Miracle-dz/UniFAR-FTUs huggingface dataset:
Ethnic/Racial Differences in Specific Mathematics Skills of Texas High School Boys: A Texas Multiyear Statewide Analysis

Investigated in this study was the extent to which differences were present in overall mathematics achievement for Texas high school boys by their ethnicity/race (i.e., Asian, White, Hispanic, and Black). Statewide data were obtained from the Texas Education Agency Public Education Information Management System on all high school boys for the 2004-2005 through the 2011-2012 school years. Inferential statistical procedures yielded statistically significant differences for all 8 school years. For each of the mathematics proficiency levels (i.e., Met Standard, Commended Performance, Higher Education Readiness Component), a stair-step effect was present. Asian boys outperformed White, Hispanic, and Black boys in all school years, with one exception in which Asian and White boys had similar proficiency levels. In all 8 school years, Black boys had the poorest mathematics achievement on each proficiency level, with Hispanic boys having the next poorest mathematics proficiency. Implications are discussed and suggestions for policy and practice are made.
An Investigation into the impact of techno-entrepreneurship education on self- employment Farzin,

Research has shown that techno-entrepreneurship is economically significant. Therefore, it is suggested that teaching techno-entrepreneurship may be important because such programmes would prepare current and future generations of learners to recognise and act on high-technology opportunities. Education in technoentrepreneurship may increase the knowledge of how to start one’s own enterprise and recognise the technological opportunities for commercialisation to improve decision-making about starting a new venture; also it influence decisions about capturing the business opportunities and turning them into successful ventures. Universities can play a main role in connecting and networking technoentrepreneurship students towards a cooperative attitude with real business practice and industry knowledge. To investigate and answer whether education for techno-entrepreneurs really helps, this paper choses a comparison of literature reviews as its method of research. After reviewing literature related to the impact of technoentrepreneurship education on self-employment 6 studies which had similar aim and objective to this paper were. These particular papers were selected based on a keywords search and as their aim, objectives, and gaps were close to the current research. In addition, they were all based on the influence of techno-entrepreneurship education in self-employment and intention of students to start new ventures. The findings showed that teaching techno-entrepreneurship education may have an influence on students’ intention and their future self-employment, but which courses should be covered and the duration of programmes, needs further investigation. Keywords—Techno-entrepreneurship education, training, higher education, intention, self-employment.
INTERIOR POLICY DIFFERENTIATION

Animals develop novel skills not only through the interaction with the environment but also from the influence of the others. In this work we model the social influence into the scheme of reinforcement learning, enabling the agents to learn both from the environment and from their peers1. Specifically, we first define a metric to measure the distance between policies then quantitatively derive the definition of uniqueness. Unlike previous precarious joint optimization approaches, the social uniqueness motivation in our work is imposed as a constraint to encourage the agent to learn a policy different from the existing agents while still solve the primal task. The resulting algorithm, namely Interior Policy Differentiation (IPD), is able to learn a collection of policies that can solve a given task with distinct behaviors and brings about performance improvement as a byproduct in some cases.2.
The Effects of Note-Taking on Mind Wandering and Learning: Is Taking Notes on Printed PowerPoint Slides Effective?

Note-taking is one of the most universally employed strategies for enhancing learning, but are all note-taking styles created equal? The present study compared how taking notes on lecture slide printouts (“full slides”) vs. taking notes on blank lined paper (“freehand notes”) would affect learning, situational interest, and in-lecture mind wandering. After completing a pretest on statistics, participants ( N = 48) were randomly assigned to either take freehand notes or take notes on full slides while watching a 30-min introductory statistics lecture video, which was occasionally interrupted with thought probes to measure mind wandering. Participants then completed a questionnaire about their situational interest in the video lecture and statistics, followed by three posttests to assess their learning from the video. Results indicated that there was a nonsignificant trend suggesting that learning was slightly increased for the full slides group, but situational interest did not differ between the two conditions. The full slides group, however, experienced significantly less task-unrelated mind wandering and more on-task thoughts than the freehand notes group, a pattern that was especially clear when the analysis focused on only those participants who actually took notes during the lecture. These results imply that full slide note-taking could be beneficial for keeping students focused during a lecture.
Soil Moisture Data Assimilation to Estimate Irrigation Water Use

Knowledge of irrigation is essential to support food security, manage depleting water resources, and comprehensively understand the global water and energy cycles. Despite the importance of understanding irrigation, little consistent information exists on the amount of water that is applied for irrigation. In this study, we develop and evaluate a new method to predict daily to seasonal irrigation magnitude using a particle batch smoother data assimilation approach, where land surface model soil moisture is applied in different configurations to understand how characteristics of remotely sensed soil moisture may impact the performance of the method. The study employs a suite of synthetic data assimilation experiments, allowing for systematic diagnosis of known error sources. Assimilation of daily synthetic soil moisture observations with zero noise produces irrigation estimates with a seasonal bias of 0.66% and a correlation of 0.95 relative to a known truth irrigation. When synthetic observations were subjected to an irregular overpass interval and random noise similar to the Soil Moisture Active Passive satellite (0.04 cm3 cm−3), irrigation estimates produced a median seasonal bias of <1% and a correlation of 0.69. When systematic biases commensurate with those between NLDAS‐2 land surface models and Soil Moisture Active Passive are imposed, irrigation estimates show larger biases. In this application, the particle batch smoother outperformed the particle filter. The presented framework has the potential to provide new information into irrigation magnitude over spatially continuous domains, yet its broad applicability is contingent upon identifying new method(s) of determining irrigation schedule and correcting biases between observed and simulated soil moisture, as these errors markedly degraded performance.
ICLR 2020 “ branches ” “ feathers ” “ fur ” “ shells ” “ eyespots ” “ stripes ” Direct feature visualizations Representation Inversion

An important goal in deep learning is to learn versatile, high-level feature representations of input data. However, standard networks’ representations seem to possess shortcomings that, as we illustrate, prevent them from fully realizing this goal. In this work, we show that robust optimization can be re-cast as a tool for enforcing priors on the features learned by deep neural networks. It turns out that representations learned by robust models address the aforementioned shortcomings and make significant progress towards learning a high-level encoding of inputs. In particular, these representations are approximately invertible, while allowing for direct visualization and manipulation of salient input features. More broadly, our results indicate adversarial robustness as a promising avenue for improving learned representations.
Rapid publication Human nuclear factor kB essential modulator mutation can result in immunodeficiency without ectodermal dysplasia

Background: Many receptors rely on the appropriate activation of nuclear factor (NF) kB to induce cellular function. This process depends critically on the phosphorylation of the inhibitor of NF-kB (IkB) by the IkB kinase. This targets IkB for ubiquitination and degradation, allowing NF-kB to translocate to the nucleus, where it can direct transcription. Hypomorphic human mutations affecting one IkB kinase component, the NF-kB essential modulator (NEMO), result in impaired signaling from receptors required for ectodermal development and immune function. Male subjects with these mutant NEMOmolecules have an X-linked syndrome known as ectodermal dysplasia with immunodeficiency, which is characterized by severe infections, with herpesviruses, bacteria, and mycobacterial susceptibility. Objective: We sought to genetically and biochemically characterize a patient with a mutant NEMO molecule without ectodermal abnormalities. Methods: We evaluated NEMO in a patient who had immunodeficiency and atypical mycobacterial infection but normal ectoderm. Results: We identified a novel NEMO mutant causing immunodeficiency without ectodermal dysplasia. The mutation, which altered the exon 9 splice site, was present in cells of ectodermal and hematopoetic origin and resulted in
Sparse Transformer: Concentrated Attention Through Explicit Selection

Self-attention-based Transformer has demonstrated the state-of-the-art performances in a number of natural language processing tasks. Self attention is able to model long-term dependencies, but it may suffer from the extraction of irrelevant information in the context. To tackle the problem, we propose a novel model called Sparse Transformer. Sparse Transformer is able to improve the concentration of attention on the global context through an explicit selection of the most relevant segments. Extensive experimental results on a series of natural language processing tasks, including neural machine translation, image captioning, and language modeling, all demonstrate the advantages of Sparse Transformer in model performance. Sparse Transformer reaches the state-of-the-art performances in the IWSLT 2015 English-to-Vietnamese translation and IWSLT 2014 German-to-English translation. In addition, we conduct qualitative analysis to account for Sparse Transformer’s superior performance.
ConQUR: Mitigating Delusional Bias in Deep Q-learning

Delusional bias is a fundamental source of error in approximate Q-learning. To date, the only techniques that explicitly address delusion require comprehensive search using tabular value estimates. In this paper, we develop efficient methods to mitigate delusional bias by training Q-approximators with labels that are "consistent" with the underlying greedy policy class. We introduce a simple penalization scheme that encourages Q-labels used across training batches to remain (jointly) consistent with the expressible policy class. We also propose a search framework that allows multiple Q-approximators to be generated and tracked, thus mitigating the effect of premature (implicit) policy commitments. Experimental results demonstrate that these methods can improve the performance of Q-learning in a variety of Atari games, sometimes dramatically.
Parental Support, Sibling Influences and Family Dynamics across the Development of Canadian Interuniversity Student-Athletes

A vast body of research focuses on the role of parents in athlete development; however, little is known about developmental influences of siblings. In general, family dynamics (i.e., patterns of relating or interacting among family members) have yet to be investigated in youth sport contexts. This study examines how family dynamics and the individual roles of parents and siblings influence the development of Canadian interuniversity student-athletes over time. Participants included four male and six female student-athletes. Each participant took part in a qualitative retrospective timeline interview. All data was subjected to a thematic analysis. Results indicate that siblings and parents play separate yet intricately connected roles in athlete development throughout childhood and adolescence. Overall, participants described a cohesive family unit built on shared values and joint participation in sport activities. They described stable and dynamic forms of support from their parents over time, and positive and negative sibling influences. These findings offer valuable insight into the dynamic nature of parent and sibling relationships with athletes in youth sport and beyond, as well as how these relationships operate in the broader family environment to optimize (and, at times, hinder) athletic development.
Learning Curves for Deep Neural Networks: A field theory perspective

A series of recent works established a rigorous correspondence between very wide deep neural networks (DNNs), trained in a particular manner, and noiseless Bayesian Inference with a certain Gaussian Process (GP) known as the Neural Tangent Kernel (NTK). Here we leverage this correspondence to provide explicit analytical predictions for the learning curves of DNNs on any fixed target function. Focusing on datasets whose input measure is uniform on a hypersphere, our predictions match experimental curves very well and reveal a strong implicit bias towards functions which are low order polynomials of the input. Two novel analytical tools underlay our approach. First a renormalization-group approach is used to show that noiseless GP inference using NTK, which lacks a good analytical handle, can be well approximated by noisy GP inference on a related kernel we call the renormalized NTK. Second a field theory reformulation of the problem allows a controlled perturbative expansion of the test error in the inverse dataset size. We believe our approach lays the foundations for a more precise quantitative understanding of DNNs.
A Case Study of In-Service Elementary School Teachers’ Professional Development in Astronomy

The purpose of this study is to explore the in-service training and professional development of elementary school teachers in relation to their knowledge of and abilities in astronomy. The teaching content for the in-service training in this study mainly concentrates on the scientific revolution in astronomy, the solar system, planets, stars, space probes and a visit to astronomical science museum. The elementary school teachers are first classified into groups so as to conduct a group learning strategy and to enable them to complete group work and a special astronomical report at the end of the semester. This study adopts a case study method and the main sources for data collection and analysis consist of the group work of the elementary school teachers, their astronomical special reports, feedback on learning and other learning portfolios. The main research results are: (1) the enrichment in the learning attitude of elementary school teachers towards astronomy; (2) the growth in the astronomical concepts of the elementary school teachers; (3) the improvement in confidence of elementary school teachers regarding their astronomy teaching.
use pruning together with layer-wise quantization and additional Huffman coding

Compressed forms of deep neural networks are essential in deploying large-scale computational models on resource-constrained devices. Contrary to analogous domains where large-scale systems are build as a hierarchical repetition of smallscale units, the current practice in Machine Learning largely relies on models with non-repetitive components. In the spirit of molecular composition with repeating atoms, we advance the state-of-the-art in model compression by proposing Atomic Compression Networks (ACNs), a novel architecture that is constructed by recursive repetition of a small set of neurons. In other words, the same neurons with the same weights are stochastically re-positioned in subsequent layers of the network. Empirical evidence suggests that ACNs achieve compression rates of up to three orders of magnitudes compared to fine-tuned fully-connected neural networks (88× to 1116× reduction) with only a fractional deterioration of classification accuracy (0.15% to 5.33%). Moreover our method can yield sub-linear model complexities and permits learning deep ACNs with less parameters than a logistic regression with no decline in classification accuracy.
Learning Variational Latent Dynamics: Towards Model-based Imitation and Control

In this paper, we learn dynamics from highdimensional demonstrations to facilitate model-based prediction and robot control. The proposed approach leverages the progress in variational-bayes and sequence modeling, extracting a low-dimensional latent space so the dynamical relations of interest can be compactly represented and learned. Different from existing works, our model captures latent dynamics in a more general form and features efficient inference for pattern filtering, prediction and synthesis. The extracted feature mapping and latent dynamics can be naturally integrated in robot learning, yielding task imitation from raw data and prediction-based reproduction. The performance of latent dynamics learning and model-based imitation is shown in three tasks: 1) reconstructing and predicting images of bouncing balls movement with an accuracy competitive to the state-of-theart; 2) synthesizing diverse handwriting image sequences; 3) learning to strike a ball under partial visual input, with results significantly outperforming baselines.
WP / 18 / 048 Inequality , ICT and Financial Access in Africa 1 Forthcoming : Technological Forecasting and Social Change

This study investigates the role of information and communication technology (ICT) on income inequality through financial development dynamics of depth (money supply and liquid liabilities), efficiency (at banking and financial system levels), activity (from banking and financial system perspectives) and size, in 48 African countries for the period 1996 to 2014. The empirical evidence is based on Generalised Method of Moments. While both financial depth and size are established to reduce inequality contingent on ICT, only the effect of financial depth in reducing inequality is robust to the inclusion of time invariant variables to the set of strictly exogenous variables. We extend the analysis by decomposing financial depth into its components, namely: formal, informal, semi-formal and non-formal financial sectors. The findings based on this extension show that ICT reduces income inequality through formal financial sector development and financial sector formalization as opposed to informal financial sector development and financial sector informalization. The study contributes at the same time to the macroeconomic literature on measuring financial development and responds to the growing field of addressing post-2015 Sustainable Development Goals (SDGs) inequality challenges by means of ICT and financial access. JEL Classification : I30; L96; O16;O55.
Education as opportunity? The causal effect of education on labor market outcomes in Jordan

ABSTRACT This paper studies the impact of the 1988/1989 educational reform in Jordan which extended mandatory schooling from nine to ten years and restructured secondary schooling. Despite weakness in the Jordanian labor market, our estimates suggest that an additional year of required schooling in the late 1980s was sufficient to improve labor force participation, employment, and wages. These effects were initially largest for women, while males with more education were also slightly more likely to be self-employed, work longer hours, and earn higher wages. We show that the extensive margin gains we observe for women persist over the life cycle, while intensive margin gains materialize only later in life. In contrast, the impacts for men strengthen over the life-cycle. These patterns are consistent with a persistent influence of traditional gender norms in Jordanian society influencing labor market decision making.
DDDT_A_198018 1717..1727

1College of Pharmacy & Division of Life and Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea; 2College of Pharmacy, Chungbuk National University, Cheongju-si 28160, Korea; 3Department of Thoracic and Cardiovascular Surgery, Bundang CHA Medical Center, CHA University, Seongnam, Gyeonggi-do, Korea; 4Department of Thoracic & Cardiovascular Surgery, Yonsei University Medical Center, Seoul 03722, Korea; 5College of Pharmacy, Hanyang University, Ansan 15588, Korea
How Did Order-Flow Impact Bond Prices During the European Sovereign Debt Crisis?

The impact of trades on price dynamics in the European sovereign debt markets is of significant importance to policy makers and market participants. This paper uses high-frequency quote and transaction data from the MTS European sovereign bond inter-dealer platform to investigate price-order-flow dynamics from July 2005 until December 2011 for Germany, France, Portugal, Italy, Ireland, Spain and Greece. We find that order-flow had a larger impact on quote revision in a relatively low-intensity trading environment than in a relatively high-intensity trading environment implying that informed traders should only execute in low-intensity trading environments when they value immediacy over discretion. This analysis is consistent with the limited prior literature for European debt markets. Our analysis indicates that this relationship persists during turbulent market conditions. Also, we find that the impact of order-flow on subsequent trades was larger during periods of high-trading intensity implying that market participants use order splitting trading strategies.
Scaling-Translation-Equivariant Networks with Decomposed Convolutional Filters

Encoding the scale information explicitly into the representation learned by a convolutional neural network (CNN) is beneficial for many computer vision tasks especially when dealing with multiscale inputs. We study, in this paper, a scaling-translation-equivariant (ST-equivariant) CNN with joint convolutions across the space and the scaling group, which is shown to be both sufficient and necessary to achieve equivariance for the regular representation of the scaling-translation group ST . To reduce the model complexity and computational burden, we decompose the convolutional filters under two pre-fixed separable bases and truncate the expansion to low-frequency components. A further benefit of the truncated filter expansion is the improved deformation robustness of the equivariant representation, a property which is theoretically analyzed and empirically verified. Numerical experiments demonstrate that the proposed scaling-translation-equivariant network with decomposed convolutional filters (ScDCFNet) achieves significantly improved performance in multiscale image classification and better interpretability than regular CNNs at a reduced model size.