nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive Ensemble Learning with Confidence Bounds for Personalized Diagnosis
Venue: AAAI Workshop: Expanding the Boundaries of Health Informatics Using AI

With the advances in the field of medical informatics, automated clinical decision support systems are becoming the de facto standard in personalized diagnosis. In order to establish high accuracy and confidence in personalized diagnosis, massive amounts of distributed, heterogeneous, correlated and highdimensional patient data from different sources such as wearable sensors, mobile applications, Electronic Health Record (EHR) databases etc. need to be processed. This requires learning both locally and globally due to privacy constraints and/or distributed nature of the multi modal medical data. In the last decade, a large number of meta learning techniques have been proposed in which local learners make online predictions based on their locally collected data instances, and feed these predictions to an ensemble learner, which fuses them and issues a global prediction. However, most of these works do not provide performance guarantees or, when they do, these guarantees are asymptotic. None of these existing works provide confidence estimates about the issued predictions or rate of learning guarantees for the ensemble learner. In this paper, we provide a systematic ensemble learning method called Hedged Bandits, which comes with both long run (asymptotic) and short run (rate of learning) performance guarantees. Moreover, we show that our proposed method outperforms all existing ensemble learning techniques, even in the presence of concept drift.
Genome Investigations of Vector Competence in Aedes aegypti to Inform Novel Arbovirus Disease Control Approaches
Venue: Insects

Dengue (DENV) yellow fever, chikungunya, and Zika virus transmission to humans by a mosquito host is confounded by both intrinsic and extrinsic variables. Besides virulence factors of the individual arboviruses, likelihood of virus transmission is subject to variability in the genome of the primary mosquito vector, Aedes aegypti. The "vectorial capacity" of A. aegypti varies depending upon its density, biting rate, and survival rate, as well as its intrinsic ability to acquire, host and transmit a given arbovirus. This intrinsic ability is known as "vector competence" Based on whole transcriptome analysis, several genes and pathways have been predicated to have an association with a susceptible or refractory response in A. aegypti to DENV infection. However, the functional genomics of vector competence of A. aegypti is not well understood, primarily due to lack of integrative approaches in genomic or transcriptomic studies. In this review, we focus on the present status of genomics studies of DENV vector competence in A. aegypti as limited information is available relative to the other arboviruses. We propose future areas of research needed to facilitate the integration of vector and virus genomics and environmental factors to work towards better understanding of vector competence and vectorial capacity in natural conditions.
An end to end assembly of the Aedes aegypti genome

We present an end to end genome assembly of a female Aedes aegypti mosquito, which spreads viral diseases such as yellow fever, dengue, chikungunya, and Zika to humans. The assembly is based on an earlier genome published in 2007 and improved in 2013. The new assembly has a scaffold N50 of 419Mb, with 96.9% of the ungapped sequence anchored to chromosomes. We used the new assembly to examine the conservation of A. aegypti chromosomes. Our results suggest that synteny is strongly conserved between Ae. aegypti and An. gambiae. Comparison to D. melanogaster highlights the extent to which the identity of entire chromosome arms is preserved across dipterans.
A geometric approach to robotic unfolding of garments
Venue: Robotics Auton. Syst.

This work presents a novel approach to autonomous unfolding of garments by means of a dual arm robotic manipulator. The proposed approach is based on the observation that a garment can be brought to an approximately planar configuration if it is held by two points on its outline. This step facilitates the detection of another set of points that when grasped the garment will naturally unfold. A robust method for successively detecting such boundary points on images of garments hanging from a single point was developed. The manipulated garment is then laid on a flat surface and matched to a set of foldable templates using shape analysis techniques. Using the established correspondences with the template's landmark points the garment is re grasped by such two points that it will naturally unfold in a spread out configuration. The adopted framework has been experimentally evaluated using a dual industrial manipulator and a variety of garments. The produced results indicate the feasibility and robustness of the proposed approach. A geometric approach for robotic unfolding of garments is proposed.Folds and hemline edges are detected and used as initial grasp points.Unfolding is completed through template matching with foldable templates.Garment classification is also achieved through template matching.Experiments in a variety of garments prove the method's robustness.
What's Good for the Goose? Examining the Impact of Gender neutral and Gender specific Factors in the Assessment and Treatment of Female and Male Justice involved Youth

In response to female youths' increased visibility in the legal system, more attention has been paid to understanding girls' pathways to justice system involvement, risk for re offending, and rehabilitative needs. Widely used risk assessment and case management tools based on the Risk Need Responsivity (RNR) framework are largely gender neutral. Gender responsive scholars have long advocated for the importance of additional gender specific factors in guiding the assessment and treatment of female justice involved youth. The dissertation is comprised of two papers which examine the contribution of proposed gender specific factors alongside well established RNR factors in the prediction of recidivism, and how service provision aimed at intervening with these factors impacts recidivism for both male and female justice involved youth. Paper 1 explores the relationship between trauma, criminogenic needs and recidivism. I first sought to define the distinct constructs often referred to under the umbrella term 'trauma' PTSD symptomology, maltreatment, and childhood adversity. The relationships between these factors, well established criminogenic needs, and recidivism were examined and compared in a matched sample of 50 female and 50 male justice involved youth. Females were significantly iii more likely than males to have experienced multiple forms of maltreatment. Several maltreatment and childhood adversity factors were significantly and positively related to criminogenic needs. PTSD symptomology and childhood adversity were not significant predictors of recidivism; however, maltreatment was the strongest predictor of recidivism for both males and females in a model that included well established risk factors. Gender was not found to be moderating the relationship between maltreatment and recidivism. Implications of the findings for theory and practice are discussed. Paper 2 examines the contribution of both criminogenic needs and several additional proposed 'female' gender specific factors to risk assessment and rehabilitative treatment. Female youth were more likely than male youth to have proposed 'female' gender specific needs but these needs alone did not predict recidivism. Successfully matching services to youths' criminogenic needs predicted reduced recidivism for both male and female youth. For youth who had 'female' gender specific needs, successful matching of services to these needs also predicted reduced recidivism for both genders. Theoretical and practice implications of these results are discussed.
Low circulating ghrelin levels in women with polycystic ovary syndrome: a systematic review and meta analysis.
Venue: Endocrine journal

Although numerous, human subject studies evaluating the relationship between circulating ghrelin levels and polycystic ovary syndrome (PCOS) risk have yielded inconsistent findings. We aimed to quantitatively assess the association by summarizing all available evidence from human subject studies. The PubMed and Web of Science databases were searched up to February 2015 for eligible studies. Studies were eligible if they reported circulating ghrelin levels in women with PCOS and healthy women controls. A fixed or random effects model was used to pool risk estimations. Twenty studies including 894 PCOS patients and 574 controls were included in the meta analysis. The studies had fair methodological quality. The pooling analysis of all available studies revealed that ghrelin levels were significantly lower in PCOS patients than in controls, with standardized mean difference of 0.40 (95% CI: 0.73, 0.08) The significant association persisted in many subgroup strata. However, the heterogeneity across studies was considerable and not eliminated in subgroup analyses. Meta regression analysis further suggested that the heterogeneity might be relevant to variability in study location, PCOS relevant factors like HOMA IR ratio, as well as other factors not assessed. In conclusion, our meta analysis suggested that ghrelin levels were significantly lower in PCOS patients than in controls. Further studies with large sample sizes are warranted to replicate our findings.
Stress drop variability of shallow earthquakes extracted from a global database of source time functions

We use the new global database of source time functions (STFs) and focal mechanisms proposed by Vallee (2013) using the automatic SCARDEC method (Vallee et al. 2011) to constrain earthquake rupture duration and variability. This database has the advantage of being very consistent since all the events with moment magnitudes M w>5.8 that have occurred during the last 20 years were reanalyzed with the same method and the same station configuration. We analyze 1754 shallow earthquakes (depth<35 km) and use high quality criteria for the STFs, which result in the selection of 660 events. Among these, 313 occurred on the subduction interface (SUB events) and 347 outside (NOT SUB events) We obtain that for a given magnitude, STF duration is log normally distributed and that STFs are longer for SUB than NOT SUB events. We then estimate the stress drop using a proxy for the rupture process duration obtained from the measurement of the maximum amplitude of the STF. The resulting stress drop is independent of magnitude and is about 2.5 times smaller for the subduction events compared with the other events. Assuming a constant rupture velocity and source model, the resulting standard deviation of the stress drop is 1.13 for the total dataset (natural log) and about 1 for separate datasets. These values are significantly lower than the ones generally obtained from corner frequency analyses with global databases ~1.5 for Allmann and Shearer, 2009) and are closer to the values inferred from strong motion measurements ~0.5 as reported by Cotton et al. 2013) This indicates that the epistemic variability is reduced by the use of STF properties, which allows us to better approach the natural variability of the source process, related to stress drop variability and/or variation in the rupture velocity.
Two phase flow boiling pressure drop in small channels

Two phase flow boiling in small channels finds a variety of applications in power and process industries. Heat transfer, boiling flow regimes, flow instabilities, pressure drop and dry out are some of the key issues related to two phase flow boiling in channels. In this work, the focus is on pressure drop in two phase flow boiling in tubes of 19 mm diameter. These tubes are typically used in steam generators. Relatively limited experimental database is available on 19 mm ID tube. Therefore, in the present work, the experimental set up is designed for studying flow boiling in 19 mm ID tube in such a way that any of the different flow regimes occurring in a steam generator tube (from pre heating of sub cooled water to dry out) can be investigated by varying inlet conditions. The reported results cover a reasonable range of heat and mass flux conditions such as 9 27 kW/m2 and 2.9 5.9 kg/m2 s respectively. In this paper, various existing correlations are assessed against experimental data for the pressure drop in a single, vertical channel during flow boiling of water at near atmospheric pressure. A special feature of these experiments is that time dependent pressures are measured at four locations along the channel. The steady state pressure drop is estimated and the identification of boiling flow regimes is done with transient characteristics using time series analysis. Experimental data and corresponding results are compared with the reported correlations. The results will be useful for understanding key aspects of flow boiling in small channels.
Streamlining DevOps automation for Cloud applications using TOSCA as standardized metamodel
Venue: Future Gener. Comput. Syst.

DevOps as an emerging paradigm aims to tightly integrate developers with operations personnel. This enables fast and frequent releases in the sense of continuously delivering new iterations of a particular application. Users and customers of today's Web applications and mobile apps running in the Cloud expect fast feedback to problems and feature requests. Thus, it is a critical competitive advantage to be able to respond quickly. Besides cultural and organizational changes that are necessary to apply DevOps in practice, tooling is required to implement end to end automation of deployment processes. Automation is the key to efficient collaboration and tight integration between development and operations. The DevOps community is constantly pushing new approaches, tools, and open source artifacts to implement such automated processes. However, as all these proprietary and heterogeneous DevOps automation approaches differ from each other, it is hard to integrate and combine them to deploy applications in the Cloud using an automated deployment process. In this paper we present a systematic classification of DevOps artifacts and show how different kinds of artifacts can be discovered and transformed toward TOSCA, which is an emerging standard. We present an integrated modeling and runtime framework to enable the seamless and interoperable integration of different approaches to model and deploy application topologies. The framework is implemented by an open source, end to end toolchain. Moreover, we validate and evaluate the presented approach to show its practical feasibility based on a detailed case study, in particular considering the performance of the transformation toward TOSCA. Classification of DevOps artifacts and their usage.Integrated, standards driven modeling runtime framework based on TOSCA.Discovery, transformation, and APIfication of DevOps artifacts.Enrichment and deployment of application topologies.Evaluation of artifact transformation and comprehensive case study.
Native Cloud Applications: Why Monolithic Virtualization Is Not Their Foundation
Venue: CLOSER

Due to the current hype around cloud computing, the term 'native cloud application' becomes increasingly popular. It suggests an application to fully benefit from all the advantages of cloud computing. Many users tend to consider their applications as cloud native if the application is just bundled as a monolithic virtual machine or container. Even though virtualization is fundamental for implementing the cloud computing paradigm, a virtualized application does not automatically cover all properties of a native cloud application. In this work, which is an extension of a previous paper, we propose a definition of a native cloud application by specifying the set of characteristic architectural properties, which a native cloud application has to provide. We demonstrate the importance of these properties by introducing a typical scenario from current practice that moves an application to the cloud. The identified properties and the scenario especially show why virtualization alone is insufficient to build native cloud applications. We also outline how native cloud applications respect the core principles of service oriented architectures, which are currently hyped a lot in the form of microservice architectures. Finally, we discuss the management of native cloud applications using container orchestration approaches as well as the cloud standard TOSCA.
Genetic causes of Parkinson's disease in the Maltese: a study of selected mutations in LRRK2, MTHFR, QDPR and SPR
Venue: BMC Medical Genetics

BackgroundMutations in Leucine rich repeat kinase 2 NM_198578 (LRRK2 c.6055G A (p.G2019S) LRRK2 c.4321C G (p.R1441G) and alpha synuclein NM_000345 (SNCA c.209G A (p.A53T) genes causing Parkinson's disease (PD) are common in Mediterranean populations. Variants in the Quinoid Dihydropteridine Reductase NM_000320 (QDPR c.68G A (p.G23D) Sepiapterin Reductase NM_003124 (SPR c.596 2A G) and Methylenetetrahydrofolate Reductase NM_005957 (MTHFR c.677C T and c.1298A C) genes are frequent in Malta and potential candidates for PD.Methods178 cases and 402 control samples from Malta collected as part of the Geoparkinson project were genotyped for MTHFR polymorphisms, QDPR and SPR mutations. Only PD and parkinsonism cases were tested for SNCA and LRRK2 mutations.ResultsLRRK2 c.4321C G and SNCA c.209G A were not detected. The LRRK2 c.6055G A mutation was found in 3.1 of Maltese PD cases. The QDPR mutation was found in both cases and controls and did not increase risk for PD. The SPR mutation was found in controls only. The odds ratios for MTHFR polymorphisms were not elevated.ConclusionsThe LRRK2 c.6055G A is a cause of PD in the Maltese, whilst QDPR c.68G A, SPR c.596 2A G and MTHFR c.677C T and c.1298A C are not important determinants of PD.
ENHANCING CLOUD APPLICATION DEVOPS USING DYNAMICALLY TAILORED DEPLOYMENT ENGINES
Venue: CloudCom 2016

Shortening software release cycles increasingly becomes a critical competitive advantage as today's users, customers, and other stakeholders expect quick responses to occurring issues and feature requests. DevOps practices and Cloud computing are two key paradigms to tackle these issues by enabling rapid and continuous delivery of applications, utilizing automated software delivery pipelines. However, it is a complex and sophisticated challenge to implement such pipelines by installing, configuring, orchestrating, and integrating the required deployment automation solutions. Therefore, we present a method in conjunction with a framework and implementation to dynamically generate tailored deployment automation engines for specific application stacks, which are packaged in a portable manner to run them on various platforms and infrastructures. The core of our work is based on generating APIs for arbitrary deployment executables such as scripts and plans that perform different tasks in the automated deployment process. As a result, deployment tasks can be triggered through generated API endpoints, abstracting from lower level, technical details of diverse deployment automation tooling. Beside a quantitative evaluation, we discuss two case studies in this context, one focusing on microservice architectures, the other one considering application functionality and its relation to deployment functionality.
High resolution mapping of global surface water and its long term changes
Venue: Nature

The location and persistence of surface water (inland and coastal) is both affected by climate and human activity and affects climate, biological diversity and human wellbeing. Global data sets documenting surface water location and seasonality have been produced from inventories and national descriptions, statistical extrapolation of regional data and satellite imagery, but measuring long term changes at high resolution remains a challenge. Here, using three million Landsat satellite images, we quantify changes in global surface water over the past 32 years at 30 metre resolution. We record the months and years when water was present, where occurrence changed and what form changes took in terms of seasonality and persistence. Between 1984 and 2015 permanent surface water has disappeared from an area of almost 90,000 square kilometres, roughly equivalent to that of Lake Superior, though new permanent bodies of surface water covering 184,000 square kilometres have formed elsewhere. All continental regions show a net increase in permanent water, except Oceania, which has a fractional (one per cent) net loss. Much of the increase is from reservoir filling, although climate change is also implicated. Loss is more geographically concentrated than gain. Over 70 per cent of global net permanent water loss occurred in the Middle East and Central Asia, linked to drought and human actions including river diversion or damming and unregulated withdrawal. Losses in Australia and the USA linked to long term droughts are also evident. This globally consistent, validated data set shows that impacts of climate change and climate oscillations on surface water occurrence can be measured and that evidence can be gathered to show how surface water is altered by human activities. We anticipate that this freely available data will improve the modelling of surface forcing, provide evidence of state and change in wetland ecotones (the transition areas between biomes) and inform water management decision making.
Influence of extreme weather disasters on global crop production
Venue: Nature

In recent years, several extreme weather disasters have partially or completely damaged regional crop production. While detailed regional accounts of the effects of extreme weather disasters exist, the global scale effects of droughts, floods and extreme temperature on crop production are yet to be quantified. Here we estimate for the first time, to our knowledge, national cereal production losses across the globe resulting from reported extreme weather disasters during 1964 2007. We show that droughts and extreme heat significantly reduced national cereal production by 9 10% whereas our analysis could not identify an effect from floods and extreme cold in the national data. Analysing the underlying processes, we find that production losses due to droughts were associated with a reduction in both harvested area and yields, whereas extreme heat mainly decreased cereal yields. Furthermore, the results highlight ~7% greater production damage from more recent droughts and 8 11% more damage in developed countries than in developing ones. Our findings may help to guide agricultural priorities in international disaster risk reduction and adaptation efforts.
Record breaking warming and extreme drought in the Amazon rainforest during the course of El Nino 2015 2016
Venue: Scientific reports

The El Nino Southern Oscillation (ENSO) is the main driver of interannual climate extremes in Amazonia and other tropical regions. The current 2015/2016 EN event was expected to be as strong as the EN of the century in 1997/98, with extreme heat and drought over most of Amazonian rainforests. Here we show that this protracted EN event, combined with the regional warming trend, was associated with unprecedented warming and a larger extent of extreme drought in Amazonia compared to the earlier strong EN events in 1982/83 and 1997/98. Typical EN like drought conditions were observed only in eastern Amazonia, whilst in western Amazonia there was an unusual wetting. We attribute this wet dry dipole to the location of the maximum sea surface warming on the Central equatorial Pacific. The impacts of this climate extreme on the rainforest ecosystems remain to be documented and are likely to be different to previous strong EN events.