arXiv subjects

Qi Li

Publications and source records attributed to Qi Li.

At least 469 records · Page 26Linked to original sources

arXiv2020

Improving Relation Extraction with Knowledge-attention

While attention mechanisms have been proven to be effective in many NLP tasks, majority of them are data-driven. We propose a novel knowledge-attention encoder which incorporates prior knowledge from external lexical resources into deep neural networks for relation extraction task. Furthermore, we present three effective ways of integrating knowledge-attention with self-attention to maximize the utilization of both knowledge and data. The proposed relation extraction system is end-to-end and fully attention-based. Experiment results show that the proposed knowledge-attention mechanism has complementary strengths with self-attention, and our integrated models outperform existing CNN, RNN, and self-attention based models. State-of-the-art performance is achieved on TACRED, a complex and large-scale relation extraction dataset.

cs.CL↗

arXiv2020

Adversarial Example in Remote Sensing Image Recognition

With the wide application of remote sensing technology in various fields, the accuracy and security requirements for remote sensing images (RSIs) recognition are also increasing. In recent years, due to the rapid development of deep learning in the field of image recognition, RSI recognition models based on deep convolution neural networks (CNNs) outperform traditional hand-craft feature techniques. However, CNNs also pose security issues when they show their capability of accurate classification. By adding a very small variation of the adversarial perturbation to the input image, the CNN model can be caused to produce erroneous results with extremely high confidence, and the modification of the image is not perceived by the human eye. This added adversarial perturbation image is called an adversarial example, which poses a serious security problem for systems based on CNN model recognition results. This paper, for the first time, analyzes adversarial example problem of RSI recognition under CNN models. In the experiments, we used different attack algorithms to fool multiple high-accuracy RSI recognition models trained on multiple RSI datasets. The results show that RSI recognition models are also vulnerable to adversarial examples, and the models with different structures trained on the same RSI dataset also have different vulnerabilities. For each RSI dataset, the number of features also affects the vulnerability of the model. Many features are good for defensive adversarial examples. Further, we find that the attacked class of RSI has an attack selectivity property. The misclassification of adversarial examples of the RSIs are related to the similarity of the original classes in the CNN feature space. In addition, adversarial examples in RSI recognition are of great significance for the security of remote sensing applications, showing a huge potential for future research.

cs.CV↗

arXiv2020

On the logarithmic profile of temperature in the atmospheric convective boundary layers

Wall-bounded turbulent flows are widely observed in natural and engineering systems, such as air flows near the Earth's surface, water flows in rivers, and flows around a car or a plane. The universal logarithmic velocity profile in wall-bounded turbulent flows proposed by von Kármán in 1930 is one of the few exact physical descriptions of turbulence. However, the mean velocity and temperature profiles cannot be adequately described by this universal log law when buoyancy effects are present. Monin-Obukhov similarity theory (MOST), proposed in 1954, has been the cornerstone theory to account for these buoyancy effects and to describe the atmospheric boundary layer. MOST has been used in almost all global weather, climate and hydrological models to describe the dependence of the mean velocity, temperature and scalar profiles on buoyancy. According to MOST, the logarithmic temperature profile breaks down as buoyancy effects become important. In contrast, here we show that this long-standing MOST theory does not apply for temperature. We propose a new theory for the logarithmic profile of near-wall temperature, which corrects MOST pitfalls and is supported by both high-resolution direct numerical simulations and field observations of the convective atmospheric boundary layer. Buoyancy effects do not modify the logarithmic nature but instead modulate the slope of the temperature profile compared to the universal von Kármán slope. The new formulation has widespread applications such as in climate models, where the proposed new temperature log law should lead to more realistic continental surface temperature, which are strongly impacted by buoyancy.

physics.flu-dyn↗

arXiv2020

Enhanced Valley Zeeman Splitting in Fe-Doped Monolayer MoS2

The Zeeman effect offers unique opportunities for magnetic manipulation of the spin degree of freedom (DOF). Recently, valley Zeeman splitting, referring to the lifting of valley degeneracy, has been demonstrated in two-dimensional transition metal dichalcogenides (TMDs) at liquid helium temperature. However, to realize the practical applications of valley pseudospins, the valley DOF must be controllable by a magnetic field at room temperature, which remains a significant challenge. Magnetic doping in TMDs can enhance the Zeeman splitting, however, to achieve this experimentally is not easy. Here, we report unambiguous magnetic manipulation of valley Zeeman splitting at 300 K (g = -6.4) and 10 K (g = -11) in a CVD-grown Fe-doped MoS2 monolayer; the effective g factor can be tuned to -20.7 by increasing the Fe dopant concentration, which represents an approximately fivefold enhancement as compared to undoped MoS2. Our measurements and calculations reveal that the enhanced splitting and geff factors are due to the Heisenberg exchange interaction of the localized magnetic moments (Fe 3d electrons) with MoS2 through the d-orbital hybridization.

cond-mat.mtrl-sci↗

arXiv2020

Discovery and timing of pulsars in the globular cluster M13 with FAST

We report the discovery of a binary millisecond pulsar (namely PSR J1641+3627F or M13F) in the globular cluster M13 (NGC 6205) and timing solutions of M13A to F using observations made with the Five-hundred-metre Aperture Spherical radio Telescope (FAST). PSR J1641+3627F has a spin period of 3.00 ms and an orbital period of 1.4 days. The most likely companion mass is 0.16 M$_{\odot}$. M13A to E all have short spin periods and small period derivatives. We also confirm that the binary millisecond pulsar PSR J1641$+$3627E (also M13E) is a black widow with a companion mass around 0.02 M$_{\odot}$. We find that all the binary systems have low eccentricities compared to those typical for globular cluster pulsars and that they decrease with distance from the cluster core. This is consistent with what is expected as this cluster has a very low encounter rate per binary.

astro-ph.HE↗

arXiv2020

On Carbon Nanotubes in the Interstellar Medium

Since their discovery in 1991, carbon nanotubes (CNTs) -- a novel one-dimensional carbon allotrope -- have attracted considerable interest worldwide because of their potential technological applications such as electric and optical devices. In the astrophysical context, CNTs may be present in the interstellar space since many of the other allotropes of carbon (e.g., amorphous carbon, fullerenes, nanodiamonds, graphite, polycyclic aromatic hydrocarbons, and possibly graphene as well) are known to be widespread in the Universe, as revealed by presolar grains in carbonaceous primitive meteorites and/or by their fingerprint spectral features in astronomical spectra. In addition, there are also experimental and theoretical pathways to the formation of CNTs in the interstellar medium (ISM). In this work, we examine their possible presence in the ISM by comparing the observed interstellar extinction curve with the ultraviolet/optical absorption spectra experimentally obtained for single-walled CNTs of a wide range of diameters and chiralities. Based on the absence in the interstellar extinction curve of the ~4.5 and 5.25 eV $π$-plasmon absorption bands which are pronounced in the experimental spectra of CNTs, we place an upper limit of ~10 ppm of C/H (i.e., ~4% of the total interstellar C) on the interstellar CNT abundance.

astro-ph.GA↗

arXiv2019

Dual affine moment invariants

Affine transformation is one of the most common transformations in nature, which is an important issue in the field of computer vision and shape analysis. And affine transformations often occur in both shape and color space simultaneously, which can be termed as Dual-Affine Transformation (DAT). In general, we should derive invariants of different data formats separately, such as 2D color images, 3D color objects, or even higher-dimensional data. To the best of our knowledge, there is no general framework to derive invariants for all of these data formats. In this paper, we propose a general framework to derive moment invariants under DAT for objects in M-dimensional space with N channels, which can be called dual-affine moment invariants (DAMI). Following this framework, we present the generating formula of DAMI under DAT for 3D color objects. Then, we instantiated a complete set of DAMI for 3D color objects with orders and degrees no greater than 4. Finally, we analyze the characteristic of these DAMI and conduct classification experiments to evaluate the stability and discriminability of them. The results prove that DAMI is robust for DAT. Our derivation framework can be applied to data in any dimension with any number of channels.

cs.CV↗

arXiv2019

A3GAN: An Attribute-aware Attentive Generative Adversarial Network for Face Aging

Face aging, which aims at aesthetically rendering a given face to predict its future appearance, has received significant research attention in recent years. Although great progress has been achieved with the success of Generative Adversarial Networks (GANs) in synthesizing realistic images, most existing GAN-based face aging methods have two main problems: 1) unnatural changes of high-level semantic information (e.g. facial attributes) due to the insufficient utilization of prior knowledge of input faces, and 2) distortions of low-level image content including ghosting artifacts and modifications in age-irrelevant regions. In this paper, we introduce A3GAN, an Attribute-Aware Attentive face aging model to address the above issues. Facial attribute vectors are regarded as the conditional information and embedded into both the generator and discriminator, encouraging synthesized faces to be faithful to attributes of corresponding inputs. To improve the visual fidelity of generation results, we leverage the attention mechanism to restrict modifications to age-related areas and preserve image details. Moreover, the wavelet packet transform is employed to capture textural features at multiple scales in the frequency space. Extensive experimental results demonstrate the effectiveness of our model in synthesizing photorealistic aged face images and achieving state-of-the-art performance on popular face aging datasets.

cs.CV↗

arXiv2019

Deep learning for subgrid-scale turbulence modeling in large-eddy simulations of the atmospheric boundary layer

In large-eddy simulations, subgrid-scale (SGS) processes are parameterized as a function of filtered grid-scale variables. First-order, algebraic SGS models are based on the eddy-viscosity assumption, which does not always hold for turbulence. Here we apply supervised deep neural networks (DNNs) to learn SGS stresses from a set of neighboring coarse-grained velocity from direct numerical simulations (DNSs) of the atmospheric boundary layer at friction Reynolds numbers Re_τ up to 1243 without invoking the eddy-viscosity assumption. The DNN model was found to produce higher correlation of SGS stresses compared to the Smagorinsky model and the Smagorinsky-Bardina mixed model in the surface and mixed layers and can be applied to different grid resolutions and various stability conditions ranging from near neutral to very unstable. The additional information on potential temperature and pressure were found not to be useful for SGS modeling. Deep learning thus demonstrates great potential for LESs of geophysical turbulence.

physics.flu-dyn↗

arXiv2019

Entanglement spectrum edge reconstruction and correlation hole of the FQH liquids

The edge of the electronic fractional quantum Hall (FQH) system obeys the law of the chiral Luttinger liquid theory due to its intrinsic topological properties and the relation of bulk-edge correspondence. However, in a realistic experimental system, such as the usual Hall bar setup, the soften of the background confinement potential can induce the reconstruction of the edge spectrum which breaks the chirality and universality of the FQH edge. The entanglement spectrum (ES) of the FQH ground state has the same counting structure as that in the energy spectrum indicating the topological characters of the quantum state. In this work, we report that the ES can also have an edge reconstruction while sweeping the area of the sub-system in real space cut. Moreover, we found the critical area of the sub-system matches accurately with the intrinsic building block of the fractional quantum Hall liquids, namely the correlation hole of the FQH liquids. The above results seem like be universal after our studying a series of typical FQH states, such as two Laughlin states at $ν= 1/3$ and $ν= 1/5$, and the Moore-Read state for $ν= 5/2$.

cond-mat.str-el↗

arXiv2019

Age Progression and Regression with Spatial Attention Modules

Age progression and regression refers to aesthetically render-ing a given face image to present effects of face aging and rejuvenation, respectively. Although numerous studies have been conducted in this topic, there are two major problems: 1) multiple models are usually trained to simulate different age mappings, and 2) the photo-realism of generated face images is heavily influenced by the variation of training images in terms of pose, illumination, and background. To address these issues, in this paper, we propose a framework based on conditional Generative Adversarial Networks (cGANs) to achieve age progression and regression simultaneously. Particularly, since face aging and rejuvenation are largely different in terms of image translation patterns, we model these two processes using two separate generators, each dedicated to one age changing process. In addition, we exploit spatial attention mechanisms to limit image modifications to regions closely related to age changes, so that images with high visual fidelity could be synthesized for in-the-wild cases. Experiments on multiple datasets demonstrate the ability of our model in synthesizing lifelike face images at desired ages with personalized features well preserved, and keeping age-irrelevant regions unchanged.

cs.CV↗

arXiv2019

How Much Graphene in Space?

The possible presence of graphene in the interstellar medium (ISM) is examined by comparing the interstellar extinction curve with the ultraviolet absorption of graphene calculated from its dielectric functions experimentally obtained with the electron energy loss spectroscopy (EELS) method. Based on the absence in the interstellar extinction curve of the ~2755 Angstrom $π$--$π^{\ast}$ electronic interband transition of graphene, we place an upper limit of ~20ppm of C/H on the interstellar graphene abundance, exceeding the previous estimate by a factor of ~3 which made use of the dielectric functions measured with the spectroscopic ellipsometry (SE) method. Compared with the SE method which measures graphene in air (and hence its surface is contaminated) in a limited energy range of ~0.7--5 eV, the EELS probes a much wider energy range of ~0--50 eV and is free of contamination. The fact that the EELS dielectric functions are substantially smaller than that of SE naturally explains why a higher upper limit on the graphene abundance is derived with EELS. Inspired by the possible detection of C$_{24}$, a planar graphene sheet, in several Galactic and extragalactic planetary nebulae, we also examine the possible presence of C$_{24}$ in the diffuse ISM by comparing the model IR emission of C$_{24}$ with the observed IR emission of the Galactic cirrus and the diffuse ISM toward $l = 44^{o}20^\prime$ and $b=-0^{o}20^\prime$. An upper limit of ~20 ppm on C$_{24}$ is also derived from the absence of the characteristic vibrational bands of C$_{24}$ at ~6.6, 9.8 and 20 $μ$m in the observed IR emission.

astro-ph.GA↗

arXiv2019

The Dust-to-Gas and Dust-to-Metals Ratio in Galaxies from z=0-6

We present predictions for the evolution of the galaxy dust-to-gas (DGR) and dust-to-metal (DTM) ratios from z=0 to 6, using a model for the production, growth, and destruction of dust grains implemented into the \simba\ cosmological hydrodynamic galaxy formation simulation. In our model, dust forms in stellar ejecta, grows by the accretion of metals, and is destroyed by thermal sputtering and supernovae. Our simulation reproduces the observed dust mass function at z=0, but modestly under-predicts the mass function by ~x3 at z ~ 1-2. The z=0 DGR vs metallicity relationship shows a tight positive correlation for star-forming galaxies, while it is uncorrelated for quenched systems. There is little evolution in the DGR-metallicity relationship between z=0-6. We use machine learning techniques to search for the galaxy physical properties that best correlate with the DGR and DTM. We find that the DGR is primarily correlated with the gas-phase metallicity, though correlations with the depletion timescale, stellar mass and gas fraction are non-negligible. We provide a crude fitting relationship for DGR and DTM vs. the gas-phase metallicity, along with a public code package that estimates the DGR and DTM given a set of galaxy physical properties.

astro-ph.GA↗

arXiv2019

VeriML: Enabling Integrity Assurances and Fair Payments for Machine Learning as a Service

Machine Learning as a Service (MLaaS) allows clients with limited resources to outsource their expensive ML tasks to powerful servers. Despite the huge benefits, current MLaaS solutions still lack strong assurances on: 1) service correctness (i.e., whether the MLaaS works as expected); 2) trustworthy accounting (i.e., whether the bill for the MLaaS resource consumption is correctly accounted); 3) fair payment (i.e., whether a client gets the entire MLaaS result before making the payment). Without these assurances, unfaithful service providers can return improperly-executed ML task results or partially trained ML models while asking for over-claimed rewards. Moreover, it is hard to argue for wide adoption of MLaaS to both the client and the service provider, especially in the open market without a trusted third party. In this paper, we present VeriML, a novel and efficient framework to bring integrity assurances and fair payments to MLaaS. With VeriML, clients can be assured that ML tasks are correctly executed on an untrusted server and the resource consumption claimed by the service provider equals to the actual workload. We strategically use succinct non-interactive arguments of knowledge (SNARK) on randomly-selected iterations during the ML training phase for efficiency with tunable probabilistic assurance. We also develop multiple ML-specific optimizations to the arithmetic circuit required by SNARK. Our system implements six common algorithms: linear regression, logistic regression, neural network, support vector machine, Kmeans and decision tree. The experimental results have validated the practical performance of VeriML.

cs.CR↗

arXiv2019

Two Dimensional Router: Design and Implementation

Higher dimensional classification has attracted more attentions with increasing demands for more flexible services in the Internet. In this paper, we present the design and implementation of a two dimensional router (TwoD router), that makes forwarding decisions based on both destination and source addresses. This TwoD router is also a key element in our current effort towards two dimensional IP routing. With one more dimension, the forwarding table will grow explosively given a straightforward implementation. As a result, it is impossible to fit the forwarding table to the current TCAM, which is the de facto standard despite its limited capacity. To solve the explosion problem, we propose a forwarding table structure with a novel separation of TCAM and SRAM. As such, we move the redundancies in expensive TCAM to cheaper SRAM, while the lookup speed is comparable with conventional routers. We also design the incremental update algorithms that minimize the number of accesses to memory. We evaluate our design with a real implementation on a commercial router, Bit-Engine 12004, with real data sets. Our design does not need new devices, which is favorable for adoption. The results also show that the performance of our TwoD router is promising.

cs.NI↗

arXiv2019

Canonical interpretation of $Y(10750)$ and $Υ(10860)$ in the $Υ$ family

Inspired by the new resonance $Y(10750)$, we calculate the masses and two-body OZI-allowed strong decays of the higher vector bottomonium sates within both screened and linear potential models. We discuss the possibilities of $Υ(10860)$ and $Y(10750)$ as mixed states via the $S-D$ mixing. Our results suggest that $Y(10750)$ and $Υ(10860)$ might be explained as mixed states between $5S$- and $4D$-wave vector $b\bar{b}$ states. The $Y(10750)$ and $Υ(10860)$ resonances may correspond to the mixed states dominated by the $4D$- and $5S$-wave components, respectively. The mass and the strong decay behaviors of the $Υ(11020)$ resonance are consistent with the assignment of the $Υ(6S)$ state in the potential models.

hep-ph↗

arXiv2019

Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects

Despite excellent performance on stationary test sets, deep neural networks (DNNs) can fail to generalize to out-of-distribution (OoD) inputs, including natural, non-adversarial ones, which are common in real-world settings. In this paper, we present a framework for discovering DNN failures that harnesses 3D renderers and 3D models. That is, we estimate the parameters of a 3D renderer that cause a target DNN to misbehave in response to the rendered image. Using our framework and a self-assembled dataset of 3D objects, we investigate the vulnerability of DNNs to OoD poses of well-known objects in ImageNet. For objects that are readily recognized by DNNs in their canonical poses, DNNs incorrectly classify 97% of their pose space. In addition, DNNs are highly sensitive to slight pose perturbations. Importantly, adversarial poses transfer across models and datasets. We find that 99.9% and 99.4% of the poses misclassified by Inception-v3 also transfer to the AlexNet and ResNet-50 image classifiers trained on the same ImageNet dataset, respectively, and 75.5% transfer to the YOLOv3 object detector trained on MS COCO.

cs.CV↗

arXiv2019

Attribute-aware Face Aging with Wavelet-based Generative Adversarial Networks

Since it is difficult to collect face images of the same subject over a long range of age span, most existing face aging methods resort to unpaired datasets to learn age mappings. However, the matching ambiguity between young and aged face images inherent to unpaired training data may lead to unnatural changes of facial attributes during the aging process, which could not be solved by only enforcing identity consistency like most existing studies do. In this paper, we propose a attribute-aware face aging model with wavelet-based Generative Adversarial Networks (GANs) to address the above issues. To be specific, we embed facial attribute vectors into both generator and discriminator of the model to encourage each synthesized elderly face image to be faithful to the attribute of its corresponding input. In addition, a wavelet packet transform (WPT) module is incorporated to improve the visual fidelity of generated images by capturing age-related texture details at multiple scales in the frequency space. Qualitative results demonstrate the ability of our model to synthesize visually plausible face images, and extensive quantitative evaluation results show that the proposed method achieves state-of-the-art performance on existing datasets.

cs.CV↗