nils-herrmann/clean_scirepeval huggingface dataset:
Puberty alters renal water handling.

We investigated the influence of sex and puberty stage on circadian urine production and levels of antidiuretic hormone [arginine vasopressin (AVP)] in healthy children. Thirty-nine volunteers (9 prepuberty boys, 10 prepuberty girls, 10 midpuberty boys, and 10 midpuberty girls) were included. All participants underwent a 24-h circadian inpatient study under standardized conditions regarding Na(+) and fluid intake. Blood samples were drawn every 4 h for measurements of plasma AVP, serum 17-β-estradiol, and testosterone, and urine was fractionally collected for measurements of electrolytes, aquaporin (AQP)2, and PGE2. We found a marked nighttime decrease in diuresis (from 1.69 ± 0.08 to 0.86 ± 0.06 ml·kg(-1)·h(-1), P < 0.001) caused by a significant nighttime increase in solute-free water reabsorption (TcH2O; day-to-night ratio: 0.64 ± 0.07, P < 0.001) concurrent with a significant decrease in osmotic excretion (day-to-night ratio: 1.23 ± 0.06, P < 0.001). Plasma AVP expressed a circadian rhythm (P < 0.01) with a nighttime increase and peak levels at midnight (0.49 ± 0.05 pg/ml). The circadian plasma AVP rhythm was not influenced by sex (P = 0.56) or puberty stage (P = 0.73). There was significantly higher nighttime TcH2O in prepuberty children. This concurred with increased nighttime urinary AQP2 excretion in prepuberty children. Urinary PGE2 exhibited a circadian rhythm independent of sex or puberty stage. Levels of serum 17β-estradiol and testosterone were as expected for sex and puberty stage, and no effect on the AVP-AQP2-TcH2O axis was observed. This study found a circadian rhythm of plasma AVP independent of sex and puberty stage, although nighttime TcH2O was higher and AQP2 excretion was more pronounced in prepuberty children, suggesting higher prepuberty renal AVP sensitivity.
Quality of Experience of VoIP Service: A Survey of Assessment Approaches and Open Issues

This survey gives a comprehensive review of recent advances related to the topic of VoIP QoE (Quality of user' Experience). It starts by providing some insight into the QoE arena and outlines the principal building blocks of a VoIP application. The sources of impairments over data IP networks are identified and distinguished from signal-oriented sources of quality degradation observed over telecom networks. An overview of existing subjective and objective methodologies for the assessment of the QoE of voice conversations is then presented outlining how subjective and objective speech quality methodologies have evolved to consider time-varying QoS transport networks. A description of practical procedures for measuring VoIP QoE and illustrative results is then given. Utilization methodology of several speech quality assessment frameworks is summarized. A survey of emerging single-ended parametric-model speech quality assessment algorithms dedicated to VoIP service is then given. In particular, after presenting a primitive single-ended parametric-model algorithm especially conceived for the evaluation of VoIP conversations, new artificial assessors of VoIP service are detailed. In particular, we describe speech quality assessment algorithms that consider, among others, packet loss burstiness, unequal importance of speech wave, and transient loss of connectivity. The following section concentrates on the integration of VoIP service over mobile data networks. The impact of quality-affecting phenomena, such as handovers and CODEC changeover are enumerated and some primary subjective results are summarized. The survey concludes with a review of open issues relating to automatic assessment of VoIP.
Keyword search on external memory data graphs

Keyword search on graph structured data has attracted a lot of attention in recent years. Graphs are a natural “lowest common denominator” representation which can combine relational, XML and HTML data. Responses to keyword queries are usually modeled as trees that connect nodes matching the keywords. In this paper we address the problem of keyword search on graphs that may be significantly larger than memory. We propose a graph representation technique that combines a condensed version of the graph (the “supernode graph”) which is always memory resident, along with whatever parts of the detailed graph are in a cache, to form a multi-granular graph representation. We propose two alternative approaches which extend existing search algorithms to exploit multigranular graphs; both approaches attempt to minimize IO by directing search towards areas of the graph that are likely to give good results. We compare our algorithms with a virtual memory approach on several real data sets. Our experimental results show significant benefits in terms of reduction in IO due to our algorithms.
Glioblastoma microvesicles transport RNA and protein that promote tumor growth and provide diagnostic biomarkers

Glioblastoma tumour cells release microvesicles (exosomes) containing mRNA, miRNA and angiogenic proteins. These microvesicles are taken up by normal host cells, such as brain microvascular endothelial cells. By incorporating an mRNA for a reporter protein into these microvesicles, we demonstrate that messages delivered by microvesicles are translated by recipient cells. These microvesicles are also enriched in angiogenic proteins and stimulate tubule formation by endothelial cells. Tumour-derived microvesicles therefore serve as a means of delivering genetic information and proteins to recipient cells in the tumour environment. Glioblastoma microvesicles also stimulated proliferation of a human glioma cell line, indicating a self-promoting aspect. Messenger RNA mutant/variants and miRNAs characteristic of gliomas could be detected in serum microvesicles of glioblastoma patients. The tumour-specific EGFRvIII was detected in serum microvesicles from 7 out of 25 glioblastoma patients. Thus, tumour-derived microvesicles may provide diagnostic information and aid in therapeutic decisions for cancer patients through a blood test.
Does Automated Refactoring Obviate Systematic Editing?

When developers add features and fix bugs, they often make systematic edits---similar edits to multiple locations. Systematic edits may indicate that developers should instead refactor to eliminate redundancy. This paper explores this question by designing and implementing a fully automated refactoring tool called Rase, which performs clone removal. Rase (1) extracts common code guided by a systematic edit; (2) creates new types and methods as needed; (3) parameterizes differences in types, methods, variables, and expressions; and (4) inserts return objects and exit labels based on control and data flow. To our knowledge, this functionality makes Rase the most advanced refactoring tool for automated clone removal.
 We evaluate Rase with real-world systematic edits and compare to method based clone removal. Rase successfully performs clone removal in 30 of 56 method pairs (n=2) and 20 of 30 method groups (n≥3) with systematic edits. We find that scoping refactoring based on systematic edits (58%), rather than the entire method (33%), increases the applicability of automated clone removal. Automated refactoring is not feasible in the other 42% cases, which indicates that automated refactoring does not obviate the need for systematic editing.
Accounting for Language Changes Over Time in Document Similarity Search

Given a query document, ranking the documents in a collection based on how similar they are to the query is an essential task with extensive applications. For collections that contain documents whose creation dates span several decades, this task is further complicated by the fact that the language changes over time. For example, many terms add or lose one or more senses to meet people’s evolving needs. To address this problem, we present methods that take advantage of two types of information to account for the language change. The first is the citation network that often exists within the collection, which can be used to link related documents with significantly different creation dates (and hence different language use). The second is the changes in the usage frequency of terms that occur over time, which can indicate changes in their senses and uses. These methods utilize the preceding information while estimating the representation of both documents and terms within the context of nonprobabilistic static and dynamic topic models. Our experiments on two real-world datasets that span more than 40 years show that our proposed methods improve the retrieval performance of existing models and that these improvements are statistically significant.
Automatic Summarization of Events from Social Media

Social media services such as Twitter generate phenomenal volume of content for most real-world events on a daily basis. Digging through the noise and redundancy to understand the important aspects of the content is a very challenging task. We propose a search and summarization framework to extract relevant representative tweets from an unfiltered tweet stream in order to generate a coherent and concise summary of an event. We introduce two topic models that take advantage of temporal correlation in the data to extract relevant tweets for summarization. The summarization framework has been evaluated using Twitter data on four real-world events. Evaluations are performed using Wikipedia articles on the events as well as using Amazon Mechanical Turk (MTurk) with human readers (MTurkers). Both experiments show that the proposed models outperform traditional LDA and lead to informative summaries.
Structure-Based Statistical Features and Multivariate Time Series Clustering

We propose a new method for clustering multivariate time series. A univariate time series can be represented by a fixed-length vector whose components are statistical features of the time series, capturing the global structure. These descriptive vectors, one for each component of the multivariate time series, are concatenated, before being clustered using a standard fast clustering algorithm such as k-means or hierarchical clustering. Such statistical feature extraction also serves as a dimension-reduction procedure for multivariate time series. We demonstrate the effectiveness and simplicity of our proposed method by clustering human motion sequences: dynamic and high-dimensional multivariate time series. The proposed method based on univariate time series structure and statistical metrics provides a novel, yet simple and flexible way to cluster multivariate time series data efficiently with promising accuracy. The success of our method on the case study suggests that clustering may be a valuable addition to the tools available for human motion pattern recognition research.
Electrically Small, Low-Profile, Huygens Circularly Polarized Antenna

The design, simulation studies, and experimental verification of an electrically small, low-profile, broadside-radiating Huygens circularly polarized (HCP) antenna are reported. To realize its unique circular polarization cardioid-shaped radiation characteristics in a compact structure, two pairs of the metamaterial-inspired near-field resonant parasitic elements, the Egyptian axe dipole (EAD) and the capacitively loaded loop (CLL), are integrated into a crossed-dipole configuration. The EAD (CLL) elements act as the orthogonal electric dipole (magnetic dipole) radiators. Balanced broadside-radiated electric and magnetic field amplitudes with the requisite 90° phase difference between them are realized by exciting these two pairs of electric and magnetic dipoles with a specially designed, unbalanced crossed-dipole structure. The electrically small (ka = 0.73) design operates at 1575 MHz. It is low profile  $0.04\lambda _{\mathbf {0}}$ , and its entire volume is only  $0.0018\lambda _{\mathbf {0}}^{\mathbf {3}}$ . A prototype of this optimized HCP antenna system was fabricated, assembled, and tested. The measured results are in good agreement with their simulated values. They demonstrate that the prototype HCP antenna resonates at 1584 MHz with a 0.6 dB axial ratio, and produces the predicted Huygens cardioid-shaped radiation patterns. The measured peak realized LHCP gain was 2.7 dBic, and the associated front-to-back ratio was 17.7 dB.
Optimal autoscaling in a IaaS cloud

An application provider leases resources (i.e., virtual machine instances) of variable configurations from a IaaS provider over some lease duration (typically one hour). The application provider (i.e., consumer) would like to minimize their cost while meeting all service level obligations (SLOs). The mechanism of adding and removing resources at runtime is referred to as autoscaling. The process of autoscaling is automated through the use of a management component referred to as an autoscaler. This paper introduces a novel autoscaling approach in which both cloud and application dynamics are modeled in the context of a stochastic, model predictive control problem. The approach exploits trade-off between satisfying performance related objectives for the consumer's application while minimizing their cost. Simulation results are presented demonstrating the efficacy of this new approach.
ARES: An Application of Impossible Spaces for Natural Locomotion in VR

Natural locomotion in room-scale virtual reality (VR) is constrained by the user's immediate physical space. To overcome this obstacle, researchers have established the use of the impossible space design mechanic. This game illustrates the applied use of impossible spaces for enhancing the aesthetics of, and presence within, a room-scale VR game. This is done by creating impossible spaces with a gaming narrative intent. First, locomotion and impossible spaces in VR are surveyed; second, a VR game called Ares is put forth as a prototype; and third, a user study is briefly explored.
N 400 amplitudes as change in a probabilistic representation of meaning : A neural network model

The N400 component of the event-related brain potential has aroused much interest because it is thought to provide an online measure of meaning processing in the brain. Yet, the underlying process of meaning construction remains incompletely understood. Here, we present a computationally explicit account of this process and the emerging representation of sentence meaning. We simulate N400 amplitudes as the change induced by an incoming stimulus in an implicit and probabilistic representation of meaning captured by the hidden unit activation pattern in a neural network model of sentence comprehension, and propose that the process underlying the N400 also drives implicit learning in the network. We account for a broad range of empirically observed N400 effects which have previously been difficult to capture within a single integrated framework.
The Charcot Lecture | beating MS: a story of B cells, with twists and turns.

Autoimmune B cells play a major role in mediating tissue damage in multiple sclerosis (MS). In MS, B cells are believed to cross the blood-brain barrier and undergo stimulation, antigen-driven affinity maturation and clonal expansion within the supportive CNS environment. These highly restricted populations of clonally expanded B cells and plasma cells can be detected in MS lesions, in cerebrospinal fluid, and also in peripheral blood. In phase II trials in relapsing MS, monoclonal antibodies that target circulating CD20-positive B lymphocytes dramatically reduced disease activity. These beneficial effects occurred within weeks of treatment, indicating that a direct effect on B cells--and likely not on putative autoantibodies--was responsible. The discovery that depletion of B cells has an impact on MS biology enabled a paradigm shift in understanding how the inflammatory phase of MS develops, and will hopefully lead to development of increasingly selective therapies against culprit B cells and related humoral immune system pathways. More broadly, these studies illustrate how lessons learned from the bedside have unique power to inform translational research. They highlight the essential role of clinician scientists, currently endangered, who navigate the rocky and often unpredictable terrain between the worlds of clinical medicine and biomedical research.
Accurate Segmentation of Partially Overlapping Cervical Cells Based on Dynamic Sparse Contour Searching and GVF Snake Model

Overlapping cells segmentation is one of the challenging topics in medical image processing. In this paper, we propose to approximately represent the cell contour as a set of sparse contour points, which can be further partitioned into two parts: the strong contour points and the weak contour points. We consider the cell contour extraction as a contour points locating problem and propose an effective and robust framework for segmentation of partially overlapping cells in cervical smear images. First, the cell nucleus and the background are extracted by a morphological filtering-based K-means clustering algorithm. Second, a gradient decomposition-based edge enhancement method is developed for enhancing the true edges belonging to the center cell. Then, a dynamic sparse contour searching algorithm is proposed to gradually locate the weak contour points in the cell overlapping regions based on the strong contour points. This algorithm involves the least squares estimation and a dynamic searching principle, and is thus effective to cope with the cell overlapping problem. Using the located contour points, the Gradient Vector Flow Snake model is finally employed to extract the accurate cell contour. Experiments have been performed on two cervical smear image datasets containing both single cells and partially overlapping cells. The high accuracy of the cell contour extraction result validates the effectiveness of the proposed method.
A Smart Power Meter to Monitor Energy Flow in Smart Grids: The Role of Advanced Sensing and IoT in the Electric Grid of the Future

This paper aims to describe the role of advanced sensing systems in the electric grid of the future. In detail, the project, development, and experimental validation of a smart power meter are described in the following. The authors provide an outline of the potentialities of the sensing systems and IoT to monitor efficiently the energy flow among nodes of an electric network. The described power meter uses the metrics proposed in the IEEE Standard 1459–2010 to analyze and process voltage and current signals. Information concerning the power consumption and power quality could allow the power grid to route efficiently the energy by means of more suitable decision criteria. The new scenario has changed the way to exchange energy in the grid. Now, energy flow must be able to change its direction according to needs. Energy cannot be now routed by considering just only the criterion based on the simple shortening of transmission path. So, even energy coming from a far node should be preferred, if it has higher quality standards. In this view, the proposed smart power meter intends to support the smart power grid to monitor electricity among different nodes in an efficient and effective way.
Along-Track Focusing of Airborne Radar Sounding Data From West Antarctica for Improving Basal Reflection Analysis and Layer Detection

This paper presents focused synthetic aperture radar (SAR) processing of airborne radar sounding data acquired with the High-Capability Radar Sounder system at 60 MHz. The motivation is to improve basal reflection analysis for water detection and to improve layer detection and tracking. The processing and reflection analyses are applied to data from Kamb Ice Stream, West Antarctica. The SAR processor correlates the radar data with reference echoes from subsurface point targets. The references are 1-D responses limited by the pulse nadir footprint or 2-D responses that include echo tails. Unfocused SAR and incoherent integration are included for comparison. Echoes are accurately preserved from along-track slopes up to about 0.5deg for unfocused SAR, 3deg for 1-D correlations, and 10deg for 2-D correlations. The noise/clutter levels increase from unfocused SAR to 1-D and 2-D correlations, but additional gain compensates at the basal interface. The basal echo signal-to-noise ratio improvement is typically about 5 dB, and up to 10 dB for 2-D correlations in rough regions. The increased noise degrades the clarity of internal layers in the 2-D correlations, but detection of layers with slopes greater than 3deg is improved. Reflection coefficients are computed for basal water detection, and the results are compared for the different processing methods. There is a significant increase in the detected water from unfocused SAR to 1-D correlations, indicating that substantial basal water exists on moderately sloped interfaces. Very little additional water is detected from the 2-D correlations. The results from incoherent integration are close to the focused SAR results, but the noise/clutter levels are much greater.
Ultrasound-guided injection of botulinum toxin in a patient with omohyoid muscle syndrome: a case report.

Omohyoid muscle syndrome has a characteristic feature of a protruding lateral neck mass during swallowing. We present a case of omohyoid muscle syndrome diagnosed based on the clinical presentation and a dynamic imaging study. Botulinum toxin was injected into the inferior belly of one omohyoid muscle, and the neck mass resolved.
A Framework for the Construction of Monolingual and Cross-lingual Word Similarity Datasets

Despite being one of the most popular tasks in lexical semantics, word similarity has often been limited to the English language. Other languages, even those that are widely spoken such as Spanish, do not have a reliable word similarity evaluation framework. We put forward robust methodologies for the extension of existing English datasets to other languages, both at monolingual and cross-lingual levels. We propose an automatic standardization for the construction of cross-lingual similarity datasets, and provide an evaluation, demonstrating its reliability and robustness. Based on our procedure and taking the RG-65 word similarity dataset as a reference, we release two high-quality Spanish and Farsi (Persian) monolingual datasets, and fifteen cross-lingual datasets for six languages: English, Spanish, French, German, Portuguese, and Farsi.
Brahmanical Idealism, Anarchical Individualism, and the Dynamics of Indian Negotiating Behavior

can be found at: International Journal of Cross Cultural Management Additional services and information for http://ccm.sagepub.com/cgi/alerts  Email Alerts: http://ccm.sagepub.com/subscriptions  Subscriptions: http://www.sagepub.com/journalsReprints.nav Reprints: http://www.sagepub.com/journalsPermissions.nav Permissions: http://ccm.sagepub.com/cgi/content/refs/4/1/39 SAGE Journals Online and HighWire Press platforms): (this article cites 32 articles hosted on the Citations