nils-herrmann/clean_scirepeval huggingface dataset:
Unique Presentation of Hematometrocolpos Mimicking Cauda Equina Syndrome: Severe Back Pain and Urinary Incontinence in an Adolescent Girl.

Imperforate hymen with hematometrocolpos in adolescent females is a rare pediatric condition. Classical presentation includes abdominal pain or a pelvic mass in female patients with primary amenorrhea. Atypical complaints and reluctance among emergency physicians to perform genital examination in the emergency department or the pediatric emergency department (PED) may delay correct diagnosis.



We report a unique, cauda equina syndrome-like presentation of hematometrocolpos secondary to imperforate hymen in a 13-year old, previously healthy girl with primary amenorrhea. In the PED, the unusual clinical presentation of severe back pain and urinary incontinence initially mimicked cauda equina syndrome and led to delayed correct diagnosis. WHY SHOULD AN EMERGENCY PHYSICIAN BE AWARE OF THIS?: The novelty of this case is a cauda equina-like presentation of imperforate hymen secondary to hematocolpos. This report illustrates the highly variable clinical presentation of this rare gynecological pediatric entity. It underlines the importance of considering this rare condition in the differential diagnosis of severe upper or lower back pain alongside voiding abnormalities including urinary retention and incontinence in adolescent females with primary amenorrhea. Above all, the importance of performing a thorough history and genital examination in this subgroup early in the investigation process in the PED emerges from this case. Essentially, excellent clinical judgment and genital examination by the emergency physician may minimize unnecessary radiological investigations and ultimately, accelerate correct diagnosis and expedite appropriate surgical treatment. However, not only pediatric and adult emergency physicians, but also pediatricians and general practitioners should be aware of this entity and its diverse clinical presentation.
Automatic Modelling , Generation And Visualisation Of Realistic 3 D Virtual Cities For Mobile Navigation

This paper presents a comprehensive procedural 3D modelling solution called Virtual City Maker, which relies on scripting algorithms for automatic generation of photorealistic virtual urban content. This solution uses the combination of aerial images, terrestrial photographic input and 2D ground maps in conjunction with a user-friendly customized interface allowing for automatically and interactively generating accurate georeferenced textured 3D virtual city content from this information, specially optimized for use with mobile devices and navigation. The paper also discusses the development and introduction of an innovative mobile virtual reality visualisation engine operating on a PDA (Personal Digital Assistant), called Virtual Navigator, which supports the import of 3D visualization file formats and also includes a front-end user-centered interface for fully immersive virtual 3D navigation. Finally, experiments with the resulting models, the engine and a number of human subjects have been conducted in order to gain insight on the issues of efficient modelling for mobile navigation but also assess some of the usability issues of the interface.
Discovery of driving patterns by trajectory segmentation

Telematics data is becoming increasingly available due to the ubiquity of devices that collect data during drives, for different purposes, such as usage based insurance (UBI), fleet management, navigation of connected vehicles, etc. Consequently, a variety of data-analytic applications have become feasible that extract valuable insights from the data. In this paper, we address the especially challenging problem of discovering behavior-based driving patterns from only externally observable phenomena (e.g. vehicle's speed). We present a trajectory segmentation approach capable of discovering driving patterns as separate segments, based on the behavior of drivers. This segmentation approach includes a novel transformation of trajectories along with a dynamic programming approach for segmentation. We apply the segmentation approach on a real-word, rich dataset of personal car trajectories provided by a major insurance company based in Columbus, Ohio. Analysis and preliminary results show the applicability of approach for finding significant driving patterns.
IOrchestra: supporting high-performance data-intensive applications in the cloud via collaborative virtualization

Multi-tier data-intensive applications are widely deployed in virtualized data centers for high scalability and reliability. As the response time is vital for user satisfaction, this requires achieving good performance at each tier of the applications in order to minimize the overall latency. However, in such virtualized environments, each tier (e.g., application, database, web) is likely to be hosted by different virtual machines (VMs) on multiple physical servers, where a guest VM is unaware of changes outside its domain, and the hypervisor also does not know the configuration and runtime status of a guest VM. As a result, isolated virtualization domains lend themselves to performance unpredictability and variance. In this paper, we propose IOrchestra, a holistic collaborative virtualization framework, which bridges the semantic gaps of I/O stacks and system information across multiple VMs, improves virtual I/O performance through collaboration from guest domains, and increases resource utilization in data centers. We present several case studies to demonstrate that IOrchestra is able to address numerous drawbacks of the current practice and improve the I/O latency of various distributed cloud applications by up to 31%.
Guidelines for the prevention of stroke in patients with stroke or transient ischemic attack: a guideline for healthcare professionals from the american heart association/american stroke association.

The aim of this updated statement is to provide comprehensive and timely evidence-based recommendations on the prevention of ischemic stroke among survivors of ischemic stroke or transient ischemic attack. Evidence-based recommendations are included for the control of risk factors, interventional approaches for atherosclerotic disease, antithrombotic treatments for cardioembolism, and the use of antiplatelet agents for noncardioembolic stroke. Further recommendations are provided for the prevention of recurrent stroke in a variety of other specific circumstances, including arterial dissections; patent foramen ovale; hyperhomocysteinemia; hypercoagulable states; sickle cell disease; cerebral venous sinus thrombosis; stroke among women, particularly with regard to pregnancy and the use of postmenopausal hormones; the use of anticoagulation after cerebral hemorrhage; and special approaches to the implementation of guidelines and their use in high-risk populations.
Gene therapy comes of age

After almost 30 years of promise tempered by setbacks, gene therapies are rapidly becoming a critical component of the therapeutic armamentarium for a variety of inherited and acquired human diseases. Gene therapies for inherited immune disorders, hemophilia, eye and neurodegenerative disorders, and lymphoid cancers recently progressed to approved drug status in the United States and Europe, or are anticipated to receive approval in the near future. In this Review, we discuss milestones in the development of gene therapies, focusing on direct in vivo administration of viral vectors and adoptive transfer of genetically engineered T cells or hematopoietic stem cells. We also discuss emerging genome editing technologies that should further advance the scope and efficacy of gene therapy approaches.
Robust emotion classification using neural network models

In the field of Brain Computer Interface, Emotion recognition plays an increasingly crucial role. As psychological understanding of emotions progresses, feature extraction along with classification of electroencephalogram (EEG) representation of these emotions becomes a more important challenge. In this work, Neural Networks as a type of high accuracy robust statistical learning model was employed in order to classify human emotions from the DEAP [7] dataset containing the measured EEG signals for Emotion Classification research. We take advantage of two Neural Network based models the first one of which is the Deep Neural Network and the other one is the Convolutional Neural Network in order to classify Valence, Arousal, Dominance and liking into two categories of Yes or No (High and Low) and to classify Valence and Arousal into three categories of (High, Normal, Low), The achieved accuracy surpasses those achieved in other papers indicating that these models carry the ability to be used as a high achieving classifier for BCI signals.
Online Learning in Limit Order Book Trade Execution

In this paper, we propose an online learning algorithm for optimal execution in the limit order book of a financial asset. Given a certain number of shares to sell and an allocated time window to complete the transaction, the proposed algorithm dynamically learns the optimal number of shares to sell via market orders at prespecified time slots within the allocated time interval. We model this problem as a Markov Decision Process (MDP), which is then solved by dynamic programming. First, we prove that the optimal policy has a specific form, which requires either selling no shares or the maximum allowed amount of shares at each time slot. Then, we consider the learning problem, in which the state transition probabilities are unknown and need to be learned on the fly. We propose a learning algorithm that exploits the form of the optimal policy when choosing the amount to trade. Interestingly, this algorithm achieves bounded regret with respect to the optimal policy computed based on the complete knowledge of the market dynamics. Our numerical results on several finance datasets show that the proposed algorithm performs significantly better than the traditional Q-learning algorithm by exploiting the structure of the problem.
Protein glycation: a firm link to endothelial cell dysfunction.

The advanced glycation end products (AGEs) are a heterogeneous class of molecules, including the following main subgroups: bis(lysyl)imidazolium cross-links, hydroimidazolones, 3-deoxyglucosone derivatives, and monolysyl adducts. AGEs are increased in diabetes, renal failure, and aging. Microvascular lesions correlate with the accumulation of AGEs, as demonstrated in diabetic retinopathy or renal glomerulosclerosis. On endothelial cells, ligation of receptor for AGE (RAGE) by AGEs induces the expression of cell adhesion molecules, tissue factor, cytokines such as interleukin-6, and monocyte chemoattractant protein-1. A chief means by which AGEs via RAGE exert their effects is by generation of reactive oxygen species, at least in part via stimulation of NADPH oxidase. Diabetes-associated vascular dysfunction in vivo can be prevented by blockade of RAGE. Thus, agents that limit AGE formation, increase the catabolism of these species, or antagonize their binding to RAGE may provide new targets for vascular protection in diabetes.
Visual information retrieval using Java and LIRE

Visual information retrieval (VIR) is an active and vibrant research area, which attempts at providing means for organizing, indexing, annotating, and retrieving visual information (images and videos) form large, unstructured repositories. The goal of VIR is to retrieve the highest number of relevant matches to a given query (often expressed as an example image and/or a series of keywords). In its early years (1995-2000) the research efforts were dominated by content-based approaches contributed primarily by the image and video processing community. During the past decade, it was widely recognized that the challenges imposed by the semantic gap (the lack of coincidence between an image's visual contents and its semantic interpretation) required a clever use of textual metadata (in addition to information extracted from the image's pixel contents) to make image and video retrieval solutions efficient and effective. The need to bridge (or at least narrow) the semantic gap has been one of the driving forces behind current VIR research. Additionally, other related research problems and market opportunities have started to emerge, offering a broad range of exciting problems for computer scientists and engineers to work on. In this tutorial, we present an overview of visual information retrieval (VIR) concepts, techniques, algorithms, and applications. Several topics are supported by examples written in Java, using Lucene (an open-source Java-based indexing and search implementation) and LIRE (Lucene Image REtrieval), an open-source Java-based library for content-based image retrieval (CBIR) written by Mathias Lux.
 After motivating the topic, we briefly review the fundamentals of information retrieval, present the most relevant and effective visual descriptors currently used in VIR, the most common indexing approaches for visual descriptors, the most prominent machine learning techniques used in connection with contemporary VIR solutions, as well as the challenges associated with building real-world, large scale VIR solutions, including a brief overview of publicly available datasets used in worldwide challenges, contests, and benchmarks. Throughout the tutorial, we integrate examples using LIRE, whose main features and design principles are also discussed. Finally, we conclude the tutorial with suggestions for deepening the knowledge in the topic, including a brief discussion of the most relevant advances, open challenges, and promising opportunities in VIR and related areas.
 The tutorial is primarily targeted at experienced Information Retrieval researchers and practitioners interested in extending their knowledge of document-based IR to equivalent concepts, techniques, and challenges in VIR. The acquired knowledge should allow participants to derive insightful conclusions and promising avenues for further investigation.
Lock-free transactional support for large-scale storage systems

In this paper, we introduce ReTSO, a reliable and efficient design for transactional support in large-scale storage systems. ReTSO uses a centralized scheme and implements snapshot isolation, a property that guarantees that read operations of a transaction read a consistent snapshot of the data stored. The centralized scheme of ReTSO enables a lock-free commit algorithm that prevents unre-leased locks of a failed transaction from blocking others. We analyze the bottlenecks in a single-server implementation of transactional logic and propose solutions for each. The experimental results show that our implementation can service up to 72K transaction per second (TPS), which is an order of magnitude larger than the maximum achieved traffic in similar data storage systems. Consequently, we do not expect ReTSO to be a bottleneck even for current large distributed storage systems.
SMALL BUSINESSES , JOB CREATION AND GROWTH : FACTS , OBSTACLES AND BEST PRACTICES

The importance of SMEs SMEs (small and medium-sized enterprises) account for 60 to 70 per cent of jobs in most OECD countries, with a particularly large share in Italy and Japan, and a relatively smaller share in the United States. Throughout they also account for a disproportionately large share of new jobs, especially in those countries which have displayed a strong employment record, including the United States and the Netherlands. Some evidence points also to the importance of age, rather than size, in job creation: young firms generate more than their share of employment. However, less than one-half of start-ups survive for more than five years and only a fraction develop into the high-growth firms which make important contributions to job creation. High job turnover poses problems for employment security; and small establishments are often exempt from giving notice to their employees. Small firms also tend to invest less in training and rely relatively more on external recruitment for raising competence. The demand for reliable, relevant and internationally comparable data on SMEs is on the rise, and statistical offices have started to expand their collection and publication of data. International comparability is still weak, however, due to divergent size-class definitions and sector classifications. To enable useful policy analysis, OECD governments need to improve their build-up of data, without creating additional obstacles for firms through the burden of excessive paper work. The greater variance in profitability, survival and growth of SMEs compared to larger firms accounts for special problems in financing. SMEs generally tend to be confronted with higher interest rates, as well as credit rationing due to shortage of collateral. The issues that arise in financing differ considerably between existing and new firms, as well as between those which grow slowly and those that grow rapidly. The expansion of private equity markets, including informal markets, has greatly improved the access to venture capital for start-ups and SMEs, but considerable differences remain among countries. Regulatory burdens remain a major obstacle for SMEs as these firms tend to be poorly equipped to deal with the problems arising from regulations. Access to information about regulations should be made available to SMEs at minimum cost. Policy makers must ensure that the compliance procedures associated with, e.g. R&D and new technologies, are not unnecessarily costly, complex or lengthy. Transparency is of particular importance to SMEs, and information technology has great potential to narrow the information …
Proposing a comprehensive framework for analysis and engineering of mobile payment business models

Although in the era of third generation (3G) mobile networks technical hurdles are minor, the continuing failure of mobile payments (m-payments) withstands the endorsement by customers and service providers. A major reason is the uncommonly high interdependency of technical, human and market factors which have to be regarded and orchestrated cohesively to solve the problem. In this paper, we apply Business Model Ontology in order to develop an m-payment business model framework based on the results of a precedent multi case study analysis of 27 m-payment procedures. The framework is depicted with a system of morphological boxes and the interrelations between the associated characteristics. Representing any m-payment business model along with its market setting and influencing decisions as instantiations, the resulting framework enables researchers and practitioners for comprehensive analysis of existing and future models and provides a helpful tool for m-payment business model engineering.
Real-time Multiple Abnormality Detection in Video Data

Automatic abnormality detection in video sequences has recently gained an increasing attention within the research community. Although progress has been seen, there are still some limitations in current research. While most systems are designed at detecting specific abnormality, others which are capable of detecting more than two types of abnormalities rely on heavy computation. Therefore, we provide a framework for detecting abnormalities in video surveillance by using multiple features and cascade classifiers, yet achieve above real-time processing speed. Experimental results on two datasets show that the proposed framework can reliably detect abnormalities in the video sequence, outperforming the current state-of-the-art methods.
Teaching intonation in discourse using speech visualization technology

Intonation, long thought to be a key to effectiveness in spoken language, is more and more commonly addressed in English language teaching through the use of speech visualization technology. While the use of visualization technology is a crucial advance in the teaching of intonation, such teaching can be further enhanced by connecting technology to an understanding of how intonation functions in discourse. This study examines the intonation of four readers reading out-of-context sentences and then the same sentences as part of coherent discourse-level texts. Two discourse-level uses of intonation, the use of intonational paragraph markers (paratones) and the distribution of tonal patterns, are discussed and implications for teaching intonation are addressed.
Smart Home Environment for Mild Cognitive Impairment Population: Solutions to Improve Care and Quality of Life

The transition stage from the natural cognitive decline of normal aging to the more serious decline of dementia is referred to as mild cognitive impairment (MCI). The cognitive changes caused in MCI are noticeable by the individuals experiencing them and by others, but the changes are not severe enough to interfere with daily life or with independent activities. Because there is a thin line between normal aging and MCI, it is difficult for individuals to discern between the two conditions. Moreover, if the symptoms of MCI are not diagnosed in time, it may lead to more serious and permanent conditions. However, if these symptoms are detected in time and proper care and precaution are taken, it is possible to prevent the condition from worsening. A smart-home environment that unobtrusively keeps track of the individual's daily living activities is a possible solution to improve care and quality of life.
Smart Menu Ordering System in Restaurant Prema

Automation plays a very important role in every field of human life. This paper contains the proposal of a fully automated menu ordering system in which the paper based menu is replaced by a user friendly Touchscreen based menu card. The system has PIC microcontroller which is interfaced with the input and output modules. The input module is the touchscreen sensor which is placed on GLCD (Graphical Liquid Crystal Display) to have a graphic image display, which takes the input from the user and provides the same information to the microcontroller. The output module is a Zigbee module which is used for communication between system at the table and system for receiving section. Microcontroller also displays the menu items on the GLCD. At the receiving end the selected items will be displayed on the LCD and by using the conveyer belt the received order will send to the particular table.
Impact Analysis of Start-Up Lost Time at Major Intersections on Sathorn Road Using a Synchro Optimization and a Microscopic SUMO Traffic Simulation

Start-up lost time is the time lost in the starting of the green time interval when a traffic signal phase changes from red to green and previously stopped vehicles in the curb line queue need time to accelerate to the desired speed. Actual traffic data analytics from newly installed loop coil detectors at all approaching upstream road segments of major intersections on Sathorn Road in Bangkok, Thailand, are used to confirm that the vehicle flow is obstructed considerably by the large start-up lost time. In this paper, the effect of a large start-up lost time is evaluated in terms of the travel time of passenger cars in a calibrated microscopic traffic simulation. The evaluation is based on the simulation of the urban mobility platform, while the traffic signal lights at major intersections are based on the standard Synchro optimization software. By the simulation, the average travel time per vehicle increases from 4% to 37% when the start-up lost time is varied from a baseline value of 1 s to the maximum value of 15 s, which potentially occurred in the actual traffic data collection in this paper. In addition, the optimal traffic signal green phase lengths increase from 2% to 42%, depending on the volume-to-capacity ratio. The similar increasing trend of optimal green time and average travel time per vehicle is observed using theoretical analysis based on M/M/1 and D/D/1 queues to support the results from Synchro. Findings of this paper are beneficial for understanding the impact of start-up lost time at signalized intersections.