Miracle-dz/UniFAR-FTUs huggingface dataset:
The Affordances of Three Play Types for Learning to Rapidly Read Ten Unfamiliar Words by Sight

This paper reports on an observational study in which I explored the affordances of three play types for learning to read ten unfamiliar words by sight. In conducting this qualitative study with two Hebrew-English speaking children (five-year-olds), I aimed to discuss alternatives to supporting children’s sight word learning. My observations confirm that play is an effective alternative for the teaching and learning of sight words, where play provided meaningful contexts for literacy learning. My observations also highlight the importance of children’s engagement and interest, which seemed to play an important role in motivating participants to continue interacting with the words.
Statistical Properties of Multivariate Distance Matrix Regression for High-Dimensional Data Analysis

Multivariate distance matrix regression (MDMR) analysis is a statistical technique that allows researchers to relate P variables to an additional M factors collected on N individuals, where P ≫ N. The technique can be applied to a number of research settings involving high-dimensional data types such as DNA sequence data, gene expression microarray data, and imaging data. MDMR analysis involves computing the distance between all pairs of individuals with respect to P variables of interest and constructing an N × N matrix whose elements reflect these distances. Permutation tests can be used to test linear hypotheses that consider whether or not the M additional factors collected on the individuals can explain variation in the observed distances between and among the N individuals as reflected in the matrix. Despite its appeal and utility, properties of the statistics used in MDMR analysis have not been explored in detail. In this paper we consider the level accuracy and power of MDMR analysis assuming different distance measures and analysis settings. We also describe the utility of MDMR analysis in assessing hypotheses about the appropriate number of clusters arising from a cluster analysis.
A Small Molecule Inhibitor of Src Family Kinases Promotes Simple Epithelial Differentiation of Human Pluripotent Stem Cells

Human pluripotent stem cells (hPSCs) provide unprecedented opportunities to study the earliest stages of human development in vitro and have the potential to provide unlimited new sources of cells for regenerative medicine. Although previous studies have reported cytokeratin 14+/p63+ keratinocyte generation from hPSCs, the multipotent progenitors of epithelial lineages have not been described and the developmental pathways regulating epithelial commitment remain largely unknown. Here we report membrane localization of β-catenin during retinoic acid (RA) – induced epithelial differentiation. In addition hPSC treatment with the Src family kinase inhibitor SU6656 modulated β-catenin localization and produced an enriched population of simple epithelial cells under defined culture conditions. SU6656 strongly upregulated expression of cytokeratins 18 and 8 (K18/K8), which are expressed in simple epithelial cells, while repressing expression of the pluripotency gene Oct4. This homogeneous population of K18+K8+Oct4− simple epithelial precursor cells can further differentiate into cells expressing keratinocyte or corneal-specific markers. These enriched hPSC-derived simple epithelial cells may provide a ready source for development and toxicology cell models and may serve as a progenitor for epithelial cell transplantation applications.
Exploring the perceptions of inhibitors and drivers of e-business progression among SMEs at different stages of e-business maturity

It is imperative for an organisation to identify potential drivers and inhibitors of e-business in order to minimise the risk and maximise the benefits derived from its e-business initiative. Although the issues of drivers and inhibitors of e-business/e-commerce have been the subject of intensive discussions elsewhere, the literature bears no indication of any attempt to look into the distinction of the drivers and inhibitors of e-business perceived by organisations at different level of e-business maturity. Hence, in this paper, it is our intention to explore this issue further. Using a stage model we developed to distinguish organisational maturity in approaching their e-business initiatives, we found that there are differences in emphasis on how organisations at different level of maturity. Arguably, by realising and understanding which issues need to be prioritised, organisation would be better equipped in anticipating any difficulties in progressing through with their e-business initiatives.
Target-responsive DNA-capped nanocontainer used for fabricating universal detector and performing logic operations

Nucleic acids have become a powerful tool in nanotechnology because of their controllable diverse conformational transitions and adaptable higher-order nanostructure. Using single-stranded DNA probes as the pore-caps for various target recognition, here we present an ultrasensitive universal electrochemical detection system based on graphene and mesoporous silica, and achieve sensitivity with all of the major classes of analytes and simultaneously realize DNA logic gate operations. The concept is based on the locking of the pores and preventing the signal-reporter molecules from escape by target-induced the conformational change of the tailored DNA caps. The coupling of ‘waking up’ gatekeeper with highly specific biochemical recognition is an innovative strategy for the detection of various targets, able to compete with classical methods which need expensive instrumentation and sophisticated experimental operations. The present study has introduced a new electrochemical signal amplification concept and also adds a new dimension to the function of graphene-mesoporous materials hybrids as multifunctional nanoscale logic devices. More importantly, the development of this approach would spur further advances in important areas, such as point-of-care diagnostics or detection of specific biological contaminations, and hold promise for use in field analysis.
Occlusion detection in visual scene using histogram of oriented gradients

Object detection is an important step in any video analysis. In this paper, we propose a novel framework for blob based occluded object detection. It detects and tracks the occluded objects in video sequences captured by a fixed camera in crowded scene with occlusion. Moreover the occlusion of an abandoned object is a critical aspect in the video surveillance. We present the system used to identify the abandoned object highlighting how the system can recognize a problem of occlusion and detect the object when it is visible again. Initially Pedestrians are detected using the pedestrian detector by computing the Histogram of Oriented Gradients descriptors (HOG), using a linear Support Vector Machine (SVM) as the classifier. In our system, the background subtraction is modeled by a Mixture of Gaussians technique (MOG). Several experiments were conducted to demonstrate the proposed method using huge video dataset show the robustness and effectiveness.
Synergy Between VEGF/VEGFR Inhibitors and Chemotherapy Agents in the Phase I Clinic

Purpose: We hypothesized that chemotherapy synergizes with VEGF/VEGFR (VEGF/R) inhibitors in patients with advanced solid malignancies. Experimental Design: Patients treated on phase I protocols between December 2004 and July 2013 (n = 1,498) were included in this analysis. The primary outcome was clinical benefit, defined as stable disease ≥6 months, complete response, or partial response. Two odds ratios (OR) for achieving clinical benefit were calculated: one for patients treated with VEGF/R inhibitors (OR with VEGF/R) and another for patients treated without (OR without VEGF/R). To compare these two ORs, an interaction term was included in the multivariate model: (chemotherapy/factor of interest)×(VEGF/R). We took significant interaction terms (Pinteraction < 0.05) to suggest effect modification (either synergy or antagonism) with VEGF/R inhibitors. Results: All patients treated with VEGF/R inhibitors exhibited higher OR for clinical benefit than those who were not [OR = 1.9; 95% confidence interval (CI), 1.5–2.4; P < 0.0001]. Use of chemotherapy agents concomitant with VEGF/R inhibitors was associated with significantly higher OR for clinical benefit compared with chemotherapy use without VEGF/R inhibitors [OR with VEGF/R = 1.6 (95% CI, 1.1–2.5) vs. OR without VEGF/R = 0.4 (95% CI, 0.3–0.6), Pinteraction = 0.02]. Specifically, the antimetabolite class was associated with the greatest increase in OR for clinical benefit [OR with VEGF/R = 2.7 (95% CI, 1.5–4.7) vs. OR without VEGF/R = 0.2 (95% CI 0.1–0.3), Pinteraction = 0.004]. Conclusions: VEGF/R inhibitor was found to synergize with chemotherapeutics. This effect was most pronounced with the antimetabolite class. Clin Cancer Res; 20(23); 5956–63. ©2014 AACR.
Bronchopulmonary dysplasia as a predictor factor for motor alteration at 6 months corrected age in premature infants.

OBJECTIVE The study aimed to assess bronchopulmonary dysplasia (BPD) as a predisposing factor for alteration in the psychomotor development index (PDI) in premature infants and verify the incidence of neuromotor alterations at 6 months corrected age. METHOD This was a prospective cohort study that followed the neuromotor development of 152 very low birth weight premature infants, with psychomotor development index as the outcome. The study used the Bayley Scale of Infant Development at 6 months corrected age, and neurological examination. RESULTS Incidence of BPD was 13.2% (n = 20). Logistic regression analysis showed an association between BPD and altered psychomotor development index (OR 3.98; 95%CI: 1.04-15.1) after adjusting for confounding variables. Neurological examination was altered in 67.1% of the 152 infants. CONCLUSION Bronchopulmonary dysplasia acted as an independent predisposing factor for alteration in the psychomotor development index in premature infants at 6 months corrected age.
Attention Sensitive Web Browsing

The attention level is an important indicator of the user's level of interest while viewing any content. The web browser is one of the most popular means to access information, and the usage of browsers in mobile devices is increasing. In this paper we analyze the use of attention as an input for web browsers. Attention can be measured easily in real time using cheap commercially available wearable EEG sensors, such as NeuroSky's MindWave. We use the measured level of attention in the following ways: as an input mechanism for navigating through the controls on the web browser such as buttons, menus and hyperlinks, to correlate the attention with the section of the webpage being browsed and make the web browser responsive to the user's attention level in real time, and as an input that is fed back to the web server enabling the web content developer to make attention sensitive websites. For each of these, we provide the implementation details and some results obtained. We also provide some pointers how the input attention level event obtained from the EEG sensors can be standardized in the W3C specification.
Principal components analysis of diet and alternatives for identifying the combination of foods that are associated with the risk of disease: a simulation study

Dietary patterns derived empirically using principal components analysis (PCA) are widely employed for investigating diet–disease relationships. In the present study, we investigated whether PCA performed better at identifying such associations than an analysis of each food on a FFQ separately, referred to here as an exhaustive single food analysis (ESFA). Data on diet and disease were simulated using real FFQ data and by assuming a number of food intakes in combination that were associated with the risk of disease. In each simulation, ESFA and PCA were employed to identify the combinations of foods that are associated with the risk of disease using logistic regression, allowing for multiple testing and adjusting for energy intake. ESFA was also separately adjusted for principal components of diet, foods that were significant in the unadjusted ESFA and propensity scores. For each method, we investigated the power with which an association between diet and disease could be identified, and the power and false discovery rate (FDR) for identifying the specific combination of food intakes. In some scenarios, ESFA had greater power to detect a diet–disease association than PCA. ESFA also typically had a greater power and a lower FDR for identifying the combinations of food intakes that are associated with the risk of disease. The FDR of both methods increased with increasing sample size, but when ESFA was adjusted for foods that were significant in the unadjusted ESFA, FDR were controlled at the desired level. These results question the widespread use of PCA in nutritional epidemiology. The adjusted ESFA identifies the combinations of foods that are causally linked to the risk of disease with low FDR and surprisingly good power.
Research on Modeling Framework of the Interactive Artificial Stock Market and the Main Structure Model

This paper gives the interactive artificial modeling framework based on the main market, and brings in breakpoints and information extraction mechanism in the simulation model, it also gives the flexible agent, semi-flexible agent, structure model of the rigid agent and the process of operation; the paper gives a multi-agent model forming structure which is based on the interests judgment. The structure model given in this paper provides a model support for the establishment of policy information, the impact on the deep-level simulation model of the stock market from external environment changes.
RIAL: Redundancy Reducing Inlining Algorithm to Map XML DTD to Relations

XML has emerged as a common standard for data exchange over the World Wide Web. One way to manage XML data is to use the power of relational databases for storing and querying them. So the hierarchical XML data should be mapped into flat relational structure. In this paper we propose an algorithm which maps DTD to relational schema and as well as content and structure it preserves the functional dependencies during the mapping process in order to produce relations with less redundancy. This is done by categorizing functional dependencies and introducing four rules to be applied to the relations created by the hybrid inlining algorithm according to each category. These rules will reduce redundancies by moving attributes, creating relations, introducing keys and preserving functional dependencies.
Quality Assessment of Exon and Gene Arrays

The quality assessment procedures discussed in this this white paper focus on using various metrics to identify outlier arrays within the data set. These metrics can identify outliers; however it is impossible to provide hard and fast rules (specific thresholds) as to which arrays to flag as outliers. Such rules need to be developed in the context of particular applications with specific types of samples, combined with balancing the cost of repeating experiments and the cost of drawing wrong conclusions.
Poisson's equation in nonlinear filtering

The goal of this paper is to gain insight into the equations arising in nonlinear filtering, as well as into the feedback particle filter introduced in recent research. The analysis is inspired by the optimal transportation literature and by prior work on variational formulation of nonlinear filtering. The construction involves a discrete-time recursion based on the successive solution of minimization problems involving the so-called forward variational representation of the elementary Bayes' formula. The construction shows that the dynamics of the nonlinear filter may be regarded as a gradient flow, or a steepest descent, for a certain energy functional with respect to the Kullback-Leibler divergence pseudo-metric. The feedback particle filter algorithm is obtained using similar analysis. This filter is a controlled system, where the control is obtained via consideration of the first order optimality conditions for the variational problem. The filter is shown to be exact, i.e., the posterior distribution of the particle matches exactly the true posterior, provided the filter is initialized with the true prior.
Effect of saline water on seed germination and early seedling growth of the halophyte quinoa

The introduction of new crops with improved salinity stress tolerance could preserve water quality and protect soil resources from further degradation, providing extra sources of food for salinized areas. In this context, we tested the salinity tolerance of a variety of quinoa. Quinoa, a rich source of minerals, proteins and antioxidants, is considered a major alternative crop to meet food shortages in this century. Our study indicated that salinity tolerance of quinoa is largely conferred by a delicate balance between osmotic adjustment and ion accumulation. Salinity reduced productivity in terms of biomass, but increased the levels of antioxidant compounds, which are important health-protecting factors in food, thus providing economic benefit.
The Roles of Dehumanization and Moral Outrage in Retributive Justice

When innocents are intentionally harmed, people are motivated to see that offenders get their “just deserts”. The severity of the punishment they seek is driven by the perceived magnitude of the harm and moral outrage. The present research extended this model of retributive justice by incorporating the role of offender dehumanization. In three experiments relying on survey methodology in Australia and the United States, participants read about different crimes that varied by type (child molestation, violent, or white collar – Studies 1 and 2) or severity (Study 3). The findings demonstrated that both moral outrage and dehumanization predicted punishment independently of the effects of crime type or crime severity. Both moral outrage and dehumanization mediated the relationship between perceived harm and severity of punishment. These findings highlight the role of offender dehumanization in punishment decisions and extend our understanding of processes implicated in retributive justice.
A dual systems theory of incontinent action

Abstract In philosophy of action, we typically aim to explain action by appealing to conative attitudes whose contents are either logically consistent propositions or can be rendered as such. Call this “the logical criterion. ” This is especially difficult to do with clear-minded, intentional incontinence since we have to explain how two judgments can have non-contradicting contents yet still aim at contradictory outcomes. Davidson devises an innovative way of doing this but compromises his ability to explain how our better judgments can cause our continent behaviors. In this essay, I preserve Davidson’s approach to the logical criterion but deviate from his broader theory of action by developing a default-interventionist dual systems theory of action. To do this, I focus on the dynamical relationship between System 1 and System 2: (1) the logical construction of value judgments in System 2 from System 1 and (2) the imaginative construction of non-propositional conative attitudes in System 1 from System 2. I draw on Street’s Humean constructivism and Peacocke’s theory of imagination for logical and imaginative construction, respectively. Within this framework, I provide a new definition of continence and incontinence that satisfies the logical criterion and explains how our better judgments can cause our continent behaviors.
Localised technical progress and choice of technique in a linear production model

Since 1969 Atkinson and Stiglitz's article economists have considered technical change as a cumulative, localised and adaptive process. However, in formal analysis technological change has always been analysed by assuming known the whole set of available techniques, by ruling out in this way important issues like path dependent inefficiencies and lock-in phenomena. In this paper we develop an adaptive model of localised choice of technique within a classical theoretical framework. Our model allows to analyse in a rigorous way the "secular" dynamics of the economy, to incorporate path dependent inefficiencies and lock-in phenomena, and to provide an initial rigorous foundations to the concept of "technological trajectory".
An experimental comparison of routing protocols in multi hop ad hoc networks

This study experimentally compares the performance of three different multi hop ad hoc network routing protocols. Traditional routing protocols have proven inadequate in wireless ad hoc networks, motivating the need for ad hoc specific routing protocols. This study tests link state, distance vector and biologically inspired approaches to routing using OLSR, Babel and BATMAN routing protocols. The importance of OSI layers is also discussed. This study concludes that the routing protocol's overhead is the largest determinant of performance in small multi hop ad hoc networks. The results show that Babel outperforms OLSR and BATMAN routing protocols and that the OSI layer of the routing protocol has little impact on performance.
MRF parameter estimation based on weighted Least Squares Fit method

In general, the Markov random field (MRF) model parameter estimation is the first step in the image modeling applications. The accuracy of the parameter estimation will have a direct impact on whether or not the application based on the model can get the right result. In this paper, it is proposed a new method called the weighted least square fit, and then used the method in the MRF model parameter estimation. The method is based on the least squares fit (LS) method, and do some improvements so as could solve the LS method is not accurate enough and noise sensitive these disadvantages. And this paper have established an evaluation system based on the Expectation Maximization theory to evaluate the accuracy of the results generated by the different methods. Experiments have proved that if choose the same equations that selected from the field and under the same calculation complexity condition, the results based on this paper's method have a higher accuracy than the original LS method.
BinSlayer: accurate comparison of binary executables

As the volume of malware inexorably rises, comparison of binary code is of increasing importance to security analysts as a method of automatically classifying new malware samples; purportedly new examples of malware are frequently a simple evolution of existing code, whose differences stem only from a need to avoid detection. This paper presents a polynomial algorithm for calculating the differences between two binaries, obtained by fusing the well-known BinDiff algorithm with the Hungarian algorithm for bi-partite graph matching. This significantly improves the matching accuracy. Additionally a meaningful metric of similarity is calculated, based on graph edit distance, from which an informed comparison of the binaries can be made. The accuracy of this method over the standard approach is demonstrated.