nils-herrmann/clean_scirepeval huggingface dataset:
Cervical aortic arch in a patient with Turner syndrome.

We report a case of a young girl with Turner syndrome presenting with a pulsatile left-sided supraclavicular swelling since birth, which proved to be the rare anomaly of a cervical aortic arch. Though elongation of the transverse aortic arch is well known in Turner syndrome, to the best of our knowledge, a cervical aortic arch has not been described in the literature.
Runtime Prediction for Scale-Out Data Analytics

Many analytics applications generate mixed workloads, i.e., workloads comprised of analytical tasks with different processing characteristics including data pre-processing, SQL, and iterative machine learning algorithms. Examples of such mixed workloads can be found in web data analysis, social media analysis, and graph analytics, where they are executed repetitively on large input datasets (e.g., Find the average user time spent on the top 10 most popular web pages on the UK domain web graph.). Scale-out processing engines satisfy the needs of these applications by distributing the data and the processing task efficiently among multiple workers that are first reserved and then used to execute the task in parallel on a cluster of machines. Finding the resource allocation that can complete the workload execution within a given time constraint, and optimizing cluster resource allocations among multiple analytical workloads motivates the need for estimating the runtime of the workload before its actual execution. Predicting runtime of analytical workloads is a challenging problem as runtime depends on a large number of factors that are hard to model a priori execution. These factors can be summarized as workload characteristics (data statistics and processing costs) , the execution configuration (deployment, resource allocation, and software settings), and the cost model that captures the interplay among all of the above parameters. While conventional cost models proposed in the context of query optimization can assess the relative order among alternative SQL query plans, they are not aimed to estimate absolute runtime. Additionally, conventional models are ill-equipped to estimate the runtime of iterative analytics that are executed repetitively until convergence and that of user defined data pre-processing operators which are not “owned” by the underlying data management system. This thesis demonstrates that runtime for data analytics can be predicted accurately by breaking the analytical tasks into multiple processing phases, collecting key input features during a reference execution on a sample of the dataset, and then using the features to build per-phase cost models. We develop prediction models for three categories of data analytics produced by social media applications: iterative machine learning, data pre-processing, and reporting SQL. The prediction framework for iterative analytics, PREDIcT, addresses the challenging problem of estimating the number of iterations, and per-iteration runtime for a class of iterative machine learning algorithms that are run repetitively until convergence. The hybrid prediction models we develop for data pre-processing tasks and for reporting SQL combine the benefits of analytical modeling with that of machine learning-based models. Through a
Systematic Hypermedia Application Design with OOHDM

In this paper we analyze the process of hypermedia applications design and implementation, focusing in particular on two critical aspects of these applications: the navigational and interface structure. We discuss the way in which we build the navigation and abstract interface models using the Object-Oriented Hypermedia Design Method (OOHDM); we show which concerns must be taken into account for each task by giving examples from a real project we are developing, the Portinari Project. We show which implementation concerns must be considered when defining interface behavior, discussing both a Toolbook and a HTML implementation of the example application.
Accurate Monocular Visual-inertial SLAM using a Map-assisted EKF Approach

This paper presents a novel tightly-coupled monocular visual-inertial Simultaneous Localization and Mapping algorithm, which provides accurate and robust localization within the globally consistent map in real time on a standard CPU. This is achieved by firstly performing the visual-inertial extended kalman filter(EKF) to provide motion estimate at a high rate. However the filter becomes inconsistent due to the well known linearization issues. So we perform a keyframe-based visual-inertial bundle adjustment to improve the consistency and accuracy of the system. In addition, a loop closure detection and correction module is also added to eliminate the accumulated drift when revisiting an area. Finally, the optimized motion estimates and map are fed back to the EKF-based visual-inertial odometry module, thus the inconsistency and estimation error of the EKF estimator are reduced. In this way, the system can continuously provide reliable motion estimates for the long-term operation. The performance of the algorithm is validated on public datasets and real-world experiments, which proves the superiority of the proposed algorithm.
Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach

Patients turn to Online Health Communities not only for information on specific conditions but also for emotional support. Previous research has indicated that the progression of emotional status can be studied through the linguistic patterns of an individual’s posts. We analyze a realworld dataset from the Mental Health section of healthboards.com. Estimated from the word usages in their posts, we find that the emotional progress across patients vary widely. We study the problem of predicting a patient’s emotional status in the future from her past posts and we propose a Recurrent Neural Network (RNN) based architecture to address it. We find that the future emotional status can be predicted with reasonable accuracy given her historical posts and participation features. Our evaluation results demonstrate the efficacy of our proposed architecture, by outperforming state-of-the-art approaches with over 0.13 reduction in Mean Absolute Error.
Interleukin 21 and its receptor are involved in NK cell expansion and regulation of lymphocyte function

Cytokines are important in the regulation of haematopoiesis and immune responses, and can influence lymphocyte development. Here we have identified a class I cytokine receptor that is selectively expressed in lymphoid tissues and is capable of signal transduction. The full-length receptor was expressed in BaF3 cells, which created a functional assay for ligand detection and cloning. Conditioned media from activated human CD3+ T cells supported proliferation of the assay cell line. We constructed a complementary DNA expression library from activated human CD3+ T cells, and identified a cytokine with a four-helix-bundle structure using functional cloning. This cytokine is most closely related to IL2 and IL15, and has been designated IL21 with the receptor designated IL21R. In vitro assays suggest that IL21 has a role in the proliferation and maturation of natural killer (NK) cell populations from bone marrow, in the proliferation of mature B-cell populations co-stimulated with anti-CD40, and in the proliferation of T cells co-stimulated with anti-CD3.
Deep Anomaly Detection with Outlier Exposure

It is important to detect anomalous inputs when deploying machine learning systems. The use of larger and more complex inputs in deep learning magnifies the difficulty of distinguishing between anomalous and in-distribution examples. At the same time, diverse image and text data are available in enormous quantities. We propose leveraging these data to improve deep anomaly detection by training anomaly detectors against an auxiliary dataset of outliers, an approach we call Outlier Exposure (OE). This enables anomaly detectors to generalize and detect unseen anomalies. In extensive experiments on natural language processing and smalland large-scale vision tasks, we find that Outlier Exposure significantly improves detection performance. We also observe that cutting-edge generative models trained on CIFAR-10 may assign higher likelihoods to SVHN images than to CIFAR-10 images; we use OE to mitigate this issue. We also analyze the flexibility and robustness of Outlier Exposure, and identify characteristics of the auxiliary dataset that improve performance.
Neural networks for computing eigenvalues and eigenvectors

Two new algorithms and associated neuron-like network architectures are proposed for solving the eigenvalue problem in real-time. The first approach is based on the solution of a set of nonlinear algebraic equations by employing optimization techniques. The second approach employs a multilayer neural network with linear artificial neurons and it exploits the continuous-time error back-propagation learning algorithm. The second approach enables us to find all the eigenvalues and the associated eigenvectors simultaneously by training the network to match some desired patterns, while the first approach is suitable to find during one run only one particular eigenvalue (e.g. an extreme eigenvalue) and the corresponding eigenvector in realtime. In order to find all eigenpairs the optimization process must be repeated in this case many times for different initial conditions. The performance and convergence behaviour of the proposed neural network architectures are investigated by extensive computer simulations.
Rolling Shutter Absolute Pose Problem with Known Vertical Direction

We present a solution to the rolling shutter (RS) absolute camera pose problem with known vertical direction. Our new solver, R5Pup, is an extension of the general minimal solution R6P, which uses a double linearized RS camera model initialized by the standard perspective P3P. Here, thanks to using known vertical directions, we avoid double linearization and can get the camera absolute pose directly from the RS model without the initialization by a standard P3P. Moreover, we need only five 2D-to-3D matches while R6P needed six such matches. We demonstrate in simulated and real experiments that our new R5Pup is robust, fast and a very practical method for absolute camera pose computation for modern cameras on mobile devices. We compare our R5Pup to the state of the art RS and perspective methods and demonstrate that it outperforms them when vertical direction is known in the range of accuracy available on modern mobile devices. We also demonstrate that when using R5Pup solver in structure from motion (SfM) pipelines, it is better to transform already reconstructed scenes into the standard position, rather than using hard constraints on the verticality of up vectors.
Reduction in hospital-associated methicillin-resistant Staphylococcus aureus and vancomycin-resistant Enterococcus with daily chlorhexidine gluconate bathing for medical inpatients.

Daily bathing with chlorhexidine gluconate (CHG) is increasingly used in intensive care units to prevent hospital-associated infections, but limited evidence exists for noncritical care settings.



A prospective crossover study was conducted on 4 medical inpatient units in an urban, academic Canadian hospital from May 1, 2014-August 10, 2015. Intervention units used CHG over a 7-month period, including a 1-month wash-in phase, while control units used nonmedicated soap and water bathing. Rates of hospital-associated methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus (VRE) colonization or infection were the primary end point. Hospital-associated S. aureus were investigated for CHG resistance with a qacA/B and smr polymerase chain reaction (PCR) and agar dilution.



Compliance with daily CHG bathing was 58%. Hospital-associated MRSA and VRE was decreased by 55% (5.1 vs 11.4 cases per 10,000 inpatient days, P = .04) and 36% (23.2 vs 36.0 cases per 10,000 inpatient days, P = .03), respectively, compared with control cohorts. There was no significant difference in rates of hospital-associated Clostridium difficile. Chlorhexidine resistance testing identified 1 isolate with an elevated minimum inhibitory concentration (8 µg/mL), but it was PCR negative.



This prospective pragmatic study to assess daily bathing for CHG on inpatient medical units was effective in reducing hospital-associated MRSA and VRE. A critical component of CHG bathing on medical units is sustained and appropriate application, which can be a challenge to accurately assess and needs to be considered before systematic implementation.
Interactive Knowledge Discovery and Data Mining in Biomedical Informatics

Biomedical research is drowning in data, yet starving for knowledge. Current challenges in biomedical research and clinical practice include information overload – the need to combine vast amounts of structured, semistructured, weakly structured data and vast amounts of unstructured information – and the need to optimize workflows, processes and guidelines, to increase capacity while reducing costs and improving efficiencies. In this paper we provide a very short overview on interactive and integrative solutions for knowledge discovery and data mining. In particular, we emphasize the benefits of including the end user into the “interactive” knowledge discovery process. We describe some of the most important challenges, including the need to develop and apply novel methods, algorithms and tools for the integration, fusion, pre-processing, mapping, analysis and interpretation of complex biomedical data with the aim to identify testable hypotheses, and build realistic models. The HCI-KDD approach, which is a synergistic combination of methodologies and approaches of two areas, Human–Computer Interaction (HCI) and Knowledge Discovery & Data Mining (KDD), offer ideal conditions towards solving these challenges: with the goal of supporting human intelligence with machine intelligence. There is an urgent need for integrative and interactive machine learning solutions, because no medical doctor or biomedical researcher can keep pace today with the increasingly large and complex data sets – often called “Big Data”.
Direct and indirect effects of perceived social support on women's infertility-related stress.

Social support can be a critical component of how a woman adjusts to infertility, yet few studies have investigated its impact on infertility-related coping and stress. We examined relationships between social support contexts and infertility stress domains, and tested if they were mediated by infertility-related coping strategies in a sample of infertile women.



The Multidimensional Scale of Perceived Social Support, the Copenhagen Multi-centre Psychosocial Infertility coping scales and the Fertility Problem Inventory were completed by 252 women seeking treatment. Structural equation modeling analysis was used to test the hypothesized multiple mediation model.



The final model revealed negative effects from perceived partner support to relationship concern (β = -0.47), sexual concern (β = -0.20) and rejection of childfree lifestyle through meaning-based coping (β = -0.04). Perceived friend support had a negative effect on social concern through active-confronting coping (β = -0.04). Finally, besides a direct negative association with social concern (β = -0.30), perceived family support was indirectly and negatively related with all infertility stress domains (β from -0.04 to -0.13) through a positive effect of active-avoidance coping. The model explained between 12 and 66% of the variance of outcomes.



Despite being limited by a convenience sampling and cross-sectional design, results highlight the importance of social support contexts in helping women deal with infertility treatment. Health professionals should explore the quality of social networks and encourage seeking positive support from family and partners. Findings suggest it might prove useful for counselors to use coping skills training interventions, by retraining active-avoidance coping into meaning-based and active-confronting strategies.
250 kW flywheel with HTS magnetic bearing for industrial use

A 250 kW / 5 kWh engineering prototype Flywheel Energy Storage System (FESS) was designed, fabricated and component tested by Adelwitz Technologiezentrum GmbH (ATZ) and L-3 Communications Magnet Motor GmbH (MM). A heavy – load vertical 0.6 ton rotor is suspended totally magnetically by an HTS radial-passive bearing on the top together with a PM bearing at the bottom. Further features are the flywheel rotor body which is manufactured from carbon fibre reinforced plastics (CFRP) in a multi-rim version and combined with an integrated high-power motor /generator. A 35 W/77 K singlestage Gifford McMahon cryo-cooler is cooling the HTS bearing to a temperature of 45 – 60 K. Functionality and efficiency of the magnetic bearing configurations, rotor control concepts and motor / generator power electric system is considered and established. Bearing stiffness parameters, damping performance, and rotational friction are measured. Testing of further components under vacuum conditions confirmed that low bearing drag and wearfree operation can be attained. The motor-generator operates with a power in excess of 250 kW and an efficiency of > 92%, including the losses of the inverters. A redundant mechanical touchdown bearing system can be activated to restore the rotor position. The separately tested flywheel components are now in the assembling status expecting first machine tests in November 2007. After studying and measuring all FESS parameters in –house the dynamical storage device will be tested in a German E.ON power station under industrial conditions.
The MPI Bioinformatics Toolkit for protein sequence analysis

The MPI Bioinformatics Toolkit is an interactive web service which offers access to a great variety of public and in-house bioinformatics tools. They are grouped into different sections that support sequence searches, multiple alignment, secondary and tertiary structure prediction and classification. Several public tools are offered in customized versions that extend their functionality. For example, PSI-BLAST can be run against regularly updated standard databases, customized user databases or selectable sets of genomes. Another tool, Quick2D, integrates the results of various secondary structure, transmembrane and disorder prediction programs into one view. The Toolkit provides a friendly and intuitive user interface with an online help facility. As a key feature, various tools are interconnected so that the results of one tool can be forwarded to other tools. One could run PSI-BLAST, parse out a multiple alignment of selected hits and send the results to a cluster analysis tool. The Toolkit framework and the tools developed in-house will be packaged and freely available under the GNU Lesser General Public Licence (LGPL). The Toolkit can be accessed at http://toolkit.tuebingen.mpg.de.
DC-DC converter power stage with cascoded transistors and automatic dead time generation

The paper presents a cascoded power stage with automatic dead time generation. The circuit is using the inter-transistor node voltages of the cascode configuration as feedback control signals to delay turning ON the power transistors. The circuit is designed as the output stage of a fully-integrated buck converter. The steady-state operation is described. The waveforms simulated on 45-nm CMOS process show that in steady-state operation the short-circuit path and body diode conductions are avoided while effective zero-voltage switching (ZVS) are provided both for ground and power supply line; the calculated dead times are in a good agreement with simulation results.
Datenbanksysteme für Business, Technologie und Web (BTW 2017), 17. Fachtagung des GI-Fachbereichs „Datenbanken und Informationssysteme" (DBIS), 6.-10. März 2017, Stuttgart, Germany, Workshopband

The energy domain currently struggles with radical legal and technological changes, such as, smart meters. This results in new use cases which can be implemented based on business process technology. Understanding and automating business processes requires to model and test them. However, existing process testing approaches frequently struggle with the testing of process resources, such as ERP systems, and negative testing. Hence, this work presents a toolchain which tackles that limitations. The approach uses an open source process engine to generate event logs and applies process mining techniques in a novel way.
Highly mobile and robust small quadruped robots

This paper describes novel highly mobile small robots called “Mini-Whegs” that can run and jump (see video). They are derived from our larger Whegs series of robots, which benefit from abstracted cockroach locomotion principles. Key to their success are the three spoked appendages, called “whegs,” which combine the speed and simplicity of wheels with the climbing mobility of legs. To be more compact than the larger Whegs vehicles, Mini-Whegs uses four whegs in an alternating diagonal gait. These 9 cm long robots can run at sustained speeds of over 10 body lengths per second and climb obstacles that are taller than their leg length. They can run forward and backward, on either side. Their robust construction allows them to tumble down a flight of stairs with no damage and carry a payload equal to twice their weight. A jumping mechanism has also been developed that enables Mini-Whegs to surmount much larger obstacles, such as stair steps.
Spike sorting for large, dense electrode arrays

Developments in microfabrication technology have enabled the production of neural electrode arrays with hundreds of closely spaced recording sites, and electrodes with thousands of sites are under development. These probes in principle allow the simultaneous recording of very large numbers of neurons. However, use of this technology requires the development of techniques for decoding the spike times of the recorded neurons from the raw data captured from the probes. Here we present a set of tools to solve this problem, implemented in a suite of practical, user-friendly, open-source software. We validate these methods on data from the cortex, hippocampus and thalamus of rat, mouse, macaque and marmoset, demonstrating error rates as low as 5%.
Towards the Internet of Smart Trains: A Review on Industrial IoT-Connected Railways

Nowadays, the railway industry is in a position where it is able to exploit the opportunities created by the IIoT (Industrial Internet of Things) and enabling communication technologies under the paradigm of Internet of Trains. This review details the evolution of communication technologies since the deployment of GSM-R, describing the main alternatives and how railway requirements, specifications and recommendations have evolved over time. The advantages of the latest generation of broadband communication systems (e.g., LTE, 5G, IEEE 802.11ad) and the emergence of Wireless Sensor Networks (WSNs) for the railway environment are also explained together with the strategic roadmap to ensure a smooth migration from GSM-R. Furthermore, this survey focuses on providing a holistic approach, identifying scenarios and architectures where railways could leverage better commercial IIoT capabilities. After reviewing the main industrial developments, short and medium-term IIoT-enabled services for smart railways are evaluated. Then, it is analyzed the latest research on predictive maintenance, smart infrastructure, advanced monitoring of assets, video surveillance systems, railway operations, Passenger and Freight Information Systems (PIS/FIS), train control systems, safety assurance, signaling systems, cyber security and energy efficiency. Overall, it can be stated that the aim of this article is to provide a detailed examination of the state-of-the-art of different technologies and services that will revolutionize the railway industry and will allow for confronting today challenges.