Miracle-dz/UniFAR-FTUs huggingface dataset:
Local central limit theorem for gradient field models

We consider the gradient field model in [−N,N] ∩ Z with a uniformly convex interaction potential. Naddaf-Spencer [22] and Miller [20] proved that the macroscopic averages of linear statistics of the field converge to a continuum Gaussian free field. In this paper we prove the distribution of φ(0)/ √ logN converges uniformly in R to a Gaussian density, with a BerryEsseen type bound. This implies the distribution of φ(0) is sufficiently ‘Gaussian like’ between [− √ logN, √ logN].
BioADAPT-MRC: adversarial learning-based domain adaptation improves biomedical machine reading comprehension task

ABSTRACT Motivation Biomedical machine reading comprehension (biomedical-MRC) aims to comprehend complex biomedical narratives and assist healthcare professionals in retrieving information from them. The high performance of modern neural network-based MRC systems depends on high-quality, large-scale, human-annotated training datasets. In the biomedical domain, a crucial challenge in creating such datasets is the requirement for domain knowledge, inducing the scarcity of labeled data and the need for transfer learning from the labeled general-purpose (source) domain to the biomedical (target) domain. However, there is a discrepancy in marginal distributions between the general-purpose and biomedical domains due to the variances in topics. Therefore, direct-transferring of learned representations from a model trained on a general-purpose domain to the biomedical domain can hurt the model’s performance. Results We present an adversarial learning-based domain adaptation framework for the biomedical machine reading comprehension task (BioADAPT-MRC), a neural network-based method to address the discrepancies in the marginal distributions between the general and biomedical domain datasets. BioADAPT-MRC relaxes the need for generating pseudo labels for training a well-performing biomedical-MRC model. We extensively evaluate the performance of BioADAPT-MRC by comparing it with the best existing methods on three widely used benchmark biomedical-MRC datasets—BioASQ-7b, BioASQ-8b and BioASQ-9b. Our results suggest that without using any synthetic or human-annotated data from the biomedical domain, BioADAPT-MRC can achieve state-of-the-art performance on these datasets. Availability and implementation BioADAPT-MRC is freely available as an open-source project at https://github.com/mmahbub/BioADAPT-MRC. Supplementary information Supplementary data are available at Bioinformatics online.
Czynnik ludzki w lotnictwie

The purpose of the research was to verify the presented theoretical model of the human factor in aviation. The paper presents a theoretical model of the human factor from a psychological perspective. This model includes the following mutually interacting factors: the subjective factor, the situational factor, risk, stress, errors, accA dents and catastrophes. The relations between elements are presented graphically. The model was verified by completing a confirmation, exploratory analysis. The obtaiA ned goodnessAofAfit indices indicate that the mode l is very well fitted to the data. The participants of the study were 318 glider and airplane pilots. The theoretical model of the human factor in aviation can be used to optimize the psychological conditions of the functioning of pilots and other persons who perform professions involving high risk and stress.
Functional sufficient dimension reduction through distance covariance

Our research proposes a novel method for reducing the dimensionality of functional data, specifically for the case where the response is a scalar and the predictor is a random function. Our method utilizes distance covariance, and has several advantages over existing methods. Unlike current techniques which require restrictive assumptions such as linear conditional mean and constant covariance, our method has mild requirements on the predictor. Additionally, our method does not involve the use of the unbounded inverse of the covariance operator. The link function between the response and predictor can be arbitrary, and our proposed method maintains the advantage of being model-free, without the need to estimate the link function. Furthermore, our method is naturally suited for sparse longitudinal data. We utilize functional principal component analysis with truncation as a regularization mechanism in the development of our method. We provide justification for the validity of our proposed method, and establish statistical consistency of the estimator under certain regularization conditions. To demonstrate the effectiveness of our proposed method, we conduct simulation studies and real data analysis. The results show improved performance compared to existing methods.
StrongSORT: Make DeepSORT Great Again

Recently, Multi-Object Tracking (MOT) has attracted rising attention, and accordingly, remarkable progresses have been achieved. However, the existing methods tend to use various basic models (e.g, detector and embedding model), and different training or inference tricks, etc. As a result, the construction of a good baseline for a fair comparison is essential. In this paper, a classic tracker, i.e., DeepSORT, is first revisited, and then is significantly improved from multiple perspectives such as object detection, feature embedding, and trajectory association. The proposed tracker, named StrongSORT, contributes a strong and fair baseline for the MOT community. Moreover, two lightweight and plug-and-play algorithms are proposed to address two inherent “missing” problems of MOT: missing association and missing detection. Specifically, unlike most methods, which associate short tracklets into complete trajectories at high computation complexity, we propose an appearance-free link model (AFLink) to perform global association without appearance information, and achieve a good balance between speed and accuracy. Furthermore, we propose a Gaussian-smoothed interpolation (GSI) based on Gaussian process regression to relieve the missing detection. AFLink and GSI can be easily plugged into various trackers with a negligible extra computational cost (1.7 ms and 7.1 ms per image, respectively, on MOT17). Finally, by fusing StrongSORT with AFLink and GSI, the final tracker (StrongSORT++) achieves state-of-the-art results on multiple public benchmarks, i.e., MOT17, MOT20, DanceTrack and KITTI. Codes are available at https://github.com/dyhBUPT/StrongSORT and https://github.com/open-mmlab/mmtracking.
Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework

Online learning in large-scale structured bandits is known to be challenging due to the curse of dimensionality. In this paper, we propose a unified meta-learning framework for a general class of structured bandit problems where the parameter space can be factorized to item-level. The novel bandit algorithm is general to be applied to many popular problems,scalable to the huge parameter and action spaces, and robust to the specification of the generalization model. At the core of this framework is a Bayesian hierarchical model that allows information sharing among items via their features, upon which we design a meta Thompson sampling algorithm. Three representative examples are discussed thoroughly. Both theoretical analysis and numerical results support the usefulness of the proposed method.
A Spatio-Causal Growth Model Explains the Pareto Principle

Under typical growth models, populations quickly lose the ability to select and sustain effects (gains and losses), as growth leaves their increasing variation (endogenous and exogeneous) uncontrolled. Under (1) Unconfounded growth, in contrast, populations preserve the ability to determine which of their variations are responsible for the gains and losses they observe (and can, consequently, carry fair selection and optimization processes). Under (2) Externally-Valid growth, effects generalize across populations’ full range of external variation (and populations can, consequently, expand across increasingly diverse conditions). The first promotes generalization of effects over populations, and the second over their environments. These alternative growth patterns allow systems in complex environments to create sustainable environments for growth, from their spatial distribution patterns.
Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation

Most dominant neural machine translation (NMT) models are restricted to make predictions only according to the local context of preceding words in a left-to-right manner. Although many previous studies try to incorporate global information into NMT models, there still exist limitations on how to effectively exploit bidirectional global context. In this paper, we propose a Confidence Based Bidirectional Global Context Aware (CBBGCA) training framework for NMT, where the NMT model is jointly trained with an auxiliary conditional masked language model (CMLM). The training consists of two stages: (1) multi-task joint training; (2) confidence based knowledge distillation. At the first stage, by sharing encoder parameters, the NMT model is additionally supervised by the signal from the CMLM decoder that contains bidirectional global contexts. Moreover, at the second stage, using the CMLM as teacher, we further pertinently incorporate bidirectional global context to the NMT model on its unconfidently-predicted target words via knowledge distillation. Experimental results show that our proposed CBBGCA training framework significantly improves the NMT model by +1.02, +1.30 and +0.57 BLEU scores on three large-scale translation datasets, namely WMT’14 English-to-German, WMT’19 Chinese-to-English and WMT’14 English-to-French, respectively.
Jet launching from binary neutron star mergers: Incorporating neutrino transport and magnetic fields

We perform general relativistic, magnetohydrodynamic (GRMHD) simulations of merging binary neutron stars incorporating neutrino transport and magnetic fields. Our new radiative transport module for neutrinos adopts a general relativistic, truncated-moment (M1) formalism. The binaries consist of two identical, irrotational stars modeled by the SLy nuclear equation of state (EOS). They are initially in quasicircular orbit and threaded with a poloidal magnetic field that extends from the stellar interior into the exterior, as in typical pulsars. We insert neutrino processes shortly after the merger and focus on the role of neutrinos in launching a jet following the collapse of the hypermassive neutron star (HMNS) remnant to a spinning black hole (BH). We treat two microphysical versions: one (a"warm-up") evolving a single neutrino species and considering only charged-current processes, and the other evolving three species $(\nu_e, \bar{\nu}_e, \nu_{\rm x})$ and related processes. We trace the evolution until the system reaches a quasiequilibrium state after BH formation. We find that the BH + disk remnant eventually launches an incipient jet. The electromagnetic Poynting luminosity is $\sim 10^{53} \rm \, erg\, s^{-1}$, consistent with that of typical short gamma-ray bursts (sGRBs). The effect of neutrino cooling shortens the lifetime of the HMNS, and lowers the amplitude of the major peak of the gravitational wave (GW) power spectrum somewhat. After BH formation, neutrinos help clear out the matter near the BH poles, resulting in lower baryon-loaded surrounding debris. The neutrino luminosity resides in the range $\sim 10^{52-53} \rm \,erg\,s^{-1}$ once quasiequilibrium is achieved. Comparing with the neutrino-free models, we observe that the inclusion of neutrinos yields similar ejecta masses and is inefficient in carrying off additional angular momentum.
Reliability Analysis Method of Deployment Mechanism of a Satellite Solar Arrays

The reliability analysis method of deployment mechanism solar arrays was proposed.Firstly,the composing of solar arrays and its deployment mechanism were introduced and the fault tree of the deployment system was built in order to qualitative analysis and quantitative analysis.Form analysis there are conclusion that the shortage of moments of twisting spring and the seizure of mechanism are the key problems which lead to the invalidation of solar arrays deployment mechanism and some methods and suggestions that to prevent those problems are also presented in the paper.Furthermore,the reliability modeling and analysis method of deployment mechanism solar arrays were proposed from three aspects of static torque margin,torque and the power by torque.
Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning

Subgraph-based graph representation learning (SGRL) has been recently proposed to deal with some fundamental challenges encountered by canonical graph neural networks (GNNs), and has demonstrated advantages in many important data science applications such as link, relation and motif prediction. However, current SGRL approaches suffer from scalability issues since they require extracting subgraphs for each training or test query. Recent solutions that scale up canonical GNNs may not apply to SGRL. Here, we propose a novel framework SUREL for scalable SGRL by co-designing the learning algorithm and its system support. SUREL adopts walk-based decomposition of subgraphs and reuses the walks to form subgraphs, which substantially reduces the redundancy of subgraph extraction and supports parallel computation. Experiments over six homogeneous, heterogeneous and higher-order graphs with millions of nodes and edges demonstrate the effectiveness and scalability of SUREL. In particular, compared to SGRL baselines, SUREL achieves 10X speed-up with comparable or even better prediction performance; while compared to canonical GNNs, SUREL achieves 50% prediction accuracy improvement.
Improving Lexical Embeddings for Robust Question Answering

Recent techniques in Question Answering (QA) have gained remarkable performance improvement with some QA models even surpassed human performance. However, the ability of these models in truly understanding the language still remains dubious and the models are revealing limitations when facing adversarial examples. To strengthen the robustness of QA models and their generalization ability, we propose a representation Enhancement via Semantic and Context constraints (ESC) approach to improve the robustness of lexical embeddings. Specifically, we insert perturbations with semantic constraints and train enhanced contextual representations via a context-constraint loss to better distinguish the context clues for the correct answer. Experimental results show that our approach gains significant robustness improvement on four adversarial test sets.
Topology-Preserving Segmentation Network: A Deep Learning Segmentation Framework for Connected Component

Medical image segmentation aims to automatically extract anatomical or pathological structures in the human body. Most objects or regions of interest are of similar patterns. For example, the relative location and the relative size of the lung and the kidney differ little among subjects. Incorporating these morphology rules as prior knowledge into the segmentation model is believed to be an effective way to enhance the accuracy of the segmentation results. Motivated by this, we propose in this work the Topology-Preserving Segmentation Network (TPSN) which can predict segmentation masks with the same topology prescribed for specific tasks. TPSN is a deformation-based model that yields a deformation map through an encoder-decoder architecture to warp the template masks into a target shape approximating the region to segment. Comparing to the segmentation framework based on pixel-wise classification, deformation-based segmentation models that warp a template to enclose the regions are more convenient to enforce geometric constraints. In our framework, we carefully design the ReLU Jacobian regularization term to enforce the bijectivity of the deformation map. As such, the predicted mask by TPSN has the same topology as that of the template prior mask.
On the Benefits of Large Learning Rates for Kernel Methods

This paper studies an intriguing phenomenon related to the good generalization performance of estimators obtained by using large learning rates within gradient descent algorithms. First observed in the deep learning literature, we show that a phenomenon can be precisely characterized in the context of kernel methods, even though the resulting optimization problem is convex. Specifically, we consider the minimization of a quadratic objective in a separable Hilbert space, and show that with early stopping, the choice of learning rate influences the spectral decomposition of the obtained solution on the Hessian's eigenvectors. This extends an intuition described by Nakkiran (2020) on a two-dimensional toy problem to realistic learning scenarios such as kernel ridge regression. While large learning rates may be proven beneficial as soon as there is a mismatch between the train and test objectives, we further explain why it already occurs in classification tasks without assuming any particular mismatch between train and test data distributions.
Dual-Branched Spatio-Temporal Fusion Network for Multihorizon Tropical Cyclone Track Forecast

A tropical cyclone (TC) is a typical extreme tropical weather system, which could cause serious disasters in transit areas. Accurate TC track forecasting is the key to reducing casualties and damages, however, long-term forecasting of TCs is a challenging problem due to their extremely high dynamics and uncertainty. Existing TC track forecasting methods mainly focus on utilizing a single modality of source data, meanwhile, suffer from limited long-term forecasting capability and high computational complexity. In this article, we propose to address the abovementioned challenges from a new perspective—by utilizing large-scale spatio-temporal multimodal historical data and advanced deep learning techniques. A novel multihorizon TC track forecasting model named dual-branched spatio-temporal fusion network (DBF-Net) is proposed and evaluated. DBF-Net contains a TC features branch that extracts temporal features from 2-D state vectors and a pressure field branch that extracts spatio-temporal features from reanalysis 3-D pressure field. We show that with the abovementioned design, DBF-Net can fully exploit the implicit associations of multimodal data, achieving advantages that unimodal data-based method does not have. Extensive experiments on 39 years of historical TCs track data in the Northwest Pacific show that our DBF-Net achieves significant accuracy improvement compared with previous TCs track forecast methods.
A natural class of cellular automata containing fractional multiplication automata, Rule 30, and others

We define the class of rapidly left expansive cellular automata, which contains fractional multiplication automata, Wolfram’s Rule 30, and many others. The definition has been shaped by a proposition of Jen on aperiodicity of columns in space-time diagrams of certain cellular automata, which generalizes to this new class. We also present results that originate from the theory of distribution modulo 1.
Reduced Order Model Closures: A Brief Tutorial

In this paper, we present a brief tutorial on reduced order model (ROM) closures. First, we carefully motivate the need for ROM closure modeling in under-resolved simulations. Then, we construct step by step the ROM closure model by extending the classical Galerkin framework to the spaces of resolved and unresolved scales. Finally, we develop the data-driven variational multiscale ROM closure and then we test it in fluid flow simulations. Our tutorial on ROM closures is structured as a sequence of questions and answers, and is aimed at first year graduate students and advanced undergraduate students. Our goal is not to explain the"how,"but the"why. "That is, we carefully explain the principles used to develop ROM closures, without focusing on particular approaches. Furthermore, we try to keep the technical details to a minimum and describe the general ideas in broad terms while citing appropriate references for details.
The scatter in the galaxy–halo connection: a machine learning analysis

We apply machine learning, a powerful method for uncovering complex correlations in high-dimensional data, to the galaxy–halo connection of cosmological hydrodynamical simulations. The mapping between galaxy and halo variables is stochastic in the absence of perfect information, but conventional machine learning models are deterministic and hence cannot capture its intrinsic scatter. To overcome this limitation, we design an ensemble of neural networks with a Gaussian loss function that predict probability distributions, allowing us to model statistical uncertainties in the galaxy–halo connection as well as its best-fit trends. We extract a number of galaxy and halo variables from the Horizon-AGN and IllustrisTNG100-1 simulations and quantify the extent to which knowledge of some subset of one enables prediction of the other. This allows us to identify the key features of the galaxy–halo connection and investigate the origin of its scatter in various projections. We find that while halo properties beyond mass account for up to 50 per cent of the scatter in the halo-to-stellar mass relation, the prediction of stellar half-mass radius or total gas mass is not substantially improved by adding further halo properties. We also use these results to investigate semi-analytic models for galaxy size in the two simulations, finding that assumptions relating galaxy size to halo size or spin are not successful.
Simplified and unified analysis of various learning problems by reduction to Multiple-Instance Learning

In statistical learning, many problem formulations have been proposed so far, such as multi-class learning, complementarily labeled learning, multi-label learning, multi-task learning, which provide theoretical models for various real-world tasks. Although they have been extensively studied, the relationship among them has not been fully investigated. In this work, we focus on a particular problem formulation called Multiple-Instance Learning (MIL), and show that various learning problems including all the problems mentioned above with some of new problems can be reduced to MIL with theoretically guaranteed generalization bounds, where the reductions are established under a new reduction scheme we provide as a by-product. The results imply that the MIL-reduction gives a simplified and unified framework for designing and analyzing algorithms for various learning problems. Moreover, we show that the MIL-reduction framework can be kernelized.
Locality-sensitive orderings and applications to reliable spanners

Chan, Har-Peled, and Jones [2020] recently developed locality-sensitive ordering (LSO), a new tool that allows one to reduce problems in the Euclidean space ℝd to the 1-dimensional line. They used LSO’s to solve a host of problems. Later, Buchin, Har-Peled, and Oláh [2019,2020] used the LSO of Chan et al. to construct very sparse reliable spanners for the Euclidean space. A highly desirable feature of a reliable spanner is its ability to withstand a massive failure: the network remains functioning even if 90% of the nodes fail. In a follow-up work, Har-Peled, Mendel, and Oláh [2021] constructed reliable spanners for general and topologically structured metrics. Their construction used a different approach, and is based on sparse covers. In this paper, we develop the theory of LSO’s in non-Euclidean metrics by introducing new types of LSO’s suitable for general and topologically structured metrics. We then construct such LSO’s, as well as constructing considerably improved LSO’s for doubling metrics. Afterwards, we use our new LSO’s to construct reliable spanners with improved stretch and sparsity parameters. Most prominently, we construct Õ(n)-size reliable spanners for trees and planar graphs with the optimal stretch of 2. Along the way to the construction of LSO’s and reliable spanners, we introduce and construct ultrametric covers, and construct 2-hop reliable spanners for the line.