Miracle-dz/UniFAR-FTUs huggingface dataset:
Resonant Dynamics of Strongly Interacting 
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 Fermionic Atoms in a Synthetic Flux Ladder

We theoretically study the dynamics of $n$-level spin-orbit coupled alkaline-earth fermionic atoms with SU($n$) symmetric interactions. We consider three dimensional lattices with tunneling along one dimension, and the internal levels treated as a synthetic dimension, realizing an $n$-leg flux ladder. Laser driving is used to couple the internal levels and to induce an effective magnetic flux through the ladder. We focus on the dense and strongly interacting regime, where in the absence of flux the system behaves as a Mott insulator with suppressed motional dynamics. At integer and fractional ratios of the laser Rabi frequency to the onsite interactions, the system exhibits resonant features in the dynamics. These resonances occur when interactions help overcome kinetic constraints upon the tunneling of atoms, thus enabling motion. Different resonances allow for the development of complex chiral current patterns. The resonances resemble the ones appearing in the longitudinal Hall resistance when the magnetic field is varied. We characterize the dynamics by studying the system's long-time relaxation behavior as a function of flux, number of internal levels $n$, and interaction strength. We observe a series of non-trivial pre-thermal plateaus caused by the emergence of resonant processes at successive orders in perturbation theory. We discuss protocols to observe the predicted phenomena under current experimental conditions.
Safer Autonomous Driving in a Stochastic, Partially-Observable Environment by Hierarchical Contingency Planning

When learning to act in a stochastic, partially observable environment, an intelligent agent should be prepared to anticipate a change in its belief of the environment state, and be capable of adapting its actions on-the-fly to changing conditions. As humans, we are able to form contingency plans when learning a task with the explicit aim of being able to correct errors in the initial control, and hence prove useful if ever there is a sudden change in our perception of the environment which requires immediate corrective action. This is especially the case for autonomous vehicles (AVs) navigating real-world situations where safety is paramount, and a strong ability to react to a changing belief about the environment is truly needed. In this paper we explore an end-to-end approach, from training to execution, for learning robust contingency plans and combining them with a hierarchical planner to obtain a robust agent policy in an autonomous navigation task where other vehicles' behaviours are unknown, and the agent's belief about these behaviours is subject to sudden, last-second change. We show that our approach results in robust, safe behaviour in a partially observable, stochastic environment, generalizing well over environment dynamics not seen during training.
Variable importance measures for heterogeneous causal effects

The recognition that personalised treatment decisions lead to better clinical outcomes has sparked recent research activity in the following two domains. Policy learning focuses on finding optimal treatment rules (OTRs), which express whether an individual would be better off with or without treatment, given their measured characteristics. OTRs optimize a pre-set population criterion, but do not provide insight into the extent to which treatment benefits or harms individual subjects. Estimates of conditional average treatment effects (CATEs) do offer such insights, but valid inference is currently difficult to obtain when data-adaptive methods are used. Moreover, clinicians are (rightly) hesitant to blindly adopt OTR or CATE estimates, not least since both may represent complicated functions of patient characteristics that provide little insight into the key drivers of heterogeneity. To address these limitations, we introduce novel nonparametric treatment effect variable importance measures (TE-VIMs). TE-VIMs extend recent regression-VIMs, viewed as nonparametric analogues to ANOVA statistics. By not being tied to a particular model, they are amenable to data-adaptive (machine learning) estimation of the CATE, itself an active area of research. Estimators for the proposed statistics are derived from their efficient influence curves and these are illustrated through a simulation study and an applied example.
Experimental Standards for Deep Learning Research: A Natural Language Processing Perspective

The field of Deep Learning (DL) has undergone explosive growth during the last decade, with a substantial impact on Natural Language Processing (NLP) as well. Yet, as with other fields employing DL techniques, there has been a lack of common experimental standards compared to more established disciplines. Starting from fundamental scientific principles, we distill ongoing discussions on experimental standards in DL into a single, widely-applicable methodology. Following these best practices is crucial to strengthening experimental evidence, improve reproducibility and enable scientific progress. These standards are further collected in a public repository to help them transparently adapt to future needs.
Constraining a possible time-variation of the speed of light along with the fine-structure constant using strong gravitational lensing and Type Ia supernovae observations

The possible time variation of the fundamental constants of nature has been an active subject of research since the large-number hypothesis was proposed by Dirac. In this paper, we propose a new method to investigate a possible time variation of the speed of light (c) along with the fine-structure constant (α) using Strong Gravitational Lensing (SGL) and Type Ia Supernovae (SNe Ia) observations. We assume a general approach to describe the mass distribution of lens-type galaxies, the one in favor of the power-law index model (PLAW). We also consider the runaway dilaton model to describe a possible time-variation of α. In order to explore the results deeply, we split the SGL sample into five sub-samples according to the lens stellar velocity dispersion and three sub-samples according to lens redshift. The results suggest that it is reasonable to treat the systems separately, but no strong indication of varying c was found.
Baseline Computation for Attribution Methods Based on Interpolated Inputs

We discuss a way to ﬁnd a well behaved baseline for attribution methods that work by feeding a neural network with a sequence of interpolated inputs between two given inputs. Then, we test it with our novel Riemann-Stieltjes Integrated Gradient-weighted Class Activation Mapping (RSI-Grad-CAM) attribution method. and our novel Riemann-Stieltjes Integrated Gradient-weighted Class Activation Mapping (RSI-Grad-CAM). 1
Generalization Error Bounds for Multiclass Sparse Linear Classifiers

We consider high-dimensional multiclass classification by sparse multinomial logistic regression. Unlike binary classification, in the multiclass setup one can think about an entire spectrum of possible notions of sparsity associated with different structural assumptions on the regression coefficients matrix. We propose a computationally feasible feature selection procedure based on penalized maximum likelihood with convex penalties capturing a specific type of sparsity at hand. In particular, we consider global sparsity, double row-wise sparsity, and low-rank sparsity, and show that with the properly chosen tuning parameters the derived plug-in classifiers attain the minimax generalization error bounds (in terms of misclassification excess risk) within the corresponding classes of multiclass sparse linear classifiers. The developed approach is general and can be adapted to other types of sparsity as well.
Defining excellence in simulation programs

A wide range of topics -essential to the development and management of successful and cost-effective simulation programs – include: Simulation Standards – best practices and program development; Types of Simulation Programs – infrastructure, framework; Simulators – types, selection and usage; Funding – fundraising, income sources; Management – asset management, policies and procedures; Environmental Design – building a simulation center; technical infrastructure; and Educational Development; Faculty Development; Research, and more, including:
Abruptly Shifting to More Authenticity: How Three Civics Teachers Leveraged Technology to Engage Their Students at the Beginning of the COVID-19 Pandemic

Abstract This study investigated how 8th-grade civics teachers kept their students engaged in learning during the abrupt shift to remote teaching caused by the COVID-19 pandemic in the spring of 2020. We analyzed the interview transcripts of 10 teachers who were piloting a new civics curriculum during the 2019–2020 school year. We found that three of 10 teachers leveraged technological tools to offer more authentic learning experiences for their students, which led to increased student engagement online. Implications for practice are discussed.
Upregulation of DGCR8, a Candidate Predisposing to Schizophrenia in Han Chinese, Contributes to Phenotypic Deficits and Neuronal Migration Delay

DiGeorge Syndrome Critical Region Gene 8 (DGCR8) is a key component of the microprocessor complex governing the maturation of most microRNAs, some of which participate in schizophrenia and neural development. Previous studies have found that the 22q11.2 locus, containing DGCR8, confers a risk of schizophrenia. However, the role of DGCR8 in schizophrenia and the early stage of neural development has remained unknown. In the present study, we try to identify the role of DGCR8 in schizophrenia from human samples and animal models. We found that the G allele and GG genotype of rs3757 in DGCR8 conferred a higher risk of schizophrenia, which likely resulted from higher expression of DGCR8 according to our test of dual-luciferase reporter system. Employed overexpression model in utero and adult mice, we also revealed that the aberrant increase of Dgcr8 delayed neuronal migration during embryological development and consequently triggered abnormal behaviors in adult mice. Together, these results demonstrate that DGCR8 may play a role in the etiology of schizophrenia through regulating neural development.
Cardiotoxicity of Anticancer Drugs: Molecular Mechanisms and Strategies for Cardioprotection

Chemotherapy and targeted therapies have significantly improved the prognosis of oncology patients. However, these antineoplastic treatments may also induce adverse cardiovascular effects, which may lead to acute or delayed onset of cardiac dysfunction. These common cardiovascular complications, commonly referred to as cardiotoxicity, not only may require the modification, suspension, or withdrawal of life-saving antineoplastic therapies, with the risk of reducing their efficacy, but can also strongly impact the quality of life and overall survival, regardless of the oncological prognosis. The onset of cardiotoxicity may depend on the class, dose, route, and duration of administration of anticancer drugs, as well as on individual risk factors. Importantly, the cardiotoxic side effects may be reversible, if cardiac function is restored upon discontinuation of the therapy, or irreversible, characterized by injury and loss of cardiac muscle cells. Subclinical myocardial dysfunction induced by anticancer therapies may also subsequently evolve in symptomatic congestive heart failure. Hence, there is an urgent need for cardioprotective therapies to reduce the clinical and subclinical cardiotoxicity onset and progression and to limit the acute or chronic manifestation of cardiac damages. In this review, we summarize the knowledge regarding the cellular and molecular mechanisms contributing to the onset of cardiotoxicity associated with common classes of chemotherapy and targeted therapy drugs. Furthermore, we describe and discuss current and potential strategies to cope with the cardiotoxic side effects as well as cardioprotective preventive approaches that may be useful to flank anticancer therapies.
Study on the Compromise Effect Under the Influence of Normative Reference Group

The compromise effect is an important context effect, but its research is still insufficient under the influence of social factors and purchase tasks. This study explores the change of compromise effect in different group norm scenarios by constructing three different group norm reference points. Three conclusions were drawn. First, the compromise effect always exists under the influence of different groups’ normative reference points if there is a compromise effect in a product set. Second, the effect value of the compromise effect will be significantly different with the change of group norm reference point. Third, group norms can indeed induce the compromise effect. Therefore, these findings would help to further enrich the literature results of the compromise effect and strengthen its application in marketing practice.
Hydro-Edaphic Gradient and Phylogenetic History Explain the Landscape Distribution of a Highly Diverse Clade of Lianas in the Brazilian Amazon

Plant distribution patterns may indicate habitat specialization either by closely related species with conserved traits or by phylogenetically distant species with converging traits. Lianas represent a large proportion of the overall tropical species diversity and abundance. Despite their importance, little is known about the relationship between habitat specialization and the phylogenetic structure of lianas, especially at the landscape scale where forest disturbances and hydro-edaphic gradients are crucial. To explore this knowledge gap, we used one of the most diverse lineages of Neotropical lianas to test whether (i) landscape environmental gradients explain liana species diversity and composition, (ii) habitat specialization is phylogenetically conserved along ecological gradients, and (iii) closely related liana species have more similar distribution patterns. We hypothesized that hydro-edaphic and forest disturbance gradients determine the compartmentalization of a subset of closely related species in different portions of the ecological gradients. To test our hypothesis, we examined the distribution of the tribe Bignonieae on 34 1-ha permanent plots systematically distributed over a 42 km2 forested landscape area in Central Amazon. We used proxies for the hydro-edaphic, forest disturbance, and soil nutrient gradients. Liana diversity increased along the hydro-edaphic gradient (i.e., toward dry plateaus), but slightly decreased along the forest disturbance gradient. Further, we found evidence of habitat specialization along the hydro-edaphic gradient on plateaus with deeper water tables, where liana assemblages are subsets of closely related species, exhibiting phylogenetic clustering. The opposite pattern was found on valleys, where liana assemblages were phylogenetically overdispersed. Our results support the role of phylogenetic niche conservatism on plateaus and a stronger environmental filter within the hydrologically dynamic valleys, associated with a functional convergence of more distantly related species. The selection of more distantly related species on hydrologically dynamic areas is a general pattern among trees, palms and now lianas. We conclude that ecological filters and phylogenetic history have played fundamental roles in structuring liana assemblages unevenly at the landscape scale. Fine-scale hydrology determines several aspects of plant community organization, whose mechanisms need to be experimentally investigated in the Amazon basin.
Foveal Phase Retardation Correlates With Optically Measured Henle Fiber Layer Thickness

This study quantified and compared phase retardation distribution in the central macula with the thickness of the Henle fiber layer (HFL). A scanning laser polarimeter (SLP) was used to acquire 20° × 40° macular-centered images, either with fixed corneal compensation or with variable corneal compensation, in two cohorts of clinically normal subjects (N = 36). Phase retardation maps from SLP imaging were used to generate a macular cross pattern (fixed compensation) or an annulus pattern (variable compensation) centered on the macula. Intensity profiles in the phase retardation maps were produced using annular regions of interest at eccentricities from 0.25° to 3°. Pixel intensity was averaged at each eccentricity, acting as a surrogate for macular phase retardation. Directional OCT images were acquired in the horizontal and vertical meridians in all subjects, allowing visualization of the HFL thickness. HFL thickness was manually segmented in each meridian and averaged. In both cohorts, phase retardation and HFL thickness were highly correlated in the central 3° assessed, providing further evidence that the source of the phase retardation signal in the central macula is dominated by the HFL and that the center of the macula on cross sectional imaging corresponds closely with the center of the macular cross on SLP imaging.
Epidemiological and Clinical Characteristics of Influenza Outbreaks Among Children in Chongqing, China

Influenza is a global serious public health threat. Seasonal influenza among children in Chongqing has been a heavy health burden. To date, few studies have examined the spatial and temporal characteristics of influenza. This research sheds new light on correlating them with influenza outbreaks with data of over 5 years (2014–2018). All cluster outbreaks among preschool and school-age children reported in Chongqing were collected through the Public Health Emergency Management Information System. The demographical, epidemiological, and clinical data of the cases were analyzed. From 2014 to 2018, a total of 111 preschool- and school-based influenza-like illness outbreaks involving 3,549 cases were identified. Several clinical symptoms that were analyzed in this study showed significant contrast between influenza A and B. Spatial autocorrelation analysis over the 5-year data detected Xiushan district being the most likely cluster. The exploration of the spatial distribution and clinical characteristics of influenza cluster of children in Chongqing could help the effective implementation of health policies. Future studies should be conducted to monitor the outbreaks of influenza among children.
The SUMO E3 Ligase MdSIZ1 Sumoylates a Cell Number Regulator MdCNR8 to Control Organ Size

Plant growth and organ size putatively associated with crop yield are regulated by a complex network of genes including ones for controlling cell proliferation. The gene fw2.2 was first identified in tomatoes and reported to govern fruit size variation through controlling cell division. In this study, we isolated a putative ortholog of the tomato fw2.2 gene from apple, Cell Number Regulator 8 (MdCNR8). Our functional analysis showed that MdCNR8 may control fruit size and root growth. MdCNR8 was mediated by the SUMO E3 ligase MdSIZ1, and SUMOylation of MdCNR8 at residue-Lys39 promoted the translocation of MdCNR8 from plasma membrane to the nucleus. The effect of MdCNR8 in inhibiting root elongation could be completely counteracted by the coexpression of MdSIZ1. Moreover, the lower cell proliferation of apple calli due to silencing MdSIZ1 could be rescued by silencing MdCNR8. Collectively, our results showed that the MdSIZ1-mediated SUMOylation is required for the fulfillment of MdCNR8 in regulating cell proliferation to control plant organ size. This regulatory interaction between MdSIZ1 and MdCNR8 will facilitate understanding the mechanism underlying the regulation of organ size.
Age Differences in Speech Perception in Noise and Sound Localization in Individuals With Subjective Normal Hearing

Hearing loss in old age, which often goes untreated, has far-reaching consequences. Furthermore, reduction of cognitive abilities and dementia can also occur, which also affects quality of life. The aim of this study was to investigate the hearing performance of seniors without hearing complaints with respect to speech perception in noise and the ability to localize sounds. Results were tested for correlations with age and cognitive performance. The study included 40 subjects aged between 60 and 90 years (mean age: 69.3 years) with not self-reported hearing problems. The subjects were screened for dementia. Audiological tests included pure-tone audiometry and speech perception in two types of background noise (continuous and amplitude-modulated noise) which was either co-located or spatially separated (multi-source noise field, MSNF) from the target speech. Sound localization ability was assessed and hearing performance was self-evaluated by a questionnaire. Speech in noise and sound localization was compared with young normal hearing adults. Although considering themselves as hearing normal, 17 subjects had at least a mild hearing loss. There was a significant negative correlation between hearing loss and dementia screening (DemTect) score. Speech perception in noise decreased significantly with age. There were significant negative correlations between speech perception in noise and DemTect score for both spatial configurations. Mean SRTs obtained in the co-located noise condition with amplitude-modulated noise were on average 3.1 dB better than with continuous noise. This gap-listening effect was severely diminished compared to a younger normal hearing subject group. In continuous noise, spatial separation of speech and noise led to better SRTs compared to the co-located masker condition. SRTs in MSNF deteriorated in modulated noise compared to continuous noise by 2.6 dB. Highest impact of age was found for speech perception scores using noise stimuli with temporal modulation in binaural test conditions. Mean localization error was in the range of young adults. Mean amount of front/back confusions was 11.5% higher than for young adults. Speech perception tests in the presence of temporally modulated noise can serve as a screening method for early detection of hearing disorders in older adults. This allows for early prescription of hearing aids.
Entrepreneurial Institutional Environment and Entrepreneurial Orientation: The Mediating Role of Entrepreneurial Passion

The entrepreneurial institutional environment is the external factor that entrepreneurial enterprises rely on for survival. Our interest is in how entrepreneurs cultivate entrepreneurial orientation in response to the highly uncertain entrepreneurial situation. Based on the cognitive appraisal theory of emotion, we analyzed the impact of the entrepreneurial institutional environment on entrepreneurial orientation through entrepreneurial passion. This study applied stepwise regression analyses to test the hypotheses on a sample of 197 entrepreneurs from the co-creation space in China. The output of the study showed that the entrepreneurial institutional environment had a positive effect on entrepreneurial orientation and that entrepreneurial passion played a mediating role between the entrepreneurial institutional environment and entrepreneurial orientation.