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Jing Zhou

Publications and source records attributed to Jing Zhou.

At least 91 records · Page 5Linked to original sources

Dependence of Chemical Abundance on the Cosmic Ray Ionization Rate in IC 348

Ions (e.g., H$_3^+$, H$_2$O$^+$) have been used extensively to quantify the cosmic-ray ionization rate (CRIR) in diffuse sightlines. However, measurements of CRIR in low-to-intermediate density gas environments are rare, especially when background stars are absent. In this work, we combine molecular line observations of CO, OH, CH, and HCO$^+$ in the star-forming cloud IC~348, and chemical models to constrain the value of CRIR and study the response of the chemical abundances distribution. The cloud boundary is found to have an $A_{\rm V}$ of approximately 4 mag. From the interior to the exterior of the cloud, the observed $^{13}$CO line intensities drop by an order of magnitude. The calculated average abundance of $^{12}$CO (assuming $^{12}$C/$^{13}$C = 65) is (1.2$\pm$0.9) $\times$10$^{-4}$, which increases by a factor of 6 from the interior to the outside regions. The average abundance of CH (3.3$\pm$0.7 $\times$ 10$^{-8}$) is in good agreement with previous findings in diffuse and translucent clouds ($A_{\rm V}$ $<$ 5 mag). However, we did not find a decline in CH abundance in regions of high extinction ($A_{\rm V}\simeq$8 mag) as previously reported in Taurus. By comparing the observed molecular abundances and chemical models, we find a decreasing trend of CRIR as $A_{\rm V}$ increases. The inferred CRIR of $ζ_{cr}$ = (4.7$\pm$1.5) $\times$ 10$^{-16}$ s$^{-1}$ at low $A_{\rm V}$ is consistent with H$^+_3$ measurements toward two nearby massive stars.

astro-ph.GA↗

Embedding Compression for Text Classification Using Dictionary Screening

In this paper, we propose a dictionary screening method for embedding compression in text classification tasks. The key purpose of this method is to evaluate the importance of each keyword in the dictionary. To this end, we first train a pre-specified recurrent neural network-based model using a full dictionary. This leads to a benchmark model, which we then use to obtain the predicted class probabilities for each sample in a dataset. Next, to evaluate the impact of each keyword in affecting the predicted class probabilities, we develop a novel method for assessing the importance of each keyword in a dictionary. Consequently, each keyword can be screened, and only the most important keywords are reserved. With these screened keywords, a new dictionary with a considerably reduced size can be constructed. Accordingly, the original text sequence can be substantially compressed. The proposed method leads to significant reductions in terms of parameters, average text sequence, and dictionary size. Meanwhile, the prediction power remains very competitive compared to the benchmark model. Extensive numerical studies are presented to demonstrate the empirical performance of the proposed method.

cs.CL↗

Properties of dense molecular gas along the major axis of M 82

Dense gas is important for galaxy evolution and star formation. Optically-thin dense-gas tracers, such as isotopologues of HCN, HCO+, etc., are very helpful to diagnose excitation conditions of dense molecular gas. However, previous studies of optically-thin dense-gas tracers were mostly focusing on average properties of galaxies as a whole, due to limited sensitivity and angular resolution. M82, a nearby prototype starburst galaxy, offers a unique case for spatially-resolved studies with single-dish telescopes. With the IRAM 30-m telescope, we observed the J = 1 - 0 transition of H13CN, HC15N, H13CO+, HN13C, H15NC, and SiO J = 2 - 1, HC3N J= 10 - 9, H2CO J = 2 - 1 toward five positions along the major axis of M82. The intensity ratios of I(HCN)/I(H13CN) and I(HCO+)/I(H13CO+) show a significant spatial variation along the major axis, with lower values in the central region than those on the disk, indicating higher optical depths in the central region. The optical depths of HCO+ lines are found to be systematically higher than those of HCN lines at all positions. Futhermore, we find that the 14N/15N ratios have an increasing gradient from the center to the outer disk.

astro-ph.GA↗

Dense Gas and Star Formation in Nearby Infrared Bright Galaxies: APEX survey of HCN and HCO+ J=2-1

Both Galactic and extragalactic studies on star formation suggest that stars form directly from dense molecular gas. To trace such high volume density gas, HCN and HCO+ J=1-0 have been widely used for their high dipole moments, relatively high abundances, and often being the strongest lines after CO. However, HCN and HCO+ J=1-0 emission could be arguably dominated by the gas components at low volume densities. HCN J=2-1 and HCO+ J=2-1, with more suitable critical densities and excitation requirements, would trace typical dense gas closely related to star formation. Here we report new observations of HCN J=2-1 and HCO+ J=2-1 towards 17 nearby infrared-bright galaxies with the APEX 12-m telescope. The correlation slopes between luminosities of HCN J=2-1, and HCO+ J=2-1 and total infrared emission are 1.03 +- 0.05 and 1.00 +- 0.05, respectively. The correlations of their surface densities, normalised with the area of radio/sub-millimeter continuum, show even tighter relations (Slopes: 0.99 +- 0.03 and 1.02 +- 0.03). The eight AGN-dominated galaxies show no significant difference from the eleven star-formation dominated galaxies in above relations. The average HCN/HCO+ ratios are 1.15 +- 0.26 and 0.98 +- 0.42 for AGN-dominated and star-formation dominated galaxies, respectively, without obvious dependencies on infrared luminosity, dust temperature, or infrared pumping. The Magellanic Clouds roughly follow the same correlations, expanding to eight orders of magnitude. On the other hand, ultra-luminous infrared galaxies with active galactic nucleus (AGN) systematically lay above the correlations, indicating potential biases introduced by AGNs.

astro-ph.GA↗

Arnold Tongues in Area-Preserving Maps

In the early 60's J. B. Keller and D. Levy discovered a fundamental property: the instability tongues in Mathieu-type equations lose sharpness with the addition of higher-frequency harmonics in the Mathieu potentials. 20 years later V. Arnold rediscovered a similar phenomenon on sharpness of Arnold tongues in circle maps (and rediscovered the result of Keller and Levy). In this paper we find a third class of objects where a similarly flavored behavior takes place: area-preserving maps of the cylinder. Speaking loosely, we show that periodic orbits of standard maps are extra fragile with respect to added drift (i.e. non-exactness) if the potential of the map is a trigonometric polynomial. That is, higher-frequency harmonics make periodic orbits more robust with respect to ``drift". The observation was motivated by the study of traveling waves in the discretized sine-Gordon equation which in turn models a wide variety of physical systems.

math.DS↗

Fermi-Polaron in a driven-dissipative background medium

The study of polaron of an open quantum system plays an important role in both verifying the effectiveness of approximate many-body theory and predicting novel quantum phenomenone in open quantum systems. In a pioneering work, Piazza et al have proposed a Fermi-polaron scheme with a lossy impurity [54], which exhibits a novel long-lived attractive polaron branch in the quantum Zeno limit. However, we would also run into a counterpart problem that an impurity scatters with an open quantum bath exciting polarons, which is what we focus in on. In this work, we conclude the molecular state under the two limits of vanishing small and infinite large dissipation intensity as well as the reason why the dissipation range leads to the decrease of the gap between the molecular state and molecule-hole continuum in the former case by means of analytically research. The spectrum functions of molecular and polaron states with different dissipation range and loss rate are investigated. We find the spectral signals of molecular and polaron states will both diffuse firstly and then revives as the dissipation is on the raise. Moreover, it is shown that the attractive and repulsive polarons show different response to the increasing dissipation range in our model. At last, we exhibit the polaron energy, residue, effective mass and two-body decay for mass balanced and imbalanced systems. Our results might be useful for future cold atom experiment on open quantum systems.

cond-mat.quant-gas↗

A Lateral AlGaN/GaN Schottky Barrier Diode with 0.36 V Turn-on Voltage and 10 kV Breakdown Voltage by Using Double Barrier Anode Structure

In this letter, we demonstrate a lateral AlGaN/GaN Schottky barrier diode (SBD) on sapphire substrate with low turn-on voltage (Von) and high breakdown voltage (VBK). By using a double barrier anode (DBA) structure formed by the mixture of Platinum (Pt) and Tantalum (Ta), the Von of the SBD can be as low as 0.36 V with a leakage current of 2.5E-6 A/mm. Supported by the high-quality carbon-doped GaN buffer on sapphire, the VBK can reach more than 10 kV with the anode-to-cathode spacing of 85 μm. Combining the VBK and the specific on-resistance (Ron,sp) of 25.1 mΩ.cm^2, the power figure of merit of the SBD can reach 4.0 GW/cm^2, demonstrating a great potential for the application in ultra-high-voltage electronics.

cond-mat.mtrl-sci↗

Improved Receivers for Optical Wireless OFDM: An Information Theoretic Perspective

We consider performance enhancement of asymmetrically-clipped optical orthogonal frequency division multiplexing (ACO-OFDM) and related optical OFDM schemes, which are variations of OFDM in intensity-modulated optical wireless communications. Unlike most existing studies on specific designs of improved receivers, this paper investigates information theoretic limits of all possible receivers. For independent and identically distributed complex Gaussian inputs, we obtain an exact characterization of information rate of ACO-OFDM with improved receivers for all SNRs. It is proved that the high-SNR gain of improved receivers asymptotically achieve 1/4 bits per channel use, which is equivalent to 3 dB in electrical SNR or 1.5 dB in optical SNR; as the SNR decreases, the maximum achievable SNR gain of improved receivers decreases monotonically to a non-zero low-SNR limit, corresponding to an information rate gain of 36.3%. For practically used constellations, we derive an upper bound on the gain of improved receivers. Numerical results demonstrate that the upper bound can be approached to within 1 dB in optical SNR by combining existing improved receivers and coded modulation. We also show that our information theoretic analyses can be extended to Flip-OFDM and PAM-DMT. Our results imply that, for the considered schemes, improved receivers may reduce the gap to channel capacity significantly at low-to-moderate SNR.

cs.IT↗

Multi-source wideband doa estimation method by frequency focusing and error weighting

In this paper, a new multi-source wideband direction of arrival (MSW-DOA) estimation method is proposed for the signal with non-uniform distribution using the sub-array of uniform linear array. Different from conventional methods, based on the free far-field model, the proposed method mainly makes two contributions. One is that the sub-array decomposition is adopted to improve the accuracy of MSW-DOA estimation by minimizing the weighted error, and the other one is that the frequency focusing procedure is optimized according to the presence probability of sound sources for reducing the influence of the sub-bands with low signal to noise ratio (SNR). Simulation results show that the proposed method can effectively improve the performance of wideband DOA estimation in the case of multiple sound sources.

eess.AS↗

Anticollinear order and degeneracy lifting in square lattice antiferromagnet LaSrCrO4

We report the static and dynamic magnetic properties of LaSrCrO$_4$, a seemingly canonical spin-3/2 square-lattice antiferromagnet that exhibits frustration between magnetic layers -- owing to their AB stacking -- and offers a rare testbed to investigate accidental-degeneracy lifting in magnetism. Neutron diffraction experiments on single-crystal samples uncover a remarkable anticollinear magnetic order below $T_N$ = 170 K characterized by a Néel arrangement of the spins within each layer and an orthogonal arrangement between adjacent layers. To understand the origin of this unusual magnetic structure, we analyze the spin-wave excitation spectrum by means of inelastic neutron scattering and bulk measurements. A spectral gap of 0.5 meV, along with a spin-flop transition at 3.2\, T, reflect the energy scale associated with the degeneracy-lifting. A minimal model to explain these observations requires both a positive biquadratic interlayer exchange and dipolar interactions, both of which are on the order of 10$^{-4}$ meV, only a few parts per million of the dominant exchange interaction $J_1 \approx 11$ meV. These results provide direct evidence for the selection of a non-collinear magnetic structure by the combined effect of two distinct degeneracy lifting interactions.

cond-mat.str-el↗

FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning

Most previous methods for text data augmentation are limited to simple tasks and weak baselines. We explore data augmentation on hard tasks (i.e., few-shot natural language understanding) and strong baselines (i.e., pretrained models with over one billion parameters). Under this setting, we reproduced a large number of previous augmentation methods and found that these methods bring marginal gains at best and sometimes degrade the performance much. To address this challenge, we propose a novel data augmentation method FlipDA that jointly uses a generative model and a classifier to generate label-flipped data. Central to the idea of FlipDA is the discovery that generating label-flipped data is more crucial to the performance than generating label-preserved data. Experiments show that FlipDA achieves a good tradeoff between effectiveness and robustness -- it substantially improves many tasks while not negatively affecting the others.

cs.CL↗

FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding

The few-shot natural language understanding (NLU) task has attracted much recent attention. However, prior methods have been evaluated under a disparate set of protocols, which hinders fair comparison and measuring progress of the field. To address this issue, we introduce an evaluation framework that improves previous evaluation procedures in three key aspects, i.e., test performance, dev-test correlation, and stability. Under this new evaluation framework, we re-evaluate several state-of-the-art few-shot methods for NLU tasks. Our framework reveals new insights: (1) both the absolute performance and relative gap of the methods were not accurately estimated in prior literature; (2) no single method dominates most tasks with consistent performance; (3) improvements of some methods diminish with a larger pretrained model; and (4) gains from different methods are often complementary and the best combined model performs close to a strong fully-supervised baseline. We open-source our toolkit, FewNLU, that implements our evaluation framework along with a number of state-of-the-art methods.

cs.CL↗

Crossover polarons in a strongly interacting Fermi superfluid

We investigate the zero-temperature quasiparticle properties of a mobile impurity immersed in a strongly interacting Fermi superfluid at the crossover from a Bose-Einstein condensate (BEC) to a Bardeen--Cooper--Schrieffer (BCS) superfluid, by using a many-body $T$-matrix approach that excludes Efimov trimer bound states. Termed BEC-BCS crossover polaron, or crossover polaron in short, this quasiparticle couples to elementary excitations of a many-body background and therefore could provide a useful probe of the underlying strongly interacting Fermi superfluid. Due to the existence of a significant pairing gap $Δ$, we find that the repulsive polaron branch becomes less well-defined. In contrast, the attractive polaron branch is protected by the pairing gap and becomes more robust at finite momentum. It remains as a delta-function peak in the impurity spectral function below a threshold $2Δ$. Above the threshold, the attractive polaron enters the particle-hole continuum and starts to get damped. We predict the polaron energy, residue and effective mass for realistic Bose-Fermi mixtures, where the minority bosonic atoms play the role of impurity. These results are practically useful for future cold-atom experiments on crossover polarons.

cond-mat.quant-gas↗

On the Capacity of MISO Optical Intensity Channels With Per-Antenna Intensity Constraints

This paper investigates the capacity of general multiple-input single-output (MISO) optical intensity channels (OICs) under per-antenna peak- and average-intensity constraints. We first consider the MISO equal-cost constrained OIC (EC-OIC), where, apart from the peak-intensity constraint, average intensities of inputs are equal to arbitrarily preassigned constants. The second model of our interest is the MISO bounded-cost constrained OIC (BC-OIC), where, as compared with the EC-OIC, average intensities of inputs are no larger than arbitrarily preassigned constants. By leveraging tools from quantile functions, stop-loss transform and convex ordering of nonnegative random variables, we prove two decomposition theorems for bounded and nonnegative random variables, based on which we equivalently transform both the EC-OIC and the BC-OIC into respective single-input single-output channels under a peak-intensity and several stop-loss mean constraints. Capacity lower and upper bounds for both channels are established, based on which the asymptotic capacity at high and low signal-to-noise-ratio are determined.

cs.IT↗

Automatic Product Copywriting for E-Commerce

Product copywriting is a critical component of e-commerce recommendation platforms. It aims to attract users' interest and improve user experience by highlighting product characteristics with textual descriptions. In this paper, we report our experience deploying the proposed Automatic Product Copywriting Generation (APCG) system into the JD.com e-commerce product recommendation platform. It consists of two main components: 1) natural language generation, which is built from a transformer-pointer network and a pre-trained sequence-to-sequence model based on millions of training data from our in-house platform; and 2) copywriting quality control, which is based on both automatic evaluation and human screening. For selected domains, the models are trained and updated daily with the updated training data. In addition, the model is also used as a real-time writing assistant tool on our live broadcast platform. The APCG system has been deployed in JD.com since Feb 2021. By Sep 2021, it has generated 2.53 million product descriptions, and improved the overall averaged click-through rate (CTR) and the Conversion Rate (CVR) by 4.22% and 3.61%, compared to baselines, respectively on a year-on-year basis. The accumulated Gross Merchandise Volume (GMV) made by our system is improved by 213.42%, compared to the number in Feb 2021.

cs.CL↗

Nonparametric C- and D-vine based quantile regression

Quantile regression is a field with steadily growing importance in statistical modeling. It is a complementary method to linear regression, since computing a range of conditional quantile functions provides a more accurate modelling of the stochastic relationship among variables, especially in the tails. We introduce a non-restrictive and highly flexible nonparametric quantile regression approach based on C- and D-vine copulas. Vine copulas allow for separate modeling of marginal distributions and the dependence structure in the data, and can be expressed through a graph theoretical model given by a sequence of trees. This way we obtain a quantile regression model, that overcomes typical issues of quantile regression such as quantile crossings or collinearity, the need for transformations and interactions of variables. Our approach incorporates a two-step ahead ordering of variables, by maximizing the conditional log-likelihood of the tree sequence, while taking into account the next two tree levels. Further, we show that the nonparametric conditional quantile estimator is consistent. The performance of the proposed methods is evaluated in both low- and high-dimensional settings using simulated and real world data. The results support the superior prediction ability of the proposed models.

stat.ME↗

Residual fourier neural operator for thermochemical curing of composites

During the curing process of composites, the temperature history heavily determines the evolutions of the field of degree of cure as well as the residual stress, which will further influence the mechanical properties of composite, thus it is important to simulate the real temperature history to optimize the curing process of composites. Since thermochemical analysis using Finite Element (FE) simulations requires heavy computational loads and data-driven approaches suffer from the complexity of highdimensional mapping. This paper proposes a Residual Fourier Neural Operator (ResFNO) to establish the direct high-dimensional mapping from any given cure cycle to the corresponding temperature histories. By integrating domain knowledge into a time-resolution independent parameterized neural network, the mapping between cure cycles to temperature histories can be learned using limited number of labelled data. Besides, a novel Fourier residual mapping is designed based on mode decomposition to accelerate the training and boost the performance significantly. Several cases are carried out to evaluate the superior performance and generalizability of the proposed method comprehensively.

cond-mat.mtrl-sci↗

Solving Partial Differential Equations with Point Source Based on Physics-Informed Neural Networks

In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs) emerges to be a promising method for solving both forward and inverse PDE problems. PDEs with a point source that is expressed as a Dirac delta function in the governing equations are mathematical models of many physical processes. However, they cannot be solved directly by conventional PINNs method due to the singularity brought by the Dirac delta function. We propose a universal solution to tackle this problem with three novel techniques. Firstly the Dirac delta function is modeled as a continuous probability density function to eliminate the singularity; secondly a lower bound constrained uncertainty weighting algorithm is proposed to balance the PINNs losses between point source area and other areas; and thirdly a multi-scale deep neural network with periodic activation function is used to improve the accuracy and convergence speed of the PINNs method. We evaluate the proposed method with three representative PDEs, and the experimental results show that our method outperforms existing deep learning-based methods with respect to the accuracy, the efficiency and the versatility.

cs.LG↗