arXiv subjects

Ning Jiang

Publications and source records attributed to Ning Jiang.

At least 145 records · Page 8Linked to original sources

arXiv2023

Multi-objective Progressive Clustering for Semi-supervised Domain Adaptation in Speaker Verification

Utilizing the pseudo-labeling algorithm with large-scale unlabeled data becomes crucial for semi-supervised domain adaptation in speaker verification tasks. In this paper, we propose a novel pseudo-labeling method named Multi-objective Progressive Clustering (MoPC), specifically designed for semi-supervised domain adaptation. Firstly, we utilize limited labeled data from the target domain to derive domain-specific descriptors based on multiple distinct objectives, namely within-graph denoising, intra-class denoising and inter-class denoising. Then, the Infomap algorithm is adopted for embedding clustering, and the descriptors are leveraged to further refine the target domain's pseudo-labels. Moreover, to further improve the quality of pseudo labels, we introduce the subcenter-purification and progressive-merging strategy for label denoising. Our proposed MoPC method achieves 4.95% EER and ranked the 1$^{st}$ place on the evaluation set of VoxSRC 2023 track 3. We also conduct additional experiments on the FFSVC dataset and yield promising results.

eess.AS↗

arXiv2023

A Gigantic Mid-Infrared Outburst in an Embedded Class-I Young Stellar Object J064722.95+031644.6

We report the serendipitous discovery of a giant mid-infrared (MIR) outburst from a previously unknown source near a star-forming region in the constellation Monoceros. The source gradually brightened by a factor of 5 from 2014 to 2016 before an abrupt rise by a factor of more than 100 in 2017. A total amplitude increase of >500 at 4.5 microns has since faded by a factor of about 10. Prior to the outburst, it was only detected at wavelengths longer than 1.8 microns in UKIDSS, Spitzer, and Herschel with a spectral energy distribution of a Class I Young Stellar Object (YSO). It has not been detected in recent optical surveys, suggesting that it is deeply embedded. With a minimum distance of 3.5 kpc, the source has a bolometric luminosity of at least 9 $L_\odot$ in the quiescent state and 400 $L_\odot$ at the peak of the eruption. The maximum accretion rate is estimated to be at least a few $10^{-5}$ $M_\odot$ year$^{-1}$. It shares several common properties with another eruptive event, WISE~J142238.82-611553.7: exceptionally large amplitude, featureless near-infrared spectrum with the exception of H_2 lines, intermediate eruption duration, an embedded Class I YSO, and a low radiative temperature (<600-700 K) in outburst. We interpret that the radiation from the inner accretion disk and young star is obscured and reprocessed by either an inflated outer disk or thick dusty outflow on scales > 6.5 AU during the outburst.

astro-ph.SR↗

arXiv2023

Sciences with the 2.5-meter Wide Field Survey Telescope (WFST)

The Wide Field Survey Telescope (WFST) is a dedicated photometric surveying facility being built jointly by the University of Science and Technology of China and the Purple Mountain Observatory. It is equipped with a 2.5-meter diameter primary mirror, an active optics system, and a mosaic CCD camera with 0.73 gigapixels on the primary focal plane for high-quality image capture over an FOV of 6.5-square-degree. It is anticipated that WFST will be set up at the Lenghu site in the summer of 2023 and begin to observe the northern sky in four optical bands (u, g, r, and i) with a range of cadences, from hourly/daily in the Deep High-Cadence Survey (DHS) program to semiweekly in the Wide-Field Survey (WFS) program, three months later. During a photometric night, a nominal 30 s exposure in the WFS program will reach a depth of 22.27, 23.32, 22.84, and 22.31 (AB magnitudes) in these four bands, respectively, allowing for the detection of a tremendous amount of transients in the low-z universe and a systematic investigation of the variability of Galactic and extragalactic objects. In the DHS program, intranight 90 s exposures as deep as 23 (u) and 24 mag (g), in combination with target of opportunity follow-ups, will provide a unique opportunity to explore energetic transients in demand for high sensitivities, including the electromagnetic counterparts of gravitational wave events, supernovae within a few hours of their explosions, tidal disruption events and fast, luminous optical transients even beyond a redshift of unity. In addition, the final 6-year co-added images, anticipated to reach g=25.8 mag in WFS or 1.5 mags deeper in DHS, will be of fundamental importance to general Galactic and extragalactic science. The highly uniform legacy surveys of WFST will serve as an indispensable complement to those of LSST that monitor the southern sky.

astro-ph.IM↗

arXiv2023

From Vlasov-Maxwell-Landau system with Coulomb potential to two-fluid incompressible Navier-Stokes-Fourier-Maxwell system with Ohm's law

In the diffusive regime, we obtain the uniform in Knudsen number estimate on the two-species Vlasov-Maxwell-Landau system with Coulomb potential around the global equilibrium. As a consequence, we justify the limit to the two-fluid incompressible Navier-Stokes-Fourier-Maxwell (NSFM) system with Ohm's law. This is the first result on the global in time uniform in Knudsen number estimate on the Boltzmann and Landau type equations with Coulomb potential coupled Vlasov-Maxwell equations.

math.AP↗

arXiv2023

AT2018dyk Revisited: a Tidal Disruption Event Candidate with Prominent Infrared Echo and Delayed X-ray Emission in a LINER Galaxy

The multiwavelength data of nuclear transient AT2018dyk, initially discovered as a changing-look low-ionization nuclear emission-line region (LINER) galaxy, has been revisited by us and found being in agreement with a tidal disruption event (TDE) scenario. The optical light curve of AT2018dyk declines as a power-law form approximately with index -5/3 yet its X-ray emission lags behind the optical peak by $\sim140$ days, both of which are typical characteristics for TDEs. The X-ray spectra are softer than normal active galactic nuclei (AGNs) although they show a slight trend of hardening. Interestingly, its rising time scale belongs to the longest among TDEs while it is nicely consistent with the theoretical prediction from its relatively large supermassive black hole (SMBH) mass ($\sim10^{7.38} M_{\odot}$). Moreover, a prominent infrared echo with peak luminosity $\sim7.4\times10^{42}~\text{erg}~\text{s}^{-1}$ has been also detected in AT2018dyk, implying an unusually dusty subparsec nuclear environment in contrast with other TDEs. In our sample, LINERs share similar covering factors with AGNs, which indicates the existence of the dusty torus in these objects. Our work suggests that the nature of nuclear transients in LINERs needs to be carefully identified and their infrared echoes offer us a unique opportunity for exploring the environment of SMBHs at low accretion rate, which has been so far poorly explored but is crucial for understanding the SMBH activity.

astro-ph.HE↗

arXiv2023

The incompressible Navier-Stokes-Fourier-Maxwell system limits of the Vlasov-Maxwell-Boltzmann system for soft potentials: the noncutoff cases and cutoff cases

We obtain the global-in-time and uniform in Knudsen number $ε$ energy estimate for the cutoff and non-cutoff scaled Vlasov-Maxwell-Boltzmann system for the soft potential. For the non-cutoff soft potential cases, our analysis relies heavily on additional dissipative mechanisms with respect to velocity, which are brought about by the strong angular singularity hypothesis, i.e. $\frac12\leq s<1$. In the case of cutoff cases, our proof relies on two new kinds of weight functions and complex construction of energy functions, and here we ask $γ\geq-1$. As a consequence, we justify the incompressible Navier-Stokes-Fourier-Maxwell equations with Ohm's law limit.

math.AP↗

arXiv2023

Quantitative global well-posedness of Boltzmann-Bose-Einstein equation and incompressible Navier-Stokes-Fourier limit

In the diffusive scaling and in the whole space, we prove the global well-posedness of the scaled Boltzmann-Bose-Einstein (briefly, BBE) equation with high temperature in the low regularity space $H^2_xL^2$. In particular, we quantify the fluctuation around the Bose-Einstein equilibrium $\mathcal{M}_{λ,T}(v)$ with respect to the parameters $λ$ and temperature $T$. Furthermore, the estimate for the diffusively scaled BBE equation is uniform to the Knudsen number $ε$. As a consequence, we rigorously justify the hydrodynamic limit to the incompressible Navier-Stokes-Fourier equations. This is the first rigorous fluid limit result for BBE.

math.AP↗

arXiv2023

Two Candidate Obscured Tidal Disruption Events Coincident with High-energy Neutrinos

Recently, three optical tidal disruption event (TDE) candidates discovered by the Zwicky Transient Facility (ZTF) have been suggested to be coincident with high-energy neutrinos. They all exhibit unusually strong dust infrared (IR) echoes, with their peak times matching the neutrino arrival time even better than the optical peaks. We hereby report on two new TDE candidates that are spatially and temporally coincident with neutrinos by matching our sample of mid-infrared outbursts in nearby galaxies (MIRONG) with Gold alerts of IceCube high-energy neutrino events up to June 2022. The two candidates show negligible optical variability according to their ZTF light curves and can therefore be classified as part of the growing population of obscured TDE candidates. The chance probability of finding two such candidates about $\sim3\%$ by redistributing the MIRONG sources randomly in the SDSS footprint, which will be as low as $\sim0.1\%$ (or $\sim0.2\%$) if we limit to sources with increased fluxes (or variability amplitudes) comparable with the matched two sources. Our findings further support the potential connection between high-energy neutrinos and TDEs in dusty environments by increasing the total number of neutrino-associated TDE and TDE candidates to five, although the underlying physics remains poorly understood.

astro-ph.HE↗

arXiv2023

The DKU-MSXF Diarization System for the VoxCeleb Speaker Recognition Challenge 2023

This paper describes the DKU-MSXF submission to track 4 of the VoxCeleb Speaker Recognition Challenge 2023 (VoxSRC-23). Our system pipeline contains voice activity detection, clustering-based diarization, overlapped speech detection, and target-speaker voice activity detection, where each procedure has a fused output from 3 sub-models. Finally, we fuse different clustering-based and TSVAD-based diarization systems using DOVER-Lap and achieve the 4.30% diarization error rate (DER), which ranks first place on track 4 of the challenge leaderboard.

eess.AS↗

arXiv2023

The DKU-MSXF Speaker Verification System for the VoxCeleb Speaker Recognition Challenge 2023

This paper is the system description of the DKU-MSXF System for the track1, track2 and track3 of the VoxCeleb Speaker Recognition Challenge 2023 (VoxSRC-23). For Track 1, we utilize a network structure based on ResNet for training. By constructing a cross-age QMF training set, we achieve a substantial improvement in system performance. For Track 2, we inherite the pre-trained model from Track 1 and conducte mixed training by incorporating the VoxBlink-clean dataset. In comparison to Track 1, the models incorporating VoxBlink-clean data exhibit a performance improvement by more than 10% relatively. For Track3, the semi-supervised domain adaptation task, a novel pseudo-labeling method based on triple thresholds and sub-center purification is adopted to make domain adaptation. The final submission achieves mDCF of 0.1243 in task1, mDCF of 0.1165 in Track 2 and EER of 4.952% in Track 3.

eess.AS↗

arXiv2023

Almost global well-posedness of 2-D Ericksen-Leslie's hyperbolic liquid crystal model for small data

This article is devoted to the two dimensional simplified Ericksen-Leslie's hyperbolic system for incompressible liquid crystal model, where the direction $d$ of liquid crystal molecules satisfies a wave map equation with an acoustical metric. We established the almost global well-posedness for small and smooth initial data near the constant equilibrium. Our proof relies on the idea of vector-field method and ghost weight method. There are two key ingredients in our proof: (i) Inspired by the gauge theory in Tataru \cite{Tataru,Tataru05}, we reformulate the wave map equation into a free wave equation with acoustical metric, where the nonlinearity is annihilated due to the geometry of $\mathbb S^1$; (ii) Motivated by the ghost weight method in Alinhac \cite{A01}, we introduce a new and important ``good unknown", the velocity $u$, which provides the additional dissipation $u/\langle t-r\rangle\in L^2_tL^2_x$. These new observations turn out to be extremely crucial in resolving the system in low dimensions.

math.AP↗

arXiv2023

On the hydrodynamics of hyperbolic liquid crystal elastomers

Liquid crystal elastomers are special cross-linked polymer materials combining the large elastic deformability of elastomers with the orientational orders of liquid crystals. This model exhibits markedly different phenomena than the liquid crystal model due to the strong coupling between mechanical elastic deformation and orientation vector. Our results are threefold. (i) First we derive the hydrodynamics of liquid crystal elastomers with inertial effect by energetic variational approach inspired by Chun Liu [33]. (ii) Then we study the hyperbolic liquid crystal elastomers from mathematical point, which is a fully quasilinear hyperbolic system. The local well-posedness for large data is proved by zero-viscosity limit method. (iii) Finally, we show the global regularity for small and smooth initial data near the constant equilibrium in three dimensions, which is achieved by carefully investigating the structure of second-order material derivatives and the cancellations in the system. This is the first global result on hyperbolic liquid crystal elastomers.

math.AP↗

arXiv2023

The NPU-MSXF Speech-to-Speech Translation System for IWSLT 2023 Speech-to-Speech Translation Task

This paper describes the NPU-MSXF system for the IWSLT 2023 speech-to-speech translation (S2ST) task which aims to translate from English speech of multi-source to Chinese speech. The system is built in a cascaded manner consisting of automatic speech recognition (ASR), machine translation (MT), and text-to-speech (TTS). We make tremendous efforts to handle the challenging multi-source input. Specifically, to improve the robustness to multi-source speech input, we adopt various data augmentation strategies and a ROVER-based score fusion on multiple ASR model outputs. To better handle the noisy ASR transcripts, we introduce a three-stage fine-tuning strategy to improve translation accuracy. Finally, we build a TTS model with high naturalness and sound quality, which leverages a two-stage framework, using network bottleneck features as a robust intermediate representation for speaker timbre and linguistic content disentanglement. Based on the two-stage framework, pre-trained speaker embedding is leveraged as a condition to transfer the speaker timbre in the source English speech to the translated Chinese speech. Experimental results show that our system has high translation accuracy, speech naturalness, sound quality, and speaker similarity. Moreover, it shows good robustness to multi-source data.

cs.SD↗

arXiv2023

AT 2023clx: the Faintest and Closest Optical Tidal Disruption Event Discovered in Nearby Star-forming Galaxy NGC 3799

We report the discovery of a faint optical tidal disruption event (TDE) in the nearby star-forming galaxy NGC 3799. Identification of the TDE is based on its position at the galaxy nucleus, a light curve declining as t^-5/3, a blue continuum with an almost constant blackbody temperature of ~12,000K, and broad (~15,000kms^-1) Balmer lines and characteristic He~II 4686A emission. The light curve of AT 2023clx peaked at an absolute magnitude of -17.16mag in the g-band and a maximum blackbody bolometric luminosity of 4.56*10^42 ergs^-1, making it the faintest TDE discovered to date. With a redshift of 0.01107 and a corresponding luminosity distance of 47.8Mpc, it is also the closest optical TDE ever discovered to our best knowledge. Furthermore, our analysis of Swift/XRT observations of AT 2023clx yields a very tight 3 sigma upper limit of 9.53*10^39 ergs^-1 in the range 0.3--10keV. AT 2023clx, together with very few other faint TDEs such as AT 2020wey, prove that there are probably a large number of faint TDEs yet to be discovered at higher redshifts, which is consistent with the prediction of luminosity functions (LFs). The upcoming deeper optical time-domain surveys, such as the Legacy Survey of Space and Time (LSST) and the Wide-Field Survey Telescope (WFST) will discover more TDEs at even lower luminosities, allowing for a more precise constraint of the low-end of the LF.

astro-ph.HE↗

arXiv2023

Artificial Neural Network Prediction of COVID-19 Daily Infection Count

It is well known that the confirmed COVID-19 infection is only a fraction of the true fraction. In this paper we use an artificial neural network to learn the connection between the confirmed infection count, the testing data, and the true infection count. The true infection count in the training set is obtained by backcasting from the death count and the infection fatality ratio (IFR). Multiple factors are taken into consideration in the estimation of IFR. We also calibrate the recovered true COVID-19 case count with an SEIR model.

q-bio.PE↗

arXiv2023

Multi-task Collaborative Pre-training and Individual-adaptive-tokens Fine-tuning: A Unified Framework for Brain Representation Learning

Structural magnetic resonance imaging (sMRI) provides accurate estimates of the brain's structural organization and learning invariant brain representations from sMRI is an enduring issue in neuroscience. Previous deep representation learning models ignore the fact that the brain, as the core of human cognitive activity, is distinct from other organs whose primary attribute is anatomy. Therefore, capturing the semantic structure that dominates interindividual cognitive variability is key to accurately representing the brain. Given that this high-level semantic information is subtle, distributed, and interdependently latent in the brain structure, sMRI-based models need to capture fine-grained details and understand how they relate to the overall global structure. However, existing models are optimized by simple objectives, making features collapse into homogeneity and worsening simultaneous representation of fine-grained information and holistic semantics, causing a lack of biological plausibility and interpretation of cognition. Here, we propose MCIAT, a unified framework that combines Multi-task Collaborative pre-training and Individual-Adaptive-Tokens fine-tuning. Specifically, we first synthesize restorative learning, age prediction auxiliary learning and adversarial learning as a joint proxy task for deep semantic representation learning. Then, a mutual-attention-based token selection method is proposed to highlight discriminative features. The proposed MCIAT achieves state-of-the-art diagnosis performance on the ADHD-200 dataset compared with several sMRI-based approaches and shows superior generalization on the MCIC and OASIS datasets. Moreover, we studied 12 behavioral tasks and found significant associations between cognitive functions and MCIAT-established representations, which verifies the interpretability of our proposed framework.

cs.CV↗

arXiv2023

Connectional-Style-Guided Contextual Representation Learning for Brain Disease Diagnosis

Structural magnetic resonance imaging (sMRI) has shown great clinical value and has been widely used in deep learning (DL) based computer-aided brain disease diagnosis. Previous approaches focused on local shapes and textures in sMRI that may be significant only within a particular domain. The learned representations are likely to contain spurious information and have a poor generalization ability in other diseases and datasets. To facilitate capturing meaningful and robust features, it is necessary to first comprehensively understand the intrinsic pattern of the brain that is not restricted within a single data/task domain. Considering that the brain is a complex connectome of interlinked neurons, the connectional properties in the brain have strong biological significance, which is shared across multiple domains and covers most pathological information. In this work, we propose a connectional style contextual representation learning model (CS-CRL) to capture the intrinsic pattern of the brain, used for multiple brain disease diagnosis. Specifically, it has a vision transformer (ViT) encoder and leverages mask reconstruction as the proxy task and Gram matrices to guide the representation of connectional information. It facilitates the capture of global context and the aggregation of features with biological plausibility. The results indicate that CS-CRL achieves superior accuracy in multiple brain disease diagnosis tasks across six datasets and three diseases and outperforms state-of-the-art models. Furthermore, we demonstrate that CS-CRL captures more brain-network-like properties, better aggregates features, is easier to optimize and is more robust to noise, which explains its superiority in theory. Our source code will be released soon.

eess.IV↗

arXiv2023

Pattern formation of a pathway-based diffusion model: linear stability analysis and an asymptotic preserving method

We investigate the linear stability analysis of a pathway-based diffusion model (PBDM), which characterizes the dynamics of the engineered Escherichia coli populations [X. Xue and C. Xue and M. Tang, P LoS Computational Biology, 14 (2018), pp. e1006178]. This stability analysis considers small perturbations of the density and chemical concentration around two non-trivial steady states, and the linearized equations are transformed into a generalized eigenvalue problem. By formal analysis, when the internal variable responds to the outside signal fast enough, the PBDM converges to an anisotropic diffusion model, for which the probability density distribution in the internal variable becomes a delta function. We introduce an asymptotic preserving (AP) scheme for the PBDM that converges to a stable limit scheme consistent with the anisotropic diffusion model. Further numerical simulations demonstrate the theoretical results of linear stability analysis, i.e., the pattern formation, and the convergence of the AP scheme.

math.NA↗