Miracle-dz/UniFAR-FTUs huggingface dataset:
Gravitational collapse and odd-parity black hole perturbations in minimal theory of bigravity

In the former part, we study the gravitational collapse of pressureless dust and find special solutions, where, in both the physical and fiducial sectors, the exterior and interior spacetime geometries are given by the Schwarzschild spacetimes and the Friedmann-Lema\^itre-Robertson-Walker universes dominated by pressureless dust, respectively, with specific time slicings. In the case where the Lagrange multipliers are trivial and have no jump across the matter interfaces in both the physical and fiducial sectors, the junction conditions across them remain the same as those in general relativity (GR). For simplicity, we foliate the interior geometry by homogeneous and isotropic spacetimes. For a spatially flat interior universe, we foliate the exterior geometry by a time-independent flat space, while for a spatiallycurved interior universe, we foliate the exterior geometry by a time-independent space with deficit solid angle. Despite the rather restrictive choice of foliations, we find interesting classes of exact solutions that represent gravitational collapse in MTBG. In the spatially flat case, under a certain tuning of the initial condition, we find exact solutions of matter collapse in which the two sectors evolve independently. In the spatially closed case, once the matter energy densities and the Schwarzschild radii are tuned between the two sectors, we find exact solutions that correspond to the Oppenheimer-Snyder model in GR. In the latter part, we study odd-parity perturbations of the Schwarzschild$-$de Sitter solutions written in the spatially flat coordinates. For the higher-multipole modes $\ell\geq2$, we find that, in general, the system reduces to that of four physical modes, where two of them are dynamical and the remaining two are shadowy, i.e., satisfying only elliptic equations.
Fractal properties of flocs formed by fluid shear and differential settling

The fractal properties of fine‐grained sediment flocs formed by fluid shear and by differential settling were studied through floc porosity–size relationships. Flocs were produced from flocculation tests in a Couette‐type flocculator in which fluid shear was the dominant collision mechanism for flocculation, and in a disk‐type flocculator in which differential settling was the dominant collision mechanism. Floc samples were then introduced into a settling tube to measure their sizes and settling speeds by a double‐exposure photographic method. The porosities of flocs were determined from the settling speed data using a porous sphere settling model. It was found that the flocs produced in the Couette‐type flocculator could be regarded as fractals, with a fractal dimension ranging from 1.83 to 1.97. However, the flocs produced in the disk‐type flocculator did not exhibit simple scaling behavior and could not be regarded as fractals. The differences in fractal property are due to the differences in floc form.
Using the Cuscore technique in the surveillance of rare health events

An adaptation from Cuscore control charts for normal distributions to the problem of monitoring the rate of occurrence of a rare event is considered. The presented scheme defines an alarm in terms of the sum of ‘scored distances' between successive events of interest. A procedure is given for using this scheme in an ‘optimal’ manner which minimizes the out of control expected delay for a given rate of false alarms. Comparisons over a number of examples, after matching the rate of false alarms, show that the present technique is more efficient than those of Shewhart and Sets, and also than Cusum under conditions of practical relevance.
Exploring Singularities in point clouds with the graph Laplacian: An explicit approach

We develop theory and methods that use the graph Laplacian to analyze the geometry of the underlying manifold of point clouds. Our theory provides theoretical guarantees and explicit bounds on the functional form of the graph Laplacian, in the case when it acts on functions defined close to singularities of the underlying manifold. We also propose methods that can be used to estimate these geometric properties of the point cloud, which are based on the theoretical guarantees.
Active Learning for Neural Machine Translation

The machine translation mechanism translates texts automatically between different natural languages, and Neural Machine Translation (NMT) has gained attention for its rational context analysis and fluent translation accuracy. However, processing low-resource languages that lack relevant training attributes like supervised data is a current challenge for Natural Language Processing (NLP). We incorporated a technique known Active Learning with the NMT toolkit Joey NMT to reach sufficient accuracy and robust predictions of low-resource language translation. With active learning, a semi-supervised machine learning strategy, the training algorithm determines which unlabeled data would be the most beneficial for obtaining labels using selected query techniques. We implemented two model-driven acquisition functions for selecting the samples to be validated. This work uses transformer-based NMT systems; baseline model (BM), fully trained model (FTM) , active learning least confidence based model (ALLCM), and active learning margin sampling based model (ALMSM) when translating English to Hindi. The Bilingual Evaluation Understudy (BLEU) metric has been used to evaluate system results. The BLEU scores of BM, FTM, ALLCM and ALMSM systems are 16.26, 22.56 , 24.54, and 24.20, respectively. The findings in this paper demonstrate that active learning techniques helps the model to converge early and improve the overall quality of the translation system.
Optimization-based Sensitivity Analysis for Unmeasured Confounding using Partial Correlations

Causal inference necessarily relies upon untestable assumptions; hence, it is crucial to assess the robustness of obtained results to violations of identification assumptions. However, such sensitivity analysis is only occasionally undertaken in practice, as many existing methods only apply to relatively simple models and their results are often difficult to interpret. We take a more flexible approach to sensitivity analysis and view it as a constrained stochastic optimization problem. This work focuses on sensitivity analysis for a linear causal effect when an unmeasured confounder and a potential instrument are present. We show how the bias of the OLS and TSLS estimands can be expressed in terms of partial correlations. Leveraging the algebraic rules that relate different partial correlations, practitioners can specify intuitive sensitivity models which bound the bias. We further show that the heuristic"plug-in"sensitivity interval may not have any confidence guarantees; instead, we propose a bootstrap approach to construct sensitivity intervals which perform well in numerical simulations. We illustrate the proposed methods with a real study on the causal effect of education on earnings and provide user-friendly visualization tools.
Slow traveling-wave solutions for the generalized surface quasi-geostrophic equation

In this paper, we systematically study the existence, asymptotic behaviors, uniqueness, and nonlinear orbital stability of traveling-wave solutions with small prop-agation speeds for the generalized surface quasi-geostrophic (gSQG) equation. Firstly we obtain the existence of a new family of global solutions via the variational method. Secondly we show the uniqueness of maximizers under our variational setting. Thirdly by using the variational framework, the uniqueness of maximizers and a concentration-compactness principle we establish some stability theorems. Moreover, after a suitable transformation, these solutions constitute the desingularization of traveling point vortex pairs.
Sample-Efficient Unsupervised Domain Adaptation of Speech Recognition Systems: A Case Study for Modern Greek

Modern speech recognition systems exhibit rapid performance degradation under domain shift. This issue is especially prevalent in data-scarce settings, such as low-resource languages, where the diversity of training data is limited. In this work, we propose M2DS2, a simple and sample-efficient fine-tuning strategy for large pre-trained speech models, based on mixed source and target domain self-supervision. We find that including source domain self-supervision stabilizes training and avoids mode collapse of the latent representations. For evaluation, we collect HParl, a 120-hour speech corpus for Greek, consisting of plenary sessions in the Greek Parliament. We merge HParl with two popular Greek corpora to create GREC-MD, a test-bed for multi-domain evaluation of Greek ASR systems. In our experiments, we find that, while other Unsupervised Domain Adaptation baselines fail in this resource-constrained environment, M2DS2 yields significant improvements for cross-domain adaptation, even when only a few hours of in-domain audio are available. When we relax the problem in a weakly supervised setting, we find that independent adaptation for audio using M2DS2 and language using simple LM augmentation techniques is particularly effective, yielding word error rates comparable to the fully supervised baselines.
E-commerce users' preferences for delivery options

Many e-commerce marketplaces oﬀer their users fast delivery options for free to meet the increasing needs of users, imposing an excessive burden on city logistics. Therefore, understanding e-commerce users’ preference for delivery options is one of the core challenges faced while designing logistics policies. To advance such understanding, this study designs and implements a stated choice survey in which respondents are faced with choice tasks among diﬀerent delivery options and time slots. The survey was completed by 4,062 users from the three major metropolitan areas in Japan. To analyze the stated choice data, mixed logit models capturing users’ taste heterogeneity as well as ﬂexible substitution patterns among choice alternatives have been estimated. The model estimation results indicate that delivery attributes including fee, time, and time slot size are signiﬁcant determinants of the delivery option choices. Associations between users’ preferences and socio-demographic characteristics, such as age, gender, teleworking frequency and the presence of a delivery box, were also suggested. Moreover, we analyzed two willingness-to-pay measures for delivery, namely, the value of delivery time savings (VODT) and the value of time slot shortening (VOTS), and applied a non-semiparametric approach with polynomial approximation to estimate their distributions in a data-oriented manner. Although VODT has a large heterogeneity among the respondents, the estimated median VODT is 25.6 JPY per day, implying that more than half of the respondents would wait an additional day if the delivery fee were increased by only 26 JPY, that is, they do not necessarily need a fast delivery option but often request it when cheap or almost free. Moreover, VOTS was found to be low, distributed with the median of 5.0 JPY per hour; that is, users do not highly value the reduction in time slot size in monetary terms. These ﬁndings on e-commerce users’ preferences can help in designing levels of service for last-mile delivery to signiﬁcantly improve its eﬃciency.
Robust Inference for Federated Meta-Learning (preprint)

Synthesizing information from multiple data sources is critical to ensure knowledge generalizability. Integrative analysis of multi-source data is challenging due to the heterogeneity across sources and data-sharing constraints due to privacy concerns. In this paper, we consider a general robust inference framework for federated meta-learning of data from multiple sites, enabling statistical inference for the prevailing model, defined as the one matching the majority of the sites. Statistical inference for the prevailing model is challenging since it requires a data-adaptive mechanism to select eligible sites and subsequently account for the selection uncertainty. We propose a novel sampling method to address the additional variation arising from the selection. Our devised CI construction does not require sites to share individual-level data and is shown to be valid without requiring the selection of eligible sites to be error-free. The proposed robust inference for federated meta-learning (RIFL) methodology is broadly applicable and illustrated with three inference problems: aggregation of parametric models, high-dimensional prediction models, and inference for average treatment effects. We use RIFL to perform federated learning of mortality risk for patients hospitalized with COVID-19 using real-world EHR data from 16 healthcare centers representing 275 hospitals across four countries.
Wall-sheared thermal convection: heat transfer enhancement and turbulence relaminarization

Abstract We studied the flow organization and heat transfer properties in two-dimensional and three-dimensional Rayleigh–Bénard cells that are imposed with different types of wall shear. The external wall shear is added with the motivation of manipulating flow mode to control heat transfer efficiency. We imposed three types of wall shear that may facilitate the single-roll, the horizontally stacked double-roll, and the vertically stacked double-roll flow modes, respectively. Direct numerical simulations are performed for fixed Rayleigh number $Ra = 10^{8}$ and fixed Prandtl number $Pr = 5.3$, while the wall-shear Reynolds number ($Re_{w}$) is in the range $60 \leqslant Re_{w} \leqslant 6000$. Generally, we found enhanced heat transfer efficiency and global flow strength with the increase of $Re_{w}$. However, even with the same magnitude of global flow strength, the heat transfer efficiency varies significantly when the cells are under different types of wall shear. An interesting finding is that by increasing the wall-shear strength, the thermal turbulence is relaminarized, and more surprisingly, the heat transfer efficiency in the laminar state is higher than that in the turbulent state. We found that the enhanced heat transfer efficiency at the laminar regime is due to the formation of more stable and stronger convection channels. We propose that the origin of thermal turbulence laminarization is the reduced amount of thermal plumes. Because plumes are mainly responsible for turbulent kinetic energy production, when the detached plumes are swept away by the wall shear, the reduced number of plumes leads to weaker turbulent kinetic energy production. We also quantify the efficiency of facilitating heat transport via external shearing, and find that for larger $Re_{w}$, the enhanced heat transfer efficiency comes at a price of a larger expenditure of mechanical energy.
A Policy Optimization Method Towards Optimal-time Stability

In current model-free reinforcement learning (RL) algorithms, stability criteria based on sampling methods are commonly utilized to guide policy optimization. However, these criteria only guarantee the infinite-time convergence of the system's state to an equilibrium point, which leads to sub-optimality of the policy. In this paper, we propose a policy optimization technique incorporating sampling-based Lyapunov stability. Our approach enables the system's state to reach an equilibrium point within an optimal time and maintain stability thereafter, referred to as"optimal-time stability". To achieve this, we integrate the optimization method into the Actor-Critic framework, resulting in the development of the Adaptive Lyapunov-based Actor-Critic (ALAC) algorithm. Through evaluations conducted on ten robotic tasks, our approach outperforms previous studies significantly, effectively guiding the system to generate stable patterns.
Eﬀective Brain Connectome: the whole-brain eﬀective connectivity from neural perturbational inference

The eﬀective brain connectome (EBC), which describes whole-brain eﬀective connectivity, is of fundamental importance for understanding information processing in the brain at both local and global scales. To date, a comprehensive mapping of the human EBC has not been achieved. Here we report a human EBC inferred through a data-driven computational framework called Neural Perturbational Inference (NPI). Speciﬁcally, we derive the human EBC by perturbing each region of a surrogate brain, an artiﬁcial neural network model trained to replicate the large-scale neural dynamics of the human brain, and observing the perturbation-induced changes in activity in other brain regions. The NPI-inferred human EBC reveals log-normally distributed strengths of both excitatory and inhibitory connections and illuminates the large-scale organization of information ﬂow within and across functional networks. The EBC sheds light on the structure-function relationship in the brain and oﬀers theoretical guidance for personalized neurostimulation.
Efficient On-Device Training via Gradient Filtering

Despite its importance for federated learning, continuous learning and many other applications, on-device training remains an open problem for EdgeAI. The problem stems from the large number of operations (e.g., floating point multiplications and additions) and memory consumption required during training by the back-propagation algorithm. Consequently, in this paper, we propose a new gradient filtering approach which enables on-device CNN model training. More precisely, our approach creates a special structure with fewer unique elements in the gradient map, thus significantly reducing the computational complexity and memory consumption of back propagation during training. Extensive experiments on image classification and semantic segmentation with multiple CNN models (e.g., MobileNet, DeepLabV3, UPerNet) and devices (e.g., Raspberry Pi and Jetson Nano) demonstrate the effectiveness and wide applicability of our approach. For example, compared to SOTA, we achieve up to 19× speedup and 77.1% memory savings on ImageNet classification with only 0.1% accuracy loss. Finally, our method is easy to implement and deploy; over 20× speedup and 90% energy savings have been observed compared to highly optimized baselines in MKLDNN and CUDNN on NVIDIA Jetson Nano. Consequently, our approach opens up a new direction of research with a huge potential for on-device training.11Code: https://github.com/SLDGroup/GradientFilter-CVPR23
Bosonization of the interacting Su-Schrieffer-Heeger model

We derive the bosonization of the interacting fermionic Su-Schrieffer-Heeger (SSH) with open boundaries. We use the classical Euler-Lagrange equations of motions of the bosonized theory to compute the density profile of the Majorana edge mode and observe excellent agreement with numerical results, notably the localization of the mode near the boundaries. Remarkably, we find that repulsive or attractive interactions do not systematically localize or delocalize the edge mode but their effects depend on the value of the staggering parameter. We provide quantitative predictions of these effects on the localization length of the edge mode.
Investigating dynamic coupling in geospace through the combined use of modeling, simulations and data analysis

Comprehensive understanding of the dynamics of the coupled solar wind-magnetosphere-ionosphere system is of utmost interest, both from the perspective of solar system astrophysics and geophysics research and from the perspective of space applications. The physical processes involved in the dynamical evolution of this complex coupled system are pertinent not only for the Sun-Earth connection, but also for major phenomena in other astrophysical systems. Furthermore, the conditions in geospace collectively termed space weather affect the ever increasing technological assets of mankind in space and therefore need to be understood, quantified and efficiently forecasted. The present collaborative paper communicates recent advances in geospace dynamic coupling research through modeling, simulations and data analysis and discusses future directions.
MHD Simulation of a Solar Eruption from Active Region 11429 Driven by a Photospheric Velocity Field

Data-driven simulation is becoming an important approach for realistically characterizing the configuration and evolution of solar active regions, revealing the onset mechanism of solar eruption events, and hopefully achieving the goal of accurate space weather forecasting, which is beyond the scope of any existing theoretical modeling. Here we performed a full 3D MHD simulation using the data-driven approach and followed the whole evolution process from the quasi-static phase to eruption successfully for solar active region (AR) NOAA 11429. The MHD system was driven at the bottom boundary by a photospheric velocity field, which is derived by the DAVE4VM method from the observed vector magnetograms. The simulation shows that a magnetic flux rope was generated by a persistent photospheric flow before the flare onset and then triggered to erupt by torus instability. Our simulation demonstrates a high degree of consistency with observations in the preeruption magnetic structure, the timescale of the quasi-static stage, the pattern of flare ribbons, as well as the time evolution of the magnetic energy injection and total unsigned magnetic flux. We further found that an eruption can also be initiated in the simulation driven by only the horizontal components of the photospheric flow, but a comparison of the different simulations indicates that the vertical flow at the bottom boundary is necessary for reproducing more realistically these observed features, emphasizing the importance of flux emergence during the development of this AR.
A Generalized Filbert Matrix

A generalized Filbert matrix is introduced, sharing properties of the Hilbert matrix and Fibonacci numbers. Explicit formulae are derived for the LU-decomposition, their inverses, and the Cholesky factorization. The approach is to use q-analysis and to leave the justification of the necessary identities to the q-version of Zeilberger’s celebrated algorithm.
Tidal disruption rate suppression by the event horizon of spinning black holes

The rate of observable tidal disruption events (TDEs) by the most massive (≳ few × 107 M⊙) black holes (BHs) is suppressed due to direct capture of stars. This suppression effect depends on the space–time geometry and holds the promise of probing the spin distribution of dormant BHs at the centres of galaxies. By extending the frozen-in approximation commonly used in the Newtonian limit, we propose a general relativistic criterion for the tidal disruption of a star of given interior structure. The rate suppression factor is then calculated for different BH masses, spins, and realistic stellar populations. We find that either a high-BH spin (≳ 0.5) or a young stellar population (≲1 Gyr) allows TDEs to be observed from BHs significantly more massive than $10^8\, \mathrm{M}_{\odot }$. We call this spin-age degeneracy (SAD). This limits our utility of the TDE rate to constrain the BH spin distribution, unless additional constraints on the age of the stellar population or the mass of the disrupted star can be obtained by modelling the TDE radiation or the stellar spectral energy distribution near the galactic nuclei.