Pulse shaper assisted short laser pulse characterization

We demonstrate that a pulse shaper is able to simultaneously act as an optical waveform generator and a short pulse characterization device when combined with an appropriate nonlinear element. We present autocorrelation measurements and their frequency resolved counterparts. We show that control over the carrier envelope phase allows continuous tuning between an intensity-like and an interferometric autocorrelation. By changing the transfer function other measurement techniques, for example STRUT, are easily realized without any modification of the optical setu

Coherent phase contrast imaging of THz phonon-polariton tunneling

We report on coherent spatiotemporal imaging of single-cycle THz waves in frustrated total internal reflection geometry. Our technique yields images of the spatiotemporal electric field distribution before and after tunneling through an air gap in between two LiNbO3 crystals. Measurements of the reflected and the transmitted THz waveforms for different tunnel distances allow for a direct comparison with results from a causal linear dispersion theory and excellent agreement is foun

Ptychographic reconstruction of attosecond pulses

We demonstrate a new attosecond pulse reconstruction modality which uses an algorithm that is derived from ptychography. In contrast to other methods, energy and delay sampling are not correlated, and as a result, the number of electron spectra to record is considerably smaller. Together with the robust algorithm, this leads to a more precise and fast convergence of the reconstruction.Comment: 12 pages, 7 figures, the MATLAB code for the method described in this paper is freely available at http://figshare.com/articles/attosecond_Extended_Ptychographyc_Iterative_Engine_ePIE_/160187

Material processing with pulsed radially and azimuthally polarized laser radiation

We report on the generation of radially and azimuthally polarized Q-switched laser radiation and its application in material processing. The power levels were sufficiently high to study micro-hole drilling in different metals. Depending on the optical properties of the metal, either radial or azimuthal polarization shows the best efficiency and the effect is attributed to waveguiding. For steel, a comparison to linearly or circularly polarized laser radiation indicates that the doughnut-shaped beam with azimuthal polarization is the most energy-efficient in producing holes of the same diameter and dept

Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science

As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning---pipeline design. We implement an open source Tree-based Pipeline Optimization Tool (TPOT) in Python and demonstrate its effectiveness on a series of simulated and real-world benchmark data sets. In particular, we show that TPOT can design machine learning pipelines that provide a significant improvement over a basic machine learning analysis while requiring little to no input nor prior knowledge from the user. We also address the tendency for TPOT to design overly complex pipelines by integrating Pareto optimization, which produces compact pipelines without sacrificing classification accuracy. As such, this work represents an important step toward fully automating machine learning pipeline design.Comment: 8 pages, 5 figures, preprint to appear in GECCO 2016, edits not yet made from reviewer comment

Effect of V and N on the microstructure evolution during continuous casting of steel

Low Carbon (LC) steel is not expected to be sensitive to hot tearing and/or cracking while microalloyed steels are known for their high cracking sensitivity during continuous casting. Experience of the Direct Sheet Plant caster at Tata Steel in Ijmuiden (the Netherlands), seems to contradict this statement. It is observed that a LC steel grade has a high risk of cracking alias hot tearing, while a High Strength Low Alloyed (HSLA) steel has a very low cracking occurrence. Another HSLA steel grade, with a similar composition but less N and V is however very sensitive to hot tearing. An extreme crack results in a breakout. A previous statistical analysis of the breakout occurrence reveals a one and a half times higher possibility of a breakout for the HSLA grade compared to the LC grade. HSLA with extra N, V shows a four times smaller possibility of breakout than LC. This study assigns the unexpected effect of the chemical composition on the hot tearing sensitivity to the role of some alloying elements such as V and N as structure refiners.This research was carried out under project number M41.5.08320 within the framework of the Research Program of the Materials innovation institute M2i (www.m2i.nl)

Ptychographic ultrafast pulse reconstruction

We demonstrate a new ultrafast pulse reconstruction modality which is somewhat reminiscent of frequency resolved optical gating but uses a modified setup and a conceptually different reconstruction algorithm that is derived from ptychography. Even though it is a second order correlation scheme it shows no time ambiguity. Moreover, the number of spectra to record is considerably smaller than in most other related schemes which, together with a robust algorithm, leads to extremely fast convergence of the reconstruction.Comment: 4 pages, 4 figures, 3 references added, new figure 2, matches published versio

Hyperparameter Importance Across Datasets

With the advent of automated machine learning, automated hyperparameter optimization methods are by now routinely used in data mining. However, this progress is not yet matched by equal progress on automatic analyses that yield information beyond performance-optimizing hyperparameter settings. In this work, we aim to answer the following two questions: Given an algorithm, what are generally its most important hyperparameters, and what are typically good values for these? We present methodology and a framework to answer these questions based on meta-learning across many datasets. We apply this methodology using the experimental meta-data available on OpenML to determine the most important hyperparameters of support vector machines, random forests and Adaboost, and to infer priors for all their hyperparameters. The results, obtained fully automatically, provide a quantitative basis to focus efforts in both manual algorithm design and in automated hyperparameter optimization. The conducted experiments confirm that the hyperparameters selected by the proposed method are indeed the most important ones and that the obtained priors also lead to statistically significant improvements in hyperparameter optimization.Comment: \c{opyright} 2018. Copyright is held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use, not for redistribution. The definitive Version of Record was published in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Minin