Miracle-dz/UniFAR-FTUs huggingface dataset:
Mapping Frequency-Specific Tone Predictions in the Human Auditory Cortex at High Spatial Resolution

Auditory inputs reaching our ears are often incomplete, but our brains nevertheless transform them into rich and complete perceptual phenomena such as meaningful conversations or pleasurable music. It has been hypothesized that our brains extract regularities in inputs, which enables us to predict the upcoming stimuli, leading to efficient sensory processing. However, it is unclear whether tone predictions are encoded with similar specificity as perceived signals. Here, we used high-field fMRI to investigate whether human auditory regions encode one of the most defining characteristics of auditory perception: the frequency of predicted tones. Two pairs of tone sequences were presented in ascending or descending directions, with the last tone omitted in half of the trials. Every pair of incomplete sequences contained identical sounds, but was associated with different expectations about the last tone (a high- or low-frequency target). This allowed us to disambiguate predictive signaling from sensory-driven processing. We recorded fMRI responses from eight female participants during passive listening to complete and incomplete sequences. Inspection of specificity and spatial patterns of responses revealed that target frequencies were encoded similarly during their presentations, as well as during omissions, suggesting frequency-specific encoding of predicted tones in the auditory cortex (AC). Importantly, frequency specificity of predictive signaling was observed already at the earliest levels of auditory cortical hierarchy: in the primary AC. Our findings provide evidence for content-specific predictive processing starting at the earliest cortical levels. SIGNIFICANCE STATEMENT Given the abundance of sensory information around us in any given moment, it has been proposed that our brain uses contextual information to prioritize and form predictions about incoming signals. However, there remains a surprising lack of understanding of the specificity and content of such prediction signaling; for example, whether a predicted tone is encoded with similar specificity as a perceived tone. Here, we show that early auditory regions encode the frequency of a tone that is predicted yet omitted. Our findings contribute to the understanding of how expectations shape sound processing in the human auditory cortex and provide further insights into how contextual information influences computations in neuronal circuits.
Line-of-Sight Reddening Predictions: Zero Points, Accuracies, the Interstellar Medium, and the Stellar Populations of Elliptical Galaxies

Revised (B-V)0-Mg2 data, which are used to test reddening predictions, are presented for 402 elliptical galaxies. These reddening predictions can tell us both what the intrinsic errors are in this relationship among gE galaxy stellar populations as well as details of nearby structure in the interstellar medium (ISM) of our Galaxy, and of the intrinsic errors in reddening predictions. Using least-squares fits, the explicit 1 σ errors in reddenings predicted by the Burstein-Heiles (BH) method and the Schlegel and coworkers (IR) method are calculated, as well as the 1 σ observational error in the (B-V)0-Mg2 for gE galaxies. It is found that in directions with E(B-V) < 0.100 mag (where most of these galaxies lie), 1 σ errors in the IR reddening predictions are 0.006–0.009 mag in E(B-V), those for BH reddening predictions are 0.011 mag, and the 1 σ agreement between the two reddening predictions is 0.007 mag. The IR predictions have an accuracy of 0.010–0.011 mag in directions with E(B-V) ≥ 0.100 mag, significantly better than those of the BH predictions (0.024–0.025). Both methods yield good evidence that gas-to-dust variations that vary by a factor of 3, both high and low, exist along many lines of sight in our Galaxy. Both methods also predict many directions with E(B-V) < 0.015 mag, despite the difference in zero point that each has assumed. The ∼0.02 higher reddening zero point in E(B-V) previously determined by Schlegel and coworkers is confirmed, primarily at the Galactic poles. Independent evidence of reddening at the north Galactic pole (NGP) is reviewed, with the conclusion that directions still exist at the NGP that have E(B-V) ≪ 0.01. Two lines of evidence suggest that IR reddenings are overpredicted in directions with high gas-to-dust ratios. As high gas-to-dust directions in the ISM also include the Galactic poles, this overprediction is the likely cause of the E(B-V) ∼ 0.02 mag larger IR reddening zero point relative to that of BH.
High capacity, small world associative memory models

Models of associative memory usually have full connectivity or, if diluted, random symmetric connectivity. In contrast, biological neural systems have predominantly local, non-symmetric connectivity. Here we investigate sparse networks of threshold units, trained with the perceptron learning rule. The units are given position and are arranged in a ring. The connectivity graph varies between being local to random via a small world regime, with short path lengths between any two neurons. The connectivity may be symmetric or non-symmetric. The results show that it is the small world networks with non-symmetric weights and non-symmetric connectivity that perform best as associative memories. It is also shown that in highly dilute networks small world architectures will produce efficiently wired associative memories, which still exhibit good pattern completion abilities.
Life Satisfaction and Value Congruence: Moderators and Extension to Constructed Socio-Demographic Groups in a Russian National Sample

Abstract. Congruence between own and reference group values promotes life satisfaction. Does this effect extend to value congruence with others with whom one does not interact but who share socio-demographic characteristics? We constructed 36 socio-demographic categories in a representative Russian national sample (N = 961). Correlations of individuals’ values with the mean value of their category indexed value congruence. Both a meta-analysis across the constructed groups and an individual level regression analysis confirmed a positive value congruence – life satisfaction effect. This effect held even when controlling for age, gender, education, religiosity, region, religion, and the 19 values on which the congruence score was based. Self-direction-action values moderated the effect: it was stronger the less important independence of action was to individuals.
Reconstruction of quantum theory on the basis of the formula of total probability

The notion of context (complex of physical conditions) is basic in this paper. We show that the main structures of quantum theory (interference of probabilities, Born’s rule, complex probabilistic amplitudes, Hilbert state space, representation of observables by operators) are present in a latent form in the classical Kolmogorov probability model. However, this model should be considered as a calculus of contextual probabilities. In our approach it is forbidden to consider abstract context independent probabilities: “first context and then probability. ” We start with the conventional formula of total probability for contextual (conditional) probabilities and then we rewrite it by eliminating combinations of incompatible contexts from consideration. In this way we obtain interference of probabilities without to appeal to the Hilbert space formalism or wave mechanics. However, we did not just reconstruct the probabilistic formalism of conventional quantum mechanics. Our contextual probabilistic model is ess.
Implementing a Positive Variation of the Good Behavior Game with the Use of a Computer-Based Program

ii ACKNOWLEDGMENTS . iii DEDICATION . iv LIST OF TABLES . viii LIST OF ILLUSTRATIONS . ix LIST OF ABBREVIATIONS . x CHAPTER I INTRODUCTION . 1 The Original Version of the Good Behavior Game . 4 The Good Behavior Game in Elementary School Classroom Settings. 5 The Good Behavior Game in Special Education Classrooms . 7 The Good Behavior Game in Preschool Settings . 8 The Good Behavior Game in High School Settings . 8 The Good Behavior Game International Settings . 10 The Good Behavior Game in Non-classroom Settings . 11 Variations of the Good Behavior Game. 12 Effect of the Good Behavior Game on Teacher Behavior . 22 Using Technology to Address Classroom Behavior . 27 Purpose of the Present Investigation . 31 CHAPTER II METHODOLOGY . 33 vi Participants and Setting. 33 Materials . 35 Dependent Variables . 36 Disruptive and Academic Engagement Behavior . 36 Teacher Praise . 36 Social Validity . 37 Data Collection . 37 Experimental Design and Data Analysis . 38 Procedures . 39 Screening and baseline . 39 Teacher training . 40 Class Orientation . 41 Intervention . 42 Withdrawal . 42 Reimplementation . 43 Interobserver agreement. 43 Treatment integrity. 44 Procedural integrity . 44 CHAPTER III RESULTS . 46 Student Behavior . 46 vii Teacher Behavior . 52 Social Validity . 56 CHAPTER IV – DISCUSSION. 58 Implications for Practice . 60 Limitations . 62 Possibilities for Future Research . 62 – Class Dojo Screenshot . 64 – IRB Approval Letter . 65 – Teacher Consent Letter. 66 – Teacher Script for the Introduction of the GBG . 68 – Classroom Observation Data Collection Sheet. 69 – Behavior Intervention Rating Scale . 70 – Treatment Integrity Checklist for the GBG. 71 Procedural Integrity Checklist for GBG Teacher Training . 72 REFERENCES . 73
Is Naïve Bayes a Good Classifier for Document Classification

Document classification is a growing interest in the research of text mining. Correctly identifying the documents into particular category is still presenting challenge because of large and vast amount of features in the dataset. In regards to the existing classifying approaches, Naive Bayes is potentially good at serving as a document classification model due to its simplicity. The aim of this paper is to highlight the performance of employing Naive Bayes in document classification. Results show that Naive Bayes is the best classifiers against several common classifiers (such as decision tree, neural network, and support vector machines) in term of accuracy and computational efficiency.
Learning uncertainty models from weather forecast performance databases using quantile regression

Forecast uncertainty information is not available in the immediate output of Numerical weather prediction (NWP) models. Such important information is required for optimal decision making processes in many domains. Prediction intervals are a prominent form of reporting the forecast uncertainty. In this paper, a series of learning methods are investigated to obtain prediction interval models by a statistical post-processing procedure involving the historical performance of an NWP system. The article investigates the application of a number of different quantile regression algorithms, including kernel quantile regression, to compute prediction intervals for target weather attributes. These quantile regression methods along with a recently proposed fuzzy clustering-based distribution fitting model are practically benchmarked in a set of experiments involving a three years long database of hourly NWP forecast and observation records. The role of different feature sets and parameters in the models are studied as well. The forecast skills of the obtained prediction intervals are evaluated not only by means of classical cross fold validation test experiments, but also subject to a new sampling variation process to assess the uncertainty of skill score measurements. The results show also how the different methods compare in terms of various quality aspects of prediction interval forecasts such as sharpness and reliability.
Rotation-based object-oriented ensemble in land use land cover classification of hyperspectral data

To classify hyperspectral images by using different techniques many classifiers have been produce better performance for object-oriented classification of hyperspectral remote sensing images. To improve the classification accuracy, first time we investigated an ensemble principle named Rotation-Based object-oriented classification of hyperspectral images (RoBOO). It is the combination of segmentation with support vector machine and nearest neighborhood algorithm. It uses random features selection and data transformation (PCA), technique to improve accuracy and diversity. In this paper we explained our new classification strategy which combines the multi-resolution segmentation (MRS) with SVM, NN classifier. MRS is used to obtain the objects by tuning the parameters included compactness, scale and shape. Furthermore, multiple classification results are produced by proposed method and results were integrated by using majority voting rule to release final results. Mostly used data transformation technique PCA was used. An experiential study on one hyperspectral dataset indicates that the suggested RoBOO slightly surpasses the single SVM and NN. The rotation-matrix with the MRS improved the performance. The effect of parameters on accuracy of RoBOO (different training sets, amount of features in each subset, compactness, scale parameters, shape) is examined as well in this document. We deduced that the integrations of SVM and NN with MRS were impressive in the object-oriented hyperspectral classification of land cover land use.
DETERMINATION OF 1-D SHEAR WAVE VELOCITIES USING THE REFRACTION MICROTREMOR METHOD

Current commonly used techniques of estimating shallow shear velocities for assessment of earthquake site response are too costly for use in most urban areas. They require large sources to be effective in noisy urban settings, or specialized independent recorders laid out in an extensive array. The refraction microtremor (ReMi) method (Louie, 2001) overcomes these problems by using standard Pwave recording equipment and ambient noise to produce average one-dimensional shear-wave profiles down to 100m depths. The combination of commonly available equipment, simple recording with no source, a wavefield transformation data processing technique, and an interactive Rayleigh-wave dispersion modeling tool exploits the most effective aspects of the microtremor, spectral analysis of surface wave (SASW), and multichannel analysis of surface wave (MASW) techniques. The slownessfrequency wavefield transformation is particularly effective in allowing accurate picking of Rayleigh-wave phase-velocity dispersion curves despite the presence of waves propagating across the linear array at high apparent velocities, higher-mode Rayleigh waves, body waves, air waves, and incoherent noise. It has been very effective for quickly and cheaply determining 30-m average shear wave-velocity (V30) and thus the NEHRP (National Earthquake Hazard Reduction Program) soil classification. In addition, it has also been used for liquefaction analysis, soil profile determination, mapping the subsurface and estimating the strength of subsurface materials, and finding buried cultural features, such as dumps and piers. In this paper we briefly discuss the method, data acquisition and interpretation of the data to yield one-dimensional shear-wave velocities.
Brands as cultural resources in children’s peer culture

ABSTRACT This article examines how children collectively appropriate brands as cultural resources. From the New Childhood Studies perspective, an ethnographic study was conducted in schools to investigate the engagement of 10- to 11-year-old children in brand culture. The findings demonstrate that, through a process similar to Corsaro’s model of interpretive reproduction, children do not simply reproduce brand culture; instead, they actively use branding to fuel their peer culture. Mastering and manipulating brands are thus sources for integration or exclusion within the peer group and for differentiation from the adult world. We show the paradoxical impacts of branding on children’s well-being and participate in the debate on their vulnerability to marketing by highlighting how they deploy brand culture to interact in their social spheres, with the consequence being that their would-be empowerment remains entangled in the “brandscape. ” Last, we contribute to a better understanding of the concept of culturally based brand literacy.
Defining National Spatial Data Infrastructures as Complex Adaptive Systems

After two decades of intensive worldwide development of (National) Spatial Data Infrastructure (NSDI) initiatives it is increasingly important to assess the outcomes in order to justify the resources spent on those programs. Many researchers throughout the world have been struggling with building NSDI assessment programs. However it is very difficult to assess NSDIs due to its complex, dynamic and constantly evolving nature. The reason for this difficulty might be that our knowledge of the real forces and mechanisms behind NSDI is still limited. This paper describes a new approach of defining NSDI. It is argued that NSDIs can be treated as Complex Adaptive Systems (Waldrop, 1993; Holland, 1996). Complex Adaptive Systems (CAS) are frequently described by the following characteristics: openness, components, non-linearity, emergency, feedback loops, adaptability, self-organization, multi-understanding, dynamics, unpredictability, sensitivity to initial conditions, scale-independence (fractal building). Both CAS and NSDI characteristics are presented, examined and compared using three case studies. Australian Dutch and Polish SDIs are analyzed in the context of Complex Adaptive Systems. This explorative and comparative study confirms that NSDI has similar properties and patterns of behaviour as CAS. Analyzing NSDI as CAS enriches our understanding of NSDIs’ complex character. As a result it appears possible to better identify and understand the key factors and conditions for NSDI functioning. The presented research is part of the Dutch program Space for Geo-Information “Development of Framework to Assess National Spatial Data Infrastructures”.
Medication knowledge of patients hospitalized for heart failure at admission and after discharge

Background A substantial aspect of health literacy is the knowledge of prescribed medication. In chronic heart failure, incomplete intake of prescribed drugs (medication non-adherence) is inversely associated with clinical prognosis. Therefore, we assessed medication knowledge in a cohort of patients with decompensated heart failure at hospital admission and after discharge in a prospective, cross-sectional study. Methods One hundred and eleven patients presenting at the emergency department with acute decompensated heart failure were included (mean age 78.4±9.2, 59% men) in the study. Patients’ medication knowledge was assessed during individual interviews at baseline, course of hospitalization, and 3 months after discharge. Individual responses were compared with the medical records of the referring general practitioner. Results Median N-terminal prohormone of brain natriuretic peptide plasma concentration in the overall population at baseline was 4,208 pg/mL (2,023–7,101 pg/mL [interquartile range]), 20 patients died between the second and third interview. The number of prescribed drugs increased from 8±3 at baseline to 9±3 after 3 months. The majority of patients did not know the correct number of their drugs. Medication knowledge decreased continuously from baseline to the third interview. At baseline, 37% (n=41) of patients stated the correct number of drugs to be taken, whereas only 18% (n=16) knew the correct number 3 months after discharge (P=0.008). Knowledge was inversely related to N-terminal prohormone of brain natriuretic peptide levels. Conclusion Medication knowledge of patients with acute decompensated heart failure is poor. Despite care in a university hospital, patients’ individual medication knowledge decreased after discharge. The study reveals an urgent need for better strategies to improve and promote the knowledge of prescribed medication in these very high-risk patients.
Deep Learning Process Prediction with Discrete and Continuous Data Features

Process prediction is a well known method to support participants in performing business processes. These methods use event logs of executed cases as a knowledge base to make predictions for running instances. A range of such techniques have been proposed for different tasks, e.g., for predicting the next activity or the remaining time of a running instance. Neural networks with Long Short-Term Memory architectures have turned out to be highly customizable and precise in predicting the next activity in a running case. Current research, however, focuses on the prediction of future activities using activity labels and resource information while further event log information, in particular discrete and continuous event data is neglected. In this paper, we show how prediction accuracy can significantly be improved by incorporating event data attributes. We regard this extension of conventional algorithms as a substantial contribution to the field of activity prediction. The new approach has been validated with a recent real-life event log.
Evaluating the psychological plausibility of word 2 vec and GloVe distributional semantic models

The representation of semantic knowledge poses a central modelling decision in many models of cognitive phenomena. However, not all such representations reflect properties observed in human semantic networks. Here, we evaluate the psychological plausibility of two distributional semantic models widely used in natural language processing: word2vec and GloVe. We use these models to construct directed and undirected semantic networks and compare them to networks of human association norms using a set of graph-theoretic analyses. Our results show that all such networks display small-world characteristics, while only undirected networks show similar degree distributions to those in the human semantic network. Directed networks also exhibit a hierarchical organization that is reminiscent of the human semantic network.
Mouse RAGE Variant 4 Is a Dominant Membrane Receptor that Does Not Shed to Generate Soluble RAGE

The receptor for advanced glycation end products (RAGE) is a multi-ligand, immunoglobulin-like receptor that has been implicated in aging-associated diseases. Recent studies have demonstrated that both human and murine Ager genes undergo extensive alternative splicing that generates multiple putative transcripts encoding different receptor isoforms. Except for the soluble isoform (esRAGE), the majority of putative RAGE isoforms remain unstudied. Profiling of murine Ager transcripts showed that variant transcript 4 (mRAGE_v4), the second most abundant transcript in lungs and multiple other tissues, encodes a receptor that lacks nine residues located within the C2 extracellular section close to the trans-membrane domain. We therefore characterized mRAGEV4 isoreceptor in comparison with the full-length mRAGE (mRAGEFL). Although differing in only nine residues, mRAGEFL and mRAGEV4 display very different cellular behaviors. While mRAGEFL undergoes constitutive, extensive shedding in the cell to generate sRAGE, mRAGEV4 hardly sheds. In addition, we found that while mRAGEFL can localize to both the plasma membrane and the endosome, mRAGEV4 is exclusively localized to the plasma membrane. These very different cellular localization patterns suggest that, in addition to their roles in sRAGE production, mRAGEFL and mRAGEV4 may play distinct, spatiotemporal roles in signaling and innate immune responses. Compared to mice, humans do not have the v4 transcript. Although hRAGE, like mRAGEFL, also localizes to the plasma membrane and the endosome, its rate of constitutive shedding is significantly lower. These observations provide valuable information regarding RAGE biology, and serve as a reference by which to create mouse models relating to human diseases.