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Objective. Electroencephalography (EEG) is a widely used neuroimaging technique known for its cost-effectiveness and user-friendliness. However, various artifacts, particularly biological artifacts like Electromyography (EMG) signals, lead to a poor signal-to-noise ratio, limiting the precision of analyses and applications. The currently reported EEG data cleaning performance largely depends on the data used for validation, and in the case of machine learning approaches, also on the data used for training. The data are typically gathered either by recruiting subjects to perform specific artifact tasks or by integrating existing datasets. Prevailing approaches, however, tend to rely on intuitive, concept-oriented data collection with minimal justification for the selection of artifacts and their quantities. Given the substantial costs associated with biological data collection and the pressing need for effective data utilization, we propose an optimization procedure for data-oriented data collection design using deep learning-based artifact detection. Approach. We apply a binary classification between artifact epochs (time intervals containing artifacts) and non-artifact epochs (time intervals containing no artifact) using three different neural architectures. Our aim is to minimize data collection efforts while preserving the cleaning efficiency. Main results. We were able to reduce the number of artifact tasks from twelve to three and decrease repetitions of isometric contraction tasks from ten to three or sometimes even just one. Significance. Our work addresses the need for effective data utilization in biological data collection, offering a systematic and dynamic quantitative approach. By providing clear justifications for the choices of artifacts and their quantity, we aim to guide future studies toward more effective and economical data collection in EEG and EMG research., Comment: Several changes of wording. Caption of figure 10 corrected

Single-photon emitters integrated in optical micro-cavities are key elements in quantum communication applications. However, optimizing their emission properties and achieving efficient cavity coupling remain significant challenges. In this study, we investigate semiconductor quantum dots (QDs) emitting in the telecom O-band and integrate them in an open fiber-cavity. Such cavities offer spatial and spectral tunability and intrinsic fiber-coupling. The design promises high collection efficiency and enables the investigation of multiple emitters in heterogeneous samples. We observe a reduction of the decay times of several individual emitters by up to a factor of $2.46(2)$ due to the Purcell effect. We comprehensively analyze the current limitations of the system, including cavity and emitter properties, the impact of the observed regime where cavity and emitter linewidths are comparable, as well as the mechanical fluctuations of the cavity length. The results elucidate the path towards efficient telecom quantum light sources., Comment: Main: 8 pages, 5 figures, 1 table; Appendix: 5 pages, 3 figures, 1 table

Many lighting methods used in computer graphics such as indirect illumination can have very high computational costs and need to be approximated for real-time applications. These costs can be reduced by means of upsampling techniques which tend to introduce artifacts and affect the visual quality of the rendered image. This paper suggests a versatile approach for accelerating the rendering of screen space methods while maintaining the visual quality. This is achieved by exploiting the low frequency nature of many of these illumination methods and the geometrical continuity of the scene. First the screen space is dynamically divided into separate sub-images, then the illumination is rendered for each sub-image in an adequate resolution and finally the sub-images are put together in order to compose the final image. Therefore we identify edges in the scene and generate masks precisely specifying which part of the image is included in which sub-image. The masks therefore determine which part of the image is rendered in which resolution. A step wise upsampling and merging process then allows optically soft transitions between the different resolution levels. For this paper, the introduced multi-resolution rendering method was implemented and tested on three commonly used lighting methods. These are screen space ambient occlusion, soft shadow mapping and screen space global illumination.

The full control of single photons is important in quantum information and quantum networking. A convenient storage device for photons is the key to memory assisted quantum communication and computing. While even a simple optical fiber can act as a convenient and reliable storage device, its storage time is tightly fixed and cannot be adapted. Therefore, the photon storage should ideally be actively controllable by external means, such as magnetic or electric control fields. In order to multiplex several photons, an active routing would also be desirable. Here we show that single photons of a semiconductor quantum dot can be deliberately delayed by an atomic vapor. Also, the output path can be selected, depending on an external magnetic field. By selecting the input polarization of the photons and by aligning the external magnetic field of the hot atomic vapor, the delay-based storage can be fine tuned to a deliberate value. With an overall delay of 25 ns, we are able to fine tune by more than 600 ps. Depending on the input polarization, the photons are routed into different output ports. The experimental data is fully resembled by a theoretical model, which describes the group velocity delay under consideration of spectral diffusion and considers the complex refractive index of the atomic vapor. The present results enable the use of an atomic vapor as a wavelength selective delay and allows for routing the single photons according to their polarization and an external magnetic field.

We suggest a method to directly deep-learn light transport, i. e., the mapping from a 3D geometry-illumination-material configuration to a shaded 2D image. While many previous learning methods have employed 2D convolutional neural networks applied to images, we show for the first time that light transport can be learned directly in 3D. The benefit of 3D over 2D is, that the former can also correctly capture illumination effects related to occluded and/or semi-transparent geometry. To learn 3D light transport, we represent the 3D scene as an unstructured 3D point cloud, which is later, during rendering, projected to the 2D output image. Thus, we suggest a two-stage operator comprising of a 3D network that first transforms the point cloud into a latent representation, which is later on projected to the 2D output image using a dedicated 3D-2D network in a second step. We will show that our approach results in improved quality in terms of temporal coherence while retaining most of the computational efficiency of common 2D methods. As a consequence, the proposed two stage-operator serves as a valuable extension to modern deferred shading approaches., Comment: Eurographics Symposium on Rendering 2019

Efficient fiber-based long-distance quantum communication via quantum repeaters relies on deterministic single-photon sources at telecom wavelengths, with the potential to exploit the existing world-wide infrastructures. For upscaling the experimental complexity in quantum networking, two-photon interference (TPI) of remote non-classical emitters in the low-loss telecom bands is of utmost importance. With respect to TPI of distinct emitters, several experiments have been conducted, e.g., using trapped atoms [Beugnon2006], ions [Maunz2007], NV-centers [Bernien2012, Sipahigil2012], SiV-centers [Sipahigil2014], organic molecules [Lettow2010] and semiconductor quantum dots (QDs) [Patel2010, Flagg2010, He2013b, Gold2014, Giesz2015, Thoma2017, Reindl2017, Zopf2017]; however, the spectral range was far from the highly desirable telecom C-band. Here, we report on TPI at 1550 nm between down-converted single photons from remote QDs [Michler2017Book], demonstrating quantum frequency conversion [Zaske2012, Ates2012, Kambs2016] as precise and stable mechanism to erase the frequency difference between independent emitters. On resonance, a TPI-visibility of (29+-3)% has been observed, being only limited by spectral diffusion processes of the individual QDs [Robinson2000, Kuhlmann2015]. Up to 2-km of additional fiber channel has been introduced in both or individual signal paths with no influence on TPI-visibility, proving negligible photon wave-packet distortion. The present experiment is conducted within a local fiber network covering several rooms between two floors of the building. Our studies pave the way to establish long-distance entanglement distribution between remote solid-state emitters including interfaces with various quantum hybrid systems [DeGreve2012,Maring2017,Bock2017,Maring2018]., Comment: J. H. Weber and B. Kambs contributed equally to this work

As a fundamental building block for quantum computation and communication protocols, the correct verification of the two-photon interference (TPI) contrast between two independent quantum light sources is of utmost importance. Here, we experimentally demonstrate how frequently present blinking dynamics and changes in emitter brightness critically affect the Hong-Ou-Mandel-type (HOM) correlation histograms of remote TPI experiments measured via the commonly utilized setup configuration. We further exploit this qualitative and quantitative explanation of the observed correlation dynamics to establish an alternative interferometer configuration, which is overcoming the discussed temporal fluctuations, giving rise to an error-free determination of the remote TPI visibility. We prove full knowledge of the obtained correlation by reproducing the measured correlation statistics via Monte-Carlo simulations. As exemplary system, we make use of two pairs of remote semiconductor quantum dots, however, the same conclusions apply for TPI experiments with flying qubits from any kind of remote solid state quantum emitters.