arXiv subjects

Tong Chen

Publications and source records attributed to Tong Chen.

At least 271 records · Page 15Linked to original sources

arXiv2019

Non-local Attention Optimized Deep Image Compression

This paper proposes a novel Non-Local Attention Optimized Deep Image Compression (NLAIC) framework, which is built on top of the popular variational auto-encoder (VAE) structure. Our NLAIC framework embeds non-local operations in the encoders and decoders for both image and latent feature probability information (known as hyperprior) to capture both local and global correlations, and apply attention mechanism to generate masks that are used to weigh the features for the image and hyperprior, which implicitly adapt bit allocation for different features based on their importance. Furthermore, both hyperpriors and spatial-channel neighbors of the latent features are used to improve entropy coding. The proposed model outperforms the existing methods on Kodak dataset, including learned (e.g., Balle2019, Balle2018) and conventional (e.g., BPG, JPEG2000, JPEG) image compression methods, for both PSNR and MS-SSIM distortion metrics.

eess.IV↗

arXiv2019

Using Structured Input and Modularity for Improved Learning

We describe a method for utilizing the known structure of input data to make learning more efficient. Our work is in the domain of programming languages, and we use deep neural networks to do program analysis. Computer programs include a lot of structural information (such as loop nests, conditional blocks, and data scopes), which is pertinent to program analysis. In this case, the neural network has to learn to recognize the structure, and also learn the target function for the problem. However, the structural information in this domain is readily accessible to software with the availability of compiler tools and parsers for well-defined programming languages. Our method for utilizing the known structure of input data includes: (1) pre-processing the input data to expose relevant structures, and (2) constructing neural networks by incorporating the structure of the input data as an integral part of the network design. The method has the effect of modularizing the neural network which helps break down complexity, and results in more efficient training of the overall network. We apply this method to an example code analysis problem, and show that it can achieve higher accuracy with a smaller network size and fewer training examples. Further, the method is robust, performing equally well on input data with different distributions.

cs.NE↗

arXiv2019

Gated Context Model with Embedded Priors for Deep Image Compression

A deep image compression scheme is proposed in this paper, offering the state-of-the-art compression efficiency, against the traditional JPEG, JPEG2000, BPG and those popular learning based methodologies. This is achieved by a novel conditional probably model with embedded priors which can accurately approximate the entropy rate for rate-distortion optimization. It utilizes three separable stacks to eliminate the blind spots in the receptive field for better probability prediction and computation reduction. Those embedded priors can be further used to help the image reconstruction when fused with latent features, after passing through the proposed information compensation network (ICN). Residual learning with generalized divisive normalization (GDN) based activation is used in our encoder and decoder with fast convergence rate and efficient performance. We have evaluated our model and other methods using rate-distortion criteria, where distortion is measured by multi-scale structural similarity (MS-SSIM). We have also discussed the impacts of various distortion metrics on the reconstructed image quality. Besides, a field study on perceptual quality is also given via a dedicated subjective assessment, to compare the efficiency of our proposed methods and other conventional image compression methods.

eess.IV↗

arXiv2019

Neural Video Compression using Spatio-Temporal Priors

The pursuit of higher compression efficiency continuously drives the advances of video coding technologies. Fundamentally, we wish to find better "predictions" or "priors" that are reconstructed previously to remove the signal dependency efficiently and to accurately model the signal distribution for entropy coding. In this work, we propose a neural video compression framework, leveraging the spatial and temporal priors, independently and jointly to exploit the correlations in intra texture, optical flow based temporal motion and residuals. Spatial priors are generated using downscaled low-resolution features, while temporal priors (from previous reference frames and residuals) are captured using a convolutional neural network based long-short term memory (ConvLSTM) structure in a temporal recurrent fashion. All of these parts are connected and trained jointly towards the optimal rate-distortion performance. Compared with the High-Efficiency Video Coding (HEVC) Main Profile (MP), our method has demonstrated averaged 38% Bjontegaard-Delta Rate (BD-Rate) improvement using standard common test sequences, where the distortion is multi-scale structural similarity (MS-SSIM).

eess.IV↗

arXiv2019

Growth and Thermo-driven Crystalline Phase Transition of Metastable Monolayer 1T'-WSe2 Thin Film

Two-dimensional (2D) transition metal dichalcogenides MX2 (M = Mo, W, X = S, Se, Te) attracts enormous research interests in recent years. Its 2H phase possesses an indirect to direct bandgap transition in 2D limit, and thus shows great application potentials in optoelectronic devices [1]. The 1T' crystalline phase transition can drive the monolayer MX2 to be a 2D topological insulator. Here we realized the molecular beam epitaxial (MBE) growth of both the 1T' and 2H phase monolayer WSe2 on bilayer graphene (BLG) substrate. The crystalline structures of these two phases were characterized using scanning tunneling microscopy. The monolayer 1T'-WSe2 was found to be metastable, and can transform into 2H phase under post-annealing procedure. The phase transition temperature of 1T'-WSe2 grown on BLG is lower than that of 1T' phase grown on 2H-WSe2 layers. This thermo-driven crystalline phase transition makes the monolayer WSe2 to be an ideal platform for the controlling of topological phase transitions in 2D materials family.

cond-mat.mtrl-sci↗

arXiv2018

Topological spin excitations in honeycomb ferromagnet CrI$_3$

In two dimensional honeycomb ferromagnets, bosonic magnon quasiparticles (spin waves) may either behave as massless Dirac fermions or form topologically protected edge states. The key ingredient defining their nature is the next-nearest neighbor Dzyaloshinskii-Moriya (DM) interaction that breaks the inversion symmetry of the lattice and discriminates chirality of the associated spin-wave excitations. Using inelastic neutron scattering, we find that spin waves of the insulating honeycomb ferromagnet CrI$_3$ ($T_C=61$ K) have two distinctive bands of ferromagnetic excitations separated by a $\sim$4 meV gap at the Dirac points. These results can only be understood by considering a Heisenberg Hamiltonian with DM interaction, thus providing experimental evidence that spin waves in CrI$_3$ can have robust topological properties useful for dissipationless spintronic applications.

cond-mat.str-el↗

arXiv2018

A Training-based Identification Approach to VIN Adversarial Examples

With the rapid development of Artificial Intelligence (AI), the problem of AI security has gradually emerged. Most existing machine learning algorithms may be attacked by adversarial examples. An adversarial example is a slightly modified input sample that can lead to a false result of machine learning algorithms. The adversarial examples pose a potential security threat for many AI application areas, especially in the domain of robot path planning. In this field, the adversarial examples obstruct the algorithm by adding obstacles to the normal maps, resulting in multiple effects on the predicted path. However, there is no suitable approach to automatically identify them. To our knowledge, all previous work uses manual observation method to estimate the attack results of adversarial maps, which is time-consuming. Aiming at the existing problem, this paper explores a method to automatically identify the adversarial examples in Value Iteration Networks (VIN), which has a strong generalization ability. We analyze the possible scenarios caused by the adversarial maps. We propose a training-based identification approach to VIN adversarial examples by combing the path feature comparison and path image classification. We evaluate our method using the adversarial maps dataset, show that our method can achieve a high-accuracy and faster identification than manual observation method.

cs.CR↗

arXiv2018

Gradient Band-based Adversarial Training for Generalized Attack Immunity of A3C Path Finding

As adversarial attacks pose a serious threat to the security of AI system in practice, such attacks have been extensively studied in the context of computer vision applications. However, few attentions have been paid to the adversarial research on automatic path finding. In this paper, we show dominant adversarial examples are effective when targeting A3C path finding, and design a Common Dominant Adversarial Examples Generation Method (CDG) to generate dominant adversarial examples against any given map. In addition, we propose Gradient Band-based Adversarial Training, which trained with a single randomly choose dominant adversarial example without taking any modification, to realize the "1:N" attack immunity for generalized dominant adversarial examples. Extensive experimental results show that, the lowest generation precision for CDG algorithm is 91.91%, and the lowest immune precision for Gradient Band-based Adversarial Training is 93.89%, which can prove that our method can realize the generalized attack immunity of A3C path finding with a high confidence.

cs.LG↗

arXiv2018

Unusual phonon density of states and response to the superconducting transition in the In-doped topological crystalline insulator Pb$_{0.5}$Sn$_{0.5}$Te

We present inelastic neutron scattering results of phonons in (Pb$_{0.5}$Sn$_{0.5}$)$_{1-x}$In$_x$Te powders, with $x=0$ and 0.3. The $x=0$ sample is a topological crystalline insulator, and the $x=0.3$ sample is a superconductor with a bulk superconducting transition temperature $T_c$ of 4.7 K. In both samples, we observe unexpected van Hove singularities in the phonon density of states at energies of 1--2.5 meV, suggestive of local modes. On cooling the superconducting sample through $T_c$, there is an enhancement of these features for energies below twice the superconducting-gap energy. We further note that the superconductivity in (Pb$_{0.5}$Sn$_{0.5}$)$_{1-x}$In$_x$Te occurs in samples with normal-state resistivities of order 10 m$Ω$~cm, indicative of bad-metal behavior. Calculations based on density functional theory suggest that the superconductivity is easily explainable in terms of electron-phonon coupling; however, they completely miss the low-frequency modes and do not explain the large resistivity. While the bulk superconducting state of (Pb$_{0.5}$Sn$_{0.5}$)$_{0.7}$In$_{0.3}$Te appears to be driven by phonons, a proper understanding will require ideas beyond simple BCS theory.

cond-mat.supr-con↗

arXiv2018

Deep Sequence Learning with Auxiliary Information for Traffic Prediction

Predicting traffic conditions from online route queries is a challenging task as there are many complicated interactions over the roads and crowds involved. In this paper, we intend to improve traffic prediction by appropriate integration of three kinds of implicit but essential factors encoded in auxiliary information. We do this within an encoder-decoder sequence learning framework that integrates the following data: 1) offline geographical and social attributes. For example, the geographical structure of roads or public social events such as national celebrations; 2) road intersection information. In general, traffic congestion occurs at major junctions; 3) online crowd queries. For example, when many online queries issued for the same destination due to a public performance, the traffic around the destination will potentially become heavier at this location after a while. Qualitative and quantitative experiments on a real-world dataset from Baidu have demonstrated the effectiveness of our framework.

cs.CV↗

arXiv2018

Deep Image Compression via End-to-End Learning

We present a lossy image compression method based on deep convolutional neural networks (CNNs), which outperforms the existing BPG, WebP, JPEG2000 and JPEG as measured via multi-scale structural similarity (MS-SSIM), at the same bit rate. Currently, most of the CNNs based approaches train the network using a L2 loss between the reconstructions and the ground-truths in the pixel domain, which leads to over-smoothing results and visual quality degradation especially at a very low bit rate. Therefore, we improve the subjective quality with the combination of a perception loss and an adversarial loss additionally. To achieve better rate-distortion optimization (RDO), we also introduce an easy-to-hard transfer learning when adding quantization error and rate constraint. Finally, we evaluate our method on public Kodak and the Test Dataset P/M released by the Computer Vision Lab of ETH Zurich, resulting in averaged 7.81% and 19.1% BD-rate reduction over BPG, respectively.

eess.IV↗

arXiv2018

Dynamic spin-lattice coupling and nematic fluctuations in NaFeAs

We use inelastic neutron scattering to study acoustic phonons and spin excitations in single crystals of NaFeAs, a parent compound of iron pnictide superconductors. NaFeAs exhibits a tetragonal-to-orthorhombic structural transition at $T_s\approx 58$ K and a collinear antiferromagnetic (AF) order at $T_N\approx 45$ K. While longitudinal and out-of-plane transverse acoustic phonons behave as expected, the in-plane transverse acoustic phonons reveal considerable softening on cooling to $T_s$, and then harden on approaching $T_N$ before saturating below $T_N$. In addition, we find that spin-spin correlation lengths of low-energy magnetic excitations within the FeAs layer and along the $c$-axis increase dramatically below $T_s$, and show weak anomaly across $T_N$. These results suggest that the electronic nematic phase present in the paramagnetic tetragonal phase is closely associated with dynamic spin-lattice coupling, possibly arising from the one-phonon-two-magnon mechanism.

cond-mat.str-el↗

arXiv2017

Nonsymmorphic symmetry protected node-line semimetal in the trigonal YH3

Using ab initio calculations based on density-functional theory and effective model analysis, we propose that the trigonal YH3(Space Group: P-3c1) at ambient pressure is a node-line semimetal when spin-orbit coupling (SOC) is ignored. This trigonal YH3 has very clean electronic structure near Fermi level and its nodal lines locate very closely to the Fermi energy, which makes it a perfect system for model analysis. Symmetry analysis shows that the nodal ring in this compound is protected by the glide-plane symmetry, where the band inversion of |Y+,dxz> and |H1-,s> orbits at Gamma point is responsible for the formation of the nodal lines. When SOC is included, the line nodes are prohibited by the glide-plane symmetry, and a small gap (~5 meV) appears, which leads YH3 to be a strong topological insulator with Z2 indices (1,000). Thus the glide-plane symmetry plays an opposite role in the formation of the nodal lines in cases without and with SOC. As the SOC-induced gap is so small that can be neglected, this P-3c1 YH3 may be a good candidate for experimental explorations on the fundamental physics of topological node-line semimetals. We find the surface states of this P-3c1 phase are somehow unique and may be helpful to identify the real ground state of YH3 in the experiment.

cond-mat.mtrl-sci↗

arXiv2017

Implementing implicit OpenMP data sharing on GPUs

OpenMP is a shared memory programming model which supports the offloading of target regions to accelerators such as NVIDIA GPUs. The implementation in Clang/LLVM aims to deliver a generic GPU compilation toolchain that supports both the native CUDA C/C++ and the OpenMP device offloading models. There are situations where the semantics of OpenMP and those of CUDA diverge. One such example is the policy for implicitly handling local variables. In CUDA, local variables are implicitly mapped to thread local memory and thus become private to a CUDA thread. In OpenMP, due to semantics that allow the nesting of regions executed by different numbers of threads, variables need to be implicitly \emph{shared} among the threads of a contention group. In this paper we introduce a re-design of the OpenMP device data sharing infrastructure that is responsible for the implicit sharing of local variables in the Clang/LLVM toolchain. We introduce a new data sharing infrastructure that lowers implicitly shared variables to the shared memory of the GPU. We measure the amount of shared memory used by our scheme in cases that involve scalar variables and statically allocated arrays. The evaluation is carried out by offloading to K40 and P100 NVIDIA GPUs. For scalar variables the pressure on shared memory is relatively low, under 26\% of shared memory utilization for the K40, and does not negatively impact occupancy. The limiting occupancy factor in that case is register pressure. The data sharing scheme offers the users a simple memory model for controlling the implicit allocation of device shared memory.

cs.PL↗

arXiv2017

Effective Resistivity in Collisionless Magnetic Reconnection

Magnetic reconnection (MR) in collisionless plasma is often attributed to the off-diagonal electron Reynolds stress, which can give rise to a large induction electric field in the reconnection region. However, in magneto-hydro-dynamics (MHD) simulations of MR, it is difficult to implement the full Reynolds stress, which is kinetic in nature. In this paper, we propose a theoretical model of effective resistivity from the first principle of particle dynamics. The derived theoretical formulation of the effective resistivity is verified by full particle-in-cell (PIC) simulations, and the corresponding physics is discussed.

physics.plasm-ph↗

arXiv2017

When Point Process Meets RNNs: Predicting Fine-Grained User Interests with Mutual Behavioral Infectivity

Predicting fine-grained interests of users with temporal behavior is important to personalization and information filtering applications. However, existing interest prediction methods are incapable of capturing the subtle degreed user interests towards particular items, and the internal time-varying drifting attention of individuals is not studied yet. Moreover, the prediction process can also be affected by inter-personal influence, known as behavioral mutual infectivity. Inspired by point process in modeling temporal point process, in this paper we present a deep prediction method based on two recurrent neural networks (RNNs) to jointly model each user's continuous browsing history and asynchronous event sequences in the context of inter-user behavioral mutual infectivity. Our model is able to predict the fine-grained interest from a user regarding a particular item and corresponding timestamps when an occurrence of event takes place. The proposed approach is more flexible to capture the dynamic characteristic of event sequences by using the temporal point process to model event data and timely update its intensity function by RNNs. Furthermore, to improve the interpretability of the model, the attention mechanism is introduced to emphasize both intra-personal and inter-personal behavior influence over time. Experiments on real datasets demonstrate that our model outperforms the state-of-the-art methods in fine-grained user interest prediction.

cs.LG↗

arXiv2017

High-pressure phases of Weyl semimetals NbP, NbAs, TaP and TaAs

The high pressure phase diagrams of TaAs family (NbP, NbAs, TaP and TaAs) are explored systematically using crystal structure search together with first-principles calculations. Our calculations show that NbAs and TaAs have similar phase diagrams and possess the same structural phase transition sequence of I41md $\rightarrow$ P-6m2 $\rightarrow$ P21/c $\rightarrow$ Pm-3m, with a bit different transition pressures. The phase transition sequence of NbP and TaP are somewhat different from that of NbAs and TaAs. In which new structures emerge, for instance, the Cmcm structure in NbP and the Pmmn structure in TaP. It is found interestingly that in the electronic structure of the high-pressure phase P-6m2-NbAs, there are Weyl points and triple degenerate points coexisted, similar to the high-pressure P-6m2-TaAs.

cond-mat.mtrl-sci↗

arXiv2017

Strain-induced quantum topological phase transitions in Na3Bi

Strain can be used as an effective tool to tune the crystal structure of materials and hence to modify their electronic structures, including topological properties. Here, taking Na3Bi as a paradigmatic example, we demonstrated with first-principles calculations and k$\cdot$p models that the topological phase transitions can be induced by various types of strains. For instance, the Dirac semimetal phase of ambient Na3Bi can be tuned into a topological insulator (TI) phase by uniaxial strain along the h100i axis. Hydrostatic pressure can let the ambient structure transfer into a new thermodynamically stable phase with Fm-3m symmetry, coming with a perfect parabolic semimetal having a single contact point between the conduction and valence bands, exactly at $Γ$ point on the Fermi level like $α$-Sn. Furthermore, uniaxial strain in the <100> direction can tune the new parabolic semimetal phase into a Dirac semimetal, while shear strains in both the <100> and <111> directions can take the new parabolic semimetal phase into a TI. k$\cdot$p models are constructed to gain more insights into these quantum topological phase transitions. At last, we calculated surface states of Fm-3m Na3Bi without and with strains to verify these topological transitions.

cond-mat.mtrl-sci↗