Wrocław University of Technology

About: Wrocław University of Technology is a education organization based out in Wrocław, Poland. It is known for research contribution in the topics: Laser & Computer science. The organization has 13115 authors who have published 31279 publications receiving 338694 citations.

Ultra-broadband dissipative soliton and noise-like pulse generation from a normal dispersion mode-locked Tm-doped all-fiber laser.

Abstract: We report generation of ultra-broadband dissipative solitons and noise-like pulses from a simple, fully fiberized mode-locked Tm-doped fiber laser. The oscillator operates in the normal net dispersion regime and is mode-locked via nonlinear polarization evolution. Depending on the cavity dispersion, the laser was capable of generating 60 nm or 100 nm broad dissipative solitons. These are the broadest spectra generated from a normal dispersion mode-locked Tm-doped fiber laser so far. The same oscillator might also operate in the noise-like pulse regime with extremely broad emission spectra (over 300 nm), which also significantly outperforms the previous reports.

Monolayered MoSe2: a candidate for room temperature polaritonics

Abstract: We acknowledge financial support by the state of Bavaria. EC, AK and SH acknowledge the EPSRC Programme "Hybrid Polaritonics" (EP/M025330/1) for support. ST acknowledges support from NSF DMR-1552220. CS acknowledges support by the European Research Council within the project UnLiMIt-2D (grant number 679288).

CCR: A combined cleaning and resampling algorithm for imbalanced data classification

Abstract: Abstract Imbalanced data classification is one of the most widespread challenges in contemporary pattern recognition. Varying levels of imbalance may be observed in most real datasets, affecting the performance of classification algorithms. Particularly, high levels of imbalance make serious difficulties, often requiring the use of specially designed methods. In such cases the most important issue is often to properly detect minority examples, but at the same time the performance on the majority class cannot be neglected. In this paper we describe a novel resampling technique focused on proper detection of minority examples in a two-class imbalanced data task. The proposed method combines cleaning the decision border around minority objects with guided synthetic oversampling. Results of the conducted experimental study indicate that the proposed algorithm usually outperforms the conventional oversampling approaches, especially when the detection of minority examples is considered.