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Lei Huang

Publications and source records attributed to Lei Huang.

At least 163 records · Page 9Linked to original sources
arXiv2023

Observational Evidence of a Centi-parsec Supermassive Black Hole Binary Existing in the Nearby Galaxy M81

Studying a centi-parsec supermassive black hole binary (SMBHB) would allow us to explore a new parameter space in active galactic nuclei, and these objects are also potential sources of gravitational waves. We report evidence that an SMBHB with an orbital period of about 30 yr may be resident in the nearby galactic nucleus M81. This orbital period and the known mass of M81 imply an orbital separation of about 0.02 pc. The jet emanating from the primary black hole showed a short period of jet wobbling at about 16.7 yr, superposing a long-term precession at a timescale of several hundred years. Periodic radio and X-ray outbursts were also found two times per orbital period, which could be explained by a double-peaked mass accretion rate variation per binary orbit. If confirmed, M81 would be one of the closest SMBHB candidates, providing a rare opportunity to study the final parsec problem.

astro-ph.GA↗
arXiv2023

FIXED: Frustratingly Easy Domain Generalization with Mixup

Domain generalization (DG) aims to learn a generalizable model from multiple training domains such that it can perform well on unseen target domains. A popular strategy is to augment training data to benefit generalization through methods such as Mixup~\cite{zhang2018mixup}. While the vanilla Mixup can be directly applied, theoretical and empirical investigations uncover several shortcomings that limit its performance. Firstly, Mixup cannot effectively identify the domain and class information that can be used for learning invariant representations. Secondly, Mixup may introduce synthetic noisy data points via random interpolation, which lowers its discrimination capability. Based on the analysis, we propose a simple yet effective enhancement for Mixup-based DG, namely domain-invariant Feature mIXup (FIX). It learns domain-invariant representations for Mixup. To further enhance discrimination, we leverage existing techniques to enlarge margins among classes to further propose the domain-invariant Feature MIXup with Enhanced Discrimination (FIXED) approach. We present theoretical insights about guarantees on its effectiveness. Extensive experiments on seven public datasets across two modalities including image classification (Digits-DG, PACS, Office-Home) and time series (DSADS, PAMAP2, UCI-HAR, and USC-HAD) demonstrate that our approach significantly outperforms nine state-of-the-art related methods, beating the best performing baseline by 6.5\% on average in terms of test accuracy. Code is available at: https://github.com/jindongwang/transferlearning/tree/master/code/deep/fixed.

cs.CV↗
arXiv2023

How many supermassive black hole binaries are detectable through tracking relative motions by sub/millimeter VLBI

The sub/millimeter wavelengths (86-690 GHz) very long baseline interferometry (VLBI) will provide $\sim5-40\ μ$as angular resolution, $\sim10$ mJy baseline sensitivity, and $\sim 1\ μ$as/yr proper motion precision, which can directly detect supermassive black hole binary (SMBHB) systems by imaging the two visible sources and tracking their relative motions. Such a way exhibits an advantage compared to indirect detect methods of observing periodic signals in motion and light curves, which are difficult to confirm from competing models. Moreover, tracking relative motion at sub/millimeter wavelengths is more reliable, as there is a negligible offset between the emission region and the black hole center. In this way, it is unnecessary to correct the black hole location by a prior of jet morphology as it would be required at longer wavelengths. We extend the formalism developed in D'Orazio & Loeb (2018) to link the observations with the orbital evolution of SMBHBs from the $\lesssim$10 kpc dynamical friction stages to the $\lesssim 0.01$ pc gravitational radiation stages, and estimate the detectable numbers of SMBHBs. By assuming 5\% of AGNs holding SMBHBs, we find that the number of detectable SMBHBs with redshift $z\le 0.5$ and mass $M\leq 10^{11}M_\odot$ is about 20. Such detection relies heavily on proper motion precision and sensitivity. Furthermore, we propose that the simultaneous multi-frequency technique plays a key role in meeting the observational requirements.

astro-ph.GA↗
arXiv2023

Distributed Linear Regression with Compositional Covariates

With the availability of extraordinarily huge data sets, solving the problems of distributed statistical methodology and computing for such data sets has become increasingly crucial in the big data area. In this paper, we focus on the distributed sparse penalized linear log-contrast model in massive compositional data. In particular, two distributed optimization techniques under centralized and decentralized topologies are proposed for solving the two different constrained convex optimization problems. Both two proposed algorithms are based on the frameworks of Alternating Direction Method of Multipliers (ADMM) and Coordinate Descent Method of Multipliers(CDMM, Lin et al., 2014, Biometrika). It is worth emphasizing that, in the decentralized topology, we introduce a distributed coordinate-wise descent algorithm based on Group ADMM(GADMM, Elgabli et al., 2020, Journal of Machine Learning Research) for obtaining a communication-efficient regularized estimation. Correspondingly, the convergence theories of the proposed algorithms are rigorously established under some regularity conditions. Numerical experiments on both synthetic and real data are conducted to evaluate our proposed algorithms.

stat.ML↗
arXiv2023

Towards Defending Multiple $\ell_p$-norm Bounded Adversarial Perturbations via Gated Batch Normalization

There has been extensive evidence demonstrating that deep neural networks are vulnerable to adversarial examples, which motivates the development of defenses against adversarial attacks. Existing adversarial defenses typically improve model robustness against individual specific perturbation types (\eg, $\ell_{\infty}$-norm bounded adversarial examples). However, adversaries are likely to generate multiple types of perturbations in practice (\eg, $\ell_1$, $\ell_2$, and $\ell_{\infty}$ perturbations). Some recent methods improve model robustness against adversarial attacks in multiple $\ell_p$ balls, but their performance against each perturbation type is still far from satisfactory. In this paper, we observe that different $\ell_p$ bounded adversarial perturbations induce different statistical properties that can be separated and characterized by the statistics of Batch Normalization (BN). We thus propose Gated Batch Normalization (GBN) to adversarially train a perturbation-invariant predictor for defending multiple $\ell_p$ bounded adversarial perturbations. GBN consists of a multi-branch BN layer and a gated sub-network. Each BN branch in GBN is in charge of one perturbation type to ensure that the normalized output is aligned towards learning perturbation-invariant representation. Meanwhile, the gated sub-network is designed to separate inputs added with different perturbation types. We perform an extensive evaluation of our approach on commonly-used dataset including MNIST, CIFAR-10, and Tiny-ImageNet, and demonstrate that GBN outperforms previous defense proposals against multiple perturbation types (\ie, $\ell_1$, $\ell_2$, and $\ell_{\infty}$ perturbations) by large margins.

cs.CV↗
arXiv2023

High-rate discretely-modulated continuous-variable quantum key distribution using quantum machine learning

We propose a high-rate scheme for discretely-modulated continuous-variable quantum key distribution (DM CVQKD) using quantum machine learning technologies, which divides the whole CVQKD system into three parts, i.e., the initialization part that is used for training and estimating quantum classifier, the prediction part that is used for generating highly correlated raw keys, and the data-postprocessing part that generates the final secret key string shared by Alice and Bob. To this end, a low-complexity quantum k-nearest neighbor (QkNN) classifier is designed for predicting the lossy discretely-modulated coherent states (DMCSs) at Bob's side. The performance of the proposed QkNN-based CVQKD especially in terms of machine learning metrics and complexity is analyzed, and its theoretical security is proved by using semi-definite program (SDP) method. Numerical simulation shows that the secret key rate of our proposed scheme is explicitly superior to the existing DM CVQKD protocols, and it can be further enhanced with the increase of modulation variance.

quant-ph↗
arXiv2023

White-light superflare and long-term activity of the nearby M7 type binary EI~Cnc observed with GWAC system

Stellar white-light flares are believed to play an essential role on the physical and chemical properties of the atmosphere of the surrounding exoplanets. Here we report an optical monitoring campaign on the nearby flaring system EI~Cnc carried out by the Ground-based Wide Angle Cameras (GWAC) and its dedicated follow-up telescope. A superflare, coming from the brighter component EI~CncA, was detected and observed, in which four components are required to properly model the complex decay light curve. The lower limit of flare energy in the $R-$band is estimated to be $3.3\times10^{32}$ ergs. 27 flares are additionally detected from the GWAC archive data with a total duration of 290 hours. The inferred cumulative flare frequency distribution follows a quite shallow power-law function with a slope of $β=-0.50\pm 0.03$ over the energy range between $10^{30}$ and $10^{33}$ erg, which reinforces the trend that stars cooler than M4 show enhanced superflare activity. The flares identified in EI~Cnc enable us to extend the $τ-E$ relationship previously established in the white-light superflares of solar-type stars down to an energy as low as $\sim10^{30}$erg (i.e., by three orders): $τ\propto E^{0.42\pm0.02}$, which suggests a common flare mechanism for stars with a type from M to solar-like, and implies an invariant of $B^{1/3}\upsilon_{\rm A}$ in the white-light flares.

astro-ph.SR↗
arXiv2023

DiffDTM: A conditional structure-free framework for bioactive molecules generation targeted for dual proteins

Advances in deep generative models shed light on de novo molecule generation with desired properties. However, molecule generation targeted for dual protein targets still faces formidable challenges including protein 3D structure data requisition for model training, auto-regressive sampling, and model generalization for unseen targets. Here, we proposed DiffDTM, a novel conditional structure-free deep generative model based on a diffusion model for dual targets based molecule generation to address the above issues. Specifically, DiffDTM receives protein sequences and molecular graphs as inputs instead of protein and molecular conformations and incorporates an information fusion module to achieve conditional generation in a one-shot manner. We have conducted comprehensive multi-view experiments to demonstrate that DiffDTM can generate drug-like, synthesis-accessible, novel, and high-binding affinity molecules targeting specific dual proteins, outperforming the state-of-the-art (SOTA) models in terms of multiple evaluation metrics. Furthermore, we utilized DiffDTM to generate molecules towards dopamine receptor D2 and 5-hydroxytryptamine receptor 1A as new antipsychotics. The experimental results indicate that DiffDTM can be easily plugged into unseen dual targets to generate bioactive molecules, addressing the issues of requiring insufficient active molecule data for training as well as the need to retrain when encountering new targets.

cs.LG↗
arXiv2023

One-Bit Spectrum Sensing for Cognitive Radio

Spectrum sensing in cognitive radio necessitates effective monitoring of wide bandwidths, which requires high-rate sampling. Traditional spectrum sensing methods employing high-precision analog-to-digital converters (ADCs) result in increased power consumption and expensive hardware costs. In this paper, we explore blind spectrum sensing utilizing one-bit ADCs. We derive a closed-form detector based on Rao's test and demonstrate its equivalence with the second-order eigenvalue-moment-ratio test. Furthermore, a near-exact distribution based on the moment-based method, and an approximate distribution in the low signal-to-noise ratio (SNR) regime with the use of the central limit theorem, are obtained. Theoretical analysis is then performed and our results show that the performance loss of the proposed detector is approximately $2$ dB ($π/2$) compared to detectors employing $\infty$-bit ADCs when SNR is low. This loss can be compensated for by using approximately $2.47$ ($π^2/4$) times more samples. In addition, we unveil that the efficiency of incoherent accumulation in one-bit detection is the square root of that of coherent accumulation. Simulation results corroborate the correctness of our theoretical calculations.

eess.SP↗
arXiv2023

Fine-grained Policy-driven I/O Sharing for Burst Buffers

A burst buffer is a common method to bridge the performance gap between the I/O needs of modern supercomputing applications and the performance of the shared file system on large-scale supercomputers. However, existing I/O sharing methods require resource isolation, offline profiling, or repeated execution that significantly limit the utilization and applicability of these systems. Here we present ThemisIO, a policy-driven I/O sharing framework for a remote-shared burst buffer: a dedicated group of I/O nodes, each with a local storage device. ThemisIO preserves high utilization by implementing opportunity fairness so that it can reallocate unused I/O resources to other applications. ThemisIO accurately and efficiently allocates I/O cycles among applications, purely based on real-time I/O behavior without requiring user-supplied information or offline-profiled application characteristics. ThemisIO supports a variety of fair sharing policies, such as user-fair, size-fair, as well as composite policies, e.g., group-then-user-fair. All these features are enabled by its statistical token design. ThemisIO can alter the execution order of incoming I/O requests based on assigned tokens to precisely balance I/O cycles between applications via time slicing, thereby enforcing processing isolation. Experiments using I/O benchmarks show that ThemisIO sustains 13.5-13.7% higher I/O throughput and 19.5-40.4% lower performance variation than existing algorithms. For real applications, ThemisIO significantly reduces the slowdown by 59.1-99.8% caused by I/O interference.

cs.DC↗
arXiv2023

A Lightweight Recurrent Learning Network for Sustainable Compressed Sensing

Recently, deep learning-based compressed sensing (CS) has achieved great success in reducing the sampling and computational cost of sensing systems and improving the reconstruction quality. These approaches, however, largely overlook the issue of the computational cost; they rely on complex structures and task-specific operator designs, resulting in extensive storage and high energy consumption in CS imaging systems. In this paper, we propose a lightweight but effective deep neural network based on recurrent learning to achieve a sustainable CS system; it requires a smaller number of parameters but obtains high-quality reconstructions. Specifically, our proposed network consists of an initial reconstruction sub-network and a residual reconstruction sub-network. While the initial reconstruction sub-network has a hierarchical structure to progressively recover the image, reducing the number of parameters, the residual reconstruction sub-network facilitates recurrent residual feature extraction via recurrent learning to perform both feature fusion and deep reconstructions across different scales. In addition, we also demonstrate that, after the initial reconstruction, feature maps with reduced sizes are sufficient to recover the residual information, and thus we achieved a significant reduction in the amount of memory required. Extensive experiments illustrate that our proposed model can achieve a better reconstruction quality than existing state-of-the-art CS algorithms, and it also has a smaller number of network parameters than these algorithms. Our source codes are available at: https://github.com/C66YU/CSRN.

cs.CV↗
arXiv2023

Prompt-to-afterglow transition of optical emission in a long gamma-ray burst consistent with a fireball

Long gamma-ray bursts (GRBs), which signify the end-life collapsing of very massive stars, are produced by extremely relativistic jets colliding into circumstellar medium. Huge energy is released both in the first few seconds, namely the internal dissipation phase that powers prompt emissions, and in the subsequent self-similar jet-deceleration phase that produces afterglows observed in broad-band electromagnetic spectrum. However, prompt optical emissions of GRBs have been rarely detected, seriously limiting our understanding of the transition between the two phases. Here we report detection of prompt optical emissions from a gamma-ray burst (i.e. GRB 201223A) using a dedicated telescope array with a high temporal resolution and a wide time coverage. The early phase coincident with prompt γ-ray emissions show a luminosity in great excess with respect to the extrapolation of γ-rays, while the later luminosity bump is consistent with onset of the afterglow. The clearly detected transition allows us to differentiate physical processes contributing to early optical emissions and to diagnose the composition of the jet

astro-ph.HE↗
arXiv2023

A huge-amplitude white-light superflare on a L0 brown dwarf discovered by GWAC survey

White-light superflares from ultra cool stars are thought to be resulted from magnetic reconnection, but the magnetic dynamics in a fully convective star is not clear yet. In this paper, we report a stellar superflare detected with the Ground Wide Angle Camera (GWAC), along with rapid follow-ups with the F60A, Xinglong 2.16m and LCOGT telescopes. The effective temperature of the counterpart is estimated to be $2200\pm50$K by the BT-Settl model, corresponding to a spectral type of L0. The $R-$band light curve can be modeled as a sum of three exponential decay components, where the impulsive component contributes a fraction of 23\% of the total energy, while the gradual and the shallower decay phases emit 42\% and 35\% of the total energy, respectively. The strong and variable Balmer narrow emission lines indicate the large amplitude flare is resulted from magnetic activity. The bolometric energy released is about $6.4\times10^{33}$ ergs, equivalent to an energy release in a duration of 143.7 hours at its quiescent level. The amplitude of $ΔR=-8.6 $mag ( or $ΔV=-11.2$ mag), placing it one of the highest amplitudes of any ultra cool star recorded with excellent temporal resolution. We argue that a stellar flare with such rapidly decaying and huge amplitude at distances greater than 1 kpc may be false positive in searching for counterparts of catastrophic events such as gravitational wave events or gamma-ray bursts, which are valuable in time-domain astronomy and should be given more attention.

astro-ph.SR↗
arXiv2023

One-Bit Covariance Reconstruction with Non-zero Thresholds: Algorithm and Performance Analysis

Covariance matrix reconstruction is a topic of great significance in the field of one-bit signal processing and has numerous practical applications. Despite its importance, the conventional arcsine law with zero threshold is incapable of recovering the diagonal elements of the covariance matrix. To address this limitation, recent studies have proposed the use of non-zero clipping thresholds. However, the relationship between the estimation error and the sampling threshold is not yet known. In this paper, we undertake an analysis of the mean squared error by computing the Fisher information matrix for a given threshold. Our results reveal that the optimal threshold can vary considerably, depending on the variances and correlation coefficients. As a result, it is inappropriate to use a constant threshold to encompass parameters that vary widely. To mitigate this issue, we present a recovery scheme that incorporates time-varying thresholds. Our approach differs from existing methods in that it utilizes the exact values of the threshold, rather than its statistical properties, to enhance the estimation performance. Our simulations, including the direction-of-arrival estimation problem, demonstrate the efficacy of the developed scheme, especially in complex scenarios where the covariance elements are widely separated.

eess.SP↗
arXiv2023

WHC: Weighted Hybrid Criterion for Filter Pruning on Convolutional Neural Networks

Filter pruning has attracted increasing attention in recent years for its capacity in compressing and accelerating convolutional neural networks. Various data-independent criteria, including norm-based and relationship-based ones, were proposed to prune the most unimportant filters. However, these state-of-the-art criteria fail to fully consider the dissimilarity of filters, and thus might lead to performance degradation. In this paper, we first analyze the limitation of relationship-based criteria with examples, and then introduce a new data-independent criterion, Weighted Hybrid Criterion (WHC), to tackle the problems of both norm-based and relationship-based criteria. By taking the magnitude of each filter and the linear dependence between filters into consideration, WHC can robustly recognize the most redundant filters, which can be safely pruned without introducing severe performance degradation to networks. Extensive pruning experiments in a simple one-shot manner demonstrate the effectiveness of the proposed WHC. In particular, WHC can prune ResNet-50 on ImageNet with more than 42% of floating point operations reduced without any performance loss in top-5 accuracy.

cs.CV↗
arXiv2022

Scale-Semantic Joint Decoupling Network for Image-text Retrieval in Remote Sensing

Image-text retrieval in remote sensing aims to provide flexible information for data analysis and application. In recent years, state-of-the-art methods are dedicated to ``scale decoupling'' and ``semantic decoupling'' strategies to further enhance the capability of representation. However, these previous approaches focus on either the disentangling scale or semantics but ignore merging these two ideas in a union model, which extremely limits the performance of cross-modal retrieval models. To address these issues, we propose a novel Scale-Semantic Joint Decoupling Network (SSJDN) for remote sensing image-text retrieval. Specifically, we design the Bidirectional Scale Decoupling (BSD) module, which exploits Salience Feature Extraction (SFE) and Salience-Guided Suppression (SGS) units to adaptively extract potential features and suppress cumbersome features at other scales in a bidirectional pattern to yield different scale clues. Besides, we design the Label-supervised Semantic Decoupling (LSD) module by leveraging the category semantic labels as prior knowledge to supervise images and texts probing significant semantic-related information. Finally, we design a Semantic-guided Triple Loss (STL), which adaptively generates a constant to adjust the loss function to improve the probability of matching the same semantic image and text and shorten the convergence time of the retrieval model. Our proposed SSJDN outperforms state-of-the-art approaches in numerical experiments conducted on four benchmark remote sensing datasets.

cs.CV↗
arXiv2022

Impact of the nonthermal electron radiation effects on the horizon scale image structure of Sagittarius A*

The Event Horizon Telescope (EHT), with $\sim$20 $μ$as high angular resolution, recently resolved the millimeter image of the suppermassive black hole in the Galaxy, Sagittarius A*. This opens a new window to study the plasma on horizon scales. The accreting disk probably contains a small fraction of nonthermal electrons and their emissions should contribute to the observed image. We study if such contributions are sufficient to cause structural differences detectable by current and future observational capabilities. We introduce nonthermal electrons in a semi-analytical accretion disk, which considers viscosity-leading heating processes, and adopt a continued hybrid electron energy distribution of thermal distribution and power-law tail. We generate the black hole images and extract the structural features as crescent parameters. We find the existence of nonthermal electron radiation makes the crescent much brighter, slightly larger, moderately thicker, and much more symmetric. When the nonthermal connecting Lorentz factor $γ_c=65$, which is equivalent to the nonthermal electrons accounting for $\sim1.5$% of the totals, nonthermal effects cause $\sim2$% size difference at 230 GHz. Comparing with the structural changes caused by other physical factors, including inclination between the system and the observer, black hole spin, and interstellar medium scattering effects, we find that although nonthermal electron radiation takes the most unimportant role at 230 GHz, it becomes more significant at 345 GHz.

astro-ph.HE↗
arXiv2022

Magnetic activities and parameters of 43 flare stars in the GWAC archive

In the archive of the Ground Wide Angle Camera (GWAC), we found 43 white light flares from 43 stars, among which, three are sympathetic or homologous flares, and one of them also has a quasi-periodic pulsation with a period of $13.0\pm1.5$ minutes. Among these 43 flare stars, there are 19 new active stars and 41 stars that have available TESS and/or K2 light curves, from which we found 931 stellar flares. We also obtained rotational or orbital periods of 34 GWAC flare stars, of which 33 are less than 5.4 days, and ephemerides of three eclipsing binaries from these light curves. Combining with low resolution spectra from LAMOST and the Xinglong 2.16m telescope, we found that $L_{\rm Hα}/L_{\rm bol}$ are in the saturation region in the rotation-activity diagram. From the LAMOST medium-resolution spectrum, we found that Star \#3 (HAT 178-02667) has double H$α$ emissions which imply it is a binary, and two components are both active stars. Thirteen stars have flare frequency distributions (FFDs) from TESS and/or K2 light curves. These FFDs show that the flares detected by GWAC can occur at a frequency of 0.5 to 9.5 yr$^{-1}$. The impact of flares on habitable planets was also studied based on these FFDs, and flares from some GWAC flare stars may produce enough energetic flares to destroy ozone layers, but none can trigger prebiotic chemistry on their habitable planets.

astro-ph.SR↗