the optimal control of a system which is connected to a controller over a finite rate noiseless channel. While structural results on the optimal encoding and control have been obtained
Externí odkaz:
http://arxiv.org/abs/2411.13884
Autor:
Kesim, Ege, Helli, Selahattin Serdar
Parameter efficient finetuning (PEFT) methods are widely used in LLMs and generative models in computer vision. Especially one can use multiple of these during inference to change the behavior of the base model. In this paper we investigated whether
Externí odkaz:
http://arxiv.org/abs/2411.14064
Establishing the existence of exact or near Markov or stationary perfect Nash equilibria in nonzero-sum Markov games over Borel spaces remains a challenging problem, with few positive results to date. In this paper, we establish the existence of appr
Externí odkaz:
http://arxiv.org/abs/2411.10805
Traditional machine learning approaches assume that data comes from a single generating mechanism, which may not hold for most real life data. In these cases, the single mechanism assumption can result in suboptimal performance. We introduce a cluste
Externí odkaz:
http://arxiv.org/abs/2411.06572