Zobrazeno 1 - 10
of 98 327
pro vyhledávání: '"Dô, P."'
Fuzzy Neural Networks (FNNs) are effective machine learning models for classification tasks, commonly based on the Takagi-Sugeno-Kang (TSK) fuzzy system. However, when faced with high-dimensional data, especially with noise, FNNs encounter challenges
Externí odkaz:
http://arxiv.org/abs/2410.13390
Autor:
Do, Thao
Extracting fine-grained OCR text from aged documents in diacritic languages remains challenging due to unexpected artifacts, time-induced degradation, and lack of datasets. While standalone spell correction approaches have been proposed, they show li
Externí odkaz:
http://arxiv.org/abs/2410.13305
We perform a systematic study of inelastic nuclear rainbow scattering for the \oc system to the 2$^+$ (4.44 MeV) state of $^{12}$C at incident energies of 100--608 MeV with the coupled-channels method. The recently generalized nearside-farside decomp
Externí odkaz:
http://arxiv.org/abs/2410.13234
The rapid evolution of Brain-Computer Interfaces (BCIs) has significantly influenced the domain of human-computer interaction, with Steady-State Visual Evoked Potentials (SSVEP) emerging as a notably robust paradigm. This study explores advanced clas
Externí odkaz:
http://arxiv.org/abs/2410.12267
Autor:
Wu, Yuli, Nguyen, Do Dinh Tan, Konermann, Henning, Yilmaz, Rüveyda, Walter, Peter, Stegmaier, Johannes
This study proposes a retinal prosthetic simulation framework driven by visual fixations, inspired by the saccade mechanism, and assesses performance improvements through end-to-end optimization in a classification task. Salient patches are predicted
Externí odkaz:
http://arxiv.org/abs/2410.11688
Strong solution and approximation of time-dependent radial Dunkl processes with multiplicative noise
We study the strong existence and uniqueness of solutions within a Weyl chamber for a class of time-dependent particle systems driven by multiplicative noise. This class includes well-known processes in physics and mathematical finance. We propose a
Externí odkaz:
http://arxiv.org/abs/2410.10457
Deep learning methods - consisting of a class of deep neural networks (DNNs) trained by a stochastic gradient descent (SGD) optimization method - are nowadays key tools to solve data driven supervised learning problems. Despite the great success of S
Externí odkaz:
http://arxiv.org/abs/2410.10533
Effective decision-making in partially observable environments demands robust memory management. Despite their success in supervised learning, current deep-learning memory models struggle in reinforcement learning environments that are partially obse
Externí odkaz:
http://arxiv.org/abs/2410.10132
Robotics has gained significant attention due to its autonomy and ability to automate in the nuclear industry. However, the increasing complexity of robots has led to a growing demand for advanced simulation and control methods to predict robot behav
Externí odkaz:
http://arxiv.org/abs/2410.09213
In this article, we present the mathematical analysis of the convergence of the linearized Crank-Nicolson Galerkin method for a nonlinear Schrodinger problem related to a domain with a moving boundary. The convergence analysis of the numerical method
Externí odkaz:
http://arxiv.org/abs/2410.08910