nils-herrmann/clean_scirepeval huggingface dataset:
School burnout and engagement in the context of demands-resources model.

A four-wave longitudinal study tested the demands-resources model in the school context.



To examine the applicability of the demands-resources to the school context.



Data of 1,709 adolescents were gathered, once during the transition from comprehensive to post-comprehensive education, twice during post-comprehensive education, and once 2 years later.



The hypotheses were supported, path analysis showing that study demands were related to school burnout a year later, while study resources were related to schoolwork engagement. Self-efficacy was positively related to engagement and negatively to burnout. School burnout predicted schoolwork engagement negatively 1 year later. Engagement was positively related to life satisfaction 2 years later, while burnout was related to depressive symptoms. Finally, burnout mediated the relationship between study demands and mental health outcomes.



The demands-resources model can usefully be applied to the school context, including the associations between school-related burnout and engagement among adolescents. The model comprises two processes, the energy-depleting process and the motivational process.
A graph-based hyper-heuristic for educational timetabling problems

This paper presents an investigation of a simple generic hyper-heuristic approach upon a set of widely used constructive heuristics (graph coloring heuristics) in timetabling. Within the hyperheuristic framework, a Tabu Search approach is employed to search for permutations of graph heuristics which are used for constructing timetables in exam and course timetabling problems. This underpins a multi-stage hyper-heuristic where the Tabu Search employs permutations upon a different number of graph heuristics in two stages. We study this graph-based hyper-heuristic approach within the context of exploring fundamental issues concerning the search space of the hyper-heuristic (the heuristic space) and the solution space. Such issues have not been addressed in other hyper-heuristic research. These approaches are tested on both exam and course benchmark timetabling problems and are compared with the fine-tuned bespoke state-of-the-art approaches. The results are within the range of the best results reported in the literature. The approach described here represents a significantly more generally applicable approach than the current state of the art in the literature. Future work will extend this hyper-heuristic framework by employing methodologies which are applicable on a wider range of timetabling and scheduling problems. to appear in European Journal of Operational Research, 2006
An Application of Inverse Reinforcement Learning to Medical Records of Diabetes Treatment

It is an important issue to utilize large amount of medical records which are being accumulated on medical information systems to improve the quality of medical treatment. The process of medical treatment can be considered as a sequential interaction process between doctors and patients. From this viewpoint, we have been modeling medical records using Markov decision processes (MDPs). Using our model, we can simulate the future of each patient and evaluate each treatment. In order to do so, the reward function of the MDP should be specified. However, there is no explicit information regarding the reward value in medical records. In this study we report our results of applying an inverse reinforcement learning (IRL) algorithm to medical records of diabetes treatment to explore the reward function that doctors have in mind during their treatments.
Evolving Neural Turing Machines for Reward-based Learning

An unsolved problem in neuroevolution (NE) is to evolve artificial neural networks (ANN) that can store and use information to change their behavior online. While plastic neural networks have shown promise in this context, they have difficulties retaining information over longer periods of time and integrating new information without losing previously acquired skills. Here we build on recent work by Graves et al.  who extended the capabilities of an ANN by combining it with an external memory bank trained through gradient descent. In this paper, we introduce an evolvable version of their Neural Turing Machine (NTM) and show that such an approach greatly simplifies the neural model, generalizes better, and does not require accessing the entire memory content at each time-step. The Evolvable Neural Turing Machine (ENTM) is able to solve a simple copy tasks and for the first time, the continuous version of the double T-Maze, a complex reinforcement-like learning problem. In the T-Maze learning task the agent uses the memory bank to display adaptive behavior that normally requires a plastic ANN, thereby suggesting a complementary and effective mechanism for adaptive behavior in NE.
The clinical use of mindfulness meditation for the self-regulation of chronic pain

Ninety chronic pain patients were trained in mindfulness meditation in a 10-week Stress Reduction and Relaxation Program. Statistically significant reductions were observed in measures of present-moment pain, negative body image, inhibition of activity by pain, symptoms, mood disturbance, and psychological symptomatology, including anxiety and depression. Pain-related drug utilization decreased and activity levels and feelings of self-esteem increased. Improvement appeared to be independent of gender, source of referral, and type of pain. A comparison group of pain patients did not show significant improvement on these measures after traditional treatment protocols. At follow-up, the improvements observed during the meditation training were maintained up to 15 months post-meditation training for all measures except present-moment pain. The majority of subjects reported continued high compliance with the meditation practice as part of their daily lives. The relationship of mindfulness meditation to other psychological methods for chronic pain control is discussed.
Empirical Evaluation of Rectified Activations in Convolutional Network

In this paper we investigate the performance of different types of rectified activation functions in convolutional neural network: standard rectified linear unit (ReLU), leaky rectified linear unit (Leaky ReLU), parametric rectified linear unit (PReLU) and a new randomized leaky rectified linear units (RReLU). We evaluate these activation function on standard image classification task. Our experiments suggest that incorporating a nonzero slope for negative part in rectified activation units could consistently improve the results. Thus our findings are negative on the common belief that sparsity is the key of good performance in ReLU. Moreover, on small scale dataset, using deterministic negative slope or learning it are both prone to overfitting. They are not as effective as using their randomized counterpart. By using RReLU, we achieved 75.68% accuracy on CIFAR-100 test set without multiple test or ensemble.
An Optimal Algorithm for Mutual Exclusion in Computer Networks

An algorithm is proposed that creates mutual exclusion in a computer network whose nodes communicate only by messages and do not share memory. The algorithm sends only 2*(N 1) messages, where N is the number of nodes in the network per critical section invocation. This number of messages is at a minimum if parallel, distributed, symmetric control is used; hence, the algorithm is optimal in this respect. The time needed to achieve mutual exclusion is also minimal under some general assumptions. As in Lamport's "bakery algorithm," unbounded sequence numbers are used to provide first-come firstserved priority into the critical section. It is shown that the number can be contained in a fixed amount of memory by storing it as the residue of a modulus. The number of messages required to implement the exclusion can be reduced by using sequential node-by-node processing, by using broadcast message techniques, or by sending information through timing channels. The "readers and writers" problem is solved by a simple modification of the algorithm and the modifications necessary to make the algorithm robust are described.
A review on License Plate Recognition system algorithms

License plate recognition(LPR)system contributed to facilitate the human work and make it easier. It is an image processing technology, which uses number plate to identify the vehicles. This paper aims to investigate in the existing LPR research by categorizing existing methods by analyzing the pros/cons of these features, and comparing them in terms of the algorithms that involve and recognition performance. We focus on the recent studies and their limitations, since each country has a different environment and format of licence plate.
A framework to build bespoke auto-tuners with structured Bayesian optimisation

Due to their complexity, modern computer systems expose many configuration parameters which users must manually tune to maximise the performance of their applications. To relieve users of this burden, auto-tuning has emerged as an alternative in which a black-box optimiser iteratively evaluates configurations to find efficient ones. A popular auto-tuning technique is Bayesian optimisation, which uses the results to incrementally build a probabilistic model of the impact of the parameters on performance. This allows the optimisation to quickly focus on efficient regions of the configuration space. Unfortunately, for many computer systems, either the configuration space is too large to develop a good model, or the time to evaluate performance is too long to be executed many times. In this dissertation, I argue that by extracting a small amount of domain specific knowledge about a system, it is possible to build a bespoke auto-tuner with significantly better performance than its off-the-shelf counterparts. This could be performed, for example, by a system engineer who has a good understanding of the underlying system behaviour and wants to provide performance portability. This dissertation presents BOAT, a framework to build BespOke Auto-Tuners. BOAT offers a novel set of abstractions designed to make the exposition of domain knowledge easy and intuitive. First, I introduce Structured Bayesian Optimisation (SBO), an extension of the Bayesian optimisation algorithm. SBO can leverage a bespoke probabilistic model of the system's behaviour, provided by the system engineer, to rapidly converge to high performance configurations. The model can benefit from observing many runtime measurements per evaluation of the system, akin to the use of profilers. Second, I present Probabilistic-C++ a lightweight, high performance probabilistic programming library. It allows users to declare a probabilistic models of their system's behaviour and expose it to an SBO. Probabilistic programming is a recent tool from the Machine Learning community making the declaration of structured probabilistic models intuitive. Third, I present a new optimisation scheduling abstraction which offers a structured way to express optimisations which themselves execute other optimisations. For example, this is useful to express Bayesian optimisations, which each iteration execute a numerical optimisation. Furthermore, it allows users to easily implement decompositions which exploit the loose coupling of subsets of the configuration parameters to optimise them almost independently.
A Triaxial Accelerometer Calibration Method Using a Mathematical Model

This paper presents a new triaxial accelerometer calibration method using a mathematical model of six calibration parameters: three gain factors and three biases. The fundamental principle of the proposed calibration method is that the sum of the triaxial accelerometer outputs is equal to the gravity vector when the accelerometer is stationary. The proposed method requires the triaxial accelerometer to be placed in six different tilt angles to estimate the six calibration parameters. Since the mathematical model of the calibration parameters is nonlinear, an iterative method is used. The results are verified via simulations by comparing the estimated gain factors and biases with the true gain factors and biases. The simulation results confirm that the proposed method is applicable in extreme cases where the gain factor is 1000 V/(m/s2) and the bias is ±100 V, as well as the cases where the gain factor is 0.001 V/(m/s2) and the bias is 0 V. The proposed calibration method is also experimentally tested with two different triaxial accelerometers, and the results are validated using a mechanical inclinometer. The experimental results show that the proposed method can accurately estimate gain factors and biases even when the initial guesses are not close to the true values. In addition, the proposed method has a low computational cost because the calculation is simple, and the iterative method usually converges within three iteration steps. The error sources of the experiments are discussed in this paper.
SUITOR: an attentive information system

Attentive systems pay attention to what users do so that they can attend to what users need. Such systems track user behavior, model user interests, and anticipate user desires and actions. Because the general class of attentive systems is broad — ranging from human butlers to web sites that profile users — we have focused specifically on attentive information systems, which observe user actions with information resources, model user information states, and suggest information that might be helpful to users. In particular, we describe an implemented system, Simple User Interest Tracker (Suitor), that tracks computer users through multiple channels — gaze, web browsing, application focus — to determine their interests and to satisfy their information needs. By observing behavior and modeling users, Suitor finds and displays potentially relevant information that is both timely and non-disruptive to the users' ongoing activities.
Cross-layer secure cyber-physical control system design for networked 3D printers

Due to the high costs of 3D-printing infrastructure, outsourcing the production to third parties specializing in 3D-printing process becomes necessary. The integration of a 3D-printing system with networked communications constitutes a cyber-physical system, bringing new security challenges. Adversaries can explore the vulnerabilities of networks to damage the physical parts of the system. In this paper, we explore the vulnerabilities of 3D-printing systems, and design a cross-layer approach for the system. At the physical layer, we use a Markov jump system to model the system and develop a robust control policy to deal with uncertainties. At the cyber-layer, we apply FlipIt game to model the contention between the defender and attacker for the control of the 3D-printing system. To connect these two layers, we develop a Stackelberg framework to capture the interactions between cyber-layer attacker and defender game and the physical-layer controller and disturbance game, and define a new equilibrium concept that captures interdependence of the zero-sum and FlipIt games. We present numerical examples to better understand the equilibria and design defense strategies for 3D printers as a tradeoff between security and robustness.
Rethinking the fear circuit: the central nucleus of the amygdala is required for the acquisition, consolidation, and expression of Pavlovian fear conditioning.

In the standard model of pavlovian fear learning, sensory input from neutral and aversive stimuli converge in the lateral nucleus of the amygdala (LA), in which alterations in synaptic transmission encode the association. During fear expression, the LA is thought to engage the central nucleus of the amygdala (CE), which serves as the principal output nucleus for the expression of conditioned fear responses. In the present study, we reexamined the roles of LA and CE. Specifically, we asked whether CE, like LA, might also be involved in fear learning and memory consolidation. Using functional inactivation methods, we first show that CE is involved not only in the expression but also the acquisition of fear conditioning. Next, we show that inhibition of protein synthesis in CE after training impairs fear memory consolidation. These findings indicate that CE is not only involved in fear expression but, like LA, is also involved in the learning and consolidation of pavlovian fear conditioning.
Deep learning via Hessian-free optimization

We develop a 2nd-order optimization method based on the “Hessian-free” approach, and apply it to training deep auto-encoders. Without using pre-training, we obtain results superior to those reported by Hinton & Salakhutdinov (2006) on the same tasks they considered. Our method is practical, easy to use, scales nicely to very large datasets, and isn’t limited in applicability to autoencoders, or any specific model class. We also discuss the issue of “pathological curvature” as a possible explanation for the difficulty of deeplearning and how 2nd-order optimization, and our method in particular, effectively deals with it.
Band Selection in Multispectral Images by Minimization of Dependent Information

In this paper, a band selection technique for hyperspectral image data is proposed. Supervised feature extraction techniques allow a reduction of the dimensionality to extract relevant features through a labeled training set. This implies an analysis of the existing class distributions, which usually means, in the case of hyperspectral imaging, a large number of samples, making the labeling process difficult. A possible alternative could be the use of information measures, which are the basis of the proposed method. The present approach basically behaves as an unsupervised feature selection criterion, to obtain the relevant spectral bands from a set of sample images. The relations of information content between spectral bands are analyzed, leading to the proposed technique based on the minimization of the dependent information between spectral bands, while trying to maximize the conditional entropies of the selected bands
Coccydynia: an overview of the anatomy, etiology, and treatment of coccyx pain.

Despite its small size, the coccyx has several important functions. Along with being the insertion site for multiple muscles, ligaments, and tendons, it also serves as one leg of the tripod-along with the ischial tuberosities-that provides weight-bearing support to a person in the seated position. The incidence of coccydynia (pain in the region of the coccyx) has not been reported, but factors associated with increased risk of developing coccydynia include obesity and female gender.



This article provides an overview of the anatomy, physiology, and treatment of coccydynia.



Conservative treatment is successful in 90% of cases, and many cases resolve without medical treatment. Treatments for refractory cases include pelvic floor rehabilitation, manual manipulation and massage, transcutaneous electrical nerve stimulation, psychotherapy, steroid injections, nerve block, spinal cord stimulation, and surgical procedures.



A multidisciplinary approach employing physical therapy, ergonomic adaptations, medications, injections, and, possibly, psychotherapy leads to the greatest chance of success in patients with refractory coccyx pain. Although new surgical techniques are emerging, more research is needed before their efficacy can be established.
Salivary cortisol levels in children adopted from romanian orphanages.

Six and a half years after adoption. 6- to 12-year-old children reared in Romanian orphanages for more than 8 months in their first years of life (RO. n = 18) had higher cortisol levels over the daytime hours than did early adopted (EA, < or = 4 months of age, n = 15) and Canadian born (CB, n = 27) children. The effect was marked, with 22% of the RO children exhibiting cortisol levels averaged over the day that exceeded the mean plus 2 SD of the EA and CB levels. Furthermore, the longer beyond 8 months that the RO children remained institutionalized the higher their cortisol levels. Cortisol levels for EA children did not differ in any respect from those of CB comparison children. This latter finding reduces but does not eliminate concerns that the results could be due to prenatal effects or birth family characteristics associated with orphanage placement. Neither age at cortisol sampling nor low IQ measured earlier appeared to explain the findings. Because the conditions in Romanian orphanages at the time these children were adopted were characterized by multiple risk factors, including gross privation of basic needs and exposure to infectious agents, the factor(s) that produced the increase in cortisol production cannot be determined. Nor could we determine whether these results reflected effects on the limbic-hypothalamic-pituitary-adrenal axis directly or were mediated by differences in parent-child interactions or family stress occasion by behavioral problems associated with prolonged orphanage care in this sample.
Connectivity in a citation network: The development of DNA theory

The study of citation networks for both articles and journals is routine. In general, these analyses proceed by considering the similarity of articles or journals and submitting the set of similarity measures to some clustering or scaling procedure. Two common methods are found in bibliomettic coupling, where two citing articles are similar to the extent they cite the same literature, and co-citation analysis where cited articles are similar to the extent they are cited by the same citing articles. Methods based on structural and regular equivalence also seek to partition the article based on their positional location. Such methods have in common focus on the articles and partitions of them. We propose a quite different approach where the connective threads through a network are preserved and the focus is on the links in the network rather than on the nodes. Variants of the depth first search algorithm are used to detect and represent the mainstream of the literature of a clearly delineated area of scientific research. The specific citation network is one that consists of ties among the key events and papers that lead to the discovery and modeling of DNA together with the final experimental confirmation of its representation.
Design & construction of a Vertical Axis Wind Turbine

A wind turbine is a device that converts kinetic energy from the wind into electrical power. Vertical Axis Wind Turbine (VAWT) is one type of wind turbine where the main rotor shaft is set vertically and it can capture wind from any direction. The aim of this work is to develop a theoretical model for the design and performance of Darrieus type vertical axis wind turbine for small scale energy applications. A small 3 bladed turbine (prototype) is constructed and investigated the performance for low wind velocity. The model is based on NACA 0018 airfoil & light wood is used as blade material. The full scale Vertical Axis Wind Turbine is made for 36 inch height, 24 inch diameter, blade cord length is 3.937 inch & blade height is 24 inch. A 100 watt 24 volt brushless DC motor is used to measure output power. The whirling speed of blade & electric power output for the corresponding speed is measured through Tachometer & Wattmeter. The power curves show the relation between the rotational wind speed of the turbine and the power produced for a range of wind speeds. This approach indicates to develop vertical axis wind turbine with better performance to meet the increasing power demand.