arXiv subjects

Qian Yang

Publications and source records attributed to Qian Yang.

At least 127 records · Page 7Linked to original sources

arXiv2020

Optical Variability of the Dwarf AGN NGC 4395 from the Transiting Exoplanet Survey Satellite

We present optical light curves from the Transiting Exoplanet Survey Satellite (TESS) for the archetypical dwarf active galactic nucleus (AGN) in the nearby galaxy NGC 4395 hosting a $\sim 10^5\,M_\odot$ supermassive black hole (SMBH). Significant variability is detected on timescales from weeks to hours before reaching the background noise level. The $\sim$month-long, 30 minute-cadence, high-precision TESS light curve can be well fit by a simple damped random walk (DRW) model, with the damping timescale $τ_{\rm DRW}$ constrained to be $2.3_{-0.7}^{+1.8}$~days ($1σ$). NGC 4395 lies almost exactly on the extrapolation of the $τ_{\rm DRW}-M_{\rm BH}$ relation measured for AGNs with BH masses that are more than three orders of magnitude larger. The optical variability periodogram can be well fit by a broken power law with the high-frequency slope ($-1.88\pm0.15$) and the characteristic timescale ($τ_{\rm br}\equiv 1/(2πf_{\rm br})=1.4_{-0.5}^{+1.9}\,$days) consistent with the DRW model within 1$σ$. This work demonstrates the power of TESS light curves in identifying low-mass accreting SMBHs with optical variability, and a potential global $τ_{\rm DRW}-M_{\rm BH}$ relation that can be used to estimate SMBH masses with optical variability measurements.

astro-ph.GA↗

arXiv2020

Attacking Recommender Systems with Augmented User Profiles

Recommendation Systems (RS) have become an essential part of many online services. Due to its pivotal role in guiding customers towards purchasing, there is a natural motivation for unscrupulous parties to spoof RS for profits. In this paper, we study the shilling attack: a subsistent and profitable attack where an adversarial party injects a number of user profiles to promote or demote a target item. Conventional shilling attack models are based on simple heuristics that can be easily detected, or directly adopt adversarial attack methods without a special design for RS. Moreover, the study on the attack impact on deep learning based RS is missing in the literature, making the effects of shilling attack against real RS doubtful. We present a novel Augmented Shilling Attack framework (AUSH) and implement it with the idea of Generative Adversarial Network. AUSH is capable of tailoring attacks against RS according to budget and complex attack goals, such as targeting a specific user group. We experimentally show that the attack impact of AUSH is noticeable on a wide range of RS including both classic and modern deep learning based RS, while it is virtually undetectable by the state-of-the-art attack detection model.

cs.IR↗

arXiv2020

Dust Reverberation Mapping in Distant Quasars from Optical and Mid-Infrared Imaging Surveys

The size of the dust torus in Active Galactic Nuclei (AGN) and their high-luminosity counterparts, quasars, can be inferred from the time delay between UV/optical accretion disk continuum variability and the response in the mid-infrared (MIR) torus emission. This dust reverberation mapping (RM) technique has been successfully applied to $\sim 70$ $z\lesssim 0.3$ AGN and quasars. Here we present first results of our dust RM program for distant quasars covered in the SDSS Stripe 82 region combining $\sim 20$-yr ground-based optical light curves with 10-yr MIR light curves from the WISE satellite. We measure a high-fidelity lag between W1-band (3.4 $μ$m) and $g$ band for 587 quasars over $0.3\lesssim z\lesssim 2$ ($\left \sim 0.8$) and two orders of magnitude in quasar luminosity. They tightly follow (intrinsic scatter $\sim 0.17$ dex in lag) the IR lag-luminosity relation observed for $z<0.3$ AGN, revealing a remarkable size-luminosity relation for the dust torus over more than four decades in AGN luminosity, with little dependence on additional quasar properties such as Eddington ratio and variability amplitude. This study motivates further investigations in the utility of dust RM for cosmology, and strongly endorses a compelling science case for the combined 10-yr Vera C. Rubin Observatory Legacy Survey of Space and Time (optical) and 5-yr Nancy Grace Roman Space Telescope 2$μ$m light curves in a deep survey for low-redshift AGN dust RM with much lower luminosities and shorter, measurable IR lags. The compiled optical and MIR light curves for 7,384 quasars in our parent sample are made public with this work.

astro-ph.GA↗

arXiv2020

Tunable lattice reconstruction and bandwidth of flat bands in magic-angle twisted bilayer graphene

The interplay between interlayer van der Waals interaction and intralayer lattice distortion can lead to structural reconstruction in slightly twisted bilayer graphene (TBG) with the twist angle being smaller than a characteristic angle θc. Experimentally, the θc is demonstrated to be very close to the magic angle (θ ~ 1.05°). In this work, we address the transition between reconstructed and unreconstructed structures of the TBG across the magic angle by using scanning tunnelling microscopy (STM). Our experiment demonstrates that both the two structures are stable in the TBG around the magic angle. By applying a STM tip pulse, we show that the two structures can be switched to each other and the bandwidth of the flat bands, which plays a vital role in the emergent strongly correlated states in the magic-angle TBG, can be tuned. The observed tunable lattice reconstruction and bandwidth of the flat bands provide an extra control knob to manipulate the exotic electronic states of the TBG near the magic angle.

cond-mat.mes-hall↗

arXiv2020

Characterization of Optical Light Curves of Extreme Variability Quasars Over a ~16-yr Baseline

We study the optical light curves - primarily probing the variable emission from the accretion disk - of ~ 900 extreme variability quasars (EVQs, with maximum flux variations more than 1 mag) over an observed-frame baseline of ~ 16 years using public data from the SDSS Stripe 82, PanSTARRS-1 and the Dark Energy Survey. We classify the multi-year long-term light curves of EVQs into three categories roughly in the order of decreasing smoothness: monotonic decreasing or increasing (3.7%), single broad peak and dip (56.8%), and more complex patterns (39.5%). The rareness of monotonic cases suggests that the major mechanisms driving the extreme optical variability do not operate over timescales much longer than a few years. Simulated light curves with a damped random walk model generally under-predict the first two categories with smoother long-term trends. Despite the different long-term behaviors of these EVQs, there is little dependence of the long-term trend on the physical properties of quasars, such as their luminosity, BH mass, and Eddington ratio. The large dynamic range of optical flux variability over multi-year timescales of these EVQs allows us to explore the ensemble correlation between the short-term (< 6 months) variability and the seasonal-average flux across the decade-long baseline (the rms-mean flux relation). We find that unlike the results for X-ray variability studies, the linear short-term flux variations do not scale with the seasonal-average flux, indicating different mechanisms that drive the short-term flickering and long-term extreme variability of accretion disk emission. Finally, we present a sample of 16 EVQs, where the approximately bell-shaped large amplitude variation in the light curve can be reasonably well fit by a simple microlensing model.

astro-ph.GA↗

arXiv2020

Faster On-Device Training Using New Federated Momentum Algorithm

Mobile crowdsensing has gained significant attention in recent years and has become a critical paradigm for emerging Internet of Things applications. The sensing devices continuously generate a significant quantity of data, which provide tremendous opportunities to develop innovative intelligent applications. To utilize these data to train machine learning models while not compromising user privacy, federated learning has become a promising solution. However, there is little understanding of whether federated learning algorithms are guaranteed to converge. We reconsider model averaging in federated learning and formulate it as a gradient-based method with biased gradients. This novel perspective assists analysis of its convergence rate and provides a new direction for more acceleration. We prove for the first time that the federated averaging algorithm is guaranteed to converge for non-convex problems, without imposing additional assumptions. We further propose a novel accelerated federated learning algorithm and provide a convergence guarantee. Simulated federated learning experiments are conducted to train deep neural networks on benchmark datasets, and experimental results show that our proposed method converges faster than previous approaches.

cs.LG↗

arXiv2020

The Third Data Release of the Beijing-Arizona Sky Survey

The Beijing-Arizona Sky Survey (BASS) is a wide and deep imaging survey to cover a 5400 deg$^2$ area in the Northern Galactic Cap with the 2.3m Bok telescope using two filters ($g$ and $r$ bands). The Mosaic $z$-band Legacy Survey (MzLS) covers the same area in $z$ band with the 4m Mayall telescope. These two surveys will be used for spectroscopic targeting of the Dark Energy Spectroscopic Instrument (DESI). The BASS survey observations were completed in 2019 March. This paper describes the third data release (DR3) of BASS, which contains the photometric data from all BASS and MzLS observations between 2015 January and 2019 March. The median astrometric precision relative to {\it Gaia} positions is about 17 mas and the median photometric offset relative to the PanSTARRS1 photometry is within 5 mmag. The median $5σ$ AB magnitude depths for point sources are 24.2, 23.6, and 23.0 mag for $g$, $r$, and $z$ bands, respectively. The photometric depth within the survey area is highly homogeneous, with the difference between the 20\% and 80\% depth less than 0.3 mag. The DR3 data, including raw data, calibrated single-epoch images, single-epoch photometric catalogs, stacked images, and co-added photometric catalogs, are publicly accessible at \url{http://batc.bao.ac.cn/BASS/doku.php?id=datarelease:home}.

astro-ph.GA↗

arXiv2019

Understanding Broad Mg II Variability in Quasars with Photoionization: Implications for Reverberation Mapping and Changing-Look Quasars

The broad Mg II line in quasars has distinct variability properties compared with broad Balmer lines: it is less variable, and usually does not display a "breathing" mode, the increase in the average cloud distance when luminosity increases. We demonstrate that these variability properties of Mg II can be reasonably well explained by simple Locally Optimally Emitting Cloud (LOC) photoionization models, confirming earlier photoionization results. In the fiducial LOC model, the Mg II-emitting gas is on average more distant from the ionizing source than the H$α$/H$β$ gas, and responds with a lower amplitude to continuum variations. If the broad-line region (BLR) is truncated at a physical radius of $\sim 0.3$ pc (for a $10^{8.5}M_{\odot}$ BH accreting at Eddington ratio of 0.1), most of the Mg II flux will always be emitted near this outer boundary and hence will not display breathing. These results indicate that reverberation mapping results on broad Mg II, while generally more difficult to obtain due to the lower line responsivity, can still be used to infer the Mg II BLR size and hence black hole mass. But it is possible that Mg II does not have a well defined intrinsic BLR size-luminosity relation for individual quasars, even though a global one for the general population may still exist. The dramatic changes in broad H$α$/H$β$ emission in the observationally-rare changing-look quasars are fully consistent with photoionization responses to extreme continuum variability, and the LOC model provides natural explanations for the persistence of broad Mg II in changing-look quasars defined on H$α$/H$β$, and the rare population of broad Mg II emitters in the spectra of massive inactive galaxies.

astro-ph.GA↗

arXiv2019

Graph-Driven Generative Models for Heterogeneous Multi-Task Learning

We propose a novel graph-driven generative model, that unifies multiple heterogeneous learning tasks into the same framework. The proposed model is based on the fact that heterogeneous learning tasks, which correspond to different generative processes, often rely on data with a shared graph structure. Accordingly, our model combines a graph convolutional network (GCN) with multiple variational autoencoders, thus embedding the nodes of the graph i.e., samples for the tasks) in a uniform manner while specializing their organization and usage to different tasks. With a focus on healthcare applications (tasks), including clinical topic modeling, procedure recommendation and admission-type prediction, we demonstrate that our method successfully leverages information across different tasks, boosting performance in all tasks and outperforming existing state-of-the-art approaches.

cs.LG↗

arXiv2019

Macroscopic visualization of fast electrochemical reaction of SrCoOx oxygen sponge

Strontium cobaltite (SrCoOx) is known as a material showing fast topotactic electrochemical Redox reaction so-called oxygen sponge. Although atomic scale phenomenon of the oxidation of SrCoO2.5 into SrCoO3 is known, the macroscopic phenomenon has not been clarified yet thus far. Here, we visualize the electrochemical oxidation of SrCoOx macroscopically. SrCoOx epitaxial films with various oxidation states were prepared by the electrochemical oxidation of SrCoO2.5 film into SrCoO3-d film. Steep decrease of both resistivity and the absolute value of thermopower of electrochemically oxidized SrCoOx epitaxial films indicated the columnar oxidation firstly occurred along with the surface normal and then spread in the perpendicular to the normal. Further, we directly visualized the phenomena using the conductive AFM. This macroscopic image of the electrochemical oxidation would be useful to develop a functional device utilizing the electrochemical redox reaction of SrCoOx.

cond-mat.mtrl-sci↗

arXiv2019

Two Case Studies of Experience Prototyping Machine Learning Systems in the Wild

Throughout the course of my Ph.D., I have been designing the user experience (UX) of various machine learning (ML) systems. In this workshop, I share two projects as case studies in which people engage with ML in much more complicated and nuanced ways than the technical HCML work might assume. The first case study describes how cardiology teams in three hospitals used a clinical decision-support system that helps them decide whether and when to implant an artificial heart to a heart failure patient. I demonstrate that physicians cannot draw on their decision-making experience by seeing only patient data on paper. They are also confused by some fundamental premises upon which ML operates. For example, physicians asked: Are ML predictions made based on clinicians' best efforts? Is it ethical to make decisions based on previous patients' collective outcomes? In the second case study, my collaborators and I designed an intelligent text editor, with the goal of improving authors' writing experience with NLP (Natural Language Processing) technologies. We prototyped a number of generative functionalities where the system provides phrase-or-sentence-level writing suggestions upon user request. When writing with the prototype, however, authors shared that they need to "see where the sentence is going two paragraphs later" in order to decide whether the suggestion aligns with their writing; Some even considered adopting machine suggestions as plagiarism, therefore "is simply wrong". By sharing these unexpected and intriguing responses from these real-world ML users, I hope to start a discussion about such previously-unknown complexities and nuances of -- as the workshop proposal states -- "putting ML at the service of people in a way that is accessible, useful, and trustworthy to all".

cs.HC↗

arXiv2019

Improving Textual Network Learning with Variational Homophilic Embeddings

The performance of many network learning applications crucially hinges on the success of network embedding algorithms, which aim to encode rich network information into low-dimensional vertex-based vector representations. This paper considers a novel variational formulation of network embeddings, with special focus on textual networks. Different from most existing methods that optimize a discriminative objective, we introduce Variational Homophilic Embedding (VHE), a fully generative model that learns network embeddings by modeling the semantic (textual) information with a variational autoencoder, while accounting for the structural (topology) information through a novel homophilic prior design. Homophilic vertex embeddings encourage similar embedding vectors for related (connected) vertices. The proposed VHE promises better generalization for downstream tasks, robustness to incomplete observations, and the ability to generalize to unseen vertices. Extensive experiments on real-world networks, for multiple tasks, demonstrate that the proposed method consistently achieves superior performance relative to competing state-of-the-art approaches.

cs.LG↗

arXiv2019

Ouroboros: On Accelerating Training of Transformer-Based Language Models

Language models are essential for natural language processing (NLP) tasks, such as machine translation and text summarization. Remarkable performance has been demonstrated recently across many NLP domains via a Transformer-based language model with over a billion parameters, verifying the benefits of model size. Model parallelism is required if a model is too large to fit in a single computing device. Current methods for model parallelism either suffer from backward locking in backpropagation or are not applicable to language models. We propose the first model-parallel algorithm that speeds the training of Transformer-based language models. We also prove that our proposed algorithm is guaranteed to converge to critical points for non-convex problems. Extensive experiments on Transformer and Transformer-XL language models demonstrate that the proposed algorithm obtains a much faster speedup beyond data parallelism, with comparable or better accuracy. Code to reproduce experiments is to be found at \url{https://github.com/LaraQianYang/Ouroboros}.

cs.CL↗

arXiv2019

An Unusual Mid-Infrared Flare in a Type 2 AGN: An Obscured Turning-on AGN or Tidal Disruption Event?

We report the discovery of an exceptional MIR flare in a Type 2 AGN, SDSS J165726.81+234528.1, at $z=0.059$. This object brightened by 3 mag in the Wide-field Infrared Survey Explorer (WISE) $W1$ and $W2$ bands between 2015 and 2017 (and is fading since 2018), without significant changes ($\lesssim$ 0.2 mag) in the optical over the same period of time. Based on the WISE light curves and near-IR imaging, the flare is more significant at longer wavelengths, suggesting an origin of hot dust emission. The estimated black hole mass ($\sim 10^{6.5}\,M_\odot$) from different methods places its peak bolometric luminosity around the Eddington limit. The high luminosity of the MIR flare and its multi-year timescale suggest that it most likely originated from reprocessed dust radiation in an extended torus surrounding the AGN, instead of from stellar explosions. The MIR color variability is consistent with known changing-look AGN and tidal disruption events (TDEs), but inconsistent with normal supernovae. We suggest that it is a turning-on Type 2 AGN or TDE, where the optical variability is obscured by the dust torus during the transition. This MIR flare event reveals a population of dramatic nuclear transients that are missed in the optical.

astro-ph.GA↗

arXiv2019

Chirped temporal solitons in driven optical resonators

Temporal solitons in driven microresonator, fiber-resonator, and bulk enhancement cavities enable attractive optical sources for spectroscopy, communications, and metrology. Here we present theoretical and experimental observations of a new class of temporal optical soliton characterized by pulses with large and positive chirp in normal dispersion resonators with strong spectral filtering. Numerical simulations reveal stable waveforms over a wide new range of parameters including highly chirped pulses at large drive powers. Chirped temporal solitons matching predictions are observed in experiments with normal dispersion fiber resonators strongly driven with nanosecond pulses. Scaling laws are developed and provide simple design guidelines for generating chirped temporal solitons in bulk- and micro-resonator, in addition to fiber-resonator platforms. The relationship between the chirped solutions and other stable waveforms in normal and anomalous dispersion resonators is examined. Chirped temporal solitons represent a promising new resource for frequency-comb and ultrashort-pulse generation.

physics.optics↗

arXiv2019

Learning Compressed Sentence Representations for On-Device Text Processing

Vector representations of sentences, trained on massive text corpora, are widely used as generic sentence embeddings across a variety of NLP problems. The learned representations are generally assumed to be continuous and real-valued, giving rise to a large memory footprint and slow retrieval speed, which hinders their applicability to low-resource (memory and computation) platforms, such as mobile devices. In this paper, we propose four different strategies to transform continuous and generic sentence embeddings into a binarized form, while preserving their rich semantic information. The introduced methods are evaluated across a wide range of downstream tasks, where the binarized sentence embeddings are demonstrated to degrade performance by only about 2% relative to their continuous counterparts, while reducing the storage requirement by over 98%. Moreover, with the learned binary representations, the semantic relatedness of two sentences can be evaluated by simply calculating their Hamming distance, which is more computational efficient compared with the inner product operation between continuous embeddings. Detailed analysis and case study further validate the effectiveness of proposed methods.

cs.CL↗

arXiv2019

Spectral Variability of a Sample of Extreme Variability Quasars and Implications for the MgII Broad-line Region

We present new Gemini/GMOS optical spectroscopy of 16 extreme variability quasars (EVQs) that dimmed by more than 1.5 mag in the $g$ band between the Sloan Digital Sky Survey (SDSS) and the Dark Energy Survey (DES) epochs (separated by a few years in the quasar rest frame). The quasar sample covers a redshift range of $0.5 < z < 2.1$. Nearly half of these EVQs brightened significantly (by more than 0.5 mag in the $g$ band) in a few years after reaching their previous faintest state, and some EVQs showed rapid (non-blazar) variations of greater than 1-2 mag on timescales of only months. Leveraging on the large dynamic range in continuum variability between the earlier SDSS and the new GMOS spectra, we explore the associated variations in the broad Mg II,$\lambda2798$ line, whose variability properties have not been well studied before. The broad Mg II flux varies in the same direction as the continuum flux, albeit with a smaller amplitude, which indicates at least some portion of Mg II is reverberating to continuum changes. However, the width (FWHM) of Mg II does not vary accordingly as continuum changes for most objects in the sample, in contrast to the case of the broad Balmer lines. Using the width of broad Mg II to estimate the black hole mass therefore introduces a luminosity-dependent bias.

astro-ph.GA↗

arXiv2019

Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes

Clinical decision support tools (DST) promise improved healthcare outcomes by offering data-driven insights. While effective in lab settings, almost all DSTs have failed in practice. Empirical research diagnosed poor contextual fit as the cause. This paper describes the design and field evaluation of a radically new form of DST. It automatically generates slides for clinicians' decision meetings with subtly embedded machine prognostics. This design took inspiration from the notion of "Unremarkable Computing", that by augmenting the users' routines technology/AI can have significant importance for the users yet remain unobtrusive. Our field evaluation suggests clinicians are more likely to encounter and embrace such a DST. Drawing on their responses, we discuss the importance and intricacies of finding the right level of unremarkableness in DST design, and share lessons learned in prototyping critical AI systems as a situated experience.

cs.HC↗