Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamics of finite-size spheroids in turbulent flow: the roles of flow structures and particle boundary layers

Abstract We study the translational and rotational dynamics of neutrally buoyant finite-size spheroids in hydrodynamic turbulence by means of fully resolved numerical simulations. We examine axisymmetric shapes, from oblate to prolate, and the particle volume dependences. We show that the accelerations and rotations experienced by non-spherical inertial-scale particles result from volume filtered fluid forces and torques, similar to spherical particles. However, the particle orientations carry signatures of preferential alignments with the surrounding flow structures, which are reflected in distinct axial and lateral fluctuations for accelerations and rotation rates. The randomization of orientations does not occur even for particles with volume-equivalent diameter size in the inertial range, here up to $60$ dissipative units ($\eta$) at Taylor-scale Reynolds number ${Re_{\lambda }=120}$. Additionally, we demonstrate that the role of fluid boundary layers around the particles cannot be neglected in reaching a quantitative understanding of particle statistical dynamics, as they affect the intensities of the angular velocities and the relative importance of tumbling with respect to spinning rotations. This study brings to the fore the importance of inertial-scale flow structures in homogeneous and isotropic turbulence and their impacts on the transport of neutrally buoyant bodies with sizes in the inertial range.
Hitting probabilities of Gaussian random fields and collision of eigenvalues of random matrices

Let X = {X(t), t ∈ R} be a centered Gaussian random field with values in R d satisfying certain conditions and let F ⊂ R be a Borel set. In our main theorem, we provide a sufficient condition for F to be polar for X, i.e. P ( X(t) ∈ F for some t ∈ R ) = 0, which improves significantly the main result in Dalang et al [7], where the case of F being a singleton was considered. We provide a variety of examples of Gaussian random field for which our result is applicable. Moreover, by using our main theorem, we solve a problem on the existence of collisions of the eigenvalues of random matrices with Gaussian random field entries that was left open in Jaramillo and Nualart [14] and Song et al [21].
Context-Aware Discrimination Detection in Job Vacancies using Computational Language Models

Discriminatory job vacancies are disapproved worldwide, but remain persistent. Discrimination in job vacancies can be explicit by directly referring to demographic memberships of candidates. More implicit forms of discrimination are also present that may not always be illegal but still influence the diversity of applicants. Explicit written discrimination is still present in numerous job vacancies, as was recently observed in the Netherlands. Current efforts for the detection of explicit discrimination concern the identification of job vacancies containing potentially discriminating terms such as"young"or"male". However, automatic detection is inefficient due to low precision: e.g."we are a young company"or"working with mostly male patients"are phrases that contain explicit terms, while the context shows that these do not reflect discriminatory content. In this paper, we show how machine learning based computational language models can raise precision in the detection of explicit discrimination by identifying when the potentially discriminating terms are used in a discriminatory context. We focus on gender discrimination, which indeed suffers from low precision when filtering explicit terms. First, we created a data set for gender discrimination in job vacancies. Second, we investigated a variety of computational language models for discriminatory context detection. Third, we evaluated the capability of these models to detect unforeseen discriminating terms in context. The results show that machine learning based methods can detect explicit gender discrimination with high precision and help in finding new forms of discrimination. Accordingly, the proposed methods can substantially increase the effectiveness of detecting job vacancies which are highly suspected to be discriminatory. In turn, this may lower the discrimination experienced at the start of the recruitment process.
$H^{1+\alpha}$ estimates for the fully nonlinear parabolic thin obstacle problem

We study the regularity of the viscosity solution to the fully nonlinear parabolic thin obstacle problem. In particular, we prove that the solution is local H1+α on each side of the smooth obstacle, for some small α > 0. Following the method which was first introduced for the harmonic case by Caffarelli in 1979, we extend the results of FernándezReal (2016) who treated the fully nonlinear elliptic case. Our results also extend those of Chatzigeorgiou (2019) in two ways. First, we do not assume solutions nor operators to be symmetric. Second, our estimates are local, in the sense that do not rely on the boundary data.
TACTiS: Transformer-Attentional Copulas for Time Series

The estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. However, the practical utility of such estimates is limited by how accurately they quantify predictive uncertainty. In this work, we address the problem of estimating the joint predictive distribution of high-dimensional multivariate time series. We propose a versatile method, based on the transformer architecture, that estimates joint distributions using an attention-based decoder that provably learns to mimic the properties of non-parametric copulas. The resulting model has several desirable properties: it can scale to hundreds of time series, supports both forecasting and interpolation, can handle unaligned and non-uniformly sampled data, and can seamlessly adapt to missing data during training. We demonstrate these properties empirically and show that our model produces state-of-the-art predictions on multiple real-world datasets.
Ordered domains in sheared dense suspensions: The link to viscosity and the disruptive effect of friction

Monodisperse suspensions of Brownian colloidal spheres crystallize at high densities, and ordering under shear has been observed at densities below the crystallization threshold. We perform large-scale simulations of a model suspension containing over [Formula: see text] particles to quantitatively study the ordering under shear and to investigate its link to the rheological properties of the suspension. We find that at high rates, for [Formula: see text], the shear flow induces an ordering transition that significantly decreases the measured viscosity. This ordering is analyzed in terms of the development of layering and planar order, and we determine that particles are packed into hexagonal crystal layers (with numerous defects) that slide past each other. By computing local [Formula: see text] and [Formula: see text] order parameters, we determine that the defects correspond to chains of particles in a squarelike lattice. We compute the individual particle contributions to the stress tensor and discover that the largest contributors to the shear stress are primarily located in these lower density, defect regions. The defect structure enables the formation of compressed chains of particles to resist the shear, but these chains are transient and short-lived. The inclusion of a contact friction force allows the stress-bearing structures to grow into a system-spanning network, thereby disrupting the order and drastically increasing the suspension viscosity.
MetaKG: Meta-Learning on Knowledge Graph for Cold-Start Recommendation

A knowledge graph (KG) consists of a set of interconnected typed entities and their attributes. Recently, KGs are popularly used as the auxiliary information to enable more accurate, explainable, and diverse user preference recommendations. Specifically, existing KG-based recommendation methods target modeling high-order relations/dependencies from long connectivity user-item interactions hidden in KG. However, most of them ignore the cold-start problems (i.e., user cold-start and item cold-start) of recommendation analytics, which restricts their performance in scenarios when involving new users or new items. Inspired by the success of meta-learning on scarce training samples, we propose a novel meta-learning based framework called MetaKG, which encompasses a collaborative-aware meta learner and a knowledge-aware meta learner, to capture meta users’ preference and entities’ knowledge for cold-start recommendations. The collaborative-aware meta learner aims to locally aggregate user preferences for each preference learning task. In contrast, the knowledge-aware meta learner is to globally generalize knowledge representation across different user preference learning tasks. Guided by two meta learners, MetaKG can effectively capture the high-order collaborative relations and semantic representations, which could be easily adapted to cold-start scenarios. Besides, we devise a novel adaptive task scheduler which can adaptively select the informative tasks for meta learning in order to prevent the model from being corrupted by noisy tasks. Extensive experiments on various cold-start scenarios using three real datasets demonstrate that our presented MetaKG outperforms all the existing state-of-the-art competitors in terms of effectiveness, efficiency, and scalability.
Notions of solution and weak-strong uniqueness criteria for the Navier-Stokes equations in Lorentz spaces

u ∈ L∞t L 2 x ∩ L 2 tH 1 x ∩ L 2p p−n t L p,∞ x to the Navier-Stokes equations satisfies the energy equality, and that such a solution u is unique among all solutions v ∈ L∞t L 2 x ∩L 2 tH 1 x satisfying the energy inequality. This extends well-known results due to G. Prodi (1959) and J. Serrin (1963), which treated the Lebesgue space Lpx rather than the larger Lorentz (and ‘weak Lebesgue’) space L p,∞ x . In doing so, we also prove the equivalence of various notions of solutions in L x , generalizing in particular a result proved for the Lebesgue setting in Fabes-Jones-Riviere (1972).
On the absence of shock waves and vacuum birefringence in Born–Infeld electrodynamics

We study the interaction of two counter–propagating electromagnetic waves in vacuum in the Born–Infeld electrodynamics. First we investigate the Born case for linearly polarized beams, E · B = 0, i.e., G2=0 (crossed field configuration), which is identical for Born–Infeld and Born electrodynamics; subsequently we study the general Born–Infeld case for beams which are nonlinearly polarized, G2≠0. In both cases, we show that the nonlinear field equations decouple using self-similar solutions and investigate the shock wave formation. We show that the only nonlinear solutions are exceptional traveling wave solutions which propagate with constant speed and which do not turn into shocks for our approximation. We obtain two types of exceptional wave solutions, then we numerically analyze which phase velocities correspond to the counter- or co-propagating beams and subsequently we determine the direction of propagation of the exceptional waves.
Evidence-aware Fake News Detection with Graph Neural Networks

The prevalence and perniciousness of fake news has been a critical issue on the Internet, which stimulates the development of automatic fake news detection in turn. In this paper, we focus on the evidence-based fake news detection, where several evidences are utilized to probe the veracity of news (i.e., a claim). Most previous methods first employ sequential models to embed the semantic information and then capture the claim-evidence interaction based on different attention mechanisms. Despite their effectiveness, they still suffer from two main weaknesses. Firstly, due to the inherent drawbacks of sequential models, they fail to integrate the relevant information that is scattered far apart in evidences for veracity checking. Secondly, they neglect much redundant information contained in evidences that may be useless or even harmful. To solve these problems, we propose a unified Graph-based sEmantic sTructure mining framework, namely GET in short. Specifically, different from the existing work that treats claims and evidences as sequences, we model them as graph-structured data and capture the long-distance semantic dependency among dispersed relevant snippets via neighborhood propagation. After obtaining contextual semantic information, our model reduces information redundancy by performing graph structure learning. Finally, the fine-grained semantic representations are fed into the downstream claim-evidence interaction module for predictions. Comprehensive experiments have demonstrated the superiority of GET over the state-of-the-arts.
Improving the Sample-Complexity of Deep Classification Networks with Invariant Integration

Leveraging prior knowledge on intraclass variance due to transformations is a powerful method to improve the sample complexity of deep neural networks. This makes them applicable to practically important use-cases where training data is scarce. Rather than being learned, this knowledge can be embedded by enforcing invariance to those transformations. Invariance can be imposed using group-equivariant convolutions followed by a pooling operation. For rotation-invariance, previous work investigated replacing the spatial pooling operation with invariant integration which explicitly constructs invariant representations. Invariant integration uses monomials which are selected using an iterative approach requiring expensive pre-training. We propose a novel monomial selection algorithm based on pruning methods to allow an application to more complex problems. Additionally, we replace monomials with different functions such as weighted sums, multi-layer perceptrons and self-attention, thereby streamlining the training of invariant-integration-based architectures. We demonstrate the improved sample complexity on the Rotated-MNIST, SVHN and CIFAR-10 datasets where rotation-invariant-integration-based Wide-ResNet architectures using monomials and weighted sums outperform the respective baselines in the limited sample regime. We achieve state-of-the-art results using full data on Rotated-MNIST and SVHN where rotation is a main source of intraclass variation. On STL-10 we outperform a standard and a rotation-equivariant convolutional neural network using pooling.
Truncated mass divergence in a Mott metal

Significance Strong repulsive interactions in metals can cause electronic grid-lock, producing a Mott insulator. The strongly correlated metallic state on the brink of such Mott localization is associated with some of the most challenging emergent phenomena in condensed matter research. This study tracks the electronic charge carrier concentration and velocity in a clean three-dimensional material tuned toward the Mott insulating state by applied pressure. Our results show that carriers slow down dramatically as the Mott transition is approached, confirming experimentally the Brinkman–Rice description of the correlated Mott metal, first articulated more than fifty years ago. These findings inform the study of more complex materials such as cuprate, organic, and iron-based superconductors, Moiré superlattice systems, and Kondo lattice materials.
A Ranking Game for Imitation Learning

We propose a new framework for imitation learning -- treating imitation as a two-player ranking-based game between a policy and a reward. In this game, the reward agent learns to satisfy pairwise performance rankings between behaviors, while the policy agent learns to maximize this reward. In imitation learning, near-optimal expert data can be difficult to obtain, and even in the limit of infinite data cannot imply a total ordering over trajectories as preferences can. On the other hand, learning from preferences alone is challenging as a large number of preferences are required to infer a high-dimensional reward function, though preference data is typically much easier to collect than expert demonstrations. The classical inverse reinforcement learning (IRL) formulation learns from expert demonstrations but provides no mechanism to incorporate learning from offline preferences and vice versa. We instantiate the proposed ranking-game framework with a novel ranking loss giving an algorithm that can simultaneously learn from expert demonstrations and preferences, gaining the advantages of both modalities. Our experiments show that the proposed method achieves state-of-the-art sample efficiency and can solve previously unsolvable tasks in the Learning from Observation (LfO) setting. Project video and code can be found at https://hari-sikchi.github. io/rank-game/
Distribution Regression with Sliced Wasserstein Kernels

The problem of learning functions over spaces of probabilities - or distribution regression - is gaining significant interest in the machine learning community. A key challenge behind this problem is to identify a suitable representation capturing all relevant properties of the underlying functional mapping. A principled approach to distribution regression is provided by kernel mean embeddings, which lifts kernel-induced similarity on the input domain at the probability level. This strategy effectively tackles the two-stage sampling nature of the problem, enabling one to derive estimators with strong statistical guarantees, such as universal consistency and excess risk bounds. However, kernel mean embeddings implicitly hinge on the maximum mean discrepancy (MMD), a metric on probabilities, which may fail to capture key geometrical relations between distributions. In contrast, optimal transport (OT) metrics, are potentially more appealing. In this work, we propose an OT-based estimator for distribution regression. We build on the Sliced Wasserstein distance to obtain an OT-based representation. We study the theoretical properties of a kernel ridge regression estimator based on such representation, for which we prove universal consistency and excess risk bounds. Preliminary experiments complement our theoretical findings by showing the effectiveness of the proposed approach and compare it with MMD-based estimators.
High-Specific Impulse Operation of the BPT-4000 Hall Thruster for NASA Science Missions

To meet the propulsive requirements, cost constraints, and scientific objectives of NASA deep-space missions, high-performance, low-cost electric propulsion (EP) options are needed. Commercial EP technology has matured sufficiently that ion and Hall thrusters are now leading candidates for many of these missions. A commercial Hall thruster system is as much as 50% less expensive than an equivalent ion thruster system, but the maximum specific impulse of commercial, flight-qualified Hall thrusters is presently 2000 s. Extending the specific impulse to 2500-3000 s would increase the Hall thruster mission capture to include nearly all of the missions where EP was applicable. To assess the suitability of extending the specific impulse range of a candidate commercial Hall thruster, a test campaign designed to assess the performance and plasma properties of Aerojet’s BPT-4000 during high-specific impulse operation was executed. An inverted-pendulum thrust stand was used for performance measurements. An RPA, ExB probe, and emissive probe were used to measure the ion energy, ion species’ current fractions, and plasma potential, respectively, in the far-field plume. For operating conditions spanning 500-800 V discharge voltage and 2.5-5.5 kW discharge power, thrust varied from 134-288 mN, specific impulse from 1980-2720 s, and efficiency from 0.50-0.59. It was concluded that operating the BPT4000 at specific impulses of 2600 s and 4.5 kW discharge power was within the existing capabilities of the magnetic circuit, thermal margins, and voltage isolation. Based on these results, a delta-qualification for high specific impulse operation and higher propellant throughput via additional testing of the qualification thruster is recommended in order to ready the thruster for near-term infusion into NASA science missions.
The evolution of ultra-massive carbon oxygen white dwarfs

Ultra-massive white dwarfs ($\rm M_{WD} \gtrsim 1.05\, M_{\odot}$) are considered powerful tools to study type Ia supernovae explosions, merger events, the occurrence of physical processes in the Super Asymptotic Giant Branch (SAGB) phase, and the existence of high magnetic fields. Traditionally, ultra-massive white dwarfs are expected to harbour oxygen-neon (ONe) cores. However, new observations and recent theoretical studies suggest that the progenitors of some ultra-massive white dwarfs can avoid carbon burning, leading to the formation of ultra-massive white dwarfs harbouring carbon-oxygen (CO) cores. Here we present a set of ultra-massive white dwarf evolutionary sequences with CO cores for a wide range of metallicity and masses. We take into account the energy released by latent heat and phase separation during the crystallization process and by $^{22}$Ne sedimentation. Realistic chemical profiles resulting from the full computation of progenitor evolution are considered. We compare our CO ultra-massive white dwarf models with ONe models. We conclude that CO ultra-massive white dwarfs evolve significantly slower than their ONe counterparts mainly for three reasons: their larger thermal content, the effect of crystallization, and the effect of $^{22}$Ne sedimentation. We also provide colors in several photometric bands on the basis of new model atmospheres. These CO ultra-massive white dwarf models, together with the ONe ultra-massive white dwarf models, provide an appropriate theoretical framework to study the ultra-massive white dwarf population in our Galaxy.
PrivFair: a Library for Privacy-Preserving Fairness Auditing

Machine learning (ML) has become prominent in applications that directly affect people's quality of life, including in healthcare, justice, and finance. ML models have been found to exhibit discrimination based on sensitive attributes such as gender, race, or disability. Assessing if an ML model is free of bias remains challenging to date, and by definition has to be done with sensitive user characteristics that are subject of anti-discrimination and data protection law. Existing libraries for fairness auditing of ML models offer no mechanism to protect the privacy of the audit data. We present PrivFair, a library for privacy-preserving fairness audits of ML models. Through the use of Secure Multiparty Computation (MPC), PrivFair protects the confidentiality of the model under audit and the sensitive data used for the audit, hence it supports scenarios in which a proprietary classifier owned by a company is audited using sensitive audit data from an external investigator. We demonstrate the use of PrivFair for group fairness auditing with tabular data or image data, without requiring the investigator to disclose their data to anyone in an unencrypted manner, or the model owner to reveal their model parameters to anyone in plaintext.