The Not-So-Sterile 4th Neutrino: Constraints on New Gauge Interactions from Neutrino Oscillation Experiments

Sterile neutrino models with new gauge interactions in the sterile sector are phenomenologically interesting since they can lead to novel effects in neutrino oscillation experiments, in cosmology and in dark matter detectors, possibly even explaining some of the observed anomalies in these experiments. Here, we use data from neutrino oscillation experiments, in particular from MiniBooNE, MINOS and solar neutrino experiments, to constrain such models. We focus in particular on the case where the sterile sector gauge boson $A'$ couples also to Standard Model particles (for instance to the baryon number current) and thus induces a large Mikheyev-Smirnov-Wolfenstein potential. For eV-scale sterile neutrinos, we obtain strong constraints especially from MINOS, which restricts the strength of the new interaction to be less than $\sim 10$ times that of the Standard Model weak interaction unless active-sterile neutrino mixing is very small ($\sin^2 \theta_{24} \lesssim 10^{-3}$). This rules out gauge forces large enough to affect short baseline experiments like MiniBooNE and it imposes nontrivial constraints on signals from sterile neutrino scattering in dark matter experiments.Comment: 19 pages, 9 figure

Explaining the frequency of contact between generations in Germany

A consideration of recent demographic trends, the historically unique longevity, and the political discussion about social security and care for the elderly makes it apparent that the topic of intergenerational relationships is getting more and more important – not only for politics, but also for social research in the field of family sociology and gerontology. A closer look at the huge quantity of studies in this field reveals a number of limitations for Germany. Firstly, only some aspects of intergenerational relationships are captured in the present empirical data. Secondly, most studies focus on the relations between adult children and their older parents. Information about intergenerational relationships founded on a broader empirical basis is missing. And, as a third point, the theoretical debate reveals some significant conceptual shortcomings. To narrow these gaps, this paper first discusses the theoretical and empirical results of the current debate about intergenerational relations. In a second step our own empirical data are presented: these capture many different aspects of the relations between generations. Finally, suggestions will be made for ways to close the theoretical gap

Dimensions and Levels of Mentoring: Empirical Findings of the First German Inventory and Implications for Future Practice

A lack of evidence-based quantitative research prevents further progress in mentoring research. In particular, standardized diagnostic instruments for exploration, evaluation and structured feedback for mentors and mentees are needed. The purpose of this study is to explore factors and levels which are crucial to the process of mentoring mentees.  The study has two objectives: The first is to expand the present empirical knowledge of basic dimensions and mentoring styles by developing the first German inventory. The second is to examine how the dimensions of the inventory are related to other qualities in the process of mentoring.Conducted at three universities of education in Austria, the data were collected during the school practice of advanced student-teachers who were guided by mentor teachers. 405 future teachers serving as mentees evaluated 206 mentors. In order to gather information on mentoring dimensions, a specially developed German questionnaire with 53 items was offered to the mentees in order to assess how often certain qualities in mentoring were experienced previously.  Concerning the results, five factors could be found while conducting an exploratory factor analysis: Professional support, collegiality, levels of work, efficiency and confidence. Some of these factors have been validated by independent variables. The inventory developed is a step to facilitate two objectives:  The first is theoretical by inspiring further research concerning the complexity of mentoring processes.  The second one is a practical one concerning a tool for collaborative reflection between mentor and mentee.  The results implicate that mentoring has to be conceptualized as a professional practice with the resources of professional training and professional contexts

Demographische Revolution, Transformation oder rationale Anpassung? Zur Entwicklung von Geburtenzahlen, Eheschließungen und Scheidungen in der ehemaligen DDR

Die Verfasser analysieren in ihrem Beitrag die demographischen Veränderungen, die sich in den Jahren ab 1989 auf dem Gebiet der ehemaligen DDR abspielen. Sie gelangen zu der Überzeugung, daß sich der Rückgang der Geburtenzahlen, Eheschließungen und Scheidungen nicht auf ein einheitliches Erklärungsmuster zurückführen läßt, sondern daß jeweils spezifische Konstellationen berücksichtigt werden müssen. (TL2

Simultaneous Clutter Detection and Semantic Segmentation of Moving Objects for Automotive Radar Data

The unique properties of radar sensors, such as their robustness to adverse weather conditions, make them an important part of the environment perception system of autonomous vehicles. One of the first steps during the processing of radar point clouds is often the detection of clutter, i.e. erroneous points that do not correspond to real objects. Another common objective is the semantic segmentation of moving road users. These two problems are handled strictly separate from each other in literature. The employed neural networks are always focused entirely on only one of the tasks. In contrast to this, we examine ways to solve both tasks at the same time with a single jointly used model. In addition to a new augmented multi-head architecture, we also devise a method to represent a network's predictions for the two tasks with only one output value. This novel approach allows us to solve the tasks simultaneously with the same inference time as a conventional task-specific model. In an extensive evaluation, we show that our setup is highly effective and outperforms every existing network for semantic segmentation on the RadarScenes dataset.Comment: Published at IEEE International Conference on Intelligent Transportation Systems (ITSC), Bilbao, ESP, 202

Familie in der Krise? Heirat und Familienbildung im Vergleich verschiedener Geburtskohorten

"Die Zukunft der Familie, ihre Krise oder allgemein Entwicklungstendenzen des familialen Lebens sind immer wieder Thema der veröffentlichten Meinung. 'Ehe light' oder 'Das Ende der bürgerlichen Familie' sind dabei die Schlagworte. Begründet werden diese Thesen dabei nicht nur durch Einzelfälle, sondern auch fast immer mit dem Hinweis auf die sinkenden Heiratszahlen und die zurückgehenden Geburten. Lassen sich diese Thesen aber nun wirklich mit den Entwicklungen des Heiratsverhaltens und der Fertilität untermauern? Um diese Frage wenigstens ansatzweise zu beantworten, sollen im folgenden diese beiden, für das gesamte familiale Handeln zentralen Entscheidungen, in einer längerfristigen historischen Perspektive betrachtet werden, wobei hierzu eine Längsschnitts- und Kohortenperspektive eingenommen wird." (Autorenreferat

Phylogenomic analysis of natural products biosynthetic gene clusters allows discovery of arseno-organic metabolites in model streptomycetes

We are indebted with Marnix Medema, Paul Straight and Sean Rovito, for useful discussions and critical reading of the manuscript, as well as with Alicia Chagolla and Yolanda Rodriguez of the MS Service of Unidad Irapuato, Cinvestav, and Araceli Fernandez for technical support in high-performance computing. This work was funded by Conacyt Mexico (grants No. 179290 and 177568) and FINNOVA Mexico (grant No. 214716) to FBG. PCM was funded by Conacyt scholarship (No. 28830) and a Cinvestav posdoctoral fellowship. JF and JFK acknowledge funding from the College of Physical Sciences, University of Aberdeen, UK.Peer reviewedPublisher PD

Detection of Condensed Vehicle Gas Exhaust in LiDAR Point Clouds

LiDAR sensors used in autonomous driving applications are negatively affected by adverse weather conditions. One common, but understudied effect, is the condensation of vehicle gas exhaust in cold weather. This everyday phenomenon can severely impact the quality of LiDAR measurements, resulting in a less accurate environment perception by creating artifacts like ghost object detections. In the literature, the semantic segmentation of adverse weather effects like rain and fog is achieved using learning-based approaches. However, such methods require large sets of labeled data, which can be extremely expensive and laborious to get. We address this problem by presenting a two-step approach for the detection of condensed vehicle gas exhaust. First, we identify for each vehicle in a scene its emission area and detect gas exhaust if present. Then, isolated clouds are detected by modeling through time the regions of space where gas exhaust is likely to be present. We test our method on real urban data, showing that our approach can reliably detect gas exhaust in different scenarios, making it appealing for offline pre-labeling and online applications such as ghost object detection.Comment: Accepted for ITSC202

Energy-based Detection of Adverse Weather Effects in LiDAR Data

Autonomous vehicles rely on LiDAR sensors to perceive the environment. Adverse weather conditions like rain, snow, and fog negatively affect these sensors, reducing their reliability by introducing unwanted noise in the measurements. In this work, we tackle this problem by proposing a novel approach for detecting adverse weather effects in LiDAR data. We reformulate this problem as an outlier detection task and use an energy-based framework to detect outliers in point clouds. More specifically, our method learns to associate low energy scores with inlier points and high energy scores with outliers allowing for robust detection of adverse weather effects. In extensive experiments, we show that our method performs better in adverse weather detection and has higher robustness to unseen weather effects than previous state-of-the-art methods. Furthermore, we show how our method can be used to perform simultaneous outlier detection and semantic segmentation. Finally, to help expand the research field of LiDAR perception in adverse weather, we release the SemanticSpray dataset, which contains labeled vehicle spray data in highway-like scenarios. The dataset is available at http://dx.doi.org/10.18725/OPARU-48815 .Comment: Accepted for publication in IEEE Robotics and Automation Letters (RA-L

Robust 3D Object Detection in Cold Weather Conditions

Adverse weather conditions can negatively affect LiDAR-based object detectors. In this work, we focus on the phenomenon of vehicle gas exhaust condensation in cold weather conditions. This everyday effect can influence the estimation of object sizes, orientations and introduce ghost object detections, compromising the reliability of the state of the art object detectors. We propose to solve this problem by using data augmentation and a novel training loss term. To effectively train deep neural networks, a large set of labeled data is needed. In case of adverse weather conditions, this process can be extremely laborious and expensive. We address this issue in two steps: First, we present a gas exhaust data generation method based on 3D surface reconstruction and sampling which allows us to generate large sets of gas exhaust clouds from a small pool of labeled data. Second, we introduce a point cloud augmentation process that can be used to add gas exhaust to datasets recorded in good weather conditions. Finally, we formulate a new training loss term that leverages the augmented point cloud to increase object detection robustness by penalizing predictions that include noise. In contrast to other works, our method can be used with both grid-based and point-based detectors. Moreover, since our approach does not require any network architecture changes, inference times remain unchanged. Experimental results on real data show that our proposed method greatly increases robustness to gas exhaust and noisy data.Comment: Ora