Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty

Cross-domain few-shot learning (CD-FSL) has drawn increasing attention for handling large differences between the source and target domains--an important concern in real-world scenarios. To overcome these large differences, recent works have considered exploiting small-scale unlabeled data from the target domain during the pre-training stage. This data enables self-supervised pre-training on the target domain, in addition to supervised pre-training on the source domain. In this paper, we empirically investigate which pre-training is preferred based on domain similarity and few-shot difficulty of the target domain. We discover that the performance gain of self-supervised pre-training over supervised pre-training becomes large when the target domain is dissimilar to the source domain, or the target domain itself has low few-shot difficulty. We further design two pre-training schemes, mixed-supervised and two-stage learning, that improve performance. In this light, we present six findings for CD-FSL, which are supported by extensive experiments and analyses on three source and eight target benchmark datasets with varying levels of domain similarity and few-shot difficulty. Our code is available at https://github.com/sungnyun/understanding-cdfsl.
DQLAP: Deep Q-Learning Recommender Algorithm with Update Policy for a Real Steam Turbine System

In modern industrial systems, diagnosing faults in time and using the best methods becomes more and more crucial. It is possible to fail a system or to waste resources if faults are not detected or are detected late. Machine learning and deep learning have proposed various methods for data-based fault diagnosis, and we are looking for the most reliable and practical ones. This paper aims to develop a framework based on deep learning and reinforcement learning for fault detection. We can increase accuracy, overcome data imbalance, and better predict future defects by updating the reinforcement learning policy when new data is received. By implementing this method, we will see an increase of 3% in all evaluation metrics, an improvement in prediction speed, and 3% - 4% in all evaluation metrics compared to typical backpropagation multi-layer neural network prediction with similar parameters.
A Fractional Finite Difference Method for Solving the Fractional Poisson Equation Based on the Shifted Grünwald Estimate

In this study a fractional Poisson equation is scrutinized through finite difference using the shifted Grunwald estimate. A novel method is proposed numerically. The existence and uniqueness of the solution for the fractional Poisson equation is proved. Exact and numerical solutions are constructed and compared. Then numerical results show the efficiency of the proposed method.
GOTCHA: Real-Time Video Deepfake Detection via Challenge-Response

With the rise of AI-enabled Real-Time Deepfakes (RTDFs), the integrity of online video interactions has become a growing concern. RTDFs have now made it feasible to replace an imposter's face with their victim in live video interactions. Such advancement in deepfakes also coaxes detection to rise to the same standard. However, existing deepfake detection techniques are asynchronous and hence ill-suited for RTDFs. To bridge this gap, we propose a challenge-response approach that establishes authenticity in live settings. We focus on talking-head style video interaction and present a taxonomy of challenges that specifically target inherent limitations of RTDF generation pipelines. We evaluate representative examples from the taxonomy by collecting a unique dataset comprising eight challenges, which consistently and visibly degrades the quality of state-of-the-art deepfake generators. These results are corroborated both by humans and a new automated scoring function, leading to 88.6% and 80.1% AUC, respectively. The findings underscore the promising potential of challenge-response systems for explainable and scalable real-time deepfake detection in practical scenarios. We provide access to data and code at https://github.com/mittalgovind/GOTCHA-Deepfakes
Equal Experience in Recommender Systems

We explore the fairness issue that arises in recommender systems. Biased data due to inherent stereotypes of particular groups (e.g., male students' average rating on mathematics is often higher than that on humanities, and vice versa for females) may yield a limited scope of suggested items to a certain group of users. Our main contribution lies in the introduction of a novel fairness notion (that we call equal experience), which can serve to regulate such unfairness in the presence of biased data. The notion captures the degree of the equal experience of item recommendations across distinct groups. We propose an optimization framework that incorporates the fairness notion as a regularization term, as well as introduce computationally-efficient algorithms that solve the optimization. Experiments on synthetic and benchmark real datasets demonstrate that the proposed framework can indeed mitigate such unfairness while exhibiting a minor degradation of recommendation accuracy.
Curved Representation Space of Vision Transformers

Neural networks with self-attention (a.k.a. Transformers) like ViT and Swin have emerged as a better alternative to traditional convolutional neural networks (CNNs). However, our understanding of how the new architecture works is still limited. In this paper, we focus on the phenomenon that Transformers show higher robustness against corruptions than CNNs, while not being overconfident. This is contrary to the intuition that robustness increases with confidence. We resolve this contradiction by empirically investigating how the output of the penultimate layer moves in the representation space as the input data moves linearly within a small area. In particular, we show the following. (1) While CNNs exhibit fairly linear relationship between the input and output movements, Transformers show nonlinear relationship for some data. For those data, the output of Transformers moves in a curved trajectory as the input moves linearly. (2) When a data is located in a curved region, it is hard to move it out of the decision region since the output moves along a curved trajectory instead of a straight line to the decision boundary, resulting in high robustness of Transformers. (3) If a data is slightly modified to jump out of the curved region, the movements afterwards become linear and the output goes to the decision boundary directly. In other words, there does exist a decision boundary near the data, which is hard to find only because of the curved representation space. This explains the underconfident prediction of Transformers. Also, we examine mathematical properties of the attention operation that induce nonlinear response to linear perturbation. Finally, we share our additional findings, regarding what contributes to the curved representation space of Transformers, and how the curvedness evolves during training.
Efficient Image Super-Resolution using Vast-Receptive-Field Attention

The attention mechanism plays a pivotal role in designing advanced super-resolution (SR) networks. In this work, we design an efficient SR network by improving the attention mechanism. We start from a simple pixel attention module and gradually modify it to achieve better super-resolution performance with reduced parameters. The specific approaches include: (1) increasing the receptive field of the attention branch, (2) replacing large dense convolution kernels with depth-wise separable convolutions, and (3) introducing pixel normalization. These approaches paint a clear evolutionary roadmap for the design of attention mechanisms. Based on these observations, we propose VapSR, the VAst-receptive-field Pixel attention network. Experiments demonstrate the superior performance of VapSR. VapSR outperforms the present lightweight networks with even fewer parameters. And the light version of VapSR can use only 21.68% and 28.18% parameters of IMDB and RFDN to achieve similar performances to those networks. The code and models are available at https://github.com/zhoumumu/VapSR.
WHOLE-HEART MOTION-RESOLVED FREE-RUNNING FAT-FRACTION MAPPING

To develop a free-running 3D radial multi-echo GRE (ME-GRE) framework for enabling cardiac- and respiratory-motion-resolved whole-heart fat fraction (FF) quantification. Multi-echo (N TE = 8) readouts optimized for water-fat separation and quantification were integrated within a continuous non-ECG-triggered free-breathing 3D radial GRE acquisition. Motion resolution was achieved with Pilot Tone (PT) navigation, and the extracted cardiac and respiratory signals were compared to those obtained with a self-gating (SG) approach. Following an XD-GRASP image reconstruction, FF, R2*, and B 0 maps, as well as fat and water images were generated with a maximum-likelihood fitting algorithm. The acquisition, reconstruction and post-processing framework was tested in 10 healthy volunteers at 1.5T using N TE = 4 and N TE = 8 echoes, and the resulting separated images and maps were compared with a standard free-breathing ECG-triggered acquisition.
Approximate Bayesian Computation for astronomical model analysis: a case study in galaxy demographics and morphological transformation at high redshift

Free to read Approximate Bayesian Computation’ (ABC) represents a powerful methodology for the analysis of complex stochastic systems for which the likelihood of the observed data under an arbitrary set of input parameters may be entirely intractable – the latter condition rendering useless the standard machinery of tractable likelihood-based, Bayesian statistical inference [e.g. conventional Markov chain Monte Carlo (MCMC) simulation]. In this paper, we demonstrate the potential of ABC for astronomical model analysis by application to a case study in the morphological transformation of high-redshift galaxies. To this end, we develop, first, a stochastic model for the competing processes of merging and secular evolution in the early Universe, and secondly, through an ABC-based comparison against the observed demographics of massive (Mgal > 1011 M⊙) galaxies (at 1.5 < z < 3) in the Cosmic Assembly Near-IR Deep Extragalatic Legacy Survey (CANDELS)/Extended Groth Strip (EGS) data set we derive posterior probability densities for the key parameters of this model. The ‘Sequential Monte Carlo’ implementation of ABC exhibited herein, featuring both a self-generating target sequence and self-refining MCMC kernel, is amongst the most efficient of contemporary approaches to this important statistical algorithm. We highlight as well through our chosen case study the value of careful summary statistic selection, and demonstrate two modern strategies for assessment and optimization in this regard. Ultimately, our ABC analysis of the high-redshift morphological mix returns tight constraints on the evolving merger rate in the early Universe and favours major merging (with disc survival or rapid reformation) over secular evolution as the mechanism most responsible for building up the first generation of bulges in early-type discs.
CHANDRA X-RAY AND HUBBLE SPACE TELESCOPE IMAGING OF OPTICALLY SELECTED KILOPARSEC-SCALE BINARY ACTIVE GALACTIC NUCLEI. I. NATURE OF THE NUCLEAR IONIZING SOURCES

Kiloparsec-scale binary active galactic nuclei (AGNs) signal active supermassive black hole (SMBH) pairs in merging galaxies. Despite their significance, unambiguously confirmed cases remain scarce and most have been discovered serendipitously. In a previous systematic search, we optically identified four kpc-scale binary AGNs from candidates selected with double-peaked narrow emission lines at z = 0.1–0.2. Here, we present Chandra and Hubble Space Telescope Wide Field Camera 3 (WFC3) imaging of these four systems. We critically examine and confirm the binary-AGN scenario for two of the four targets, by combining high angular resolution X-ray imaging spectroscopy with Chandra ACIS-S, better nuclear position constraints from WFC3 F105W imaging, and direct starburst estimates from WFC3 F336W imaging; for the other two targets, the existing data are still consistent with the binary-AGN scenario, but we cannot rule out the possibility of only one AGN ionizing gas in both merging galaxies. We find tentative evidence for a systematically smaller X-ray-to-[O iii] luminosity ratio and/or higher Compton-thick fraction in optically selected kpc-scale binary AGNs than in single AGNs, possibly caused by a higher nuclear gas column due to mergers and/or a viewing angle bias related to the double-peak narrow-line selection. While our result lends some further support to the general approach of optically identifying kpc-scale binary AGNs, it also highlights the challenge and ambiguity of X-ray confirmation.
Cfl3d Contribution to the AIAA Supersonic Shock Boundary Layer Interaction Workshop

This paper documents the CFL3D contribution to the AIAA Supersonic Shock Boundary Layer Interaction Workshop, held in Orlando, Florida in January 2010. CFL3D is a Reynolds-averaged Navier-Stokes code. Four shock boundary layer interaction cases are computed using a one-equation turbulence model widely used for other aerodynamic problems of interest. Two of the cases have experimental data available at the workshop, and two of the cases do not. The effect of grid, flux scheme, and thin-layer approximation are investigated. Comparisons are made to the available experimental data. All four cases exhibit strong three-dimensional behavior in and near the interaction regions, resulting from influences of the tunnel side-walls.
INFRARED SUPERNOVA REMNANTS AND THEIR INFRARED-TO-X-RAY FLUX RATIOS

Recent high-resolution infrared space missions have revealed supernova remnants (SNRs) of diverse morphology in infrared (IR) dust emission that are often very different from their X-ray appearance. The observed range of infrared-to-X-ray (IRX) flux ratios of SNRs is also wide. For a sample of 20 Galactic SNRs, we obtain their IR and X-ray properties and investigate the physical causes for such large differences. We find that the observed IRX flux ratios ( R IRX,obs ) are related to the IRX morphology, with SNRs with the largest R IRX,obs showing anticorrelated IRX morphology. By analyzing the relation of R IRX,obs to X-ray and IR parameters, we show that the R IRX,obs of some SNRs agrees with theoretical ratios of SNR shocks in which dust grains are heated and destroyed by collisions with plasma particles. For the majority of SNRs, however, R IRX,obs values are either significantly smaller or significantly larger than the theoretical ratios. The latter SNRs have relatively low dust temperatures. We discuss how the natural and/or environmental properties of SNRs could have affected the IRX flux ratios and the IRX morphology of these SNRs. We conclude that the SNRs with largest R IRX,obs are probably located in a dense environment and that their IR emission is from dust heated by shock radiation rather than by collisions. Our result suggests that the IRX flux ratio, together with dust temperature, can be used to infer the nature of unresolved SNRs in external galaxies.
EFFECT OF INTERACTING RAREFACTION WAVES ON RELATIVISTICALLY HOT JETS

The effect of rarefaction acceleration on the propagation dynamics and structure of relativistically hot jets is studied through relativistic hydrodynamic simulations. We emphasize the nonlinear interaction of rarefaction waves excited at the interface between a cylindrical jet and the surrounding medium. From simplified one-dimensional (1D) models with radial jet structure, we find that a decrease in the relativistic pressure due to the interacting rarefaction waves in the central zone of the jet transiently yields a more powerful boost of the bulk jet than that expected from single rarefaction acceleration. This leads to a cyclic in situ energy conversion between thermal and bulk kinetic energies, which induces radial oscillating motion of the jet. The oscillation timescale is characterized by the initial pressure ratio of the jet to the ambient medium and follows a simple scaling relation, τoscillation∝(Pjet, 0/Pamb, 0)1/2. Extended two-dimensional simulations confirm that this radial oscillating motion in the 1D system manifests as modulation of the structure of the jet in a more realistic situation where a relativistically hot jet propagates through an ambient medium. We find that when the ambient medium has a power-law pressure distribution, the size of the reconfinement region along the propagation direction of the jet in the modulation structure λ evolves according to a self-similar relation λ∝tα/2, where α is the power-law index of the pressure distribution.
Exploring the reality of density substructures in the Palomar 5 stellar stream

We present an analysis of the presence of substructures in the stellar stream of the Palomar 5 globular cluster, as derived from Sloan Digital Sky Survey data. Using a matched filter technique, we recover the positions and sizes of overdensities reported in previous studies. To explore the reality of these structures, we also create an artificial model of the stream, in which we construct a realistic background on top of which we add a perfectly smooth stream structure, taking into account the effects of photometric completeness and interstellar extinction. We find that the smooth artificial stream then shows similarly-pronounced substructures as the real structure. Interestingly, our best-fit N-body simulation does display real projected density variations linked to stellar epicyclic motions, but these become less significant when taking into account the SDSS star-count constraints. The substructures found when applying our matched filter technique to the N-body particles converted into observable stars are thus mostly unrelated to these epicyclic motions. This analysis suggests that the majority of the previously-detected substructures along the tidal tail of Palomar 5 are artefacts of observational inhomogeneities.
Examining the consumption of radical content on YouTube

Significance Daily share of news consumption on YouTube, a social media platform with more than 2 billion monthly users, has increased in the last few years. Constructing a large dataset of users’ trajectories across the full political spectrum during 2016–2019, we identify several distinct communities of news consumers, including “far-right” and “anti-woke. ” Far right is small and not increasing in size over the observation period, while anti-woke is growing, and both grow in consumption per user. We find little evidence that the YouTube recommendation algorithm is driving attention to this content. Our results indicate that trends in video-based political news consumption are determined by a complicated combination of user preferences, platform features, and the supply-and-demand dynamics of the broader web. Although it is under-studied relative to other social media platforms, YouTube is arguably the largest and most engaging online media consumption platform in the world. Recently, YouTube’s scale has fueled concerns that YouTube users are being radicalized via a combination of biased recommendations and ostensibly apolitical “anti-woke” channels, both of which have been claimed to direct attention to radical political content. Here we test this hypothesis using a representative panel of more than 300,000 Americans and their individual-level browsing behavior, on and off YouTube, from January 2016 through December 2019. Using a labeled set of political news channels, we find that news consumption on YouTube is dominated by mainstream and largely centrist sources. Consumers of far-right content, while more engaged than average, represent a small and stable percentage of news consumers. However, consumption of “anti-woke” content, defined in terms of its opposition to progressive intellectual and political agendas, grew steadily in popularity and is correlated with consumption of far-right content off-platform. We find no evidence that engagement with far-right content is caused by YouTube recommendations systematically, nor do we find clear evidence that anti-woke channels serve as a gateway to the far right. Rather, consumption of political content on YouTube appears to reflect individual preferences that extend across the web as a whole.
Low-Reynolds-number oscillating boundary layers on adiabatic slopes

Abstract We investigate the instabilities and transition mechanisms of Boussinesq stratified boundary layers on sloping boundaries when subjected to oscillatory body forcing parallel to the slope. We examine idealized forms of boundary layers on hydraulically smooth abyssal slopes in tranquil mid- to low-latitude regions, where low-wavenumber internal tides gently heave isopycnals up and down adiabatic slopes in the absence of mean flows, high-wavenumber internal tides, shelf breaks, resonant tide–bathymetry interactions (critical slopes) and other phenomena associated with turbulence ‘hot spots’. In non-rotating low-Reynolds-number flow, increased stratification on the downslope phase has a relaminarizing effect, while on the upslope phase we find transition-to-turbulence pathways arise from shear production triggered by gravitational instabilities. When rotation is significant (low slope Burger numbers) we find that boundary layer turbulence is sustained throughout the oscillation period, resembling stratified Stokes–Ekman layer turbulence. Simulation results suggest that oscillating boundary layers on smooth slopes at low Reynolds number ($\textit {Re}\leqslant 840$), unity Prandtl number and slope Burger numbers greater than unity do not cause significant irreversible turbulent buoyancy flux (mixing), and that flat-bottom dissipation rate models derived from the tide amplitude are accurate within an order of magnitude.
A critical analysis of turbulence dissipation in near-wall flows, based on stereo particle image velocimetry and direct numerical simulation data

Abstract An experiment was performed using stereo particle image velocimetry (SPIV) in the Laboratoire de Mécanique des Fluides de Lille boundary layer facility to determine all the derivative moments needed to estimate the average dissipation rate of the turbulence kinetic energy $\epsilon = 2 \nu \langle {\mathsf{s}}_{ij}{\mathsf{s}}_{ij} \rangle$, where ${\mathsf{s}}_{ij}$ is the fluctuating strain rate and $\langle ~\rangle$ denotes ensemble averages. Also measured were all the moments of the full average deformation rate tensor, as well as all of the first, second and third fluctuating velocity moments except those involving pressure. The Reynolds number was $Re_\theta = 7634$ or $Re_\tau = 2598$. The present paper gives the measured average dissipation, $\epsilon$ and the derivative moments comprising it. The results are compared with the earlier measurements of Balint, Wallace & Vukolavcevic (J. Fluid Mech., vol. 228, 1991, pp. 53–86) and Honkan & Andreopoulos (J. Fluid Mech., vol. 350, 1997, pp. 29–96) at lower Reynolds numbers and to new results from a plane channel flow DNS at comparable Reynolds number. Of special interest is the prediction by George & Castillo (Appl. Mech. Rev., vol. 50, 1997, pp. 689–729) and Wosnik, Castillo & George (J. Fluid Mech., vol. 421, 2000, pp. 115–145) that $\epsilon ^+ \propto {x_2^+}^{-1}$ for streamwise homogeneous flows and a nearly indistinguishable power law, $\epsilon \propto {x_2^+}^{\gamma -1}$, for boundary layers. In spite of the modest Reynolds number, the predictions seem to be correct. Then the statistical character of the velocity derivatives is examined in detail, and a particular problem is identified with the breakdown of local homogeneity inside $x_2^+ = 100$. A more general alternative for partially homogeneous turbulence flows is offered which is consistent with the observations. With the help of DNS, the spatial characteristics of the dissipation very near the wall are also examined in detail.