arXiv subjects

Yanan Wang

Publications and source records attributed to Yanan Wang.

At least 91 records · Page 5Linked to original sources

arXiv2023

The radio detection and accretion properties of the peculiar nuclear transient AT 2019avd

AT 2019avd is a nuclear transient detected from infrared to soft X-rays, though its nature is yet unclear. The source has shown two consecutive flaring episodes in the optical and the infrared bands and its second flare was covered by X-ray monitoring programs. During this flare, the UVOT/Swift photometries revealed two plateaus: one observed after the peak and the other one appeared ~240 days later. Meanwhile, our NICER and XRT/Swift campaigns show two declines in the X-ray emission, one during the first optical plateau and one 70-90 days after the optical/UV decline. The evidence suggests that the optical/UV could not have been primarily originated from X-ray reprocessing. Furthermore, we detected a timelag of ~16-34 days between the optical and UV emission, which indicates the optical likely comes from UV reprocessing by a gas at a distance of 0.01-0.03 pc. We also report the first VLA and VLBA detection of this source at different frequencies and different stages of the second flare. The information obtained in the radio band - namely a steep and a late-time inverted radio spectrum, a high brightness temperature and a radio-loud state at late times - together with the multiwavelength properties of AT 2019avd suggests the launching and evolution of outflows such as disc winds or jets. In conclusion, we propose that after the ignition of black hole activity in the first flare, a super-Eddington flaring accretion disc formed and settled to a sub-Eddington state by the end of the second flare, associated with a compact radio outflow.

astro-ph.HE↗

arXiv2022

Nanomechanical Resonators: Toward Atomic Scale

The quest for realizing and manipulating ever smaller man-made movable structures and dynamical machines has spurred tremendous endeavors, led to important discoveries, and inspired researchers to venture to new grounds. Scientific feats and technological milestones of miniaturization of mechanical structures have been widely accomplished by advances in machining and sculpturing ever shrinking features out of bulk materials such as silicon. With the flourishing multidisciplinary field of low-dimensional nanomaterials, including one-dimensional (1D) nanowires/nanotubes, and two-dimensional (2D) atomic layers such as graphene/phosphorene, growing interests and sustained efforts have been devoted to creating mechanical devices toward the ultimate limit of miniaturization--genuinely down to the molecular or even atomic scale. These ultrasmall movable structures, particularly nanomechanical resonators that exploit the vibratory motion in these 1D and 2D nano-to-atomic-scale structures, offer exceptional device-level attributes, such as ultralow mass, ultrawide frequency tuning range, broad dynamic range, and ultralow power consumption, thus holding strong promises for both fundamental studies and engineering applications. In this Review, we offer a comprehensive overview and summary of this vibrant field, present the state-of-the-art devices and evaluate their specifications and performance, outline important achievements, and postulate future directions for studying these miniscule yet intriguing molecular-scale machines.

cond-mat.mes-hall↗

arXiv2022

Complete Cross-triplet Loss in Label Space for Audio-visual Cross-modal Retrieval

The heterogeneity gap problem is the main challenge in cross-modal retrieval. Because cross-modal data (e.g. audiovisual) have different distributions and representations that cannot be directly compared. To bridge the gap between audiovisual modalities, we learn a common subspace for them by utilizing the intrinsic correlation in the natural synchronization of audio-visual data with the aid of annotated labels. TNN-CCCA is the best audio-visual cross-modal retrieval (AV-CMR) model so far, but the model training is sensitive to hard negative samples when learning common subspace by applying triplet loss to predict the relative distance between inputs. In this paper, to reduce the interference of hard negative samples in representation learning, we propose a new AV-CMR model to optimize semantic features by directly predicting labels and then measuring the intrinsic correlation between audio-visual data using complete cross-triple loss. In particular, our model projects audio-visual features into label space by minimizing the distance between predicted label features after feature projection and ground label representations. Moreover, we adopt complete cross-triplet loss to optimize the predicted label features by leveraging the relationship between all possible similarity and dissimilarity semantic information across modalities. The extensive experimental results on two audio-visual double-checked datasets have shown an improvement of approximately 2.1% in terms of average MAP over the current state-of-the-art method TNN-CCCA for the AV-CMR task, which indicates the effectiveness of our proposed model.

cs.MM↗

arXiv2022

Hybrid topological photonic crystals

Photonic topological phases offering unprecedented manipulation of electromagnetic waves have attracted much research interest which, however, have been mostly restricted to a single band gap. Here, we report on the experimental discovery of hybrid topological photonic crystals which host simultaneously quantum anomalous Hall and valley Hall phases in different photonic band gaps. The underlying hybrid topological phase manifests itself in the edge responses as the coexistence of the chiral edge states and valley Hall edge states in different frequency ranges. We experimentally verify such an emergent phenomenon and show that such a feature enables novel multiplexing of photon transport in the edge channels. Our study reveals a situation with coexisting topology of distinct nature in a single photonic system that may enable frequency-dependent filtering and manipulation of topological edge photons.

cond-mat.mes-hall↗

arXiv2022

High energy Millihertz quasi-periodic oscillations in 1A 0535+262 with Insight-HXMT challenge current models

We studied the millihertz quasi-periodic oscillation (mHz QPO) in the 2020 outburst of the Be/X-ray binary 1A 0535+262 using Insight-HXMT data over a broad energy band. The mHz QPO is detected in the 27-120 keV energy band. The QPO centroid frequency is correlated with the source flux, and evolves in the 35-95 mHz range during the outburst. The QPO is most significant in the 50-65 keV band, with a significance of ~ 8 sigma, but is hardly detectable (<2 sigma) in the lowest (1-27 keV) and highest (>120 keV) energy bands. Notably, the detection of mHz QPO above 80 keV is the highest energy at which mHz QPOs have been detected so far. The fractional rms of the mHz QPO first increases and then decreases with energy, reaching the maximum amplitude at 50-65 keV. In addition, at the peak of the outburst, the mHz QPO shows a double-peak structure, with the difference between the two peaks being constant at ~0.02 Hz, twice the spin frequency of the neutron star in this system. We discuss different scenarios explaining the generation of the mHz QPO, including the beat frequency model, the Keplerian frequency model, the model of two jets in opposite directions, and the precession of the neutron star, but find that none of them can explain the origin of the QPO well. We conclude that the variability of non-thermal radiation may account for the mHz QPO, but further theoretical studies are needed to reveal the physical mechanism.

astro-ph.HE↗

arXiv2022

Determination of QPO properties in the presence of strong broad-band noise: a case study on the data of MAXI J1820+070

Accurate calculation of the phase lags of quasi-periodic oscillations (QPOs) will provide insight into their origin. In this paper we investigate the phase lag correction method which has been applied to calculate the intrinsic phase lags of the QPOs in MAXI J1820+070. We find that the traditional additive model between BBN and QPOs in the time domain is rejected, but the convolution model is accepted. By introducing a convolution mechanism in the time domain, the Fourier cross-spectrum analysis shows that the phase lags between QPOs components in different energy bands will have a simple linear relationship with the phase lags between the total signals, so that the intrinsic phase lags of the QPOs can be obtained by linear correction. The power density spectrum (PDS) thus requires a multiplicative model to interpret the data. We briefly discuss a physical scenario for interpreting the convolution. In this scenario, the corona acts as a low-pass filter, the Green's function containing the noise is convolved with the QPOs to form the low-frequency part of the PDS, while the high-frequency part requires an additive component. We use a multiplicative PDS model to fit the data observed by Insight-HXMT. The overall fitting results are similar compared to the traditional additive PDS model. Neither the width nor the centroid frequency of the QPOs obtained from each of the two PDS models were significantly different, except for the r.m.s. of the QPOs. Our work thus provides a new perspective on the coupling of noise and QPOs.

astro-ph.HE↗

arXiv2022

Insight-HXMT Study of the Inner Accretion Disk in the Black Hole Candidate EXO 1846--031

We study the spectral evolution of the black hole candidate EXO 1846$-$031 during its 2019 outburst, in the 1--150 keV band,with the {\it {Hard X-ray Modulation Telescope}}. The continuum spectrum is well modelled with an absorbed disk-blackbody plus cutoff power-law, in the hard, intermediate and soft states. In addition, we detect an $\approx$6.6 keV Fe emission line in the hard intermediate state. Throughout the soft intermediate and soft states, the fitted inner disk radius remains almost constant; we suggest that it has settled at the innermost stable circular orbit (ISCO). However, in the hard and hard intermediate states, the apparent inner radius was unphysically small (smaller than ISCO), even after accounting for the Compton scattering of some of the disk photons by the corona in the fit. We argue that this is the result of a high hardening factor, $f_{\rm col}\approx2.0-2.7$, in the early phases of outburst evolution, well above the canonical value of 1.7 suitable to a steady disk. We suggest that the inner disk radius was close to ISCO already in the low/hard state. Furthermore, we propose that this high value of hardening factor in the relatively hard state is probably caused by the additional illuminating of the coronal irradiation onto the disk. Additionally, we estimate the spin parameter with the continuum-fitting method, over a range of plausible black hole masses and distances. We compare our results with the spin measured with the reflection-fitting method and find that the inconsistency of the two results is partly caused by the different choices of $f_{\rm col}$.

astro-ph.HE↗

arXiv2022

Cloning Outfits from Real-World Images to 3D Characters for Generalizable Person Re-Identification

Recently, large-scale synthetic datasets are shown to be very useful for generalizable person re-identification. However, synthesized persons in existing datasets are mostly cartoon-like and in random dress collocation, which limits their performance. To address this, in this work, an automatic approach is proposed to directly clone the whole outfits from real-world person images to virtual 3D characters, such that any virtual person thus created will appear very similar to its real-world counterpart. Specifically, based on UV texture mapping, two cloning methods are designed, namely registered clothes mapping and homogeneous cloth expansion. Given clothes keypoints detected on person images and labeled on regular UV maps with clear clothes structures, registered mapping applies perspective homography to warp real-world clothes to the counterparts on the UV map. As for invisible clothes parts and irregular UV maps, homogeneous expansion segments a homogeneous area on clothes as a realistic cloth pattern or cell, and expand the cell to fill the UV map. Furthermore, a similarity-diversity expansion strategy is proposed, by clustering person images, sampling images per cluster, and cloning outfits for 3D character generation. This way, virtual persons can be scaled up densely in visual similarity to challenge model learning, and diversely in population to enrich sample distribution. Finally, by rendering the cloned characters in Unity3D scenes, a more realistic virtual dataset called ClonedPerson is created, with 5,621 identities and 887,766 images. Experimental results show that the model trained on ClonedPerson has a better generalization performance, superior to that trained on other popular real-world and synthetic person re-identification datasets. The ClonedPerson project is available at https://github.com/Yanan-Wang-cs/ClonedPerson.

cs.CV↗

arXiv2022

Sequential Search with Off-Policy Reinforcement Learning

Recent years have seen a significant amount of interests in Sequential Recommendation (SR), which aims to understand and model the sequential user behaviors and the interactions between users and items over time. Surprisingly, despite the huge success Sequential Recommendation has achieved, there is little study on Sequential Search (SS), a twin learning task that takes into account a user's current and past search queries, in addition to behavior on historical query sessions. The SS learning task is even more important than the counterpart SR task for most of E-commence companies due to its much larger online serving demands as well as traffic volume. To this end, we propose a highly scalable hybrid learning model that consists of an RNN learning framework leveraging all features in short-term user-item interactions, and an attention model utilizing selected item-only features from long-term interactions. As a novel optimization step, we fit multiple short user sequences in a single RNN pass within a training batch, by solving a greedy knapsack problem on the fly. Moreover, we explore the use of off-policy reinforcement learning in multi-session personalized search ranking. Specifically, we design a pairwise Deep Deterministic Policy Gradient model that efficiently captures users' long term reward in terms of pairwise classification error. Extensive ablation experiments demonstrate significant improvement each component brings to its state-of-the-art baseline, on a variety of offline and online metrics.

cs.IR↗

arXiv2021

A universal model for the formation energy prediction of inorganic compounds

Harnessing the recent advance in data science and materials science, it is feasible today to build predictive models for materials properties. In this study, we employ the data of high-throughput quantum mechanics calculations based on 170,714 inorganic crystalline compounds to train a machine learning model for formation energy prediction. Different from the previous work, our model reaches a fairly good predictive ability (R2=0.982 and MAE=0.07 eVatom-1, DenseNet model) and meanwhile can be universally applied to the large phase space of inorganic materials. The improvement comes from several effective structure-dependent descriptors that are proposed to take the information of electronegativity and structure into account. This model can provide a useful tool to search for new materials in a vast phase space in a fast and cost-effective manner.

cond-mat.mtrl-sci↗

arXiv2021

Cavity Quantum Electrodynamics Design with Single Photon Emitters in Hexagonal Boron Nitride

Hexagonal boron nitride (h-BN), a prevalent insulating crystal for dielectric and encapsulation layers in two-dimensional (2D) nanoelectronics and a structural material in 2D nanoelectromechanical systems (NEMS), has also rapidly emerged as a promising platform for quantum photonics with the recent discovery of optically active defect centers and associated spin states. Combined with measured emission characteristics, here we propose and numerically investigate the cavity quantum electrodynamics (cavity-QED) scheme incorporating these defect-enabled single photon emitters (SPEs) in h-BN microdisk resonators. The whispering-gallery nature of microdisks can support multiple families of cavity resonances with different radial and azimuthal mode indices simultaneously, overcoming the challenges in coinciding a single point defect with the maximum electric field of an optical mode both spatially and spectrally. The excellent characteristics of h-BN SPEs, including exceptional emission rate, considerably high Debye-Waller factor, and Fourier transform limited linewidth at room temperature, render strong coupling with the ratio of coupling to decay rates g/max(γ,\k{appa}) predicated as high as 500. This study not only provides insight into the emitter-cavity interaction, but also contributes toward realizing h-BN photonic components, such as low-threshold microcavity lasers and high-purity single photon sources, critical for linear optics quantum computing and quantum networking applications.

physics.optics↗

arXiv2020

A variable ionized disk wind in the black-hole candidate EXO 1846-031

After 34 years, the black-hole candidate EXO 1846-031 went into outburst again in 2019. We investigate its spectral properties in the hard intermediate and the soft states with NuSTAR and Insight-HXMT. A reflection component has been detected in the two spectral states but possibly originating from different illumination spectra: in the intermediate state, the illuminating source is attributed to a hard coronal component, which has been commonly observed in other X-ray binaries, whereas in the soft state the reflection is probably produced by the disk self-irradiation. Both cases support EXO 1846-031 as a low inclination system of ~40 degrees. An absorption line is clearly detected at ~7.2 keV in the hard intermediate state, corresponding to a highly ionized disk wind (log ξ > 6.1) with a velocity up to 0.06c. Meanwhile, quasi-simultaneous radio emissions have been detected before and after the X-rays, implying the co-existence of disk winds and jets in this system. Additionally, the observed wind in this source is potentially driven by magnetic forces. The absorption line disappeared in the soft state and a narrow emission line appeared at ~6.7 keV on top of the reflection component, which may be evidence for disk winds, but data with the higher spectral resolution are required to examine this.

astro-ph.HE↗

arXiv2020

Offline Meta-level Model-based Reinforcement Learning Approach for Cold-Start Recommendation

Reinforcement learning (RL) has shown great promise in optimizing long-term user interest in recommender systems. However, existing RL-based recommendation methods need a large number of interactions for each user to learn a robust recommendation policy. The challenge becomes more critical when recommending to new users who have a limited number of interactions. To that end, in this paper, we address the cold-start challenge in the RL-based recommender systems by proposing a meta-level model-based reinforcement learning approach for fast user adaptation. In our approach, we learn to infer each user's preference with a user context variable that enables recommendation systems to better adapt to new users with few interactions. To improve adaptation efficiency, we learn to recover the user policy and reward from only a few interactions via an inverse reinforcement learning method to assist a meta-level recommendation agent. Moreover, we model the interaction relationship between the user model and recommendation agent from an information-theoretic perspective. Empirical results show the effectiveness of the proposed method when adapting to new users with only a single interaction sequence. We further provide a theoretical analysis of the recommendation performance bound.

cs.LG↗

arXiv2020

STMARL: A Spatio-Temporal Multi-Agent Reinforcement Learning Approach for Cooperative Traffic Light Control

The development of intelligent traffic light control systems is essential for smart transportation management. While some efforts have been made to optimize the use of individual traffic lights in an isolated way, related studies have largely ignored the fact that the use of multi-intersection traffic lights is spatially influenced and there is a temporal dependency of historical traffic status for current traffic light control. To that end, in this paper, we propose a novel SpatioTemporal Multi-Agent Reinforcement Learning (STMARL) framework for effectively capturing the spatio-temporal dependency of multiple related traffic lights and control these traffic lights in a coordinating way. Specifically, we first construct the traffic light adjacency graph based on the spatial structure among traffic lights. Then, historical traffic records will be integrated with current traffic status via Recurrent Neural Network structure. Moreover, based on the temporally-dependent traffic information, we design a Graph Neural Network based model to represent relationships among multiple traffic lights, and the decision for each traffic light will be made in a distributed way by the deep Q-learning method. Finally, the experimental results on both synthetic and real-world data have demonstrated the effectiveness of our STMARL framework, which also provides an insightful understanding of the influence mechanism among multi-intersection traffic lights.

cs.MA↗

arXiv2020

Vision Skills Needed to Answer Visual Questions

The task of answering questions about images has garnered attention as a practical service for assisting populations with visual impairments as well as a visual Turing test for the artificial intelligence community. Our first aim is to identify the common vision skills needed for both scenarios. To do so, we analyze the need for four vision skills---object recognition, text recognition, color recognition, and counting---on over 27,000 visual questions from two datasets representing both scenarios. We next quantify the difficulty of these skills for both humans and computers on both datasets. Finally, we propose a novel task of predicting what vision skills are needed to answer a question about an image. Our results reveal (mis)matches between aims of real users of such services and the focus of the AI community. We conclude with a discussion about future directions for addressing the visual question answering task.

cs.HC↗

arXiv2020

SEMDOT: Smooth-Edged Material Distribution for Optimizing Topology Algorithm

Element-based topology optimization algorithms capable of generating smooth boundaries have drawn serious attention given the significance of accurate boundary information in engineering applications. The basic framework of a new element-based continuum algorithm is proposed in this paper. This algorithm is based on a smooth-edged material distribution strategy that uses solid/void grid points assigned to each element. Named Smooth-Edged Material Distribution for Optimizing Topology (SEMDOT), the algorithm uses elemental volume fractions which depend on the densities of grid points in the Finite Element Analysis (FEA) model rather than elemental densities. Several numerical examples are studied to demonstrate the application and effectiveness of SEMDOT. In these examples, SEMDOT proved to be capable of obtaining optimized topologies with smooth and clear boundaries showing better or comparable performance compared to other topology optimization methods. Through these examples, first, the advantages of using the Heaviside smooth function are discussed in comparison to the Heaviside step function. Then, the benefits of introducing multiple filtering steps in this algorithm are shown. Finally, comparisons are conducted to exhibit the differences between SEMDOT and some well-established element-based algorithms. The validation of the sensitivity analysis method adopted in SEMDOT is conducted using a typical compliant mechanism design case. In addition, this paper provides the Matlab code of SEMDOT for educational and academic purposes.

cs.CE↗

arXiv2020

Surpassing Real-World Source Training Data: Random 3D Characters for Generalizable Person Re-Identification

Person re-identification has seen significant advancement in recent years. However, the ability of learned models to generalize to unknown target domains still remains limited. One possible reason for this is the lack of large-scale and diverse source training data, since manually labeling such a dataset is very expensive and privacy sensitive. To address this, we propose to automatically synthesize a large-scale person re-identification dataset following a set-up similar to real surveillance but with virtual environments, and then use the synthesized person images to train a generalizable person re-identification model. Specifically, we design a method to generate a large number of random UV texture maps and use them to create different 3D clothing models. Then, an automatic code is developed to randomly generate various different 3D characters with diverse clothes, races and attributes. Next, we simulate a number of different virtual environments using Unity3D, with customized camera networks similar to real surveillance systems, and import multiple 3D characters at the same time, with various movements and interactions along different paths through the camera networks. As a result, we obtain a virtual dataset, called RandPerson, with 1,801,816 person images of 8,000 identities. By training person re-identification models on these synthesized person images, we demonstrate, for the first time, that models trained on virtual data can generalize well to unseen target images, surpassing the models trained on various real-world datasets, including CUHK03, Market-1501, DukeMTMC-reID, and almost MSMT17. The RandPerson dataset is available at https://github.com/VideoObjectSearch/RandPerson.

cs.CV↗

arXiv2020

The evolution of the broadband temporal features observed in the black-hole transient MAXI J1820+070 with Insight-HXMT

We study the evolution of the temporal properties of MAXI 1820+070 during the 2018 outburst in its hard state from MJD 58190 to 58289 with Insight-HXMT in a broad energy band 1-150 keV. We find different behaviors of the hardness ratio, the fractional rms and time lag before and after MJD 58257, suggesting a transition occurred at around this point. The observed time lags between the soft photons in the 1-5 keV band and the hard photons in higher energy bands, up to 150 keV, are frequency-dependent: the time lags in the low-frequency range, 2-10 mHz, are both soft and hard lags with a timescale of dozens of seconds but without a clear trend along the outburst; the time lags in the high-frequency range, 1-10 Hz, are only hard lags with a timescale of tens of milliseconds; first increase until around MJD 58257 and decrease after this date. The high-frequency time lags are significantly correlated to the photon index derived from the fit to the quasi-simultaneous NICER spectrum in the 1-10 keV band. This result is qualitatively consistent with a model in which the high-frequency time lags are produced by Comptonization in a jet.

astro-ph.HE↗