Miracle-dz/UniFAR-FTUs huggingface dataset:
The Proximal ID Algorithm

Unobserved confounding is a fundamental obstacle to establishing valid causal conclusions from observational data. Two complementary types of approaches have been developed to address this obstacle: obtaining identification using fortuitous external aids, such as instrumental variables or proxies, or by means of the ID algorithm, using Markov restrictions on the full data distribution encoded in graphical causal models. In this paper we aim to develop a synthesis of the former and latter approaches to identification in causal inference to yield the most general identification algorithm in multivariate systems currently known -- the proximal ID algorithm. In addition to being able to obtain nonparametric identification in all cases where the ID algorithm succeeds, our approach allows us to systematically exploit proxies to adjust for the presence of unobserved confounders that would have otherwise prevented identification. In addition, we outline a class of estimation strategies for causal parameters identified by our method in an important special case. We illustrate our approach by simulation studies and a data application.
Learning Dynamical System for Grasping Motion

Dynamical System has been widely used for encoding trajectories from human demonstration, which has the inherent adaptability to dynamically changing environments and robustness to perturbations. In this paper we propose a framework to learn a dynamical system that couples position and orientation based on a diffeomorphism. Different from other methods, it can realise the synchronization between positon and orientation during the whole trajectory. Online grasping experiments are carried out to prove its effectiveness and online adaptability.
Floquet graphene antidot lattices

We establish the theoretical foundation of the Floquet graphene antidot lattice, whereby massless Dirac fermions are driven periodically by a circularly polarized electromagnetic field, while having their motion excluded from an array of nanoholes. The properties of interest are encoded in the quasienergy spectra, which are computed non-perturbatively within the Floquet formalism. We find that a rich Floquet phase diagram emerges as the amplitude of the drive field is varied. Notably, the Dirac dispersion can be restored in real time relative to the gapped equilibrium state, which may enable the creation of an optoelectronic switch or a dynamically tunable electronic waveguide. As the amplitude is increased, the ability to shift the quasienergy gap between high-symmetry points can change which crystal momenta dominate in the scattering processes relevant to electronic transport and optical emission. Furthermore, the bands can be flattened near the Γ point, which is indicative of selective dynamical localization. Lastly, quadratic and linear dispersions emerge in orthogonal directions at the M point, signaling a Floquet semi-Dirac material. Importantly, all our predictions are valid for experimentally accessible near-IR radiation, which corresponds to the high-frequency limit for the graphene antidot lattice. Cycling between engineered Floquet electronic phases may play a key role in the development of next-generation on-chip devices for optoelectronic applications.
Entropy stable non-oscillatory fluxes: An optimized wedding of entropy conservative flux with non-oscillatory flux

Abstract This work frames the problem of constructing non-oscillatory entropy stable fluxes as a least square optimization problem. A flux sign stability condition is defined for a pair of entropy conservative flux (F∗) and a non-oscillatory flux (Fs). This novel approach paves a way to construct non-oscillatory entropy stable flux (F̂) as a simple combination of (F∗ and Fs) which inherently optimize the numerical diffusion in the entropy stable flux (F̂) such that it reduces to the underlying non-oscillatory flux (Fs) in the flux sign stable region. This robust approach is (i) agnostic to the choice of flux pair (F∗, Fs), (ii) does not require the computation of costly dissipation operator and high order reconstruction of scaled entropy variable to construct the diffusion term. Various non-oscillatory entropy stable fluxes are constructed and exhaustive computational results for standard test problems are given which show that fully discrete schemes using these entropy stable fluxes do not exhibit nonphysical spurious oscillations in approximating the discontinuities compared to the non-oscillatory schemes using underlying fluxes (Fs) only. Moreover, these entropy stable schemes maintain the formal order of accuracy of the lower order flux in the pair.
A Two-Pass Lower Bound for Semi-Streaming Maximum Matching

We prove a lower bound on the space complexity of two-pass semi-streaming algorithms that approximate the maximum matching problem. The lower bound is parameterized by the density of Ruzsa-Szemeredi graphs: * Any two-pass semi-streaming algorithm for maximum matching has approximation ratio at least $(1- \Omega(\frac{\log{RS(n)}}{\log{n}}))$, where $RS(n)$ denotes the maximum number of induced matchings of size $\Theta(n)$ in any $n$-vertex graph, i.e., the largest density of a Ruzsa-Szemeredi graph. Currently, it is known that $n^{\Omega(1/\! \log\log{n})} \leq RS(n) \leq \frac{n}{2^{O(\log^*{\! (n)})}}$ and closing this (large) gap between upper and lower bounds has remained a notoriously difficult problem in combinatorics. Under the plausible hypothesis that $RS(n) = n^{\Omega(1)}$, our lower bound is the first to rule out small-constant approximation two-pass semi-streaming algorithms for the maximum matching problem, making progress on a longstanding open question in the graph streaming literature.
Locally Interpretable Model Agnostic Explanations using Gaussian Processes

Owing to tremendous performance improvements in data-intensive domains, machine learning (ML) has garnered immense interest in the research community. However these ML models turn out to be black boxes, which are tough to interpret, resulting in a direct decrease in productivity. Local Interpretable Model-Agnostic Explanations (LIME) is a popular technique for explaining prediction of a single instance. Although LIME is simple and versatile, it suffers from instability in the generated explanations. In this paper, we propose a Gaussian process (GP) based variation of locally interpretable models. We employ a smart sampling strategy based on the acquisition functions in Bayesian optimization. Further, we employ the automatic relevance determination based covariance function in GP, with separate length-scale parameters for each feature, where the reciprocal of length-scale parameters serve as feature explanations. We illustrate the performance of the proposed technique on two real-world datasets, and demonstrate the superior stability of the proposed technique. Furthermore, we demonstrate that the proposed technique is able to generate faithful explanations using much fewer samples as compared to LIME.
UWB Radar for through Foliage Imaging using Cyclic Prefix-based OFDM

Abstract This paper proposes the use of sufficient cyclic prefix (CP) OFDM synthetic aperture radar (SAR) for foliage penetration (FOPEN). The foliage introduces phase and amplitude fluctuation which cause the sidelobes to increase and affects the final image of the obscured targets. The wideband CP-based OFDM SAR inherently eliminates the sidelobes that arise from the interference between targets on the same range line. The integrated sidelobe level ratio (ISLR) of the CP-based OFDM signal along the range direction is lower than that of the random noise signal by 2 dB for foliage penetration application, while the peak sidelobe level ratio (PSLR) are almost the same of both of the two signals.
IADA: Iterative Adversarial Data Augmentation Using Formal Verification and Expert Guidance

Neural networks (NNs) are widely used for clas-siﬁcation tasks for their remarkable performance. However, the robustness and accuracy of NNs heavily depend on the training data. In many applications, massive training data is usually not available. To address the challenge, this paper proposes an iterative adversarial data augmentation (IADA) framework to learn neural network models from insufﬁcient amount of training data. The method uses formal veriﬁcation to identify the most “confusing” input samples, and leverages human guidance to safely and iteratively augment the training data with these samples. The proposed framework is applied to an artiﬁcial 2D dataset, the MNIST dataset, and a human motion dataset. By applying IADA to fully-connected NN classiﬁers, we show that our training method can improve the robustness and accuracy of the learned model. By comparing to regular supervised training, on the MNIST dataset, the average perturbation bound improved 107 . 4%. The classiﬁcation accuracy improved 1 . 77%, 3 . 76%, 10 . 85% on the 2D dataset, the MNIST dataset, and the human motion dataset respectively.
On boosting the power of Chatterjee’s rank correlation

Chatterjee (2021)’s ingenious approach to estimating a measure of dependence first proposed by Dette et al. (2013) based on simple rank statistics has quickly caught attention. This measure of dependence has the appealing property of being between 0 and 1, and being 0 or 1 if and only if the corresponding pair of random variables is independent or one is a measurable function of the other almost surely. However, more recent studies (Cao & Bickel, 2020; Shi et al., 2022) showed that independence tests based on Chatterjee’s rank correlation are unfortunately rate-inefficient against various local alternatives and they call for variants. We answer this call by proposing an improvement to Chatterjee’s rank correlation that still consistently estimates the same dependence measure but provably achieves near-parametric efficiency in testing against Gaussian rotation alternatives. This is possible by incorporating many right nearest neighbours in constructing the correlation coefficients. We thus overcome the “only one disadvantage” of Chatterjee’s rank correlation (Chatterjee, 2021, Section 7).
Polynomial bounds for chromatic number. III. Excluding a double star

A “double star” is a tree with two internal vertices. It is known that the Gyárfás–Sumner conjecture holds for double stars, that is, for every double star H $H$ , there is a function f H ${f}_{H}$ such that if G $G$ does not contain H $H$ as an induced subgraph then χ ( G ) ≤ f H ( ω ( G ) ) $\chi (G)\le {f}_{H}(\omega (G))$ (where χ , ω $\chi ,\omega $ are the chromatic number and the clique number of G $G$ ). Here we prove that f H ${f}_{H}$ can be chosen to be a polynomial.
Data-driven Analysis of Gender Differences and Similarities in Scratch Programs

Block-based programming environments such as Scratch are an essential entry point to computer science. In order to create an effective learning environment that has the potential to address the gender imbalance in computer science, it is essential to better understand gender-specific differences in how children use such programming environments. In this paper, we explore gender differences and similarities in Scratch programs along two dimensions: In order to understand what motivates girls and boys to use Scratch, we apply a topic analysis using unsupervised machine learning for the first time on Scratch programs, using a dataset of 317 programs created by girls and boys in the range of 8–10 years. In order to understand how they program for these topics, we apply automated program analysis on the code implemented in these projects. We find that, in-line with common stereotypes, girls prefer topics that revolve around unicorns, celebrating, dancing and music, while boys tend to prefer gloomy topics with bats and ghouls, or competitive ones such as soccer or basketball. Girls prefer animations and stories, resulting in simpler control structures, while boys create games with more loops and conditional statements, resulting in more complex programs. Considering these differences can help to improve the learning outcomes and the resulting computing-related self-concepts, which are prerequisites for developing a longer-term interest in computer science.
Optical Variability of a Newly Discovered Blazar Sample from the BZCAT Catalog

In an optical monitoring program to characterize the variability properties of blazars, we observed 10 sources from the Roma-BZCAT catalog for 26 nights in V and R bands during 2014 October to 2015 June with two telescopes located in India. The sample includes mainly newly discovered BL Lacertae objects (BL Lacs) for which the redshift of some sources is not yet known. We present the results of flux and color variations of the sample on intraday and short timescales obtained by using the power-enhanced F-test and the nested-ANOVA tests, along with their spectral behavior. We find significant intraday variability in the single flat-spectrum radio quasar in our sample, having an amplitude of variation ∼12%. Although a few of the BL Lacs showed probable variation in some nights, none of them passed the variability tests at 99.9% significance level. We find that 78% of the sample showed significant negative color–magnitude correlations, i.e., a redder-when-brighter spectral evolution. Those that do not show strong or clear chromatism predominantly exhibit a redder-when-brighter trend. Unlike on hourly timescales, the high-synchrotron-peaked blazars in the sample (BZGJ0656+4237, BZGJ0152+0147, and BZBJ1728+5013) show strong flux variation on timescales of days to months, where again we detect a decreasing trend of the spectral slope with brightness. We observe a global steepening of the optical spectrum with increasing flux on the intranight timescale for the entire blazar sample. The nonvariability in the BL Lacs in our sample might be caused by the distinct contribution from the disk as well as from other components in the studied energy range.
The Integrated Probabilistic Data Association Filter Adapted to Lie Groups

The integrated probabilistic data association filter (IPDAF) is a target tracking algorithm based on the probabilistic data association filter that calculates a statistical measure that indicates if an estimated representation of the target properly represents the target or is generated from non-target-originated measurements. The main contribution of this article is to adapt the IPDAF to constant velocity target models that evolve on connected, unimodular Lie groups, and where the measurements are also defined on a Lie group. We present an example where the methods developed in the article are applied to the problem of tracking a ground vehicle on the special Euclidean group SE(2).
Convergence Rate Analysis for Fixed-Point Iterations of Generalized Averaged Nonexpansive Operators

We estimate convergence rates for fixed-point iterations of a class of nonlinear operators which are partially motivated from solving convex optimization problems. We introduce the notion of the generalized averaged nonexpansive (GAN) operator with a positive exponent, and provide a convergence rate analysis of the fixed-point iteration of the GAN operator. The proposed generalized averaged nonexpansiveness is weaker than the averaged nonexpansiveness while stronger than nonexpansiveness. We show that the fixed-point iteration of a GAN operator with a positive exponent converges to its fixed-point and estimate the local convergence rate (the convergence rate in terms of the distance between consecutive iterates) according to the range of the exponent. We prove that the fixed-point iteration of a GAN operator with a positive exponent strictly smaller than 1 can achieve an exponential global convergence rate (the convergence rate in terms of the distance between an iterate and the solution). Furthermore, we establish the global convergence rate of the fixed-point iteration of a GAN operator, depending on both the exponent of generalized averaged nonexpansiveness and the exponent of the H$\ddot{\text{o}}$lder regularity, if the GAN operator is also H$\ddot{\text{o}}$lder regular. We then apply the established theory to three types of convex optimization problems that appear often in data science to design fixed-point iterative algorithms for solving these optimization problems and to analyze their convergence properties.
Neonatal platelet physiology and implications for transfusion

Abstract The neonatal hemostatic system is different from that of adults. The differences in levels of procoagulant and anticoagulant factors and the evolving equilibrium in secondary hemostasis during the transition from fetal/neonatal life to infancy, childhood, and adult life are known as “developmental hemostasis. ” In regard to primary hemostasis, while the number (150,000–450,000/µl) and structure of platelets in healthy neonates closely resemble those of adults, there are significant functional differences between neonatal and adult platelets. Specifically, platelets derived from both cord blood and neonatal peripheral blood are less reactive than adult platelets to agonists, such as adenosine diphosphate (ADP), epinephrine, collagen, thrombin, and thromboxane (TXA2) analogs. This platelet hyporeactivity is due to differences in expression levels of key surface receptors and/or in signaling pathways, and is more pronounced in preterm neonates. Despite these differences in platelet function, bleeding times and PFA-100 closure times (an in vitro test of whole-blood primary hemostasis) are shorter in healthy full-term infants than in adults, reflecting enhanced primary hemostasis. This paradoxical finding is explained by the presence of factors in neonatal blood that increase the platelet-vessel wall interaction, such as high von Willebrand factor (vWF) levels, predominance of ultralong vWF multimers, high hematocrit, and high red cell mean corpuscular volume. Thus, the hyporeactivity of neonatal platelets should not be viewed as a developmental deficiency, but rather as an integral part of a developmentally unique, but well balanced, primary hemostatic system. In clinical practice, due to the high incidence of bleeding (especially intraventricular hemorrhage, IVH) among preterm infants, neonatologists frequently transfuse platelets to non-bleeding neonates when platelet counts fall below an arbitrary limit, typically higher than that used in older children and adults. However, recent studies have shown that prophylactic platelet transfusions not only fail to decrease bleeding in preterm neonates, but are associated with increased neonatal morbidity and mortality. In this review, we will describe the developmental differences in platelet function and primary hemostasis between neonates and adults, and will analyze the implications of these differences to platelet transfusion decisions.
Revisioning the ENP Among Emergency Workforce Changes: Specialty or Population?

The COVID-19 pandemic has highlighted emergency care in unanticipated ways, reshaping emergency care delivery and highlighting the emergency department (ED) workforce. Ultimately, these changes have significant implications for emergency nurse practitioner (ENP) education and practice. This editorial highlights historical trends in ED practice, the role and scope of nurse practitioner (NP) practice in emergency care, and recent evidence of the emergency medicine (EM) workforce surplus to support reenvisioning the future ENP profession (American College of Emergency Physicians [ACEP], 2021; Bennett et al., 2020; Reiter & Allen, 2020).
Automated Screening for Social Anxiety, Generalized Anxiety, and Depression From Objective Smartphone-Collected Data: Cross-sectional Study

Background The lack of access to mental health care could be addressed, in part, through the development of automated screening technologies for detecting the most common mental health disorders without the direct involvement of clinicians. Objective smartphone-collected data may contain sufficient information about individuals’ behaviors to infer their mental states and therefore screen for anxiety disorders and depression. Objective The objective of this study is to compare how a single set of recognized and novel features, extracted from smartphone-collected data, can be used for predicting generalized anxiety disorder (GAD), social anxiety disorder (SAD), and depression. Methods An Android app was designed, together with a centralized server system, to collect periodic measurements of objective smartphone data. The types of data included samples of ambient audio, GPS location, screen state, and light sensor data. Subjects were recruited into a 2-week observational study in which the app was run on their personal smartphones. The subjects also completed self-report severity measures of SAD, GAD, and depression. The participants were 112 Canadian adults from a nonclinical population. High-level features were extracted from the data of 84 participants, and predictive models of SAD, GAD, and depression were built and evaluated. Results Models of SAD and depression achieved a significantly greater screening accuracy than uninformative models (area under the receiver operating characteristic means of 0.64, SD 0.13 and 0.72, SD 0.12, respectively), whereas models of GAD failed to be predictive. Investigation of the model coefficients revealed key features that were predictive of SAD and depression. Conclusions We demonstrate the ability of a common set of features to act as predictors in the models of both SAD and depression. This suggests that the types of behaviors that can be inferred from smartphone-collected data are broad indicators of mental health, which can be used to study, assess, and track psychopathology simultaneously across multiple disorders and diagnostic boundaries.
Amelioratory effect of neoandrographolide on myocardial ischemic‐reperfusion injury by its anti‐inflammatory and anti‐apoptotic activities

In the present study, we aimed to evaluate the cardioprotective effect of neoandrographolide (Neo) on myocardial ischemia/reperfusion injury (I/R) models and explore its possible mechanism. We randomly and equally divided male mice into sham‐operation, I/R, and I/R + Neo groups. H9C2 cell line and primary neonatal rat cardiomyocytes were induced into the simulated I/R's status and used to further validate the Neo's role in vitro. Heart systolic function, indexes of myocardial injury (IMI), infarct size, pathological change, cell apoptosis, inflammatory cytokines, and indexes related to apoptotic and NF‐κB signaling pathways were analyzed in vivo or in vitro after the Neo treatment. Compared to the I/R group, Neo significantly suppressed IMI, infarct size, inflammatory cell infiltration, cell apoptosis, inflammatory cytokines, bax, cleaved caspase‐3, P‐IKBa, and P‐NF‐κB protein expressions, and the translocation of NF‐kB subunit p65 from the cytoplasm to the nucleus in vivo or in vitro. Still, ejected fraction, fractional shortening, and the bcl‐2 protein expression were notably increased after the Neo treatment. Neo could be developed into a new drug for treating myocardial I/R by inhibiting myocardial inflammation and apoptosis, which was closely related to suppressing the activation of bax/bcl‐2 and NF‐κB signaling pathways.