arXiv subjects

Nan Ding

Publications and source records attributed to Nan Ding.

At least 55 records · Page 3Linked to original sources

arXiv2020

Multi-wavelength Selected Compton-thick AGNs in Chandra Deep Field-South Survey

Even in deep X-ray surveys, Compton-thick active galactic nuclei (CT AGNs, ${\rm N_H} \geqslant 1.5~\times~10^{24}~{\rm cm}^{-2}$) are difficult to be identified due to X-ray flux suppression and their complex spectral shape. However, the study of CT AGNs is vital for understanding the rapid growth of black holes and the origin of cosmic X-ray background. In the local universe, the fraction of CT AGNs accounts for 30% of the whole AGN population. We may expect a higher fraction of CT AGNs in deep X-ray surveys, however, only 10% of AGNs have been identified as CT AGNs in the 7 Ms \textit{Chandra} Deep Field-South (CDFS) survey. In this work, we select 51 AGNs with abundant multi-wavelength data. Using the method of the mid-infrared (mid-IR) excess, we select hitherto unknown 8 CT AGN candidates in our sample. Seven of these candidates can confirm as CT AGN based on the multi-wavelength identification approach, and a new CT AGN (XID 133) is identified through the mid-IR diagnostics. We also discuss the X-ray origin of these eight CT AGNs and the reason why their column densities were underestimated in previous studies. We find that the multi-wavelength approaches of selecting CT AGNs are highly efficient, provided the high quality of observational data. We also find that CT AGNs have a higher Eddington ratio than non-CT AGNs, and that both CT AGNs and non-CT AGNs show similar properties of host galaxies.

astro-ph.GA↗

arXiv2020

From the Fermi blazar sequence to the relation between Fermi blazars and gamma-ray Narrow-line Seyfert 1 Galaxies

We use the third catalog of blazars detected by Fermi/LAT (3LAC) and gamma-ray Narrow-line Seyfert 1 Galaxies (gamma-NLSy1s) to study the blazar sequence and relationship between them. Our results are as follows: (i) There is a weak anti-correlation between synchrotron peak frequency and peak luminosity for both Fermi blazars and gamma-NLSy1s, which supports the blazar sequence. However, after Doppler correction, the inverse correlation disappeared, which suggests that anti-correlation between synchrotron peak frequency and peak luminosity is affected by the beaming effect. (ii) There is a significant anti-correlation between jet kinetic power and synchrotron peak frequency for both Fermi blazars and gamma-NLSy1s, which suggests that the gamma-NLSy1s could fit well into the original blazar sequence. (iii) According to previous work, the relationship between synchrotron peak frequency and synchrotron curvature can be explained by statistical or stochastic acceleration mechanisms. There are significant correlations between synchrotron peak frequency and synchrotron curvature for whole sample, Fermi blazars and BL Lacs, respectively. The slopes of the correlation are consistent with statistical acceleration. For FSRQs, LBLs, IBLs, HBLs, and gamma-NLS1s, we also find a significant correlation, but in these cases the slopes can not be explained by previous theoretical models. (iv) The slope of relation between synchrotron peak frequency and synchrotron curvature in gamma-NLS1s is large than that of FSRQs and BL Lacs. This result may imply that the cooling dominates over the acceleration process for FSRQs and BL Lacs, while gamma-NLS1s is the opposite.

astro-ph.HE↗

arXiv2020

A two-zone blazar radiation model for "orphan" neutrino flares

In this work, we investigate the 2014-2015 neutrino flare associated with the blazar TXS 0506+056 and a recently discovered muon neutrino event IceCube-200107A in spatial coincidence with the blazar 4FGL J0955.1+3551, under the framework of a two-zone radiation model of blazars where an inner/outer blob close to/far from the supermassive black hole are invoked. An interesting feature that the two sources share in common is that no evidence of GeV gamma-ray activity is found during the neutrino detection period, probably implying a large opacity for GeV gamma rays in the neutrino production region. In our model, continuous particle acceleration/injection takes place in the inner blob at the jet base, where the hot X-ray corona of the supermassive black hole provides target photon fields for efficient neutrino production and strong GeV gamma-ray absorption. We show that this model can self-consistently interpret the neutrino emission from both two blazars in a large parameter space. In the meantime, the dissipation processes in outer blob are responsible for the simultaneous multi-wavelength emission of both sources. In agreement with previous studies of TXS 0506+056 and, an intense MeV emission from the induced electromagnetic cascade in the inner blob is robustly expected to accompany the neutrino flare in our model could be used to test the model with the next-generation MeV gamma-ray detector in the future.

astro-ph.HE↗

arXiv2020

Improving Text Generation Evaluation with Batch Centering and Tempered Word Mover Distance

Recent advances in automatic evaluation metrics for text have shown that deep contextualized word representations, such as those generated by BERT encoders, are helpful for designing metrics that correlate well with human judgements. At the same time, it has been argued that contextualized word representations exhibit sub-optimal statistical properties for encoding the true similarity between words or sentences. In this paper, we present two techniques for improving encoding representations for similarity metrics: a batch-mean centering strategy that improves statistical properties; and a computationally efficient tempered Word Mover Distance, for better fusion of the information in the contextualized word representations. We conduct numerical experiments that demonstrate the robustness of our techniques, reporting results over various BERT-backbone learned metrics and achieving state of the art correlation with human ratings on several benchmarks.

cs.CL↗

arXiv2020

TeaForN: Teacher-Forcing with N-grams

Sequence generation models trained with teacher-forcing suffer from issues related to exposure bias and lack of differentiability across timesteps. Our proposed method, Teacher-Forcing with N-grams (TeaForN), addresses both these problems directly, through the use of a stack of N decoders trained to decode along a secondary time axis that allows model parameter updates based on N prediction steps. TeaForN can be used with a wide class of decoder architectures and requires minimal modifications from a standard teacher-forcing setup. Empirically, we show that TeaForN boosts generation quality on one Machine Translation benchmark, WMT 2014 English-French, and two News Summarization benchmarks, CNN/Dailymail and Gigaword.

cs.CL↗

arXiv2020

Detection of a high-confidence quasi-periodic oscillation in radio light curve of the high redshift FSRQ PKS J0805-0111

In this work, we have searched quasi-periodic oscillations (QPOs) in the 15 GHz light curve of the FSRQ PKS J0805-0111 monitored by the Owens Valley Radio Observatory (OVRO) 40 m telescope during the period from January 9,2008 to May 9,2019, using the weighted wavelet Z-transform (WWZ) and the Lomb-Scargle Periodogram (LSP) techniques. This is the first time to search for periodic radio signal in the FSRQ PKS J0805-0111 by these two methods. All two methods consistently reveal a repeating signal with a periodicity of 3.38$\pm$ 0.8 years ($>$99.7\% confidence level). In order to determining the significance of the periods, the false alarm probability method was used, and a large number of Monte Carlo simulations were performed. As possible explanations, we discuss a number of scenarios including the thermal instability of thin disks scenario, the spiral jet scenario and the binary supermassive black hole scenario, we expected that the binary black hole scenario, where the QPO is caused by the precession of the binary black holes, is the most likely explanation. FSRQ PKS J0805-0111 thus could be a good binary black hole candidate. In the binary black hole scenario, the distance between the primary black hole and the secondary black hole is about $1.71\times 10^{16}$ cm.

astro-ph.HE↗

arXiv2020

Attention that does not Explain Away

Models based on the Transformer architecture have achieved better accuracy than the ones based on competing architectures for a large set of tasks. A unique feature of the Transformer is its universal application of a self-attention mechanism, which allows for free information flow at arbitrary distances. Following a probabilistic view of the attention via the Gaussian mixture model, we find empirical evidence that the Transformer attention tends to "explain away" certain input neurons. To compensate for this, we propose a doubly-normalized attention scheme that is simple to implement and provides theoretical guarantees for avoiding the "explaining away" effect without introducing significant computational or memory cost. Empirically, we show that the new attention schemes result in improved performance on several well-known benchmarks.

cs.LG↗

arXiv2020

Talking-Heads Attention

We introduce "talking-heads attention" - a variation on multi-head attention which includes linearprojections across the attention-heads dimension, immediately before and after the softmax operation.While inserting only a small number of additional parameters and a moderate amount of additionalcomputation, talking-heads attention leads to better perplexities on masked language modeling tasks, aswell as better quality when transfer-learning to language comprehension and question answering tasks.

cs.LG↗

arXiv2020

Multicolor Optical Monitoring of the Blazar S5 0716+714 from 2017 to 2019

We continuously monitored the blazar S5 0716+714 in the optical $g$, $r$ and $i$ bands from Nov. 10, 2017 to Jun. 06, 2019. The total number of observations is 201 nights including 26973 data points. This is a very large quasi-simultaneous multicolor sample for the blazar. The average time spans and time resolutions are 3.4 hours and 2.9 minutes per night, respectively. During the period of observations, the target source in the $r$ band brightens from $14^{\rm m}.16$ to $12^{\rm m}.29$ together with five prominent sub-flares, and then first becomes fainter to $14^{\rm m}.76$ and again brightens to $12^{\rm m}.94$ with seven prominent sub-flares. For the long-term variations, we find a strong flatter when brighter (FWB) trend at a low flux state and then a weak FWB trend at a higher flux state. A weak FWB trend at a low flux state and then a strong FWB trend at a higher flux state are also reported. Most of sub-flares show the strong FWB trends, except for two flares with a weak FWB trend. The particle acceleration and cooling mechanisms together with the superposition of different FWB-slopes from sub-flares are likely to explain the optical color behaviours. A scenario of bent jet is discussed.

astro-ph.HE↗

arXiv2020

LOGAN: High-Performance GPU-Based X-Drop Long-Read Alignment

Pairwise sequence alignment is one of the most computationally intensive kernels in genomic data analysis, accounting for more than 90% of the runtime for key bioinformatics applications. This method is particularly expensive for third-generation sequences due to the high computational cost of analyzing sequences of length between 1Kb and 1Mb. Given the quadratic overhead of exact pairwise algorithms for long alignments, the community primarily relies on approximate algorithms that search only for high-quality alignments and stop early when one is not found. In this work, we present the first GPU optimization of the popular X-drop alignment algorithm, that we named LOGAN. Results show that our high-performance multi-GPU implementation achieves up to 181.6 GCUPS and speed-ups up to 6.6x and 30.7x using 1 and 6 NVIDIA Tesla V100, respectively, over the state-of-the-art software running on two IBM Power9 processors using 168 CPU threads, with equivalent accuracy. We also demonstrate a 2.3x LOGAN speed-up versus ksw2, a state-of-art vectorized algorithm for sequence alignment implemented in minimap2, a long-read mapping software. To highlight the impact of our work on a real-world application, we couple LOGAN with a many-to-many long-read alignment software called BELLA, and demonstrate that our implementation improves the overall BELLA runtime by up to 10.6x. Finally, we adapt the Roofline model for LOGAN and demonstrate that our implementation is near-optimal on the NVIDIA Tesla V100s.

q-bio.GN↗

arXiv2020

iqiyi Submission to ActivityNet Challenge 2019 Kinetics-700 challenge: Hierarchical Group-wise Attention

In this report, the method for the iqiyi submission to the task of ActivityNet 2019 Kinetics-700 challenge is described. Three models are involved in the model ensemble stage: TSN, HG-NL and StNet. We propose the hierarchical group-wise non-local (HG-NL) module for frame-level features aggregation for video classification. The standard non-local (NL) module is effective in aggregating frame-level features on the task of video classification but presents low parameters efficiency and high computational cost. The HG-NL method involves a hierarchical group-wise structure and generates multiple attention maps to enhance performance. Basing on this hierarchical group-wise structure, the proposed method has competitive accuracy, fewer parameters and smaller computational cost than the standard NL. For the task of ActivityNet 2019 Kinetics-700 challenge, after model ensemble, we finally obtain an averaged top-1 and top-5 error percentage 28.444% on the test set.

cs.CV↗

arXiv2019

Physical properties of the CDFS X-ray sources through fitting spectral energy distributions

The physical parameters of galaxies and/or AGNs can be derived by fitting their multi-band spectral energy distributions (SEDs). By using CIGALE code, we perform multi-band SED fitting (from ultraviolet to infrared) for 791 X-ray sources (518 AGNs and 273 normal galaxies) in the 7 Ms Chandra Deep Field-south survey (CDFS). We consider the contributions from AGNs and adopt more accurate redshifts than published before. Therefore, more accurate star formation rates (SFRs) and stellar masses (M$_*$) are derived. We classify the 518 AGNs into type-I and type-II based on their optical spectra and their SEDs. Moreover, six AGN candidates are selected from the 273 normal galaxies based on their SEDs. Our main results are as follows: (1) the host galaxies of AGNs have larger M$_*$ than normal galaxies, implying that AGNs prefer to host in massive galaxies; (2) the specific star formation rates (sSFRs) of AGN host galaxies are different from those of normal galaxies, suggesting that AGN feedback may play an important role in the star formation activity; (3) we find that the fraction of optically obscured AGNs in CDFS decreases with the increase of intrinsic X-ray luminosity, which is consistent with previous studies;(4) the host galaxies of type-I AGNs tend to have lower M$_*$ than type-II AGNs, which may suggest that dust in the host galaxy may also contribute to the optical obscuration of AGNs.

astro-ph.GA↗

arXiv2018

Physical properties variation across the green valley for local galaxies

We have selected a sample of nearby galaxies from Sloan Digital Sky Survey Data Release 7 (SDSS DR7) to investigate the physical properties variation from blue cloud to green valley to red sequence. The sample is limited in a narrow range in color-stellar mass diagram. After splitting green valley galaxies into two parts---a bluer green valley (green 1) and a redder one (green 2) and three stellar mass bins, we investigate the physical properties variation across the green valley region. Our main results are as following: (i) The percentages of pure bulge and bulge-dominated/elliptical galaxies increase gradually from blue cloud to red sequence while the percentages of pure disk and disk-dominated/spiral galaxies decrease gradually in all stellar mass bins and different environments; (ii) With the analysis of morphological and structural parameters (e.g., concentration (C) and the stellar mass surface density within the central 1Kpc ($Σ_{1}$)), red galaxies show the most luminous and compact cores than both green valley and blue galaxies while blue galaxies show the opposite behavior in all stellar mass bins. (iii) A strong negative (positive) relationship between bulge-to-total light ratio (B/T) and specific star formation rate (sSFR) ($D_{4000}$) is found from blue to red galaxies. Our results indicate that the growth of bulge plays an important role when the galaxies change from the blue cloud, to green valley, and to the red sequence.

astro-ph.GA↗

arXiv2018

SHAPED: Shared-Private Encoder-Decoder for Text Style Adaptation

Supervised training of abstractive language generation models results in learning conditional probabilities over language sequences based on the supervised training signal. When the training signal contains a variety of writing styles, such models may end up learning an 'average' style that is directly influenced by the training data make-up and cannot be controlled by the needs of an application. We describe a family of model architectures capable of capturing both generic language characteristics via shared model parameters, as well as particular style characteristics via private model parameters. Such models are able to generate language according to a specific learned style, while still taking advantage of their power to model generic language phenomena. Furthermore, we describe an extension that uses a mixture of output distributions from all learned styles to perform on-the fly style adaptation based on the textual input alone. Experimentally, we find that the proposed models consistently outperform models that encapsulate single-style or average-style language generation capabilities.

cs.CL↗

arXiv2017

Cold-Start Reinforcement Learning with Softmax Policy Gradient

Policy-gradient approaches to reinforcement learning have two common and undesirable overhead procedures, namely warm-start training and sample variance reduction. In this paper, we describe a reinforcement learning method based on a softmax value function that requires neither of these procedures. Our method combines the advantages of policy-gradient methods with the efficiency and simplicity of maximum-likelihood approaches. We apply this new cold-start reinforcement learning method in training sequence generation models for structured output prediction problems. Empirical evidence validates this method on automatic summarization and image captioning tasks.

cs.LG↗

arXiv2017

Characterizing Quantum Supremacy in Near-Term Devices

A critical question for the field of quantum computing in the near future is whether quantum devices without error correction can perform a well-defined computational task beyond the capabilities of state-of-the-art classical computers, achieving so-called quantum supremacy. We study the task of sampling from the output distributions of (pseudo-)random quantum circuits, a natural task for benchmarking quantum computers. Crucially, sampling this distribution classically requires a direct numerical simulation of the circuit, with computational cost exponential in the number of qubits. This requirement is typical of chaotic systems. We extend previous results in computational complexity to argue more formally that this sampling task must take exponential time in a classical computer. We study the convergence to the chaotic regime using extensive supercomputer simulations, modeling circuits with up to 42 qubits - the largest quantum circuits simulated to date for a computational task that approaches quantum supremacy. We argue that while chaotic states are extremely sensitive to errors, quantum supremacy can be achieved in the near-term with approximately fifty superconducting qubits. We introduce cross entropy as a useful benchmark of quantum circuits which approximates the circuit fidelity. We show that the cross entropy can be efficiently measured when circuit simulations are available. Beyond the classically tractable regime, the cross entropy can be extrapolated and compared with theoretical estimates of circuit fidelity to define a practical quantum supremacy test.

quant-ph↗

arXiv2016

Understanding Image and Text Simultaneously: a Dual Vision-Language Machine Comprehension Task

We introduce a new multi-modal task for computer systems, posed as a combined vision-language comprehension challenge: identifying the most suitable text describing a scene, given several similar options. Accomplishing the task entails demonstrating comprehension beyond just recognizing "keywords" (or key-phrases) and their corresponding visual concepts. Instead, it requires an alignment between the representations of the two modalities that achieves a visually-grounded "understanding" of various linguistic elements and their dependencies. This new task also admits an easy-to-compute and well-studied metric: the accuracy in detecting the true target among the decoys. The paper makes several contributions: an effective and extensible mechanism for generating decoys from (human-created) image captions; an instance of applying this mechanism, yielding a large-scale machine comprehension dataset (based on the COCO images and captions) that we make publicly available; human evaluation results on this dataset, informing a performance upper-bound; and several baseline and competitive learning approaches that illustrate the utility of the proposed task and dataset in advancing both image and language comprehension. We also show that, in a multi-task learning setting, the performance on the proposed task is positively correlated with the end-to-end task of image captioning.

cs.CL↗

arXiv2016

Multilingual Word Embeddings using Multigraphs

We present a family of neural-network--inspired models for computing continuous word representations, specifically designed to exploit both monolingual and multilingual text. This framework allows us to perform unsupervised training of embeddings that exhibit higher accuracy on syntactic and semantic compositionality, as well as multilingual semantic similarity, compared to previous models trained in an unsupervised fashion. We also show that such multilingual embeddings, optimized for semantic similarity, can improve the performance of statistical machine translation with respect to how it handles words not present in the parallel data.

cs.CL↗