Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical modelling of γ-ray emission produced by electrons originating from the magnetospheres of millisecond pulsars in globular clusters

Globular clusters are source of gamma-ray radiation. At GeV energies, their emission is attributed to magnetospheric activity of millisecond pulsars residing in the clusters. Inverse Compton scattering of ambient photon fields on relativistic particles diffusing through cluster environment is thought to be the source of GeV-TeV emission of globular clusters. Using pair starved polar cap model gamma-ray emission from synthetic millisecond pulsar was modelled. In addition to pulsar emission characteristics, the synthetic pulsar model yielded spectra of electrons escaping pulsar magnetosphere. To simulate gamma-ray emission of globular cluster, both products of synthetic millisecond pulsar modelling were used. Gamma-ray spectra of synthetic millisecond pulsars residing in the cluster were summed to produce the magnetospheric component of cluster emission. Electrons ejected by these pulsars were injected into synthetic globular cluster environment. Their diffusion and interaction, both, with cluster magnetic field and ambient photon fields, were performed with Bednarek & Sitarek (2007) model yielding ICS component of cluster emission. The sum of the magnetospheric and ICS components gives the synthetic gamma-ray spectrum of globular cluster. The synthetic cluster spectrum stretches from GeV to TeV energies. Detailed modelling was preformed for two globular clusters: Ter 5 and 47 Tuc. Simulations are able to reproduce (within errors) the shape and the flux level of the GeV part of the spectrum observed for both clusters with the Fermi/LAT instrument. The synthetic flux level obtained in the TeV part of the clusters' spectrum is in agreement with a H.E. S.S. upper limit determined for 47 Tuc, and with emission level recently detected for Ter 5 with H.E. S.S. telescope. The synthetic globular cluster model, however, is not able to reproduce the exact shape of the TeV spectrum observed for Ter 5.
Wanna Get Away? Regression Discontinuity Estimation of Exam School Effects Away From the Cutoff

In regression discontinuity (RD) studies exploiting an award or admissions cutoff, causal effects are nonparametrically identified for those near the cutoff. The effect of treatment on inframarginal applicants is also of interest, but identification of such effects requires stronger assumptions than those required for identification at the cutoff. This article discusses RD identification and estimation away from the cutoff. Our identification strategy exploits the availability of dependent variable predictors other than the running variable. Conditional on these predictors, the running variable is assumed to be ignorable. This identification strategy is used to study effects of Boston exam schools for inframarginal applicants. Identification based on the conditional independence assumptions imposed in our framework yields reasonably precise and surprisingly robust estimates of the effects of exam school attendance on inframarginal applicants. These estimates suggest that the causal effects of exam school attendance for 9th grade applicants with running variable values well away from admissions cutoffs differ little from those for applicants with values that put them on the margin of acceptance. An extension to fuzzy designs is shown to identify causal effects for compliers away from the cutoff. Supplementary materials for this article are available online.
Who is friends with whom? : Patterns of inter- and intraethnic friendships of mainstream and immigrant early adolescents in Germany

Multiethnic schools provide opportunities for interethnic contact and the development of positive interethnic relations. Yet, some children develop such relations more easily than others. In the present study, we were interested in patterns of interand intraethnic friendships and the relative likeability of certain ethnic groups in ethnically heterogeneous schools. The sample comprised 842 early adolescents (Mage = 11.50 years, SDage = .71; 53% male) from 64 countries of origin who attend multiethnic schools in Southwest Germany. In line with our expectations, interethnic friendships are to a large extent formed on the basis of cultural distance, with more friendships occurring between groups that are culturally more similar. Further, the likeability of children from different ethnic groups follows the so-called ethnic hierarchy, a rank order of different ethnic groups, which is based on perceived similarity with the mainstream group. Interventions to improve early adolescents’ interethnic friendships should aim to reduce perceptions of cultural distance and ethnic hierarchies in intergroup settings.
Application of mixed solvents for elution of organophosphate pesticides extracted from raw propolis by matrix solid-phase dispersion and analysis by GC-MS

The excessive application of organophosphate pesticides (OPPs) in crop fields close to hives and its application in beekeeping practices are potential sources of contamination of propolis. Pesticides were extracted from raw propolis by matrix solid-phase dispersion. Because of the complex chemical composition of propolis, binary solvent mixtures based on acetonitrile and a relatively non-polar solvent in different proportions were tested for the selective elution of OPPs. The effect of mixed solvents on clean-up was evaluated by GC-MS and the main interfering compounds co-eluted with pesticides were identified. In addition, three volumes of the solvent mixture and the volume of the sample were evaluated in an experimental factorial design. The final experimental conditions were 1 mL of dissolved propolis and 8 mL of acetonitrile/dichloromethane (g4r = 25: 75). Mean recoveries ranged from 55 % to 96 % (relative standard deviation < 8 %) with an inter-day precision lower than 24 %. The proposed method was applied to the analysis of real samples obtained from local beekeepers.
Association of Self-Presentational Strategies on Facebook and Positive Feedback in Adolescence – A Two-Study Approach

As Facebook enables adolescents to present themselves positively by means of their Facebook profile and to gain positive feedback for doing so, the aim of the current study is to investigate which kinds of adolescents’ positive self-presentation in profile pictures and status updates are associated with positive feedback in terms of Likes from the Facebook community, considering gender differences. Data for this study were based on adolescents aged 14–17 years, pursuing a two-method approach, using questionnaire data (N = 703; 71.7% female; Mage = 15.76 years) as well as data from content analysis (N = 143; 63% female; Mage = 15.72 years). Hierarchical multiple regressions were used. The results suggest that the self-presentational strategy of ingratiation seems to be the most advantageous in eliciting positive feedback. In particular, presenting oneself in the company of friends as well as posting about spending time with friends were related to positive feedback.
Explaining Presidential Popularity: How Ad Hoc Theorizing, Misplaced Emphasis, and Insufficient Care in Measuring One's Variables Refuted Common Sense and Led Conventional Wisdom Down the Path of Anomalies

Within the last ten years a new conventional wisdom has surfaced in political science which tells us that presidents inexorably become less popular over time. Not much else matters. Neither the economy, nor the Vietnam War, not even Watergate seems to have had much independent effect on presidential popularity once time is taken into account. Before embracing these conclusions we need to reconsider the method that produced them. I argue that previous research too willingly accepted time as an explanatory variable, enshrouding it with theoretical meaning. To preserve its explanatory power alternative, substantive variables were shortchanged in their operational definitions and measurement. In this article I reverse the emphasis. Here, time is rejected as an explanatory variable and is employed only as a diagnostic indicator of the adequacy of the equations. A variety of alternative representations of real-world forces such as the economy and war are tested and some considerably improve the time-series correlation between the environment and presidential popularity. With these substantive variables I propose a simpler, if less glamorous, theory of presidential popularity consisting of two hypotheses: first, popularity is related to real events and conditions, and second, that it responds slowly to environmental change. Popularity is then both experiential and incremental. The findings for Presidents Truman through Nixon support this common-sense view. The Korean War (measured by U.S. casualties), the Vietnam War (measured by the number of bombing missions over North Vietnam and the U.S. war dead), the economy (especially six-month changes in consumer prices), Watergate, international “rally” events, and early term surges of approval all contribute independently to short-term fluctuations in presidential popularity. Moreover, as predicted, popularity appears to be autoregressive even when represented by an instrumental variables surrogate measure to minimize serial correlation. When the equations are specified in this way, time proves to be unnecessary in order to explain trends in presidential popularity.
Algebraic and combinatorial expansion in random simplicial complexes

In this paper we consider the expansion properties and the spectrum of the combinatorial Laplace operator of a d‐dimensional Linial–Meshulam random simplicial complex, above the cohomological connectivity threshold. We consider the spectral gap of the Laplace operator and the Cheeger constant as this was introduced by Parzanchevski, Rosenthal, and Tessler. We show that with high probability the spectral gap of the random simplicial complex as well as the Cheeger constant are both concentrated around the minimum co‐degree of among all (d−1) ‐faces. Furthermore, we consider a random walk on such a complex, which generalizes the standard random walk on a graph. We show that the associated conductance is with high probability bounded away from 0, resulting in a bound on the mixing time that is logarithmic in the number of vertices of the complex.
Causal inference of brain connectivity from fMRI with $\psi$-Learning Incorporated Linear non-Gaussian Acyclic Model ($\psi$-LiNGAM)

Functional connectivity (FC) has become a primary means of understanding brain functions by identifying brain network interactions and, ultimately, how those interactions produce cognitions. A popular definition of FC is by statistical associations between measured brain regions. However, this could be problematic since the associations can only provide spatial connections but not causal interactions among regions of interests. Hence, it is necessary to study their causal relationship. Directed acyclic graph (DAG) models have been applied in recent FC studies but often encountered problems such as limited sample sizes and large number of variables (namely high-dimensional problems), which lead to both computational difficulty and convergence issues. As a result, the use of DAG models is problematic, where the identification of DAG models in general is nondeterministic polynomial time hard (NP-hard). To this end, we propose a ψ-learning incorporated linear nonGaussian acyclic model (ψ-LiNGAM). We use the association model (ψ-learning) to facilitate causal inferences and the model works well especially for high-dimensional cases. Our simulation results demonstrate that the proposed method is more robust and accurate than several existing ones in detecting graph structure and direction. We then applied it to the resting state fMRI (rsfMRI) data obtained from the publicly available Philadelphia Neurodevelopmental Cohort (PNC) to study the cognitive variance, which includes 855 individuals aged 822 years. Therein, we have identified three types of hub structure: the in-hub, outhub and sum-hub, which correspond to the centers of receiving, sending and relaying information, respectively. We also detected 16 most important pairs of causal flows. Several of the results have been verified to be biologically significant. To facilitate reproducible research, we make the code to be publicly available at https://github.com/Aiying0512/psi-LiNGAM.
Selective Question Answering under Domain Shift

To avoid giving wrong answers, question answering (QA) models need to know when to abstain from answering. Moreover, users often ask questions that diverge from the model’s training data, making errors more likely and thus abstention more critical. In this work, we propose the setting of selective question answering under domain shift, in which a QA model is tested on a mixture of in-domain and out-of-domain data, and must answer (i.e., not abstain on) as many questions as possible while maintaining high accuracy. Abstention policies based solely on the model’s softmax probabilities fare poorly, since models are overconfident on out-of-domain inputs. Instead, we train a calibrator to identify inputs on which the QA model errs, and abstain when it predicts an error is likely. Crucially, the calibrator benefits from observing the model’s behavior on out-of-domain data, even if from a different domain than the test data. We combine this method with a SQuAD-trained QA model and evaluate on mixtures of SQuAD and five other QA datasets. Our method answers 56% of questions while maintaining 80% accuracy; in contrast, directly using the model’s probabilities only answers 48% at 80% accuracy.
Behind the Fertility-Education Nexus: What Triggered the French Development Process?

The education-fertility relationship is a central element of the models explaining the transition to sustained economic growth. In this paper, we use a three-stages least squares estimator to disentangle the causality direction of this relationship. Controlling for a wide array of socio-economic, cultural, and geographical determinants, our cliometric contribution on French counties during the nineteenth century corroborates the existence of a single negative causal link from fertility to education. We put forward the hypothesis that in France a decrease in fertility is strongly associated to greater schooling.
Ultra-thin Skin Electronics for High Quality and Continuous Skin-Sensor-Silicon Interfacing

Skin-inspired electronics emerges as a new paradigm due to the increasing demands for conformable and high-quality skin-sensor-silicon (SSS) interfacing in wearable, electronic skin and health monitoring applications. Advances in ultra-thin, flexible, stretchable and conformable materials have made skin electronics feasible. In this paper, we prototyped an active electrode (with a thickness $\leq 2$ um), which integrates the electrode with a thin-film transistor (TFT) based amplifier, to effectively suppress motion artifacts. The fabricated ultra-thin amplifier can achieve a gain of 32 dB at 20 kHz, demonstrating the feasibility of the proposed active electrode. Using atrial fibrillation (AF) detection for electrocardiogram (ECG) as an application driver, we further develop a simulation framework taking into account all elements including the skin, the sensor, the amplifier and the silicon chip. Systematic and quantitative simulation results indicate that the proposed active-electrode can effectively improve the signal quality under motion noises (achieving $\ge 30$ dB improvement in signal-to-noise ratio (SNR)), which boosts classification accuracy by more than 19% for AF detection.
Goodness-of-Fit Test for Self-Exciting Processes

Recently there have been many research efforts in developing generative models for self-exciting point processes, partly due to their broad applicability for real-world applications, notably self- and mutual- exciting point processes. However, rarely can we quantify how well the generative model captures the nature or ground-truth since it is usually unknown. The challenge typically lies in the fact that the generative models typically provide, at most, good approximations to the ground-truth (e.g., through the rich representative power of neural networks), but they cannot be precisely the ground-truth. We thus cannot use the classic goodness-of-fit test framework to evaluate their performance. In this paper, we provide goodness-of-fit tests for generative models by leveraging a new connection of this problem with the classical statistical theory of mismatched maximum-likelihood estimator (MLE). We present a non-parametric self-normalizing test statistic for the goodness-of-fit test based on Generalized Score (GS) statistics. We further establish asymptotic properties for MLE of the Quasi-model (Quasi-MLE), asymptotic $\chi^2$ null distribution and power function of GS statistic. Numerical experiments validate the asymptotic null distribution as well as the consistency of our proposed GS test.
Intriguing generalization and simplicity of adversarially trained neural networks

Adversarial training has been the topic of dozens of studies and a leading method for defending against adversarial attacks. Yet, it remains unknown (a) how adversarially-trained classifiers (a.k.a "robust" classifiers) generalize to new types of out-of-distribution examples; and (b) what hidden representations were learned by robust networks. In this paper, we perform a thorough, systematic study to answer these two questions on AlexNet, GoogLeNet, and ResNet-50 trained on ImageNet. While robust models often perform on-par or worse than standard models on unseen distorted, texture-preserving images (e.g. blurred), they are consistently more accurate on texture-less images (i.e. silhouettes and stylized). That is, robust models rely heavily on shapes, in stark contrast to the strong texture bias in standard ImageNet classifiers (Geirhos et al. 2018). Remarkably, adversarial training causes three significant shifts in the functions of hidden neurons. That is, each convolutional neuron often changes to (1) detect pixel-wise smoother patterns; (2) detect more lower-level features i.e. textures and colors (instead of objects); and (3) be simpler in terms of complexity i.e. detecting more limited sets of concepts.
Dynamic Tensor Rematerialization

Checkpointing enables training larger models by freeing intermediate activations and recomputing them on demand. Previous checkpointing techniques are difficult to generalize to dynamic models because they statically plan recomputations offline. We present Dynamic Tensor Rematerialization (DTR), a greedy online algorithm for heuristically checkpointing arbitrary models. DTR is extensible and general: it is parameterized by an eviction policy and only collects lightweight metadata on tensors and operators. Though DTR has no advance knowledge of the model or training task, we prove it can train an $N$-layer feedforward network on an $\Omega(\sqrt{N})$ memory budget with only $\mathcal{O}(N)$ tensor operations. Moreover, we identify a general eviction heuristic and show how it allows DTR to automatically provide favorable checkpointing performance across a variety of models and memory budgets.
Opponent Modelling with Local Information Variational Autoencoders

Modelling the behaviours of other agents (opponents) is essential for understanding how agents interact and making effective decisions. Existing methods for opponent modelling commonly assume knowledge of the local observations and chosen actions of the modelled opponents, which can significantly limit their applicability. We propose a new modelling technique based on variational autoencoders which uses only the local observations of the agent under control: its observed world state, chosen actions, and received rewards. The model is jointly trained with the agent's decision policy using deep reinforcement learning techniques. We provide a comprehensive evaluation and ablation study in diverse multi-agent tasks, showing that our method achieves significantly higher returns than a baseline method which does not use opponent modelling, and comparable performance to an ideal baseline which has full access to opponent information.
Intensity- and temperature- dependent carrier recombination in InAs/In(As1-xSbx) type-II superlattices

Our time-resolved measurements for carrier recombination are reported as a midwave infrared InAs/InAs0.66Sb0. 34 type-II superlattice (T2SL) function of pump intensity and sample temperature. By including the T2SL doping level in the analysis, the Shockley-Read-Hall (SRH), radiative, and Auger recombination components of the carrier lifetime are uniquely distinguished at each temperature. SRH is the limiting recombination mechanism for excess carrier densities less than the doping level (the low-injection regime) and temperatures less than 175 K. A SRH defect energy of 95 meV, either below the T2SL conduction-band edge or above the T2SL valence-band edge, is identified. Auger recombination limits the carrier lifetimes for excess carrier densities greater than the doping level (the high-injection regime) for all temperatures tested. Additionally, at temperatures greater than 225 K, Auger recombination also limits the low-injection carrier lifetime due to the onset of the intrinsic temperature range and large intrinsic carrier densities. Radiative recombination is found to not have a significant contribution to the total lifetime for all temperatures and injection regimes, with the data implying a photon recycling factor of 15. Using the measured lifetime data, diffusion currents are calculated and compared to calculated Hg1-xCdxTe dark current, indicating that the T2SL can have a lowermore » dark current with mitigation of the SRH defect states. Our results illustrate the potential for InAs/InAs1-xSbx T2SLs as absorbers in infrared photodetectors. « less
Monetary Policy Tradeoffs between Financial Stability and Price Stability

We analyze the impact of interest rate policy on financial stability in an environment where banks can experience runs on their short-term liabilities forcing them to sell assets at fire sale prices. Price adjustment frictions and a state-dependent risk of financial crisis create the possibility of a policy tradeoff between price stability and financial stability. Focusing on Taylor rules with monetary policy possibly reacting to banks' short-term liabilities, we find that the optimized policy uses the extra tool to support investment at the expense of higher inflation and output volatility.
In sickness and in health: pivotal role of vitamin D

Within the last several years, frequency of vitamin D testing has multiplied substantially all over the world, since it has been shown to have an important role in many diseases and conditions. Even though liquid chromatography - tandem mass spectrometry (LC-MS/MS) has been identified as “gold standard” method for vitamin D measurement, most laboratories still use immunochemistry methods. Besides analytical problems (hydrophobicity, low circulating concentrations, ability to bind to lipids, albumins and vitamin D binding protein, presence of multiple vitamin D metabolites and variable ratios of 25(OH)D2 and 25(OH)D3 in the blood), vitamin D shows great preanalytical variability, since its concentration is drastically influenced by seasonal changes, exposure to sun, type of clothes or sun block creams. Vitamin D is mostly measured in serum or plasma, but new studies are showing importance of measuring vitamin D in pleural effusions, breast milk, urine, synovial fluid and saliva. Besides the main role in calcium homeostasis and bone metabolism, many studies linked vitamin D deficiency with cancer, cardiovascular diseases, diabetes, fertility and many other conditions. However, even though initial observational studies indicated that supplementation with vitamin D might be beneficial in disease development and progression; first results of well-designed randomized controlled prospective studies did not find differences in frequency of cardiovascular events or invasive cancer between patients taking vitamin D supplementation compared to placebo. In the light of these recent findings, validity of excessive vitamin D testing remains an open question.