Miracle-dz/UniFAR-FTUs huggingface dataset:
Iterative initial condition reconstruction

Motivated by recent developments in perturbative calculations of the nonlinear evolution of large-scale structure, we present an iterative algorithm to reconstruct the initial conditions in a given volume starting from the dark matter distribution in real space. In our algorithm, objects are first moved back iteratively along estimated potential gradients, with a progressively reduced smoothing scale, until a nearly uniform catalog is obtained. The linear initial density is then estimated as the divergence of the cumulative displacement, with an optional second-order correction. This algorithm should undo nonlinear effects up to one-loop order, including the higher-order infrared resummation piece. We test the method using dark matter simulations in real space. At redshift $z=0$, we find that after eight iterations the reconstructed density is more than $95\%$ correlated with the initial density at $k\le 0.35\; h\mathrm{Mpc}^{-1}$. The reconstruction also reduces the power in the difference between reconstructed and initial fields by more than 2 orders of magnitude at $k\le 0.2\; h\mathrm{Mpc}^{-1}$, and it extends the range of scales where the full broadband shape of the power spectrum matches linear theory by a factor of 2-3. As a specific application, we consider measurements of the baryonic acoustic oscillation (BAO) scale that can be improved by reducing the degradation effects of large-scale flows. In our idealized dark matter simulations, the method improves the BAO signal-to-noise ratio by a factor of 2.7 at $z=0$ and by a factor of 2.5 at $z=0.6$, improving standard BAO reconstruction by $70\%$ at $z=0$ and $30\%$ at $z=0.6$, and matching the optimal BAO signal and signal-to-noise ratio of the linear density in the same volume. For BAO, the iterative nature of the reconstruction is the most important aspect.
Hippocampal cellular functional organization for fear memory: Effects of sleep

Memory is vital to our daily existence. Although a large number of studies have suggested that the hippocampus is dedicated to long‐term memory, understanding how memory is anatomically encoded within the hippocampal neuronal network is still lacking. Previously our laboratory showed that hippocampal pyramidal cells are organized in cell clusters to encode both spatial and episodic memory. Based on these findings, we hypothesized that “cluster‐type” is a functional organization principal in the hippocampus to encode all types of memory. Here, we tested whether contextual fear, another hippocampus‐dependent memory, is also organized in cell clusters. We further investigated the possibility that post‐learning sleep may affect functional organization. Cluster formation was examined by assessing the topographic localization of active cells using immediate early gene (IEG, Zif268) imaging methods. The first experiment provides evidence of a cluster‐type organization in the hippocampus for fear memory by showing a spatial distribution of adjacent Zif268 positive cells. Exposure to the context itself, without electric shocks, induced a similar cellular formation; however, the degree of clustering was significantly lower. The second experiment provides evidence that sleep plays a role in the refinement and long‐term stability of the clusters. The present results confirm the existence of a cluster‐type topographic functional neuronal organization in the hippocampus for memory, and further suggest that post‐learning sleep enhances the cluster‐type organization.
Management of cardiovascular disease risk in people with comorbid mental illness: A cross sectional study in Australian general practice

1 The George Institute for Global Health, University of New South Wales, Sydney, NSW, Australia 2 School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia 3 Westmead Applied Research Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia 4 Centre for Primary Health Care and Equity, University of New South Wales, Sydney, NSW, Australia 5 Faculty of Health Sciences & Medicine, Bond University, Gold Coast, QLD, Australia 6 School of Population Health, University of New South Wales, Sydney, NSW, Australia
A Survey of Bone Grafting Practice Patterns in North American Cleft Surgeons

Introduction Alveolar bone grafting aims to restore bony continuity of the alveolus and provide optimal periodontal support for teeth adjacent to the cleft. We created a survey of cleft surgeons to assess the current standard of care regarding this procedure. Methods A multiple choice survey was implemented using Qualtrics software and emailed to a list of 708 surgeons from the ACPA membership directory. Correlation between various provider factors and treatment practices was assessed with Fisher's exact test and likelihood ratio tests. Results The response rate was 17.5%. Eighty-seven percent of providers preferred to perform grafts prior to secondary canine eruption while 10% favored before central incisor eruption. Eighty-one percent favored palatal expansion prior to bone grafting. Wide variability existed regarding the time to initiate postoperative orthodontics; 43% waited 4 to 6 months. Sixty-four percent of surgeons now utilize cone beam CT to assess graft take. The majority of respondents utilized cancellous bone autograft (92%) from the anterior iliac crest (97%) as graft material. Seventy percent used three or more modalities for post-operative pain control management. Early career surgeons (0–5 years) appeared more likely to use non-autologous materials (p < .01) for grafting. Conclusion Alveolar bone grafting prior to secondary canine eruption remains the most common strategy but other protocols are employed. Surgeons utilize multiple modalities for radiographic evaluation and most often use autologous cancellous bone as the primary grafting material. There is no true consensus on the perioperative timing and sequencing of orthodontic manipulation while principles of multimodal perioperative pain control appear widely accepted.
Tooth Alignment Network Based on Landmark Constraints and Hierarchical Graph Structure

Automatic tooth alignment target prediction is vital in shortening the planning time of orthodontic treatments and aligner designs. Generally, the quality of alignment targets greatly depends on the experience and ability of dentists and has enormous subjective factors. Therefore, many knowledge-driven alignment prediction methods have been proposed to help inexperienced dentists. Unfortunately, existing methods tend to directly regress tooth motion, which lacks clinical interpretability. Tooth anatomical landmarks play a critical role in orthodontics because they are effective in aiding the assessment of whether teeth are in close arrangement and normal occlusion. Thus, we consider anatomical landmark constraints to improve tooth alignment results. In this article, we present a novel tooth alignment neural network for alignment target predictions based on tooth landmark constraints and a hierarchical graph structure. We detect the landmarks of each tooth first and then construct a hierarchical graph of jaw-tooth-landmark to characterize the relationship between teeth and landmarks. Then, we define the landmark constraints to guide the network to learn the normal occlusion and predict the rigid transformation of each tooth during alignment. Our method achieves better results with the architecture built for tooth data and landmark constraints and has better explainability than previous methods with regard to clinical tooth alignments.
The Evolution and Global Spatiotemporal Dynamics of Senecavirus A

Recurrent outbreaks and global epidemics of senecavirus A-associated vesicular disease have caused heavy economic losses and have threatened the development of the pig industry. However, the question of where the virus comes from has been one of the biggest puzzles due to the stealthy nature of the virus. ABSTRACT Recurrent outbreaks of senecavirus A (SVA)-associated vesicular disease have led to a large number of infected pigs being culled and has caused considerable economic losses to the swine industry. Although SVA was discovered 2 decades ago, knowledge about the evolutionary and transmission histories of SVA remains unclear. Herein, we performed an integrated analysis of the recombination, phylogeny, selection, and spatiotemporal dynamics of SVA. Phylogenetic analysis demonstrated that SVA diverged into two main branches, clade I (pre-2007 strains) and clade II (post-2007 strains). Importantly, analysis of selective strength showed that clade II was evolving under relaxed selection compared with clade I. Positive selection analysis identified 27 positive selective sites, most of which are located on the outer surface of capsid protomer or on the important functional domains of nonstructure proteins. Bayesian phylodynamics suggested that the estimated time to the most recent common ancestor of SVA was around 1986, and the estimated substitution rate of SVA was 3.3522 × 10−3 nucleotide substitutions/site/year. Demographic history analysis revealed that the effective population size of SVA has experienced a gradually increasing trend with slight fluctuation until 2017 followed by a sharp decline. Notably, Bayesian phylogeographic analysis inferred that Brazil might be the source of SVA’s global transmission since 2015. In summary, these data illustrated that the ongoing evolution of SVA drove the lineage-specific innovation and potentially phenotypically important variation. Our study sheds new light on the fundamental understanding of SVA evolution and spread history. IMPORTANCE Recurrent outbreaks and global epidemics of senecavirus A-associated vesicular disease have caused heavy economic losses and have threatened the development of the pig industry. However, the question of where the virus comes from has been one of the biggest puzzles due to the stealthy nature of the virus. Consequently, tracing the source, evolution, and transmission pattern of SVA is a very challenging task. Based on the most comprehensive analysis, we revealed the origin time, rapid evolution, epidemic dynamics, and selection of SVA. We observed two main genetic branches, clade I (pre-2007 strains) and clade II (post-2007 strains), and described the epidemiological patterns of SVA in different countries. We also first identified Brazil as the source of SVA’s global transmission since 2015. Findings in this study provide important implications for the control and prevention of the virus.
Aspects of Unstructured Grids and Finite-Volume Solvers for the Euler and Navier-Stokes Equations

One of the major achievements in engineering science has been the development of computer algorithms for solving nonlinear differential equations such as the Navier-Stokes equations. These algorithms are now used in the practical engineering design of devices such as cars and airplanes as well as theoretical studies of complex phenomena such as fluid turbulence. In past years, limited computer resources have motivated the development of efficient numerical methods in computational fluid dynamics (CFD) utilizing structured meshes. These meshes are comprised of systematic arrays of quadrilateral or hexahedral cells. The use of structured meshes greatly simplifies the implementation of CFD algorithms on conventional computers. Structured meshes also permit the use of highly efficient solution techniques such as alternating direction implicit (ADI) iteration schemes or multigrid. Following the dramatic improvement in computing speed in recent years, emphasis has shifted towards the design of algorithms capable of treating complex geometries. The automatic generation of structured grids about complex geometries is problematic. Unstructured grids offer one promising alternative technique for treating these general geometries. Unstructured meshes have irregular connectivity and usually contain triangles and/or quadrilaterals in two dimensions and tetrahedra and/or hexahedra in three dimensions. The generation and use of unstructured grids poses new challenges in computational fluid dynamics. This is true for both grid generation as well as for the design of algorithms for flow solution. The purpose of these notes is to present recent developments in the unstructured grid generation and flow solution technology.
Suicide risk and mental health in the general population of French Polynesia

Objective There are no reports on the burden of suicidal ideation and suicide attempts in the general population of French Polynesia (FP). We aimed to improve suicide prevention and mental health care by assessing the prevalence of suicide risk and major mental health disorders and care among adults in FP. Methods We conducted the Mental Health in General Population Survey in FP during 2015 to 2017. Participants were selected using the quota method to obtain a representative sample of the general population. Suicide risk and psychiatric diagnoses were assessed using the Mini International Neuropsychiatric Interview. Results We included 968 people aged 18 years or older. The prevalence of current suicidal ideation (13.1%) and current (2.6%) and lifetime suicide attempts (18.6%), as well as mental health disorders (42.8%), was high in FP. A notable proportion of participants with these conditions did not seek medical assistance. Conclusion A high prevalence of suicide risk and mental health disorders was found in the general population of FP. Suicide prevention and mental health plans are needed in FP that include better access to primary care for the diagnosis and treatment of mental health disorders. Further research is needed to clarify cultural risk and protective factors.
Critical behavior of the two-dimensional N-component Landau-Ginzburg Hamiltonian with cubic anisotropy

We study the two-dimensional N-component Landau-Ginzburg Hamiltonian with cubic anisotropy. We compute and analyze the fixed-dimension perturbative expansion of the renormalization-group functions to four loops. The relations of these models with N-color Ashkin-Teller models, discrete cubic models, the planar model with fourth-order anisotropy, and the structural phase transition in adsorbed monolayers are discussed. Our results for N=2 (XY model with cubic anisotropy) are compatible with the existence of a line of fixed points joining the Ising and the O(2) fixed points. Along this line the exponent η has the constant value 1/4, while the exponent v runs in a continuous and monotonic way from I to [from Ising to O(2)]. In the four-loop approximation, for N≥3 we find a cubic fixed point in the region u,v≥0.
Community assembly history alters relationships between biodiversity and ecosystem functions during restoration

Abstract Relationships between biodiversity and ecosystem functioning depend on the processes structuring community assembly. However, predicting biodiversity‐ecosystem functioning (BEF) relationships based on community assembly remains challenging because assembly outcomes are often contingent on history and the consequences of history for ecosystem functions are poorly understood. In a grassland restoration experiment, we isolated the role of history for the relationships between plant biodiversity and multiple ecosystem functions by initiating assembly in three different years, while controlling for all other aspects of community assembly. We found that two aspects of assembly history—establishment year and succession—altered species and trait community trajectories, which in turn altered net primary productivity, decomposition rates, and floral resources. Moreover, history altered BEF relationships (which ranged from positive to negative), both within and across functions, by modifying the causal pathways linking species identity, traits, diversity, and ecosystem functions. Our results show that the interplay of deterministic succession and environmental stochasticity during establishment mediate historical contingencies that cause variation in biodiversity and ecosystem functions, even under otherwise identical assembly conditions. An explicit attention to history is needed to understand why biodiversity‐ecosystem function relationships vary in natural ecosystems: a critical question at the intersection of fundamental theory and applications to environmental change biology and ecosystem restoration.
Domain Adaptive Algorithm Based on Multi-Manifold Embedded Distributed Alignment for Brain-Computer Interfaces

The use of transfer learning in brain-computer interfaces (BCIs) has potential applications. As electroencephalogram (EEG) signals vary among different paradigms and subjects, existing EEG transfer learning algorithms mainly focus on the alignment of the original space. They may not discover hidden details owing to the low-dimensional structure of EEG. To effectively transfer data from a source to target domain, a multi-manifold embedding domain adaptive algorithm is proposed for BCI. First, we aligned the EEG covariance matrix in the Riemannian manifold and extracted the characteristics of each source domain in the tangent space to reflect the differences between different source domains. Subsequently, we mapped the extracted characteristics to the Grassmann manifold to obtain a common feature representation. In domain adaptation, the geometric and statistical attributes of EEG data were considered simultaneously, and the target domain divergence matrix was updated with pseudo-labels to maximize the inter-class distance and minimize the intra-class distance. Datasets generated via BCIs were used to verify the effectiveness of the algorithm. Under two experimental paradigms, namely single-source to single-target and multi-source to single-target, the average accuracy of the algorithm on three datasets was 73.31% and 81.02%, respectively, which is more than that of several state-of-the-art EEG cross-domain classification approaches. Our multi-manifold embedded domain adaptive method achieved satisfactory results on EEG transfer learning. The method can achieve effective EEG classification without a same subject's training set.
Bridging the View Disparity Between Radar and Camera Features for Multi-Modal Fusion 3D Object Detection

Environmental perception with the multi-modal fusion is crucial in autonomous driving to increase accuracy, completeness, and robustness. This paper focuses on utilizing millimeter-wave (MMW) radar and camera sensor fusion for 3D object detection. A novel method that realizes the feature-level fusion under the bird's-eye view (BEV) for a better feature representation is proposed. Firstly, radar points are augmented with temporal accumulation and sent to a spatial-temporal encoder for radar feature extraction. Meanwhile, multi-scale image 2D features which adapt to various spatial scales are obtained by image backbone and neck model. Then, image features are transformed to BEV with the designed view transformer. In addition, this work fuses the multi-modal features with a two-stage fusion model called point-fusion and ROI-fusion, respectively. Finally, a detection head regresses objects category and 3D locations. Experimental results demonstrate that the proposed method realizes the state-of-the-art (SOTA) performance under the most crucial detection metrics–mean average precision (mAP) and nuScenes detection score (NDS) on the challenging nuScenes dataset.
Tuning the conductance topology in solids

The inertia of trapping and detrapping of nonequilibrium charge carriers affects the electrochemical and transport properties of both bulk and nanoscopic structures in a very peculiar way. An emerging memory response with hysteresis in the current–voltage response and its eventual multiple crossing, produced by this universally available ingredient, are signatures of this process. Here, we deliver a microscopic and analytical solution for these behaviors, understood as the modulation of the topology of the current–voltage loops. Memory emergence thus becomes a characterization tool for intrinsic features that affect the electronic transport of solids such as the nature and number of trapping sites, intrinsic symmetry constraints, and natural relaxation time scales. This method is also able to reduce the seeming complexity of frequency-dependent electrochemical impedance and cyclic voltammetry observable for a variety of systems to a combination of simple microscopic ingredients.
Generalized LOAM: LiDAR Odometry Estimation With Trainable Local Geometric Features

This letter presents a LiDAR odometry estimation framework called Generalized LOAM. Our proposed method is generalized in that it can seamlessly fuse various local geometric shapes around points to improve the position estimation accuracy compared to the conventional LiDAR odometry and mapping (LOAM) method. To utilize continuous geometric features for LiDAR odometry estimation, we incorporate tiny neural networks into a generalized iterative closest point (GICP) algorithm. These neural networks improve the data association metric and the matching cost function using local geometric features. Experiments with the KITTI benchmark demonstrate that our proposed method reduces relative trajectory errors compared to the other LiDAR odometry estimation methods.
On the distribution of individual causal effects of binary exposures using latent variable models

In recent years the ﬁeld of causal inference from observational data has emerged rapidly. This literature has focused on (conditional) average causal eﬀect estimation. When (remaining) variability of individual causal eﬀects (ICEs) is considerable, average eﬀects may be less informative, and possibly misleading for an individual. The fundamental problem of causal inference precludes the estimation of the joint distribution of potential outcomes without making assumptions, while this distribution is necessary to describe the heterogeneity of causal eﬀects. In this paper we describe these assumptions and present a family of ﬂexible latent variable models that can be used to study individual eﬀect modiﬁcation and estimate the ICE distribution from cross-sectional data. We will also discuss how the distribution is aﬀected by misspeciﬁcation of the error distribution or ignoring possible confounding-eﬀect heterogeneity. How latent variable models can be applied and validated in practice is illustrated in a case study on the eﬀect of Hepatic Steatosis on a clinical precursor to heart failure. Assuming that there is (i) no unmeasured confounding and (ii) independence of the individual eﬀect modiﬁer and the potential outcome under no exposure, we conclude that the individual causal eﬀect distribution deviates from Gaussian. We estimate that the ‘treatment’ beneﬁt rate in the population is 23 . 7% ( 95% Bayesian credible interval: 2 . 6% , 53 . 7% ) despite a harming average eﬀect.
Sequence entropy tuples and mean sensitive tuples

Abstract Using the idea of local entropy theory, we characterize the sequence entropy tuple via mean forms of the sensitive tuple in both topological and measure-theoretical senses. For the measure-theoretical sense, we show that for an ergodic measure-preserving system, the $\mu $ -sequence entropy tuple, the $\mu $ -mean sensitive tuple, and the $\mu $ -sensitive in the mean tuple coincide, and give an example to show that the ergodicity condition is necessary. For the topological sense, we show that for a certain class of minimal systems, the mean sensitive tuple is the sequence entropy tuple.