Miracle-dz/UniFAR-FTUs huggingface dataset:
ABINet++: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Spotting

Scene text spotting is of great importance to the computer vision community due to its wide variety of applications. Recent methods attempt to introduce linguistic knowledge for challenging recognition rather than pure visual classification. However, how to effectively model the linguistic rules in end-to-end deep networks remains a research challenge. In this paper, we argue that the limited capacity of language models comes from 1) implicit language modeling; 2) unidirectional feature representation; and 3) language model with noise input. Correspondingly, we propose an autonomous, bidirectional and iterative ABINet++ for scene text spotting. First, the autonomous suggests enforcing explicitly language modeling by decoupling the recognizer into vision model and language model and blocking gradient flow between both models. Second, a novel bidirectional cloze network (BCN) as the language model is proposed based on bidirectional feature representation. Third, we propose an execution manner of iterative correction for the language model which can effectively alleviate the impact of noise input. Additionally, based on an ensemble of the iterative predictions, a self-training method is developed which can learn from unlabeled images effectively. Finally, to polish ABINet++ in long text recognition, we propose to aggregate horizontal features by embedding Transformer units inside a U-Net, and design a position and content attention module which integrates character order and content to attend to character features precisely. ABINet++ achieves state-of-the-art performance on both scene text recognition and scene text spotting benchmarks, which consistently demonstrates the superiority of our method in various environments especially on low-quality images. Besides, extensive experiments including in English and Chinese also prove that, a text spotter that incorporates our language modeling method can significantly improve its performance both in accuracy and speed compared with commonly used attention-based recognizers. Code is available at https://github.com/FangShancheng/ABINet-PP.
On Holistic Multi-Step Cyberattack Detection via a Graph-based Correlation Approach

While digitization of distribution grids through information and communications technology brings numerous benefits, it also increases the grid's vulnerability to serious cyber attacks. Unlike conventional systems, attacks on many industrial control systems such as power grids often occur in multiple stages, with the attacker taking several steps at once to achieve its goal. Detection mechanisms with situational awareness are needed to detect orchestrated attack steps as part of a coherent attack campaign. To provide a foundation for detection and prevention of such attacks, this paper addresses the detection of multi-stage cyber attacks with the aid of a graph-based cyber intelligence database and alert correlation approach. Specifically, we propose an approach to detect multi-stage attacks by lever-aging heterogeneous data to form a knowledge base and employ a model-based correlation approach on the generated alerts to identify multi-stage cyber attack sequences taking place in the network. We investigate the detection quality of the proposed approach by using a case study of a multi-stage cyber attack campaign in a future-orientated power grid pilot.
DrapeNet: Generating Garments and Draping them with Self-Supervision

Recent approaches to drape garments quickly over arbitrary human bodies leverage self-supervision to eliminate the need for large training sets. However, they are designed to train one network per clothing item, which severely limits their generalization abilities. In our work, we rely on self-supervision to train a single network to drape multiple garments. This is achieved by predicting a 3D deformation ﬁeld conditioned on the latent codes of a generative network, which models garments as unsigned distance ﬁelds. Our pipeline can generate and drape previously unseen garments of any topology, whose shape can be edited by manipulating their latent codes. Being fully differentiable, our formulation makes it possible to recover accurate 3D models of garments from partial observations – images or 3D scans – via gradient descent. Our code will be made publicly available.
The Isaac Newton Telescope Monitoring Survey of Local Group Dwarf Galaxies. V. The Star Formation History of Sagittarius Dwarf Irregular Galaxy Derived from Long-period Variable Stars

We conducted an optical monitoring survey of the Sagittarius dwarf irregular galaxy (SagDIG) during the period of 2016 June–2017 October, using the 2.5 m Isaac Newton Telescopeat La Palama. Our goal was to identify long-period variable stars (LPVs), namely, asymptotic giant branch stars (AGBs) and red supergiant stars, to obtain the star formation history of isolated, metal-poor SagDIG. For our purpose, we used a method that relies on evaluating the relation between luminosity and the birth mass of these most evolved stars. We found 27 LPV candidates within 2 half-light radii of SagDIG. 10 LPV candidates were in common with previous studies, including one extreme-AGB (x-AGB). By adopting the metallicity Z = 0.0002 for older populations and Z = 0.0004 for younger ages, we estimated that the star formation rate changes from 0.0005 ± 0.0002 M ⊙ yr−1 kpc−2 (13 Gyr ago) to 0.0021 ± 0.0010 M ⊙ yr−1 kpc−2 (0.06 Gyr ago). Like many dwarf irregular galaxies, SagDIG has had continuous star formation activity across its lifetime, though with different rates, and experiences an enhancement of star formation since z ≃ 1. We also evaluated the total stellar mass within 2 half-light radii of SagDIG for three choices of metallicities. For metallicity Z = 0.0002 and 0.0004, we estimated the stellar mass M* = (5.4 ± 2.3) × 106 and (3.0 ± 1.3) × 106 M ⊙, respectively. Additionally, we determined a distance modulus of μ = 25.27 ± 0.05 mag, using the tip of the red giant branch.
Neural tangent kernel analysis of PINN for advection-diffusion equation

Physics-informed neural networks (PINNs) numerically approximate the solution of a partial differential equation (PDE) by incorporating the residual of the PDE along with its initial/boundary conditions into the loss function. In spite of their partial success, PINNs are known to struggle even in simple cases where the closed-form analytical solution is available. In order to better understand the learning mechanism of PINNs, this work focuses on a systematic analysis of PINNs for the linear advection-diffusion equation (LAD) using the Neural Tangent Kernel (NTK) theory. Thanks to the NTK analysis, the effects of the advection speed/diffusion parameter on the training dynamics of PINNs are studied and clariﬁed. We show that the training difﬁculty of PINNs is a result of 1) the so-called ’spectral bias’, which leads to difﬁculty in learning high-frequency behaviours; and 2) convergence rate disparity between different loss components that results in training failure. The latter occurs even in the cases where the solution of the underlying PDE does not exhibit high-frequency behaviour. Furthermore, we observe that this training difﬁculty manifests itself, to some extent, differently in advection-dominated and diffusion-dominated regimes. Different strategies to address these issues are also discussed. In particular, it is demonstrated that periodic activation functions can be used to partly resolve the spectral bias issue.
Measurements of large streamwise vortices in an open‐channel flow

The moving-bed flume, where the cross-sectional mean velocity is zero, has proven to be useful for visualization of coherent structures in the flow, especially streamwise vortices with a size scale equal to the depth. The temporal mean of these streamwise vortices is turbulence driven secondary motion. Hydrogen bubbles illuminated by a plane of laser light are used to visualize and measure these vortices. Flow visualization at the zero mean velocity point provides a unique view of the streamwise vortices without the interference of the mean flow velocity.
DENSE: Data-Free One-Shot Federated Learning

One-shot Federated Learning (FL) has recently emerged as a promising approach, which allows the central server to learn a model in a single communication round. Despite the low communication cost, existing one-shot FL methods are mostly impractical or face inherent limitations, \eg a public dataset is required, clients' models are homogeneous, and additional data/model information need to be uploaded. To overcome these issues, we propose a novel two-stage \textbf{D}ata-fre\textbf{E} o\textbf{N}e-\textbf{S}hot federated l\textbf{E}arning (DENSE) framework, which trains the global model by a data generation stage and a model distillation stage. DENSE is a practical one-shot FL method that can be applied in reality due to the following advantages: (1) DENSE requires no additional information compared with other methods (except the model parameters) to be transferred between clients and the server; (2) DENSE does not require any auxiliary dataset for training; (3) DENSE considers model heterogeneity in FL, \ie different clients can have different model architectures. Experiments on a variety of real-world datasets demonstrate the superiority of our method.For example, DENSE outperforms the best baseline method Fed-ADI by 5.08\% on CIFAR10 dataset.
Tired of Over-smoothing? Stress Graph Drawing Is All You Need!

In designing and applying graph neural networks, we often fall into some optimization pitfalls, the most deceptive of which is that we can only build a deep model by solving over-smoothing. The fundamental reason is that we do not understand how graph neural networks work. Stress graph drawing can offer a unique viewpoint to message iteration in the graph, such as the root of the over-smoothing problem lies in the inability of graph models to maintain an ideal distance between nodes. We further elucidate the trigger conditions of over-smoothing and propose Stress Graph Neural Networks. By introducing the attractive and repulsive message passing from stress iteration, we show how to build a deep model without preventing over-smoothing, how to use repulsive information, and how to optimize the current message-passing scheme to approximate the full stress message propagation. By performing different tasks on 23 datasets, we verified the effectiveness of our attractive and repulsive models and the derived relationship between stress iteration and graph neural networks. We believe that stress graph drawing will be a popular resource for understanding and designing graph neural networks.
Overcoming Concept Shift in Domain-Aware Settings through Consolidated Internal Distributions

We develop an algorithm to improve the predictive performance of a pre-trained model under \textit{concept shift} without retraining the model from scratch when only unannotated samples of initial concepts are accessible. We model this problem as a domain adaptation problem, where the source domain data is inaccessible during model adaptation. The core idea is based on consolidating the intermediate internal distribution, learned to represent the source domain data, after adapting the model. We provide theoretical analysis and conduct extensive experiments on five benchmark datasets to demonstrate that the proposed method is effective.
Testing the identification of causal effects in observational data

This study demonstrates the existence of a testable condition for the identification of the causal effect of a treatment on an outcome in observational data, which relies on two sets of variables: observed covariates to be controlled for and a suspected instrument. Under a causal structure commonly found in empirical applications, the testable conditional independence of the suspected instrument and the outcome given the treatment and the covariates has two implications. First, the instrument is valid, i.e. it does not directly affect the outcome (other than through the treatment) and is unconfounded conditional on the covariates. Second, the treatment is unconfounded conditional on the covariates such that the treatment effect is identified. We suggest tests of this conditional independence based on machine learning methods that account for covariates in a data-driven way and investigate their asymptotic behavior and finite sample performance in a simulation study. We also apply our testing approach to evaluating the impact of fertility on female labor supply when using the sibling sex ratio of the first two children as supposed instrument, which by and large points to a violation of our testable implication for the moderate set of socio-economic covariates considered.
Spectroscopy of high-temperature solar flare plasmas

The past decade has seen great improvements in the quality of X -ray spectra of solar flares obtained from spacecraft. Such spectra show lines emitted by highly ionized atoms of abundant elements which make up high-temperature plasma contained within coronal magnetic flux tubes. This plasma is probably energized at or a little before the flare impulsive stage, as revealed by bursts of hard X-rays. Temperature and density conditions can be deduced from ratios of line intensities, as well as element abundances under certain conditions. In this paper, several examples of line ratios to deduce these are given. Analysis shows that there is a wide range of electron temperatures - generally from 2 x 106 K to 20 x 106 K - though sometimes even higher. Electron densities of around 1017-1018 m-3 have been derived, higher values occurring at the flare peak or just before, and then declining. The physical conditions of the hot plasma are now precisely enough known from X -ray spectroscopy that models of flares which have been constructed in the past can be constrained. The most profitable direction for research in this area in the near future would in fact appear to be for a much better linking of the findings from X -ray spectra and modelling of plasma in flux tubes to understand better the flare process in general.
Two spin liquid phases in the spatially anisotropic triangular Heisenberg model

The quantum spin-1/2 antiferromagnetic Heisenberg model on a two dimensional triangular lattice geometry with spatial anisotropy is relevant to describe materials like ${\rm Cs_2 Cu Cl_4}$ and organic compounds like {$\kappa$-(ET)$_2$Cu$_2$(CN)$_3$}. The strength of the spatial anisotropy can increase quantum fluctuations and can destabilize the magnetically ordered state leading to non conventional spin liquid phases. In order to understand these intriguing phenomena, quantum Monte Carlo methods are used to study this model system as a function of the anisotropic strength, represented by the ratio $J'/J$ between the intra-chain nearest neighbor coupling $J$ and the inter-chain one $J'$. We have found evidence of two spin liquid regions. The first one is stable for small values of the coupling $J'/J \alt 0.65$, and appears gapless and fractionalized, whereas the second one is a more conventional spin liquid with a small spin gap and is energetically favored in the region $0.65\alt J'/J \alt 0.8$. We have also shown that in both spin liquid phases there is no evidence of broken translation symmetry with dimer or spin-Peirls order or any broken spatial reflection symmetry of the lattice. The various phases are in good agreement with the experimental findings, thus supporting the existence of spin liquid phases in two dimensional quantum spin-1/2 systems.
Participant Recruitment From Low- and Middle-Income Countries for Pivotal Trials of Drugs Approved by the U.S. Food and Drug Administration

BACKGROUND Many participants in clinical trials supporting U.S. Food and Drug Administration (FDA) drug approvals are recruited from outside the United States, including from low- and middle-income countries (LMICs). Where participants are recruited for pivotal trials has implications for ethical research conduct and generalizability. OBJECTIVE To describe LMIC recruitment for pivotal trials of newly approved drugs for cancer, neurologic disease, and cardiovascular disease. DESIGN Cross-sectional analysis. SETTING Pivotal trials of new cancer, cardiovascular, and neurologic drugs approved from 2012 to 2019 matched to ClinicalTrials.gov, FDA records, and publications. MEASUREMENTS Host countries and available per country enrollments were extracted. The primary end point was the proportion of pivotal trials enrolling participants in LMICs. The secondary end point was the proportion of pivotal trial participants contributed by LMICs for each indication area. RESULTS Data were obtained from 66 new drugs and 144 pivotal clinical trials. All cardiovascular approvals (12 drugs, 29 trials) and neurologic approvals (26 drugs, 54 trials) were analyzed, as well as a random sample of cancer approvals (28 of 85 drugs [33%]) matched to their pivotal trials (61 of 210 trials [29%]). Among the trials, 56% in cancer, 79% in cardiovascular disease, and 56% in neurology recruited from an LMIC. For multicountry trials, country-level enrollment figures were not available for 71 trials (55%). For those reporting per country enrollment, the percentage of participants recruited from LMICs was 8% for cancer trials, 36% for cardiovascular trials, and 17% for neurology trials. LIMITATIONS The study was limited to FDA-approved drugs in 3 areas, including a sample of cancer drugs. Pivotal trials of nonapproved drugs or drugs for other indications were not captured. CONCLUSION Most pivotal trials for FDA-approved drugs recruit from LMICs. Publications and FDA documents generally do not provide country-level data on recruitment. PRIMARY FUNDING SOURCE None.
Probing a simplified 
W′
 model of 
R(D(*))
 anomalies using 
b
 tags, 
τ
 leptons, and missing energy

We study the LHC sensitivity to a $W'$ produced via bottom and charm quarks and decaying to $\tau$ flavor leptons in the mass range 200-1000 GeV. We show that the extra $b$ quarks necessitated by the production mechanism can improve the background rejection compared to an inclusive analysis relying solely on $\tau$-tagging and $E_{T}^{\text{miss}}$. We present prospective limits on the couplings and compare them to the best fit to the $R(D^{(\ast)})$ anomalies in $B$ meson decays.
PARSIVEL Snow Observations: A Critical Assessment

The performance of the laser-optical Particle Size Velocity (PARSIVEL) disdrometer is evaluated to determine the characteristics of falling snow. PARSIVEL’s measuring principle is reexamined to detect its limitations and pitfalls when applied to solid precipitation. This study uses snow observations taken during the Canadian Cloudsat/Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Validation Project (C3VP) campaign, when two PARSIVEL instruments were collocated with a single twodimensional disdrometer (2-DVD), which allows more detailed observation of snowflakes. When characterizing the snowflake size, PARSIVEL instruments inherently retrieve only one size parameter, which is approximately equal to the widest horizontal dimension (more accurately with large snowflakes) and that has no microphysical meaning. Unlike for raindrops, the equivolume PARSIVEL diameter—the PARSIVEL output variable—has no physical counterpart for snowflakes. PARSIVEL’s fall velocity measurement may not be accurate for a single snowflake particle. This is due to the internally assumed relationship between horizontal and vertical snow particle dimensions. The uncertainty originates from the shape-related factor, which tends to depart more and more from unity with increasing snowflake sizes and can produce large errors. When averaging over a large number of snowflakes, the correction factor is size dependent with a systematic tendency to an underestimation of the fall speed (but never exceeding 20%). Compared to a collocated 2-DVD for long-lasting events, PARSIVEL seems to overestimate the number of small snowflakes and large particles. The disagreement between PARSIVEL and 2-DVD snow measurements can only be partly ascribed to PARSIVEL intrinsic limitations (border effects and sizing problems), but it has to deal with the difficulties and drawbacks of both instruments in fully characterizing snow properties.
Near-wall physics of a shear-driven three-dimensional turbulent boundary layer with varying crossflow

The near-wall physics and turbulence structure of a planar shear-driven three-dimensional turbulent boundary layer (3DTBL) with varying strengths of crossflow are examined in a specialized facility. Spanwise shear modifies the near-wall turbulence structure. Flow visualization reveals a reduction of mean streak length by as much as 50%, while streak spanwise spacing remains constant. Power spectra of velocity confirm this shift towards higher temporal frequencies, corresponding to decreased streamwise length scales. Particle image velocimetry (PIV) measurements indicate that the spanwise shear increases the number and strength of flow structures that interact in the inner region of the boundary layer. This leads to increased momentum transfer between low- and high-speed fluids, resulting in a thickening of the inner region of the boundary layer. In addition, the organized two-dimensional boundary layer spanwise vorticity layer along the wall surface is disrupted and forms strong vortical structures that are lifted off the wall surface and diffuse into the boundary layer with streamwise distance. The transverse vorticity experiences a similar alteration. Streamwise velocity profiles exhibit an increasing velocity deficit with increased crossflow, consistent with the thickening of the inner region of the boundary layer. Increases in the normal Reynolds stresses are associated with interaction of the secondary flow structures and modifications to vortical structures with increasing three-dimensionality and higher relative wall speed. Significant increases are observed for the most highly sheared cases with translating wall velocities 2.0 and 2.75 times the free-stream velocity. This increase in the normal stresses leads to an increase in the turbulent kinetic energy over the belt surface, which diffuses into the inner region of the boundary layer with streamwise distance. The Reynolds shear stresses also exhibit a significant increase in magnitude over the belt surface owing to enhanced turbulence production over the translating wall section. In general, three-dimensional effects are confined to the area close to the wall and the crossflow increases the interaction of secondary flow structures leading to modifications of the vorticity in the inner region of the boundary layer. These flow distortions disrupt the near-wall streak structure and result in increased levels of turbulent kinetic energy and Reynolds stresses, particularly over the translating wall section.
Identification of Flow Structures on a LP Turbine Blade Due to Periodic Passing Wakes

The paper describes the flow structures on the suction surface of a highly cambered low-pressure turbine (LPT) blade (T106 profile) subjected to periodic convective wakes. A separation bubble on the rear half of the suction side of the blade was found to form under the operating condition due to the highly diffusive boundary layer. Interactions of migrating wakes with this separated boundary layer trigger rollup of the shear layer leading to transition and the appearance of coherent vortices. To characterize the dynamics of these large-scale structures, a proper orthogonal decomposition is pursued on both the fluctuating velocity and the vorticity fields generated by large-eddy simulations (LESs) of wake passing over the LPT blade for a Reynolds number Re = 1.6 X 105. The first two modes clearly depict the rollup of the unstable shear layer and formation of large-scale vortex loops that contain a major fraction of the fluctuation energy. The present LES, at least in a qualitative sense, illustrates the large-scale motions in the outer layer and dynamics of vortical structures in a separated boundary layer excited by external perturbations.