arXiv subjects

Xin Liu

Publications and source records attributed to Xin Liu.

At least 883 records · Page 49Linked to original sources

arXiv2020

The Curious Case of PHL 293B: A Long-Lived Transient in a Metal-Poor Blue Compact Dwarf Galaxy

We report on small-amplitude optical variability and recent dissipation of the unusually persistent broad emission lines in the blue compact dwarf galaxy PHL 293B. The galaxy's unusual spectral features (P Cygni-like profiles with $\sim$800 km s$^{-1}$ blueshifted absorption lines) have resulted in conflicting interpretations of the nature of this source in the literature. However, analysis of new Gemini spectroscopy reveals the broad emission has begun to fade after being persistent for over a decade prior. Precise difference imaging light curves constructed with the Sloan Digital Sky Survey and the Dark Energy Survey reveal small-amplitude optical variability of $\sim$0.1 mag in the g band offset by $100\pm21$ pc from the brightest pixel of the host. The light curve is well-described by an active galactic nuclei (AGN)-like damped random walk process. However, we conclude that the origin of the optical variability and spectral features of PHL 293B is due to a long-lived stellar transient, likely a Type IIn supernova or non-terminal outburst, mimicking long-term AGN-like variability. This work highlights the challenges of discriminating between scenarios in such extreme environments, relevant to searches for AGNs in dwarf galaxies. This is the second long-lived transient discovered in a blue compact dwarf, after SDSS1133. Our result implies such long-lived stellar transients may be more common in metal-deficient galaxies. Systematic searches for low-level variability in dwarf galaxies will be possible with the upcoming Legacy Survey of Space and Time at Vera C. Rubin Observatory.

astro-ph.GA↗

arXiv2020

Extreme-Scale Density Functional Theory High Performance Computing of DGDFT for Tens of Thousands of Atoms using Millions of Cores on Sunway TaihuLight

High performance computing (HPC) is a powerful tool to accelerate the Kohn-Sham density functional theory (KS-DFT) calculations on modern heterogeneous supercomputers. Here, we describe a massively extreme-scale parallel and portable implementation of discontinuous Galerkin density functional theory (DGDFT) method on the Sunway TaihuLight supercomputer. The DGDFT method uses the adaptive local basis (ALB) functions generated on-the-fly during the self-consistent field (SCF) iteration to solve the KS equations with the high precision comparable to that of plane-wave basis set. In particular, the DGDFT method adopts a two-level parallelization strategy that makes use of different types of data distribution, task scheduling, and data communication schemes, and combines with the feature of master-slave multi-thread heterogeneous parallelism of SW26010 processor, resulting in extreme-scale HPC KS-DFT calculations on the Sunway TaihuLight supercomputer. We show that the DGDFT method can scale up to 8,519,680 processing cores (131,072 core groups) on the Sunway TaihuLight supercomputer for investigating the electronic structures of two-dimensional (2D) metallic graphene systems containing tens of thousands of carbon atoms.

physics.comp-ph↗

arXiv2020

Interfacial-hybridization-modified Ir Ferromagnetism and Electronic Structure in LaMnO$_3$/SrIrO$_3$ Superlattices

Artificially fabricated 3$d$/5$d$ superlattices (SLs) involve both strong electron correlation and spin-orbit coupling in one material by means of interfacial 3$d$-5$d$ coupling, whose mechanism remains mostly unexplored. In this work we investigated the mechanism of interfacial coupling in LaMnO$_3$/SrIrO$_3$ SLs by several spectroscopic approaches. Hard x-ray absorption, magnetic circular dichroism and photoemission spectra evidence the systematic change of the Ir ferromagnetism and the electronic structure with the change of the SL repetition period. First-principles calculations further reveal the mechanism of the SL-period dependence of the interfacial electronic structure and the local properties of the Ir moments, confirming that the formation of Ir-Mn molecular orbital is responsible for the interfacial coupling effects. The SL-period dependence of the ratio between spin and orbital components of the Ir magnetic moments can be attributed to the realignment of electron spin during the formation of the interfacial molecular orbital. Our results clarify the nature of interfacial coupling in this prototypical 3$d$/5$d$ SL system and the conclusion will shed light on the study of other strongly correlated and spin-orbit coupled oxide hetero-interfaces.

cond-mat.str-el↗

arXiv2020

High-$T_c$ Superconductor Fe(Se,Te) Monolayer: an Intrinsic, Scalable and Electrically-tunable Majorana Platform

A monolayer of the high-$T_c$ superconductor FeTe$_{1-x}$Se$_x$ has been predicted to realize a topologically non-trivial state with helical edge modes at its boundary, providing a novel intrinsic system to search for topological superconductivity and Majorana zero modes. Evidence in favor of a topological phase transition and helical edge modes has been identified in recent experiments \cite{Peng2019}. We propose to create Majorana zero modes by applying an in-plane magnetic field to the FeTe$_{1-x}$Se$_x$ monolayer and by tuning the local chemical potential via electric gating. Owing to the anisotropic magnetic couplings on edges from a topological band inversion, an in-plane magnetic field drives the system into an intrinsic high-order topological superconductor phase with Majorana corner modes, without fabricating heterostructures. Furthermore, we demonstrate that Majorana zero modes can occur at other different locations, including the domain wall of chemical potentials at one edge and certain type of tri-junction in the 2D bulk. Our study not only demonstrates FeTe$_{1-x}$Se$_x$ monolayer as a promising Majorana platform with scalability and electrical tunability and within reach of contemporary experimental capability, but also provides a general principle to search for realistic realization of high-order topological superconductivity.

cond-mat.supr-con↗

arXiv2020

Electric-field-controllable high-spin SrRuO3 driven by a solid ionic junction

Controlling magnetism and spin structures in strongly correlated systems by using electric field is of fundamental importance but challenging. Here, a high-spin ruthenate phase is achieved via a solid ionic chemical junction at SrRuO3/SrTiO3 interface with distinct formation energies and diffusion barriers of oxygen vacancies, analogue to electronic band alignment in semiconductor heterojunction. Oxygen vacancies trapped within this interfacial SrRuO3 reconstruct Ru-4d electronic structure and orbital occupancy, leading to an enhanced magnetic moment. Furthermore, an interfacial magnetic phase can be switched reversibly by electric-field-rectifying oxygen migration in a solid-state ionic gating device, providing a framework for atomic design of functionalities in strongly correlated oxides using a way of solid chemistry.

cond-mat.str-el↗

arXiv2020

Design admissibility and de la Garza phenomenon in multi-factor experiments

The determination of an optimal design for a given regression problem is an intricate optimization problem, especially for models with multivariate predictors. Design admissibility and invariance are main tools to reduce the complexity of the optimization problem and have been successfully applied for models with univariate predictors. In particular several authors have developed sufficient conditions for the existence of saturated designs in univariate models, where the number of support points of the optimal design equals the number of parameters. These results generalize the celebrated de la Garza phenomenon (de la Garza, 1954) which states that for a polynomial regression model of degree $p-1$ any optimal design can be based on at most $p$ points. This paper provides - for the first time - extensions of these results for models with a multivariate predictor. In particular we study a geometric characterization of the support points of an optimal design to provide sufficient conditions for the occurrence of the de la Garza phenomenon in models with multivariate predictors and characterize properties of admissible designs in terms of admissibility of designs in conditional univariate regression models.

math.ST↗

arXiv2020

Learning Diverse Fashion Collocation by Neural Graph Filtering

Fashion recommendation systems are highly desired by customers to find visually-collocated fashion items, such as clothes, shoes, bags, etc. While existing methods demonstrate promising results, they remain lacking in flexibility and diversity, e.g. assuming a fixed number of items or favoring safe but boring recommendations. In this paper, we propose a novel fashion collocation framework, Neural Graph Filtering, that models a flexible set of fashion items via a graph neural network. Specifically, we consider the visual embeddings of each garment as a node in the graph, and describe the inter-garment relationship as the edge between nodes. By applying symmetric operations on the edge vectors, this framework allows varying numbers of inputs/outputs and is invariant to their ordering. We further include a style classifier augmented with focal loss to enable the collocation of significantly diverse styles, which are inherently imbalanced in the training set. To facilitate a comprehensive study on diverse fashion collocation, we reorganize Amazon Fashion dataset with carefully designed evaluation protocols. We evaluate the proposed approach on three popular benchmarks, the Polyvore dataset, the Polyvore-D dataset, and our reorganized Amazon Fashion dataset. Extensive experimental results show that our approach significantly outperforms the state-of-the-art methods with over 10% improvements on the standard AUC metric on the established tasks. More importantly, 82.5% of the users prefer our diverse-style recommendations over other alternatives in a real-world perception study.

cs.CV↗

arXiv2020

Resonant Contributions to Three-body $B\to KKK$ Decays in Perturbative QCD Approach

In this work, we study the ($S$, $P$ and $D$)-wave $K^+K^-$ contributions to $B\to KKK$ decays in the perturbative QCD approach at leading order. Within the two-meson wave functions describing the nonperturbative dynamics in the kaon-pair for different waves, we calculate the branching fractions and the direct $CP$ asymmetries of these decay modes in the corresponding resonance regions. Most of our numerical results are well consistent with the current measurements. We note that the narrow-width approximation is invalid in the quasi-two-body decays $B\to Kf_0(980)\to KKK$. For other decays, under the narrow-width approximation we can extract the branching fractions of the corresponding two-body decays involving the intermediate resonant states, and the related branching fractions agree with the current experimental data well. Furthermore, we also predict the corresponding quasi-two-body decays $B\to Kπ^+π^-$, which are expected to be measured in the ongoing LHCb and Belle-II experiments.

hep-ph↗

arXiv2020

XGPT: Cross-modal Generative Pre-Training for Image Captioning

While many BERT-based cross-modal pre-trained models produce excellent results on downstream understanding tasks like image-text retrieval and VQA, they cannot be applied to generation tasks directly. In this paper, we propose XGPT, a new method of Cross-modal Generative Pre-Training for Image Captioning that is designed to pre-train text-to-image caption generators through three novel generation tasks, including Image-conditioned Masked Language Modeling (IMLM), Image-conditioned Denoising Autoencoding (IDA), and Text-conditioned Image Feature Generation (TIFG). As a result, the pre-trained XGPT can be fine-tuned without any task-specific architecture modifications to create state-of-the-art models for image captioning. Experiments show that XGPT obtains new state-of-the-art results on the benchmark datasets, including COCO Captions and Flickr30k Captions. We also use XGPT to generate new image captions as data augmentation for the image retrieval task and achieve significant improvement on all recall metrics.

cs.CL↗

arXiv2020

$B_{(s)} \to η_c(P,V)$ decays and effects of the next-to-leading order contributions in the perturbative QCD approach

By employing the perturbative QCD (PQCD) factorization approach, we studied the sixteen $B/B_s \to η_c (π, K, η^{(\prime)},ρ,K^*,ω,ϕ)$ decays with the inclusion of the currently known next-to-leading order (NLO) contributions. We found the following main points: (a) for the five measured $B \to η_c (K,K^*)$ and $B_s\to η_cϕ$ decays, the NLO contributions can provide $ (80-180)\%$ enhancements to the leading order (LO) PQCD predictions of their branching ratios, which play an important role to help us to interpret the data; (b) for the seven ratios $R_{1,\cdots,7}$ of the branching ratios defined among the properly selected pair of the considered decay modes, the PQCD predictions for the values of $R_{3,4,5}$ agree well with those currently available measurements from BaBar and Belle Collaboration; (c) for $B^0 \to η_c K_S^0$ decay, the PQCD predictions for both the direct and mixing induced CP asymmetries do agree very well with the measured values within errors; and (d) the PQCD predictions for ratios $R_{1,2}$ and $R_{6,7}$ also agree with the general expectations and will be tested by the future experiments.

hep-ph↗

arXiv2020

Poisson stable solutions for stochastic differential equations with Lévy noise

In this paper, we use a unified framework to study Poisson stable (including stationary, periodic, quasi-periodic, almost periodic, almost automorphic, Birkhoff recurrent, almost recurrent in the sense of Bebutov, Levitan almost periodic, pseudo-periodic, pseudo-recurrent and Poisson stable) solutions for semilinear stochastic differential equations driven by infinite dimensional Lévy noise with large jumps. Under suitable conditions on drift, diffusion and jump coefficients, we prove that there exist solutions which inherit the Poisson stability of coefficients. Further we show that these solutions are globally asymptotically stable in square-mean sense. Finally, we illustrate our theoretical results by several examples.

math.DS↗

arXiv2020

A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer Learning

Due to insufficient training data and the high computational cost to train a deep neural network from scratch, transfer learning has been extensively used in many deep-neural-network-based applications. A commonly used transfer learning approach involves taking a part of a pre-trained model, adding a few layers at the end, and re-training the new layers with a small dataset. This approach, while efficient and widely used, imposes a security vulnerability because the pre-trained model used in transfer learning is usually publicly available, including to potential attackers. In this paper, we show that without any additional knowledge other than the pre-trained model, an attacker can launch an effective and efficient brute force attack that can craft instances of input to trigger each target class with high confidence. We assume that the attacker has no access to any target-specific information, including samples from target classes, re-trained model, and probabilities assigned by Softmax to each class, and thus making the attack target-agnostic. These assumptions render all previous attack models inapplicable, to the best of our knowledge. To evaluate the proposed attack, we perform a set of experiments on face recognition and speech recognition tasks and show the effectiveness of the attack. Our work reveals a fundamental security weakness of the Softmax layer when used in transfer learning settings.

cs.LG↗

arXiv2020

Real-Space Imaging of the Ordered Small Molecule Orientations in Porous Frameworks by Electron Microscopy

The real-space imaging of small molecules is always challenging under the electron microscopes, but highly demanded for investigating various nanoscale interactions, such as hydrogen bond and van der Waals (vdW) force. Especially, identifying the host-guest interactions in porous materials directly at the molecular level will bring a deeper insight into the behaviors of guest molecules during the sorption, catalysis, gas separation and energy storage. In this work, we directly resolved the ordered configurations of p-xylenes (PXs) adsorbed in ZSM-5 frameworks by the scanning transmission electron microscopy (STEM) with the integrated differential phase contrast (iDPC) technique to identify the host-guest vdW interactions. Based on these observations, we revealed that the PXs in one straight channel modified the channel geometry with a coherent orientation. And the adjacent straight channels were deformed up to 8.8% along the different directions corresponding to three dominant PX configurations, resulting a negligible overall expansion of ZSM-5 lattices. Then, we could also image the disorder and desorption of PXs in ZSM-5 channels during the in situ heating. This work not only helped us to study the host-guest vdW interactions and the sorption behaviors of PXs in ZSM-5, but also provided an efficient tool for further imaging and studying other single-molecule behaviors under STEMs.

cond-mat.mtrl-sci↗

arXiv2020

ASER: A Large-scale Eventuality Knowledge Graph

Understanding human's language requires complex world knowledge. However, existing large-scale knowledge graphs mainly focus on knowledge about entities while ignoring knowledge about activities, states, or events, which are used to describe how entities or things act in the real world. To fill this gap, we develop ASER (activities, states, events, and their relations), a large-scale eventuality knowledge graph extracted from more than 11-billion-token unstructured textual data. ASER contains 15 relation types belonging to five categories, 194-million unique eventualities, and 64-million unique edges among them. Both intrinsic and extrinsic evaluations demonstrate the quality and effectiveness of ASER.

cs.AI↗

arXiv2019

On Universal Scaling of Distributed Queues under Load Balancing

This paper considers the steady-state performance of load balancing algorithms in a many-server system with distributed queues. The system has $N$ servers, and each server maintains a local queue with buffer size $b-1,$ i.e. a server can hold at most one job in service and $b-1$ jobs in the queue. Jobs in the same queue are served according to the first-in-first-out (FIFO) order. The system is operated in a heavy-traffic regime such that the workload per server is $λ= 1 - N^{-α}$ for $0.5\leq α<1.$ We identify a set of algorithms such that the steady-state queues have the following universal scaling, where {\em universal} means that it holds for any $α\in[0.5,1)$: (i) the number of of busy servers is $λN-o(1);$ and (ii) the number of servers with two jobs (one in service and one in queue) is $O(N^α\log N);$ and (iii) the number of servers with more than two jobs is $O\left(\frac{1}{N^{r(1-α)-1}}\right),$ where $r$ can be any positive integer independent of $N.$ The set of load balancing algorithms that satisfy the sufficient condition includes join-the-shortest-queue (JSQ), idle-one-first (I1F), and power-of-$d$-choices (Po$d$) with $d\geq N^α\log^2 N.$ We further argue that the waiting time of such an algorithm is near optimal order-wise.

math.PR↗

arXiv2019

Large-scale Mobile App Identification Using Deep Learning

Many network services and tools (e.g. network monitors, malware-detection systems, routing and billing policy enforcement modules in ISPs) depend on identifying the type of traffic that passes through the network. With the widespread use of mobile devices, the vast diversity of mobile apps, and the massive adoption of encryption protocols (such as TLS), large-scale encrypted traffic classification becomes increasingly difficult. In this paper, we propose a deep learning model for mobile app identification that works even with encrypted traffic. The proposed model only needs the payload of the first few packets for classification, and, hence, it is suitable even for applications that rely on early prediction, such as routing and QoS provisioning. The deep model achieves between 84% to 98% accuracy for the identification of 80 popular apps. We also perform occlusion analysis to bring insight into what data is leaked from SSL/TLS protocol that allows accurate app identification. Moreover, our traffic analysis shows that many apps generate not only app-specific traffic, but also numerous ambiguous flows. Ambiguous flows are flows generated by common functionality modules, such as advertisement and traffic analytics. Because such flows are common among many different apps, identifying the source app that generates ambiguous flows is challenging. To address this challenge, we propose a CNN+LSTM model that uses adjacent flows to learn the order and pattern of multiple flows, to better identify the app that generates them. We show that such flow association considerably improves the accuracy, particularly for ambiguous flows. Furthermore, we show that our approach is robust to mixed traffic scenarios where some unrelated flows may appear in adjacent flows. To the best of our knowledge, this is the first work that identifies the source app for ambiguous flows.

cs.NI↗

arXiv2019

A Trio of Massive Black Holes Caught in the Act of Merging

We report the discovery of SDSS J0849+1114 as the first known triple Type 2 Seyfert nucleus. It represents three active black holes that are identified from new spatially resolved optical slit spectroscopy using the Dual Imaging Spectrograph on the 3.5 m telescope at the Apache Point Observatory. We also present new complementary observations including the Hubble Space Telescope Wide Field Camera 3 U- and Y-band imaging, Chandra Advanced CCD Imaging Spectrometer S-array X-ray 0.5--8 keV imaging spectroscopy, and NSF Karl G. Jansky Very Large Array radio 9.0 GHz imaging in its most extended A configuration. These comprehensive multiwavelength observations, when combined together, strongly suggest that all three nuclei are active galactic nuclei. While they are now still at kiloparsec-scale separations, where the host-galaxy gravitational potential dominates, the black holes may evolve into a bound triple system in $\lesssim$2 Gyr. These triple merger systems may explain the overly massive stellar cores that have been observed in some elliptical galaxies such as M87, which are expected to be unique gravitational wave sources. Similar systems may be more common in the early universe, when galaxy mergers are thought to have been more frequent.

astro-ph.GA↗

arXiv2019

Spectral Energy Distributions of Candidate Periodically-Variable Quasars: Testing the Binary Black Hole Hypothesis

Periodic quasars are candidates for binary supermassive black holes (BSBHs) efficiently emitting low frequency gravitational waves. Recently, $\sim$150 candidates were identified from optical synoptic surveys. However, they may be false positives caused by stochastic quasar variability given the few cycles covered (typically 1.5). To independently test the binary hypothesis, we search for evidence of truncated or gapped circumbinary accretion discs (CBDs) in their spectral energy distributions (SEDs). Our work is motivated by CBD simulations that predict flux deficits as cutoffs from central cavities opened by secondaries or notches from minidiscs around both BHs. We find that candidate periodic quasars show SEDs similar to those of control quasars matched in redshift and luminosity. While seven of 138 candidates show a blue cutoff in the IR-optical-UV SED, six of which may represent CBDs with central cavities, the red SED fraction is similar to that in control quasars, suggesting no correlation between periodicity and SED anomaly. Alternatively, dust reddening may cause red SEDs. The fraction of extremely radio-loud quasars, e.g., blazars (with $R>100$), is tentatively higher than that in control quasars (at 2.5$σ$). Our results suggest that, assuming most periodic candidates are robust, IR-optical-UV SEDs of CBDs are similar to those of accretion discs of single BHs, if the periodicity is driven by BSBHs; the higher blazar fraction may signal precessing radio jets. Alternatively, most current candidate periodic quasars identified from few-cycle light curves may be false positives. Their tentatively higher blazar fraction and lower Eddington ratios may both be caused by selection biases.

astro-ph.GA↗