Hot-spot formation in stacks of intrinsic Josephson junctions in Bi2Sr2CaCu2O8

We have studied experimentally and numerically temperature profiles and the formation of hot spots in intrinsic Josephson junction stacks in Bi2Sr2CaCu2O8 (BSCCO). The superconducting stacks are biased in a state where all junctions are resistive. The formation of hot spots in this system is shown to arise mainly from the strongly negative temperature coefficient of the c-axis resistivity of BSCCO at low temperatures. This leads to situations where the maximum temperature in the hot spot can be below or above the superconducting transition temperature Tc. The numerical simulations are in good agreement with the experimental observations

An iconic language for the graphical representation of medical concepts

<p>Abstract</p> <p>Background</p> <p>Many medication errors are encountered in drug prescriptions, which would not occur if practitioners could remember the drug properties. They can refer to drug monographs to find these properties, however drug monographs are long and tedious to read during consultation. We propose a two-step approach for facilitating access to drug monographs. The first step, presented here, is the design of a graphical language, called VCM.</p> <p>Methods</p> <p>The VCM graphical language was designed using a small number of graphical primitives and combinatory rules. VCM was evaluated over 11 volunteer general practitioners to assess if the language is easy to learn, to understand and to use. Evaluators were asked to register their VCM training time, to indicate the meaning of VCM icons and sentences, and to answer clinical questions related to randomly generated drug monograph-like documents, supplied in text or VCM format.</p> <p>Results</p> <p>VCM can represent the various signs, diseases, physiological states, life habits, drugs and tests described in drug monographs. Grammatical rules make it possible to generate many icons by combining a small number of primitives and reusing simple icons to build more complex ones. Icons can be organized into simple sentences to express drug recommendations. Evaluation showed that VCM was learnt in 2 to 7 hours, that physicians understood 89% of the tested VCM icons, and that they answered correctly to 94% of questions using VCM (versus 88% using text, <it>p </it>= 0.003) and 1.8 times faster (<it>p </it>< 0.001).</p> <p>Conclusion</p> <p>VCM can be learnt in a few hours and appears to be easy to read. It can now be used in a second step: the design of graphical interfaces facilitating access to drug monographs. It could also be used for broader applications, including the design of interfaces for consulting other types of medical document or medical data, or, very simply, to enrich medical texts.</p

Comparative web search questions

We analyze comparative questions, i.e., questions asking to compare different items, that were submitted to Yandex in 2012. Responses to such questions might be quite different from the simple “ten blue links” and could, for example, aggregate pros and cons of the different options as direct answers. However, changing the result presentation is an intricate decision such that the classification of comparative questions forms a highly precision-oriented task. From a year-long Yandex log, we annotate a random sample of 50,000 questions; 2.8% of which are comparative. For these annotated questions, we develop a precision-oriented classifier by combining carefully hand-crafted lexico-syntactic rules with feature-based and neural approaches—achieving a recall of 0.6 at a perfect precision of 1.0. After running the classifier on the full year log (on average, there is at least one comparative question per second), we analyze 6,250 comparative questions using more fine-grained subclasses (e.g., should the answer be a “simple” fact or rather a more verbose argument) for which individual classifiers are trained. An important insight is that more than 65% of the comparative questions demand argumentation and opinions, i.e., reliable direct answers to comparative questions require more than the facts from a search engine’s knowledge graph. In addition, we present a qualitative analysis of the underlying comparative information needs (separated into 14 categories like consumer electronics or health), their seasonal dynamics, and possible answers from community question answering platforms. © 2020 Copyright held by the owner/author(s).This work has been partially supported by the DFG through the project “ACQuA: Answering Comparative Questions with Arguments” (grants BI 1544/7-1 and HA 5851/2-1) as part of the priority program “RATIO: Robust Argumentation Machines” (SPP 1999). We thank Yandex and Mail.Ru for granting access to the data. The study was partially conducted during Pavel Braslavski’s research stay at the Bauhaus-Universität Weimar in 2018 supported by the DAAD. We also thank Ekaterina Shirshakova and Valentin Dittmar for their help in question annotation