Miracle-dz/UniFAR-FTUs huggingface dataset:
Troubling literacy: monolingual assumptions, multilingual contexts, and language teacher expertise

The current educational context in many English speaking countries is one where literacy is understood to be essentially monolingual in orientation; that is, an understanding of literacy around a single common language, with the emphasis on identifying universal, normative ‘standards’ and ‘benchmarks’, such as the National Assessment Program-Literacy and Numeracy in Australia, the Primary National Strategy in the United Kingdom, and No Child Left Behind in the United States. This paper aims to problematise such assumptions by examining how teachers, themselves, understand “teaching literacy” when their students come to the teaching/learning relationship with a first language other than English. With a focus on teachers working with bilingual and multilingual students in their early stages of acquiring English as a second language (ESL), the study thus acknowledges the increasingly diverse sociolinguistic profile of students in Australia and elsewhere in the varying degrees of communicative competence they bring to the mainstream in their use of ESL. Moreover, it draws on ESL teacher knowledge and expertise which has been identified as increasingly marginalised within mainstream educational discourse. Through case studies of three ESL teachers in the middle years, salient themes around the interrelated notions of literacy as learning, language for literacy and language as literacy were identified with respect to the literacy needs of second language learners.
An integrated reweighting theory of perceptual learning

Improvements in performance on visual tasks due to practice are often specific to a retinal position or stimulus feature. Many researchers suggest that specific perceptual learning alters selective retinotopic representations in early visual analysis. However, transfer is almost always practically advantageous, and it does occur. If perceptual learning alters location-specific representations, how does it transfer to new locations? An integrated reweighting theory explains transfer over retinal locations by incorporating higher level location-independent representations into a multilevel learning system. Location transfer is mediated through location-independent representations, whereas stimulus feature transfer is determined by stimulus similarity at both location-specific and location-independent levels. Transfer to new locations/positions differs fundamentally from transfer to new stimuli. After substantial initial training on an orientation discrimination task, switches to a new location or position are compared with switches to new orientations in the same position, or switches of both. Position switches led to the highest degree of transfer, whereas orientation switches led to the highest levels of specificity. A computational model of integrated reweighting is developed and tested that incorporates the details of the stimuli and the experiment. Transfer to an identical orientation task in a new position is mediated via more broadly tuned location-invariant representations, whereas changing orientation in the same position invokes interference or independent learning of the new orientations at both levels, reflecting stimulus dissimilarity. Consistent with single-cell recording studies, perceptual learning alters the weighting of both early and midlevel representations of the visual system.
Using probabilistic programs as proposals

Monte Carlo inference has asymptotic guarantees, but can be slow when using generic proposals. Handcrafted proposals that rely on user knowledge about the posterior distribution can be efficient, but are difficult to derive and implement. This paper proposes to let users express their posterior knowledge in the form of \emph{proposal programs}, which are samplers written in probabilistic programming languages. One strategy for writing good proposal programs is to combine domain-specific heuristic algorithms with neural network models. The heuristics identify high probability regions, and the neural networks model the posterior uncertainty around the outputs of the algorithm. Proposal programs can be used as proposal distributions in importance sampling and Metropolis-Hastings samplers without sacrificing asymptotic consistency, and can be optimized offline using inference compilation. Support for optimizing and using proposal programs is easily implemented in a sampling-based probabilistic programming runtime. The paper illustrates the proposed technique with a proposal program that combines RANSAC and neural networks to accelerate inference in a Bayesian linear regression with outliers model.
Improving Simulation for Network Research

New protocols and algorithms are being developed to meet changing operational requirements in the Internet. Simulation is a vital tool to quickly and inexpensively explore the behavior of these new protocol across the range of topologies, cross-traÆc, and interactions that might occur in the Internet. This paper describes ns, a widely used, multi-protocol network simulator designed to address the needs of networking researchers. Ns provides multiple levels of abstraction to permit simulations to span a wide-range of scales, emulation, where real-world packets can enter the simulator. We describe the ns architecture and examine the range of ways simulation and ns are used in networking research.
The assessment of parent-to-infant attachment: Development of a self-report questionnaire instrument

Abstract In most research involving maternal infant attachment, the attachment construct has been assessed by behavioural observation. The problems inherent in this approach, including its high cost and labour-intensive nature, are briefly reviewed. It is suggested that the behavioural approach could be usefully complemented by enquiry into the mother's subjective experiences towards her infant. The development of a 19 item self-report questionnaire to assess mother-to-infant attachment is described. Using a sample of approximately 200 women assessed at 4 weeks, 4 months and 8 months postnatally, the psychometric properties of the instrument are presented. Acceptable levels of internal consistency and test-retest reliability were found. The factor structure of the questionnaire revealed four factors which accounted for approximately 40% of the variance in attachment scores: pleasure in proximity, acceptance, tolerance, and competence as parent. Evidence supporting the construct validity of the questionnai.
Immunodeficiency Virus Type 1 Infectious Molecular Clones of Human Multi-Protease-Inhibitor-Resistant Prototypical Recombinant

Published Ahead of Print 24 June 2013. 10.1128/AAC.00614-13. 2013, 57(9):4290. DOI: Antimicrob. Agents Chemother. Schiffer, Susan P. Holmes and Robert W. Shafer Liu, George L. Melikian, David A. Katzenstein, Celia A. Vici Varghese, Yumi Mitsuya, W. Jeffrey Fessel, Tommy F. Immunodeficiency Virus Type 1 Infectious Molecular Clones of Human Multi-Protease-Inhibitor-Resistant Prototypical Recombinant
PD output feedback control design for industrial robotic manipulators

This paper presents an output feedback PD-type controller for the trajectory tracking control of robotic manipulators. A linear observer is used to estimate the unknown velocity signals of the nonlinear manipulator. In the first part of this paper, we propose a PD-like output-feedback control law. The Lyapunov method is used to characterize the asymptotic property of all the signals in the closed-loop error model dynamics. This property sets the bound on the tracking error trajectory of the closed-loop system. In the second part, we remove the nominal model dynamics from the control design to formulate a model-independent PD-type output feedback approach. Using an asymptotic analysis for the singularly perturbed closed-loop model, we guarantee that all the signals under the proposed PD output feedback design are bounded and their bounds can be made arbitrarily small by using observer-controller gains. Implementation results demonstrate the potential application of the proposed method on real systems.
Unfolding physiological state: mortality modelling in intensive care units

Accurate knowledge of a patient's disease state and trajectory is critical in a clinical setting. Modern electronic healthcare records contain an increasingly large amount of data, and the ability to automatically identify the factors that influence patient outcomes stand to greatly improve the efficiency and quality of care. We examined the use of latent variable models (viz. Latent Dirichlet Allocation) to decompose free-text hospital notes into meaningful features, and the predictive power of these features for patient mortality. We considered three prediction regimes: (1) baseline prediction, (2) dynamic (time-varying) outcome prediction, and (3) retrospective outcome prediction. In each, our prediction task differs from the familiar time-varying situation whereby data accumulates; since fewer patients have long ICU stays, as we move forward in time fewer patients are available and the prediction task becomes increasingly difficult. We found that latent topic-derived features were effective in determining patient mortality under three timelines: in-hospital, 30 day post-discharge, and 1 year post-discharge mortality. Our results demonstrated that the latent topic features important in predicting hospital mortality are very different from those that are important in post-discharge mortality. In general, latent topic features were more predictive than structured features, and a combination of the two performed best. The time-varying models that combined latent topic features and baseline features had AUCs that reached 0.85, 0.80, and 0.77 for in-hospital, 30 day post-discharge and 1 year post-discharge mortality respectively. Our results agreed with other work suggesting that the first 24 hours of patient information are often the most predictive of hospital mortality. Retrospective models that used a combination of latent topic features and structured features achieved AUCs of 0.96, 0.82, and 0.81 for in-hospital, 30 day, and 1-year mortality prediction. Our work focuses on the dynamic (time-varying) setting because models from this regime could facilitate an on-going severity stratification system that helps direct care-staff resources and inform treatment strategies.
PHARAOH: A collaborative crowdsourcing platform for PHenotyping And Regional Analysis Of Histology

Deep learning has proven to be capable of automating key aspects of histopathologic analysis, but its continual reliance on large expert-annotated training datasets hinders widespread adoption. Here, we present an online collaborative portal that streamlines tissue image annotation to promote the development and sharing of custom computer vision models for PHenotyping And Regional Analysis Of Histology (PHARAOH; https://www.pathologyreports.ai/). PHARAOH uses a weakly supervised active learning framework whereby patch-level image features are leveraged to organize large swaths of tissue into morphologically-uniform clusters for batched human annotation. By providing cluster-level labels on only a handful of cases, we show how custom PHARAOH models can be developed and used to guide the quantification of cellular features that correlate with molecular, pathologic and patient outcome data. Both custom model design and feature extraction pipelines are amenable to crowdsourcing making PHARAOH a fully scalable systems-level solution for the systematic expansion and cataloging of computational pathology applications.
Structural factors influencing menstruating school girls' health and well-being in Tanzania

The importance of girls' education for population health outcomes in low-income countries is well documented. Despite this critical relationship, the global public health community has tended to overlook aspects of the school environment that may hinder girls' pubertal transitions in schools across low-income countries. Minimal empirical attention has been given to structural and environmental factors, both physical and social, within the school experience that may impact on pubescent and newly menstruating girls' school attendance and participation and, ultimately, their health and well-being. This article describes an in-depth study conducted in Kilimanjaro, Tanzania that explored post-pubescent girls' experiences of managing their menstrual flow in school environments. The study identified eight fundamental structural and environmental factors impacting on girls' school-going experiences, such as insufficient water and sanitation and the disciplinary nature of the academic atmosphere. The public health and education fields are long overdue to collectively address these pubertal-related challenges facing schoolgirls in low-income countries.
Source (or Part of the following Source): Type Article Title Internal State of Monkey Primary Visual Cortex (v1) Predicts Figure Ground Perception Author(s) Internal State of Monkey Primary Visual Cortex (v1) Predicts Figure–ground Perception Materials and Methods

Copyright It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content licence (like Creative Commons). UvA-DARE is a service provided by the library of the University of Amsterdam (http://dare.uva.nl) When stimulus information enters the visual cortex, it is rapidly processed for identification. However, sometimes the processing of the stimulus is inadequate and the subject fails to notice the stimulus. Human psychophysical studies show that this occurs during states of inattention or absent-mindedness. At a neurophysiological level, it remains unclear what these states are. To study the role of cortical state in perception, we analyzed neural activity in the monkey primary visual cortex before the appearance of a stimulus. We show that, before the appearance of a reported stimulus, neural activity was stronger and more correlated than for a not-reported stimulus. This indicates that the strength of neural activity and the functional connectivity between neurons in the primary visual cortex participate in the perceptual processing of stimulus information. Thus, to detect a stimulus, the visual cortex needs to be in an appropriate state. Introduction Vision depends on the cerebral cortex. Retinal information enters the primary visual cortex (V1) via the thalamus and, from there on, is transferred to the higher visual areas of the cerebral cortex. At these higher levels, more and more elaborate processing of the visual information occurs. In addition, feedback connections (Mignard and Malpeli, 1991; Salin and Bullier, 1995) and horizontal connections (Gilbert, 1993) provide recurrent interactions between localized, low-level information and more global, high-level information (Lamme and Roelfsema, 2000). These kinds of interactions have been identified as the basis for various modu-latory influences on the neuronal activity in V1 The observed modulations often reflect relatively high-level perceptual attributes of the stimuli that fall within the small receptive fields of the neurons. For example, perceived brightness (Rossi et al., 1996), perceptual grouping (Kapadia et al., 1995), or figure– ground segregation (Lamme, 1995) may modify the response to an otherwise identical receptive field stimulus. We reported recently a direct link between these modulations and the animal's percept. More specifically, we showed that, when figure– ground modulation in V1 does not occur, the animal does not perceive the figure (Supèr et al., 2001). Assuming that these modulations depend on recurrent interactions …
5′-Triphosphate-RNA-independent activation of RIG-I via RNA aptamer with enhanced antiviral activity

RIG-I is a cytosolic receptor for non-self RNA that mediates immune responses against viral infections through IFNα/β production. In an attempt to identify novel tools that modulate IFNα/β production, we used SELEX technology to screen RNA aptamers that specifically target RIG-I protein. Most of the selected RIG-I aptamers contained polyU motifs in the second half regions that played critical roles in the activation of RIG-I-mediated IFNβ production. Unlike other known ligands, RIG-I aptamer bound and activated RIG-I in a 5′-triphosphate-independent manner. The helicase and RD domain of RIG-I were used for aptamer binding, but intact RIG-I protein was required to exert aptamer-mediated signaling activation. Furthermore, replication of NDV, VSV and influenza virus in infected host cells was efficiently blocked by pre- or post-treatment with RIG-I aptamer. Based on these data, we propose that RIG-I aptamer has strong potential to be an antiviral agent that specifically boosts the RIG-I-dependent signaling cascade.
Creativity and innovation in Saudi Arabia: An overview

Abstract The concepts of creativity and innovation are often used interchangeably in the literature. However, some authors may discriminate between the two concepts. Such authors usually consider creativity as an internal and rational process of creating new ideas, while innovation refers to the implementation of such ideas. As the global forces become increasingly inevitable, many business experts are predicting that workforce creativity and innovation will be the most important factors in establishing and maintaining a competitive advantage. Because of its theoretical and practical significance, how to foster innovation in organizations has become a fast-growing research area in the field of management. According to the global innovation index Saudi Arabia was ranked at 41 ahead of Italy, PolaneZ Turkey and China. However, its innovation output index was low at ‘98’ place. The purpose of this conceptual-paper is to examine the current efforts towards organizational creativity and innovation particularly in the context of Arab world especially Saudi industry. This theoretical paper also aims to ident6 and comprehend the barriers towards creativity and innovation in terms of organizational effectiveness. This paper has found that Saudi Arabia is doing well in attracting and retaining innovation factors (such as human skills, government support and investment in R&D, and increasingly knowledge through education-industry linkages). Still, there is a need for combination of right policies and good human capital to improve ‘creative outcomes’
A mighty wind

Developments in the world of wind continue to happen at record speed. The world as a whole is in the midst of grappling with an epochal transition from a system dominated by fossil and nuclear fuel to one that relies much more heavily on renewable energy. No technology breakthroughs are required for the United States to achieve the scenario of 20% of electricity from wind by 2030. Instead, many evolutionary steps executed with technical skill, which can cumulatively result in a 30-40% improvement in the cost effectiveness of wind technology over the next few decades, are expected to occur.
Virtual reality in anesthesia “simulation”

Simulation in anesthesia is a field that has revolutionized the teaching outlook. The uncommon grave situations are no more unseen. The ability of these devices to test and give a taste of nerves to an anesthetist is actually preparing him for a safe future management when the need be. The role of simulation in testing a new device for its likely success in clinical world can be foreseen. Mastering a difficult skill no longer subjects a patient to danger. These advanced methods not only see how anesthetist responds to environment, but also how the OT environment reacts to him. The review highlights how technology will help us become technically sound clinicians for tomorrow.
Angiogenic Role of LYVE-1–Positive Macrophages in Adipose Tissue

Here we report the discovery of a characteristic dense vascular network (DVN) in the tip portion of epididymal adipose tissue in adult mice. The DVN is formed by angiogenesis rather than by vasculogenesis, and has functional blood circulation. This DVN and its subsequent branching may provide a new functional route for adipogenesis. The recruitment, infiltration, and accumulation of bone marrow-derived LYVE-1+ macrophages in the tip region are crucial for the formation of the DVN. Matrix metalloproteinases (MMPs) and the VEGF-VEGFR2 system are responsible not only for the formation of the DVN, but also for the recruitment and infiltration of LYVE-1+ macrophages into the epididymal adipose tissue tip region. SDF-1, but not the MCP-1-CCR2 system, is a critical factor in recruitment and ongoing retention of macrophages in this area. We also demonstrate that the tip region of epididymal adipose tissue is highly hypoxic, and thus provides a microenvironment conducive to the high expression and enhanced activities of VEGF, VEGFR2, MMPs, and SDF-1 in autocrine and paracrine manners, to create an ideal niche for the recruitment, retention, and angiogenic action of macrophages. These findings shed light on the complex interplay between macrophage infiltration, angiogenesis, and adipogenesis in the tip region of adult epididymal adipose tissue, and provide novel insight into the regulation of alternative outgrowth of adipose tissue.
Comprehension Calibration and Recall Prediction Accuracy of Texts: Reading Skill, Reading Strategies, and Effort.

High school students at 3 levels of verbal skill rated their own recall (prediction accuray) and comprehension (calibration accuracy) of 3 expository texts accompanied by 3 different sets of instructions. All sets of instructions emphasized reading for understanding, and two of them also involved key words (given or personally selected), which were to be used during study. Students assessed which instructions they preferred and estimated their general verbal and memory skills. Three major results were obtained: (a) Students seemed to assess their general verbal and memory skills quite well. (b) Acceptable levels of comprehension calibration and recall prediction accuracy were found. Verbal-skill differences were found for recall prediction accuracy but not for comprehension calibration accuracy. (c) Students had study preference-the most preferred way to study increased performance but reduced prediction accuracy.
Robust 1-bit Compressive Sensing via Gradient Support Pursuit

This paper studies a formulation of 1-bit Compressed Sensing (CS) problem based on the maximum likelihood estimation framework. In order to solve the problem we apply the recently proposed Gradient Support Pursuit algorithm, with a minor modification. Assuming the proposed objective function has a Stable Restricted Hessian, the algorithm is shown to accurately solve the 1-bit CS problem. Furthermore, the algorithm is compared to the state-of-the-art 1-bit CS algorithms through numerical simulations. The results suggest that the proposed method is robust to noise and at mid to low input SNR regime it achieves the best reconstruction SNR vs. execution time trade-off.