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Unsupervised feature learning has emerged as a promising tool in learning representations from unlabeled data. However, it is still challenging to learn useful high-level features when the data contains a significant amount of irrelevant patterns. Although feature selection can be used for such complex data, it may fail when we have to build a learning(More)
While determining model complexity is an important problem in machine learning, many feature learning algorithms rely on cross-validation to choose an optimal number of features, which is usually challenging for online learning from a massive stream of data. In this paper, we propose an incremental feature learning algorithm to determine the optimal model(More)
Roughly speaking, this update rule is based on the following idea: increase the number of feature increments when the performance improves (i.e., the model is not at optimum), and decrease the number of feature increments when there is minimal or no performance improvement (i.e., the model has converged). From this intuition, we consider the following(More)
For InAs/GaAs(001) quantum dot (QD) system, the wetting layer (WL) evolution and its temperature dependence were studied using reflectance difference spectroscopy and were analyzed with a rate equation model. WL thicknesses showed a monotonic increase at relatively low growth temperatures but showed an initial increase and then decrease at higher(More)
We report the time-resolved excited state ultrafast dynamics of single unit cell (1 UC) thick FeSe films on SrTiO_{3} (STO), with FeTe capping layers. By measuring the photoexcited quasiparticles' density and lifetime, we unambiguously identify a superconducting (SC) phase transition, with a transition temperature T_{c} of 68 (-5/+2)  K and a SC gap of(More)
Interface charge transfer and electron-phonon coupling have been suggested to play a crucial role in the recently discovered high-temperature superconductivity of single unit-cell FeSe films on SrTiO3. However, their origin remains elusive. Here, using ultraviolet photoemission spectroscopy and element-sensitive X-ray photoemission spectroscopy, we identify(More)
The fictitious domain method with H1-penalty for elliptic problems is considered. We propose a new way to derive the sharp error estimates between the solutions of original elliptic problems and their H1-penalty problems. We find our method of analysis is applicable to parabolic problems while retaining the sharpness of the error estimates. We also prove(More)
Analysis of the fictitious domain method with L 2-penalty for elliptic and parabolic problems Abstract. The fictitious domain method with L 2-penalty for elliptic and parabolic problems are considered, respectively. The regularity theorems and a priori estimates for L 2-penalty problems are given. We derive error estimates for penalization and finite(More)