Miracle-dz/UniFAR-FTUs huggingface dataset:
Acceleration and Expansion of a Coronal Mass Ejection in the High Corona: Role of Magnetic Reconnection

The important role played by magnetic reconnection in the early acceleration of coronal mass ejections (CMEs) has been widely discussed. However, as CMEs may have expansion speeds comparable to their propagation speeds in the corona, it is not clear whether and how reconnection contributes to the true acceleration and expansion separately. To address this question, we analyze the dynamics of a moderately fast CME on 2013 February 27, associated with a continuous acceleration of its front into the high corona, even though its speed had reached ∼700 km s−1, which is faster than the solar wind. The apparent acceleration of the CME is found to be due to its expansion in the radial direction. The true acceleration of the CME, i.e., the acceleration of its center, is then estimated by taking into account the expected deceleration caused by the drag force of the solar wind acting on a fast CME. It is found that the true acceleration and the radial expansion have similar magnitudes. We find that magnetic reconnection occurs after the eruption of the CME and continues during its propagation in the high corona, which contributes to its dynamic evolution. Comparison between the apparent acceleration related to the expansion and the true acceleration that compensates the drag shows that, for this case, magnetic reconnection contributes almost equally to the expansion and to the acceleration of the CME. The consequences of these measurements for the evolution of CMEs as they transit from the corona to the heliosphere are discussed.
The open Haldane–Shastry chain: thermodynamics and criticality

We study the thermodynamics and criticality of the su(m|n) Haldane–Shastry chain of BC N type with a general chemical potential term. We first derive a complete description of the spectrum of this model in terms of BC N -type motifs, from which we deduce a representation for the partition function as the trace of a product of site-dependent transfer matrices. In the thermodynamic limit, this formula yields a simple expression for the free energy per spin in terms of the Perron–Frobenius eigenvalue of the continuum limit of the transfer matrix. Evaluating this eigenvalue we obtain closed-form expressions for the thermodynamic functions of the chains with m, n ⩽ 2. Using the motif-based description of the spectrum derived here, we study in detail the ground state of these models and their low energy excitations. In this way we identify the critical intervals in chemical potential space and compute their corresponding Fermi velocities. By contrast with previously studied models of this type, we find in some cases two types of low energy excitations with linear energy-quasimomentum relation. Finally, we determine the central charge of all the critical phases by analyzing the low-temperature behavior of the expression for the free energy per spin.
Separable Self-attention for Mobile Vision Transformers

Mobile vision transformers (MobileViT) can achieve state-of-the-art performance across several mobile vision tasks, including classification and detection. Though these models have fewer parameters, they have high latency as compared to convolutional neural network-based models. The main efficiency bottleneck in MobileViT is the multi-headed self-attention (MHA) in transformers, which requires $O(k^2)$ time complexity with respect to the number of tokens (or patches) $k$. Moreover, MHA requires costly operations (e.g., batch-wise matrix multiplication) for computing self-attention, impacting latency on resource-constrained devices. This paper introduces a separable self-attention method with linear complexity, i.e. $O(k)$. A simple yet effective characteristic of the proposed method is that it uses element-wise operations for computing self-attention, making it a good choice for resource-constrained devices. The improved model, MobileViTv2, is state-of-the-art on several mobile vision tasks, including ImageNet object classification and MS-COCO object detection. With about three million parameters, MobileViTv2 achieves a top-1 accuracy of 75.6% on the ImageNet dataset, outperforming MobileViT by about 1% while running $3.2\times$ faster on a mobile device. Our source code is available at: \url{https://github.com/apple/ml-cvnets}
A symmetrized parametric finite element method for anisotropic surface diffusion ii. three dimensions

For the evolution of a closed surface under anisotropic surface diﬀusion with a general anisotropic surface energy γ ( n ) in three dimensions (3D), where n is the unit outward normal vector, by introducing a novel symmetric positive deﬁnite surface energy matrix Z k ( n ) depending on a stabilizing function k ( n ) and the Cahn-Hoﬀman ξ -vector, we present a new symmetrized variational formulation for anisotropic surface diﬀusion with weakly or strongly anisotropic surface energy, which preserves two important structures including volume conservation and energy dissipation. Then we propose a structural-preserving parametric ﬁnite element method (SP-PFEM) to discretize the symmetrized variational problem, which preserves the volume in the discretized level. Under a relatively mild and simple condition on γ ( n ), we show that SP-PFEM is unconditionally energy- stable for almost all anisotropic surface energies γ ( n ) arising in practical applications. Extensive numerical results are reported to demonstrate the eﬃciency and accuracy as well as energy dissipation of the proposed SP-PFEM for solving anisotropic surface diﬀusion in 3D.
Filter-regular sequences and mixed multiplicities

Let $S$ be a finitely generated standard multigraded algebra over an Artinian local ring $A$; $M$ a finitely generated multigraded $S$-module. This paper answers to the question when mixed multiplicities of $M$ are positive and characterizes them in terms of lengths of $A$-modules. As an application, we get results on mixed multiplicities of ideals (Proposition 4.4 and Theorem 4.5), and recover the results of Risler and Teissier [Te](see Remark 4.8), Trung and Verma [TV](see Remark 4.6).
Perspectives of Persons With Type 2 Diabetes Toward Diabetes Self-Management: A Qualitative Study

Background. The global increase in type 2 diabetes (T2D) poses a significant public health challenge worldwide. Due to the chronic and complex nature of the disease, management does not depend on pharmacotherapy alone; rather, a comprehensive medical and self-management plan is required. Diabetes Self-Management Education programs should address diabetes-specific behaviors and be grounded in patients’ perceptions of their disease and its consequences. Aim. The study aims to understand perceptions of diabetes self-management (DSM) among persons of different nationalities with T2D living in Qatar. Method. A phenomenological qualitative study was implemented using four focus group interviews using Triandis Interpersonal Behavior Model to understand factors influencing DSM perceptions and behaviors. Therefore, deductive content analysis was used, also inductive content analysis used to find new emerging themes. Twenty-nine participants with T2D of 12 different nationalities were recruited. Results. Content analyses revealed that participants were mainly motivated to manage their diabetes by fear of future health complications—and the resulting impact on their families—triggered by witnessing these complications on other family members and friends. Factors that influenced DSM included cultural factors such as food content and holidays’ customs, weather conditions, psychological distress, and social factors such as lack of family support. Furthermore, access to healthy food, number of working hours and time, and cost of DSM supplies were found to be among the barriers to DSM. Conclusion. Understanding and considering patients’ perspectives is a core component of culturally competent and evidence-based DSM programs, and it should be considered in any DSM program.
Semileptonic Decays of Heavy Omega Baryons in a Quark Model

The semileptonic decays of {Omega}{sub c} and {Omega}{sub b} are treated in the framework of a constituent quark model developed in a previous paper on the semileptonic decays of heavy {Lambda} baryons. Analytic results for the form factors for the decays to ground states and a number of excited states are evaluated. For {Omega}{sub b} to {Omega}{sub c} the form factors obtained are shown to satisfy the relations predicted at leading order in the heavy-quark effective theory at the non-recoil point. A modified fit of nonrelativistic and semirelativistic Hamiltonians generates configuration-mixed baryon wave functions from the known masses and the measured {Lambda}{sub c}{sup +} {yields} {Lambda}e{sup +}{nu} rate, with wave functions expanded in both harmonic oscillator and Sturmian bases. Decay rates of {Omega}{sub b} to pairs of ground and excited {Omega}{sub c} states related by heavy-quark symmetry calculated using these configuration-mixed wave functions are in the ratios expected from heavy-quark effective theory, to a good approximation. Our predictions for the semileptonic elastic branching fraction of {Omega}{sub Q} vary minimally within the models we use. We obtain an average value of (84 {+-} 2%) for the fraction of {Omega}{sub c} {yields} {Xi}{sup (*)} decays to ground states, and 91% for themore » fraction of {Omega}{sub c} {yields} {Omega}{sup (*)} decays to the ground state {Omega}. The elastic fraction of {Omega}{sub b} {yields} {Omega}{sub c} ranges from about 50% calculated with the two harmonic-oscillator models, to about 67% calculated with the two Sturmian models. « less
Surgical Outcomes, Trends, and Risk Factors of Distal Triceps Repairs

Background: Distal triceps ruptures are rare, and complete ruptures are commonly treated with surgery. Studies of patients in small cohorts with distal triceps tear have reported outcomes and risk factors; however, large-scale data are scant. This study seeks to determine current trends, outcomes, and risk factors of distal triceps tears. Methods: Within a large insurance claims database, distal triceps repair patients were identified through Current Procedural Terminology coding with concomitant distal triceps International Classification of Diseases, 9th Revision/10th Revision diagnosis codes and 1-year active status before and after surgery. Demographics, total costs, 90-day complications, and revision rates within 1 year of index surgery were analyzed. Logistic regression was performed for revision and complication rates using sex, age, and comorbidities (anabolic steroid use, diabetes, ischemic heart disease, tobacco use, rheumatoid arthritis, and chronic kidney disease). Results: A total of 8143 patients were included in the cohort. Male patients and patients aged 40 to 59 years comprised most of the study population. The postoperative complication rate was 5.8%, and the 1-year revision rate was 2.6%. Male sex, age >60 years, ischemic heart disease, rheumatoid arthritis, and chronic kidney disease were statistically significant risk factors for higher 90-day complication rates. Anabolic steroid use significantly increased the risk of surgical revision. Conclusions: Distal triceps repairs in this large cohort study occur most frequently in men aged 40 to 59 years. Complications are generally low, with age >60 years, male sex, ischemic heart disease, rheumatoid arthritis, and chronic kidney disease as risk factors for 90-day complications and prior anabolic steroid use as a risk factor for 1-year revision surgery. This information can help to improve education and expectations of this procedure.
Equivariant Fixed Point Theory

We reexamine equivariant generalizations of the Lefschetz number and Reidemeister trace using categorical traces. This gives simple, conceptual descriptions of the invariants as well as direct comparisons to previously defined generalizations. These comparisons are illuminating applications of the additivity and multiplicativity of the categorical trace.
Linkage of calling and burnout among Chinese airline pilots: the role of psychological capital and organizational identification

ABSTRACT Objectives. Burnout of airline pilots has an extremely adverse impact on their physical and mental state, their work efficiency and safe operation of airlines, and is thus an important issue in occupational safety and health. Therefore, it is crucial to identify factors that may reduce burnout among airline pilots. Drawing on conservation of resources theory, the present study investigates the relationships between calling, psychological capital, organizational identification and burnout in order to understand the mechanisms underlying pilot burnout. Method. A cross-sectional survey was conducted to measure calling, psychological capital, organizational identification and burnout among a sample consisting of 242 Chinese commercial airline pilots (age: M 31.60 years, SD 6.44). Correlation analysis, mediation analysis and moderated analysis were conducted. Results. The results showed that calling has a direct and negative effect on pilot burnout, and an indirect effect on burnout through influencing psychological capital. Moreover, organizational identification was found to positively moderate the negative relationship between calling and pilot burnout. Conclusion. This study contributes to deepening theoretical research on burnout, and provides empirical evidence for effective intervention in the burnout of airline pilots, which is beneficial to reduce pilot human error and ensure flight safety. Future research directions are also discussed.
Self-supervised Re-renderable Facial Albedo Reconstruction from Single Image

Reconstructing high-fidelity 3D facial texture from a single image is a quite challenging task due to the lack of complete face information and the domain gap between the 3D face and 2D image. Further, obtaining re-renderable 3D faces has become a strongly desired property in many applications, where the term 're-renderable' demands the facial texture to be spatially complete and disentangled with environmental illumination. In this paper, we propose a new self-supervised deep learning framework for reconstructing high-quality and re-renderable facial albedos from single-view images in-the-wild. Our main idea is to first utilize a prior generation module based on the 3DMM proxy model to produce an unwrapped texture and a globally parameterized prior albedo. Then we apply a detail refinement module to synthesize the final texture with both high-frequency details and completeness. To further make facial textures disentangled with illumination, we propose a novel detailed illumination representation which is reconstructed with the detailed albedo together. We also design several novel regularization losses on both the albedo and illumination maps to facilitate the disentanglement of these two factors. Finally, by leveraging a differentiable renderer, each face attribute can be jointly trained in a self-supervised manner without requiring ground-truth facial reflectance. Extensive comparisons and ablation studies on challenging datasets demonstrate that our framework outperforms state-of-the-art approaches.
A new proof of long-range scattering for critical nonlinear Schrödinger equations

We present a new proof of global existence and long range scattering, from small initial data, for the one-dimensional cubic gauge invariant nonlinear Schr\"odinger equation, and for Hartree equations in dimension $n \geq 2$. The proof relies on an analysis in Fourier space, related to the recent works of Germain, Masmoudi and Shatah on space-time resonances. An interesting feature of our approach is that we are able to identify the long range phase correction term through a very natural stationary phase argument.
A Survey on Deep Learning Based Channel Estimation in Doubly Dispersive Environments

Wireless communications systems are impacted by multi-path fading and Doppler shift in dynamic environments, where the channel becomes doubly-dispersive and its estimation becomes an arduous task. Only a few pilots are used for channel estimation in conventional approaches to preserve high data rate transmission. Consequently, such estimators experience a significant performance degradation in high mobility scenarios. Recently, deep learning has been employed for doubly-dispersive channel estimation due to its low-complexity, robustness, and good generalization ability. Against this backdrop, the current paper presents a comprehensive survey on channel estimation techniques based on deep learning by deeply investigating different methods. The study also provides extensive experimental simulations followed by a computational complexity analysis. After considering different parameters such as modulation order, mobility, frame length, and deep learning architecture, the performance of the studied estimators is evaluated in several mobility scenarios. In addition, the source codes are made available online in order to make the results reproducible.
Additional Fibonacci-Bernoulli relations

We continue our study on relationships between Fibonacci (Lucas) numbers and Bernoulli numbers and polynomials. The derivations of our results are based on functional equations for the respective generating functions, which in our case are combinations of hyperbolic functions. Special cases and some corollaries will highlight interesting aspects of our findings.
Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches

Stochastic variance reduction has proven effective at accelerating ﬁrst-order algorithms for solving convex ﬁnite-sum optimization tasks such as empirical risk minimization. Incorporating additional second-order information has proven helpful in further improving the performance of these ﬁrst-order methods. However, comparatively little is known about the beneﬁts of using variance reduction to accelerate popular stochastic second-order methods such as Subsampled Newton. To address this, we propose Stochastic Variance-Reduced Newton (SVRN), a ﬁnite-sum minimization algorithm which enjoys all the beneﬁts of second-order methods: simple unit step size, easily parallelizable large-batch operations, and fast local convergence, while at the same time taking advantage of variance reduction to achieve improved convergence rates (per data pass) for smooth and strongly convex problems. We show that SVRN can accelerate many stochastic second-order methods (such as Subsampled Newton) as well as iterative least squares solvers (such as Iterative Hessian Sketch), and it compares favorably to popular ﬁrst-order methods with variance reduction. as well as easily scalable large-batch operations. Our empirical results show that SVRN can be effectively used to accelerate stochastic second-order methods, such as Subsampled Newton, when performing convex ﬁnite-sum minimization.
Which models are innately best at uncertainty estimation?

Due to the comprehensive nature of this paper, it has been updated and split into two separate papers:"A Framework For Benchmarking Class-out-of-distribution Detection And Its Application To ImageNet"and"What Can We Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers". We recommend reading them instead. Deep neural networks must be equipped with an uncertainty estimation mechanism when deployed for risk-sensitive tasks. This paper studies the relationship between deep architectures and their training regimes with their corresponding selective prediction and uncertainty estimation performance. We consider both in-distribution uncertainties and class-out-of-distribution ones. Moreover, we consider some of the most popular estimation performance metrics previously proposed including AUROC, ECE, AURC, and coverage for selective accuracy constraint. We present a novel and comprehensive study of selective prediction and the uncertainty estimation performance of 484 existing pretrained deep ImageNet classifiers that are available at popular repositories. We identify numerous and previously unknown factors that affect uncertainty estimation and examine the relationships between the different metrics. We find that distillation-based training regimes consistently yield better uncertainty estimations than other training schemes such as vanilla training, pretraining on a larger dataset and adversarial training. We also provide strong empirical evidence showing that ViT is by far the most superior architecture in terms of uncertainty estimation performance, judging by any aspect, in both in-distribution and class-out-of-distribution scenarios.
Diffusion-GAN: Training GANs with Diffusion

Generative adversarial networks (GANs) are challenging to train stably, and a promising remedy of injecting instance noise into the discriminator input has not been very effective in practice. In this paper, we propose Diffusion-GAN, a novel GAN framework that leverages a forward diffusion chain to generate Gaussian-mixture distributed instance noise. Diffusion-GAN consists of three components, including an adaptive diffusion process, a diffusion timestep-dependent discriminator, and a generator. Both the observed and generated data are diffused by the same adaptive diffusion process. At each diffusion timestep, there is a different noise-to-data ratio and the timestep-dependent discriminator learns to distinguish the diffused real data from the diffused generated data. The generator learns from the discriminator's feedback by backpropagating through the forward diffusion chain, whose length is adaptively adjusted to balance the noise and data levels. We theoretically show that the discriminator's timestep-dependent strategy gives consistent and helpful guidance to the generator, enabling it to match the true data distribution. We demonstrate the advantages of Diffusion-GAN over strong GAN baselines on various datasets, showing that it can produce more realistic images with higher stability and data efficiency than state-of-the-art GANs.
Robustness Evaluation and Adversarial Training of an Instance Segmentation Model

To evaluate the robustness of non-classifier models, we propose probabilistic local equivalence, based on the notion of randomized smoothing, as a way to quantitatively evaluate the robustness of an arbitrary function. In addition, to understand the effect of adversarial training on non-classifiers and to investigate the level of robustness that can be obtained without degrading performance on the training distribution, we apply Fast is Better than Free adversarial training together with the TRADES robust loss to the training of an instance segmentation network. In this direction, we were able to achieve a symmetric best dice score of 0.85 on the TuSimple lane detection challenge, outperforming the standardly-trained network's score of 0.82. Additionally, we were able to obtain an F-measure of 0.49 on manipulated inputs, in contrast to the standardly-trained network's score of 0. We show that probabilisitic local equivalence is able to successfully distinguish between standardly-trained and adversarially-trained models, providing another view of the improved robustness of the adversarially-trained models.
Variational regularization with oversmoothing penalty term in Banach spaces

In the present work, we discuss variational regularization for ill-posed nonlinear problems with focus on an oversmoothing penalty term. This means in our model that the searched-for solution of the considered nonlinear operator equation does not belong to the domain of definition of the penalty functional. In the past years, such variational regularization has been investigated comprehensively in Hilbert scales. Our present study tries to continue and to extend those investigations to Banach scales. This new study includes convergence rates results for a priori choices of the regularization parameter, both for H\"older-type smoothness and low order-type smoothness. The necessary tools for low order smoothness in the Banach space setting are provided.