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Dong Li

Publications and source records attributed to Dong Li.

At least 505 records · Page 28Linked to original sources
arXiv2019

Quantum coherence fraction

As an analogy of fully entangled fraction in the framework of entanglement theory, we have introduced the notion of quantum coherence fraction $C_{\mathcal{F}}$, which quantifies the closeness between a given state and the set of maximally coherent states. By providing an alternative formulation of the robustness of coherence $C_{\mathcal{R}}$, we have elucidated the relationship between quantum coherence fraction and the normalized version of $C_{\mathcal{R}}$ (i.e., $\overline{C}_{\mathcal{R}}$), where the role of genuinely incoherent operations (GIO) is highlighted. Numerical simulation shows that though as expected $C_{\mathcal{F}}$ is upper bounded by $\overline{C}_{\mathcal{R}}$, $C_{\mathcal{F}}$ constitutes a good approximation to $\overline{C}_{\mathcal{R}}$ especially in low-dimensional Hilbert spaces. Even more intriguingly, we can analytically prove that $C_{\mathcal{F}}$ is exactly equivalent to $\overline{C}_{\mathcal{R}}$ for qubit and qutrit states. Moreover, some intuitive properties and implications of $C_{\mathcal{F}}$ are also indicated.

quant-ph↗
arXiv2019

Non-Volatile Superconductivity in an Insulating Copper Oxide Induced via Ionic Liquid Gating

Manipulating the superconducting states of high-T_c cuprate superconductors in an efficient and reliable way is of great importance for their applications in next-generation electronics. Traditional methods are mostly based on a trial-and-error method that is difficult to implement and time consuming. Here, employing ionic liquid gating, a selective control of volatile and non-volatile superconductivity is achieved in pristine insulating Pr_2CuO_{4\pmδ} film, based on two distinct mechanisms: 1) with positive electric fields, the film can be reversibly switched between non-superconducting and superconducting states, attributed to the carrier doping effect. 2) The film becomes more resistive by applying negative bias voltage up to -4 V, but strikingly, a non-volatile superconductivity is achieved once the gate voltage is removed. Such a persistent superconducting state represents a novel phenomenon in copper oxides, resulting from the doping healing of oxygen vacancies in copper-oxygen planes as unraveled by high-resolution scanning transmission electron microscope and in-situ x-ray diffraction experiments. The effective manipulation and mastering of volatile/non-volatile superconductivity in the same parent cuprate opens the door to more functionalities for superconducting electronics, as well as supplies flexible samples for investigating the nature of quantum phase transitions in high-T_c superconductors.

cond-mat.supr-con↗
arXiv2019

Graph Attention Memory for Visual Navigation

Visual navigation in complex environments is inefficient with traditional reactive policy or general-purposed recurrent policy. To address the long-term memory issue, this paper proposes a graph attention memory (GAM) architecture consisting of memory construction module, graph attention module and control module. The memory construction module builds the topological graph based on supervised learning by taking the exploration prior. Then, guided attention features are extracted with the graph attention module. Finally, the deep reinforcement learning based control module makes decisions based on visual observations and guided attention features. Detailed convergence analysis of GAM is presented in this paper. We evaluate GAM-based navigation system in two complex 3D environments. Experimental results show that the GAM-based navigation system significantly improves learning efficiency and outperforms all baselines in average success rate.

cs.CV↗
arXiv2019

Improving constant in end-point Poincaré inequality on Hamming cube

We improve the constant $\fracπ{2}$ in $L^1$-Poincaré inequality on Hamming cube. For Gaussian space the sharp constant in $L^1$ inequality is known, and it is $\sqrt{\fracπ{2}}$. For Hamming cube the sharp constant is not known, and $\sqrt{\fracπ{2}}$ gives an estimate from below for this sharp constant. On the other hand, L. Ben Efraim and F. Lust-Piquard have shown an estimate from above: $C_1\le \fracπ{2}$. There are at least two other independent proofs of the same estimate from above (we write down them in this note). Since those proofs are very different from the proof of Ben Efraim and Lust-Piquard but gave the same constant, that might have indicated that constant is sharp. But here we give a better estimate from above, showing that $C_1$ is strictly smaller than $\fracπ{2}$. It is still not clear whether $C_1> \sqrt{\fracπ{2}}$. We discuss this circle of questions and the computer experiments.

math.PR↗
arXiv2019

Emergent superconductivity in single crystalline $\mathrm{MgTi}_2\mathrm{O}_4$ films via structural engineering

Spinel compounds have demonstrated rich functionalities but rarely shown superconductivity. Here, we report the emergence of superconductivity in the spinel $\mathrm{MgTi}_2\mathrm{O}_4$, known to be an insulator with a complicated order. The superconducting transition is achieved by engineering a superlattice of $\mathrm{MgTi}_2\mathrm{O}_4$ and $\mathrm{SrTiO}_3$. The onset transition temperature in the $\mathrm{MgTi}_2\mathrm{O}_4$ layer can be tuned from 0 to 5 K in such geometry, concurrently with a stretched $c$-axis (from 8.51 to 8.53 Å) compared to the bulk material. Such a positive correlation without saturation suggests ample room for the further enhancement. Intriguingly, the superlattice exhibits isotropic upper critical field $H_{\mathrm{c}2}$ that breaks the Pauli limit, distinct from the highly anisotropic feature of interface superconductivity. The origin of superconductivity in the $\mathrm{MgTi}_2\mathrm{O}_4$ layer is understood in combination with the electron energy loss spectra and the first-principles electronic structure calculations, which point to the birth of superconductivity in the $\mathrm{MgTi}_2\mathrm{O}_4$ layer by preventing the Ti-Ti dimerization. Our discovery not only provides a platform to explore the interplay between the superconductivity and other exotic states, but also opens a new window to realize superconductivity in the spinel compounds as well as other titanium oxides.

cond-mat.supr-con↗
arXiv2019

Non-standard inference for augmented double autoregressive models with null volatility coefficients

This paper considers an augmented double autoregressive (DAR) model, which allows null volatility coefficients to circumvent the over-parameterization problem in the DAR model. Since the volatility coefficients might be on the boundary, the statistical inference methods based on the Gaussian quasi-maximum likelihood estimation (GQMLE) become non-standard, and their asymptotics require the data to have a finite sixth moment, which narrows applicable scope in studying heavy-tailed data. To overcome this deficiency, this paper develops a systematic statistical inference procedure based on the self-weighted GQMLE for the augmented DAR model. Except for the Lagrange multiplier test statistic, the Wald, quasi-likelihood ratio and portmanteau test statistics are all shown to have non-standard asymptotics. The entire procedure is valid as long as the data is stationary, and its usefulness is illustrated by simulation studies and one real example.

econ.EM↗
arXiv2019

Statistical distribution of quantum correlation induced by multiple scattering in the disordered medium

For the quantum correlations between scattered modes in the disordered media, the previous works focus mainly on the cases where the inputs are non-superposed states, for instance, products of Fock states [Phys. Rev. Letts. 105 (2010) 090501]. A natural question that arises is how the superpositions affect the quantum correlations. Following this trail, the comparison between superpositions and products of Fock states is performed. It is found an interesting phenomenon that for the superposition and the corresponding product of Fock state (non-Gaussian states), their averaged quantum correlations are nearly same, whereas the distributions of their quantum correlations might be different. Therefore, superpositions may affect the distributions of the quantum correlations. In addition, to examine how the Gaussian states affect the quantum correlations, we compare the typical Gaussian states with the non-Gaussian states (superpositions and products of Fock states). It is discovered that the non-Gaussian-state input could result in the quantum correlation that is either positive or negative, depending on the number of the input modes and the number of the photons in each mode, whereas the Gaussian-state input always leads to the non-negative quantum correlation. Besides, it is demonstrated that with the increase of the disorder strength, the mean strength of the quantum correlation increases for multi-mode-state inputs (except for multi-mode-coherent-state inputs). These results may be useful to control and adjust the quantum properties of scattered modes after the quantized lights propagating through the disordered medium.

quant-ph↗
arXiv2019

Architecture-Aware, High Performance Transaction for Persistent Memory

Byte-addressable non-volatile main memory (NVM) demands transactional mechanisms to access and manipulate data on NVM atomically. Those transaction mechanisms often employ a logging mechanism (undo logging or redo logging). However, the logging mechanisms can bring large runtime overhead (8%-49% in our evaluation), and 41%-78% of the overhead attributes to the frequent cache-line flushing. Such large overhead significantly diminishes the performance benefits offered by NVM. In this paper, we introduce a new method to reduce the overhead of cache-line flushing for logging-based transactions. Different from the traditional method that works at the program level and leverages program semantics to reduce the logging overhead, we introduce architecture awareness. In particular, we do not flush certain cache blocks, as long as they are estimated to be eliminated out of the cache because of the hardware caching mechanism (e.g., the cache replacement algorithm). Furthermore, we coalesce those cache blocks with low dirtiness to improve the efficiency of cache-line flushing. We implement an architecture-aware, high performance transaction runtime system for persistent memory, Archapt. Our results show that comparing with an undo logging (PMDK) and a redo logging (Mnemosyne), Archapt reduces cache-line flushing by 66% and improves system throughput by 19% on average (42% at most). Our crash tests with four hardware caching policies show that Archapt provides a strong guarantee on crash consistency.

cs.DC↗
arXiv2019

Runtime Concurrency Control and Operation Scheduling for High Performance Neural Network Training

Training neural network often uses a machine learning framework such as TensorFlow and Caffe2. These frameworks employ a dataflow model where the NN training is modeled as a directed graph composed of a set of nodes. Operations in neural network training are typically implemented by the frameworks as primitives and represented as nodes in the dataflow graph. Training NN models in a dataflow-based machine learning framework involves a large number of fine-grained operations. Those operations have diverse memory access patterns and computation intensity. How to manage and schedule those operations is challenging, because we have to decide the number of threads to run each operation (concurrency control) and schedule those operations for good hardware utilization and system throughput. In this paper, we extend an existing runtime system (the TensorFlow runtime) to enable automatic concurrency control and scheduling of operations. We explore performance modeling to predict the performance of operations with various thread-level parallelism. Our performance model is highly accurate and lightweight. Leveraging the performance model, our runtime system employs a set of scheduling strategies that co-run operations to improve hardware utilization and system throughput. Our runtime system demonstrates a big performance benefit. Comparing with using the recommended configurations for concurrency control and operation scheduling in TensorFlow, our approach achieves 33% performance (execution time) improvement on average (up to 49%) for three neural network models, and achieves high performance closing to the optimal one manually obtained by the user.

cs.DC↗
arXiv2019

Strict Stationarity Testing and GLAD Estimation of Double Autoregressive Models

In this article we develop a tractable procedure for testing strict stationarity in a double autoregressive model and formulate the problem as testing if the top Lyapunov exponent is negative. Without strict stationarity assumption, we construct a consistent estimator of the associated top Lyapunov exponent and employ a random weighting approach for its variance estimation, which in turn are used in a t-type test. We also propose a GLAD estimation for parameters of interest, relaxing key assumptions on the commonly used QMLE. All estimators, except for the intercept, are shown to be consistent and asymptotically normal in both stationary and explosive situations. The finite-sample performance of the proposed procedures is evaluated via Monte Carlo simulation studies and a real dataset of interest rates is analyzed.

math.ST↗
arXiv2019

Enhanced Expressive Power and Fast Training of Neural Networks by Random Projections

Random projections are able to perform dimension reduction efficiently for datasets with nonlinear low-dimensional structures. One well-known example is that random matrices embed sparse vectors into a low-dimensional subspace nearly isometrically, known as the restricted isometric property in compressed sensing. In this paper, we explore some applications of random projections in deep neural networks. We provide the expressive power of fully connected neural networks when the input data are sparse vectors or form a low-dimensional smooth manifold. We prove that the number of neurons required for approximating a Lipschitz function with a prescribed precision depends on the sparsity or the dimension of the manifold and weakly on the dimension of the input vector. The key in our proof is that random projections embed stably the set of sparse vectors or a low-dimensional smooth manifold into a low-dimensional subspace. Based on this fact, we also propose some new neural network models, where at each layer the input is first projected onto a low-dimensional subspace by a random projection and then the standard linear connection and non-linear activation are applied. In this way, the number of parameters in neural networks is significantly reduced, and therefore the training of neural networks can be accelerated without too much performance loss.

cs.LG↗
arXiv2018

A Preliminary Study of Neural Network-based Approximation for HPC Applications

Machine learning, as a tool to learn and model complicated (non)linear relationships between input and output data sets, has shown preliminary success in some HPC problems. Using machine learning, scientists are able to augment existing simulations by improving accuracy and significantly reducing latencies. Our ongoing research work is to create a general framework to apply neural network-based models to HPC applications. In particular, we want to use the neural network to approximate and replace code regions within the HPC application to improve performance (i.e., reducing the execution time) of the HPC application. In this paper, we present our preliminary study and results. Using two applications (the Newton-Raphson method and the Lennard-Jones (LJ) potential in LAMMP) for our case study, we achieve up to 2.7x and 2.46x speedup, respectively.

cs.PF↗
arXiv2018

PARIS: Predicting Application Resilience Using Machine Learning

Extreme-scale scientific applications can be more vulnerable to soft errors (transient faults) as high-performance computing systems increase in scale. The common practice to evaluate the resilience to faults of an application is random fault injection, a method that can be highly time consuming. While resilience prediction modeling has been recently proposed to predict application resilience in a faster way than fault injection, it can only predict a single class of fault manifestation (SDC) and there is no evidence demonstrating that it can work on previously unseen programs, which greatly limits its re-usability. We present PARIS, a resilience prediction method that addresses the problems of existing prediction methods using machine learning. Using carefully-selected features and a machine learning model, our method is able to make resilience predictions of three classes of fault manifestations (success, SDC, and interruption) as opposed to one class like in current resilience prediction modeling. The generality of our approach allows us to make prediction on new applications, i.e., previously unseen applications, providing large applicability to our model. Our evaluation on 125 programs shows that PARIS provides high prediction accuracy, 82% and 77% on average for predicting the rate of success and interruption, respectively, while the state-of-the-art resilience prediction model cannot predict them. When predicting the rate of SDC, PARIS provides much better accuracy than the state-of-the-art (38% vs. -273%). PARIS is much faster (up to 450x speedup) than the traditional method (random fault injection).

cs.DC↗
arXiv2018

Signatures of magnetic reconnection at the footpoints of fan shape jets on a light bridge driven by photospheric convective motions

Dynamical jets are generally found on Light bridges (LBs), which are key to studying sunspots decays. So far, their formation mechanism is not fully understood. In this paper, we used state-of-the-art observations from the Goode Solar Telescope, the Interface Region Imaging Spectrograph, the Spectro-Polarimeter on board Hinode and the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory to analyze the fan shape jets on LBs in detail. Continuous upward motion of the jets in ascending phase is found from the H$α$ velocity, which lasts for 12 minutes and is associated with the H$α$ line wing enhancements. Two mini jets appear upon the bright fronts of the fan shape jets visible in the AIA 171 Å and 193 Å channels, with a time interval as short as 1 minute. Two kinds of small scale convective motions are identified in the photospheric images, along with the H$α$ line wing enhancements. One seems to be associated with the formation of a new convection cell and the other manifests as the motion of a dark lane passing through the convection cell. The finding of three lobes Stokes V profiles and their inversion with NICOLE code indicates that there is magnetic field lines with opposite polarities in LBs. From the H$α$ -0.8 Å images, we found ribbon like brightenings propagating along the LBs, possibly indicating slipping reconnection. Our observation supports that the fan shape jets under study are caused by the magnetic reconnection and photospheric convective motions play an important role in triggering the magnetic reconnection.

astro-ph.SR↗
arXiv2018

Transition-region explosive events produced by plasmoid instability

Magnetic reconnection is thought to be a key process in most of solar eruptions. Thanks to high-resolution observations and simulations, the studied scale of reconnection process has become smaller and smaller. Spectroscopic observations show that the reconnection site can be very small, which always exhibits a bright core and two extended wings with fast speeds, i.e., transition-region explosive events. In this paper, using the PLUTO code, we perform a 2-D magnetohydrodynamic simulation to investigate the small-scale reconnection in double current sheets. Based on our simulation results, such as the line-of-sight velocity, number density and plasma temperature, we can synthesize the line profile of Si IV 1402.77 A which is a well known emission line to study the transition-region explosive events on the Sun. The synthetic line profile of Si IV 1402.77 A is complex with a bright core and two broad wings which can extend to be nearly 200 km/s. Our simulation results suggest that the transition-region explosive events on the Sun are produced by plasmoid instability during the small-scale magnetic reconnection.

astro-ph.SR↗
arXiv2018

Reinforcement Learning and Deep Learning based Lateral Control for Autonomous Driving

This paper investigates the vision-based autonomous driving with deep learning and reinforcement learning methods. Different from the end-to-end learning method, our method breaks the vision-based lateral control system down into a perception module and a control module. The perception module which is based on a multi-task learning neural network first takes a driver-view image as its input and predicts the track features. The control module which is based on reinforcement learning then makes a control decision based on these features. In order to improve the data efficiency, we propose visual TORCS (VTORCS), a deep reinforcement learning environment which is based on the open racing car simulator (TORCS). By means of the provided functions, one can train an agent with the input of an image or various physical sensor measurement, or evaluate the perception algorithm on this simulator. The trained reinforcement learning controller outperforms the linear quadratic regulator (LQR) controller and model predictive control (MPC) controller on different tracks. The experiments demonstrate that the perception module shows promising performance and the controller is capable of controlling the vehicle drive well along the track center with visual input.

cs.LG↗
arXiv2018

FlipTracker: Understanding Natural Error Resilience in HPC Applications

As high-performance computing systems scale in size and computational power, the danger of silent errors, i.e., errors that can bypass hardware detection mechanisms and impact application state, grows dramatically. Consequently, applications running on HPC systems need to exhibit resilience to such errors. Previous work has found that, for certain codes, this resilience can come for free, i.e., some applications are naturally resilient, but few studies have shown the code patterns---combinations or sequences of computations---that make an application naturally resilient. In this paper, we present FlipTracker, a framework designed to extract these patterns using fine-grained tracking of error propagation and resilience properties, and we use it to present a set of computation patterns that are responsible for making representative HPC applications naturally resilient to errors. This not only enables a deeper understanding of resilience properties of these codes, but also can guide future application designs towards patterns with natural resilience.

cs.DC↗