Miracle-dz/UniFAR-FTUs huggingface dataset:
A Navier-Stokes Solver for Compressible Turbulent Flows on Quadtree and
 Octree Based Cartesian Grids

Cartesian grids represent a special extent in unstructured grid literature. They employ chiefly created algorithms to produce automatic meshing while simulating flows around complex geometries without considering shape of the bodies. In this article, firstly, it is intended to produce regionally developed Cartesian meshes for two dimensional and three dimensional, disordered geometries to provide solutions hierarchically. Secondly, accurate results for turbulent flows are developed by finite volume solver (GeULER-NaTURe) with both geometric and solution adaptations. As a result, a “hands-off” flow solver based on Cartesian grids as the preprocessor is performed using object-oriented programming. Spalart-Allmaras turbulence model added Reynolds Averaged Navier Stokes equations are solved for the flows around airfoils and wings. The solutions are validated and verified by one two dimensional and one three dimensional turbulent flow common test cases in literature. Both case studies disclose the efficaciousness of the developed codes and qualify in convergence and accuracy.
Sperm morphology and comparison morphometry measurements of two species of bats, Molossus molossus and Molossops temminckii (Chiroptera: Molossidae)

Sperm morphology is considered a species‐specific character and has been used as a tool in the classification of numerous mammalian taxa. Neotropical bats have been poorly studied, and important aspects on sperm morphology have not been elucidated. The aim of the present study was to describe and compare the sperm morphology and morphometry of Molossus molossus and Molossops temminckii. A total of 14 adults specimens were analyzed from the Colección Mamíferos Lillo, Universidad Nacional de Tucumán: five M. molossus and nine M. temminckii. The epididymis were extracted and macerated in Farmer's solution, followed by a coloration with different stains. To carry out the description and morphometric analysis, microphotographs were taken under an optical, epifluorescence, and scanning electron microscope (SEM). A total of 50 sperm from each individual were measured for morphometric analysis. The length and width of the head, midpiece and tail were taken as variables. Sperm from M. molossus and M. temminckii were practically identical, both morphologically and morphometrically. In both species, a distal bulge was observed at the end of the intermediate piece in a percentage greater than 85%. The main characteristics shared between the species were: presence of acrosomal blebs in the upper half of the head of the spermatozoa; cephalic equatorial segment with filiform ornamentations; intermembrane space of head apex wedge‐shaped; helical middle piece and annulus at the end of middle piece. In the present study, SEM allowed us to visualize structures, such as acrosomal vesicles, that were not detected with other types of microscopy.
Semantic-embedded Similarity Prototype for Scene Recognition

Due to the high inter-class similarity caused by the complex composition and the co-existing objects across scenes, numerous studies have explored object semantic knowledge within scenes to improve scene recognition. However, a resulting challenge emerges as object information extraction techniques require heavy computational costs, thereby burdening the network considerably. This limitation often renders object-assisted approaches incompatible with edge devices in practical deployment. In contrast, this paper proposes a semantic knowledge-based similarity prototype, which can help the scene recognition network achieve superior accuracy without increasing the computational cost in practice. It is simple and can be plug-and-played into existing pipelines. More specifically, a statistical strategy is introduced to depict semantic knowledge in scenes as class-level semantic representations. These representations are used to explore correlations between scene classes, ultimately constructing a similarity prototype. Furthermore, we propose to leverage the similarity prototype to support network training from the perspective of Gradient Label Softening and Batch-level Contrastive Loss, respectively. Comprehensive evaluations on multiple benchmarks show that our similarity prototype enhances the performance of existing networks, all while avoiding any additional computational burden in practical deployments. Code and the statistical similarity prototype will be available soon.
Assessing Student Errors in Experimentation Using Artificial Intelligence and Large Language Models: A Comparative Study with Human Raters

Identifying logical errors in complex, incomplete or even contradictory and overall heterogeneous data like students' experimentation protocols is challenging. Recognizing the limitations of current evaluation methods, we investigate the potential of Large Language Models (LLMs) for automatically identifying student errors and streamlining teacher assessments. Our aim is to provide a foundation for productive, personalized feedback. Using a dataset of 65 student protocols, an Artificial Intelligence (AI) system based on the GPT-3.5 and GPT-4 series was developed and tested against human raters. Our results indicate varying levels of accuracy in error detection between the AI system and human raters. The AI system can accurately identify many fundamental student errors, for instance, the AI system identifies when a student is focusing the hypothesis not on the dependent variable but solely on an expected observation (acc. = 0.90), when a student modifies the trials in an ongoing investigation (acc. = 1), and whether a student is conducting valid test trials (acc. = 0.82) reliably. The identification of other, usually more complex errors, like whether a student conducts a valid control trial (acc. = .60), poses a greater challenge. This research explores not only the utility of AI in educational settings, but also contributes to the understanding of the capabilities of LLMs in error detection in inquiry-based learning like experimentation.
Conforming Finite Element Function Spaces in Four Dimensions, Part 1: Foundational Principles and the Tesseract

The stability, robustness, accuracy, and efficiency of space-time finite element methods crucially depend on the choice of approximation spaces for test and trial functions. This is especially true for high-order, mixed finite element methods which often must satisfy an inf-sup condition in order to ensure stability. With this in mind, the primary objective of this paper and a companion paper is to provide a wide range of explicitly stated, conforming, finite element spaces in four-dimensions. In this paper, we construct explicit high-order conforming finite elements on 4-cubes (tesseracts); our construction uses tools from the recently developed `Finite Element Exterior Calculus'. With a focus on practical implementation, we provide details including Piola-type transformations, and explicit expressions for the volumetric, facet, face, edge, and vertex degrees of freedom. In addition, we establish important theoretical properties, such as the exactness of the finite element sequences, and the unisolvence of the degrees of freedom.
Late Quaternary history of the Owens Valley fault zone, eastern California, and surface rupture associated with the 1872 earthquake

The right-lateral Owens Valley fault zone (OVFZ) in eastern California extends north about 100 km from near the northwest shore of Owens Lake to beyond Big Pine. It passes through Lone Pine near the eastern base of the Alabama Hills and follows the floor of Owens Valley northward to the Poverty Hills, where it steps 3 km to the left and continues northwest across Crater Mountain and through Big Pine. Data from one site suggest an average net slip rate for the OVFZ of 1.5 [+-] 1 mm/yr for the past 300 ky. Several other sites yield an average Holocene net slip rate of 2 [+-] 1 mm/yr. The OVFZ apparently has experienced three major Holocene earthquakes. The minimum average recurrence interval is 5,000 years at the subsidiary Lone Pine fault, whereas it is 3,300 to 5,000 years elsewhere along the OVFZ. The prehistoric earthquakes are not dated, so an average recurrence interval need not apply. However, roughly equal (characteristic) displacement apparently happened during each Holocene earthquake. The Owens Valley fault zone accommodates some of the relative motion (dextral shear) between the North American and Pacific plates along a discrete structure. This shear occurs in the Walker Lane belt ofmore » normal and strike-slip faults within the mainly extensional Basin and Range Province. In Owens Valley displacement is partitioned between the OVFZ and the nearby, subparallel, and purely normal range-front faults of the Sierra Nevada. Compared to the OVFZ, these range-front normal faults are very discontinuous and have smaller Holocene slip rates of 0.1 to 0.8 mm/yr, dip slip. Contemporary activity on adjacent faults of such contrasting styles suggests large temporal fluctuations in the relative magnitudes of the maximum and intermediate principal stresses while the extension direction remains consistently east-west. « less
The Asymptotic Properties of the One-Sample Spatial Rank Methods

For a set of $p$-variate data points $\boldsymbol y_1,\ldots,\boldsymbol y_n$, there are several versions of multivariate median and related multivariate sign test proposed and studied in the literature. In this paper we consider the asymptotic properties of the multivariate extension of the Hodges-Lehmann (HL) estimator, the spatial HL-estimator, and the related test statistic. The asymptotic behavior of the spatial HL-estimator and the related test statistic when $n$ tends to infinity are collected, reviewed, and proved, some for the first time though being used already for a longer time. We also derive the limiting behavior of the HL-estimator when both the sample size $n$ and the dimension $p$ tend to infinity.
Black Hole Spin Measurements in LMC X-1 and Cyg X-1 Are Highly Model Dependent

The black hole spin parameter, a *, was measured to be close to its maximum value of 1 in many accreting X-ray binaries. In particular, a * ≳ 0.9 was found in a number of studies of LMC X-1. These measurements were claimed to take into account both statistical and systematic uncertainties. We perform new measurements using a recent simultaneous observation by NICER and NuSTAR, providing a data set of high quality. We use the disk continuum method together with improved models for coronal Comptonization. With the standard relativistic disk model and optically thin Comptonization, we obtain values of a * similar to those obtained before. We then consider modifications to the standard model. Using a color correction of 2, we find a * ≈ 0.64–0.84. We then consider disks with dissipation in surface layers. To account for that, we assume the standard disk is covered by a warm and optically thick Comptonizing layer. Our model with the lowest χ 2 then yields a * ≈ 0.40 − 0.32 + 0.41 . In order to test the presence of such effects in other sources, we also study an X-ray observation of Cyg X-1 by Suzaku in the soft state. We confirm the previous findings of a * > 0.99 using the standard model, but then we find a weakly constrained a * ≈ 0.82 − 0.74 + 0.16 when including an optically thick Comptonizing layer. We conclude that determinations of the spin using the continuum method can be highly sensitive to the assumptions about the disk structure.
Augmented Negative Sampling for Collaborative Filtering

Negative sampling is essential for implicit-feedback-based collaborative filtering, which is used to constitute negative signals from massive unlabeled data to guide supervised learning. The state-of-the-art idea is to utilize hard negative samples that carry more useful information to form a better decision boundary. To balance efficiency and effectiveness, the vast majority of existing methods follow the two-pass approach, in which the first pass samples a fixed number of unobserved items by a simple static distribution and then the second pass selects the final negative items using a more sophisticated negative sampling strategy. However, selecting negative samples from the original items in a dataset is inherently restricted due to the limited available choices, and thus may not be able to contrast positive samples well. In this paper, we confirm this observation via carefully designed experiments and introduce two major limitations of existing solutions: ambiguous trap and information discrimination. Our response to such limitations is to introduce “augmented” negative samples that may not exist in the original dataset. This direction renders a substantial technical challenge because constructing unconstrained negative samples may introduce excessive noise that eventually distorts the decision boundary. To this end, we introduce a novel generic augmented negative sampling (ANS) paradigm and provide a concrete instantiation. First, we disentangle hard and easy factors of negative items. Next, we generate new candidate negative samples by augmenting only the easy factors in a regulated manner: the direction and magnitude of the augmentation are carefully calibrated. Finally, we design an advanced negative sampling strategy to identify the final augmented negative samples, which considers not only the score function used in existing methods but also a new metric called augmentation gain. Extensive experiments on real-world datasets demonstrate that our method significantly outperforms state-of-the-art baselines. Our code is publicly available at https://github.com/Asa9aoTK/ANS-Recbole.
A matrix-free parallel two-level deflation preconditioner for the two-dimensional Helmholtz problems

We propose a matrix-free parallel two-level-deflation preconditioner combined with the Complex Shifted Laplacian preconditioner(CSLP) for the two-dimensional Helmholtz problems. The Helmholtz equation is widely studied in seismic exploration, antennas, and medical imaging. It is one of the hardest problems to solve both in terms of accuracy and convergence, due to scalability issues of the numerical solvers. Motivated by the observation that for large wavenumbers, the eigenvalues of the CSLP-preconditioned system shift towards zero, deflation with multigrid vectors, and further high-order vectors were incorporated to obtain wave-number-independent convergence. For large-scale applications, high-performance parallel scalable methods are also indispensable. In our method, we consider the preconditioned Krylov subspace methods for solving the linear system obtained from finite-difference discretization. The CSLP preconditioner is approximated by one parallel geometric multigrid V-cycle. For the two-level deflation, the matrix-free Galerkin coarsening as well as high-order re-discretization approaches on the coarse grid are studied. The results of matrix-vector multiplications in Krylov subspace methods and the interpolation/restriction operators are implemented based on the finite-difference grids without constructing any coefficient matrix. These adjustments lead to direct improvements in terms of memory consumption. Numerical experiments of model problems show that wavenumber independence has been obtained for medium wavenumbers. The matrix-free parallel framework shows satisfactory weak and strong parallel scalability.
Potential Atmospheric Compositions of TRAPPIST-1 c Constrained by JWST/MIRI Observations at 15 μm

The first James Webb Space Telescope observations of TRAPPIST-1 c showed a secondary eclipse depth of 421 ± 94 ppm at 15 μm, which is consistent with a bare rock surface or a thin, O2-dominated, low-CO2 atmosphere. Here we further explore potential atmospheres for TRAPPIST-1 c by comparing the observed secondary eclipse depth to synthetic spectra of a broader range of plausible environments. To self-consistently incorporate the impact of photochemistry and atmospheric composition on atmospheric thermal structure and predicted eclipse depth, we use a two-column climate model coupled to a photochemical model and simulate O2-dominated, Venus-like, and steam atmospheres. We find that a broader suite of plausible atmospheric compositions are also consistent with the data. For lower-pressure atmospheres (0.1 bar), our O2–CO2 atmospheres produce eclipse depths within 1σ of the data, consistent with the modeling results of Zieba et al. However, for higher-pressure atmospheres, our models produce different temperature–pressure profiles and are less pessimistic, with 1–10 bar O2, 100 ppm CO2 models within 2. 0σ–2. 2σ of the measured secondary eclipse depth and up to 0.5% CO2 within 2. 9σ. Venus-like atmospheres are still unlikely. For thin O2 atmospheres of 0.1 bar with a low abundance of CO2 (∼100 ppm), up to 10% water vapor can be present and still provide an eclipse depth within 1σ of the data. We compared the TRAPPIST-1 c data to modeled steam atmospheres of ≤3 bars, which are 1. 7σ–1. 8σ from the data and not conclusively ruled out. More data will be required to discriminate between possible atmospheres or more definitively support the bare rock hypothesis.
Simplified and Improved Bounds on the VC-Dimension for Elastic Distance Measures

We study range spaces, where the ground set consists of either polygonal curves in $\mathbb{R}^d$ or polygonal regions in the plane that may contain holes and the ranges are balls defined by an elastic distance measure, such as the Hausdorff distance, the Fr\'echet distance and the dynamic time warping distance. The range spaces appear in various applications like classification, range counting, density estimation and clustering when the instances are trajectories, time series or polygons. The Vapnik-Chervonenkis dimension (VC-dimension) plays an important role when designing algorithms for these range spaces. We show for the Fr\'echet distance of polygonal curves and the Hausdorff distance of polygonal curves and planar polygonal regions that the VC-dimension is upper-bounded by $O(dk\log(km))$ where $k$ is the complexity of the center of a ball, $m$ is the complexity of the polygonal curve or region in the ground set, and $d$ is the ambient dimension. For $d \geq 4$ this bound is tight in each of the parameters $d, k$ and $m$ separately. For the dynamic time warping distance of polygonal curves, our analysis directly yields an upper-bound of $O(\min(dk^2\log(m),dkm\log(k)))$.
Automated Sizing and Training of Efficient Deep Autoencoders using Second Order Algorithms

We propose a multi-step training method for designing generalized linear classifiers. First, an initial multi-class linear classifier is found through regression. Then validation error is minimized by pruning of unnecessary inputs. Simultaneously, desired outputs are improved via a method similar to the Ho-Kashyap rule. Next, the output discriminants are scaled to be net functions of sigmoidal output units in a generalized linear classifier. We then develop a family of batch training algorithm for the multi layer perceptron that optimizes its hidden layer size and number of training epochs. Next, we combine pruning with a growing approach. Later, the input units are scaled to be the net function of the sigmoidal output units that are then feed into as input to the MLP. We then propose resulting improvements in each of the deep learning blocks thereby improving the overall performance of the deep architecture. We discuss the principles and formulation regarding learning algorithms for deep autoencoders. We investigate several problems in deep autoencoders networks including training issues, the theoretical, mathematical and experimental justification that the networks are linear, optimizing the number of hidden units in each layer and determining the depth of the deep learning model. A direct implication of the current work is the ability to construct fast deep learning models using desktop level computational resources. This, in our opinion, promotes our design philosophy of building small but powerful algorithms. Performance gains are demonstrated at each step. Using widely available datasets, the final network's ten fold testing error is shown to be less than that of several other linear, generalized linear classifiers, multi layer perceptron and deep learners reported in the literature.
Taming the Power of Diffusion Models for High-Quality Virtual Try-On with Appearance Flow

Virtual try-on is a critical image synthesis task that aims to transfer clothes from one image to another while preserving the details of both humans and clothes. While many existing methods rely on Generative Adversarial Networks (GANs) to achieve this, flaws can still occur, particularly at high resolutions. Recently, the diffusion model has emerged as a promising alternative for generating high-quality images in various applications. However, simply using clothes as a condition for guiding the diffusion model to inpaint is insufficient to maintain the details of the clothes. To overcome this challenge, we propose an exemplar-based inpainting approach that leverages a warping module to guide the diffusion model's generation effectively. The warping module performs initial processing on the clothes, which helps to preserve the local details of the clothes. We then combine the warped clothes with clothes-agnostic person image and add noise as the input of diffusion model. Additionally, the warped clothes is used as local conditions for each denoising process to ensure that the resulting output retains as much detail as possible. Our approach, namely Diffusion-based Conditional Inpainting for Virtual Try-ON(DCI-VTON), effectively utilizes the power of the diffusion model, and the incorporation of the warping module helps to produce high-quality and realistic virtual try-on results. Experimental results on VITON-HD demonstrate the effectiveness and superiority of our method. Source code and trained models will be publicly released at: https://github.com/bcmi/DCI-VTON-Virtual-Try-On.
Uncertainty Quantification for Image-based Traffic Prediction across Cities

Despite the strong predictive performance of deep learning models for traffic prediction, their widespread deployment in real-world intelligent transportation systems has been restrained by a lack of interpretability. Uncertainty quantification (UQ) methods provide an approach to induce probabilistic reasoning, improve decision-making and enhance model deployment potential. To gain a comprehensive picture of the usefulness of existing UQ methods for traffic prediction and the relation between obtained uncertainties and city-wide traffic dynamics, we investigate their application to a large-scale image-based traffic dataset spanning multiple cities and time periods. We compare two epistemic and two aleatoric UQ methods on both temporal and spatio-temporal transfer tasks, and find that meaningful uncertainty estimates can be recovered. We further demonstrate how uncertainty estimates can be employed for unsupervised outlier detection on changes in city traffic dynamics. We find that our approach can capture both temporal and spatial effects on traffic behaviour in a representative case study for the city of Moscow. Our work presents a further step towards boosting uncertainty awareness in traffic prediction tasks, and aims to highlight the value contribution of UQ methods to a better understanding of city traffic dynamics.