nils-herrmann/clean_scirepeval huggingface dataset:
Factors associated with career satisfaction and burnout among US neurosurgeons: results of a nationwide survey.

OBJECT :The object of this study was to identify and quantify predictors of burnout and career satisfaction among US neurosurgeons.



All US members (3247) of the American Association of Neurological Surgeons (AANS) were invited to participate in a survey between September and December 2012. Responses were evaluated through univariate analysis. Factors independently associated with burnout and career satisfaction were determined using multivariable logistic regression. Subgroup analysis of academic and nonacademic neurosurgeons was performed as well.



The survey response rate was 24% (783 members). The majority of respondents were male, 40-60 years old, in a stable relationship, with children, working in a group or university practice, and trained in a subspecialty. More than 80% of respondents reported being at least somewhat satisfied with their career, and 70% would choose a career in neurosurgery again; however, only 26% of neurosurgeons believed their professional lives would improve in the future, and 52% believed it would worsen. The overall burnout rate was 56.7%. Factors independently associated with both burnout and career satisfaction included achieving a balance between work and life outside the hospital (burnout OR 0.45, satisfaction OR 10.0) and anxiety over future earnings and/or health care reform (burnout OR 1.96, satisfaction OR 0.32). While the burnout rate for nonacademic neurosurgeons (62.9%) was higher than that for academic neurosurgeons (47.7%), academicians who had practiced for over 20 years were less likely to be satisfied with their careers.



The rates of burnout and career satisfaction were both high in this survey study of US neurosurgeons. The negative effects of burnout on the lives of surgeons, patients, and their families require further study and probably necessitate the development of interventional programs at local, regional, and even national levels.
Hybrid PAPR Reduction Scheme for FBMC/OQAM Systems Based on Multi Data Block PTS and TR Methods

The filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) is being studied by many researchers as a key enabler for the fifth-generation air interface. In this paper, a hybrid peak-to-average power ratio (PAPR) reduction scheme is proposed for FBMC/OQAM signals by utilizing multi data block partial transmit sequence (PTS) and tone reservation (TR). In the hybrid PTS-TR scheme, the data blocks signal is divided into several segments, and the number of data blocks in each segment is determined by the overlapping factor. In each segment, we select the optimal data block to transmit and jointly consider the adjacent overlapped data block to achieve minimum signal power. Then, the peak reduction tones are utilized to cancel the peaks of the segment FBMC/OQAM signals. Simulation results and analysis show that the proposed hybrid PTS-TR scheme could provide better PAPR reduction than conventional PTS and TR schemes in FBMC/OQAM systems. Furthermore, we propose another multi data block hybrid PTS-TR scheme by exploiting the adjacent multi overlapped data blocks, called as the multi hybrid (M-hybrid) scheme. Simulation results show that the M-hybrid scheme can achieve about 0.2-dB PAPR performance better than the hybrid PTS-TR scheme.
Combining Context-Awareness with Wearable Computing for Emotion-based Contents Service

One important but often overlooked aspect of human contexts of ubiquitous computing environment is human’s emotional status. And, there are no realistic and robust humancentric contents services so far, because there are few considers about combining context awareness computing with wearable computing for improving suitability of contents to each user’s needs. In this paper, we discuss combining context awareness computing with wearable computing to develop more effective personalized services. And we propose new algorithms to develop efficiently personalized emotion based content service system.
Postcoital bleeding in a case of recto-vaginal endometriosis

Rectovaginal Endometriosis (RVE) is a severe form of endometriosis classified by Kirtner as stage 4 [1,2]. It is less frequent than peritoneal or ovarian endometriosis affecting 3.8% to 37% of patients with endometriosis [3,4]. RVE infiltrates the rectum, vagina, and rectovaginal septum, up to obliteration of the pouch of Douglas . Endometriotic nodules exceeding 30 mm in diameter have 17.9% risk of ureteral involvement , while 5.3% to 12% of patients have bowel endometriosis, most commonly found in the recto-sigmoid involving 74% of those patients [3,4].
The Influence of Peer Pressure on Criminal Behaviour May

Peer pressure is a reoccurring phenomenon in criminal or deviant behaviour especially, as it pertains to adolescents. It may begin in early childhood of about 5years and increase through childhood to become more intense in adolescence years. This paper examines how peer pressure is present in adolescents and how it may influence or create the leverage to non-conformity to societal norms and laws. The paper analyses the process and occurrence of peer influence and pressure on individuals and groups within the framework of the social learning and the social control theories. Major features of the peer pressure process are identified as group dynamics, delinquent peer subculture, peer approval of delinquent behaviour and sanctions for non-conformity which include ridicule, mockery, ostracism and even mayhem or assault in some cases. Also, the paper highlights acceptance and rejection as key concepts that determine the sway or gladiation of adolescents to deviant and criminal behaviour. Finally, it concludes that peer pressure exists for conformity and in delinquent subculture, the result is conformity to criminal codes and behaviour. The paper recommends more urgent, serious and offensive grass root approaches by governments and institutions against this growing threat to the continued peace, orderliness and development of society.
A Compact UWB Antenna for On-Body Applications

A new compact planar ultrawideband (UWB) antenna designed for on-body communications is presented. The antenna is characterized in free space, on a homogeneous phantom modeling a human arm, and on a realistic high-resolution whole-body voxel model. In all configurations it demonstrates very satisfactory features for on-body propagation. The results are presented in terms of return loss, radiation pattern, efficiency, and E-field distribution. The antenna shows very good performance within the 3-11.2 GHz range, and therefore it might be used successfully for the 3.1-10.6 GHz IR-UWB systems. The simulation results for the return loss and radiation patterns are in good agreement with measurements. Finally, a time-domain analysis over the whole-body voxel model is performed for impulse radio applications, and transmission scenarios with several antennas placed on the body are analyzed and compared.
Face Recognition-A Tool for Automated Attendance System

Student attendance in a classroom is a time consuming task when done manually. Face recognition is a biometric powerful tool to authenticate a person. The other biometric attendance methods waste time as the students have to make a queue to touch their thumb on the scanning device. So, automated attendance system using face recognition is the efficient one for student attendance. Image acquisition, face detection and face recognition are the three parts of this system. The issue of fake attendance and proxies can be solved by using this system. This paper gives an overview of some known methods used to implement this system and also the benefits and drawbacks of it. This paper also proposes an efficient method by which we can implement such a system with high accuracy.  Student attendance, biometric, automated attendance system, face detection, face recognition
A broadband high efficiency class AB GaN HEMT balanced power amplifier

In this paper, a broadband high efficiency Class-AB balanced power amplifier (PA) is presented. The proposed PA offers a high efficiency of > 43% for a band of 400 MHz to 1.4 GHz. The broadband matching circuits were realized with microstrip-radial-stubs (MRS) on low loss Rogers 5880 substrate with 0.78 mm thickness and 2.2 dielectric constant. The input and output matching is better than −13 dB throughout the band. The PA delivers maximum output power of 41.5 dBm with a flat gain of 11.4–13.5 dB. Due to high gain, stability, efficiency, and broadband, the proposed PA is thus suitable for recent and upcoming wireless communication systems.
An experiment in discovering personally meaningful places from location data

As mobile devices become location-aware, they offer the promise of powerful new applications. While computers work with physical locations like latitude and longitude, people think and speak in terms of places, like "my office" or ``Sue's house''. Therefore, location-aware applications must incorporate the notion of places to achieve their full potential. This requires systems to acquire the places that are meaningful for each user. Previous work has explored algorithms to discover personal places from location data. However, we know of no empirical, quantitative evaluations of these algorithms, so the question of how well they work currently is unanswered. We report here on an experiment that begins to provide an answer; we show that a place discovery algorithm can do a good job of discovering places that are meaningful to users. The results have important implications for system design and open up interesting avenues for future research.
Meditation experience is associated with differences in default mode network activity and connectivity.

Many philosophical and contemplative traditions teach that "living in the moment" increases happiness. However, the default mode of humans appears to be that of mind-wandering, which correlates with unhappiness, and with activation in a network of brain areas associated with self-referential processing. We investigated brain activity in experienced meditators and matched meditation-naive controls as they performed several different meditations (Concentration, Loving-Kindness, Choiceless Awareness). We found that the main nodes of the default-mode network (medial prefrontal and posterior cingulate cortices) were relatively deactivated in experienced meditators across all meditation types. Furthermore, functional connectivity analysis revealed stronger coupling in experienced meditators between the posterior cingulate, dorsal anterior cingulate, and dorsolateral prefrontal cortices (regions previously implicated in self-monitoring and cognitive control), both at baseline and during meditation. Our findings demonstrate differences in the default-mode network that are consistent with decreased mind-wandering. As such, these provide a unique understanding of possible neural mechanisms of meditation.
A service identification model for service oriented architecture

Service Oriented Architecture facilitates more feature as compared to legacy architecture which makes this architecture widely accepted by the industry. Service oriented architecture provides feature like reusability, composability, distributed deployment. Service of SOA is governed by SOA governance board in which they provide approval to create the services and also provide space to expose the particular services. Sometime many services are kept in a repository which creates service identification issue. Service identification is one of the most critical aspects in service oriented architecture. The services must be defined or identified keeping reuse and usage in different business contexts in mind. Rigorous review of Identified service should be done prior to development of the services. Identification of the authenticated service is challenging to development teams due to several reasons such as lack of business process documentation, lack of expert analyst, and lack of business executive involvement, lack of reuse of services, lack of right decision to choose the appropriate service. In some of the cases we have replica of same service exist, which creates difficulties in service identification. Existing design approaches of SOA doesn't take full advantage whereas proposed model is compatible more advantageous and increase the performance of the services. This paper proposes a model which will help in clustering the service repository based on service functionality. Service identification will be easy if we follow distributed repository based on functionality for our services. Generally in case of web services where service response time should be minimal, searching in whole repository delays response time. The proposed model will reduce the response time of the services and will also helpful in identifying the correct services within the specified time.
Recurrent neural network training with preconditioned stochastic gradient descent

Recurrent neural networks (RNN), especially the ones requiring extremely long term memories, are difficult to training. Hence, they provide an ideal testbed for benchmarking the performance of optimization algorithms. This paper reports test results of a recently proposed preconditioned stochastic gradient descent (PSGD) algorithm on RNN training. We find that PSGD may outperform Hessian-free optimization which achieves the state-of-the-art performance on the target problems, although it is only slightly more complicated than stochastic gradient descent (SGD) and is user friendly, virtually a tuning free algorithm.
Selective difficulty in recognising facial expressions of emotion in boys with ADHD

Research on emotion understanding in ADHD shows inconsistent results. This study uses control methods to investigate two questions about recognition and understanding of emotional expressions in 36 five- to eleven-year-old boys with ADHD:  Do they find this task more difficult than judging non-emotional information from faces, thus suggesting a specific social-cognitive impairment?  Are their judgements about faces impaired by general limitations on task performance, such as impulsive responding? In Part 1, 19 boys with ADHD and 19 age-matched typically developing boys matched facial expressions of emotion to situations, and did a control non-emotional face-processing task. Boys with ADHD performed more poorly than age-matches on both tasks, but found the emotion task harder than the non-emotion task. In Part 2, 17 boys with ADHD and 13 five-to six-year-old typically developing boys performed the same tasks, but with an ‘inhibitory scaffolding’ procedure to prevent impulsive responding. Boys with ADHD performed as well as the younger controls on the non-emotional task, but still showed impairments in the emotion task. Boys with ADHD may show poorer task performance because of general cognitive factors, but also showed selective problems in matching facial emotions to situations.
Common errors in the execution of preoperative templating for primary total hip arthroplasty.

We reviewed 75 primary total hip arthroplasty preoperative and postoperative radiographs and recorded limb length discrepancy, change in femoral offset, acetabular position, neck cut, and femoral component positioning. Interobturator line, as a technique to measure preoperative limb length discrepancy, had the least amount of variance when compared with interteardrop and intertuberosity lines (Levene test, P = .0527). The most common error in execution of preoperative templating was excessive limb lengthening (mean, 3.52 mm), primarily due to inferior acetabular cup positioning (Pearson correlation coefficient, P = .036). Incomplete medialization of the acetabular component contributed the most to offset discrepancy. The most common errors in the execution of preoperative templating resulted in excessive limb lengthening and increased offset. Identifying these errors can lead to more accurate templating techniques and improved intraoperative execution.
A 14 nm SoC platform technology featuring 2nd generation Tri-Gate transistors, 70 nm gate pitch, 52 nm metal pitch, and 0.0499 um2 SRAM cells, optimized for low power, high performance and high density SoC products

A leading edge 14 nm SoC platform technology based upon the 2nd generation Tri-Gate transistor technology [5] has been optimized for density, low power and wide dynamic range. 70 nm gate pitch, 52 nm metal pitch and 0.0499 um2 HDC SRAM cells are the most aggressive design rules reported for 14/16 nm node SoC process to achieve Moore's Law 2x density scaling over 22 nm node. High performance NMOS/PMOS drive currents of 1.3/1.2 mA/um, respectively, have been achieved at 0.7 V and 100 nA/um off-state leakage, 37%/50% improvement over 22 nm node. Ultra-low power NMOS/PMOS drives are 0.50/0.32 mA/um at 0.7 V and 15pA/um Ioff. This technology also deploys high voltage I/O transistors to support up to 3.3 V I/O. A full suite of analog, mixed-signal and RF features are also supported.
OPTIMIZATION OF TEXT CLASSIFICATION USING SUPERVISED AND UNSUPERVISED LEARNING APPROACH

Text Classification, also known as text categorization, is the task of automatically allocating unlabeled documents into predefined categories. Text Classification means allocating a document to one or more categories or classes. The ability to accurately perform a classification task depends on the representations of documents to be classified. Text representations transform the textural documents into a compact format. Text Classification plays an important role in information mining, summarization, text recovery and question-answering. It uses several tools from information retrieval (IR) and Machine Learning. Here we are reviewing the effectiveness of different supervised and unsupervised learning approaches in text classification. KeywordsText Mining, Text Classification, Feature Extraction,Term Weighting, Linear SVC, SGD,K-Mean with cosine similarity.
Real-time eye tracking using a smart camera

Real-time eye and iris tracking is important for handsoff gaze-based password entry, instrument control by paraplegic patients, Internet user studies, as well as homeland security applications. In this project, a smart camera, LabVIEW and vision software tools are utilized to generate eye detection and tracking algorithms. The algorithms are uploaded to the smart camera for on-board image processing. Eye detection refers to finding eye features in a single frame. Eye tracking is achieved by detecting the same eye features across multiple image frames and correlating them to a particular eye. The algorithms are tested for eye detection and tracking under different conditions including different angles of the face, head motion speed, and eye occlusions to determine their usability for the proposed applications. This paper presents the implemented algorithms and performance results of these algorithms on the smart camera.
Hyperparameters and Tuning Strategies for Random Forest

The random forest algorithm (RF) has several hyperparameters that have to be set by the user, e.g., the number of observations drawn randomly for each tree and whether they are drawn with or without replacement, the number of variables drawn randomly for each split, the splitting rule, the minimum number of samples that a node must contain and the number of trees. In this paper, we first provide a literature review on the parameters’ influence on the prediction performance and on variable importance measures. It is well known that in most cases RF works reasonably well with the default values of the hyperparameters specified in software packages. Nevertheless, tuning the hyperparameters can improve the performance of RF. In the second part of this paper, after a brief overview of tuning strategies we demonstrate the application of one of the most established tuning strategies, model-based optimization (MBO). To make it easier to use, we provide the tuneRanger R package that tunes RF with MBO automatically. In a benchmark study on several datasets, we compare the prediction performance and runtime of tuneRanger with other tuning implementations in R and RF with default hyperparameters.
Redox modulation of the NMDA receptor

Redox modulation has been recognized to be an important mechanism of regulation for the N-methyl-d-aspartate (NMDA) receptor. Sulfhydryl reducing agents enhance, whereas oxidizing agents decrease, NMDA-evoked currents. Multiple cysteine residues located in different NMDA receptor subunits have been identified as molecular determinants underlying redox modulation. The NMDA receptor is also regulated by nitric oxide (NO)-related species directly, not involving cyclic GMP, but the molecular mechanism of this action has heretofore not been entirely clear. The confusion arose at least partly due to the fact that various redox forms of NO (NO+, NO•, NO−, each having an additional electron compared with the previous) have distinct mechanisms of action. Recently, a critical cysteine residue (Cys 399) on the NR2A subunit has been shown to react under physiological conditions with NO by S-nitrosylation (transfer of the NO+ to cysteine thiol) or by reaction with NO− (nitroxyl anion) to underlie this form of modulation.
User generated soundscapes activating museum visitors

Museums are seeking various ways to attract and engage audience. Activities like virtual museums, mobile audio guides, augmented reality as well as interactive digital stories aim to provide interactive experiences for an audience with familiarity of digital interaction. Our target is to enable visitor's role change from a passive audience to an active participator as content creator. We concentrate on audio augmentation for museum context because audio provides another perspective into the content in question. In this paper, we discuss our experiences of bringing user generated soundscapes as a part of museums' activities by developing a soundscape platform and a number of mobile applications running on it. Most of the functionality is distributed as ease of use and intuitive mobile applications, which have been evaluated in real context in two workshops. The workshops had different user groups and goals. The first one evaluated ease of use and feelings evoked of the young audiences, and the second one ease of use and acceptance of use for other museums than the Museum of Technology, which was our primary design environment.
A novel approach to implement a shop bot on distributed web crawler

Shopping Agent is a kind of Web application software that, when queried by the customer, provides him/her with the consolidated list of the information about all the retail products relating to a query from various e-commerce sites and resources. This helps customers to decide on the best site that provides nearest, cheapest and most reliable product that they desire to buy. This paper aims to develop a distributed crawler to help on-line shoppers to compare the prices of the requested products from different vendors and get the best deal at one place. The crawling usually consumes large set of computer resources to process the vast amount of data in fat e-commerce servers in a real world scenario. So the alternative way is to use map-reduce paradigm to process large amount of data by forming Hadoop cluster of cheap commodity hardware. Therefore, this paper describes implementation of a shopping agent on a distributed web crawler using map-Reduce paradigm to crawl the web pages.