LLukas22/scidocs huggingface dataset:
Learning in the presence of concept drift and hidden contexts

On-line learning in domains where the target concept depends on some hidden context poses serious problems. A changing context can induce changes in the target concepts, producing what is known as concept drift. We describe a family of learning algorithms that flexibly react to concept drift and can take advantage of situations where contexts reappear. The general approach underlying all these algorithms consists of (1) keeping only a window of currently trusted examples and hypotheses; (2) storing concept descriptions and reusing them when a previous context re-appears; and (3) controlling both of these functions by a heuristic that constantly monitors the system's behavior. The paper reports on experiments that test the systems' perfomance under various conditions such as different levels of noise and different extent and rate of concept drift.
EAU guidelines on renal cell carcinoma: the 2010 update.

CONTEXT AND OBJECTIVES
The European Association of Urology Guideline Group for renal cell carcinoma (RCC) has prepared these guidelines to help clinicians assess the current evidence-based management of RCC and to incorporate the present recommendations into daily clinical practice.


EVIDENCE ACQUISITION
The recommendations provided in the current updated guidelines are based on a thorough review of available RCC guidelines and review articles combined with a systematic literature search using Medline and the Cochrane Central Register of Controlled Trials.


EVIDENCE SYNTHESIS
A number of recent prospective randomised studies concerning RCC are now available with a high level of evidence, whereas earlier publications were based on retrospective analyses, including some larger multicentre validation studies, meta-analyses, and well-designed controlled studies.


CONCLUSIONS
These guidelines contain information for the treatment of an individual patient according to a current standardised general approach. Updated recommendations concerning diagnosis, treatment, and follow-up can improve the clinical handling of patients with RCC.
Hypertext versions of journal articles: computer-aided linking and realistic human-based evaluation

My overall objective is to develop and evaluate ways of automatically incorporating hypertext links into pre-existing scholarly journal articles. I describe a rule-based approach for making three types of links (structural, de nition, and semantic). Structural links are a way of making explicit some connections between parts of the text. De nition links connect the use of a term, de ned elsewhere in the document, to that de nition. Links that connect parts of text that discuss similar things are semantic links. I distinguish several types of semantic links. I use two information retrieval (IR) systems (Cornell's SMART system and Bellcore's Latent Semantic Indexing) to select links based on the content of the articles. I conducted an experiment to compare the performance of the links forged using these two systems. The e ectiveness of the links (and the rules used to make them) is tested by people reading the hypertext versions for information under a time constraint. A within-subjects experimental design was used. Each of the nineteen experimental participants read one version of each of three scholarly articles in a di erent hypertext form (one had only simple links, the others had de nition links and semantic links selected using one of the IR systems). Subjects' preferences were also measured Although I used three survey articles from published sources for my evaluation experiment there was no di erence in reader preference or performance on the basis of article. Subjects ratings of the utility of the various links shows a signi cant preference for structural links over semantic links. De nition links were preferred to structural links, although the result was not signi cant. No di erence between the links created using the two IR systems was detected. iii However there were signi cant di erences in the times that readers spent on documents created using the various treatments When they read in documents with only structural links readers were more likely to have read the whole article, and their satisfaction scores were inversely proportional to their comprehension score. The method of evaluating hypertext versions of journal articles for use by researchers may be applied to other hypertext versions.
Eccentric contractions disrupt FKBP12 content in mouse skeletal muscle

Strength deficits associated with eccentric contraction-induced muscle injury stem, in part, from impaired voltage-gated sarcoplasmic reticulum (SR) Ca(2+) release. FKBP12 is a 12-kD immunophilin known to bind to the SR Ca(2+) release channel (ryanodine receptor, RyR1) and plays an important role in excitation-contraction coupling. To assess the effects of eccentric contractions on FKBP12 content, we measured anterior crural muscle (tibialis anterior [TA], extensor digitorum longus [EDL], extensor hallucis longus muscles) strength and FKBP12 content in pellet and supernatant fractions after centrifugation via immunoblotting from mice before and after a single bout of either 150 eccentric or concentric contractions. There were no changes in peak isometric torque or FKBP12 content in TA muscles after concentric contractions. However, FKBP12 content was reduced in the pelleted fraction immediately after eccentric contractions, and increased in the soluble protein fraction 3 day after injury induction. FKBP12 content was correlated (P = 0.025; R(2) = 0.38) to strength deficits immediately after injury induction. In summary, eccentric contraction-induced muscle injury is associated with significant alterations in FKBP12 content after injury, and is correlated with changes in peak isometric torque.
Educating Experienced IT Professionals by Addressing Industry's Needs

Academia and industry disagree about what students should learn in school versus on the job. When the University of Alabama at Birmingham developed a graduate program in electrical and computer engineering, they consulted industry executives to develop a curriculum that addresses industry's needs without compromising academic fundamentals.
Endogenous auxin and jasmonic acid levels are differentially modulated by abiotic stresses in rice

Abiotic stresses such as drought, salinity, and adverse temperatures are major limiting factors for plant growth and reproduction. Plant responses to these stresses are coordinated by arrays of regulatory networks including the induction of endogenous abscisic acid (ABA), a well documented phytohormone for stress responses. However, whether or how these abiotic stresses affect the endogenous biosynthesis or metabolism of other phytohormones remains largely unknown. Here, we report the changes of endogenous indole-3-acetic acid (IAA) and jasmonic acid (JA) levels and expression of genes related to the biosynthesis or signaling of these hormones in rice under various abiotic stress conditions. The IAA content was decreased after drought stress, but it was significantly increased under cold and heat stresses. And the auxin-regulated gravitropism of root tip was inhibited by cold stress. Many genes involved in the IAA biosynthesis and signaling were changed in transcript level under these stresses, and the changes were essentially in agreement with the change of endogenous IAA level. Interestingly, the endogenous JA content was increased markedly under drought and cold stresses, but it was reduced by heat stress. Accordingly, many genes involved in JA biosynthesis and signaling were induced by drought and cold treatment but these genes were significantly suppressed by heat stress. We concluded that endogenous levels of IAA and JA were differentially regulated by abiotic stresses in rice, implying diverse roles of these hormones in stress responses.
Better physician-patient relationships are associated with higher reported adherence to antiretroviral therapy in patients with HIV infection

BACKGROUND: There is little evidence to support the widely accepted assertion that better physician-patient relationships result in higher rates of adherence with recommended therapies. OBJECTIVE: To determine whether and which aspects of a better physician-patient relationship are associated with higher rates of adherence with antiretroviral therapies for persons with HIV infection. DESIGN: Cross-sectional analysis. SETTING: Twenty-two outpatient HIV practices in a metropolitan area. PARTICIPANTS: Five hundred fifty-four patients with HIV infection taking antiretroviral medications. MEASUREMENTS: We measured adherence using a 4-item self-report scale (α=0.75). We measured core aspects of physician-patient relationships using 6 previously tested scales (general communication, HIV-specific information, participatory decision making, overall satisfaction, willingness to recommend physician, and physician trust; α>0.70 for all) and 1 new scale, adherence dialogue (α=0.92). For adherence dialogue, patients rated their physician at understanding and solving problems with antiretroviral therapy regimens. RESULTS: Mean patient age was 42 years, 15% were female, 73% were white, and 57% reported gay or bisexual sexual contact as their primary HIV risk factor. In multivariable models that accounted for the clustering of patients within physicians’ practices, 6 of the 7 physician-patient relationship quality variables were significantly (P<.05) associated with adherence. In all 7 models worse adherence was independently associated (P<.05) with lower age, not believing in the importance of antiretroviral therapy, and worse mental health. CONCLUSIONS: This study showed that multiple, mutable dimensions of the physician-patient relationship were associated with medication adherence in persons with HIV infection, suggesting that physician-patient relationship quality is a potentially important point of intervention to improve patients’ medication adherence. In addition, our data suggest that it is critical to investigate and incorporate patients’ belief systems about antiretroviral therapy into adherence discussions, and to identify and treat mental disorders.
Robust Large-Scale Localization in 3D Point Clouds Revisited

We tackle the problem of getting a full 6-DOF pose estimation of a query image inside a given point cloud. This technical report re-evaluates the algorithms proposed by Y. Li et al. “Worldwide Pose Estimation using 3D Point Cloud” [1]. Our code computes poses from 3 or 4 points, with both known and unknown focal length. The results can easily be displayed and analyzed with Meshlab. We found both advantages and shortcomings of the methods proposed. Furthermore, additional priors and parameters for point selection, RANSAC and pose quality estimate (inlier test) are proposed and applied.
A fast-switching current-pulse driver for LED backlight

In this paper, a fast-switching current-pulse driver for light emitting diodes (LED) backlight is proposed. It uses a regulated drain current mirror (RD-CM) [1] and a high-voltage NMOS transistor (HV-NMOS). It achieves the fast-response current-pulse switching by using a dynamic gain-boosting amplifier (DGB-AMP). The DGB-AMP does not discharge the large HV-NMOS gate capacitance of the RD-CM when the output current switch turns off. Therefore, it does not need to charge the HV-NMOS gate capacitance when the switch turns on. The proposed current-pulse driver achieves the fast current switching by removing the repetitive gate discharging and charging. Simulation results were verified with measurements performed on a fabricated chip using a 5V/40V 0.5um BCD process. It reduces the switching delay to 360ns from 700ns of the conventional current-pulse driver.
Diabetes mellitus and abnormal glucose tolerance development after gestational diabetes: A three-year, prospective, randomized, clinical-based, Mediterranean lifestyle interventional study with parallel groups.

BACKGROUND & AIMS
Women with prior gestational diabetes mellitus (GDM) have a high risk of developing type 2 diabetes mellitus (DM2) in later life. The study aim was to evaluate the efficacy of a lifestyle intervention for the prevention of glucose disorders (impaired fasting glucose, impaired glucose tolerance or DM2) in women with prior GDM.


METHODS
A total of 260 women with prior GDM who presented with normal fasting plasma glucose at six to twelve weeks postpartum were randomized into two groups: a Mediterranean lifestyle intervention group (n = 130) who underwent an educational program on nutrition and a monitored physical activity program and a control group (n = 130) with a conventional follow-up. A total of 237 women completed the three-year follow-up (126 in the intervention group and 111 in the control group). Their glucose disorders rates, clinical and metabolic changes and rates of adherence to the Mediterranean lifestyle were analyzed.


RESULTS
Less women in the intervention group (42.8%) developed glucose disorders at the end of the three-year follow-up period compared with the control group (56.75%), p < 0.05. The multivariate analysis indicated a reduction in the rate of glucose disorders with a BMI of less than 27 kg/m(2) (OR 0.28; 0.12-0.65; p < 0.003), low fat intake pattern (OR 0.30; 0.13-0.70; p < 0.005), low saturated fat pattern (OR 0.30; 0.13-0.69; p < 0.005) and healthy fat pattern (OR 0.34; 0.12-0.94; p < 0.04).


CONCLUSIONS
Lifestyle intervention was effective for the prevention of glucose disorders in women with prior GDM. Body weight gain and an unhealthy fat intake pattern were found to be the most predictive factors for the development of glucose disorders. Current Controlled trials: ISRCTN24165302. http://www.controlled-trials.com/isrctn/pf/24165302.
Grid connected Converters for Photovoltaic, State of the Art, Ideas for Improvement of Transformerless Inverters

The paper presents a short overview of the state of the art for grid tied PV inverters at low and medium power level (1..100 kW), mainly intended for rooftop applications. The inverters are categorized according to the configuration of the PV system, the configuration of the conversion stages within the inverter and whether they use transformers or not [1], [3]. After the introduction of the state of the art of inverters for PV systems with and without transformers, the paper focuses on some known problems and challenges for transformerless inverters. Topologies without transformers have big advantages like low weight, volume and cost. In addition they often reach higher efficiencies than topologies with transformers. Therefore they are important for future developments.
Classification of Underwater Signals Using Neural Networks

In this paper, four kinds of neural network classifiers have been used for the classification of underwater passive sonar signals radiated by ships. Classification process can be divided into two stages. In the preprocessing and feature extraction stage, Two-Pass Split-Windows (TPSW) algorithm is used to extract tonal features from the average power spectral density (APSD) of the input data. In the classification stage, four kinds of static neural network classifiers are used to evaluate the classification results, inclusive of the probabilistic based classifier-Probabilistic Neural Network (PNN), the hyperplane based classifier-Multilayer Perceptron (MLP), the kernel based classifierAdaptive Kernel Classifier (AKC), and the exemplar based classifierLearning Vector Quantization (LVQ). For comparison, the same classifiers but using Dyadic Wavelet Transform (DWT) in the feature extraction are used to evaluate the performance of the proposed method. Experimental results show that feature extraction using TPSW gives better classification performance than using DWT, but require more computation time. In neural network classifiers, exemplar classifiers give better performance than the others both in learning speed and classification rate. Moreover, the classifier using LVQ families with data extracted by DWT can reach the same correction rate (100%) as the classifier using various networks with data extracted by TPSW. Detail discussion for experimental results are also included.
Parameter determination of support vector machine and feature selection using simulated annealing approach

Support vector machine (SVM) is a novel pattern classification method that is valuable in many applications. Kernel parameter setting in the SVM training process, along with the feature selection, significantly affects classification accuracy. The objective of this study is to obtain the better parameter values while also finding a subset of features that does not degrade the SVM classification accuracy. This study develops a simulated annealing (SA) approach for parameter determination and feature selection in the SVM, termed SA-SVM. To measure the proposed SA-SVM approach, several datasets in UCI machine learning repository are adopted to calculate the classification accuracy rate. The proposed approach was compared with grid search which is a conventional method of performing parameter setting, and various other methods. Experimental results indicate that the classification accuracy rates of the proposed approach exceed those of grid search and other approaches. The SA-SVM is thus useful for parameter determination and feature selection in the SVM. # 2007 Elsevier B.V. All rights reserved.
eproducibility and Accuracy of Echocardiographic easurements of Left Ventricular Parameters sing Real-Time Three-Dimensional Echocardiography

OBJECTIVES We sought to determine whether assessment of left ventricular (LV) function with real-time (RT) three-dimensional echocardiography (3DE) could reduce the variation of sequential LV measurements and provide greater accuracy than two-dimensional echocardiography (2DE). BACKGROUND Real-time 3DE has become feasible as a standard clinical tool, but its accuracy for LV assessment has not been validated. METHODS Unselected patients (n 50; 41 men; age, 64 8 years) presenting for evaluation of LV function were studied with 2DE and RT-3DE. Test-retest variation was performed by a complete restudy by a separate sonographer within 1 h without alteration of hemodynamics or therapy. Magnetic resonance imaging (MRI) images were obtained during a breath-hold, and measurements were made off-line. RESULTS The test-retest variation showed similar measurements for volumes but wider scatter of LV mass measurements with M-mode and 2DE than 3DE. The average MRI end-diastolic volume was 172 53 ml; LV volumes were underestimated by 2DE (mean difference, 54 33; p 0.01) but only slightly by RT-3DE ( 4 29; p 0.31). Similarly, end-systolic volume by MRI (91 53 ml) was underestimated by 2DE (mean difference, 28 28; p 0.01) and by RT-3DE (mean difference, 3 18; p 0.23). Ejection fraction by MRI was similar by 2DE (p 0.76) and RT-3DE (p 0.74). Left ventricular mass (183 50 g) was overestimated by M-mode (mean difference, 68 86 g; p 0.01) and 2DE (16 57; p 0.04) but not RT-3DE (0 38 g; p 0.94). There was good interand intra-observer correlation between RT-3DE by two sonographers for volumes, ejection fraction, and mass. CONCLUSIONS Real-time 3DE is a feasible approach to reduce test-retest variation of LV volume, ejection fraction, and mass measurements in follow-up LV assessment in daily practice. (J Am Coll Cardiol 2004;44:878–86) © 2004 by the American College of Cardiology Foundation
Image Segmentation Based on 2D Otsu Method with Histogram Analysis

Image segmentation plays an important role in image analysis and computer vision system. Among all segmentation techniques, the automatic thresholding methods are widely used because of their advantages of simple implement and time saving. Otsu method is one of thresholding methods and frequently used in various fields. Two-dimensional (2D) Otsu method behaves well in segmenting images of low signal-to-noise ratio than one-dimensional (1D). But it gives satisfactory results only when the numbers of pixels in each class are close to each other. Otherwise, it gives the improper results. In this paper, 2D histogram projection is used to correct the Otsu threshold. The 1D histograms are acquired by 2D histogram projection in x and y axes and a fast algorithm for searching the extrema of the projected histogram is proposed based on the wavelet transform in this paper. Experimental results show that the proposed method performs better than the traditional Otsu method for our renal biopsy samples.
Review of Relation Extraction Methods: What Is New Out There?

Relation extraction is a part of Information Extraction and an established task in Natural Language Processing. This paper presents an overview of the main directions of research and recent advances in the field. It reviews various techniques used for relation extraction including knowledge-based, supervised and self-supervised methods. We also mention applications of relation extraction and identify current trends in the way the field is developing.
An algebraic SU(1,1) solution for the relativistic hydrogen atom

Abstract The bound eigenfunctions and spectrum of a Dirac hydrogen atom are found taking advantage of the SU ( 1 , 1 ) Lie algebra in which the radial part of the problem can be expressed. For defining the algebra we need to add to the description an additional angular variable playing essentially the role of a phase. The operators spanning the algebra are used for defining ladder operators for the radial eigenfunctions of the relativistic hydrogen atom and for evaluating its energy spectrum. The status of the Johnson–Lippman operator in this algebra is also investigated.
Parallel Processing Systems for Big Data: A Survey

The volume, variety, and velocity properties of big data and the valuable information it contains have motivated the investigation of many new parallel data processing systems in addition to the approaches using traditional database management systems (DBMSs). MapReduce pioneered this paradigm change and rapidly became the primary big data processing system for its simplicity, scalability, and fine-grain fault tolerance. However, compared with DBMSs, MapReduce also arouses controversy in processing efficiency, low-level abstraction, and rigid dataflow. Inspired by MapReduce, nowadays the big data systems are blooming. Some of them follow MapReduce's idea, but with more flexible models for general-purpose usage. Some absorb the advantages of DBMSs with higher abstraction. There are also specific systems for certain applications, such as machine learning and stream data processing. To explore new research opportunities and assist users in selecting suitable processing systems for specific applications, this survey paper will give a high-level overview of the existing parallel data processing systems categorized by the data input as batch processing, stream processing, graph processing, and machine learning processing and introduce representative projects in each category. As the pioneer, the original MapReduce system, as well as its active variants and extensions on dataflow, data access, parameter tuning, communication, and energy optimizations will be discussed at first. System benchmarks and open issues for big data processing will also be studied in this survey.