arXiv subjects

Yong Zhang

Publications and source records attributed to Yong Zhang.

At least 451 records · Page 25Linked to original sources

arXiv2022

Self-Attention for Incomplete Utterance Rewriting

Incomplete utterance rewriting (IUR) has recently become an essential task in NLP, aiming to complement the incomplete utterance with sufficient context information for comprehension. In this paper, we propose a novel method by directly extracting the coreference and omission relationship from the self-attention weight matrix of the transformer instead of word embeddings and edit the original text accordingly to generate the complete utterance. Benefiting from the rich information in the self-attention weight matrix, our method achieved competitive results on public IUR datasets.

cs.CL↗

arXiv2022

ATOMS: ALMA Three-millimeter Observations of Massive Star-forming regions-IX. A pilot study towards IRDC G034.43+00.24 on multi-scale structures and gas kinematics

We present a comprehensive study of the gas kinematics associated with density structures at different spatial scales in the filamentary infrared dark cloud, G034.43+00.24 (G34). This study makes use of the H13CO+ (1-0) molecular line data from the ALMA Three-millimeter Observations of Massive Star-forming regions (ATOMS) survey, which has spatial and velocity resolution of 0.04 pc and 0.2 km/s, respectively. Several tens of dendrogram structures have been extracted in the position-position-velocity space of H13CO+, which include 21 small-scale leaves and 20 larger-scale branches. Overall, their gas motions are supersonic but they exhibit the interesting behavior where leaves tend to be less dynamically supersonic than the branches. For the larger-scale, branch structures, the observed velocity-size relation (i.e., velocity variation/dispersion versus size) are seen to follow the Larson scaling exponent while the smaller-scale, leaf structures show a systematic deviation and display a steeper slope. We argue that the origin of the observed kinematics of the branch structures is likely to be a combination of turbulence and gravity-driven ordered gas flows. In comparison, gravity-driven chaotic gas motion is likely at the level of small-scale leaf structures. The results presented in our previous paper and this current follow-up study suggest that the main driving mechanism for mass accretion/inflow observed in G34 varies at different spatial scales. We therefore conclude that a scale-dependent combined effect of turbulence and gravity is essential to explain the star-formation processes in G34.

astro-ph.GA↗

arXiv2022

EBHI:A New Enteroscope Biopsy Histopathological H&E Image Dataset for Image Classification Evaluation

Background and purpose: Colorectal cancer has become the third most common cancer worldwide, accounting for approximately 10% of cancer patients. Early detection of the disease is important for the treatment of colorectal cancer patients. Histopathological examination is the gold standard for screening colorectal cancer. However, the current lack of histopathological image datasets of colorectal cancer, especially enteroscope biopsies, hinders the accurate evaluation of computer-aided diagnosis techniques. Methods: A new publicly available Enteroscope Biopsy Histopathological H&E Image Dataset (EBHI) is published in this paper. To demonstrate the effectiveness of the EBHI dataset, we have utilized several machine learning, convolutional neural networks and novel transformer-based classifiers for experimentation and evaluation, using an image with a magnification of 200x. Results: Experimental results show that the deep learning method performs well on the EBHI dataset. Traditional machine learning methods achieve maximum accuracy of 76.02% and deep learning method achieves a maximum accuracy of 95.37%. Conclusion: To the best of our knowledge, EBHI is the first publicly available colorectal histopathology enteroscope biopsy dataset with four magnifications and five types of images of tumor differentiation stages, totaling 5532 images. We believe that EBHI could attract researchers to explore new classification algorithms for the automated diagnosis of colorectal cancer, which could help physicians and patients in clinical settings.

eess.IV↗

arXiv2022

Spatially decomposed $γ$-ray features surrounding SNR Kes 79 & PSR J1853+0056

There have been substantial improvements on Fermi Large Area Telescope (LAT) data and analysis tools since the last analysis on the mid-aged supernova remnant (SNR) Kes 79 (Auchettl et al. 2014). Recent multi-wavelength studies confirmed its interaction with molecular clouds. About $0.36\degr$ north from Kes 79, a powerful pulsar -- PSR J1853+0056 also deserves our attention. In this work, we analyse the 11.5-year Fermi-LAT data to investigate the $γ$-ray feature in/around this complex region. Our result shows a more significant detection ($\sim$34.8$σ$ in 0.1--50 GeV) for this region. With $\ge$5 GeV data, we detect two extended sources -- Src-N (the brighter one; radius $\approx0.31\degr$) concentrated at the north of the SNR while enclosing PSR J1853+0056, and Src-S (radius $\approx0.58\degr$) concentrated at the south of the SNR. Their spectra have distinct peak energies ($\sim$1.0 GeV for Src-N and $\lesssim$0.5 GeV for Src-S), suggesting different origins for them. In our hadronic model that includes the leaked cosmic-rays (CRs) from the shock-cloud collision, even with extreme values of parameters, SNR Kes 79 can by no means provide enough CRs reaching clouds at Src-N to explain the local GeV spectrum. We propose that the Src-N emission could be predominantly reproduced by a putative pulsar wind nebula (PWN) powered by PSR J1853+0056. On the other hand, our same hadronic model can reproduce a majority of the GeV emission at Src-S with typical values of parameters, while the three known pulsars inside Src-S release a total power that is too low to account for half of its $γ$-ray emission.

astro-ph.HE↗

arXiv2022

ATOMS: ALMA Three-millimeter Observations of Massive Star-forming regions -- VIII. A search for hot cores by using C$_2$H$_5$CN, CH$_3$OCHO and CH$_3$OH lines

Hot cores characterized by rich lines of complex organic molecules are considered as ideal sites for investigating the physical and chemical environments of massive star formation. We present a search for hot cores by using typical nitrogen- and oxygen-bearing complex organic molecules (C$_2$H$_5$CN, CH$_3$OCHO and CH$_3$OH), based on ALMA Three-millimeter Observations of Massive Star-forming regions (ATOMS). The angular resolutions and line sensitivities of the ALMA observations are better than 2 arcsec and 10 mJy/beam, respectively. A total of 60 hot cores are identified with 45 being newly detected, in which the complex organic molecules have high gas temperatures ($>$ 100 K) and small source sizes ($<$ 0.1 pc). So far this is the largest sample of hot cores observed with similar angular resolution and spectral coverage. The observations have also shown nitrogen and oxygen differentiation in both line emission and gas distribution in 29 hot cores. Column densities of CH$_3$OH and CH$_3$OCHO increase as rotation temperatures rise. The column density of CH$_3$OCHO correlates tightly with that of CH$_3$OH. The pathways for production of different species are discussed. Based on the spatial position difference between hot cores and UC~H{\sc ii} regions, we conclude that 24 hot cores are externally heated while the other hot cores are internally heated. The observations presented here will potentially help establish a hot core template for studying massive star formation and astrochemistry.

astro-ph.GA↗

arXiv2022

Jointly Learning Knowledge Embedding and Neighborhood Consensus with Relational Knowledge Distillation for Entity Alignment

Entity alignment aims at integrating heterogeneous knowledge from different knowledge graphs. Recent studies employ embedding-based methods by first learning the representation of Knowledge Graphs and then performing entity alignment via measuring the similarity between entity embeddings. However, they failed to make good use of the relation semantic information due to the trade-off problem caused by the different objectives of learning knowledge embedding and neighborhood consensus. To address this problem, we propose Relational Knowledge Distillation for Entity Alignment (RKDEA), a Graph Convolutional Network (GCN) based model equipped with knowledge distillation for entity alignment. We adopt GCN-based models to learn the representation of entities by considering the graph structure and incorporating the relation semantic information into GCN via knowledge distillation. Then, we introduce a novel adaptive mechanism to transfer relational knowledge so as to jointly learn entity embedding and neighborhood consensus. Experimental results on several benchmarking datasets demonstrate the effectiveness of our proposed model.

cs.LG↗

arXiv2022

The Properties and Evolutions of Starspots on Three Detached Eclipsing Binaries in the LAMOST-Kepler survey

The spotted detached eclipsing binary (DEB) offers insights into starspots on the binary. Three spotted DEBs, KIC 8097825, KIC 6859813, and KIC 5527172, which were observed by the Kepler photometry and LAMOST spectroscopy, are studied in this work. The physical parameters of binaries are determined by binary modeling. The sizes, lifetimes, and single/double-dip ratio (SDR) of starspots are derived by starspot analysis. KIC 8097825 has large starspots. KIC 6859813 has a spot rotation period shorter than its orbital period but the system should be synchronized inferred from timescale estimation. The difference may be the result of the surface differential rotation. The KIC 5527172 has a long spot lifetime and an M dwarf component with an inflation radius. The primaries of these binaries and the secondary of KIC 8097825 have spots. Adding spotted DEBs of literature, we compare the starspots on binaries with those on the single stars. The spot sizes of starspots on 65% binaries are smaller than the median of those on single stars. The lifetimes of starspots on binaries are consistent with those on single stars when the rotation periods are larger than 3 days. SDRs for half of the binaries are consistent with those of single star systems, while another half are smaller. The relative lifetime positively correlates with the RMS and SDR but negatively correlates with the rotation period. These relations are similar to those of spots on the single star systems. Binaries with luminosity ratios close to the unit tend to have more double dips.

astro-ph.SR↗

arXiv2022

ATOMS: ALMA Three-millimeter Observations of Massive Star-forming regions -- VII. A catalogue of SiO clumps from ACA observations

To understand the nature of SiO emission, we conducted ACA observations of the SiO (2-1) lines toward 146 massive star-forming regions, as part of the ALMA Three-millimeter Observations of Massive Star-forming regions (ATOMS) survey. We detected SiO emission in 128 (87.7$\%$) sources and identified 171 SiO clumps, 105 of which are spatially separated from 3 mm continuum emission. A large amount of the SiO line profiles (60$\%$) are non-Gaussian. The velocity dispersion of the SiO lines ranges from 0.3 to 5.43 km s$^{-1}$. In 63 sources the SiO clumps are associated with H$_\rm{II}$ regions characterized by H40$α$ emission. We find that 68$\%$ (116) of the SiO clumps are associated with strong outflows. The median velocity dispersion of the SiO line for outflow sources and non-outflow sources is 1.91 km s$^{-1}$ and 0.99 km s$^{-1}$, respectively. These results indicate that outflow activities could be connected to strongly shocked gas. The velocity dispersion and [SiO]/[H$^{13}$CO$^+$] intensity ratio do not show any correlation with the dust temperature and particle number density of clumps. We find a positive correlation between the SiO line luminosity and the bolometric luminosity, implying stronger shock activities are associated with more luminous proto-clusters. The SiO clumps in associations with H$_\rm{II}$ regions were found to show a steeper feature in $L_\rm{sio}$/$L_\rm{bol}$. The SiO line luminosity and the fraction of shocked gas have no apparent evidence of correlation with the evolutionary stages traced by luminosity to mass ratio ($L_\rm{bol}/M$).

astro-ph.GA↗

arXiv2021

Dewetting Characteristics of Contact Lenses Coated with Wetting Agents

Hypothesis: Although wetting agents have been developed to limit tear film dewetting over contact lenses, systematic analyses correlating wetting agents properties to mechanisms of the tear film destabilization are not readily available. Clarifying destabilization characteristics across key physio-chemical variables will provide a rational basis for identifying optimal wetting agents. Experiments: We employ an in-house, in vitro platform to comprehensively evaluate drainage and dewetting dynamics of five wetting agents across seventeen different formulations and two model tear film solutions: phosphate-buffered saline (PBS) and artificial tear solution (ATS). We consider the film thickness evolution, film thickness at breakup, dewetted front propagation, and develop correlations to contact angle to compare the samples. Findings: Zwitterionic wetting agents effectively stabilize the tear film by reducing the film thickness at the onset of dewetting, and delaying dewetted region propagation across the lens. Furthermore, tuning wetting agent surface concentrations in binary mixtures can enhance wetting characteristics. Finally, despite disparities in wetting agent molecular properties, the time to dewet $50\%$ of the lens scales linearly with the product of the receding contact angle and contact angle hysteresis. Hence, we fundamentally establish the importance of minimizing both the absolute contact angle values and contact angle hysteresis for effective wetting performance.

cond-mat.soft↗

arXiv2021

ML4CO: Is GCNN All You Need? Graph Convolutional Neural Networks Produce Strong Baselines For Combinatorial Optimization Problems, If Tuned and Trained Properly, on Appropriate Data

The 2021 NeurIPS Machine Learning for Combinatorial Optimization (ML4CO) competition was designed with the goal of improving state-of-the-art combinatorial optimization solvers by replacing key heuristic components with machine learning models. The competition's main scientific question was the following: is machine learning a viable option for improving traditional combinatorial optimization solvers on specific problem distributions, when historical data is available? This was motivated by the fact that in many practical scenarios, the data changes only slightly between the repetitions of a combinatorial optimization problem, and this is an area where machine learning models are particularly powerful at. This paper summarizes the solution and lessons learned by the Huawei EI-OROAS team in the dual task of the competition. The submission of our team achieved the second place in the final ranking, with a very close distance to the first spot. In addition, our solution was ranked first consistently for several weekly leaderboard updates before the final evaluation. We provide insights gained from a large number of experiments, and argue that a simple Graph Convolutional Neural Network (GCNNs) can achieve state-of-the-art results if trained and tuned properly.

cs.LG↗

arXiv2021

Mining Minority-class Examples With Uncertainty Estimates

In the real world, the frequency of occurrence of objects is naturally skewed forming long-tail class distributions, which results in poor performance on the statistically rare classes. A promising solution is to mine tail-class examples to balance the training dataset. However, mining tail-class examples is a very challenging task. For instance, most of the otherwise successful uncertainty-based mining approaches struggle due to distortion of class probabilities resulting from skewness in data. In this work, we propose an effective, yet simple, approach to overcome these challenges. Our framework enhances the subdued tail-class activations and, thereafter, uses a one-class data-centric approach to effectively identify tail-class examples. We carry out an exhaustive evaluation of our framework on three datasets spanning over two computer vision tasks. Substantial improvements in the minority-class mining and fine-tuned model's performance strongly corroborate the value of our proposed solution.

cs.CV↗

arXiv2021

Personalized Cross-Silo Federated Learning on Non-IID Data

Non-IID data present a tough challenge for federated learning. In this paper, we explore a novel idea of facilitating pairwise collaborations between clients with similar data. We propose FedAMP, a new method employing federated attentive message passing to facilitate similar clients to collaborate more. We establish the convergence of FedAMP for both convex and non-convex models, and propose a heuristic method to further improve the performance of FedAMP when clients adopt deep neural networks as personalized models. Our extensive experiments on benchmark data sets demonstrate the superior performance of the proposed methods.

cs.LG↗

arXiv2021

A recurrent neural network approach for remaining useful life prediction utilizing a novel trend features construction method

Data-driven methods for remaining useful life (RUL) prediction normally learn features from a fixed window size of a priori of degradation, which may lead to less accurate prediction results on different datasets because of the variance of local features. This paper proposes a method for RUL prediction which depends on a trend feature representing the overall time sequence of degradation. Complete ensemble empirical mode decomposition, followed by a reconstruction procedure, is created to build the trend features. The probability distribution of sensors' measurement learned by conditional neural processes is used to evaluate the trend features. With the best trend feature, a data-driven model using long short-term memory is developed to predict the RUL. To prove the effectiveness of the proposed method, experiments on a benchmark C-MAPSS dataset are carried out and compared with other state-of-the-art methods. Comparison results show that the proposed method achieves the smallest root mean square values in prediction of all RUL.

eess.SP↗

arXiv2021

Tailoring Magnetic Exchange Interactions in Ferromagnet-Intercalated MnBi2Te4 Superlattices

The intrinsic magnetic topological insulator MnBi2Te4 (MBT) has provided a platform for the successful realization of exotic quantum phenomena. To broaden the horizons of MBT-based material systems, we intercalate ferromagnetic MnTe layers to construct the [(MBT)(MnTe)m]N superlattices by molecular beam epitaxy. The effective incorporation of ferromagnetic spacers mediates the anti-ferromagnetic interlayer coupling among the MBT layers through the exchange spring effect at the MBT/MnTe hetero-interfaces. Moreover, the precise control of the MnTe thickness enables the modulation of relative strengths among the constituent magnetic orders, leading to tunable magnetoelectric responses, while the superlattice periodicity serves as an additional tuning parameter to tailor the spin configurations of the synthesized multi-layers. Our results demonstrate the advantages of superlattice engineering for optimizing the magnetic interactions in MBT-family systems, and the ferromagnet-intercalated strategy opens up new avenues in magnetic topological insulator structural design and spintronic applications.

cond-mat.mtrl-sci↗

arXiv2021

The Stellar "Snake" I: Whole Structure and Properties

To complement our previous discovery of the young snake-like structure in the solar neighborhood and reveal the structure's full extent, we build two samples of stars within the Snake and its surrounding territory from {\tt Gaia EDR3}. With the friends-of-friends algorithm, we identify 2694 and 9615 Snake member candidates from the two samples. Thirteen open clusters are embedded in these member candidates. By combining the spectroscopic data from multiple surveys, we investigate the comprehensive properties of the candidates and find that they \thj{are very likely to} belong to one sizable structure, since most of the components are well bridged in their spatial distributions, and follow a single stellar population with an age of $30-40$\,Myr and solar metallicity. This sizable structure is best explained as hierarchically primordial, and probably formed from a filamentary giant molecular cloud with unique formation history in localized regions. To analyze the dynamics of the Snake, we divide the structure into five groups according to their tangential velocities; we find that the groups are expanding at a coherent rate ($κ_X\sim3.0\,\times10^{-2}\,\rm km\,s^{-1}\,pc^{-1}$) along the length of the structure ($X$-direction). \thj{The corresponding expansion age ($τ\sim33$\,Myr) is highly consistent with the age of the Snake}. With over ten thousand member stars, the Snake is an ideal laboratory to study nearby coeval stellar formation, stellar physics, and environmental evolution over a large spatial extent.

astro-ph.GA↗

arXiv2021

Photo-induced Macro/Mesoscopic Scale Ion Segregation in Mixed-Halide Perovskites: Ring Structure and Ionic Plasma Oscillations

Contrary to the common belief that light-induced halide ion segregation in a mixed halide al-loy occurs within the illuminated area, we find that the Br ions released by light diffuse away from the area, which generates a counter-balancing Coulombic force between the anion deficit and surplus region, together resulting in a macro/mesoscopic size anion ring surrounding the center, showing a photoluminescence ring. Upon removing the illumination, the displaced anions return to the illuminated area, and the restoring force leads to a damped ultra-low-frequency oscillatory ion motion, which may be the first observation of an ionic plasma oscillation in solids.

physics.app-ph↗

arXiv2021

Near-field energy transfer between graphene and magneto-optic media

We consider the near-field radiative energy transfer between two separated parallel plates: graphene supported by a substrate and a magneto-optic medium. We first study the scenario in which the two plates have the same temperature. An electric current through the graphene gives rise to nonequilibrium fluctuations and induces energy transfer. Both the magnitude and direction of the energy flux can be controlled by the electric current and an in-plane magnetic field in the magneto-optic medium. This is due to the interplay between the nonreciprocal photon occupation number in the graphene and nonreciprocal surface modes in the magneto-optic plate. Furthermore, we report that a tunable thermoelectric current can be generated in the graphene in the presence of a temperature difference between the two plates.

physics.optics↗

arXiv2021

The First Data Release of LAMOST Low Resolution Single Epoch Spectra

LAMOST Data Release 5, covering $\sim$17,000 $deg^2$ from $-10^{\circ}$ to $80^{\circ}$ in declination, contains 9 millions co-added low resolution spectra of celestial objects, each spectrum combined from repeat exposure of two to tens of times during Oct 2011 to Jun 2017. In this paper, We present the spectra of individual exposures for all the objects in LAMOST Data Release 5. For each spectrum, equivalent width of 60 lines from 11 different elements are calculated with a new method combining the actual line core and fitted line wings. For stars earlier than F type, the Balmer lines are fitted with both emission and absorption profiles once two components are detected. Radial velocity of each individual exposure is measured by minimizing $χ^2$ between the spectrum and its best template. Database for equivalent widths of spectral lines and radial velocities of individual spectra are available online. Radial velocity uncertainties with different stellar type and signal-to-noise ratio are quantified by comparing different exposure of the same objects. We notice that the radial velocity uncertainty depends on the time lag between observations. For stars observed in the same day and with signal-to-noise ratio higher than 20, the radial velocity uncertainty is below 5km/s, and increase to 10km/s for stars observed in different nights.

astro-ph.SR↗