LLukas22/scidocs huggingface dataset:
Reconstituting knowledge management

Purpose – The purpose of the paper is to improve traditional knowledge management models in light of complexity theory, emphasizing the importance of moving away from hierarchical relationships among data, information, knowledge, and wisdom. Design/methodology/approach – Traditional definitions and models are critically reviewed and their weaknesses highlighted. A transformational perspective of the traditional hierarchies is proposed to highlight the need to develop better perspectives. The paper demonstrates the holistic nature of data, information, knowledge, and wisdom, and how they are all based on an interpretation of existence. Findings – Existing models are logically extended, by adopting a complexity-based perspective, to propose a new model – the E2E model – which highlights the non-linear relationships among existence, data, information, knowledge, wisdom, and enlightenment, as well as the nature of understanding as the process that defines the differences among these constructs. The meaning of metas (such as meta-data, meta-information, and meta-knowledge) is discussed, and a reconstitution of knowledge management is proposed. Practical implications – The importance of understanding as a concept to create useful metaphors for knowledge management practitioners is emphasized, and the crucial importance of the metas for knowledge management is shown. Originality/value – A new model of the cognitive system of knowledge is proposed, based on application of complexity theory to knowledge management. Understanding is identified as the basis of the conversion process among an extended range of knowledge constructs, and the scope of knowledge management is redefined.
A Real Ghost Fluid Method for the Simulation of Multimedium Compressible Flow

In the previous ghost fluid methods (GFMs) developed, the focus is on the definition of ghost fluid states while the pressure and velocity in the real fluid sides are taken for granted, except for the correction made to the density at the real fluid nodes next to the interface to overcome the possible problems related to overheating. It has been found that such GFMs encounter many difficulties when applied to shock impedance matching (-like) problems due to the inability of accurately imposing interfacial conditions. By predicting the flow states for the real fluid nodes just next to the interface and the ghost fluid nodes using the Riemann problem solver, a more accurate interface boundary condition can be imposed and the said difficulties are mitigated to a large extent. This leads to the development of a proposed real-GFM in this work. A simple yet efficient extension of the present method to multidimensions is also introduced. In order to overcome issues associated with the severe bunching of level set contours due to the large flow velocity gradient, an extension (artificial) velocity field is constructed in the computation of the level set function. The present method is applied to various oneand two-dimensional problems involving strong shock-interface interaction and complex flow physics.
QUANTILE REGRESSION IN

Abstract. Quantile regression is an evolving body of statistical methods for estimating and drawing inferences about conditional quantile functions. An implementation of these methods in the R language is available in the package quantreg. This vignette offers a brief tutorial introduction to the package. R and the package quantreg are open-source software projects and can be freely downloaded from CRAN: http://cran.r-project.org.
An Implementation of the Hopcroft and Tarjan Planarity Test and Embedding Algorithm

We describe an implementation of the Hopcroft and Tarjan planarity test and em bedding algorithm The program tests the planarity of the input graph and either constructs a combinatorial embedding if the graph is planar or exhibits a Kuratowski subgraph if the graph is non planar
Tomato consumption and plasma lycopene concentration in people aged 65 y and over in a British national survey

Objective: Assessment of consumption of raw and processed tomatoes, plasma lycopene concentration and their interrelationship in a nationally representative sample of elderly British people.Setting: A 1994–1995 National Diet and Nutrition Survey: a nationwide cross-sectional sample of people aged 65 y and over living in mainland Britain, either in the community (‘free-living’, n=1275) or in institutions such as nursing homes (n=412).Methods: Consumption of raw and processed tomatoes (including those in tomato-containing composite foods) was recorded by a 4-day dietary record for each participant. Plasma lycopene concentration was assayed by HPLC. Sociodemographic and lifestyle information was recorded by trained interviewers.Results: In all, 29% of free-living people and 24% of those living in institutions did not consume any raw or processed tomatoes during the 4-day recording period. Free-living participants were more likely to consume raw tomatoes (26 vs 16%). Plasma lycopene concentration was positively associated with the weight of raw and processed tomatoes consumed, especially with processed tomatoes and their products, alcohol consumption and with plasma total (as well as HDL and non-HDL) cholesterol concentrations. Among free-living participants, increased age, cigarette smoking habit and manual occupational social class were all associated with lower plasma lycopene concentration.Conclusions: In the current population, consumption of raw and processed tomatoes varied by sociodemographic and lifestyle characteristics, and made a significant contribution to plasma lycopene concentration. Determinants of plasma lycopene concentration were age, plasma cholesterol concentration and smoking habit.
E-HowNet and Automatic Construction of a Lexical Ontology

In this paper, we propose a lexical senses representation system called E-HowNet, in which the lexical senses are defined by basic concepts. As a result, the meanings of expressions are more specific than those derived by using primitives. We also design an ontology to express the taxonomic relations between concepts and the attributes of concepts. To establish the taxonomic relations between word senses, we introduce a strategy that constructs the E-HowNet ontology automatically. We then implement the lexical ontology as a Web application1 to demonstrate the taxonomy and the search functions for querying key-terms and E-HowNet expressions in the lexicon, which contains more than 88,000 lexical senses.
A comprehensive survey of Network Function Virtualization

Today’s networks are filled with a massive and ever-growing variety of network functions that coupled with proprietary devices, which leads to network ossification and difficulty in network management and service provision. Network Function Virtualization (NFV) is a promising paradigm to change such situation by decoupling network functions from the underlying dedicated hardware and realizing them in the form of software, which are referred to as Virtual Network Functions (VNFs). Such decoupling introduces many benefits which include reduction of Capital Expenditure (CAPEX) and Operation Expense (OPEX), improved flexibility of service provision, etc. In this paper, we intend to present a comprehensive survey on NFV, which starts from the introduction of NFV motivations. Then, we explain the main concepts of NFV in terms of terminology, standardization and history, and how NFV differs from traditional middlebox based network. After that, the standard NFV architecture is introduced using a bottom up approach, based on which the corresponding use cases and solutions are also illustrated. In addition, due to the decoupling of network functionalities and hardware, people’s attention is gradually shifted to the VNFs. Next, we provide an extensive and in-depth discussion on state-of-the-art VNF algorithms including VNF placement, scheduling, migration, chaining and multicast. Finally, to accelerate the NFV deployment and avoid pitfalls as far as possible, we survey the challenges faced by NFV and the trend for future directions. In particular, the challenges are discussed from bottom up, which include hardware design, VNF deployment, VNF life cycle control, service chaining, performance evaluation, policy enforcement, energy efficiency, reliability and security, and the future directions are discussed around the current trend towards network softwarization. © 2018 Elsevier B.V. All rights reserved.
Yield curve shapes and the asymptotic short rate distribution in affine one-factor models

We consider a model for interest rates where the short rate is given under the risk-neutral measure by a time-homogeneous one-dimensional affine process in the sense of Duffie, Filipović, and Schachermayer. We show that in such a model yield curves can only be normal, inverse, or humped (i.e., endowed with a single local maximum). Each case can be characterized by simple conditions on the present short rate rt . We give conditions under which the short rate process converges to a limit distribution and describe the risk-neutral limit distribution in terms of its cumulant generating function. We apply our results to the Vasiček model, the CIR model, a CIR model with added jumps, and a model of Ornstein–Uhlenbeck type.
A portable, extensible and fast stochastic volatility model calibration using multi and many-core processors

Financial markets change precipitously and on-demand pricing and risk models must be constantly recalibrated to reduce risk. However, certain classes of models are computationally intensive to robustly calibrate to intraday pricesstochastic volatility models being an archetypal example due to the nonconvexity of the objective function. In order to accelerate this procedure through parallel implementation, financial application developers are faced with an ever growing plethora of low-level high-performance computing frameworks such as OpenMP, OpenCL, CUDA, or SIMD intrinsics, and forced to make a tradeoff between performance versus the portability, flexibility and modularity of the code required to facilitate rapid in-house model development and productionization. This paper describes the acceleration of stochastic volatility model calibration on multi-core CPUs and GPUs using the Xcelerit platform. By adopting a simple programming model, the Xcelerit platform enables the application developer to write sequential, high-level C++ code, without concern for low-level highperformance computing frameworks. This platform provides the portability, flexibility and modularity required by application developers. Speedups of up to 30x and 293x are respectively achieved on an Intel Xeon CPU and NVIDIA Tesla K40 GPU, compared to a sequential CPU implementation. The Xcelerit platform implementation is further shown to be equivalent in performance to a low-level CUDA version. Overall, we are able to reduce the entire calibration process time of the sequential implementation from 6, 189 seconds to 183.8 and 17.8 seconds on the CPU and GPU respectively without requiring the developer to reimplement in low-level high performance computing frameworks. Copyright c © 2010 John Wiley & Sons, Ltd.
Lipid metabolism enzyme ACSVL3 supports glioblastoma stem cell maintenance and tumorigenicity

Targeting cell metabolism offers promising opportunities for the development of drugs to treat cancer. We previously found that the fatty acyl-CoA synthetase VL3 (ACSVL3) is elevated in malignant brain tumor tissues and involved in tumorigenesis. This study investigates the role of ACSVL3 in the maintenance of glioblastoma multiforme (GBM) stem cell self-renewal and the capacity of GBM stem cells to initiate tumor xenograft formation. We examined ACSVL3 expression during differentiation of several GBM stem cell enriched neurosphere cultures. To study the function of ACSVL3, we performed loss-of-function by using small interfering RNAs to target ACSVL3 and examined stem cell marker expression, neurosphere formation and tumor initiation properties. ACSVL3 expression levels were substantially increased in GBM stem cell enriched neurosphere cultures and decreased after differentiation of the neurospheres. Down-regulating ACSVL3 with small inhibiting RNAs decreased the expression of markers and regulators associated with stem cell self-renewal, including CD133, ALDH, Musashi-1 and Sox-2. ACSVL3 knockdown in neurosphere cells led to increased expression of differentiation markers GFAP and Tuj1. Furthermore, ACSVL3 knockdown reduced anchorage-independent neurosphere cell growth, neurosphere-forming capacity as well as self-renewal of these GBM stem cell enriched neurosphere cultures. In vivo studies revealed that ACSVL3 loss-of-function substantially inhibited the ability of neurosphere cells to propagate orthotopic tumor xenografts. A link between ACSVL3 and cancer stem cell phenotype was further established by the findings that ACSVL3 expression was regulated by receptor tyrosine kinase pathways that support GBM stem cell self-renewal and tumor initiation, including EGFR and HGF/c-Met pathways. Our findings indicate that the lipid metabolism enzyme ACSVL3 is involved in GBM stem cell maintenance and the tumor-initiating capacity of GBM stem cell enriched-neurospheres in animals.
Cooperative Sybil Attack Detection for Position Based Applications in Privacy Preserved VANETs

In this paper, we propose a security protocol to detect sybil attacks for position based applications in privacy preserved vehicular ad hoc networks (VANETs). Vehicles in our protocol identify sybil attacks locally in a cooperative way by examining the rationality of vehicles' positions to their own neighbors. The attack detection utilizes the characteristics of communication and vehicles' GPS positions which are included in the periodically broadcasted safety related messages. No extra hardware and little communication and computation overhead will be introduced to vehicles. Therefore, our protocol is very light weighted and suitable for real applications. Moreover, a smart attacker scenario in which a malicious vehicle may adjust its communication range to avoid detection and the malicious vehicles' collusion scenario are also considered. Simulation results based on NS2 are presented to demonstrate the performance of the proposed protocol.
Recent Widespread Tree Growth Decline Despite Increasing Atmospheric CO2

BACKGROUND
The synergetic effects of recent rising atmospheric CO(2) and temperature are expected to favor tree growth in boreal and temperate forests. However, recent dendrochronological studies have shown site-specific unprecedented growth enhancements or declines. The question of whether either of these trends is caused by changes in the atmosphere remains unanswered because dendrochronology alone has not been able to clarify the physiological basis of such trends.


METHODOLOGY/PRINCIPAL FINDINGS
Here we combined standard dendrochronological methods with carbon isotopic analysis to investigate whether atmospheric changes enhanced water use efficiency (WUE) and growth of two deciduous and two coniferous tree species along a 9 degrees latitudinal gradient across temperate and boreal forests in Ontario, Canada. Our results show that although trees have had around 53% increases in WUE over the past century, growth decline (measured as a decrease in basal area increment--BAI) has been the prevalent response in recent decades irrespective of species identity and latitude. Since the 1950s, tree BAI was predominantly negatively correlated with warmer climates and/or positively correlated with precipitation, suggesting warming induced water stress. However, where growth declines were not explained by climate, WUE and BAI were linearly and positively correlated, showing that declines are not always attributable to warming induced stress and additional stressors may exist.


CONCLUSIONS
Our results show an unexpected widespread tree growth decline in temperate and boreal forests due to warming induced stress but are also suggestive of additional stressors. Rising atmospheric CO2 levels during the past century resulted in consistent increases in water use efficiency, but this did not prevent growth decline. These findings challenge current predictions of increasing terrestrial carbon stocks under climate change scenarios.
Part of Speech Tagging and Shallow Parsing of Indian Languages

This paper describes and evaluates shallow parsing of several Indian languages utilizing Conditional Random Field models. We show how performance can be substantially improved by several feature enhancements and improved modeling techniques, including expanding the chunk tag inventory, and separating punctuation from linguistic phrases. We also report results from part of speech tagging of Hindi, Bengali and Telugu using generative methods.
Monitoring of skeletal muscle oxygenation using near-infrared spectroscopy during abdominal aortic surgery

 Purpose. To examine the utility of near-infrared spectroscopy (NIRS) in assessing lower-leg perfusion, NIRS was performed on the calf muscles of patients who underwent abdominal aortic surgery. Methods. Thirty patients undergoing elective infrarenal abdominal aortic surgery for abdominal aortic aneurysm (AAA group; n = 16) and aorto-occlusive disease (AOD group; n = 14) were studied. Before induction of anesthesia, NIRS probes were placed over both calf muscles, and muscle oxygen saturation (StO2) was continuously monitored throughout the surgery. Results. The preoperative StO2 value was significantly lower in the AOD group (57.0 ± 11.2%) than in the AAA group (68.7 ± 7.0%). In both groups, StO2 significantly decreased after aortic cross-clamping; the maximal ischemic value of StO2 in the AAA group (17.8 ± 7.2%) was significantly lower than that in the AOD group (46.7 ± 17.1%). The time taken to reach maximal ischemia was significantly longer in the AAA group (30 ± 12 min) than in the AOD group (19 ± 12 min). After release of the aortic clamp, the decreased StO2 returned to the preoperative level in the AAA group, whereas it increased above the preoperative value in the AOD group. Conclusion. NIRS performed on the calf muscles is a useful method for assessing the changes in lower-leg perfusion during and after abdominal aortic surgery.
Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification

Summary
State-of-the-art light and electron microscopes are capable of acquiring large image datasets, but quantitatively evaluating the data often involves manually annotating structures of interest. This process is time-consuming and often a major bottleneck in the evaluation pipeline. To overcome this problem, we have introduced the Trainable Weka Segmentation (TWS), a machine learning tool that leverages a limited number of manual annotations in order to train a classifier and segment the remaining data automatically. In addition, TWS can provide unsupervised segmentation learning schemes (clustering) and can be customized to employ user-designed image features or classifiers.


Availability and Implementation
TWS is distributed as open-source software as part of the Fiji image processing distribution of ImageJ at http://imagej.net/Trainable_Weka_Segmentation .


Contact
ignacio.arganda@ehu.eus.


Supplementary information
Supplementary data are available at Bioinformatics online.
Enhanced Information Retrieval from Narrative German-language Clinical Text Documents using Automated Document Classification

The amount of narrative clinical text documents stored in Electronic Patient Records (EPR) of Hospital Information Systems is increasing. Physicians spend a lot of time finding relevant patient-related information for medical decision making in these clinical text documents. Thus, efficient and topical retrieval of relevant patient-related information is an important task in an EPR system. This paper describes the prototype of a medical information retrieval system (MIRS) for clinical text documents. The open-source information retrieval framework Apache Lucene has been used to implement the prototype of the MIRS. Additionally, a multi-label classification system based on the open-source data mining framework WEKA generates metadata from the clinical text document set. The metadata is used for influencing the rank order of documents retrieved by physicians. Combining information retrieval and automated document classification offers an enhanced approach to let physicians and in the near future patients define their information needs for information stored in an EPR. The system has been designed as a J2EE Web-application. First findings are based on a sample of 18,000 unstructured, clinical text documents written in German.
Prevalence and development of psychiatric disorders in childhood and adolescence.

BACKGROUND
This longitudinal community study assessed the prevalence and development of psychiatric disorders from age 9 through 16 years and examined homotypic and heterotypic continuity.


METHODS
A representative population sample of 1420 children aged 9 to 13 years at intake were assessed annually for DSM-IV disorders until age 16 years.


RESULTS
Although 3-month prevalence of any disorder averaged 13.3% (95% confidence interval [CI], 11.7%-15.0%), during the study period 36.7% of participants (31% of girls and 42% of boys) had at least 1 psychiatric disorder. Some disorders (social anxiety, panic, depression, and substance abuse) increased in prevalence, whereas others, including separation anxiety disorder and attention-deficit/hyperactivity disorder (ADHD), decreased. Lagged analyses showed that children with a history of psychiatric disorder were 3 times more likely than those with no previous disorder to have a diagnosis at any subsequent wave (odds ratio, 3.7; 95% CI, 2.9-4.9; P<.001). Risk from a previous diagnosis was high among both girls and boys, but it was significantly higher among girls. Continuity of the same disorder (homotypic) was significant for all disorders except specific phobias. Continuity from one diagnosis to another (heterotypic) was significant from depression to anxiety and anxiety to depression, from ADHD to oppositional defiant disorder, and from anxiety and conduct disorder to substance abuse. Almost all the heterotypic continuity was seen in girls.


CONCLUSIONS
The risk of having at least 1 psychiatric disorder by age 16 years is much higher than point estimates would suggest. Concurrent comorbidity and homotypic and heterotypic continuity are more marked in girls than in boys.