Miracle-dz/UniFAR-FTUs huggingface dataset:
Planning to save and executive function in preschool children

Preschoolers’ ability to create, and implement, a plan to save a limited resource for future use was examined. Children were given five marbles then spent 3 minutes with a lessdesirable opportunity to use them (for most, a simple marble run), then 3 minutes with a more-desirable opportunity (a complex marble run), for two trials. Half of the children were given the opportunity to create an explicit plan by allocating their marbles between bowls (similar to budgeting their resource). The opportunity to plan facilitated saving on Trial 1. Children not given the opportunity to plan performed poorly on Trial 1, but learned to save by Trial 2. All children in the Planning condition, regardless o f whether they planned to save, performed similarly on a measure of planning ability. Saving was not correlated with measures of inhibitory control, indicating that something beyond these skills may account for performance on the Savings task. Acknowledgements First, I would like to thank my supervisor, Dr. Deepthi Kamawar. Her guidance, generosity, and enthusiasm during this project have been sincerely appreciated. I would like to express my thanks to Etelle Bourassa, June Drayton-Callender, Dr. Jo-Anne LeFevre and Dr. Johanna Peetz for all they have done to help me throughout the duration of this program. I would also like to extend my thanks the members of the Children’s Representational Development Lab, especially Corrie, Andrea and Jenny. I am extremely grateful for the help and support they have each provided. I would like to express my gratitude to my family and friends for their continual support, especially Adam, for his limitless encouragement and patience during this program. Special thanks to Blair, Raelyne, and Christine for providing two years of constant optimism and support. Finally, I would like to thank each of the parents and children who participated in this project. Abstract Table of
Insights into Second Language Reading: LOOKING AT THE WHOLE

This critical volume, provides an in-depth analysis of second language reading's multiple dimensions.The paperback edition describes the complexity of reading and explains how reading differs in a first and second language. The book is broad in scope, covering all major aspects of the reading process and synthesizing all current reading research. The author provides a cross-linguistic orientation, explaining how first and second languages can mutually facilitate one another. This important volume offers strategies for enhancing literary acquisition, second-language learning and bilingual processing, and will serve as a valuable guide for graduate students, professors, researchers and foreign language teachers.
Project PALMMS (Physical Activity Levels and Family Medical Histories): An undergraduate sample

v List of Figures . ix List of Tables . x Chapter 1: Introduction . 1 Common Predictors and Correlates of Physical Inactivity . 2 Further Possible Reasons for the Decline in Physical Activity . 3 Importance of Addressing Physical Activity in the College Population . 4 Beliefs of Chronic Illness Development . 4 Prediction of Physical Activity through the Theory of Planned Behavior (TPB) . 6 TPB, Benefits & Barriers, and Physical Activity . 7 Perceptions Influencing Physical Activity . 8 Perceived Barriers to Physical Activity . 8 Perceived Benefits to Physical Activity . 9 Family Medical History . 9 FMH, PA, and Chronic Illness Development . 11 Purpose of the Study . 13
Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions

Despite the ubiquity of the Gaussian process regression model, few theoretical results are available that account for the fact that parameters of the covariance kernel typically need to be estimated from the dataset. This article provides one of the first theoretical analyses in the context of Gaussian process regression with a noiseless dataset. Specifically, we consider the scenario where the scale parameter of a Sobolev kernel (such as a Mat\'{e}rn kernel) is estimated by maximum likelihood. We show that the maximum likelihood estimation of the scale parameter alone provides significant adaptation against misspecification of the Gaussian process model in the sense that the model can become "slowly" overconfident at worst, regardless of the difference between the smoothness of the data-generating function and that expected by the model. The analysis is based on a combination of techniques from nonparametric regression and scattered data interpolation. Empirical results are provided in support of the theoretical findings.
Regularization Helps with Mitigating Poisoning Attacks: Distributionally-Robust Machine Learning Using the Wasserstein Distance

We use distributionally-robust optimization for machine learning to mitigate the effect of data poisoning attacks. We provide performance guarantees for the trained model on the original data (not including the poison records) by training the model for the worst-case distribution on a neighbourhood around the empirical distribution (extracted from the training dataset corrupted by a poisoning attack) defined using the Wasserstein distance. We relax the distributionally-robust machine learning problem by finding an upper bound for the worst-case fitness based on the empirical sampled-averaged fitness and the Lipschitz-constant of the fitness function (on the data for given model parameters) as regularizer. For regression models, we prove that this regularizer is equal to the dual norm of the model parameters. We use the Wine Quality dataset, the Boston Housing Market dataset, and the Adult dataset for demonstrating the results of this paper.
Decremental SSSP in Weighted Digraphs: Faster and Against an Adaptive Adversary

Given a dynamic digraph $G = (V,E)$ undergoing edge deletions and given $s\in V$ and $\epsilon>0$, we consider the problem of maintaining $(1+\epsilon)$-approximate shortest path distances from $s$ to all vertices in $G$ over the sequence of deletions. Even and Shiloach (J.~ACM'$81$) give a deterministic data structure for the exact version of the problem with total update time $O(mn)$. Henzinger et al. (STOC'$14$, ICALP'$15$) give a Monte Carlo data structure for the approximate version with improved total update time $ O(mn^{0.9 + o(1)}\log W)$ where $W$ is the ratio between the largest and smallest edge weight. A drawback of their data structure is that they only work against an oblivious adversary, meaning that the sequence of deletions needs to be fixed in advance. This limits its application as a black box inside algorithms. We present the following $(1+\epsilon)$-approximate data structures: (1) the first data structure is Las Vegas and works against an adaptive adversary; it has total expected update time $\tilde O(m^{2/3}n^{4/3})$ for unweighted graphs and $\tilde O(m^{3/4}n^{5/4}\log W)$ for weighted graphs, (2) the second data structure is Las Vegas and assumes an oblivious adversary; it has total expected update time $\tilde O(\sqrt m n^{3/2})$ for unweighted graphs and $\tilde O(m^{2/3}n^{4/3}\log W)$ for weighted graphs, (3) the third data structure is Monte Carlo and is correct w.h. p.~against an oblivious adversary; it has total expected update time $\tilde O((mn)^{7/8}\log W) = \tilde O(mn^{3/4}\log W)$. Each of our data structures can be queried at any stage of $G$ in constant worst-case time; if the adversary is oblivious, a query can be extended to also report such a path in time proportional to its length. Our update times are faster than those of Henzinger et al.~for all graph densities.
Merger rate density of binary black holes formed in open clusters

Several binary black holes (BBHs) have been observed using gravitational wave detectors. For the formation mechanism of BBHs, two main mechanisms, isolated binary evolution and dynamical formation in dense star clusters, have been suggested. Future observations are expected to provide more information about BBH distributions, and it will help us to distinguish the two formation mechanisms. For the star cluster channel, globular clusters have mainly been investigated. However, recent simulations have suggested that BBH formation in open clusters is not negligible. We estimate a local merger rate density of BBHs originated from open clusters using the results of our $N$-body simulations of open clusters with four different metallicities. We find that the merger rate per cluster is the highest for our 0.1 solar metallicity model. Assuming a cosmic star formation history and a metallicity evolution with dispersion, we estimate the local merger rate density of BBHs originated from open clusters to be $\sim 70~{\rm yr}^{-1} {\rm Gpc}^{-3}$. This value is comparable to the merger rate density expected from the first and second observation runs of LIGO and Virgo. In addition, we find that BBH mergers obtained from our simulations can reproduce the distribution of primary mass and mass ratio of merging BBHs estimated from the LIGO and Virgo observations.
General prescription for global 
U(1)
’s in 6D SCFTs

We present a general prescription for determining the global U(1) symmetries of six-dimensional superconformal field theories (6D SCFTs). We use the quiver-like gauge theory description of the tensor branch to identify candidate U(1) symmetries which can act on generalized matter. The condition that these candidate U(1)'s are free of Adler-Bell-Jackiw (ABJ) anomalies provides bottom-up constraints for U(1)'s. This agrees with the answer obtained from symmetry breaking patterns induced by Higgs branch flows. We provide numerous examples illustrating the details of this proposal. In the F-theory realization of these theories, some of these symmetries originate from deformations of non-abelian flavor symmetries localized on a component of the discriminant, while others come from an additional generator of the Mordell-Weil group. We also provide evidence that some of these global U(1)'s do not arise from gauge symmetries, as would happen in taking a decoupling limit of a model coupled to six-dimensional supergravity.
Entropy numbers and Marcinkiewicz-type discretization theorem

This paper studies the behavior of the entropy numbers of classes of functions with bounded integral norms from a given finite dimensional linear subspace. Upper bounds of these entropy numbers in the uniform norm are obtained and applied to establish a Marcinkiewicz type discretization theorem for integral norms of functions from a given finite dimensional subspace.
Cross Currency Valuation and Hedging in the Multiple Curve Framework

We generalize the results of Bielecki and Rutkowski (2015) on funding and collateraliza- tion to a multi-currency framework and link their results with those of Piterbarg (2012), Moreni and Pallavicini (2017), and Fujii et al. (2010b). In doing this, we provide a complete study of absence of arbitrage in a multi-currency market where, in each single monetary area, multiple interest rates coexist. We first characterize absence of arbitrage in the case without collateral. After that we study collateralization schemes in a very general situation: the cash flows of the contingent claim and those associated to the collateral agreement can be specified in any currency. We study both segregation and rehypothecation and allow for cash and risky collateral in arbitrary currency specifications. Absence of arbitrage and pricing in the presence of collateral are discussed under all possible combinations of conventions. Our work provides a reference for the analysis of wealth dynamics, we also provide valuation formulas that are a useful foundation for cross-currency curve construction techniques. Our framework provides also a solid foundation for the construction of multi-currency simulation models for the generation of exposure profiles in the context of xVA calculations.
H-OWAN: Multi-distorted Image Restoration with Tensor 1x1 Convolution

It is a challenging task to restore images from their variants with combined distortions. In the existing works, a promising strategy is to apply parallel "operations" to handle different types of distortion. However, in the feature fusion phase, a small number of operations would dominate the restoration result due to the features' heterogeneity by different operations. To this end, we introduce the tensor 1x1 convolutional layer by imposing high-order tensor (outer) product, by which we not only harmonize the heterogeneous features but also take additional non-linearity into account. To avoid the unacceptable kernel size resulted from the tensor product, we construct the kernels with tensor network decomposition, which is able to convert the exponential growth of the dimension to linear growth. Armed with the new layer, we propose High-order OWAN for multi-distorted image restoration. In the numerical experiments, the proposed net outperforms the previous state-of-the-art and shows promising performance even in more difficult tasks.
The relevance of learning quantum physics from the perspective of the secondary school student: A case study

Studying quantum physics in upper secondary school is now a standard practice (Stadermann et al., 2019). But given the context of science education, with low recruitment numbers in higher education and poor attitudes towards science, it remains a question whether students find the learning of quantum physics relevant. In this study, we explore how students perceive the importance of quantum physics and technology and whether their perception changes after an intervention, namely the "Quantum Rules! " visit. We also aim to understand if they overall feel that learning quantum is relevant or not. In order to answer these questions, we followed a mixed-methods approach, combining both questionnaires and interviews. The quantitative analysis showed that the "Quantum Rules! " intervention has a positive effect on students' perception of the relevance of quantum physics and technology, especially regarding how important they feel quantum science is for society. Nevertheless, the qualitative information revealed that although students may find quantum physics and technology important for society, that does not necessarily mean that they find learning quantum physics relevant. We found that students believe the latter is relevant to them only if they find it interesting. We therefore rediscover the common expression "important, but not for me," and we further propose that this perception derives from students not seeing the societal relevance of learning quantum physics.
Infodemiological Study Using Google Trends on Coronavirus Epidemic in Wuhan, China

The recent emergence of a new coronavirus (COVID-19) has gained a high cover in public media and worldwide news. The virus has caused a viral pneumonia in tens of thousands of people in Wuhan, a central city of China. This short paper gives a brief introduction on how the demand for information on this new epidemic is reported through Google Trends. The reported period is 31 December 2020 to 20 March 2020. The authors draw conclusions on current infodemiological data on COVID-19 using three main search keywords: coronavirus, SARS and MERS. Two approaches are set. First is the worldwide perspective, second – the Chinese one, which reveals that in China this disease in the first days was more often referred to SARS then to general coronaviruses, whereas worldwide, since the beginning, it is more often referred to coronaviruses.
Quaternion matrix regression for color face recognition

Regression analysis-based approaches have been widely studied for face recognition (FR) in the past several years. More recently, to better deal with some difficult conditions such as occlusions and illumination, nuclear norm based matrix regression methods have been proposed to characterize the low-rank structure of the error image, which generalize the one-dimensional, pixel-based error model to the two-dimensional structure. These methods, however, are inherently devised for grayscale image based FR and without exploiting the color information which is proved beneficial for FR of color face images. Benefiting from quaternion representation, which is capable of encoding the cross-channel correlation of color images, we propose a novel color FR method by formulating the color FR problem as a nuclear norm based quaternion matrix regression (NQMR). We further develop a more robust model called R-NQMR by using the logarithm of the nuclear norm, instead of the original nuclear norm, which adaptively assigns weights on different singular values, and then extend it to deal with the mixed noise. The proposed models, then, are solved using the effective alternating direction multiplier method (ADMM). Experiments on several public face databases demonstrate the superior performance and efficacy of the proposed approaches for color FR, especially for some difficult conditions (occlusion, illumination and mixed noise) over some state-of-the-art regression analysis-based approaches.
Estimating the Jet Power of Mrk 231 during the 2017–2018 Flare

Long-term 17.6 GHz radio monitoring of the broad absorption-line quasar, Mrk 231, detected a strong flare in late 2017. This triggered four epochs of Very Long Baseline Array (VLBA) observations from 8.4 to 43 GHz over a 10 week period as well as an X-ray observation with NuSTAR. This was the third campaign of VLBA monitoring that we have obtained. The 43 GHz VLBA was degraded in all epochs, with only 7 of 10 antennas available in three epochs and 8 in the first epoch. However, useful results were obtained due to a fortuitous capturing of a complete, short 100 mJy flare at 17.6 GHz, both growth and decay. This provided useful constraints on the physical model of the ejected plasma that were not available in previous campaigns. We consider four classes of models: discrete ejections (both protonic and positronic) and jetted (protonic and positronic). The most viable model is a “dissipative bright knot” in a faint background leptonic jet with an energy flux ∼1043 erg s−1. Inverse Compton scattering calculations (based on these models) in the ambient quasar photon field explains the lack of a detectable increase in X-ray luminosity measured by NuSTAR. We show that the core (the bright knot) moves toward a nearby secondary at ≈0.97c. The background jet is much fainter. Evidently, the high-frequency VLBA core does not represent the point of origin of blazar jets, in general, and optical depth “core shift” estimates of jet points of origin can be misleading.
A Chandra X-Ray Survey of Optically Selected AGN Pairs

We present a Chandra archival study of optically selected active galactic nucleus (AGN) pairs at a median redshift . Out of 1286 AGN pairs (with projected separations rp < 100 kpc and velocity offsets Δv < 600 km s−1) optically identified from the Sloan Digital Sky Survey Seventh Data Release, we find 67 systems with archival Chandra observations, which represents the largest sample of optically selected AGN pairs studied in the X-ray. Among the 67 AGN pairs, 21 systems have both nuclei detected in the X-ray, 36 have one nucleus detected in the X-ray, and 10 have no X-ray detection. The X-ray detection rate, 78/134 = 58% (±7% 1σ Poisson errors), is significantly higher than that (23/134 = 17% ± 4%) of a comparison sample of star-forming galaxy pairs, lending support to the optical AGN classification. In the conservative case where X-ray contamination from star formation is removed, the X-ray detection rate becomes 27% ± 4%, consistent with predictions from the latest galaxy merger simulations. The 2–10 keV X-ray luminosity L2–10 keV increases with decreasing projected separation in AGN pairs for rp ≳ 15 kpc, suggesting an enhancement of black hole accretion even in early-stage mergers. On the other hand, L2–10 keV appears to decrease with decreasing projected separation at rp ≲15 kpc, which is contradictory to predictions from merger simulations. The apparent decrease in L2–10 keV of AGN pairs at rp ≲ 15 kpc may be caused by (i) enhanced absorbing columns from merger-induced gas inflows, (ii) feedback effects from early-stage mergers, and/or (iii) small number statistics. Future X-ray studies with larger samples are needed to put our results on firmer statistical ground.
Chromospheric activity in bright contact binary stars

Context. Studying chromospheric activity of contact binaries is an effective way of revealing the magnetic activity processes of these systems. One efficient but somewhat neglected method for this purpose is to follow the changes of the Hα line profiles via optical spectroscopy. Aims. Our goal is to perform a comprehensive preliminary analysis based on the optical spectral signs of chromospheric activity on the largest sample of contact binaries to date. Methods. We collected optical echelle spectra on 12 bright contact binaries in 17 nights. We derived new radial velocity curves from our observations. For quantifying the apparent chromospheric activity levels of the systems, we subtracted self-constructed synthetic spectra from the observed ones and measured the equivalent widths of the residual Hα-profiles at each observed epoch. Our well-sampled data set allowed us to study the short-term variations of chromospheric activity levels as well as to search for correlations between them and some basic physical parameters of the systems. Results. Fitting the radial velocity curves, we re-determined the mass ratios and systemic velocities of all observed objects. We found that chromospheric activity levels of the studied systems show various changes during the orbital revolution: we see either flat, one-peaked, or two-peaked distributions of equivalent width vs. the orbital phase. The first case means that the activity level is probably constant, while the latter two cases suggest the presence of one or two active longitudes at the stellar surfaces. Our correlation diagrams show that mean chromospheric activity levels may be related to the orbital periods, B−V color indices, inverse Rossby numbers, and temperature differences of the components. At the same time, no clear trend is visible with respect to the mass ratios, inclinations, or fill-out factors of the systems. A- and W-type contact binaries in our sample show similar distributions on each of the studied correlation diagrams.
Facebook Ads Monitor: An Independent Auditing System for Political Ads on Facebook

The 2016 United States presidential election was marked by the abuse of targeted advertising on Facebook. Concerned with the risk of the same kind of abuse to happen in the 2018 Brazilian elections, we designed and deployed an independent auditing system to monitor political ads on Facebook in Brazil. To do that we first adapted a browser plugin to gather ads from the timeline of volunteers using Facebook. We managed to convince more than 2000 volunteers to help our project and install our tool. Then, we use a Convolution Neural Network (CNN) to detect political Facebook ads using word embeddings. To evaluate our approach, we manually label a data collection of 10k ads as political or non-political and then we provide an in-depth evaluation of proposed approach for identifying political ads by comparing it with classic supervised machine learning methods. Finally, we deployed a real system that shows the ads identified as related to politics. We noticed that not all political ads we detected were present in the Facebook Ad Library for political ads. Our results emphasize the importance of enforcement mechanisms for declaring political ads and the need for independent auditing platforms.