Einstein Center Digital Future

ABSTRACT Recent studies on hedging hydrological extremes on water utilities showed the potential benefits of index-based insurance. However, the interest and willingness of water utilities to adopt such insurance schemes remain unclear. This study investigates the interest of Brazilian water utilities in index-based insurance for droughts and floods, assessed through an online survey. Results show a willingness to pay (WTP) of 1.4% of their annual revenue for drought and 1.3% for flood coverage. Factors influencing WTP for drought schemes include the utility’s location, availability of alternative water sources, and the participant’s gender. WTP for flood schemes is influenced by the availability of alternative water sources, abstraction type, participants’ role, and water source type. This evidence of demand for such insurance products suggests that index-based insurance could open new business opportunities and improve hydroclimatic risk management for water utilities.

Errors in data are a key challenge in modern data management and processing systems. Monitoring and mitigating risks associated with errors in data transformations and downstream applications, such as Machine Learning (ML) model training, requires a profound understanding of error generation and impact of errors on data pipelines. Unfortunately, scientific progress in the field is facing two main challenges: For one, research on data errors often does not adhere to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles, which impedes reproducibility and comparisons. Second, existing data error models are oversimplified and fail to capture the complex statistical dependencies underlying the types and distributions of errors observed in real-world data. Building on prior work in the database management systems and statistics literature, we extend the theory on missing values to encompass a broader range of errors in tables and provide an overview of relevant error types. Combining error sampling mechanisms often observed in real data with a comprehensive categorization of errors, we introduce a latent factor model for tabular data errors that is simple to implement and can effectively model realistic error dependencies. Error sampling is decoupled from error types, which allows for simple extensions with more error types or sampling mechanisms. Using established benchmarks, we evaluate our model in two application scenarios, data cleaning and tabular ML tasks. In a comprehensive suite of experiments we demonstrate the impact of realistic error models on data cleaning benchmarks. Our results also show that a simple generative error model captures a wide range of error mechanisms and offers a convenient formalization of data perturbations to improve the generalizability, robustness and reproducibility of data cleaning research.

BackgroundObjective measurements estimated that 1.57 billion people globally had hearing loss in 2019. However, where audiologists are sparse, self-reported measures have been suggested as alternatives to assess burden. Moreover, research suggests this number varies by gender, due to biological and social mechanisms. Further refining our knowledge of the global prevalence of hearing loss will provide better understanding of which groups are most affected and how to best allocate resources.MethodsUsing Gateway to Global Ageing data and the South African National Income Dynamics Study, we estimated the prevalence and men:women ratio of self-reported hearing loss and hearing aid use for 28 countries. Hearing loss was measured as having fair/poor hearing or reporting hearing aid use. We included 664 580 observations of individuals aged 50+ years.ResultsWe found that the four countries with the highest levels of hearing loss (China 65%, South Korea 39%, Mexico 33%, Brazil 31%) also had the lowest reported hearing aid use (as low as 1% in China). Though men were more likely than women to report hearing loss in nearly all countries, especially in regions with higher hearing aid use (USA, Northern/Western Europe), China, South Korea and South Africa reported no gender difference. As the prevalence of hearing loss and hearing aid use increased with age, gender differences generally decreased.ConclusionInternational variation underscores the importance of country context in shaping perceived hearing and hearing aid use. Regions with high levels of hearing loss and low hearing aid use may be especially amenable to interventions.

Various leakage detection algorithms have been proposed in the literature to pursue prompt leakage identification in water distribution networks using sensor data. Some use stochastic process control (SPC) methods, where the leak identification is considered an anomaly detection problem. However, several practical challenges emerge from SPC in leakage detection, including outliers, random fluctuations, and non-normally distributed and autocorrelated data. Here, we contribute a comparative analysis of seven advanced SPC techniques based on cumulative sum for leakage identification in water distribution networks, selected based on an extensive literature review of approximately 100 publications. We test their usability for leakage detection by integrating them into a state-of-the-art data-driven detection method and demonstrating them on the L-Town benchmark network. Ultimately, this study offers actionable recommendations to guide the selection of change point detection methods for leakage detection: our results indicate that transformations are best combined with robust statistics. Nonparametric and adaptive methods are suited to retrieve reliable alarms under heterogeneous conditions. Lastly, incorporating a decorrelation step improves the detection performance if irregular water demand patterns occur in the network.

Abstract Interfacing light from solid-state single-photon sources with scalable and robust room-temperature quantum memories has been a long-standing challenge in photonic quantum information technologies due to inherent noise processes and time-scale mismatches between the operating conditions of solid-state and atomic systems. Here, we demonstrate storage of single photons from a semiconductor quantum dot (QD) device in a room-temperature atomic vapor memory and their on-demand retrieval. A deterministically fabricated InGaAs QD light source emits single photons at the wavelength of the cesium D1 line at 895 nm which exhibit an inhomogeneously broadened linewidth of 5.1(7) GHz and are subsequently stored in a low-noise ladder-type cesium vapor memory. We show control over the interaction between the single photons and the atomic vapor, allowing for variable retrieval times of up to 19.8(3) ns. A maximum internal efficiency of η int = 0.6 ( 1 ) % is achieved. Our results expand the application space of both room-temperature vapor memories and semiconductor QDs in future quantum network architectures.

Abstract: Technology-assisted supplemental work (TASW) has become a common demand in flexible work arrangements. Building on the effort–recovery model, we investigated how two TASW events after work (i. e., continuing work tasks, being contacted) affect psychological detachment and relaxation in the evening and, in turn, emotional exhaustion the next morning. With an event-oriented approach, we analyzed daily diary data of 215 employees (686 daily observations over five consecutive workdays). The results of multilevel path analysis showed a negative direct effect (a) of both TASW events on psychological detachment and (b) of being contacted on relaxation. A similar pattern was found for the indirect effects on exhaustion. The direct relationships between TASW events and emotional exhaustion were not significant. We conclude that employees should avoid TASW whenever possible in order to sufficiently recover from work and begin their next workday refreshed.

This study presents sector-coupled PyPSA-Earth: a novel global open-source energy system optimization model that incorporates major demand sectors and energy carriers in high spatial and temporal resolution, to enable energy transition studies worldwide. The model includes a workflow that automatically downloads and processes the necessary demand, supply and transmission data to co-optimize investment and operation of energy systems of countries or regions of Earth. The workflow provides the user with tools to forecast future demand scenarios and allows for custom user-defined data in several aspects. Sector-coupled PyPSA-Earth introduces novelty by offering users a comprehensive methodology to generate readily available sector-coupled data and model of any region worldwide, starting from raw and open data sources. The model provides flexibility in terms of spatial and temporal detail, allowing the user to tailor it to their specific needs. The capabilities of the model are demonstrated through two showcases for Egypt and Brazil. The Egypt case quantifies the relevant role of PV, exceeding 35 GW, and electrolysis in Suez and Damietta regions, for meeting 16% of the EU hydrogen demand. Complementarily, the Brazil case confirms the model’s ability in handling hydrogen planning infrastructure, including repurposing of existing gas networks which results in 146 M€ lower costs than building new pipelines. The results prove the suitability of sector-coupled PyPSA-Earth to meet the needs of policymakers, developers, and scholars in advancing the energy transition. The authors invite the interested individuals and institutions to collaborate in the future developments of the model within PyPSA meets Earth initiative. • Open-source sector-coupled energy model with global coverage and high resolution. • Readily available workflow for supply, demand and transmission data worldwide. • Future demand scenario forecast tool with the option of custom data integration. • Global sub-national demand database with assets geo-locations, based on open data. • Showcases for hydrogen export and infrastructure expansion in Egypt and Brazil.

The solar wind, a stream of charged particles originating from the Sun, poses significant risks to technology and astronauts. It is driven by large structures on the solar surface like coronal holes and active regions, which can be identified in extreme ultra-violet (EUV) solar images several days before they become geoeffective. In this work, we propose to use a distributional regression algorithm to forecast the solar wind speed at the Lagrange 1 point from solar images. Instead of predicting a single value, this method models the entire conditional distribution as a function of input features. It allows computing the uncertainty of predictions and specifying the probability of the solar wind speed exceeding certain thresholds, which is especially useful for extreme event predictions like coronal mass ejections and high-speed solar wind streams. We employ a convolutional neural network to encode solar images from multiple wavelength channels into unstructured low-dimensional representations. Using a semi-structured distributional regression approach, we couple the deep learning encoder with structured physical input parameters, such as past solar wind properties and solar cycle information. Thereby, we incorporate physical knowledge into the model and enhance explainability. We predict the solar wind speed distributions with a one-hour cadence four days in advance. We train and evaluate our method using cross-validation on 15 years of data and compare it to current state-of-the-art models. We find that it provides an accurate forecast and especially models the heavy-tailed solar wind speed distribution well. We further show the advantages over standard regression approaches and how to use the predicted conditional quantiles to improve extreme event predictions, highlighting the potential for operational space weather forecasts.

Event-based visual odometry is a specific branch of visual simultaneous localization and mapping (SLAM) techniques, which aims at solving tracking and mapping subproblems (typically in parallel), by exploiting the special working principles of neuromorphic (i.e., event-based) cameras. Due to the motion-dependent nature of event data, explicit data association (i.e., feature matching) under large-baseline viewpoint changes is difficult to establish, making direct methods a more rational choice. However, state-of-the-art direct methods are limited by the high computational complexity of the mapping subproblem and the degeneracy of camera pose tracking in certain degrees of freedom (DoF) in rotation. In this article, we tackle these issues by building an event-based stereo visual-inertial odometry system, which is built upon a direct pipeline known as event-based stereo visual odometry (ESVO). Specifically, to speed up the mapping operation, we propose an efficient strategy for sampling contour points according to the local dynamics of events. The mapping performance is also improved in terms of structure completeness and local smoothness by merging the temporal stereo and static stereo results. To circumvent the degeneracy of camera pose tracking in recovering the pitch and yaw components of general 6-DoF motion, we introduce IMU measurements as motion priors via preintegration. To this end, a compact back-end is proposed for continuously updating the IMU bias and predicting the linear velocity, enabling an accurate motion prediction for camera pose tracking. The resulting system scales well with modern high-resolution event cameras and leads to better global positioning accuracy in large-scale outdoor environments. Extensive evaluations on five publicly available datasets featuring different resolutions and scenarios justify the superior performance of the proposed system against five state-of-the-art methods. Compared to ESVO, our new pipeline significantly reduces the camera pose tracking error by 40%–80% and 20%–80% in terms of absolute trajectory error and relative pose error, respectively; at the same time, the mapping efficiency is improved by a factor of five. We release our pipeline as an open-source software for future research in this field.