nils-herrmann/clean_scirepeval huggingface dataset:
Relationship between Transformational Leadership and Employee Retention among Healthcare Professionals in the United States

With the endemic health care professional shortage seen both in the United States as well as globally, retaining staff is a high priority. Much like other organizations who rely on human capital, health care professionals are the most important resource for improving patient outcomes, and for achieving organizational performance. Leaders must effectively manage their employees in order to retain top talent and meet organizational goals. One reason for high turnover rates among healthcare professionals is the lack of recognition they are given by their leaders. With this in mind, the purpose of this study was to examine the relationship between transformational leadership components and turnover intentions of health care professionals. One hundred and twenty seven healthcare professionals from the United States participated in the survey. Participants were recruited through a LinkedIn group of healthcare professionals. More than 100 healthcare professionals from the United States responded to an online survey that contained the Global Transformational Leadership Scale, The Turnover Intention Scale, and demographic questions to describe the sample. A correlational analysis was conducted to determine the type of relationship between the health care professionali s perceptions of their supervisorsi transformational leadership and their intent to turnover. The results of this study support the theory of transformational leadership. The health care professionalsi turnover intentions were negatively correlated with the transformational leadership components, which indicate that as their turnover intentions increased their positive rating of their supervisorsi transformational leadership behaviors decreased.
TRANSFORMATIONAL LEADERSHIP, EMPLOYEE OUTCOMES, AND PERFORMANCE OF UNIVERSITIES IN KENYA

University education in Kenya has undergone major reforms in the last ten years thatpose challenges to both employees and universities. To address these challenges, universities needproactive leadership and a motivated workforce which transformational leadership can provide asit has the ability to motivate and empower employees to better organizational performance. Thisstudy sought to determine the factors that influence the relationship between transformationalleadership and performance and if this relationship is direct or it is mediated by other factors. Thestudy employed a positivist approach to research and used a descriptive survey research design.Data was collected from the top leadership of the 52 fully fledged universities in Kenya using aquestionnaire. A response rate of 73% was realized. Descriptive statistics were used to obtain ageneral understanding of the universities while different statistical techniques such as regressionanalysis and correlation analysis were used to analyse data and test the hypotheses. The resultssupported all the hypotheses and showed positive and statistically significant relationships betweentransformational leadership and performance and between transformational leadership andemployee outcomes. Employee outcomes fully mediate the relationship between transformationalleadership and performance. It emerged that transformational leadership behaviour of the topleadership of universities in Kenya led to high employee performance and organizationaleffectiveness. Specifically the findings suggest that in terms of policy, universities need visionaryleadership and sound policies that will strengthen their position as a fundamental sector ingenerating human capital for the county's developmental and economic needs. In terms of practice,the findings of this study are useful to the leadership of Kenyan universities in the formulation ofstrategies for improving performance and in developing leadership training policies for universitiesin Kenya. The results of this study have significant implications for theory in that they add to thebody of knowledge on the mediating effect of employee outcomes on the relationship betweentransformational leadership and performance as well as the universality of the transformationaltransactionaltheory across nations and societies
Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change
Venue: ACL

Understanding how words change their meanings over time is key to models of language and cultural evolution, but historical data on meaning is scarce, making theories hard to develop and test. Word embeddings show promise as a diachronic tool, but have not been carefully evaluated. We develop a robust methodology for quantifying semantic change by evaluating word embeddings (PPMI, SVD, word2vec) against known historical changes. We then use this methodology to reveal statistical laws of semantic evolution. Using six historical corpora spanning four languages and two centuries, we propose two quantitative laws of semantic change: (i) the law of conformity the rate of semantic change scales with an inverse power law of word frequency; (ii) the law of innovation independent of frequency, words that are more polysemous have higher rates of semantic change.
Cooperative Inverse Reinforcement Learning
Venue: NIPS

For an autonomous system to be helpful to humans and to pose no unwarranted risks, it needs to align its values with those of the humans in its environment in such a way that its actions contribute to the maximization of value for the humans. We propose a formal definition of the value alignment problem as cooperative inverse reinforcement learning (CIRL) A CIRL problem is a cooperative, partial information game with two agents, human and robot; both are rewarded according to the human's reward function, but the robot does not initially know what this is. In contrast to classical IRL, where the human is assumed to act optimally in isolation, optimal CIRL solutions produce behaviors such as active teaching, active learning, and communicative actions that are more effective in achieving value alignment. We show that computing optimal joint policies in CIRL games can be reduced to solving a POMDP, prove that optimality in isolation is suboptimal in CIRL, and derive an approximate CIRL algorithm.
The SDSS IV extended baryon oscillation spectroscopic survey: Overview and early data

In a six year program started in 2014 July, the Extended Baryon Oscillation Spectroscopic Survey (eBOSS) will conduct novel cosmological observations using the BOSS spectrograph at Apache Point Observatory. These observations will be conducted simultaneously with the Time Domain Spectroscopic Survey (TDSS) designed for variability studies and the Spectroscopic Identification of eROSITA Sources (SPIDERS) program designed for studies of X ray sources. In particular, eBOSS will measure with percent level precision the distance redshift relation with baryon acoustic oscillations (BAO) in the clustering of matter. eBOSS will use four different tracers of the underlying matter density field to vastly expand the volume covered by BOSS and map the large scale structures over the relatively unconstrained redshift range 0.6 0.6 sample of BOSS galaxies. With ~195,000 new emission line galaxy redshifts, we expect BAO measurements of d_A(z) to an accuracy of 3.1% and H(z) to 4.7% at an effective redshift of z 0.87. A sample of more than 500,000 spectroscopically confirmed quasars will provide the first BAO distance measurements over the redshift range 0.9 2.1; these new data will enhance the precision of dA(z) and H(z) at z 2.1 by a factor of 1.44 relative to BOSS. Furthermore, eBOSS will provide improved tests of General Relativity on cosmological scales through redshift space distortion measurements, improved tests for non Gaussianity in the primordial density field, and new constraints on the summed mass of all neutrino species. Here, we provide an overview of the cosmological goals, spectroscopic target sample, demonstration of spectral quality from early data, and projected cosmological constraints from eBOSS.
Evaluation Measures for the SemEval 2016 Task 4 "Sentiment Analysis in Twitter" (Draft: Version 1.13)

This informal document details the evaluation measures that will be used in SemEval2016 Task 4 "Sentiment Analysis in Twitter" a revamped edition of SemEval 2015 Task 10 (Rosenthal et al. 2015) Note: changes that have been made to the present document since Version 1.0 onwards are typeset in blue. Task 4 consists of five subtasks; the evaluation measures that we will use for them will be discussed in Sections 1 to 5. Subtasks B to E conceptually form a "2x2 matrix"  where the rows indicate the goal of the task (classification vs. quantification) and the columns indicate the granularity of the task (two point scale vs. five point scale) Note that, for Subtasks B to E, the dataset is subdivided into a number of "topics" and the subtask needs to be carried out independently for each topic. As a result, each of the evaluation measures described below is "macroaveraged" across the topics, i.e. we compute the measure individually for each topic, and we then average the results across the topics.
Association of AR V7 on Circulating Tumor Cells as a Treatment Specific Biomarker With Outcomes and Survival in Castration Resistant Prostate Cancer.
Venue: JAMA oncology

A critical decision in the management of metastatic castration resistant prostate cancer (mCRPC) is when to administer an androgen receptor signaling (ARS) inhibitor or a taxane.  To determine if pretherapy nuclear androgen receptor splice variant 7 (AR V7) protein expression and localization on circulating tumor cells (CTCs) is a treatment specific marker for response and outcomes between ARS inhibitors and taxanes.  because they were not initiating ARS or taxane therapy; and 18 were excluded for processing time constraints, leaving 161 patients for analysis. Between December 2012 and March 2015, blood was collected and processed from patients with progressive mCRPC immediately prior to new line of systemic therapy. Patients were followed up to 3 years.  Prostate specific antigen (PSA) response, time receiving therapy, radiographic progression free survival (rPFS) and overall survival (OS) Results Overall, of 193 prospectively collected blood samples from 161 men with mCRPC, 191 were evaluable (128 pre ARS inhibitor and 63 pretaxane) AR V7 positive CTCs were found in 34 samples (18% including 3% of first line, 18% of second line, and 31% of third or greater line samples. Patients whose samples had AR V7 positive CTCs before ARS inhibition had resistant posttherapy PSA changes (PTPC) shorter rPFS, shorter time on therapy, and shorter OS than those without AR V7 positive CTCs. Overall, resistant PTPC were seen in 65 of 112 samples (58% without detectable AR V7 positive CTCs prior to ARS inhibition. There were statistically significant differences in OS but not in PTPC, time on therapy, or rPFS for patients with or without pretherapy AR V7 positive CTCs treated with a taxane. A multivariable model adjusting for baseline factors associated with survival showed superior OS with taxanes relative to ARS inhibitors when AR V7 positive CTCs were detected pretherapy (hazard ratio, 0.24; 95% CI, 0.10 0.57; P .035)  The results validate CTC nuclear expression of AR V7 protein in men with mCRPC as a treatment specific biomarker that is associated with superior survival on taxane therapy over ARS directed therapy in a clinical practice setting. Continued examination of this biomarker in prospective studies will further aid clinical utility.
Clinical Applications of Circulating Tumor Cells and Circulating Tumor DNA as Liquid Biopsy.
Venue: Cancer discovery

UNLABELLED "Liquid biopsy" focusing on the analysis of circulating tumor cells (CTC) and circulating cell free tumor DNA (ctDNA) in the blood of patients with cancer has received enormous attention because of its obvious clinical implications for personalized medicine. Analyses of CTCs and ctDNA have paved new diagnostic avenues and are, to date, the cornerstones of liquid biopsy diagnostics. The present review focuses on key areas of clinical applications of CTCs and ctDNA, including detection of cancer, prediction of prognosis in patients with curable disease, monitoring systemic therapies, and stratification of patients based on the detection of therapeutic targets or resistance mechanisms.  The application of CTCs and ctDNA for the early detection of cancer is of high public interest, but it faces serious challenges regarding specificity and sensitivity of the current assays. Prediction of prognosis in patients with curable disease can already be achieved in several tumor entities, particularly in breast cancer. Monitoring the success or failure of systemic therapies (i.e. chemotherapy, hormonal therapy, or other targeted therapies) by sequential measurements of CTCs or ctDNA is also feasible. Interventional studies on treatment stratification based on the analysis of CTCs and ctDNA are needed to implement liquid biopsy into personalized medicine. Cancer Discov; 6(5) 479 91. (c)2016 AACR.
The biology of circulating tumor cells
Venue: Oncogene

Metastasis is a biologically complex process consisting of numerous stochastic events which may tremendously differ across various cancer types. Circulating tumor cells (CTCs) are cells that are shed from primary tumors and metastatic deposits into the blood stream. CTCs bear a tremendous potential to improve our understanding of steps involved in the metastatic cascade, starting from intravasation of tumor cells into the circulation until the formation of clinically detectable metastasis. These efforts were propelled by novel high resolution approaches to dissect the genomes and transcriptomes of CTCs. Furthermore, capturing of viable CTCs has paved the way for innovative culturing technologies to study fundamental characteristics of CTCs such as invasiveness, their kinetics and responses to selection barriers, such as given therapies. Hence the study of CTCs is not only instrumental as a basic research tool, but also allows the serial monitoring of tumor genotypes and may therefore provide predictive and prognostic biomarkers for clinicians. Here, we review how CTCs have contributed to significant insights into the metastatic process and how they may be utilized in clinical practice.
Clusters of circulating tumor cells traverse capillary sized vessels
Venue: Proceedings of the National Academy of Sciences

Metastasis is responsible for 90% of cancer related deaths and is driven by tumor cells circulating in blood. However, it is believed that only individual tumor cells can reach distant organs because multicellular clusters are too large to pass through narrow capillaries. Here, we collected evidence by examining clusters in microscale devices, computational simulations, and animals, which suggest that this assumption is incorrect, and that clusters may transit through capillaries by unfolding into single file chains. This previously unidentified cell behavior may explain why previous experiments reported that clusters were more efficient at seeding metastases than equal numbers of single tumor cells, and has led to a strategy that, if applied clinically, may reduce the incidence of metastasis in patients. Multicellular aggregates of circulating tumor cells (CTC clusters) are potent initiators of distant organ metastasis. However, it is currently assumed that CTC clusters are too large to pass through narrow vessels to reach these organs. Here, we present evidence that challenges this assumption through the use of microfluidic devices designed to mimic human capillary constrictions and CTC clusters obtained from patient and cancer cell origins. Over 90% of clusters containing up to 20 cells successfully traversed 5 to 10 mm constrictions even in whole blood. Clusters rapidly and reversibly reorganized into single file chain like geometries that substantially reduced their hydrodynamic resistances. Xenotransplantation of human CTC clusters into zebrafish showed similar reorganization and transit through capillary sized vessels in vivo. Preliminary experiments demonstrated that clusters could be disrupted during transit using drugs that affected cellular interaction energies. These findings suggest that CTC clusters may contribute a greater role to tumor dissemination than previously believed and may point to strategies for combating CTC cluster initiated metastasis.
New Security Architecture for Big Data Hadoop

Hadoop is a system that provides distributed job processing on large clusters using commodity servers and distributed file system that allows storing and managing petabytes of data which is collected from disparate data sources. Although Hadoop is used on private clusters behind firewalls and physical security, Hadoop is often runs in public cloud for providing shared multi tenant service and used to store sensitive data; as a result strong authorization and authentication is required. Hadoop has two vulnerabilities that can dramatically impact its compromise resilience. That is, overloaded authentication key and the lack of fine grained access control at the data access level. Providing security to Hadoop is more challenging due to all the interactions do not follow the classic client server pattern: the file system is partitioned and distributed over the network, requiring authorization checks at multiple points; the system scales to thousands of servers and tens of thousands of concurrent tasks; submitted batch job is executed at a later time on nodes different from the node on which the client authenticated and submitted the job and jobs from different users are executed on the same node. To address these challenges, the base Kerberos authentication protocol is used which is supplemented by delegation, access token, and job tokens. This paper examines current security mechanisms of Hadoop clusters using Kerberos supplemented by other tokens, and then we analyzed the security problems and risks of Hadoop. Further, we proposed a new authentication protocol inside Hadoop and usage of modified tokens for improving security.
Network security analysis using Big Data technology
Venue: SoutheastCon 2016

With the evolution of networks, threats or attacks with the intention of disrupting service or stealing confidential data are increasing tremendously. Networks have to be monitored constantly and protected against attacks. In this paper, a new method to analyze network traffic using Big Data techniques is introduced. This approach detects anomalous activities being carried out and malicious data being transmitted over the networks through processing and loading traffic data into Hive database in Hadoop Distributed File System (HDFS) environment, and analyzing the data using Hive queries. The results of using this method to detect attacks on the sample dataset are also presented.
Infrared small target detection based on visual saliency
Venue: 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC)

Infrared small target detection is complex, especially when the background of the image is very complicated. This paper mainly deal with FLIR image which is from shipborne infrared warning system, and our task is to precisely segment small targets of the image. Inspired by the visual attention mechanism, we propose a new visual saliency algorithm for infrared detection of small targets in the sky sea background. This paper mainly include two parts. The first part use visual saliency to detect, and the second part segment targets based on Manifold Ranking.
Equivalent circuit models and model validation of SiC MOSFET oscillation phenomenon
Venue: 2016 IEEE Energy Conversion Congress and Exposition (ECCE)

SiC MOSFETs are known to provide a better performance compared to Si IGBTs. However, they can also introduce undesirable behaviors like switching oscillations due to the existence of parasitic elements. In this paper, we introduce the turn on and turn off switching equivalent circuit models and validate these models with commercially available SiC MOSFETs. Based on the models, theoretical analysis is carried out and the guidance of damping circuit design is provided. Circuit simulations and experimental measurements are performed to validate the modeling method as a general treatment for ultrafast switching applications. In addition, a method of extracting parasitic inductances of packaged SiC MOSFETs using a network analyzer is introduced. The measured parasitic inductance values prove to be more realistic than the values estimated by the device manufacturer when being used in the equivalent circuit models. Furthermore, a detailed justification and experimental validation on the assumption of gate loop resistive contribution are provided.
Deterministic modeling of the exposure of individual participants in the National Health and Nutrition Examination Survey (NHANES) to polychlorinated biphenyls.
Venue: Environmental science. Processes impacts

A population's exposure to persistent organic pollutants, e.g. polychlorinated biphenyls (PCBs) is typically assessed through national biomonitoring programs, such as the United States National Health and Nutrition Examination Survey (NHANES) To complement statistical methods, we use a deterministic modeling approach to establish mechanistic links between human contaminant concentrations and factors (e.g. age, diet, lipid mass) deemed responsible for the often considerable variability in these concentrations. Lifetime exposures to four PCB congeners in 6128 participants from NHANES 1999 2004 are simulated using the ACC Human model supplied with individualized input parameters obtained from NHANES questionnaires (e.g. birth year, sex, body mass index, dietary composition, reproductive behavior) Modeled and measured geometric mean PCB 153 concentrations in NHANES participants of 13.3 and 22.0 ng g 1 lipid, respectively, agree remarkably well, although lower model measurement agreement for air, water, and food suggests that this is partially due to fortuitous error cancellation. The model also reproduces trends in the measured data with key factors such as age, parity and sex. On an individual level, 62% of all modeled concentrations are within a factor of three of their corresponding measured values (Spearman rs 0.44) However, the model attributes more of the inter individual variability to differences in dietary lipid intake than is indicated by the measured data. While the model succeeds in predicting levels and trends on the population level, the accuracy of individual specific predictions would need to be improved for refined exposure characterization in epidemiological studies.
Investigation of large scale propagation for outdoor parking lot environment for 5G wireless communications
Venue: 2016 IEEE 3rd International Symposium on Telecommunication Technologies (ISTT)

The congestion at the frequency band below 6 GHz and the enormous amount of available raw bandwidth above 6 GHz, directed researchers to utilize millimeter wave spectrum as an option to increase mobile broadband capacity. Thus the needs to investigate the ultra wide band frequency for different scenarios are become a critical issue nowadays. In this paper, an investigation of the car reflection case study for a different number of the parking lot rows at 26 GHz frequency band is proposed. The measurement campaigns were conducted at the University Technology Malaysia (UTM) Kuala Lumper, Malaysia and used the directional horn and omni antennas for transmitter and receiver side, respectively. The close in free space (CI) and floating intercept (FI) path loss models are investigated at 26 GHz frequency band. Then the new path gain factor is proposed to display the power degrading with respect to the number of car rows. The results show that CI model is more suitable compared to FI model at the particular scenario.