Miracle-dz/UniFAR-FTUs huggingface dataset:
Hydrodynamic irreversibility of non-Brownian suspensions in highly confined duct flow

Abstract The irreversible behaviour of a highly confined non-Brownian suspension of spherical particles at low Reynolds number in a Newtonian fluid is studied experimentally and numerically. In the experiment, the suspension is confined in a thin rectangular channel that prevents complete particle overlap in the narrow dimension and is subjected to an oscillatory pressure-driven flow. In the small cross-sectional dimension, particles rapidly separate to the walls, whereas in the large dimension, features reminiscent of shear-induced migration in bulk suspensions are recovered. Furthermore, as a consequence of the channel geometry and the development and application of a single-camera particle tracking method, three-dimensional particle trajectories are obtained that allow us to directly associate relative particle proximity with the observed migration. Companion simulations of a steadily flowing suspension highly confined between parallel plates are conducted using the force coupling method, which also show rapid migration to the walls as well as other salient features observed in the experiment. While we consider relatively low volume fractions compared to most prior work in the area, we nevertheless observe significant and rapid migration, which we attribute to the high degree of confinement.
DNN-Compressed Domain Visual Recognition with Feature Adaptation

Learning-based image compression was shown to achieve a competitive performance with state-of-the-art transform-based codecs. This motivated the development of new learning-based visual compression standards such as JPEG-AI. Of particular interest to these emerging standards is the development of learning-based image compression systems targeting both humans and machines. This paper is concerned with learning-based compression schemes whose compressed-domain representations can be utilized to perform visual processing and computer vision tasks directly in the compressed domain. In our work, we adopt a learning-based compressed-domain classification framework for performing visual recognition using the compressed-domain latent representation at varying bit-rates. We propose a novel feature adaptation module integrating a lightweight attention model to adaptively emphasize and enhance the key features within the extracted channel-wise information. Also, we design an adaptation training strategy to utilize the pretrained pixel-domain weights. For comparison, in addition to the performance results that are obtained using our proposed latent-based compressed-domain method, we also present performance results using compressed but fully decoded images in the pixel domain as well as original uncompressed images. The obtained performance results show that our proposed compressed-domain classification model can distinctly outperform the existing compressed-domain classification models, and that it can also yield similar accuracy results with a much higher computational efficiency as compared to the pixel-domain models that are trained using fully decoded images.
Superconformal indices of $\mathcal{N}=4$ Chern-Simons matter theories

Gaiotto and Witten found that one can construct 3d $\mathcal{N}=4$ Chern-Simons matter theories by using $\mathcal{N}=4$ SCFT whose momentum map of global symmetries satisfy special condition. Usually, one uses free hypermultiplet and twisted hypermultiplet, and more recently it was found that strongly coupled theory such as 3d version of $T_N$ theory and Argyres-Douglas matter can also be used. In this paper, we compute superconformal index of these $\mathcal{N}=4$ theories and derive the Coulomb/Higgs limit. Our results determine the moduli space of vacua, which is used to check various interesting mirror symmetry involving CSM theory and usual $\mathcal{N}=4$ gauge theory.
Band-filling-controlled magnetism from transition metal intercalation in $N_{1/3}$NbS$_2$ revealed with first-principles calculations

We present a first-principles study of the effect of 3$d$ transition metal intercalation on the magnetic properties of the 2H-NbS$_2$ system, using spin-resolved density functional theory calculations to investigate the electronic structure of $N_{1/3}$NbS$_2$ ($N$ = Ti, V, Cr, Mn, Fe, Co, Ni). We are able to accurately determine the magnetic moments and crystal field splitting, and find that the magnetic properties of the materials are determined by a mechanism based on filling rigid bands with electrons from the intercalant. We predict the dominant magnetic interaction of these materials by considering Fermi surface nesting, finding agreement with experiment where data are available.
LatentPINNs: Generative physics-informed neural networks via a latent representation learning

Physics-informed neural networks (PINNs) are promising to replace conventional partial differential equation (PDE) solvers by offering more accurate and flexible PDE solutions. However, they are hampered by the relatively slow convergence and the need to perform additional, potentially expensive, training for different PDE parameters. To solve this limitation, we introduce latentPINN, a framework that utilizes latent representations of the PDE parameters as additional (to the coordinates) inputs into PINNs and allows for training over the distribution of these parameters. Motivated by the recent progress on generative models, we promote the use of latent diffusion models to learn compressed latent representations of the PDE parameters distribution and act as input parameters to NN functional solutions. We use a two-stage training scheme in which the first stage, we learn the latent representations for the distribution of PDE parameters. In the second stage, we train a physics-informed neural network over inputs given by randomly drawn samples from the coordinate space within the solution domain and samples from the learned latent representation of the PDE parameters. We test the approach on a class of level set equations given by the nonlinear Eikonal equation. We specifically share results corresponding to three different sets of Eikonal parameters (velocity models). The proposed method performs well on new phase velocity models without the need for any additional training.
Unraveling Joint Evolution of Bars, Star Formation, and Active Galactic Nuclei of Disk Galaxies

We aim to unravel the interplay between bars, star formation (SF), and active galactic nuclei (AGNs) in barred galaxies. To this end, we utilize the SDSS DR12 to select a sample of nearby (0.02 < z < 0.06) disk galaxies that are suitable for bar examination (M r < −20.12 and inclination ≲53°). We identify 3662 barred galaxies and measure the length and axis ratio of each bar. We invent new bar parameters that mitigate the stellar and bulge mass biases and show, for the first time, that the evolution of non-AGN and AGN-hosting barred galaxies should be tracked using different bar parameters: the bar length for non-AGN galaxies and the bar axis ratio for AGN-hosting galaxies. Our analysis confirms that barred galaxies have a higher specific SF rate than unbarred control galaxies. Moreover, we find a positive correlation of bar length with both the SF enhancement and the centrally star-forming galaxy fraction, indicating the interconnectivity of bars and SF through the bar-driven gas inflow. We also find that, while the AGN fraction of barred galaxies is the same as that of the unbarred control sample, galaxies hosting more massive black holes (BHs) have rounder (i.e., higher axis ratio) bars, implying that the bar is not a cause of AGN activity; rather, AGNs appear to regulate bars. Our findings corroborate theoretical predictions that bars in non-AGN galaxies grow in length, and bars in AGN-hosting galaxies become rounder as BHs grow and eventually get destroyed.
Fast Traversability Estimation for Wild Visual Navigation

Natural environments such as forests and grasslands are challenging for robotic navigation because of the false perception of rigid obstacles from high grass, twigs, or bushes. In this work, we propose Wild Visual Navigation (WVN), an online self-supervised learning system for traversability estimation which uses only vision. The system is able to continuously adapt from a short human demonstration in the field. It leverages high-dimensional features from self-supervised visual transformer models, with an online scheme for supervision generation that runs in real-time on the robot. We demonstrate the advantages of our approach with experiments and ablation studies in challenging environments in forests, parks, and grasslands. Our system is able to bootstrap the traversable terrain segmentation in less than 5 min of in-field training time, enabling the robot to navigate in complex outdoor terrains - negotiating obstacles in high grass as well as a 1.4 km footpath following. While our experiments were executed with a quadruped robot, ANYmal, the approach presented can generalize to any ground robot.
Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Data

Model averaging is a widely adopted technique in federated learning (FL) that aggregates multiple client models to obtain a global model. Remarkably, model averaging in FL can yield a superior global model, even when client models are trained with non-convex objective functions and on heterogeneous local datasets. However, the rationale behind its success remains poorly understood. To shed light on this issue, we first visualize the loss landscape of FL over client and global models to illustrate their geometric properties. The visualization shows that the client models encompass the global model within a common basin, and interestingly, the global model may deviate from the bottom of the basin while still outperforming the client models. To gain further insights into model averaging in FL, we decompose the expected loss of the global model into five factors related to the client models. Specifically, our analysis reveals that the loss of the global model after early training mainly arises from \textit{i)} the client model's loss on non-overlapping data between client datasets and the global dataset and \textit{ii)} the maximum distance between the global and client models. Based on these findings from our loss landscape visualization and loss decomposition, we propose utilizing iterative moving averaging (IMA) on the global model at the late training phase to reduce its deviation from the expected minimum, while constraining client exploration to limit the maximum distance between the global and client models. Our experiments demonstrate that incorporating IMA into existing FL methods significantly improves their accuracy and training speed on various heterogeneous data setups of benchmark datasets.
Adam-Smith at SemEval-2023 Task 4: Discovering Human Values in Arguments with Ensembles of Transformer-based Models

This paper presents the best-performing approach alias “Adam Smith” for the SemEval-2023 Task 4: “Identification of Human Values behind Arguments”. The goal of the task was to create systems that automatically identify the values within textual arguments. We train transformer-based models until they reach their loss minimum or f1-score maximum. Ensembling the models by selecting one global decision threshold that maximizes the f1-score leads to the best-performing system in the competition. Ensembling based on stacking with logistic regressions shows the best performance on an additional dataset provided to evaluate the robustness (“Nahj al-Balagha”). Apart from outlining the submitted system, we demonstrate that the use of the large ensemble model is not necessary and that the system size can be significantly reduced.
Prox-DBRO-VR: A Unified Analysis on Decentralized Byzantine-Resilient Composite Stochastic Optimization with Variance Reduction and Non-Asymptotic Convergence Rates

Decentralized Byzantine-resilient stochastic gradient algorithms resolve efficiently large-scale optimization problems in adverse conditions, such as malfunctioning agents, software bugs, and cyber attacks. This paper targets on handling a class of generic composite optimization problems over multi-agent cyberphysical systems (CPSs), with the existence of an unknown number of Byzantine agents. Based on the proximal mapping method, two variance-reduced (VR) techniques, and a norm-penalized approximation strategy, we propose a decentralized Byzantine-resilient and proximal-gradient algorithmic framework, dubbed Prox-DBRO-VR, which achieves an optimization and control goal using only local computations and communications. To reduce asymptotically the variance generated by evaluating the noisy stochastic gradients, we incorporate two localized variance-reduced techniques (SAGA and LSVRG) into Prox-DBRO-VR, to design Prox-DBRO-SAGA and Prox-DBRO-LSVRG. Via analyzing the contraction relationships among the gradient-learning error, robust consensus condition, and optimal gap, the theoretical result demonstrates that both Prox-DBRO-SAGA and Prox-DBRO-LSVRG, with a well-designed constant (resp., decaying) step-size, converge linearly (resp., sub-linearly) inside an error ball around the optimal solution to the optimization problem under standard assumptions. The trade-offs between the convergence accuracy and the number of Byzantine agents in both linear and sub-linear cases are characterized. In simulation, the effectiveness and practicability of the proposed algorithms are manifested via resolving a sparse machine-learning problem over multi-agent CPSs under various Byzantine attacks.
Helping Visually Impaired People Take Better Quality Pictures

Perception-based image analysis technologies can be used to help visually impaired people take better quality pictures by providing automated guidance, thereby empowering them to interact more confidently on social media. The photographs taken by visually impaired users often suffer from one or both of two kinds of quality issues: technical quality (distortions), and semantic quality, such as framing and aesthetic composition. Here we develop tools to help them minimize occurrences of common technical distortions, such as blur, poor exposure, and noise. We do not address the complementary problems of semantic quality, leaving that aspect for future work. The problem of assessing, and providing actionable feedback on the technical quality of pictures captured by visually impaired users is hard enough, owing to the severe, commingled distortions that often occur. To advance progress on the problem of analyzing and measuring the technical quality of visually impaired user-generated content (VI-UGC), we built a very large and unique subjective image quality and distortion dataset. This new perceptual resource, which we call the LIVE-Meta VI-UGC Database, contains 40K real-world distorted VI-UGC images and 40K patches, on which we recorded 2.7M human perceptual quality judgments and 2.7M distortion labels. Using this psychometric resource we also created an automatic limited vision picture quality and distortion predictor that learns local-to-global spatial quality relationships, achieving state-of-the-art prediction performance on VI-UGC pictures, significantly outperforming existing picture quality models on this unique class of distorted picture data. We also created a prototype feedback system that helps to guide users to mitigate quality issues and take better quality pictures, by creating a multi-task learning framework. The dataset and models can be accessed at: https://github.com/mandal-cv/visimpaired.
The Cohomology Ring of the Deligne-Mumford Moduli Space of Real Rational Curves with Conjugate Marked Points

It is a long-established and heavily-used fact that the integral cohomology ring of the Deligne-Mumford moduli space of (complex) rational curves is the polynomial ring on the boundary divisors modulo the ideal generated by the obvious geometric relations between them. We show that the rational cohomology ring of the Deligne-Mumford moduli space of real rational curves with conjugate marked points only is the polynomial ring on certain (``complex") boundary divisors and real boundary hypersurfaces modulo the ideal generated by the obvious geometric relations between them and the geometric relation in positive dimension and codimension identified in a previous paper.
NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist

In this study, we analyze automatic evaluation metrics for Natural Language Generation (NLG), specifically task-agnostic metrics and human-aligned metrics. Task-agnostic metrics, such as Perplexity, BLEU, BERTScore, are cost-effective and highly adaptable to diverse NLG tasks, yet they have a weak correlation with human. Human-aligned metrics (CTC, CtrlEval, UniEval) improves correlation level by incorporating desirable human-like qualities as training objective. However, their effectiveness at discerning system-level performance and quality of system outputs remain unclear.We present metric preference checklist as a framework to assess the effectiveness of automatic metrics in three NLG tasks: Text Summarization, Dialogue Response Generation, and Controlled Generation. Our proposed framework provides access: (i) for verifying whether automatic metrics are faithful to human preference, regardless of their correlation level to human; and (ii) for inspecting the strengths and limitations of NLG systems via pairwise evaluation. We show that automatic metrics provide a better guidance than human on discriminating system-level performance in Text Summarization and Controlled Generation tasks. We also show that multi-aspect human-aligned metric (UniEval) is not necessarily dominant over single-aspect human-aligned metrics (CTC, CtrlEval) and task-agnostic metrics (BLEU, BERTScore), particularly in Controlled Generation tasks.
Automating privacy decisions -where to draw the line?

Users are often overwhelmed by privacy decisions to manage their personal data, which can happen on the web, in mobile, and in IoT environments. These decisions can take various forms -such as decisions for setting privacy permissions or privacy preferences, decisions responding to consent requests, or to intervene and “reject” processing of one’s personal data -, and each can have different legal impacts. In all cases and for all types of decisions, scholars and industry have been proposing tools to better automate the process of privacy decisions at different levels, in order to enhance usability. We provide in this paper an overview of the main challenges raised by the automation of privacy decisions, together with a classification scheme of the existing and envisioned work and proposals addressing automation of privacy decisions.
Peculiar Spectral Evolution of the Type I Supernova 2019eix: A Possible Double Detonation from a Helium Shell on a Sub-Chandrasekhar-mass White Dwarf

We present photometric and spectroscopic data for the nearby Type I supernova (SN Ia) 2019eix (originally classified as an SN Ic), from the day of its discovery up to 100 days after maximum brightness. Before maximum light, SN 2019eix resembles a typical SN Ic, albeit lacking the usual O i feature. Its light curve is similar to the typical SN Ic with decline rates (ΔM 15,V = 0.84) and absolute magnitude M V = −18.35. However, after maximum light, this SN has unusual spectroscopic features, a large degree of line blending, significant line blanketing in the blue (λ < 5000 Å), and strong Ca ii absorption features during and after peak brightness. These unusual spectral features are similar to models of subluminous thermonuclear explosions, specifically double-detonation models of SNe Ia. Photometrically, SN 2019eix appears to be somewhat brighter with slower decline rates than other double-detonation candidates. We modeled the spectra using the radiative-transfer code TARDIS using SN 1994I (an SN Ic) as a base model to see whether we could reproduce the unusual features of SN 2019eix and found them to be consistent with the exception of the O i feature. We also compared SN 2019eix with double-detonation models and found them to best match the observations of SN 2019eix, but failed to reproduce its full photometric and spectroscopic evolution.
ReLU soothes the NTK condition number and accelerates optimization for wide neural networks

Rectified linear unit (ReLU), as a non-linear activation function, is well known to improve the expressivity of neural networks such that any continuous function can be approximated to arbitrary precision by a sufficiently wide neural network. In this work, we present another interesting and important feature of ReLU activation function. We show that ReLU leads to: {\it better separation} for similar data, and {\it better conditioning} of neural tangent kernel (NTK), which are closely related. Comparing with linear neural networks, we show that a ReLU activated wide neural network at random initialization has a larger angle separation for similar data in the feature space of model gradient, and has a smaller condition number for NTK. Note that, for a linear neural network, the data separation and NTK condition number always remain the same as in the case of a linear model. Furthermore, we show that a deeper ReLU network (i.e., with more ReLU activation operations), has a smaller NTK condition number than a shallower one. Our results imply that ReLU activation, as well as the depth of ReLU network, helps improve the gradient descent convergence rate, which is closely related to the NTK condition number.
The Whole Is Greater than the Sum of Its Parts: Improving DNN-based Music Source Separation

This paper presents the crossing scheme (X-scheme) for improving the performance of deep neural network (DNN)-based music source separation (MSS) without increasing calculation cost. It consists of three components: (i) multi-domain loss (MDL), (ii) bridging operation, which couples the individual instrument networks, and (iii) combination loss (CL). MDL enables the taking advantage of the frequency- and time-domain representations of audio signals. We modify the target network, i.e., the network architecture of the original DNN-based MSS, by adding bridging paths for each output instrument to share their information. MDL is then applied to the combinations of the output sources as well as each independent source, hence we called it CL. MDL and CL can easily be applied to many DNN-based separation methods as they are merely loss functions that are only used during training and do not affect the inference step. Bridging operation does not increase the number of learnable parameters in the network. Experimental results showed that the validity of Open-Unmix (UMX) and densely connected dilated DenseNet (D3Net) extended with our X-scheme, respectively called X-UMX and X-D3Net, by comparing them with their original versions. We also verified the effectiveness of X-scheme in a large-scale data regime, showing its generality with respect to data size. X-UMX Large (X-UMXL), which was trained on large-scale internal data and used in our experiments, is newly available at https://github.com/asteroid-team/asteroid/tree/master/egs/musdb18/X-UMX.
ParaLS: Lexical Substitution via Pretrained Paraphraser

Lexical substitution (LS) aims at finding appropriate substitutes for a target word in a sentence. Recently, LS methods based on pretrained language models have made remarkable progress, generating potential substitutes for a target word through analysis of its contextual surroundings. However, these methods tend to overlook the preservation of the sentence’s meaning when generating the substitutes. This study explores how to generate the substitute candidates from a paraphraser, as the generated paraphrases from a paraphraser contain variations in word choice and preserve the sentence’s meaning. Since we cannot directly generate the substitutes via commonly used decoding strategies, we propose two simple decoding strategies that focus on the variations of the target word during decoding. Experimental results show that our methods outperform state-of-the-art LS methods based on pre-trained language models on three benchmarks.