During the Second World War, estimates of the number of tanks deployed by Germany were critically needed. The Allies adopted a successful statistical approach to estimate this information: assume that the tanks are sequentially numbered starting from, say, 1, and ending at an unknown positive integer $N$. If we observe the numbers of $k$ tanks, then the best linear unbiased estimator for $N$ is $M(1+1/k)-1$ where $M$ is the maximum observed serial number. While this approach was successful, there are many more adversarial situations where the approach for the original German Tank Problem falls short. Typically the number of ``factories'' is a possibly unknown $l>1$, and tanks produced by different factories may have serial numbers in disjoint ranges that are often separated by unknown amounts. Clark, Gonye and Miller (CGM) presented an unbiased estimator for $N$ when the minimum serial number is unknown. So if one can identify which samples correspond to which factory, one can then estimate each factory's range using CGM's method, and sum them for an estimate of the rival's total productivity. We present a procedure to estimate the total productivity and prove that it is effective when $\log l/\log k$ is sufficiently small. In the final section, we show that if we have a small number of samples, we can make an estimator that performs orders of magnitude better when given additional information about the size of the gaps.
It is largely agreed that subseasonal-to-seasonal (S2S) predictability arises from the atmospheric initial state during early lead times and from the land and ocean during intermediate and late lead times. We test this hypothesis for the large-scale mid-latitude atmosphere by training numerous XGBoost models to predict weather regimes (WRs) over North America at 1-to-8-week lead times. Each model uses a different predictor from one Earth system component (atmosphere, ocean, or land) sourced from reanalysis. According to the models, the atmosphere provides more predictability during the first two forecast weeks, and the three components performed similarly afterward. However, the skill and sources of predictability are highly dependent on the season and target WR. Our results show greater WR predictability in fall and winter, particularly for the Pacific Trough and Pacific Ridge regimes, driven primarily by the ocean (e.g., El Niño-Southern Oscillation and sea ice). For the Pacific Ridge in winter, the stratosphere also contributes significantly to predictability across most S2S lead times. Additionally, the initial large-scale tropospheric structure (encompassing the tropics and extra-tropics, e.g., Madden-Julian Oscillation) and soil conditions play a relevant role-most notably for the Greenland High regime in winter. This study highlights previously identified sources of predictability for the large-scale atmosphere and gives insight into new sources for future study. Given how closely linked WRs are to surface precipitation and temperature anomalies, storm tracks, and extreme events, the study results contribute to improving S2S prediction of surface weather.
Black carbon (BC) is an important pollutant aerosol emitted by numerous human activities, including gas flaring. Improper combustion in flaring activities can release large amounts of BC, which is harmful to human health and has a strong climate warming effect. To our knowledge, no study has ever directly monitored BC emissions from satellite imagery. Previous works quantified BC emissions indirectly, by applying emission coefficients to flaring volumes estimated from satellite imagery. Here, we develop a deep learning framework and apply it to Sentinel-2 imagery over North Africa during 2022 to detect and quantify BC emissions from gas flaring. We find that BC emissions in this region amount to about 1 million tCO$_{2,\mathrm{eq}}$, or 1 million passenger cars, more than a quarter of which are due to 10 sites alone. This work demonstrates the operational monitoring of BC emissions from flaring, a key step in implementing effective mitigation policies to reduce the climate impact of oil and gas operations.
Mohammed Bin Sumait, Aleksandra Gabryszak, Leonhard Hennig
et al.
Factuality can play an important role when automatically processing clinical text, as it makes a difference if particular symptoms are explicitly not present, possibly present, not mentioned, or affirmed. In most cases, a sufficient number of examples is necessary to handle such phenomena in a supervised machine learning setting. However, as clinical text might contain sensitive information, data cannot be easily shared. In the context of factuality detection, this work presents a simple solution using machine translation to translate English data to German to train a transformer-based factuality detection model.
Christopher N. A. Willmer, Chun Ly, Satoshi Kikuta
et al.
We present Near-Infrared (NIR) ground-based Y, J, H, and K imaging obtained in the James Webb Space Telescope North Ecliptic Pole Time Domain Field (TDF) using the MMT-Magellan Infrared Imager and Spectrometer (MMIRS) on the MMT.These new observations cover a field of approximately 230 arcmin^2 in Y, H, and K and 313 arcmin^2 in J. Using Monte Carlo simulations we estimate a 1 sigma depth relative to the background sky of (Y, J, H, K}) = (23.80, 23.53, 23.13, 23.28) in AB magnitudes for point sources at a 95% completeness level. These observations are part of the ground-based effort to characterize this region of the sky, supplementing space-based data obtained with Chandra, NuSTAR, XMM, AstroSat, HST, and JWST. This paper describes the observations and reduction of the NIR imaging and combines these NIR data with archival imaging in the visible, obtained with the Subaru Hyper-Suprime-Cam, to produce a merged catalog of 57,501 sources. The new observations reported here, plus the corresponding multi-wavelength catalog, will provide a baseline for time-domain studies of bright sources in the TDF.
As conventional flexibility providers are gradually being replaced by variable renewable energies and electricity demand keeps rising, additional flexibility will become increasingly valuable for the power system. Meanwhile, new sources of flexibility known as distributed energy resources are starting to emerge. Among them electric vehicles have a promising potential to provide short-term frequency-control services. However their integration into balancing markets faces various barriers. Based on the existing literature, this paper aims at identifying which potential barriers exist in the case of the participation of electric vehicles in German balancing markets.
The exponential growth of renewable energy capacity has brought much uncertainty to electricity prices and to electricity generation. To address this challenge, the energy exchanges have been developing further trading possibilities, especially the intraday and balancing markets. For an energy trader participating in both markets, the forecasting of imbalance prices is of particular interest. Therefore, in this manuscript we conduct a very short-term probabilistic forecasting of imbalance prices, contributing to the scarce literature in this novel subject. The forecasting is performed 30 minutes before the delivery, so that the trader might still choose the trading place. The distribution of the imbalance prices is modelled and forecasted using methods well-known in the electricity price forecasting literature: lasso with bootstrap, gamlss, and probabilistic neural networks. The methods are compared with a naive benchmark in a meaningful rolling window study. The results provide evidence of the efficiency between the intraday and balancing markets as the sophisticated methods do not substantially overperform the intraday continuous price index. On the other hand, they significantly improve the empirical coverage. The analysis was conducted on the German market, however it could be easily applied to any other market of similar structure.
The German Tank Problem dates back to World War II when the Allies used a statistical approach to estimate the number of enemy tanks produced or on the field from observed serial numbers after battles. Assuming that the tanks are labeled consecutively starting from 1, if we observe $k$ tanks from a total of $N$ tanks with the maximum observed tank being $m$, then the best estimate for $N$ is $m(1 + 1/k) - 1$. We explore many generalizations. We looked at the discrete and continuous one dimensional case. We explored different estimators such as the $L$\textsuperscript{th} largest tank, and applied motivation from portfolio theory and studied a weighted average; however, the original formula was the best. We generalized the problem in two dimensions, with pairs instead of points, studying the discrete and continuous square and circle variants. There were complications from curvature issues and that not every number is representable as a sum of two squares. We often concentrated on the large $N$ limit. For the discrete and continuous square, we tested various statistics, finding the largest observed component did best; the scaling factor for both cases is $(2k+1)/2k$. The discrete case was especially involved because we had to use approximation formulas that gave us the number of lattice points inside the circle. Interestingly, the scaling factors were different for the cases. Lastly, we generalized the problem into $L$ dimensional squares and circles. The discrete and continuous square proved similar to the two dimensional square problem. However, for the $L$\textsuperscript{th} dimensional circle, we had to use formulas for the volume of the $L$-ball, and had to approximate the number of lattice points inside it. The formulas for the discrete circle were particularly interesting, as there was no $L$ dependence in the formula.
We investigated the detailed plasma condition of the North Polar Spur (NPS)/Loop I using archival $Suzaku$ data. In previous research collisional ionization equilibrium (CIE) have been assumed for X-ray plasma state, but we also assume non-equilibrium ionization (NEI) to check the plasma condition in more detail. We found that most of the plasma in the NPS/Loop I favors the state of NEI, and has the density-weighted ionization timescale of $n_e t\sim10^{11-12}$ s cm$^{-3}$ and the electron number density $n_e\sim$ a few $\times$ 10$^{-3}$ cm$^{-3}$. The plasma shock age, $t$, or the time elapsed after the shock front passed through the plasma, is estimated to be on the order of a few $\rm{Myr}$ for the NPS/Loop I, which puts a strict lower limit to the age of the whole NPS/Loop I structure. We found that NEI results in significantly higher temperature and lower emission measure than those currently derived under CIE assumption. The electron temperature under NEI is estimated to be as high as 0.5 keV toward the brightest X-ray NPS ridge at $Δθ=-20^\circ$, which decreases to 0.3 keV at $-10^\circ$, and again increases to $\sim 0.6$ keV towards the outer edge of Loop I at $Δθ\sim0^\circ$, about twice the currently estimated temperatures. Here, $Δθ$ is the angular distance from the outer edge of Loop I. We discuss the implication of introducing NEI for the research in plasma states in astrophysical phenomena.
Marco Di Giovanni, Francesco Pierri, Christopher Torres-Lugo
et al.
Despite the increasing limitations for unvaccinated people, in many European countries there is still a non-negligible fraction of individuals who refuse to get vaccinated against SARS-CoV-2, undermining governmental efforts to eradicate the virus. We study the role of online social media in influencing individuals' opinion towards getting vaccinated by designing a large-scale collection of Twitter messages in three different languages -- French, German and Italian -- and providing public access to the data collected. Focusing on the European context, our VaccinEU dataset aims to help researchers to better understand the impact of online (mis)information about vaccines and design more accurate communication strategies to maximize vaccination coverage.
Paul-Louis Poulier, Zhi-Chao Liang, Damien Fournier
et al.
Context. In local helioseismology, the travel times of acoustic waves propagating in opposite directions along the same meridian inform us about horizontal flows in the north-south direction. The longitudinal averages of the north-south helioseismic travel-time shifts vary with the sunspot cycle. Aims. We aim to study the contribution of inflows into solar active regions to this solar-cycle variation. Methods. To do so, we identify the local flows around active regions in the horizontal flow maps obtained from correlation tracking of granulation in SDO/HMI continuum images. We compute the forward-modeled travel-time perturbations caused by these inflows using 3D sensitivity kernels. In order to compare with the observations, we average these forward-modeled travel-time perturbations over longitude and time in the same way as the measured travel times. Results. The forward-modeling approach shows that the inflows associated with active regions may account for only a fraction of the solar-cycle variations in the north-south travel-time measurements. Conclusions. The travel-time perturbations caused by the large-scale inflows surrounding the active regions do not explain in full the solar-cycle variations seen in the helioseismic measurements of the meridional circulation. Keywords: Sun: activity -- Sun: helioseismology
The aim of the present study is to follow the development of the suffixed definite article in North Germanic, in particular taking into account the unique reference expressed by the nascent article. The study is based on the corpora of Old Swedish, Old Danish and Old Icelandic texts written between 1200 and 1550. Both qualitative and quantitative methods, such as logistic regression models, are applied. The study is grounded in the notions of familiarity and uniqueness, which we explore diachronically. The results indicate that the use of the definite article is much more frequent in familiar than in unique contexts in North Germanic in the periods studied, as a greater proportion of NPs with direct anaphors is definite in the oldest extant texts, as well as throughout the later periods, than the proportion of NPs with unique referents. NPs with unique referents are further shown to constitute a non-uniform group, where the ‘more local’ unique NPs (grounded in specific knowledge) appear more frequently with a definite article than the ‘more global’ unique referents (grounded in encyclopaedic knowledge).
The current state of adoption of well-structured electronic health records and integration of digital methods for storing medical patient data in structured formats can often considered as inferior compared to the use of traditional, unstructured text based patient data documentation. Data mining in the field of medical data analysis often needs to rely solely on processing of unstructured data to retrieve relevant data. In natural language processing (NLP), statistical models have been shown successful in various tasks like part-of-speech tagging, relation extraction (RE) and named entity recognition (NER). In this work, we present GERNERMED, the first open, neural NLP model for NER tasks dedicated to detect medical entity types in German text data. Here, we avoid the conflicting goals of protection of sensitive patient data from training data extraction and the publication of the statistical model weights by training our model on a custom dataset that was translated from publicly available datasets in foreign language by a pretrained neural machine translation model. The sample code and the statistical model is available at: https://github.com/frankkramer-lab/GERNERMED
Maximilian Wich, Adrian Gorniak, Tobias Eder
et al.
Since traditional social media platforms continue to ban actors spreading hate speech or other forms of abusive languages (a process known as deplatforming), these actors migrate to alternative platforms that do not moderate users content. One popular platform relevant for the German hater community is Telegram for which limited research efforts have been made so far. This study aims to develop a broad framework comprising (i) an abusive language classification model for German Telegram messages and (ii) a classification model for the hatefulness of Telegram channels. For the first part, we use existing abusive language datasets containing posts from other platforms to develop our classification models. For the channel classification model, we develop a method that combines channel-specific content information collected from a topic model with a social graph to predict the hatefulness of channels. Furthermore, we complement these two approaches for hate speech detection with insightful results on the evolution of the hater community on Telegram in Germany. We also propose methods for conducting scalable network analyses for social media platforms to the hate speech research community. As an additional output of this study, we provide an annotated abusive language dataset containing 1,149 annotated Telegram messages.
Christoph Kern, Ruben L. Bach, Hannah Mautner
et al.
Algorithmic profiling is increasingly used in the public sector as a means to allocate limited public resources effectively and objectively. One example is the prediction-based statistical profiling of job seekers to guide the allocation of support measures by public employment services. However, empirical evaluations of potential side-effects such as unintended discrimination and fairness concerns are rare. In this study, we compare and evaluate statistical models for predicting job seekers' risk of becoming long-term unemployed with respect to prediction performance, fairness metrics, and vulnerabilities to data analysis decisions. Focusing on Germany as a use case, we evaluate profiling models under realistic conditions by utilizing administrative data on job seekers' employment histories that are routinely collected by German public employment services. Besides showing that these data can be used to predict long-term unemployment with competitive levels of accuracy, we highlight that different classification policies have very different fairness implications. We therefore call for rigorous auditing processes before such models are put to practice.
Sascha Wolfer, Alexander Koplenig, Frank Michaelis
et al.
The coronavirus pandemic may be the largest crisis the world has had to face since World War II. It does not come as a surprise that it is also having an impact on language as our primary communication tool. We present three inter-connected resources that are designed to capture and illustrate these effects on a subset of the German language: An RSS corpus of German-language newsfeeds (with freely available untruncated unigram frequency lists), a static but continuously updated HTML page tracking the diversity of the used vocabulary and a web application that enables other researchers and the broader public to explore these effects without any or with little knowledge of corpus representation/exploration or statistical analyses.
. The history of Russia and Norway and their areas in the Arctic and Russian-Norwegian relations are traditionally important for the residents of both states. Opportunities to present new documents and materials, to discuss the significance of centuries-old ties between Russia and Norway, especially in the North, are not provided so often. In early April 2019, a large-scale scientific seminar of Russian and Scandinavian historians, philologists, and archaeologists was held at the University of Tromsø — the Arctic University of Norway. It was precisely dedicated to the issues listed above. One of the reasons for the meeting was the jubilee of the Honorary Doctor of the Northern (Arctic) Federal University named after M.V. Lo-monosov, Professor J.P. Nielsen. Also, the seminar hosted the presentation of a collective scientific collection written by associates of J.P. Nielsen from various countries — “In the North, the East and the West meet.”
The present theme issue of the Scandinavian Journal of the Old Testament on the functions of psalms and prayers in the Second Temple period emanates from a two-part international conference on the same topic held in Copenhagen in May 2015 and in Helsinki in September 2015. The conferences gathered almost thirty scholars from Europe and North America working on psalms and prayers. The conferences were generously supported by the Joint Committee for Nordic Research Councils in the Humanities and Social Sciences (NOS-HS), the Emil Aaltonen Foundation, and the Academy of Finland Center of Excellence: Changes in Sacred Texts and Traditions (CSTT). The papers presented at the conferences were edited by Mika S. Pajunen and Jeremy Penner and published in 2017 (de Gruyter, BZAW, 486). Research on psalms and prayers in ancient Jewish literature is in a stage of transition in these years. In light of the wealth of firsthand evidence on the uses and functions of psalms and prayers in late Second Temple Judaism provided by the Qumran manuscripts, a view of the Masoretic Book of Psalms now in the Hebrew Bible simply as a hymnbook of Second Temple Judaism is no longer sustainable. The uses and functions of psalms and prayers now appear to have been significantly more varied and multivalent than what was previously assumed, and new models concerning the composition, categorization and functional classification of psalms and psalm collections are needed in order to advance scholarly understanding of these texts and their social and historical contexts. The main aim of the two conferences was to facilitate a discussion about fresh trajectories that research on psalms and prayers should take in the coming years. A second objective was to strengthen Nordic research collaboration in this area of Biblical Studies. Scandinavian biblical scholarship has a strong tradition in Psalm research, which over the years has been complemented by a significant expertise in Qumran studies, and we wanted to sustain and develop this particular Nordic focus and momentum.