Miracle-dz/UniFAR-FTUs huggingface dataset:
Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions

Generative adversarial networks (GANs) are often billed as"universal distribution learners", but precisely what distributions they can represent and learn is still an open question. Heavy-tailed distributions are prevalent in many different domains such as financial risk-assessment, physics, and epidemiology. We observe that existing GAN architectures do a poor job of matching the asymptotic behavior of heavy-tailed distributions, a problem that we show stems from their construction. Additionally, when faced with the infinite moments and large distances between outlier points that are characteristic of heavy-tailed distributions, common loss functions produce unstable or near-zero gradients. We address these problems with the Pareto GAN. A Pareto GAN leverages extreme value theory and the functional properties of neural networks to learn a distribution that matches the asymptotic behavior of the marginal distributions of the features. We identify issues with standard loss functions and propose the use of alternative metric spaces that enable stable and efficient learning. Finally, we evaluate our proposed approach on a variety of heavy-tailed datasets.
Bending behavior of additively manufactured lattice structures: numerical characterization and experimental validation

Selective Laser Melting (SLM) technology has undergone significant development in the past years providing unique flexibility for the fabrication of complex metamaterials such as octet-truss lattices. However, the microstructure of the final parts can exhibit significant variations due to the high complexity of the manufacturing process. Consequently, the mechanical behavior of these lattices is strongly dependent on the process-induced defects, raising the importance on the incorporation of as-manufactured geometries into the computational structural analysis. This, in turn, challenges the traditional mesh-conforming methods making the computational costs prohibitively large. In the present work, an immersed image-to-analysis framework is applied to efficiently evaluate the bending behavior of AM lattices. To this end, we employ the Finite Cell Method (FCM) to perform a threedimensional numerical analysis of the three-point bending test of a lattice structure and compare the as-designed to as-manufactured effective properties. Furthermore, we undertake a comprehensive study on the applicability of dimensionally reduced beam models to the prediction of the bending behavior of lattice beams and validate classical and strain gradient beam theories applied in combination with the FCM. The numerical findings suggest that the SLM octet-truss lattices exhibit size effects, thus, requiring a flexible framework to incorporate high-order continuum theories.
Seshadri constants and K-stability of Fano manifolds

We give a lower bound of the $\delta$-invariants of ample line bundles in terms of Seshadri constants. As applications, we prove the uniform K-stability of infinitely many families of Fano hypersurfaces of arbitrarily large index, as well as the uniform K-stability of most families of smooth Fano threefolds of Picard number one.
Polyimide as anode electrode material for rechargeable sodium batteries

A polyimide synthesized from 1,4,5,8-naphthalenetetracarboxylic dianhydride (NTCDA) and ethylene diamine (EDA) was evaluated as a new anode material for sodium ion batteries (SIB). The polyimide delivers a discharge specific capacity of 140 mA h g−1 at an average potential of 2 V vs. Na+/Na with an initial coulombic efficiency of 97.6% and exhibits an excellent cycleability with a capacity retention of 90% over 500 cycles. A full SIB with polyimide anode and Na3V2(PO4)3/C cathode and Na4Fe(CN)6/C cathode was proposed.
Spin dynamics in NaFeAs and 
NaFe0.53Cu0.47As
 probed by resonant inelastic x-ray scattering

The parent compounds of iron-based superconductors are magnetically ordered bad metals, with superconductivity appearing near a putative magnetic quantum critical point. The presence of both Hubbard repulsion and Hund’s coupling leads to rich physics in these multiorbital systems, and motivated descriptions of magnetism in terms of itinerant electrons or localized spins. The NaFe 1 − x Cu x As series consists of magnetically ordered bad metal ( x = 0), superconducting ( x ≈ 0 . 02) and magnetically ordered semiconducing / insulating ( x ≈ 0 . 5) phases, providing a platform to investigate the connection between superconductivity, magnetism and electronic correlations. Here we use x-ray absorption spectroscopy and resonant inelastic x-ray scattering to study the valence state of Fe and spin dynamics in two NaFe 1 − x Cu x As compounds ( x = 0 and 0.47). We ﬁnd that magnetism in both compounds arises from Fe 2 + atoms, and exhibits underdamped dispersive spin waves in their respective ordered states. The dispersion of spin excitations in NaFe 0 . 53 Cu 0 . 47 As is consistent with being quasi-one-dimensional. Compared to NaFeAs, the band top of spin waves in NaFe 0 . 53 Cu 0 . 47 As is slightly softened with signiﬁcantly more spectral weight of the spin excitations. Our results indicate the spin dynamics in NaFe 0 . 53 Cu 0 . 47 As arise from localized magnetic moments and suggest the iron-based superconductors are proximate to a correlated insulating state with localized iron moments.
Numerical study of the stabilization of 1D locally coupled wave equations

In this paper, we study the numerical stabilization of a 1D system of two wave equations coupled by velocities with an internal, local control acting on only one equation. In the theoretical part of this study, we distinguished two cases. In the first one, the two waves assumed propagate at the same speed. Under appropriate geometric conditions, we had proved that the energy decays exponentially. While in the second case, when the waves propagate at different speeds, under appropriate geometric conditions, we had proved that the energy decays only at a polynomial rate. In this paper, we confirmed these two results in a 1D numerical approximation. However, when the coupling region does not intersect the damping region, the stabilization of the system is still theoretically an open problem. But, here in both cases, we observed an unpredicted behavior : the energy decays at an exponential rate when the propagation speeds are the same or at a polynomial rate when they are different.
Soil aggregation as influenced by cultural practices in Saskatchewan: II. Brown and Dark Brown Chernozemic soils

The size distribution and stability of aggregates are important characteristics influencing tilth, erosion, water infiltration and nutrient dynamics in soil. We need to understand how soil management influences agregation so that we may make suitable modifications to farming practices to enhance soil stability. Two long-term crop-rotation experiments in southern Saskatchewan were used to examine the effects of management on aggregation. In 1991, soil was collected three times during the summer from the top 5 cm of soil as part of a 25-yr experiment at Swift Current (Orthic Brown Chernozemic soil) and twice as part of a 27-yr experiment at Scott (Orthic Dark Brown Chernozemic soil). The two medium-textured soils were dry-sieved by rotary sieve and then wet-sieved after (i) slow wetting and (ii) fast wetting of the 1–2-mm size fractions. The wind-erodible fraction (<0.84 mm) of soil and geometric mean diameter (GMD) of aggregates varied with antecedent precipitation. Thus, wind-erodible aggregates increased.
SGA: A Robust Algorithm for Partial Recovery of Tree-Structured Graphical Models with Noisy Samples

We consider learning Ising tree models when the observations from the nodes are corrupted by independent but non-identically distributed noise with unknown statistics. Katiyar et al. (2020) showed that although the exact tree structure cannot be recovered, one can recover a partial tree structure; that is, a structure belonging to the equivalence class containing the true tree. This paper presents a systematic improvement of Katiyar et al. (2020). First, we present a novel impossibility result by deriving a bound on the necessary number of samples for partial recovery. Second, we derive a significantly improved sample complexity result in which the dependence on the minimum correlation $\rho_{\min}$ is $\rho_{\min}^{-8}$ instead of $\rho_{\min}^{-24}$. Finally, we propose Symmetrized Geometric Averaging (SGA), a more statistically robust algorithm for partial tree recovery. We provide error exponent analyses and extensive numerical results on a variety of trees to show that the sample complexity of SGA is significantly better than the algorithm of Katiyar et al. (2020). SGA can be readily extended to Gaussian models and is shown via numerical experiments to be similarly superior.
Foundations of a Fast, Data-Driven, Machine-Learned Simulator

We introduce a novel strategy for machine-learning-based fast simulators, which is the ﬁrst that can be trained in an unsupervised manner using observed data samples to learn a predictive model of detector response and other difﬁcult-to-model transformations. Across the physical sciences, a barrier to interpreting observed data is the lack of knowledge of a detector’s imperfect resolution, which transforms and obscures the unobserved latent data. Modeling this detector response is an essential step for statistical inference, but closed-form models are often unavailable or intractable, requiring the use of computationally expensive, ad-hoc numerical simulations. Using particle physics detectors as an illustrative example, we describe a novel strategy for a fast, predictive simulator called Optimal Transport based Unfolding and Simulation (OTUS), which uses a probabilistic autoencoder to learn this transformation directly from observed data, rather than from existing simulations. Unusually, the probabilistic autoencoder’s latent space is physically meaningful, such that the decoder becomes a fast, predictive simulator for a new latent sample, and has the potential to replace Monte Carlo simulators. We provide proof-of-principle results for Z -boson and top-quark decays, but stress that our approach can be widely applied to other physical science ﬁelds.
Linear Regression With Distributed Learning: A Generalization Error Perspective

Distributed learning provides an attractive framework for scaling the learning task by sharing the computational load over multiple nodes in a network. Here, we investigate the performance of distributed learning for large-scale linear regression where the model parameters, i.e., the unknowns, are distributed over the network. We adopt a statistical learning approach. In contrast to works that focus on the performance on the training data, we focus on the generalization error, i.e., the performance on unseen data. We provide high-probability bounds on the generalization error for both isotropic and correlated Gaussian data as well as sub-gaussian data. These results reveal the dependence of the generalization performance on the partitioning of the model over the network. In particular, our results show that the generalization error of the distributed solution can be substantially higher than that of the centralized solution even when the error on the training data is at the same level for both the centralized and distributed approaches. Our numerical results illustrate the performance with both real-world image data as well as synthetic data.
Homogenization of a modified bidomain model involving imperfect transmission

We study, by means of the periodic unfolding technique, the homogenization of a modified bidomain model, which describes the propagation of the action potential in the cardiac electrophysiology. Such a model, allowing the presence of pathological zones in the heart, involves various geometries and non-standard transmission conditions on the interface between the healthy and the damaged part of the cardiac muscle.
An Empirical Study of Flaky Tests in Python

Tests that cause spurious failures without any code changes, i.e., flaky tests, hamper regression testing, increase maintenance costs, may shadow real bugs, and decrease trust in tests. While the prevalence and importance of flakiness is well established, prior research focused on Java projects, thus raising the question of how the findings generalize. In order to provide a better understanding of the role of flakiness in software development beyond Java, we empirically study the prevalence, causes, and degree of flakiness within software written in Python, one of the currently most popular programming languages. For this, we sampled 22 352 open source projects from the popular PyPI package index, and analyzed their 876 186 test cases for flakiness. Our investigation suggests that flakiness is equally prevalent in Python as it is in Java. The reasons, however, are different: Order dependency is a much more dominant problem in Python, causing 59 % of the 7 571 flaky tests in our dataset. Another 28 % were caused by test infrastructure problems, which represent a previously undocumented cause of flakiness. The remaining 13 % can mostly be attributed to the use of network and randomness APIs by the projects, which is indicative of the type of software commonly written in Python. Our data also suggests that finding flaky tests requires more runs than are often done in the literature: A 95 % confidence that a passing test case is not flaky on average would require 170 reruns.
Long time asymptotics of large data in the Kadomtsev-Petviashvili models

We consider the Kadomtsev-Petviashvili equations posed on R. For both equations, we provide sequential in time asymptotic descriptions of solutions, of arbitrarily large data, inside regions not containing lumps or line solitons, and under minimal regularity assumptions. The proof involves the introduction of two new virial identities adapted to the KP dynamics, showing decay in large regions of space, especially in the KP-I case, where no monotonicity property was previously known. Our results do not require the use of the integrability of KP and are adaptable to well-posed perturbations of KP.
Correcting Presbyopia With Autofocusing Liquid-Lens Eyeglasses

Objective: Presbyopia, an age-related ocular disorder, is characterized by the loss in the accommodative abilities of the human eye. Conventional methods of correcting presbyopia divide the field of view, thereby resulting in significant vision impairment. We demonstrate the design, assembly and evaluation of autofocusing eyeglasses for restoration of accommodation without dividing the field of view. Methods: The adaptive optics eyeglasses comprise of two variable-focus liquid lenses, a time-of-flight range sensor and low-power, dual microprocessor control electronics, housed within an ergonomic frame. Subject-specific accommodation deficiency models were utilized to demonstrate high-fidelity accommodative correction. The abilities of this system to reduce accommodation deficiency, its power consumption, response time, optical performance and MTF were evaluated. Results: Average corrected accommodation deficiencies for 5 subjects ranged from -0.021 D to 0.016 D. Each accommodation correction calculation was performed in ∼67 ms which consumed 4.86 mJ of energy. The optical resolution of the system was 10.5 cycles/degree, and featured a restorative accommodative range of 4.3 D. This system was capable of running for up to 19 hours between charge cycles and weighed ∼132 g. Conclusion: The design, assembly and performance of an autofocusing eyeglasses system to restore accommodation in presbyopes has been demonstrated. Significance: The new autofocusing eyeglasses system presented in this article has the potential to restore pre-presbyopic levels of accommodation in subjects diagnosed with presbyopia.
Dynamic pruned-enriched Rosenbluth method

Recently, Grassberger [1997, Phys. Rev. E, 56, 3682] has presented a new algorithm (‘PERM’) for simulating flexible polymer chains. This algorithm has been shown to have a good efficiency and has been used in a wide class of systems. A drawback of this algorithm is that it is static: it is therefore not suited for Markov-chain Monte Carlo simulations. Here, we present a dynamic generalization of the PERM algorithm. For a specific example, we compare the efficiency of DPERM to that of other Monte Carlo algorithms. In the case studied, we find that DPERM is only marginally more efficient. However, this result may depend on the details of the implementation.
Misplaced trust? The relationship between trust, ability to identify commercially influenced results and search engine preference

People have a high level of trust in search engines, especially Google, but only limited knowledge of them, as numerous studies have shown. This leads to the question: To what extent is this trust justified considering the lack of familiarity among users with how search engines work and the business models they are founded on? We assume that trust in Google, search engine preferences and knowledge of result types are interrelated. To examine this assumption, we conducted a representative online survey with n = 2012 German Internet users. We show that users with little search engine knowledge are more likely to trust and use Google than users with more knowledge. A contradiction revealed itself – users strongly trust Google, yet they are unable to adequately evaluate search results. For those users, this may be problematic since it can potentially affect knowledge acquisition. Consequently, there is a need to promote user information literacy to create a more solid foundation for user trust in search engines. The impact of our study lies in emphasising the need for creating appropriate training formats to promote information literacy.
A multi-perspective combined recall and rank framework for Chinese procedure terminology normalization

Medical terminology normalization aims to map the clinical mention to terminologies come from a knowledge base, which plays an important role in analyzing Electronic Health Record(EHR) and many downstream tasks. In this paper, we focus on Chinese procedure terminology normalization. The expression of terminologies are various and one medical mention may be linked to multiple terminologies. Previous study explores some methods such as multi-class classification or learning to rank(LTR) to sort the terminologies by literature and semantic information. However, these information is inadequate to find the right terminologies, particularly in multi-implication cases. In this work, we propose a combined recall and rank framework to solve the above problems. This framework is composed of a multi-task candidate generator(MTCG), a keywords attentive ranker(KAR) and a fusion block(FB). MTCG is utilized to predict the mention implication number and recall candidates with semantic similarity. KAR is based on Bert with a keywords attentive mechanism which focuses on keywords such as procedure sites and procedure types. FB merges the similarity come from MTCG and KAR to sort the terminologies from different perspectives. Detailed experimental analysis shows our proposed framework has a remarkable improvement on both performance and efficiency. ∗Corresponding author: ruanton@mail.ecust. edu.cn 1 ar X iv :2 10 1. 09 10 1v 1 [ cs . C L ] 2 2 Ja n 20 21 Procedure mentions Standard terminologies 经腹右肾上腺嗜铬细胞瘤切除术 (Transabdominal resection of right adrenal pheochromocytoma)
Automatic cerebral vessel extraction in TOF-MRA using deep learning

Deep learning approaches may help radiologists in the early diagnosis and timely treatment of cerebrovascular diseases. Accurate cerebral vessel segmentation of Time-of-Flight Magnetic Resonance Angiographs (TOFMRAs) is an essential step in this process. This study investigates deep learning approaches for automatic, fast and accurate cerebrovascular segmentation for TOF-MRAs. The performance of several data augmentation and selection methods for training a 2D and 3D U-Net for vessel segmentation was investigated in five experiments: a) without augmentation, b) Gaussian blur, c) rotation and flipping, d) Gaussian blur, rotation and flipping and e) different input patch sizes. All experiments were performed by patch-training both a 2D and 3D U-Net and predicted on a test set of MRAs. Ground truth was manually defined using an interactive threshold and region growing method. The performance was evaluated using the Dice Similarity Coefficient (DSC), Modified Hausdor Distance and Volumetric Similarity, between the predicted images and the interactively defined ground truth. The segmentation performance of all trained networks on the test set was found to be good, with DSC scores ranging from 0.72 to 0.83. Both the 2D and 3D U-Net had the best segmentation performance with Gaussian blur, rotation and flipping compared to other experiments without augmentation or only one of those augmentation techniques. Additionally, training on larger patches or slices gave optimal segmentation results. In conclusion, vessel segmentation can be optimally performed on TOF-MRAs using a trained 3D U-Net on larger patches, where data augmentation including Gaussian blur, rotation and flipping was performed on the training data.
Laryngopharyngeal Reflux Disease: Outcome of Patients After Treatment in Otolaryngology Clinics

Introduction Laryngopharyngeal reflux (LPR) is a different entity from gastroesophageal reflux disease (GERD). Patients with LPR usually present with a variety of symptoms such as hoarseness, voice fatigue, burning sensation in the throat, persistent cough, sore throat, dysphagia, a sensation of a lump in the throat, and chronic throat clearing. The management of LPR is based on medications (proton pump inhibitors) along with lifestyle and dietary modifications. It has been suggested that the Reflux Symptom Index (RSI) and Reflux Finding Score (RFS) are useful parameters to assess patients with LPR. The aim of this study is to assess the subjective and objective benefits of RFS and RSI for diagnosing and management of LPR in the tertiary care center and to find the difference in RSI and RSI scoring with respect to gender. Methods A prospective study was performed and 102 patients were included according to inclusion criteria. RFS and RSI questionnaires were filled on the first visit of patients and then treatment with proton pump inhibitors was started along with lifestyle modification instructions. Questionnaires were filled after four weeks and then 12weeks post-treatment. Repeated measure analysis of variance (ANOVA) was performed to compare the mean RFS and RSI from baseline to the end of treatment. The post hoc analysis was done using the Bonferroni test of multiple comparisons. An independent sample t-test was also used to compare the mean RFS and RSI between genders. P-values less than 0.05 were considered statistically significant Results RFS and RSI were found to be significantly decreased post-treatment after four weeks and 12 weeks post-treatment (p-value- <0.01). Eight point eight percent (8.8%) side effects were observed in the study, the change in quality of life after a three-month treatment was significantly improved among 62.7% patients, and 75.5% did lifestyle modifications. In the mean comparison of RFS and RSI with respect to gender, it was observed that the mean RFS of females samples after one month and three months of treatment were significantly less as compared to male samples, p<0.01. There was no significant mean difference observed for RSI after one month and three months of treatment with respect to gender (p>0.05). Conclusion RFS and RSI are convenient and helpful for diagnosing LPR, and they can be easily implemented in ear, nose, throat (ENT) clinics for the subjective and objective assessment of LPR. Females showed greater improvement on laryngoscopy findings (RFS scores) post-treatment as compared to males.