Miracle-dz/UniFAR-FTUs huggingface dataset:
The Silenced Dialogue: Power and Pedagogy in Educating Other People's Children

Lisa Delpit uses the debate over process-oriented versus skills-oriented writing instruction as the starting-off point to examine the "culture of power" that exists in society in general and in the educational environment in particular. She analyzes five complex rules of power that explicitly and implicitly influence the debate over meeting the educational needs of Black and poor students on all levels. Delpit concludes that teachers must teach all students the explicit and implicit rules of power as a first step toward a more just society. This article is an edited version of a speech presented at the Ninth Annual Ethnography in Education Research Forum, University of Pennsylvania, Philadelphia, Pennsylvania, February 5-6, 1988.
Using Generalized Cayley Transformations within an Inexact Rational Krylov Sequence Method

The rational Krylov sequence (RKS) method is a generalization of Arnoldi's method. It constructs an orthogonal reduction of a matrix pencil into an upper Hessenberg pencil. The RKS method is useful when the matrix pencil may be efficiently factored. This paper considers approximately solving the resulting linear systems with iterative methods. We show that a Cayley transformation leads to a more efficient and robust eigensolver than the usual shift-invert transformation when the linear systems are solved inexactly within the RKS method. A relationship with the recently introduced Jacobi--Davidson method is also established.
A Novel Multi-Class EEG-Based Sleep Stage Classification System

Sleep stage classification is one of the most critical steps in effective diagnosis and the treatment of sleep-related disorders. Visual inspection undertaken by sleep experts is a time-consuming and burdensome task. A computer-assisted sleep stage classification system is thus essential for both sleep-related disorders diagnosis and sleep monitoring. In this paper, we propose a system to classify the wake and sleep stages with high rates of sensitivity and specificity. The EEG signals of 25 subjects with suspected sleep-disordered breathing, and the EEG signals of 20 healthy subjects from three data sets are used. Every EEG epoch is decomposed into eight subband epochs each of which has a frequency band pertaining to one EEG rhythm (i.e., delta, theta, alpha, sigma, beta 1, beta 2, gamma 1, or gamma 2). Thirteen features are extracted from each subband epoch. Therefore, 104 features are totally obtained for every EEG epoch. The Kruskal–Wallis test is used to examine the significance of the features. Non-significant features are discarded. The minimal-redundancy-maximal-relevance feature selection algorithm is then used to eliminate redundant and irrelevant features. The features selected are classified by a random forest classifier. To set the system parameters and to evaluate the system performance, nested 5-fold cross-validation and subject cross-validation are performed. The performance of our proposed system is evaluated for different multi-class classification problems. The minimum overall accuracy rates obtained are 95.31% and 86.64% for nested 5-fold and subject cross-validation, respectively. The system performance is promising in terms of the accuracy, sensitivity, and specificity rates compared with the ones of the state-of-the-art systems. The proposed system can be used in health care applications with the aim of improving sleep stage classification.
Regulation of mitochondrial bioenergetic function by hydrogen sulfide. Part I. Biochemical and physiological mechanisms

Until recently, hydrogen sulfide (H2S) was exclusively viewed a toxic gas and an environmental hazard, with its toxicity primarily attributed to the inhibition of mitochondrial Complex IV, resulting in a shutdown of mitochondrial electron transport and cellular ATP generation. Work over the last decade established multiple biological regulatory roles of H2S, as an endogenous gaseous transmitter. H2S is produced by cystathionine γ‐lyase (CSE), cystathionine β‐synthase (CBS) and 3‐mercaptopyruvate sulfurtransferase (3‐MST). In striking contrast to its inhibitory effect on Complex IV, recent studies showed that at lower concentrations, H2S serves as a stimulator of electron transport in mammalian cells, by acting as a mitochondrial electron donor. Endogenous H2S, produced by mitochondrially localized 3‐MST, supports basal, physiological cellular bioenergetic functions; the activity of this metabolic support declines with physiological aging. In specialized conditions (calcium overload in vascular smooth muscle, colon cancer cells), CSE and CBS can also associate with the mitochondria; H2S produced by these enzymes, serves as an endogenous stimulator of cellular bioenergetics. The current article overviews the biochemical mechanisms underlying the stimulatory and inhibitory effects of H2S on mitochondrial function and cellular bioenergetics and discusses the implication of these processes for normal cellular physiology. The relevance of H2S biology is also discussed in the context of colonic epithelial cell physiology: colonocytes are exposed to high levels of sulfide produced by enteric bacteria, and serve as a metabolic barrier to limit their entry into the mammalian host, while, at the same time, utilizing it as a metabolic ‘fuel’.
Genome Sequence of Antibiotic-Producing Bacillus amyloliquefaciens Strain KCTC 13012

ABSTRACT We report the 4.0-Mb draft genome sequence of Bacillus amyloliquefaciens (syn. Bacillus velezensis) KCTC 13012, which exhibits a broad spectrum of antagonistic activity against bacteria and fungi and promotes plant growth as well. The genome contains an array of biosynthetic gene clusters for secondary metabolites that are comparable to those in Bacillus amyloliquefaciens subsp. plantarum FZB42T.
Speech Recognition System for District Names of Pakistan : Development , Challenges and Solutions

Speech interfaces provide people an easy and comfortable means to interact with computer systems. Speech recognition is a core component of speech interfaces which recognizes human speech in a particular language. Some small vocabulary speech recognition systems for Urdu language with reasonable accuracy have been developed but these systems are either speaker dependent or unable to handle the large accent variations that exists among people of Pakistan. This paper presents a speaker independent Urdu speech recognition system for district names of Pakistan that performs well for major accents of Urdu. Two methods have been studied and analyzed to handle accent variation. The systems have been tested in laboratory and field and their results have been reported in this paper. We conclude that accent independent system performs better for isolated words and addition of field data in the training of system improves the overall recognition accuracy. Keywords—speech recognition; isolated word; speaker independent; accent variation; field testing
Multidimensional Influences of Family Environment in Education: The Case of Socialist Czechoslovakia.

By expanding J. S. Coleman's theory of the intergenerational conversion process, social network theory, and Bourdieu's cultural capital theory, the author identified four components of family capital that are important for the educational attainment of children : human capital, financial capital, social capital, and cultural capital. Furthermore, unlike the conventional approach that focuses on parent-child relationships in families, the author defined social capital to include social relationships and networks outside the family that parents can use. The relative importance of each component of family capital for a child's education is illustrated by a case study of socialist Czechoslovakia in which social capital was operationalized by membership in the Communist Party. The study found that in addition to each component's independent influences on children's educational attainment, there may also be an interaction effect between the parents' social and human capital in the conversion process
Learning CRFs with Hierarchical Features : An Application to Go

We investigate the task of learning grid-based CRFs with hierarchical features motivated by the task of territory prediction in Go. We first analyze various independent and gridbased CRF classification models and state-ofthe-art training/inference algorithms to determine which offers the best performance across a variety of metrics. Faced with the performance drawbacks of independent models and the computational drawbacks of intractable CRF models, we introduce the BMA-Tree algorithm that uses Bayesian model averaging of tree-structured predictors to exploit hierarchical feature structure. Our results demonstrate that BMA-Tree is superior to other independent classifiers and provides a computationally efficient alternative to intractable grid-based CRF models when training is too slow or approximate inference is inadequate for the task at hand.
Direct and Indirect Implications of Pathogen Prevalence for Scientific and Technological Innovation

Rates of scientific and technological innovation vary widely across cultures, but why? Given the previously documented effects of disease threat on cultural values and traits that inhibit innovation, this variation may be due, at least in part, to regional variation in the prevalence of disease-causing pathogens. Five country-level measures of innovation were used to investigate this hypothesis. Preliminary results revealed that pathogen prevalence was significantly associated with all five measures of innovation. Further analyses revealed that pathogen prevalence significantly predicted innovation when statistically controlling for other purported causes of cross-cultural variation in innovation, such as education, wealth, and population structure. Finally, mediational analyses revealed that the effect of disease prevalence on innovation was mediated by levels of collectivism and conformity. These results demonstrate that the previously documented impact of disease threat on cultural value systems may have downstream consequences for scientific and technological innovation.
Endoplasmic Reticulum Stress Inhibition Protects against Excitotoxic Neuronal Injury in the Rat Brain

Elevated brain glutamate with activation of neuronal glutamate receptors accompanies neurological disorders, such as epilepsy and brain trauma. However, the mechanisms by which excitotoxicity triggers neuronal injury are not fully understood. We have studied the glutamate receptor agonist kainic acid (KA) inducing seizures and excitotoxic cell death. KA caused the disintegration of the endoplasmic reticulum (ER) membrane in hippocampal neurons and ER stress with the activation of the ER proteins Bip, Chop, and caspase-12. Salubrinal, inhibiting eIF2α (eukaryotic translation initiation factor 2 subunit α) dephosphorylation, significantly reduced KA-induced ER stress and neuronal death in vivo and in vitro. KA-induced rise in intracellular calcium was not affected by Salubrinal. The results show that ER responses are essential parts of excitotoxicity mediated by glutamate receptor activation and that Salubrinal decreases neuronal death in vivo. Inhibition of ER stress by small molecular compounds may be beneficial for treatment of various neuronal injuries and brain disorders.
Species richness patterns and metapopulation processes – evidence from epiphyte communities in boreo‐nemoral forests

suggested. We studied the relative importance of local environmental conditions and spatial aggregation of species richness of facultative and obligate epiphytic bryophytes and lichens within two old-growth forests in eastern Sweden. The effect of the local environment was analyzed using generalized linear models (GLM). We tested whether species richness was spatially structured by fitting variogram models to the residuals of the GLM. In addition, we analyzed the species-area relationship (area = tree diameter). Different environmental variables explained the richness of different species groups (bryophytes vs lichens, specialists vs generalists, sexual vs asexual dispersal). In most groups, the total variation explained by environmental variables was higher than the variation explained by the spatial model. Spatial aggregation was more pronounced in asexually than in sexually dispersed species. Bryophyte species richness was only poorly predicted by area, and lichen species richness was not explained by area at all. Spatial aggregation may indicate effects of dispersal limitation and metapopulation dynamics on community species richness. Our results suggest that species groups differ in habitat requirements and dispersal abilities; there were indications that presence of species with different dispersal strategies is linked to the age of the host tree. Separate analyses of the species richness of species groups that differ in the degree of habitat specialization and dispersal ability give insights into the processes determining community species richness. The poor species-area relationship, especially in lichens, may indicate species turnover rather than accumulation during the lifetime of the host tree. Epiphyte species extinctions may be mainly caused by deterministic processes, e.g. changes in habitat conditions as the host tree grows, ages and dies, rather than by stochastic population processes.
Combining Probabilistic Search, Latent Variable Analysis and Classification Models

The EM and 1 K-Means algorithms are two popular search techniques that converge to a local minimum of their respective loss functions. The EM algorithm uses partial assignment of instances while the K-Means algorithm uses exclusive assignment. We show that an exclusive random assignment (ERA) algorithm that performs exclusive assignment based on a random experiment can outperform both EM and K-Means for mixture modeling. We show that the ERA algorithm can obtain better maximum likelihood estimates on three real world data sets. On an artificial data set, we show that the ERA algorithm can produce parameter estimates that are more likely to be closer to the generating mechanism. To illustrate the practical benefits of the ERA algorithm we test its ability in a classification context. We propose Latent Variable Classifier (LVC) that combines latent variable analysis such as mixture models and classification models such as Naive Bayes classifiers. For each mixture component (cluster) a classification model is built from those observations assigned to the component. Our experiments on three UCI data sets show LVC’s obtain a greater crossvalidated accuracy than building a single classifier from the entire data set and probabilistic search out-performs the EM algorithm.
The importance of context: Vietnamese, Somali, and Iranian refugee mothers discuss their resettled lives and involvement in their children’s schools

Parental involvement in schools is regarded as critical to student success in Australia, Canada, and the USA, the world’s top refugee resettlement countries. Refugees can be disadvantaged when they are unfamiliar with the practices and when their own cultural beliefs conflict with expectations in their new communities, or when they are consumed by other pressing needs. As part of an evaluation of a nonprofit US refugee agency’s liaison program, three groups of refugee mothers from different world regions spoke about their lives and connections with their children’s schools. Focus groups revealed satisfaction with many aspects of their children’s schools as well as contrasts between the groups’ needs and experiences. Differences indicate the need for the education community and other service providers to be aware of international backgrounds that bring refugees to the countries of resettlement and cultural differences that create diverse concerns of these groups.
Negotiating Activity Restriction: A Grounded Theory of Home Management of Preterm Labor

Preterm birth, preceded by preterm labor, is a significant perinatal problem. This article describes a qualitative, grounded theory study in which 25 women treated at home for preterm labor were interviewed. The purpose was to investigate how women managed this treatment, specifically bed rest, within the context of their day-to-day life. Three management phases were identified: Phase 1, the diagnostic phase, encompassed the circumstances around the diagnosis and subsequent realization that one was at risk to deliver preterm; in Phase 2, the negotiation phase, the women confronted their multisituated reality and negotiated their needs and wants that conflicted with it; and in Phase 3, the preparation phase, the women shift their focus from prevention of preterm delivery to preparation for parenthood. The proposed theory that women manage their activity restriction through negotiations in which they attempt to balance their perceived risk against a calculus of salient variables represents a new perspective in preterm birth prevention.
L'ambivalence des consommateurs : proposition d'un nouvel outil de mesure

Literature on methodology reveals a serious problem related to the use of semantic differential scales to measure individuals’ global evaluations. Two types of evaluations are incorporated—in an artificial and unsuitable way—into these bipolar continuums that contain two opposing adjectives: indifferent reactions and ambivalent reactions. The distinction between these two types of evaluation is significant because they reflect different realities. While indifference corresponds to an absence of positive or negative reactions, an ambivalent evaluation is composed of both positive and negative reactions. This research demonstrates the advantages of using a new measurement tool, the Evaluative Space Grid (Larsen & al., 2009), developed in psychology research; this tool is able to capture the ambivalent component in consumers’ global evaluations. Using a mixed design method comprising five empirical studies, we demonstrate that the Evaluative Space Grid has psychometric properties that match those of semantic differential scales; however, in contrast to semantic differential scales, the ESG is capable of differentiating between indifferent reactions and ambivalent reactions. This research therefore constitutes a major methodological contribution that will be of interest to both researchers and practitioners.
Desktop 3-D Interactive Drama – Applying Design Principles from the Performance Arts.

Increasing emotional engagement in 3-D interactive environments is a hard, but important problem. It is important for its potential utility in increasing motivation, involvement, and engagement. These constructs are not only useful for entertainment applications, but also impact training and edutainment applications due to the impact of emotions on learning (Ulate, 2002; Wolfe, 2001). Many researchers have explored several techniques, including enhancing the story content to stimulate emotional engagement, developing new algorithms for dynamically creating stunning visual effects, and enhancing 3-D sound. Theatre and film have integrated many techniques that increase engagement, attention, and emotional involvement. In this paper, I describe a set of new design techniques integrated in an architecture that uses theatric and cinematic theories, specifically acting and screenwriting methods, to stimulate and improve emotional engagement in 3-D interactive narratives. In this paper, I discuss two research directions: (1) defining an interaction model for 3-D interactive narrative based on screenwriting theories, and (2) developing an actor-based agent architecture to simulate believable actions within an interactive narrative. The resulting architecture was implemented and tested within Mirage, an interactive story based on the Greek Tragedy Electra. Based on the critiques from several participants, I deduce that the resulting architecture presents significantly encouraging design techniques that can potentially increase emotional involvement and dramatic content of an interactive desktop 3D VR experience. The system and approach presented in this paper demonstrates an important new direction that adds to the set of techniques currently used and expand the design methodologies to include methods from disciplines, such as performance arts, theatre, and film.
Murine Microenvironment Metaprofiles Associate with Human Cancer Etiology and Intrinsic Subtypes

Purpose: Ionizing radiation is a well-established carcinogen in rodent models and a risk factor associated with human cancer. We developed a mouse model that captures radiation effects on host biology by transplanting unirradiated Trp53-null mammary tissue to sham or irradiated hosts. Gene expression profiles of tumors that arose in irradiated mice are distinct from those that arose in naïve hosts. We asked whether expression metaprofiles could discern radiation-preceded human cancer or be informative in sporadic breast cancers. Experimental Design: Affymetrix microarray gene expression data from 56 Trp53-null mammary tumors were used to define gene profiles and a centroid that discriminates tumors arising in irradiated hosts. These were applied to publicly available human cancer datasets. Results: Host irradiation induces a metaprofile consisting of gene modules representing stem cells, cell motility, macrophages, and autophagy. Human orthologs of the host irradiation metaprofile discriminated between radiation-preceded and sporadic human thyroid cancers. An irradiated host centroid was strongly associated with estrogen receptor–negative breast cancer. When applied to sporadic human breast cancers, the irradiated host metaprofile strongly associated with basal-like and claudin-low breast cancer intrinsic subtypes. Comparing host irradiation in the context of TGF-β levels showed that inflammation was robustly associated with claudin-low tumors. Conclusions: Detection of radiation-preceded human cancer by the irradiated host metaprofile raises possibilities of assessing human cancer etiology. Moreover, the association of the irradiated host metaprofiles with estrogen receptor–negative status and claudin-low subtype suggests that host processes similar to those induced by radiation underlie sporadic cancers. Clin Cancer Res; 19(6); 1353–62. ©2013 AACR.
Robust distributed model predictive control of linear systems

In this work, synthesis and closed-loop operation of robust distributed model predictive control (MPC) for linear systems using distributed optimization is discussed. Previous work has shown that a nominal MPC controller for this setup can be synthesized and operated in a purely distributed manner. This paper extends this concept to linear systems subject to additive bounded disturbance. It is shown how well-established robust MPC approaches can be applied to distributed systems. The main focus of the paper is on a thorough discussion of computational issues arising from distributed synthesis and closed-loop operation of existing robust MPC controllers. In particular, techniques for distributed synthesis of structured robust positive invariant sets and distributed constraint tightening are proposed. The paper is concluded by a numerical example which illustrates the functionality and performance of the proposed techniques.