Zobrazeno 1 - 10
of 1 293
pro vyhledávání: '"Molina, Daniel"'
Autor:
Osaba, Eneko, Villar-Rodriguez, Esther, Del Ser, Javier, Nebro, Antonio J., Molina, Daniel, LaTorre, Antonio, Suganthan, Ponnuthurai N., Coello, Carlos A. Coello, Herrera, Francisco
Publikováno v:
Swarm and Evolutionary Computation, vol. 64, p. 100888, Jul. 2021
In the last few years, the formulation of real-world optimization problems and their efficient solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In spite of decades of historical advancements on the design an
Externí odkaz:
http://arxiv.org/abs/2410.03205
Autor:
Poyatos, Javier, Del Ser, Javier, Garcia, Salvador, Ishibuchi, Hisao, Molina, Daniel, Triguero, Isaac, Xue, Bing, Yao, Xin, Herrera, Francisco
In Artificial Intelligence, there is an increasing demand for adaptive models capable of dealing with a diverse spectrum of learning tasks, surpassing the limitations of systems devised to cope with a single task. The recent emergence of General-Purp
Externí odkaz:
http://arxiv.org/abs/2407.08745
Autor:
Kodgirwar, Shantanu, Loetgering, Lars, Liu, Chang, Joseph, Aleena, Licht, Leona, Molina, Daniel S. Penagos, Eschen, Wilhelm, Rothhardt, Jan, Habeck, Michael
The limited dynamic range of the detector can impede coherent diffractive imaging (CDI) schemes from achieving diffraction-limited resolution. To overcome this limitation, a straightforward approach is to utilize high dynamic range (HDR) imaging thro
Externí odkaz:
http://arxiv.org/abs/2403.11344
Publikováno v:
Information Fusion, Volume 103, March 2024, 102135
Most applications of Artificial Intelligence (AI) are designed for a confined and specific task. However, there are many scenarios that call for a more general AI, capable of solving a wide array of tasks without being specifically designed for them.
Externí odkaz:
http://arxiv.org/abs/2307.14283
We examine the interplay between spectral bandwidth and illumination curvature in ptychography. By tailoring the divergence of the illumination, broader spectral bandwidths can be tolerated without requiring algorithmic modifications to the forward m
Externí odkaz:
http://arxiv.org/abs/2305.08954
Publikováno v:
Applied Soft Computing, 147 (2023), 110757
Evolutionary Computation algorithms have been used to solve optimization problems in relation with architectural, hyper-parameter or training configuration, forging the field known today as Neural Architecture Search. These algorithms have been combi
Externí odkaz:
http://arxiv.org/abs/2302.10253
Autor:
Eschen, Wilhelm, Liu, Chang, Molina, Daniel S. Penagos, Klas, Robert, Limpert, Jens, Rothhardt, Jan
We present high-speed and wide-field EUV ptychography at 13.5 nm wavelength using a table-top high-order harmonic source. By employing a scientific complementary metal oxide semiconductor (sCMOS) detector the scan time for sub-20 nm high-resolution m
Externí odkaz:
http://arxiv.org/abs/2302.14147
Autor:
Loetgering, Lars, Du, Mengqi, Flaes, Dirk Boonzajer, Aidukas, Tomas, Wechsler, Felix, Molina, Daniel S. Penagos, Rose, Max, Pelekanidis, Antonios, Eschen, Wilhelm, Hess, Jürgen, Wilhein, Thomas, Heintzmann, Rainer, Rothhardt, Jan, Witte, Stefan
Conventional (CP) and Fourier (FP) ptychography have emerged as versatile quantitative phase imaging techniques. While the main application cases for each technique are different, namely lens-less short wavelength imaging for CP and lens-based visibl
Externí odkaz:
http://arxiv.org/abs/2301.06595
Autor:
Liu, Chang, Eschen, Wilhelm, Loetgering, Lars, Molina, Daniel S., Klas, Robert, Iliou, Alexander, Steinert, Michael, Herkersdorf, Sebastian, Kirsche, Alexander, Pertsch, Thomas, Hillmann, Falk, Limpert, Jens, Rothhardt, Jan
Publikováno v:
PhotoniX 4, 6 (2023)
Table-top extreme ultraviolet (EUV) microscopy offers unique opportunities for label-free investigation of biological samples. Here, we demonstrate ptychographic EUV imaging of two dried, unstained model specimens: germlings of a fungus (Aspergillus
Externí odkaz:
http://arxiv.org/abs/2211.04135
Publikováno v:
Neural Networks, 158, (2023), 59-82
In recent years, Deep Learning models have shown a great performance in complex optimization problems. They generally require large training datasets, which is a limitation in most practical cases. Transfer learning allows importing the first layers
Externí odkaz:
http://arxiv.org/abs/2202.03844