Search

We study the effect of educational attainment on family formation using regression discontinuity designs generated by centralized admissions processes to both secondary and tertiary education in Finland. Admission to further education at either margin does not increase the likelihood that men form families. In contrast, women admitted to further education are more likely to both live with a partner and have children. We then pre-register and test two hypotheses which could explain each set of results using survey data. These suggest that the positive association between men's education and family formation observed in the data is driven by selection. For women, our estimates are consistent with the idea that, as increased returns to social skills shift the burden of child development from schools to parents and particularly mothers, education can make women more attractive as potential partners. [Funding for this report was provided by The Strategic Research Council, the Research Council of Finland, and Palkansaajasäätiö.]

Recent research has shown that integrating artificial intelligence (AI) into wireless communication systems can significantly improve spectral efficiency. However, the prevalent use of simulated radio channel data for training and validating neural network-based radios raises concerns about their generalization capability to diverse real-world environments. To address this, we conducted empirical over-the-air (OTA) experiments using software-defined radio (SDR) technology to test the performance of an NN-based orthogonal frequency division multiplexing (OFDM) receiver in a real-world small cell scenario. Our assessment reveals that the performance of receivers trained on diverse 3GPP TS38.901 channel models and broad parameter ranges significantly surpasses conventional receivers in our testing environment, demonstrating strong generalization to a new environment. Conversely, setting simulation parameters to narrowly reflect the actual measurement environment led to suboptimal OTA performance, highlighting the crucial role of rich and randomized training data in improving the NN-based receiver's performance. While our empirical test results are promising, they also suggest that developing new channel models tailored for training these learned receivers would enhance their generalization capability and reduce training time. Our testing was limited to a relatively narrow environment, and we encourage further testing in more complex environments.

Diffractively coupled nanoparticle arrays are promising candidates for helping to flatten many photonic devices such as lasers, lenses, and metrology instruments. Their performance, however, is directly linked with the size of the metasurfaces, limiting their applicability in nanophotonic applications. Here, we dramatically reduce array sizes of high-$Q$-factor metasurfaces by utilizing strongly dispersive media. The effect is demonstrated by theoretically and numerically studying periodic arrays of plasmonic nanoparticles embedded inside Bragg-reflector waveguides. We demonstrate array dimensions reduction up to two orders of magnitude while still achieving ultra-high $Q$-factors in excess of 10$^4$., Comment: 10 pages, 6 figures, and 6 pages supplemental material with 5 figures

We theoretically study the quality and Purcell enhancement factors in epsilon-near-zero (ENZ) optical microcavities with ideally lossless or slightly lossy structures. In the case of ENZ rectangular cavities with perfect electric conductor (PEC) walls, our results supported by numerical simulations, show that both the quality and Purcell factors grow with increasing the length of cavity, having respective functional dependencies of $n^{-3}$ and $n^{-2}$ on the effective index of refraction, $n$. Further, we study extremely low-loss all-dielectric Bragg reflection waveguide cavities, near their cutoff frequencies. In such ENZ structures, the respective quality and Purcell factors scale as \((n\kappa)^{-1}\) and $n/\kappa$, in terms of real ($n$) and imaginary ($\kappa$) parts of the cavity effective index of refraction. We believe such structures exhibiting remarkably high quality ($\sim10^6$) and Purcell factors ($\sim10^3$), can drastically enhance various linear and nonlinear optical processes, and thus find use in applications ranging from laser development to optical sensing and metrology., Comment: 5 pages, 5 figures, 40 references

Polysomnography (PSG) data is recorded and stored in various formats depending on the recording software. Although the PSG data can usually be exported to open formats, such as the European Data Format (EDF), they are limited in data types, validation, and readability. Moreover, the exported data is not harmonized, which means different datasets need customized preprocessing to conduct research on multiple datasets. In this work, we designed and implemented an open format for storage and processing of PSG data, called the Sleeplab format (SLF), which is both human and machine-readable, and has built-in validation of both data types and structures. SLF provides tools for reading, writing, and compression of the PSG datasets. In addition, SLF promotes harmonization of data from different sources, which reduces the amount of work needed to apply the same analytics pipelines to different datasets. SLF is interoperable as it utilizes the file system and commonly used file formats to store the data. The goal of developing SLF was to enable fast exploration and experimentation on PSG data, and to streamline the workflow of building analytics and machine learning applications that combine PSG data from multiple sources. The performance of SLF was tested with two open datasets of different formats (EDF and HDF5). SLF is fully open source and available at https://github.com/UEF-SmartSleepLab/sleeplab-format., Comment: 8 pages, 3 figures, 1 table

This paper investigates the feasibility of machine learning (ML)-based pilotless spatial multiplexing in multiple-input and multiple-output (MIMO) communication systems. Especially, it is shown that by training the transmitter and receiver jointly, the transmitter can learn such constellation shapes for the spatial streams which facilitate completely blind separation and detection by the simultaneously learned receiver. To the best of our knowledge, this is the first time ML-based spatial multiplexing without channel estimation pilots is demonstrated. The results show that the learned pilotless scheme can outperform a conventional pilot-based system by as much as 15-20% in terms of spectral efficiency, depending on the modulation order and signal-to-noise ratio., Comment: Presented in the 57th Asilomar Conference on Signals, Systems, and Computers

We demonstrate a simple and scalable approach to increase conversion efficiencies of nonlinear metasurfaces by incorporating them into multipass cells and by letting the pump beam to interact with the metasurfaces multiple times. We experimentally show that by metasurface design, the associated phase-matching criteria can be fulfilled. As a proof of principle, we achieve phase matching of second-harmonic generation (SHG) using a metasurface consisting of aluminium nanoparticles deposited on a glass substrate. The phase-matching condition is verified to be achieved by measuring superlinear dependence of the detected SHG as a function of number of passes. We measure an order of magnitude enhancement in the SHG signal when the incident pump traverses the metasurface up to 9 passes. Results are found to agree well with a simple model developed to estimate the generated SHG signals. We also discuss strategies to further scale-up the nonlinear signal generation. Our approach provides a clear pathway to enhance nonlinear optical responses of metasurface-based devices. The generic nature of our approach holds promise for diverse applications in nonlinear optics and photonics.

State-of-the-art automatic sleep staging methods have already demonstrated comparable reliability and superior time efficiency to manual sleep staging. However, fully automatic black-box solutions are difficult to adapt into clinical workflow and the interaction between explainable automatic methods and the work of sleep technologists remains underexplored and inadequately conceptualized. Thus, we propose a human-in-the-loop concept for sleep analysis, presenting an automatic sleep staging model (aSAGA), that performs effectively with both clinical polysomnographic recordings and home sleep studies. To validate the model, extensive testing was conducted, employing a preclinical validation approach with three retrospective datasets; open-access, clinical, and research-driven. Furthermore, we validate the utilization of uncertainty mapping to identify ambiguous regions, conceptualized as gray areas, in automatic sleep analysis that warrants manual re-evaluation. The results demonstrate that the automatic sleep analysis achieved a comparable level of agreement with manual analysis across different sleep recording types. Moreover, validation of the gray area concept revealed its potential to enhance sleep staging accuracy and identify areas in the recordings where sleep technologists struggle to reach a consensus. In conclusion, this study introduces and validates a concept from explainable artificial intelligence into sleep medicine and provides the basis for integrating human-in-the-loop automatic sleep staging into clinical workflows, aiming to reduce black-box criticism and the burden associated with manual sleep staging.

In its 60 years of existence, the field of nonlinear optics has gained momentum especially over the past two decades thanks to major breakthroughs in material science and technology. In this article, we present a new set of data tables listing nonlinear-optical properties for different material categories as reported in the literature since 2000. The papers included in the data tables are representative experimental works on bulk materials, solvents, 0D-1D-2D materials, metamaterials, fiber waveguiding materials, on-chip waveguiding materials, hybrid waveguiding systems, and materials suitable for nonlinear optics at THz frequencies. In addition to the data tables, we also provide best practices for performing and reporting nonlinear-optical experiments. These best practices underpin the selection process that was used for including papers in the tables. While the tables indeed show strong advancements in the field over the past two decades, we encourage the nonlinear-optics community to implement the identified best practices in future works. This will allow a more adequate comparison, interpretation and use of the published parameters, and as such further stimulate the overall progress in nonlinear-optical science and applications., Comment: 209 pages, 8 figures, 14 tables. Journal of Physics:Photonics (2023)

When the cornea becomes hydrated above its physiologic level it begins to significantly scatter light, loosing transparency and thus impairing eyesight. This condition, known as corneal edema, can be associated with different causes, such as corneal scarring, corneal infection, corneal inflammation, and others, making it difficult to diagnose and quantify. Previous works have shown that Second Harmonic Generation Microscopy (SHG) represents a valuable non-linear optical imaging tool to non-invasively identify and monitor changes in the collagen architecture of the cornea, potentially playing a pivotal role in future in-vivo cornea diagnostic methods. However, the interpretation of SHG data can pose significant problems when transferring such approaches to clinical settings, given the low availability of public data sets, and training resources. In this work we explore the use of three Deep Learning models, the highly popular InceptionV3 and ResNet50, alongside FLIMBA, a custom developed architecture, requiring no pre-training, to automatically detect corneal edema in SHG images of porcine cornea. We discuss and evaluate data augmentation strategies tuned to the specifics of the herein addressed application and observe that Deep Learning models building on different architectures provide complementary results. Importantly, we observe that the combined use of such complementary models boosts the overall classification performance in the case of differentiating edematous and healthy corneal tissues, up to an AU-ROC=0.98. These results have potential to be extrapolated to other diagnostics scenarios, such as differentiation of corneal edema in different stages, automated extraction of hydration level of cornea, or automated identification of corneal edema causes, and thus pave the way for novel methods for cornea diagnostics with Deep-Learning assisted non-linear optical imaging., Comment: 19 pages, 7 figures, 1 Table