arXiv subjects

Jie Liu

Publications and source records attributed to Jie Liu.

At least 577 records · Page 32Linked to original sources

arXiv2020

New timing measurement results of 16 pulsars

Pulsar's position, proper motion and parallax are important parameters in timing equations. It is a really challenging work to fit astrometric parameters accurately through pulsar timing, especially for pulsars that show irregular timing properties. As the fast development of related techniques, it is possible to measure astrometric parameters of more and more pulsars in a model$\textrm{-}$independent manner with the Very Long Baseline Interferometry (VLBI). In this work, we select 16 normal pulsars, whose parallax and proper motion have not been successfully fitted with timing observations or show obvious differences with corresponding latest VLBI solutions, and do further studies on their timing properties. After updating astrometric parameters in pulsar ephemerides with the latest VLBI measurements, we derive the latest rotation solutions of these pulsars with observation data at S and C$\textrm{-}$band obtained from the Shanghai Tian Ma Radio Telescope (TMRT). Compared with spin frequency $ν$ inferred from previous rotation solutions, the newly$\textrm{-}$fitted $ν$ show differences larger than 10$^{-9}$ Hz for most pulsars. The contribution of the Shklovsky effect to period derivative $\dot{P}$ can be properly removed taking advantages of accurate proper motion and distance of target pulsars measured by VLBI astrometry. This further leads to a precise estimate of intrinsic characteristic age $τ_{\rm c}$. Differences between the newly$\textrm{-}$measured $τ_{\rm c}$ and corresponding previous results are as large as 2% for some pulsars. VLBI astrometric parameter solutions also lead to better measurements of timing irregularities. For PSR B0154$+$61, the glitch epoch (MJD 58279.5) measured with previous ephemeris is about 13 d later than the result (MJD 58266.4) obtained with VLBI astrometric parameter solutions.

astro-ph.HE↗

arXiv2020

Deep Convolutional Neural Network-based Bernoulli Heatmap for Head Pose Estimation

Head pose estimation is a crucial problem for many tasks, such as driver attention, fatigue detection, and human behaviour analysis. It is well known that neural networks are better at handling classification problems than regression problems. It is an extremely nonlinear process to let the network output the angle value directly for optimization learning, and the weight constraint of the loss function will be relatively weak. This paper proposes a novel Bernoulli heatmap for head pose estimation from a single RGB image. Our method can achieve the positioning of the head area while estimating the angles of the head. The Bernoulli heatmap makes it possible to construct fully convolutional neural networks without fully connected layers and provides a new idea for the output form of head pose estimation. A deep convolutional neural network (CNN) structure with multiscale representations is adopted to maintain high-resolution information and low-resolution information in parallel. This kind of structure can maintain rich, high-resolution representations. In addition, channelwise fusion is adopted to make the fusion weights learnable instead of simple addition with equal weights. As a result, the estimation is spatially more precise and potentially more accurate. The effectiveness of the proposed method is empirically demonstrated by comparing it with other state-of-the-art methods on public datasets.

cs.CV↗

arXiv2020

Dipolar spin waves in uniaxial easy-axis antiferromagnets: A natural topological nodal-line semimetal

The existence of the magnetostatic surface spin waves in ferromagnets, known as Damon-Eshbach mode, was recently demonstrated to originate from the topology of the dipole-dipole interaction. In this work, we study the topological characteristics of magnons in easy-axis antiferromagnets with uniaxial anisotropy. The dipolar spin waves are found to be, driven by the dipole-dipole interaction, in a topological nodal-line semimetal phase, which hosts Damon-Eshbach-type surface modes due to the bulk-edge correspondence. The long wavelength character of dipolar spin waves makes our proposal valid for any natural uniaxial easy-axis antiferromagnet, and thus enriches the candidates of topological magnonic materials. In contrast to the nonreciprocal property in ferromagnetic case, the surface modes with opposite momentum coexist at each surface, but with different chiralities. Such a chirality-momentum or spin-momentum locking, similar to that of electronic surface states in topological insulators, offers the opportunity to design novel chirality-based magnonic devices in antiferromagnets.

cond-mat.mes-hall↗

arXiv2020

Triaging moderate COVID-19 and other viral pneumonias from routine blood tests

The COVID-19 is sweeping the world with deadly consequences. Its contagious nature and clinical similarity to other pneumonias make separating subjects contracted with COVID-19 and non-COVID-19 viral pneumonia a priority and a challenge. However, COVID-19 testing has been greatly limited by the availability and cost of existing methods, even in developed countries like the US. Intrigued by the wide availability of routine blood tests, we propose to leverage them for COVID-19 testing using the power of machine learning. Two proven-robust machine learning model families, random forests (RFs) and support vector machines (SVMs), are employed to tackle the challenge. Trained on blood data from 208 moderate COVID-19 subjects and 86 subjects with non-COVID-19 moderate viral pneumonia, the best result is obtained in an SVM-based classifier with an accuracy of 84%, a sensitivity of 88%, a specificity of 80%, and a precision of 92%. The results are found explainable from both machine learning and medical perspectives. A privacy-protected web portal is set up to help medical personnel in their practice and the trained models are released for developers to further build other applications. We hope our results can help the world fight this pandemic and welcome clinical verification of our approach on larger populations.

cs.LG↗

arXiv2020

Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation Error

We study the $L_1$-regularized maximum likelihood estimator/estimation (MLE) problem for discrete Markov random fields (MRFs), where efficient and scalable learning requires both sparse regularization and approximate inference. To address these challenges, we consider a stochastic learning framework called stochastic proximal gradient (SPG; Honorio 2012a, Atchade et al. 2014,Miasojedow and Rejchel 2016). SPG is an inexact proximal gradient algorithm [Schmidtet al., 2011], whose inexactness stems from the stochastic oracle (Gibbs sampling) for gradient approximation - exact gradient evaluation is infeasible in general due to the NP-hard inference problem for discrete MRFs [Koller and Friedman, 2009]. Theoretically, we provide novel verifiable bounds to inspect and control the quality of gradient approximation. Empirically, we propose the tighten asymptotically (TAY) learning strategy based on the verifiable bounds to boost the performance of SPG.

cs.LG↗

arXiv2020

Nanotechnology-inspired Information Processing Systems of the Future

Nanoscale semiconductor technology has been a key enabler of the computing revolution. It has done so via advances in new materials and manufacturing processes that resulted in the size of the basic building block of computing systems - the logic switch and memory devices - being reduced into the nanoscale regime. Nanotechnology has provided increased computing functionality per unit volume, energy, and cost. In order for computing systems to continue to deliver substantial benefits for the foreseeable future to society at large, it is critical that the very notion of computing be examined in the light of nanoscale realities. In particular, one needs to ask what it means to compute when the very building block - the logic switch - no longer exhibits the level of determinism required by the von Neumann architecture. There needs to be a sustained and heavy investment in a nation-wide Vertically Integrated Semiconductor Ecosystem (VISE). VISE is a program in which research and development is conducted seamlessly across the entire compute stack - from applications, systems and algorithms, architectures, circuits and nanodevices, and materials. A nation-wide VISE provides clear strategic advantages in ensuring the US's global superiority in semiconductors. First, a VISE provides the highest quality seed-corn for nurturing transformative ideas that are critically needed today in order for nanotechnology-inspired computing to flourish. It does so by dramatically opening up new areas of semiconductor research that are inspired and driven by new application needs. Second, a VISE creates a very high barrier to entry from foreign competitors because it is extremely hard to establish, and even harder to duplicate.

cs.CY↗

arXiv2020

A Comprehensive Survey of Grammar Error Correction

Grammar error correction (GEC) is an important application aspect of natural language processing techniques. The past decade has witnessed significant progress achieved in GEC for the sake of increasing popularity of machine learning and deep learning, especially in late 2010s when near human-level GEC systems are available. However, there is no prior work focusing on the whole recapitulation of the progress. We present the first survey in GEC for a comprehensive retrospect of the literature in this area. We first give the introduction of five public datasets, data annotation schema, two important shared tasks and four standard evaluation metrics. More importantly, we discuss four kinds of basic approaches, including statistical machine translation based approach, neural machine translation based approach, classification based approach and language model based approach, six commonly applied performance boosting techniques for GEC systems and two data augmentation methods. Since GEC is typically viewed as a sister task of machine translation, many GEC systems are based on neural machine translation (NMT) approaches, where the neural sequence-to-sequence model is applied. Similarly, some performance boosting techniques are adapted from machine translation and are successfully combined with GEC systems for enhancement on the final performance. Furthermore, we conduct an analysis in level of basic approaches, performance boosting techniques and integrated GEC systems based on their experiment results respectively for more clear patterns and conclusions. Finally, we discuss five prospective directions for future GEC researches.

cs.CL↗

arXiv2020

Projective manifolds whose tangent bundle contains a strictly nef subsheaf

Suppose that $X$ is a projective manifold whose tangent bundle $T_X$ contains a locally free strictly nef subsheaf. We prove that $X$ is isomorphic to a projective bundle over a hyperbolic manifold. Moreover, if the fundamental group $π_1(X)$ is virtually abelian, then $X$ is isomorphic to a projective space.

math.AG↗

arXiv2020

Localization, phases and transitions in the three-dimensional extended Lieb lattices

We study the localization properties and the Anderson transition in the 3D Lieb lattice $\mathcal{L}_3(1)$ and its extensions $\mathcal{L}_3(n)$ in the presence of disorder. We compute the positions of the flat bands, the disorder-broadened density of states and the energy-disorder phase diagrams for up to 4 different such Lieb lattices. Via finite-size scaling, we obtain the critical properties such as critical disorders and energies as well as the universal localization lengths exponent $ν$. We find that the critical disorder $W_c$ decreases from $\sim 16.5$ for the cubic lattice, to $\sim 8.6$ for $\mathcal{L}_3(1)$, $\sim 5.9$ for $\mathcal{L}_3(2)$ and $\sim 4.8$ for $\mathcal{L}_3(3)$. Nevertheless, the value of the critical exponent $ν$ for all Lieb lattices studied here and across disorder and energy transitions agrees within error bars with the generally accepted universal value $ν=1.590 (1.579,1.602)$.

cond-mat.dis-nn↗

arXiv2020

Learning to Predict More Accurate Text Instances for Scene Text Detection

At present, multi-oriented text detection methods based on deep neural network have achieved promising performances on various benchmarks. Nevertheless, there are still some difficulties for arbitrary shape text detection, especially for a simple and proper representation of arbitrary shape text instances. In this paper, a pixel-based text detector is proposed to facilitate the representation and prediction of text instances with arbitrary shapes in a simple manner. Firstly, to alleviate the effect of the target vertex sorting and achieve the direct regression of arbitrary shape text instances, the starting-point-independent coordinates regression loss is proposed. Furthermore, to predict more accurate text instances, the text instance accuracy loss is proposed as an assistant task to refine the predicted coordinates under the guidance of IoU. To evaluate the effectiveness of our detector, extensive experiments have been carried on public benchmarks which contain arbitrary shape text instances and multi-oriented text instances. We obtain 84.8% of F-measure on Total-Text benchmark. The results show that our method can reach state-of-the-art performance.

cs.CV↗

arXiv2020

Interpolative separable density fitting decomposition for accelerating Hartree-Fock exchange calculations within numerical atomic orbitals

The high cost associated with the evaluation of Hartree-Fock exchange (HFX) makes hybrid functionals computationally challenging for large systems. In this work, we present an efficient way to accelerate HFX calculations with numerical atomic basis sets. Our approach is based on the recently proposed interpolative separable density fitting (ISDF) decomposition to construct a low rank approximation of HFX matrix, which avoids explicit calculations of the electron repulsion integrals (ERIs) and significantly reduces the computational cost. We implement the ISDF method for hybrid functional (PBE0) calculations in the HONPAS package. We take benzene and polycyclic aromatic hydrocarbons molecules as examples and demonstrate that hybrid functionals with ISDF yields quite promising results at a significantly reduced computational cost. Especially, the ISDF approach reduces the total cost for evaluating HFX matrix by nearly 2 orders of magnitude compared to conventional approaches of direct evaluation of ERIs.

physics.comp-ph↗

arXiv2020

Computational Performance of a Germline Variant Calling Pipeline for Next Generation Sequencing

With the booming of next generation sequencing technology and its implementation in clinical practice and life science research, the need for faster and more efficient data analysis methods becomes pressing in the field of sequencing. Here we report on the evaluation of an optimized germline mutation calling pipeline, HummingBird, by assessing its performance against the widely accepted BWA-GATK pipeline. We found that the HummingBird pipeline can significantly reduce the running time of the primary data analysis for whole genome sequencing and whole exome sequencing while without significantly sacrificing the variant calling accuracy. Thus, we conclude that expansion of such software usage will help to improve the primary data analysis efficiency for next generation sequencing.

q-bio.GN↗

arXiv2020

Spectral self-adaptive absorber/emitter for harvesting energy from the sun and outer space

The sun (~6000 K) and outer space (~3 K) are the original heat source and sink for human beings on Earth. The energy applications of absorbing solar irradiation and harvesting the coldness of outer space for energy utilization have attracted considerable interest from researchers. However, combining these two functions in a static device for continuous energy harvesting is unachievable due to the intrinsic infrared spectral conflict. In this study, we developed spectral self-adaptive absorber/emitter (SSA/E) for daytime photothermal and nighttime radiative sky cooling modes depending on the phase transition of the vanadium dioxide coated layer. A 24-hour day-night test showed that the fabricated SSA/E has continuous energy harvesting ability and improved overall energy utilization performance, thus showing remarkable potential in future energy applications.

cond-mat.mtrl-sci↗

arXiv2020

Examples of Fano manifolds with non-pseudoeffective tangent bundle

Let $X$ be a Fano manifold. While the properties of the anticanonical divisor $-K_X$ and its multiples have been studied by many authors, the positivity of the tangent bundle $T_X$ is much more elusive. We give a complete characterisation of the pseudoeffectivity of $T_X$ for del Pezzo surfaces, hypersurfaces in the projective space and del Pezzo threefolds.

math.AG↗

arXiv2020

FLAME: A Self-Adaptive Auto-labeling System for Heterogeneous Mobile Processors

How to accurately and efficiently label data on a mobile device is critical for the success of training machine learning models on mobile devices. Auto-labeling data on mobile devices is challenging, because data is usually incrementally generated and there is possibility of having unknown labels. Furthermore, the rich hardware heterogeneity on mobile devices creates challenges on efficiently executing auto-labeling workloads. In this paper, we introduce Flame, an auto-labeling system that can label non-stationary data with unknown labels. Flame includes a runtime system that efficiently schedules and executes auto-labeling workloads on heterogeneous mobile processors. Evaluating Flame with eight datasets on a smartphone, we demonstrate that Flame enables auto-labeling with high labeling accuracy and high performance.

cs.LG↗

arXiv2020

Tencent ML-Images: A Large-Scale Multi-Label Image Database for Visual Representation Learning

In existing visual representation learning tasks, deep convolutional neural networks (CNNs) are often trained on images annotated with single tags, such as ImageNet. However, a single tag cannot describe all important contents of one image, and some useful visual information may be wasted during training. In this work, we propose to train CNNs from images annotated with multiple tags, to enhance the quality of visual representation of the trained CNN model. To this end, we build a large-scale multi-label image database with 18M images and 11K categories, dubbed Tencent ML-Images. We efficiently train the ResNet-101 model with multi-label outputs on Tencent ML-Images, taking 90 hours for 60 epochs, based on a large-scale distributed deep learning framework,i.e.,TFplus. The good quality of the visual representation of the Tencent ML-Images checkpoint is verified through three transfer learning tasks, including single-label image classification on ImageNet and Caltech-256, object detection on PASCAL VOC 2007, and semantic segmentation on PASCAL VOC 2012. The Tencent ML-Images database, the checkpoints of ResNet-101, and all the training codehave been released at https://github.com/Tencent/tencent-ml-images. It is expected to promote other vision tasks in the research and industry community.

cs.CV↗

arXiv2020

CTM: Collaborative Temporal Modeling for Action Recognition

With the rapid development of digital multimedia, video understanding has become an important field. For action recognition, temporal dimension plays an important role, and this is quite different from image recognition. In order to learn powerful feature of videos, we propose a Collaborative Temporal Modeling (CTM) block (Figure 1) to learn temporal information for action recognition. Besides a parameter-free identity shortcut, as a separate temporal modeling block, CTM includes two collaborative paths: a spatial-aware temporal modeling path, which we propose the Temporal-Channel Convolution Module (TCCM) with unshared parameters for each spatial position (H*W) to build, and a spatial-unaware temporal modeling path. CTM blocks can seamlessly be inserted into many popular networks to generate CTM Networks and bring the capability of learning temporal information to 2D CNN backbone networks, which only capture spatial information. Experiments on several popular action recognition datasets demonstrate that CTM blocks bring the performance improvements on 2D CNN baselines, and our method achieves the competitive results against the state-of-the-art methods. Code will be made publicly available.

cs.CV↗

arXiv2020

iqiyi Submission to ActivityNet Challenge 2019 Kinetics-700 challenge: Hierarchical Group-wise Attention

In this report, the method for the iqiyi submission to the task of ActivityNet 2019 Kinetics-700 challenge is described. Three models are involved in the model ensemble stage: TSN, HG-NL and StNet. We propose the hierarchical group-wise non-local (HG-NL) module for frame-level features aggregation for video classification. The standard non-local (NL) module is effective in aggregating frame-level features on the task of video classification but presents low parameters efficiency and high computational cost. The HG-NL method involves a hierarchical group-wise structure and generates multiple attention maps to enhance performance. Basing on this hierarchical group-wise structure, the proposed method has competitive accuracy, fewer parameters and smaller computational cost than the standard NL. For the task of ActivityNet 2019 Kinetics-700 challenge, after model ensemble, we finally obtain an averaged top-1 and top-5 error percentage 28.444% on the test set.

cs.CV↗