Miracle-dz/UniFAR-FTUs huggingface dataset:
Research assignments in teacher education Norwegian undergraduate student teachers’ experiences of the writing process

The aim of Norway’s reformed teacher education programme is to educate research-literate teachers who are able to integrate research-based knowledge into classroom experiences. Based on this, the aim of the present study was to gain deeper insight into student teachers’ experiences of working on their research and development (R&D) assignments in order to obtain a better understanding of the process from their perspective. More specifically, their experiences of working on the R&D assignments before, during and after the writing process were investigated through essays. Data were collected from 59 informants from two teacher education institutions, and 137 essays were analysed in line with a thematic analytical approach. The student teachers’ experiences of working on the R&D assignments were related to two themes: a positive and challenging process, and research and teacher relevance. In line with these themes, the R&D assignments should be organised so as to provide enough support for student teachers alongside the appropriate tools for managing the research task. Furthermore, R&D assignments within teacher education can be seen as a route towards connecting research and practice and further developing research-literate teachers. Utgående detta syftet studie att identifiera sätt att utforma Forsknings- och utvecklingsarbetet (FoU oppgave) på så att det upplevs som värdefullt av lärarstuderande och förbereder dem på att vara forskningskunniga deltagare i de lärandegemenskaper de möter som professionella lärare. Mer specifikt undersöks lärarstuderandes erfarenheter av Forsknings- och utvecklingsarbetet före, under och efter skrivprocessen utgående från deras skrivna essäer. Data insamlades från totalt 59 lärarstuderande från två olika lärarutbildningsinstitutioner och deras skrivna essäer (totalt 137 texter) analyserades med en tematiskt analys. Lärarstuderandes erfarenheter av Forsknings- och utvecklingsarbetet kunde beskrivas med två övergripande teman; en positiv och utmanande process, samt forsknings- och lärarrelevans. Studiens resultat visar betydelsen av att lärarutbildningen ger tillräckligt stöd till lärarstuderande under skrivandet av Forsknings- och utvecklingsarbetet samt användbara redskap för att klara av forskningsuppgiften. Vidare kan Forsknings- och utvecklingsarbetet inom lärarutbildningen ses som en möjlighet att koppla samman forskning och praktik i syfte att utbilda forskningskunniga lärare. forsknings-
The doubly regularized support vector machine

The standard L2-norm support vector machine (SVM) is a widely used tool for classification problems. The L1-norm SVM is a variant of the standard L2norm SVM, that constrains the L1-norm of the fitted coefficients. Due to the nature of the L1-norm, the L1-norm SVM has the property of automatically selecting variables, not shared by the standard L2-norm SVM. It has been argued that the L1-norm SVM may have some advantage over the L2-norm SVM, especially with high dimensional problems and when there are redundant noise variables. On the other hand, the L1-norm SVM has two drawbacks: (1) when there are several highly correlated variables, the L1-norm SVM tends to pick only a few of them, and remove the rest; (2) the number of selected variables is upper bounded by the size of the training data. A typical example where these occur is in gene microarray analysis. In this paper, we propose a doubly regularized support vector machine (DrSVM). The DrSVM uses the elastic-net penalty, a mixture of the L2-norm and the L1-norm penalties. By doing so, the DrSVM performs automatic variable selection in a way similar to the L1-norm SVM. In addition, the DrSVM encourages highly correlated variables to be selected (or removed) together. We illustrate how the DrSVM can be particularly useful when the number of variables is much larger than the size of the training data (p n). We also develop efficient algorithms to compute the whole solution paths of the DrSVM.
Updated Perspectives on the Role of Biomechanics in COPD: Considerations for the Clinician

: Patients with chronic obstructive pulmonary disease (COPD) demonstrate extra-pulmonary functional decline such as an increased prevalence of falls. Biomechanics offers insight into functional decline by examining mechanics of abnormal movement patterns. This review discusses biomechanics of functional outcomes, muscle mechanics, and breathing mechanics in patients with COPD as well as future directions and clinical perspectives. Patients with COPD demonstrate changes in their postural sway during quiet standing compared to controls, and these deficits are exacerbated when sensory information (eg, eyes closed) is manipulated. If standing balance is disrupted with a perturbation, patients with COPD are slower to return to baseline and their muscle activity is differential from controls. When walking, patients with COPD appear to adopt a gait pattern that may increase stability (eg, shorter and wider steps, decreased gait speed) in addition to altered gait variability. Biomechanical muscle mechanics (ie, tension, extensibility, elasticity, and irritability) alterations with COPD are not well documented, with relatively few articles investigating these properties. On the other hand, dyssynchronous motion of the abdomen and rib cage while breathing is well documented in patients with COPD. Newer biomechanical technologies have allowed for estimation of regional, compartmental, lung volumes during activity such as exercise, as well as respiratory muscle activation during breathing. Future directions of biomechanical analyses in COPD are trending toward wearable sensors, big data, and cloud computing. Each of these offers unique opportunities as well as challenges. Advanced analytics of sensor data can offer insight into the health of a system by quantifying complexity or fluctuations in patterns of movement, as healthy systems demonstrate flexibility and are thus adaptable to changing conditions. Biomechanics may offer clinical utility in prediction of 30-day readmissions, identifying disease severity, and patient monitoring. Biomechanics is complementary to other assessments, capturing what patients do, as well as their capability.
Examination of Social Cognition Impairments in Adults with Traumatic Brain Injury through the Awareness of Social Inference Test performance

Social cognition focuses on how people process, perceive, and apply information in social context in order to understand and infer their own behaviors and that of others. Impaired social cognition is a common, and often chronic, outcome of many neurological and psychiatric disorders, including traumatic brain injury (TBI). By lacking these social skills, there is a major disadvantage for social integration with others. In order to assess social cognition deficits in adult TBI populations, Part 1 and 3 of The Awareness of Social Inference Test (TASIT) were administered to examine social cognition impairments. A sample of 58 participants (28 TBI, 30 healthy control) were compared on their ability to accurately recognize emotions and interpret social situations. Mixed analyses of variance (MANOVA) revealed that those diagnosed with mild and severe traumatic brain injury may have difficulties with both basic aspects of social perception and more complex higher-order cognitive abilities when compared to healthy controls, showing less accuracy for both Part 1 and 3 of TASIT. These findings support previous literature that state how social perception deficits affect TBI populations, which has implications for both remediation of such impairments and for educating those who interact with people diagnosed with TBI.
Quantum Query Algorithms for Certain Problems and Generalization of Algorithm Designing Techniques

Quantum algorithms can be analyzed in a query model to compute Boolean functions where input is given in a black box, but the aim is to compute function value for arbitrary input using as few queries as possible. In this paper we concentrate on quantum query algorithm designing tasks. The main aim of research was to find new efficient algorithms and develop general algorithm designing techniques. We present several exact quantum query algorithms for certain problems that are better than classical counterparts. Next we introduce algorithm transformation methods that allow significant enlarging of sets of exactly computable functions. Finally, we propose algorithm constructing methods applicable for algorithms with specific properties that allow constructing algorithms for more complex functions preserving acceptable error probability and number of queries.
Who Do They Think We Are? Analyzing misrepresentation of races in Netflix Original films using Critical Race Theory and Social Identity Theory

3 Acknowledgements. 5 List of Tables. 6 CHAPTER ONE: Introduction . 9 CHAPTER TWO: Literature Review . 14 Social Identity Theory . 14 Ingroup Processes and Conflicts . 16 Stereotypes . 17 Critical Race Theory . 18 Whiteness . 19 White Supremacy, Media, & Television . 21 Research Questions . 23 CHAPTER THREE: Methodology . 25 Research Sample . 25 Measurement & Coding . 27 Intercoder Reliability . 31 Statistical Analysis . 32 CHAPTER FOUR: Results . 33 Initial Analysis . 33 Racial Inclusion & U.S. Proportions . 34 Race x Portrayal & Occupation . 35 Black & White x Type, Portrayal, & Film . 36 8 CHAPTER FIVE: Discussion. 37 Implications . 37 Limitations &Future Research. 39 Conclusion.39
Sliding mode control of singularly perturbed Markovian jump delayed systems with nonlinearity

This paper is concerned with sliding mode control (SMC) of a class of time‐delay nonlinear singularly perturbed Markovian jump systems. Firstly, a switching surface function is designed, and a delay‐dependent condition is derived in terms of ϵ‐independent linear matrix inequality, which guarantees that the resulting sliding mode dynamics is mean‐square exponentially stable. Then an algorithm is given to estimate the stability bound. Moreover, an adaptive SMC law is synthesized to drive the system trajectories onto the designed switching surface in a finite time. Finally, a numerical example is presented to show the effectiveness of proposed method. Copyright © 2012 John Wiley & Sons, Ltd.
Coulomb enhancement of superconducting pair-pair correlations in a 3 4 -filled model for κ − ( BEDT-TTF ) 2 X

We present the results of precise correlated-electron calculations on the monomer lattices of the organic charge-transfer solids κ-(BEDT-TTF)2X for 32 and 64 molecular sites. Our calculations are for band parameters corresponding to X = Cu[N(CN)2]Cl and Cu2(CN)3, which are semiconducting antiferromagnetic and quantum spinliquid,respectively,atambientpressure.Wehaveperformedourcalculationsforvariableelectrondensities ρ per BEDT-TTF molecule, with ρ ranging from 1t o 2. We fi nd thatd-wave superconducting pair-pair correlations are enhanced by electron-electron interactions only for a narrow carrier concentration about ρ = 1.5, which is precisely the carrier concentration where superconductivity in the charge-transfer solids occurs. Our results indicate that the enhancement in pair-pair correlations is not related to antiferromagnetic order, but to a proximate hidden spin-singlet state that manifests itself as a charge-ordered state in other charge-transfer solids. Long-range superconducting order does not appear to be present in the purely electronic model, suggesting that electronphonon interactions also must play a role in a complete theory of superconductivity.
Composition and pharmacological analysis of loach mucus

Hydrogel plays an important role in the composition of biological tissue. Muscle, cell membrane, blood vessel wall, hair and cartilage in human body can be regarded as hydrogel. The mucus on the surface of natural living organisms such as loach, andrias davidianus and earthworm is natural hydrogel. This kind of mucus with peculiar smell is produced by living organisms due to external stimulation of body surface stress response. It is a kind of self-protection. And this mucus has certain effects of antibacterial, antifatigue and enhancing immunity. The main component of mucus is collagen with shorter molecular structure and molecular weight is relatively small, and the content of collagen can reach about 40 ∼ 60 % in mucus. It has a certain bonding effect on skin surface damage, and plays the function of quickly closing wounds and killing bacteria. Hyaluronic acid (HA), active structure in oligosaccharide peptide and collagen peptide extracted from mucus, particularly type II collagen, play important roles in skin repair. This function of this natural hydrogel may open a new approach for the development of secondary functions of such organisms.
Deep Learning Assessment Method for Postoperative Pain Based on Facial Video Data

In recent years, many scholars at home and abroad have applied deep learning methods to automated pain recognition, but these methods are mostly based on discrete data, and the reliability of the evaluation results is low, making it difficult for doctors to accurately judge the disease. This paper conducts an automated pain assessment study based on continuous postoperative facial video data. First, the principle of deep learning network for pain recognition is introduced, and a computing platform based on deep learning is built. Then, based on the video data in the public pain recognition database, a deep learning-based pain recognition experiment was conducted. Finally, the experimental results of pain recognition are analyzed, which confirms the feasibility and effectiveness of the deep learning pain assessment method based on facial video data. This paper applies deep learning to pain assessment based on facial video data, which helps to assess patient pain more accurately and quickly.
The nsBETI method: an extension of the FETI method to non‐symmetrical BEM‐FEM coupled problems

The finite element tearing and interconnecting (FETI) method is recognized as an effective domain decomposition tool to achieve scalability in the solution of partitioned second‐order elasticity problems. In the boundary element tearing and interconnecting (BETI) method, a direct extension of the FETI algorithm to the BEM, the symmetric Galerkin BEM formulation, is used to obtain symmetric system matrices, making possible to apply the same FETI conjugate gradient solver. In this work, we propose a new BETI variant labeled nsBETI that allows to couple substructures modeled with the FEM and/or non‐symmetrical BEM formulations. The method connects non‐matching BEM and FEM subdomains using localized Lagrange multipliers and solves the associated non‐symmetrical flexibility equations with a Bi‐CGstab iterative algorithm. Scalability issues of nsBETI in BEM–BEM and combined BEM–FEM coupled problems are also investigated. Copyright © 2012 John Wiley & Sons, Ltd.
Horizon strings as 3D black hole microstates

We propose that 3d black holes are an ensemble of tensionless null string states. These microstates typically have non-zero winding. We evaluate their partition function in the limit of large excitation numbers and show that their combinatorics reproduces the Bekenstein-Hawking entropy and its semiclassical logarithmic corrections.
Black Box Model Explanations and the Human Interpretability Expectations - An Analysis in the Context of Homicide Prediction

Strategies based on Explainable Artificial Intelligence - XAI have promoted better human interpretability of the results of black box machine learning models. The XAI measures being currently used (Ciu, Dalex, Eli5, Lofo, Shap, and Skater) provide various forms of explanations, including global rankings of relevance of attributes. Current research points to the need for further studies on how these explanations meet the Interpretability Expectations of human experts and how they can be used to make the model even more transparent while taking into account specific complexities of the model and dataset being analyzed, as well as important human factors of sensitive real-world contexts/problems. Intending to shed light on the explanations generated by XAI measures and their interpretabilities, this research addresses a real-world classification problem related to homicide prediction, duly endorsed by the scientific community, replicated its proposed black box model and used 6 different XAI measures to generate explanations and 6 different human experts to generate what this research referred to as Interpretability Expectations - IE. The results were computed by means of comparative analysis and identification of relationships among all the attribute ranks produced, and ~49% concordance was found among attributes indicated by means of XAI measures and human experts, ~41% exclusively by XAI measures and ~10% exclusively by human experts. The results allow for answering:"Do the different XAI measures generate similar explanations for the proposed problem? ","Are the interpretability expectations generated among different human experts similar? ","Do the explanations generated by XAI measures meet the interpretability expectations of human experts? "and"Can Interpretability Explanations and Expectations work together? ", all of which concerning the context of homicide prediction.
Optimal Settings for Cryptocurrency Trading Pairs

The goal of cryptocurrencies is decentralization. In principle, all currencies have equal status. Unlike traditional stock markets, there is no default currency of denomination (fiat), thus the trading pairs can be set freely. However, it is impractical to set up a trading market between every two currencies. In order to control management costs and ensure sufficient liquidity, we must give priority to covering those large-volume trading pairs and ensure that all coins are reachable. We note that this is an optimization problem. Its particularity lies in: 1) the trading volume between most (>99.5%) possible trading pairs cannot be directly observed. 2) It satisfies the connectivity constraint, that is, all currencies are guaranteed to be tradable. To solve this problem, we use a two-stage process: 1) Fill in missing values based on a regularized, truncated eigenvalue decomposition, where the regularization term is used to control what extent missing values should be limited to zero. 2) Search for the optimal trading pairs, based on a branch and bound process, with heuristic search and pruning strategies. The experimental results show that: 1) If the number of denominated coins is not limited, we will get a more decentralized trading pair settings, which advocates the establishment of trading pairs directly between large currency pairs. 2) There is a certain room for optimization in all exchanges. The setting of inappropriate trading pairs is mainly caused by subjectively setting small coins to quote, or failing to track emerging big coins in time. 3) Too few trading pairs will lead to low coverage; too many trading pairs will need to be adjusted with markets frequently. Exchanges should consider striking an appropriate balance between them.
Standardized Medical Image Classification across Medical Disciplines

AUCMEDI is a Python-based framework for medical image classification. In this paper, we evaluate the capabilities of AUCMEDI, by applying it to multiple datasets. Datasets were specifically chosen to cover a variety of medical disciplines and imaging modalities. We designed a simple pipeline using Jupyter notebooks and applied it to all datasets. Results show that AUCMEDI was able to train a model with accurate classification capabilities for each dataset: Averaged AUC per dataset range between 0.82 and 1.0, averaged F1 scores range between 0.61 and 1.0. With its high adaptability and strong performance, AUCMEDI proves to be a powerful instrument to build widely applicable neural networks. The notebooks serve as application examples for AUCMEDI.
Minimum Age of Information in Internet of Things with Opportunistic Channel Access

This paper investigates an Internet of Things (IoT) system in which multiple devices are observing some object's physical parameters and then offloading their observations back to the BS in time with opportunistic channel access. Specifically, each device accesses the common channel through contention with a certain probability firstly and then the winner evaluates the instant channel condition and decides to accept the right of channel access or not. We analyze this system through the perspective of Age of Information (AoI), which describes the freshness of observed information. The target is to minimize average AoI by optimizing the probability of device participation in contention and the transmission rate threshold. The problem is hard to solve since the AoI expression in fractional form is complex. We first decompose the original problem into two single-variable optimization sub-problems through Dinkelbach method and Block Coordinate Descent (BCD) method. And then we transform them to Monotonic optimization problems by proving the monotonicity of the objective functions, whose global optimal solution is able to be found through Polyblock algorithm. Numerical results verify the validity of our proposed method.
Bell Inequalities Induced by Pseudo Pauli Operators on Single Logical Qubits

In most Bell tests, the measurement settings are specially chosen so that the maximal quantum violations of the Bell inequalities can be detected, or at least, the violations are strong enough to be observed. Such choices can usually associate the corresponding Bell operators to a kind of effective observables, called pseudo Pauli operators, providing us a more intuitive understanding of Bell nonlocality in some sense. Based on that, a more general quantum-to-classical approach for the constructions of Bell inequalities is developed. Using this approach, one can not only derive several kinds of well-known Bell inequalities, but also explore many new ones. Besides, we show that some quadratic Bell inequalities can be induced from the uncertainty relations of pseudo Pauli operators as well, which may shed new light on the study of uncertainty relations of some nonlocal observables.
Possible Evidence for Lorentz Invariance Violation in Gamma-Ray Burst 221009A

The preliminary detections of the gamma-ray burst 221009A up to 18 TeV by LHAASO and up to 251 TeV by Carpet 2 have been reported through Astronomer’s Telegrams and Gamma-ray Coordination Network circulars. Since this burst is at redshift z = 0.1505, these photons may at first seem to have a low probability to avoid pair production off of background radiation fields and survive to reach detectors on Earth. By extrapolating the reported 0.1–1.0 GeV Fermi Large Area Telescope spectrum from this burst to higher energies and using this to limit the intrinsic spectrum of the burst, we show that the survival of the 18 TeV photon detected by LHAASO is not unlikely with many recent extragalactic background light models, although the detection of a 251 TeV event is still very unlikely. This can be resolved if Lorentz invariance is violated at an energy scale E QG ≲ 49E Planck in the linear (n = 1) case, and E QG ≲ 10−6 E Planck in the quadratic (n = 2) case (95% confidence limits), where E Planck is the Planck energy. This could potentially be the first evidence for subluminal Lorentz invariance violation.