nils-herrmann/clean_scirepeval huggingface dataset:
Burnout as a predictor of all-cause mortality among industrial employees: a 10-year prospective register-linkage study.

Burnout, a psychological consequence of prolonged work stress, has been shown to coexist with physical and mental disorders. The aim of this study was to investigate whether burnout is related to all-cause mortality among employees.



In 1996, of 15,466 Finnish forest industry employees, 9705 participated in the 'Still Working' study and 8371 were subsequently identified from the National Population Register. Those who had been treated in a hospital for the most common causes of death prior to the assessment of burnout were excluded on the basis of the Hospital Discharge Register, resulting in a final study population of 7396 people. Burnout was measured using the Maslach Burnout Inventory-General Survey. Dates of death from 1996 to 2006 were extracted from the National Mortality Register. Mortality was predicted with Cox hazard regression models, controlling for baseline sociodemographic factors and register-based health status according to entitled medical reimbursement and prescribed medication for mental health problems, cardiac risk factors, and pain problems.



During the 10-year 10-month follow-up, a total of 199 employees had died. The risk of mortality per one-unit increase in burnout was 35% higher (95% CI 1.07-1.71) for total score and 26% higher (0.99-1.60) for exhaustion, 29% higher for cynicism (1.03-1.62), and 22% higher for diminished professional efficacy (0.96-1.55) in participants who had been under 45 at baseline. After adjustments, only the associations regarding burnout and exhaustion were statistically significant. Burnout was not related to mortality among the older employees.



Burnout, especially work-related exhaustion, may be a risk for overall survival.
2D-3D Pose Estimation of Heterogeneous Objects Using a Region Based Approach

Recently, region based methods for estimating the 3D pose of an object from a 2D image have gained increasing popularity. They do not require prior knowledge of the object’s texture, making them particularity attractive when the object’s texture is unknown a priori. Region based methods estimate the 3D pose of an object by finding the pose which maximizes the image segmentation in to foreground and background regions. Typically the foreground and background regions are described using global appearance models, and an energy function measuring their fit quality is optimized with respect to the pose parameters. Applying a region based approach on standard 2D-3D pose estimation databases shows its performance is strongly dependent on the scene complexity. In simple scenes, where the statistical properties of the foreground and background do not spatially vary, it performs well. However, in more complex scenes, where the statistical properties of the foreground or background vary, the performance strongly degrades. The global appearance models used to segment the image do not sufficiently capture the spatial variation. Inspired by ideas from local active contours, we propose a framework for simultaneous image segmentation and pose estimation using multiple local appearance models. The local appearance models are capable of capturing spatial variation in statistical properties, where global appearance models are limited. We derive an energy function, measuring the image segmentation, using multiple local regions and optimize it with respect to the pose parameters. Our experiments show a substantially higher probability of estimating the correct pose for heterogeneous objects, whereas for homogeneous objects there is minor improvement.
Transient Ischemic Rectitis as a Potential Complication after Prostatic Artery Embolization: Case Report and Review of the Literature

Prostatic artery embolization (PAE) is an alternative treatment for benign prostatic hyperplasia. Complications are primarily related to non-target embolization. We report a case of ischemic rectitis in a 76-year-old man with significant lower urinary tract symptoms due to benign prostatic hyperplasia, probably related to nontarget embolization. Magnetic resonance imaging revealed an 85.5-g prostate and urodynamic studies confirmed Inferior vesical obstruction. PAE was performed bilaterally. During the first 3 days of follow-up, a small amount of blood mixed in the stool was observed. Colonoscopy identified rectal ulcers at day 4, which had then disappeared by day 16 post PAE without treatment. PAE is a safe, effective procedure with a low complication rate, but interventionalists should be aware of the risk of rectal nontarget embolization.
VLL: a lock manager redesign for main memory database systems

Lock managers are increasingly becoming a bottleneck in database systems that use pessimistic concurrency control. In this paper, we introduce very lightweight locking (VLL), an alternative approach to pessimistic concurrency control for main memory database systems, which avoids almost all overhead associated with traditional lock manager operations. We also propose a protocol called selective contention analysis (SCA), which enables systems implementing VLL to achieve high transactional throughput under high-contention workloads. We implement these protocols both in a traditional single-machine multi-core database server setting and in a distributed database where data are partitioned across many commodity machines in a shared-nothing cluster. Furthermore, we show how VLL and SCA can be extended to enable range locking. Our experiments show that VLL dramatically reduces locking overhead and thereby increases transactional throughput in both settings.
Combining Opponent Modeling and Model-Based Reinforcement Learning in a Two-Player Competitive Game

When an opponent with a stationary and stochastic policy is encountered in a twoplayer competitive game, model-free Reinforcement Learning (RL) techniques such as Q-learning and Sarsa(λ) can be used to learn near-optimal counter strategies given enough time. When an agent has learned such counter strategies against multiple diverse opponents, it is not trivial to decide which one to use when a new unidentified opponent is encountered. Opponent modeling provides a sound method for accomplishing this in the case where a policy has already been learned against the new opponent; the policy corresponding to the most likely opponent model can be employed. When a new opponent has never been encountered previously, an appropriate policy may not be available. The proposed solution is to use model-based RL methods in conjunction with separate environment and opponent models. The model-based RL algorithms used were Dyna-Q and value iteration (VI). The environment model allows an agent to reuse general knowledge about the game that is not tied to a specific opponent. Opponent models that are evaluated include Markov chains, Mixtures of Markov chains, and Latent Dirichlet Allocation on Markov chains. The latter two models are latent variable models, which make predictions for new opponents by estimating their latent (unobserved) parameters. In some situations, I have found that this allows good predictive models to be learned quickly for new opponents given data from previous opponents. I show cases where these models have low predictive perplexity (high accuracy) for novel opponents. In theory, these opponent models would enable modelbased RL agents to learn best response strategies in conjunction with an environment model, but converting prediction accuracy to actual game performance is non-trivial. This was not achieved with these methods for the domain, which is a two-player soccer game based on a physics simulation. Model-based RL did allow for faster learning in the game, but did not take full advantage of the opponent models. The quality of the environment model seems to be a critical factor in this situation.
Context-Aware Collaborative Topic Regression with Social Matrix Factorization for Recommender Systems

Online social networking sites have become popular platforms on which users can link with each other and share information, not only basic rating information but also information such as contexts, social relationships, and item contents. However, as far as we know, no existing works systematically combine diverse types of information to build more accurate recommender systems. In this paper, we propose a novel context-aware hierarchical Bayesian method. First, we propose the use of spectral clustering for user-item subgrouping, so that users and items in similar contexts are grouped. We then propose a novel hierarchical Bayesian model that can make predictions for each user-item subgroup, our model incorporate not only topic modeling to mine item content but also social matrix factorization to handle ratings and social relationships. Experiments on an Epinions dataset show that our method significantly improves recommendation performance compared with six categories of state-of-the-art recommendation methods in terms of both prediction accuracy and recall. We have also conducted experiments to study the extent to which ratings, contexts, social relationships, and item contents contribute to recommendation performance in terms of prediction accuracy and recall.
Vanishing Point-Based Image Transforms for Enhancement of Probabilistic Occupancy Map-Based People Localization

The widespread use of vision-based surveillance systems has inspired many research efforts on people localization. In this paper, a series of novel image transforms based on the vanishing point of vertical lines is proposed for enhancement of the probabilistic occupancy map (POM)-based people localization scheme. Utilizing the characteristic that the extensions of vertical lines intersect at a vanishing point, the proposed transforms, based on image or ground plane coordinate system, aims at producing transformed images wherein each standing/walking person will have an upright appearance. Thus, the degradation in localization accuracy due to the deviation of camera configuration constraint specified can be alleviated, while the computation efficiency resulted from the applicability of integral image can be retained. Experimental results show that significant improvement in POM-based people localization for more general camera configurations can indeed be achieved with the proposed image transforms.
Skeleton-based action recognition with convolutional neural networks

Current state-of-the-art approaches to skeleton-based action recognition are mostly based on recurrent neural networks (RNN). In this paper, we propose a novel convolutional neural networks (CNN) based framework for both action classification and detection. Raw skeleton coordinates as well as skeleton motion are fed directly into CNN for label prediction. A novel skeleton transformer module is designed to rearrange and select important skeleton joints automatically. With a simple 7-layer network, we obtain 89.3% accuracy on validation set of the NTU RGB+D dataset. For action detection in untrimmed videos, we develop a window proposal network to extract temporal segment proposals, which are further classified within the same network. On the recent PKU-MMD dataset, we achieve 93.7% mAP, surpassing the baseline by a large margin.
Erupted complex odontoma delayed eruption of permanent molar.

Odontomas, benign tumors that develop in the jaw, rarely erupt into the oral cavity. We report an erupted odontoma which delayed eruption of the first molar. The patient was a 10-year-old Japanese girl who came to our hospital due to delayed eruption of the right maxillary first molar. All the deciduous teeth had been shed. The second premolar on the right side had erupted, but not the first molar. Slight inflammation of the alveolar mucosa around the first molar had exposed a tooth-like, hard tissue. Panoramic radiography revealed a radiopaque mass indicating a lesion approximately 1 cm in diameter. The border of the image was clear, and part of the mass was situated close to the occlusal surface of the first molar. The root of the maxillary right first molar was only half-developed. A clinical diagnosis of odontoma was made. The odontoma was subsequently extracted, allowing the crown of the first molar to erupt almost 5 months later. The dental germ of the permanent tooth had been displaced by the odontoma. However, after the odontoma had been extracted, the permanent tooth was still able to erupt spontaneously, as eruptive force still remained. When the eruption of a tooth is significantly delayed, we believe that it is necessary to examine the area radiographically. If there is any radiographic evidence of a physical obstruction that might delay eruption, that obstruction should be removed before any problems can arise. Regular dental checkups at schools might improve our ability to detect evidence of delayed eruption earlier.
Data Mining Techniques in EDM for Predicting the Performance of Students

In recent, growth of higher education has increased rapidly. Many new institutions, colleges and universities are being established by both the private and government sectors for the growth of education and welfare of the students. Each institution aims at producing higher and exemplary education rates by employing various teaching and grooming methods. But still there are cases of unemployment that exists among the medium and low risk students. This paper describes the use of data mining techniques to improve the efficiency of academic performance in the educational institutions. Various data mining techniques such as decision tree, association rule, nearest neighbors, neural networks, genetic algorithms, exploratory factor analysis and stepwise regression can be applied to the higher education process, which in turn helps to improve student’s performance. This type of approach gives high confidence to students in their studies. This method helps to identify the students who need special advising or counseling by the teacher which gives high quality of education. Keywords-component; Data Mining; KDD; EDM; Association Rule
Interest in the Ketogenic Diet Grows for Weight Loss and Type 2 Diabetes.

This summer, 25 overweight and obese adults participating in a tightly controlled feeding study will take up fulltime residence for 3 months at a wooded lakefront center in Ashland, Massachusetts. However, before checking in at Framingham State University’s Warren Conference Center and Inn, they will have to lose 15% of their body weight on a calorie-restricted diet with home-delivered meals. Those who pass this hurdle will be invited to the inn, where they’ll be randomly assigned to 1 of 3 equal-calorie diets: a lowfat, high-carbohydrate diet that’s either high or low in added sugar or a very lowcarbohydrate, high-fat ketogenic diet that causes the body to switch from burning carbohydrates to burning fat. The group will be the first of 5 that will participate in the trial over 3 years. Changes in body fat mass and energy expenditure will be assessed to determine if any of the diets have a unique effect on metabolism, while controlling calorie intake, in people who have already lost weight. “It’s hard to lose weight, but it’s much harder to maintain that weight loss because of well-described physiological adaptations,” said coprincipal investigator David S. Ludwig, MD, PhD, a professor of pediatrics and nutrition at Harvard Medical School and Harvard T.H. Chan School of Public Health. After most diet-induced weight loss, “hunger goes up and metabolic rate goes down, and tendency to restore fat increases.” But there are hints that the ketogenic diet may be different. A meta-analysis of 13 randomized controlled trials suggested that people on ketogenic diets tend to lose more weight and keep more of it off than people on low-fat diets. People placed on these diets often report decreased hunger, according to Amy Miskimon Goss, PhD, RD, an assistant professor at the University of Alabama at Birmingham (UAB) Nutrition Obesity Research Center. The appetitesuppressing powers of the diet aren’t fully understood but could have to do with the satiating properties of fat and protein, changes in appetite-regulating hormones on a low-carb diet, a direct hunger-reducing role of ketone bodies— the body’s main fuel source on the diet—or other factors. Additionally, the ketogenic diet may not affect metabolism the same way other diets do. In a previous study, Ludwig found that metabolism slowed by more than 400 kcal/d on a low-fat diet while there was no significant decline in metabolic rate on a very low-carb diet. Medical News & Perspectives ........p215
On the Move to Meaningful Internet Systems: OTM 2013 Workshops

Co-references are traditionally used when integrating data from different datasets. This approach has various benefits such as fault tolerance, ease of integration and traceability of provenance; however, it often results in the problem of entity consolidation, i.e., of objectively stating whether all the co-references do really refer to the same entity; and, when this is the case, whether they all convey the same intended meaning. Relying on the sole presence of a single equivalence (owl:sameAs) statement is often problematic and sometimes may even cause serious troubles. It has been observed that to indicate the likelihood of an equivalence one could use a numerically weighted measure, but the real hard questions of where precisely will these values come from arises. To answer this question we propose a methodology based on a graph clustering algorithm.
Anhedonia in schizophrenia and major depression: state or trait?

In schizophrenia and major depressive disorder, anhedonia (a loss of capacity to feel pleasure) had differently been considered as a premorbid personological trait or as a main symptom of their clinical picture. The aims of this study were to examine the pathological features of anhedonia in schizophrenic and depressed patients, and to investigate its clinical relations with general psychopathology (negative, positive, and depressive dimensions).



A total of 145 patients (80 schizophrenics and 65 depressed subjects) were assessed using the Physical Anhedonia Scale and the Social Anhedonia Scale (PAS and SAS, respectively), the Scales for the Assessment of Positive and Negative Symptoms (SAPS and SANS, respectively), the Calgary Depression Scale for Schizophrenics (CDSS), and the Hamilton Depression Rating Scale (HDRS). The statistical analysis was performed in two steps. First, the schizophrenic and depressed samples were dichotomised into 'anhedonic' and 'normal hedonic' subgroups (according to the 'double (PAS/SAS) cut-off') and were compared on the general psychopathology scores using the Mann-Whitney Z test. Subsequently, for the total schizophrenic and depressed samples, Spearman correlations were calculated to examine the relation between anhedonia ratings and the other psychopathological parameters.



In the schizophrenic sample, anhedonia reached high significant levels only in 45% of patients (n = 36). This 'anhedonic' subgroup was distinguished by high scores in the disorganisation and negative dimensions. Positive correlations of anhedonia with disorganised and negative symptoms were also been detected. In the depressed sample, anhedonia reached high significant levels in only 36.9% of subjects (n = 24). This 'anhedonic' subgroup as distinguished by high scores in the depression severity and negative dimensions. Positive correlations of anhedonia with depressive and negative symptoms were also been detected.



In the schizophrenic sample, anhedonia seems to be a specific subjective psychopathological experience of the negative and disorganised forms of schizophrenia. In the depressed sample, anhedonia seems to be a specific subjective psychopathological experience of those major depressive disorder forms with a marked clinical depression severity.
Rational Attention Allocation Over the Business Cycle

Department of Finance Stern School of Business and NBER, New York University, 44 W. 4th Street, New York, NY 10012; mkacperc@stern.nyu.edu;  NBER, and CEPR, New York University, 44 W. 4th Street, New York, NY 10012;  NBER, and CEPR, New York University, 44 W. 4th Street, New York, NY 10012;  We thank John Campbell, Joseph Chen, Xavier Gabaix, Vincent Glode,  Dijk, and seminar participants at NYU Stern (economics and finance), Harvard Business School, Chicago Booth, MIT Sloan, Yale SOM, Stanford University (economics and finance), University of California at Berkeley (economics and finance), UCLA economics, Duke economics, University of Toulouse, University of Vienna, Australian National University, University of Melbourne, University of New South Wales, University of Sydney, University of Technology Sydney, Erasmus University, University of Mannheim, University of Alberta, Concordia, Lugano, the Amsterdam Asset Pricing Retreat, the Society for Economic Dynamics meetings in Istanbul, CEPR Financial Markets conference in Gerzensee, UBC Summer Finance conference, and Econometric Society meetings in Atlanta for useful comments and suggestions. Finally, we thank the Q-group for their generous financial support.
Asymmetrical V-Shape Rotor Configuration of an Interior Permanent Magnet Machine for Improving Torque Characteristics

The purpose of this paper is to present an asymmetrical V-shape rotor configuration of an interior permanent magnet (IPM) machine for improving the torque characteristics, i.e., reducing cogging torque and torque ripple. Using this rotor configuration, the great reduction of the cogging torque and the torque ripple can be achieved. To acquire more desired torque characteristics, an optimization design is implemented using hybrid genetic algorithm-Taguchi (HGAT). Time-stepping finite-element analysis is adopted to calculate the torque characteristics for comparison and analyses. The best set of design parameters is found out; and the IPM model optimized using the HGAT shows even better torque characteristics. An IPM machine prototype having HGAT-design parameters was manufactured for test, and the effectiveness of the proposed rotor is verified by comparison between the simulation and the experiment results.
Safe Option-Critic: Learning Safety in the Option-Critic Architecture

Designing hierarchical reinforcement learning algorithms that induce a notion of safety is not only vital for safety-critical applications, but also, brings better understanding of an artificially intelligent agent’s decisions. While learning end-to-end options automatically has been fully realized recently, we propose a solution to learning safe options. We introduce the idea of controllability of states based on the temporal difference errors [11] in the optioncritic framework. We then derive the policy-gradient theorem with controllability and propose a novel framework called safe optioncritic. We demonstrate the effectiveness of our approach in the fourrooms grid-world, cartpole, and three games in the Arcade Learning Environment (ALE): MsPacman, Amidar and Q*Bert. Learning of end-to-end options with the proposed notion of safety achieves reduction in the variance of return and boosts the performance in environments with intrinsic variability in the reward structure. More importantly, the proposed algorithm outperforms the vanilla options in all the environments and primitive actions in two out of three ALE games.