Miracle-dz/UniFAR-FTUs huggingface dataset:
BASS. XXV. DR2 Broad-line-based Black Hole Mass Estimates and Biases from Obscuration

We present measurements of broad emission lines and virial estimates of supermassive black hole masses (M BH) for a large sample of ultrahard X-ray-selected active galactic nuclei (AGNs) as part of the second data release of the BAT AGN Spectroscopic Survey (BASS/DR2). Our catalog includes M BH estimates for a total of 689 AGNs, determined from the Hα, Hβ, Mg ii λ2798, and/or C iv λ1549 broad emission lines. The core sample includes a total of 512 AGNs drawn from the 70 month Swift/BAT all-sky catalog. We also provide measurements for 177 additional AGNs that are drawn from deeper Swift/BAT survey data. We study the links between M BH estimates and line-of-sight obscuration measured from X-ray spectral analysis. We find that broad Hα emission lines in obscured AGNs ( log(NH/cm−2)>22.0 ) are on average a factor of 8.0−2.4+4.1 weaker relative to ultrahard X-ray emission and about 35−12+7 % narrower than those in unobscured sources (i.e., log(NH/cm−2)<21.5 ). This indicates that the innermost part of the broad-line region is preferentially absorbed. Consequently, current single-epoch M BH prescriptions result in severely underestimated (>1 dex) masses for Type 1.9 sources (AGNs with broad Hα but no broad Hβ) and/or sources with log(NH/cm−2)≳22.0 . We provide simple multiplicative corrections for the observed luminosity and width of the broad Hα component (L[bHα] and FWHM[bHα]) in such sources to account for this effect and to (partially) remedy M BH estimates for Type 1.9 objects. As a key ingredient of BASS/DR2, our work provides the community with the data needed to further study powerful AGNs in the low-redshift universe.
Easy Adaptation to Mitigate Gender Bias in Multilingual Text Classification

Existing approaches to mitigate demographic biases evaluate on monolingual data, however, multilingual data has not been examined. In this work, we treat the gender as domains (e.g., male vs. female) and present a standard domain adaptation model to reduce the gender bias and improve performance of text classifiers under multilingual settings. We evaluate our approach on two text classification tasks, hate speech detection and rating prediction, and demonstrate the effectiveness of our approach with three fair-aware baselines.
Characterizing Error Mitigation by Symmetry Verification in QAOA

Hardware errors are a major obstacle to demonstrating quantum advantage with the quantum approximate optimization algorithm (QAOA). Recently, symmetry verification has been proposed and empirically demonstrated to boost the quantum state fidelity, the expected solution quality, and the success probability of QAOA on a superconducting quantum processor. Symmetry verification uses parity checks that leverage the symmetries of the objective function to be optimized. We develop a theoretical framework for analyzing this approach under local noise and derive explicit formulas for fidelity improvements on problems with global ℤ2 symmetry. We numerically investigate the symmetry verification on the MaxCut problem and identify the error regimes in which this approach improves the QAOA objective. We observe that these regimes correspond to the error rates present in near-term hardware. We further demonstrate the efficacy of symmetry verification on an IonQ trapped ion quantum processor where an improvement in the QAOA objective of up to 19.2% is observed.
Asymptotics for the rotating fluids and primitive systems with large ill-prepared initial data in critical spaces

In this article we study the lifespan and asymptotics (in the large rotation and stratification regime) for the Primitive system for highly ill-prepared initial data in critical spaces. Compared to our previous works, we simplified the proof and made it adaptable to the Rotating fluids system with highly ill-prepared initial data decomposed as a sum of 2D horizontal part and a very large 3D part. We also provide explicit convergence rates.
Chaos for rescaled measures on Kac’s sphere

In this article we study a relatively novel way of constructing chaotic sequences of probability measures supported on Kac's sphere, which are obtained as the law of a vector of $N$ i.i. d. variables after it is rescaled to have unit average energy. We show that, as $N$ increases, this sequence is chaotic in the sense of Kac, with respect to the Wasserstein distance, in $L^1$, in the entropic sense, and in the Fisher information sense. For many of these results, we provide explicit rates of polynomial order in $N$. In the process, we improve a quantitative entropic chaos result of Haurey and Mischler by relaxing the finite moment requirement on the densities from order $6$ to $4+\epsilon$.
medXGAN: Visual Explanations for Medical Classifiers through a Generative Latent Space

Despite the surge of deep learning in the past decade, some users are skeptical to deploy these models in practice due to their black-box nature. Specifically, in the medical space where there are severe potential repercussions, we need to develop methods to gain confidence in the models’ decisions. To this end, we propose a novel medical imaging generative adversarial framework, medXGAN (medical eXplanation GAN), to visually explain what a medical classifier focuses on in its binary predictions. By encoding domain knowledge of medical images, we are able to disentangle anatomical structure and pathology, leading to fine-grained visualization through latent interpolation. Furthermore, we optimize the latent space such that interpolation explains how the features contribute to the classifier’s output. Our method outperforms baselines such as Gradient-Weighted Class Activation Mapping (Grad-CAM) and Integrated Gradients in localization and explanatory ability. Additionally, a combination of the medXGAN with Integrated Gradients can yield explanations more robust to noise. The project page with code is available at: https://avdravid.github. io/medXGANpage/.
What do Toothbrushes do in the Kitchen? How Transformers Think our World is Structured

Transformer-based models are now predominant in NLP.They outperform approaches based on static models in many respects.This success has in turn prompted research that reveals a number of biases in the language models generated by transformers.In this paper we utilize this research on biases to investigate to what extent transformer-based language models allow for extracting knowledge about object relations (X occurs in Y; X consists of Z; action A involves using X). To this end, we compare contextualized models with their static counterparts. We make this comparison dependent on the application of a number of similarity measures and classifiers.Our results are threefold:Firstly, we show that the models combined with the different similarity measures differ greatly in terms of the amount of knowledge they allow for extracting.Secondly, our results suggest that similarity measures perform much worse than classifier-based approaches.Thirdly, we show that, surprisingly, static models perform almost as well as contextualized models – in some cases even better.
Evolutionary Algorithms for Limiting the Effect of Uncertainty for the Knapsack Problem with Stochastic Profits

Evolutionary algorithms have been widely used for a range of stochastic optimization problems in order to address complex real-world optimization problems. We consider the knapsack problem where the profits involve uncertainties. Such a stochastic setting reflects important real-world scenarios where the profit that can be realized is uncertain. We introduce different ways of dealing with stochastic profits based on tail inequalities such as Chebyshev's inequality and Hoeffding bounds that allow to limit the impact of uncertainties. We examine simple evolutionary algorithms and the use of heavy tail mutation and a problem-specific crossover operator for optimizing uncertain profits. Our experimental investigations on different benchmarks instances show the results of different approaches based on tail inequalities as well as improvements achievable through heavy tail mutation and the problem specific crossover operator.
Global Surfaces of Section and Periodic Orbits in The Spatial Isosceles Three Body Problem

We study the spatial isosceles three body problem, which is a system with two degrees of freedom after modulo the rotation symmetry. For certain choices of energy and angular momentum, we find some disk-like global surfaces of section with the Euler orbit as their common boundary, and a brake orbit passing through them. By considering the Poincar\'e maps of these global surfaces of section, we prove the existence of all kinds of different periodic orbits under certain assumption. Moreover, we are able to prove, for generic choices of masses, the system always has infinitely many periodic orbits. One of the key is to estimate the rotation numbers of the Euler orbit and the brake orbit with respect to the Poincar\'e map. For this, we establish formulas connected these numbers with the mean indices of the corresponding orbits using the Maslov-type index.
Extension of Lipschitz maps definable in Hensel minimal structures

In this paper, we establish a theorem on extension of Lipschitz maps deﬁnable in Hensel minimal, non-trivially valued ﬁelds K of equicharacteristic zero. It may be regarded as a deﬁnable, non-Archimedean, non-locally compact version of Kirszbraun’s theorem.
A note on the distribution of the extreme degrees of a random graph via the Stein-Chen method

We offer an alternative proof, using the Stein-Chen method, of Bollob\'{a}s' theorem concerning the distribution of the extreme degrees of a random graph. Our proof also provides a rate of convergence of the extreme degree to its asymptotic distribution. The same method also applies in a more general setting where the probability of every pair of vertices being connected by edges depends on the number of vertices.
Into the Depths: A New Activity Metric for High-precision Radial Velocity Measurements Based on Line Depth Variations

The discovery and characterization of extrasolar planets using radial velocity (RV) measurements is limited by noise sources from the surfaces of host stars. Current techniques to suppress stellar magnetic activity rely on decorrelation using an activity indicator (e.g., strength of the Ca ii lines, width of the cross-correlation function, broadband photometry) or measurement of the RVs using only a subset of spectral lines that have been shown to be insensitive to activity. Here, we combine the above techniques by constructing a high-signal-to-noise activity indicator, the depth metric (t) , from the most activity-sensitive spectral lines using the “line-by-line” method of Dumusque (2018). Analogous to photometric decorrelation of RVs or Gaussian progress regression modeling of activity indices, time series modeling of (t) reduces the amplitude of magnetic activity in RV measurements; in an αCenB RV time series from HARPS, the RV rms was reduced from 2.67 to 1.02 m s−1. (t) modeling enabled us to characterize injected planetary signals as small as 1 m s−1. In terms of noise reduction and injected signal recovery, (t) modeling outperforms activity mitigation via the selection of activity-insensitive spectral lines. For Sun-like stars with activity signals on the m s−1 level, the depth metric independently tracks rotationally modulated and multiyear stellar activity with a level of quality similar to that of the FWHM of the CCF and log RHK′ . The depth metric and its elaborations will be a powerful tool in the mitigation of stellar magnetic activity, particularly as a means of connecting stellar activity to physical processes within host stars.
Black holes: on the universality of the Kerr hypothesis

To what extent are all astrophysical, dark, compact objects both black holes (BHs) and described by the Kerr geometry? We embark on the exercise of defying the universality of this remarkable idea, often called the"Kerr hypothesis". After establishing its rationale and timeliness, we define a minimal set of reasonability criteria for alternative models of dark compact objects. Then, as proof of principle, we discuss concrete, dynamically robust non-Kerr BHs and horizonless imitators, that 1) pass the basic theoretical, and in particular dynamical, tests, 2) match (some of the) state of the art astrophysical observables and 3) only emerge at some (macroscopic) scales. These examples illustrate how the universality (at all macroscopic scales) of the Kerr hypothesis can be challenged.
Cosmic Reionization on Computers: Physical Origin of Long Dark Gaps in Quasar Absorption Spectra

I explore the properties of “dark gaps”—regions in quasar absorption spectra without significant transmission—with several simulations from the Cosmic Reionization On Computers (CROC) project. The CROC simulations in the largest available boxes (120 cMpc) come close to matching both the distribution of mean opacities and the frequency of dark gaps, but alas not in the same model: the run that matches the mean opacities fails to contain enough dark gaps and vice versa. Nevertheless, the run that matches the dark gap distributions serves as a counterexample to claims in the literature that the dark gap statistics requires a late end to reionization—in that run reionization ends at z = 6.7 (likely too early). While multiple factors contribute to the frequency of large dark gaps in the simulations, the primary factor that controls the overall shape of the dark gap distribution is the ionization level in voids—the lowest-density regions produce the highest transmission spikes that terminate long gaps. As the result, the dark gap distribution correlates strongly with the fraction of the spectrum above the gap detection threshold and the observed distribution is matched by the simulation in which this fraction is 2%. Hence, the gap distribution by itself does not constrain the timing of reionization, although it may do so in combination with the distribution of mean opacities.
Are Multimodal Transformers Robust to Missing Modality?

Multimodal data collected from the real world are often imperfect due to missing modalities. Therefore multimodal models that are robust against modal-incomplete data are highly preferred. Recently, Transformer models have shown great success in processing multimodal data. However, existing work has been limited to either architecture designs or pre-training strategies; whether Transformer models are naturally robust against missing-modal data has rarely been investigated. In this paper, we present the first-of-its-kind work to comprehensively investigate the behavior of Transformers in the presence of modal-incomplete data. Unsurprising, we find Transformer models are sensitive to missing modalities while different modal fusion strategies will significantly affect the robustness. What surprised us is that the optimal fusion strategy is dataset dependent even for the same Transformer model; there does not exist a universal strategy that works in general cases. Based on these findings, we propose a principle method to improve the robustness of Transformer models byautomatically searching for an optimal fusion strategy regarding input data. Experimental validations on three benchmarks support the superior performance of the proposed method.
TT deformations of supersymmetric quantum mechanics

We define a manifestly supersymmetric version of the TT deformation appropriate for a class of (0 + 1)-dimensional theories with N = 1 or N = 2 supersymmetry, including one presentation of the super-Schwarzian theory which is dual to JT supergravity. These deformations are written in terms of Noether currents associated with translations in superspace, so we refer to them collectively as f(Q) deformations. We provide evidence that the f(Q) deformations of N = 1 and N = 2 theories are on-shell equivalent to the dimensionally reduced supercurrent-squared deformations of 2d theories with N = (0, 1) and N = (1, 1) supersymmetry, respectively. In the N = 1 case, we present two forms of the f(Q) deformation which drive the same flow, and clarify their equivalence by studying the analogous equivalent deformations in the non-supersymmetric setting.
Proximal Policy Optimization Learning based Control of Congested Freeway Traffic

In this paper, a delay compensation feedback controller based on reinforcement learning is proposed to adjust the time interval of the adaptive cruise control (ACC) vehicle agents in the traffic congestion by introducing the proximal policy optimization (PPO) scheme. The high‐speed traffic flow is characterized by a two‐by‐two Aw Rasle Zhang nonlinear first‐order partial differential equations (PDEs). Unlike the backstepping delay compensation control,23 the PPO controller proposed in this paper consists of the current traffic flow velocity, the current traffic flow density and the previous one step control input. Since the system dynamics of the traffic flow are difficult to be expressed mathematically, the control gains of the three feedback can be determined via learning from the interaction between the PPO and the digital simulator of the traffic system. The performance of Lyapunov control, backstepping control and PPO control are compared with numerical simulation. The results demonstrate that PPO control is superior to Lyapunov control in terms of the convergence rate and control efforts for the traffic system without delay. As for the traffic system with unstable input delay value, the performance of PPO controller is also equivalent to that of backstepping controller. Besides, PPO is more robust than backstepping controller when the parameter is sensitive to Gaussian noise.
Gas Accretion Can Drive Turbulence in Galaxies

The driving of turbulence in galaxies is deeply connected with the physics of feedback, star formation, outflows, accretion, and radial transport in disks. The velocity dispersion of gas in galaxies therefore offers a promising observational window into these processes. However, the relative importance of each of these mechanisms remains controversial. In this work we revisit the possibility that turbulence on galactic scales is driven by the direct impact of accreting gaseous material on the disk. We measure this effect in a disk-like star-forming galaxy in IllustrisTNG, using the high-resolution cosmological magnetohydrodynamical simulation TNG50. We employ Lagrangian tracer particles with a high time cadence of only a few million years to identify accretion and other events. The energies of particles are measured by stacking the events in bins of time around the event. The average effect of each event is measured by fitting explicit models for the kinetic and turbulent energies as a function of time. These measurements are corroborated by cross-correlating the turbulent energy with other time series and searching for signals of causality, i.e., asymmetries across zero time lag. We find that accretion contributes to the large-scale turbulent kinetic energy even if it does not dominate in this ∼5 × 109 M ⊙ stellar mass galaxy. Extrapolating this finding to a range of galaxy masses, we find that there are regimes where energy from direct accretion may dominate the turbulent energy budget, particularly in disk outskirts, galaxies less massive than the Milky Way, and at redshift ∼2.
Unified Speech-Text Pre-training for Speech Translation and Recognition

In this work, we describe a method to jointly pre-train speech and text in an encoder-decoder modeling framework for speech translation and recognition. The proposed method utilizes multi-task learning to integrate four self-supervised and supervised subtasks for cross modality learning. A self-supervised speech subtask, which leverages unlabelled speech data, and a (self-)supervised text to text subtask, which makes use of abundant text training data, take up the majority of the pre-training time. Two auxiliary supervised speech tasks are included to unify speech and text modeling space. Detailed analysis reveals learning interference among subtasks. In order to alleviate the subtask interference, two pre-training configurations are proposed for speech translation and speech recognition respectively. Our experiments show the proposed method can effectively fuse speech and text information into one model. It achieves between 1.7 and 2.3 BLEU improvement above the state of the art on the MuST-C speech translation dataset and comparable WERs to wav2vec 2.0 on the Librispeech speech recognition task.
Contraction Analysis of Hopfield Neural Networks with Hebbian Learning

Motivated by advances in neuroscience and machine learning, this paper is concerned with the modeling and analysis of Hopfield neural networks with dynamic recurrent connections undergoing Hebbian learning. To capture the synaptic sparsity of neural circuits, we propose a low dimensional formulation for the model and then characterize its key dynamical properties. First, we give a biologically-inspired forward invariance result. Then, we give sufficient conditions for the non-Euclidean contractivity of the model. Our contraction analysis leads to stability and robustness of time-varying trajectories – for networks with both excitatory and inhibitory synapses governed by both Hebbian and anti-Hebbian rules. Our proposed contractivity test is based upon biologically meaningful quantities, e.g., neural and synaptic decay rate, maximum out-degree, and the maximum synaptic strength. Finally, we show that the model satisfies Dale’s principle. The effectiveness of our results is illustrated via a numerical example.
Bootstrap Motion Forecasting With Self-Consistent Constraints

We present a novel framework to bootstrap Motion forecastIng with Self-consistent Constraints (MISC). The motion forecasting task aims at predicting future trajectories of vehicles by incorporating spatial and temporal information from the past. A key design of MISC is the proposed Dual Consistency Constraints that regularize the predicted trajectories under spatial and temporal perturbation during training. Also, to model the multi-modality in motion forecasting, we design a novel self-ensembling scheme to obtain accurate teacher targets to enforce the self-constraints with multi-modality supervision. With explicit constraints from multiple teacher targets, we observe a clear improvement in the prediction performance. Extensive experiments on the Argoverse motion forecasting benchmark and Waymo Open Motion dataset show that MISC significantly outperforms the state-of-the-art methods. As the proposed strategies are general and can be easily incorporated into other motion forecasting approaches, we also demonstrate that our proposed scheme consistently improves the prediction performance of several existing methods.