Generalized drift-diffusion model for miniband superlattices

A drift-diffusion model of miniband transport in strongly coupled superlattices is derived from the single-miniband Boltzmann-Poisson transport equation with a BGK (Bhatnagar-Gross-Krook) collision term. We use a consistent Chapman-Enskog method to analyze the hyperbolic limit, at which collision and electric field terms dominate the other terms in the Boltzmann equation. The reduced equation is of the drift-diffusion type, but it includes additional terms, and diffusion and drift do not obey the Einstein relation except in the limit of high temperatures.Comment: 4 pages, 3 figures, double-column revtex. To appear as RC in PR

Artificial Intelligence and Intellectual Property Law - Position Statement of the Max Planck Institute for Innovation and Competition of 9 April 2021 on the Current Debate

This Position Statement presents a broad overview of issues arising at the intersection of AI and IP law based on the work of the Max Planck Institute for Innovation and Competition research group on Regulation of the Digital Economy. While the analysis is approached mainly from a perspective de lege lata, it also identifies questions which require further reflection de lege ferenda supported by in-depth interdisciplinary research. The scope is confined to substantive European IP law, in particular, as regards copyright, patents, designs, databases and trade secrets. Specific AI-related issues are mapped out around the core questions of IP law, namely, the eligibility for protection under the respective IP regimes, allocation of rights and the scope of protection. The structure of the analysis reflects three key components of AI: inputs required for the development of AI systems, AI as a process and the output of AI applications. Overall, it is emphasised that, while recent legal and policy discussions have mostly focused on AI-aided and AI-generated output, a more holistic view that accounts for the role of IP law across the AI innovation cycle is indispensable

Bias adjustment and ensemble recalibration methods for seasonal forecasting: a comprehensive intercomparison using the C3S dataset

This work presents a comprehensive intercomparison of diferent alternatives for the calibration of seasonal forecasts, ranging from simple bias adjustment (BA)-e.g. quantile mapping-to more sophisticated ensemble recalibration (RC) methods- e.g. non-homogeneous Gaussian regression, which build on the temporal correspondence between the climate model and the corresponding observations to generate reliable predictions. To be as critical as possible, we validate the raw model and the calibrated forecasts in terms of a number of metrics which take into account diferent aspects of forecast quality (association, accuracy, discrimination and reliability). We focus on one-month lead forecasts of precipitation and temperature from four state-of-the-art seasonal forecasting systems, three of them included in the Copernicus Climate Change Service dataset (ECMWF-SEAS5, UK Met Ofce-GloSea5 and Météo France-System5) for boreal winter and summer over two illustrative regions with diferent skill characteristics (Europe and Southeast Asia). Our results indicate that both BA and RC methods efectively correct the large raw model biases, which is of paramount importance for users, particularly when directly using the climate model outputs to run impact models, or when computing climate indices depending on absolute values/thresholds. However, except for particular regions and/or seasons (typically with high skill), there is only marginal added value-with respect to the raw model outputs-beyond this bias removal. For those cases, RC methods can outperform BA ones, mostly due to an improvement in reliability. Finally, we also show that whereas an increase in the number of members only modestly afects the results obtained from calibration, longer hindcast periods lead to improved forecast quality, particularly for RC methods.This work has been funded by the C3S activity on Evaluation and Quality Control for seasonal forecasts. JMG was partially supported by the project MULTI-SDM (CGL2015-66583-R, MINECO/FEDER). FJDR was partially funded by the H2020 EUCP project (GA 776613)

How to create an operational multi-model of seasonal forecasts?

Seasonal forecasts of variables like near-surface temperature or precipitation are becoming increasingly important for a wide range of stakeholders. Due to the many possibilities of recalibrating, combining, and verifying ensemble forecasts, there are ambiguities of which methods are most suitable. To address this we compare approaches how to process and verify multi-model seasonal forecasts based on a scientific assessment performed within the framework of the EU Copernicus Climate Change Service (C3S) Quality Assurance for Multi-model Seasonal Forecast Products (QA4Seas) contract C3S 51 lot 3. Our results underpin the importance of processing raw ensemble forecasts differently depending on the final forecast product needed. While ensemble forecasts benefit a lot from bias correction using climate conserving recalibration, this is not the case for the intrinsically bias adjusted multi-category probability forecasts. The same applies for multi-model combination. In this paper, we apply simple, but effective, approaches for multi-model combination of both forecast formats. Further, based on existing literature we recommend to use proper scoring rules like a sample version of the continuous ranked probability score and the ranked probability score for the verification of ensemble forecasts and multi-category probability forecasts, respectively. For a detailed global visualization of calibration as well as bias and dispersion errors, using the Chi-square decomposition of rank histograms proved to be appropriate for the analysis performed within QA4Seas.The research leading to these results is part of the Copernicus Climate Change Service (C3S) (Framework Agreement number C3S_51_Lot3_BSC), a program being implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission. Francisco Doblas-Reyes acknowledges the support by the H2020 EUCP project (GA 776613) and the MINECO-funded CLINSA project (CGL2017-85791-R)

PF-4var/CXCL4L1 Predicts Outcome in Stable Coronary Artery Disease Patients with Preserved Left Ventricular Function

Background: Platelet-derived chemokines are implicated in several aspects of vascular biology. However, for the chemokine platelet factor 4 variant (PF-4var/CXCL4L1), released by platelets during thrombosis and with different properties as compared to PF-4/CXCL4, its role in heart disease is not yet studied. We evaluated the determinants and prognostic value of the platelet-derived chemokines PF-4var, PF-4 and RANTES/CCL5 in patients with stable coronary artery disease (CAD). Methodology/Principal Findings: From 205 consecutive patients with stable CAD and preserved left ventricular (LV) function, blood samples were taken at inclusion and were analyzed for PF-4var, RANTES, platelet factor-4 and N-terminal pro-B-type natriuretic peptide (NT-proBNP). Patients were followed (median follow-up 2.5 years) for the combined endpoint of cardiac death, non-fatal acute myocardial infarction, stroke or hospitalization for heart failure. Independent determinants of PF-4var levels (median 10 ng/ml; interquartile range 8-16 ng/ml) were age, gender and circulating platelet number. Patients who experienced cardiac events (n = 20) during follow-up showed lower levels of PF-4var (8.5 [5.3-10] ng/ml versus 12 [8-16] ng/ml, p = 0.033). ROC analysis for events showed an area under the curve (AUC) of 0.82 (95% CI 0.73-0.90, p<0.001) for higher NT-proBNP levels and an AUC of 0.32 (95% CI 0.19-0.45, p = 0.009) for lower PF-4var levels. Cox proportional hazard analysis showed that PF-4var has an independent prognostic value on top of NT-proBNP. Conclusions: We conclude that low PF-4var/CXCL4L1 levels are associated with a poor outcome in patients with stable CAD and preserved LV function. This prognostic value is independent of NT-proBNP levels, suggesting that both neurohormonal and platelet-related factors determine outcome in these patients

Internet of Things for Environmental Sustainability and Climate Change

Our world is vulnerable to climate change risks such as glacier retreat, rising temperatures, more variable and intense weather events (e.g., floods, droughts, and frosts), deteriorating mountain ecosystems, soil degradation, and increasing water scarcity. However, there are big gaps in our understanding of changes in regional climate and how these changes will impact human and natural systems, making it difficult to anticipate, plan, and adapt to the coming changes. The IoT paradigm in this area can enhance our understanding of regional climate by using technology solutions, while providing the dynamic climate elements based on integrated environmental sensing and communications that is necessary to support climate change impacts assessments in each of the related areas (e.g., environmental quality and monitoring, sustainable energy, agricultural systems, cultural preservation, and sustainable mining). In the IoT in Environmental Sustainability and Climate Change chapter, a framework for informed creation, interpretation and use of climate change projections and for continued innovations in climate and environmental science driven by key societal and economic stakeholders is presented. In addition, the IoT cyberinfrastructure to support the development of continued innovations in climate and environmental science is discussed

A complementary study approach unravels novel players in the pathoetiology of Hirschsprung disease

Hirschsprung disease (HSCR, OMIM 142623) involves congenital intestinal obstruction caused by dysfunction of neural crest cells and their progeny during enteric nervous system (ENS) development. HSCR is a multifactorial disorder; pathogenetic variants accounting for disease phenotype are identified only in a minority of cases, and the identification of novel disease-relevant genes remains challenging. In order to identify and to validate a potential disease-causing relevance of novel HSCR candidate genes, we established a complementary study approach, combining whole exome sequencing (WES) with transcriptome analysis of murine embryonic ENS-related tissues, literature and databas