Miracle-dz/UniFAR-FTUs huggingface dataset:
He’s Too Young to Learn About That Stuff: Anti-Racist Pedagogy and Early Childhood Social Studies

Few early childhood teachers engage in critical and anti-racist forms of pedagogical practice, primarily on the basis of developmental and political concerns. With the exception of a few studies, little has been documented relative to early childhood teachers’ experiences while enacting this form of pedagogical practice. The purpose of this article is to examine my teaching experiences engaging in critical, anti-racist pedagogy through the development and implementation of a critical action research study/unit on African American history. Data from this study reveal four levels of challenges that emerged throughout the development and implementation phases of this study/unit. Finally, I discuss several implications of this study for early child-hood multicultural practice
PDS expansion and dietary choices: Evidence from edible oils in India

Using large-scale consumer expenditure survey data from the National Sample Survey Office (NSSO), this paper attempts to quantify the extent to which policy interventions in edible oils, particularly palm oil, in the Indian states of Tamil Nadu, Andhra Pradesh and Maharashtra have had an impact on household nutrition. We exploit the cross-sectional variation in the introduction of the intervention on subsidised palm oil to identify the causal effects on household’s consumption patterns and caloric intakes thereof. We conduct difference-in-differences (DID) and matched DID analyses for both rural and urban areas separately. Our DID estimation results suggest that, after the policy intervention, an average rural household in the treated district increased its daily caloric intake from palm and other minor oils by 146-154 Kcal relative to an average household in the bordering control district. This translates to an increase of 33-35 per cent from the average baseline consumption of palm and other oils. No such positive effects were however observed for urban areas. In addition, we find that the households reduced their intakes of traditionally consumed coconut and groundnut oils. The estimates from the matched DID are similar in direction and marginally higher than the standard DID estimates for caloric intakes of palm and other oils in rural areas. To capture the changes in group composition over time in a repeated cross-sectional framework, we also undertake the analysis by employing the PSM-DIDRCS estimator. The estimates were close to that found under matched DID estimation using the PSM sample. The results were also robust to heterogeneous treatment impacts and various alternative specifications. The findings from this study have policy implications for the discussion surrounding the expansion of PDS portfolios aimed at addressing the malnutrition problem in
Physics-constrained deterministic solution of time-dependent partial differential equations using deep convolutional encoder-decoders

Pattern recognition has its origins in engineering while machine learning developed from computer science. Today, artificial intelligence (AI) is a booming field with many practical applications and active research topics that deals with both pattern recognition and machine learning. We now use softwares and applications to automate routine labor, understand speech (using Natural Language Processing) or images (extracting hierarchical features and patterns for object detection and pattern recognition), make diagnoses in medicine, even intricate surgical procedures and support basic scientific research. This thesis deals with exploring the application of a specific branch of AI, or a specific tool, Deep Learning (DL) to solving real world engineering problems governed by Partial Differential equations which otherwise had been difficult to solve using existing methods till date. Here we focus on different Deep Learning based methods to deal with two such engineering problems. We also explore how a Deep Learning model can iteratively solve a non-linear Partial Differential Equation (PDE) based on a given set of initial conditions and using a Physics-governed loss function instead of traditional loss functions used to optimize Deep Learning models. Previous literature deal with utilizing a single loss function to solve such examples of PDEgoverned systems. Moreover, the most recent work proposes to solve only a simple spatially varying PDE (Darcy Flow). We extend the framework to deal with both spatial and time-varying PDEs (Burgers’ Equation). Furthermore, we also propose an alternating minimization process for optimizing neural networks as PDE-solvers. This alternatively minimizes two separate loss functions. We note that this process works just as well as the single loss minimization and in certain cases, performs even better. From a neural network standpoint, we use a Convolutional Encoder-Decoder framework as our PDE-surrogate.
Numerical Simulations and the Design of Magnetic Field-Enhanced Electron Impact Ion Source with Hollow Cylinder Structure

An electron impact ion source-adopted magnetic field-enhanced technology has been designed for enhancing the electron intensity and the ionization efficiency. Based on the ion optic focus mechanism, an electron impact ionization source was designed, and the electron entrance into the ionization chamber was designed with a hollow cylinder structure to improve the ion extraction efficiency. Numerical simulation and optimal geometry were optimized by SIMION 8.0 to provide higher electron intensity and ion transmission efficiency. To improve the electron intensity, the influence of the filament potential and magnetic intensity was investigated, and the values of 70 eV and 150 Gs were chosen in our apparatus. Based on the optimal parameters, the air in the lab and oxygen gas was detected by the homemade apparatus, and the ion intensity was detected in the positive and negative ion modes, respectively. The homemade electron impact ion source apparatus has the potential to enhance ionization efficiency applied in the mass spectrometer ionization source.
Effects of Chonic and Acute Bisphenol-A Exposure on the Cellular Stress Response

Stress granules (SGs) are cytoplasmic aggregates of mRNA and protein that form in response to environmental stress and are thought to confer protection from apoptosis. Bisphenol-A (BPA) is a ubiquitous environmental toxin linked to numerous diseases, though its relationship to SGs was previously unknown. BPA exposures within human serum levels had no significant effect on cell growth nor induced SG formation. However, high-dose acute BPA exposure greatly induced SG formation. Further, lowdose chronic BPA exposure resulted in a significant decrease in SG formation during subsequent high-dose acute BPA exposure, suggesting that chronic exposure to BPA alters cellular stress dynamics. Our results suggest that BPA may seriously alter the ability of cells to cope with environmental stress.
Survey Design and Precision of ASER Estimates

The purpose of ASER (Annual Status of Education Report) is to obtain reliable estimates of the status of children’s schooling and basic learning (reading and arithmetic level) in rural India. The survey is undertaken in rural India every year and there have been four such surveys starting from 2005. Every year a core set of questions regarding schooling status and basic learning levels are posed to children in the 3-16 age group.
Comparative Analysis: The Impact of Financial Sector Development on Economic Growth in the Non-Oil Sector in Saudi Arabia

The aim of this paper is to examine the relationship between financial sector development and economic growth in the non-oil sector in Saudi Arabia. Six measures of financial sector development are used in this study, The data used in this study are from Saudi Arabia and consisted of time series data for the period of 1985 to 2015. The data are analyzed using the Autoregressive Distributed Lag (ARDL) model. The main conclusion is that there is a positive and significant impact of financial sector development on the total economic growth of the non-oil sector and the economic growth of the non-oil public and private sector.
Physiology and Breeding 2018 , 8 ( 2 ) : 89-98 ISSN : 2008-5168 Response of coriander to salinity stress

Salinity in the soil and irrigation water is an environmental problem and a major constraint for crop production. Coriandrum sativum as a leafy vegetable is great interest for vegetable producers in the world. However, due to gradual increase in soil and water salinity, especially in Northwest Iran, the production of this vegetable has been faced with many constraints. This experiment was conducted to study the effect of salinity on some morphological and physiological traits of Coriandrum sativum. The factors were arranged as factorial based on randomized complete block design with five NaCl levels (0, 50, 100, 150 and 200 mM NaCl) and local ecotypes (Tabriz, Malayer) with three replications. The results revealed that there was interaction of salinity levels with ecotypes in terms of proline content and relative water content. The highest amounts for proline content (77 μg FWt) was recorded for Tabriz clone under 200 mM NaCl. The greatest relative water content was obtained by Tabriz clone × NaCl 0 and Malayer × NaCl 0 and 50 mM combinations. The highest dry weight of leaves, K/Na ratio and K belonged to the non-salinity condition. Highest amounts of Na accumulation, MDA and H2O2 level and ion leakage were attained with 200 mM NaCl level. Soluble sugars content were affected by both salinity and ecotype. The highest content of soluble sugars was achieved by Malayer ecotype. With increasing salinity level, the content of soluble sugars increased and the highest amount of soluble sugars content was recorded at 200 mM NaCl.
Study on the Effects of the Interphase Region on the Network Properties in Polymer Carbon Nanotube Nanocomposites

The interphase region around nanoparticles changes the percolation threshold of long and thin nanoparticles, such as carbon nanotubes (CNT) in polymer nanocomposites. In this paper, the effects of the interphase region on the percolation threshold of nanoparticles and the network fraction are studied. New percolation threshold (φP) is defined by the role of the interphase in the excluded volume of nanoparticles (Vex). Moreover, the influences of filler and interphase size on the percolation volume fraction, the fraction of nanoparticles in the network as well as the volume fraction and relative density of the filler network are investigated. The least ranges of “φP” are obtained by thin and long CNT. Similarly, a thick interphase increases the “Vex” parameter, which causes a positive role in the percolation occurrence. Also, thin CNT and a thick interphase cause the high fraction of the filler network in the nanocomposites.
Public opinion matters

Social media mining has proven useful in multiple research fields as a tool for public opinion extraction and analysis. Such mining can discover knowledge from unstructured data in booming social media sources that provide instant public responses and also capture long-term data. Environmental scientists have realized its potential and conducted various studies where public opinion matters. We focus our discussion in this article on mining social media text on environmental issues, with particular emphasis on sentiment analysis, fitting the theme of Data Science and Sustainability. The data science community today is interested in topics that overlap with environmental issues and their broader impacts on sustainability. Such work appeals to scientists focusing on areas such as smart cities, climate change and geo-informatics. Future issues emerging from this research include domain-specific multilingual mining, and advanced geo-location tagging with demographically focused sentiment analysis.
Design and Implementation of an Escape Analysis in the Context of Safety-Critical Embedded Systems

The use of a managed, type-safe language such as Standard ML, Ada Ravenscar, or Java in hard real-time and embedded systems offers productivity, safety, and dependability benefits at a reasonable cost. Static software systems, that is systems in which all relevant resource entities such as threads and their priorities, for instance, and the entire source code are known ahead of time, are particularly interesting for the deployment in safety-critical embedded systems: Code verification is rather maintainable in contrast to dynamic systems. Additionally, static analyses can incorporate information from all software and system layers to assist compilers in emitting code that is well suited to an application on a particular hardware device. It was shown in the past that a program composed in type-safe Java in combination with a static system setup can be as efficient as one that is written in C [30], which is still the most widely used language in the embedded domain. Escape analysis (EA) is one of several static-analysis techniques. It supports, for instance, runtime efficiency by enabling automated stack allocation of objects. In addition, Stilkerich et al. [27, 28] have argued that EA enables further applications in safety-critical embedded systems such as the computation of memory classes stated in the Real-Time Specification for Java (RTSJ) [6]. EA can be applied to any programming language but the quality of its results greatly benefits from the properties of a type-safe language. Notably, embedded multicore devices can positively be affected by the use of EA. Thus, we explore an ahead-of-time (AOT) escape analysis in the context of the KESO JVM featuring a Java AOT compiler targeting (deeply) embedded (hard) real-time systems.
Inference and analysis of gene regulatory networks using data mining techniques

Causality draws the relationship between the cause and the effect among a pair of items. Causality inference in Gene Regulatory Networks (GRNs) is a challenging task due to the limits of appropriate data and large dataset size of real networks. Such an inference is very important because it might give the blueprint of how a complex organism operates which might further reveal the probable cause of some of the terminal diseases. A number of techniques have been proposed and applied to infer causal relationships in various domains. However, they are not specific to regulatory network inference. In our work, we made an empirical study on a few selective methods in inferring causal GRNs. To assess the candidate methods in terms of their quality of inference we used time-series expression data from the DREAM challenges. Our findings revealed that among the selected causality finding techniques, Causation Entropy gives the best result, however, it is computationally expensive. We used the second-best approach, Transfer Entropy, to infer casual GRN of breast cancer datasets. The topological analysis of the network revealed that top out-degree genes such as SLC39A5 which are considered central genes, plays an important role in cancer progression. The main intention behind this research work was to develop an effective inference method for causal Gene Regulatory Network which is computationally less expensive and also considers the effects of time-delay during regulation of one gene by the other. We proposed a novel time delayed inferring method for GRNs using association rule mining. We tried to infer the regulatory sign of the edges i.e. whether influencing positively or negatively. Comparative results highlight that our proposed technique produced better inference results in comparison to several of the existing techniques. We applied the proposed method to perform post inference analysis on malignant cancer network and benign network. Various networks parameters like the degree distribution, average path length, clustering coefficient, network re-wiring structure analysis, network topological variation analysis, and connectivity variation analysis are performed to identify the differentially expressed genes between the two networks. To avoid inference biasness and to obtain a consensus based conclusion we made a comparative analysis between our proposed method and transfer entropy based method and also computed the average score. Such an analysis of the network has revealed four genes, namely, STON2, UMODL1, STK11, and COBL which are already under investigation for its role in various types of cancer as seen from various researches.
MBFN: A Multi-branch Face Network for Facial Analysis

We present a multi-task algorithm for simultaneous face identification, gender recognition, age estimation, several facial attributes prediction and facial expression classification by using a single deep multi-branch convolutional neural network (CNN) based on MobileFaceNet. The proposed method, called MBFN (multi-branch face network), uses the backbone network for face identification and pulls branches from the backbone for other facial tasks. It exploits the synergy among the facial tasks which enhances their individual performances. Extensive experiments show that the proposed network is able to capture global and local information in faces and achieve state-of-the-art result for most of these tasks.
Native Language Identification: The Role of Consonants

Native language identification is relevant in speech technology, second language learning, forensic analysis and cross-cultural communication. Here we explore the contribution of consonantal articulation in this process. Specifically, we investigate whether Chinese English listeners can identify different speakers' native languages by their consonant production. Subjects listened to 30 English sentences (with 24 target consonants read by three groups of English speakers (American/Chinese/French). Subjects identified the speakers' native language background by the English consonants segmented from the 30 sentences. Results indicated that some consonants present better cues to native language than others.
What Are the Benefits of Data Sharing? Uniting Supply Chain and Platform Economy Perspectives

Abstract Data as a resource and data sharing enable competitive supply chains and present-day digital platform business models. The recipe for these competitive supply chains will no longer be contingent on how different companies contract to share data in their existing business networks but how these companies make these contracts available for the others in multisided markets. Advancing the availability of data sharing contracts has led to novel internal and external operational efficiencies and to new types of strategic opportunities. Data sharing is nothing new. Approximately 49% of the companies already share data with other companies. How has data sharing emerged between companies? What types of benefits have companies reached by sharing data? Those are the two research questions we are answering in this study. Additionally, we map the trajectory of data sharing technologies and their benefits for companies. External strategic opportunities cannot be reached unless the product, service and software architectures are modular; in addition, the boundary resources are not being considered by the companies. Finally, the tools for evaluating the value capture of indirect network effects is missing from widely accepted business case valuation methods. The question remains – what type of data resources can companies treat as proprietary or as shared?
Narrowband Interference Cancellation for Index-Modulated TDS-OFDM in Underwater Acoustic Communications

In this extended abstract, the narrowband interference (NBI) cancellation problem is introduced for dual-PN-padded TDS-OFDM signal in underwater acoustic communications. PN sequence is used in differential measuring of interference and payload reconstruction. A compressed sensing-based algorithm termed initialization enhanced sparsity adaptive subspace pursuit (IESASP) is proposed to estimate the NBI differential. Index-modulated OFDM (IM-OFDM) signal is considered for performance evaluation. Simulation results show that the proposed NBI cancellation method can effectively enhance BER performance especially for IM-OFDM.
Real-time Transportation Prediction Correction using Reconstruction Error in Deep Learning

In online complex systems such as transportation system, an important work is real-time traffic prediction. Due to the data shift, data model inconsistency, and sudden change of traffic patterns (like transportation accident), the prediction result derived from an offline-built model would be unreliable. Retraining the model is usually not time affordable for online prediction, especially when the prediction model is very complex and costs a lot of training time (for example, deep neural networks). A real-time prediction correction strategy would be of great value under this situation. Traditionally, the prediction correction usually relies on the prediction error in several previous time intervals. They assume that the error pattern is similar in the current time interval, so that it is time-delayed to some extent. In this article, we propose the prediction correction strategy using the reconstruction error in the deep neural network. The reconstruction error can reflect the model’s ability on feature representation and then determine the fitness of an input data to the model. We first build the relationship between reconstruction error and prediction error. From the perspective of the prediction interval, we demonstrate that the reconstruction error is in positive relation with the prediction interval. Thus the prediction result is more reliable when the reconstruction error is smaller. Then we propose two mechanisms of real-time prediction correction using the reconstruction error. The data driven prediction correction approach selects several training instances with similar reconstruction errors to the current instance and using their average prediction error in correcting the prediction result. The model-driven approach builds several component deep neural networks in training. The component training set for each network is selected according to the reconstruction error of training instances. For a predicting instance, it first computes the reconstruction error of the sample in each component network and then averages the results by the reconstruction error and prediction interval. The model-driven approach is actually a reconstruction error-based deep neural network ensemble approach. Finally, a series of experiments demonstrated that reconstruction error based prediction correction approaches are effective in several prediction problems in transportation including traffic flow prediction on road, traffic flow prediction in entrance and exit station and travel time prediction. Besides the high overall accuracy, our approach can also provide many observations of using the reconstruction error in transportation prediction.