arXiv subjects

Long Ma

Publications and source records attributed to Long Ma.

At least 145 records · Page 8Linked to original sources

arXiv2021

Markov chains and hitting times for error accumulation in quantum circuits

We study a classical model for the accumulation of errors in multi-qubit quantum computations. By modeling the error process in a quantum computation using two coupled Markov chains, we are able to capture a weak form of time-dependency between errors in the past and future. By subsequently using techniques from the field of discrete probability theory, we calculate the probability that error quantities such as the fidelity and trace distance exceed a threshold analytically. The formulae cover fairly generic error distributions, cover multi-qubit scenarios, and are applicable to e.g. the randomized benchmarking protocol. To combat the numerical challenge that may occur when evaluating our expressions, we additionally provide an analytical bound on the error probabilities that is of lower numerical complexity. Besides this, we study a model describing continuous errors accumulating in a single qubit. Finally, taking inspiration from the field of operations research, we illustrate how our expressions can be used to e.g. decide how many gates one can apply before too many errors accumulate with high probability, and how one can lower the rate of error accumulation in existing circuits through simulated annealing.

quant-ph↗

arXiv2021

High-Performance Nanofluidic Osmotic Power Generation Enabled by Exterior Surface Charges under the Natural Salt Gradient

High-performance osmotic energy conversion (OEC) requires both high ionic selectivity and permeability in nanopores. Here, through systematical explorations of influences from individual charged nanopore surfaces on the performance of OEC, we find that the charged exterior surface on the low-concentration side (surfaceL) is essential to achieve high-performance osmotic power generation, which can significantly improve the ionic selectivity and permeability simultaneously. Detailed investigation of ionic transport indicates that electric double layers near charged surfaces provide high-speed passages for counterions. The charged surfaceL enhances cation diffusion through enlarging the effective diffusive area, and inhibits anion transport by electrostatic repulsion. Different areas of charged exterior surfaces have been considered to mimic membranes with different porosities in practical applications. Through adjusting the width of the charged ring region on the surfaceL, electric power in single nanopores increases from 0.3 to 3.4 pW with a plateau at the width of ~200 nm. The power density increases from 4200 to 4900 W/m2 and then decreases monotonously that reaches the commercial benchmark at the charged width of ~480 nm. While, energy conversion efficiency can be promoted from 4% to 26%. Our results provide useful guide in the design of nanoporous membranes for high-performance osmotic energy harvesting.

physics.chem-ph↗

arXiv2021

Tiny Transducer: A Highly-efficient Speech Recognition Model on Edge Devices

This paper proposes an extremely lightweight phone-based transducer model with a tiny decoding graph on edge devices. First, a phone synchronous decoding (PSD) algorithm based on blank label skipping is first used to speed up the transducer decoding process. Then, to decrease the deletion errors introduced by the high blank score, a blank label deweighting approach is proposed. To reduce parameters and computation, deep feedforward sequential memory network (DFSMN) layers are used in the transducer encoder, and a CNN-based stateless predictor is adopted. SVD technology compresses the model further. WFST-based decoding graph takes the context-independent (CI) phone posteriors as input and allows us to flexibly bias user-specific information. Finally, with only 0.9M parameters after SVD, our system could give a relative 9.1% - 20.5% improvement compared with a bigger conventional hybrid system on edge devices.

eess.AS↗

arXiv2021

Parton collisional effect on the conversion of geometry eccentricities into momentum anisotropies in relativistic heavy-ion collisions

We explore parton collisional effects on the conversion of geometry eccentricities into azimuthal anisotropies in Pb+Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV using a multi-phase transport model. The initial eccentricity $\varepsilon_{n}$ (n = 2,3) and flow harmonics $v_{n}$ (n = 2,3) are investigated as a function of the number of parton collisions ($N_{coll}$) during the source evolution of partonic phase. It is found that partonic collisions leads to generate elliptic flow $v_{2}$ and triangular flow $v_{3}$ in Pb+Pb collisions. On the other hand, partonic collisions also result in an evolution of the eccentricity of geometry. The collisional effect on the flow conversion efficiency is therefore studied. We find that the partons with larger $N_{coll}$ show a lower flow conversion efficiency, which reflect differential behaviors with respect to $N_{coll}$. It provides an additional insight into the dynamics of the space-momentum transformation during the QGP evolution from a transport model point of view.

nucl-th↗

arXiv2020

Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement

Low-light image enhancement plays very important roles in low-level vision field. Recent works have built a large variety of deep learning models to address this task. However, these approaches mostly rely on significant architecture engineering and suffer from high computational burden. In this paper, we propose a new method, named Retinex-inspired Unrolling with Architecture Search (RUAS), to construct lightweight yet effective enhancement network for low-light images in real-world scenario. Specifically, building upon Retinex rule, RUAS first establishes models to characterize the intrinsic underexposed structure of low-light images and unroll their optimization processes to construct our holistic propagation structure. Then by designing a cooperative reference-free learning strategy to discover low-light prior architectures from a compact search space, RUAS is able to obtain a top-performing image enhancement network, which is with fast speed and requires few computational resources. Extensive experiments verify the superiority of our RUAS framework against recently proposed state-of-the-art methods.

cs.CV↗

arXiv2020

Modulation of Ionic Current Rectification in Ultra-Short Conical Nanopores

Nanopores that exhibit ionic current rectification (ICR) behave like diodes, such that they transport ions more efficiently in one direction than the other. Conical nanopores have been shown to rectify ionic current, but only those with at least 500 nm in length exhibit significant ICR. Here, through the finite element method, we show how ICR of conical nanopores with length below 200 nm can be tuned by controlling individual charged surfaces i.e. inner pore surface (surface_inner), and exterior pore surfaces on the tip and base side (surface_tip and surface_base). The charged surface_inner and surface_tip can induce obvious ICR individually, while the effects of the charged surface_base on ICR can be ignored. The fully charged surface_inner alone could render the nanopore counterion-selective and induces significant ion concentration polarization in the tip region, which causes reverse ICR compared to nanopores with all surface charged. In addition, the direction and degree of rectification can be further tuned by the depth of the charged surface_inner. When considering the exterior membrane surface only, the charged surface_tip causes intra-pore ionic enrichment and depletion under opposite biases which results in significant ICR. Its effective region is within ~40 nm beyond the tip orifice. We also found that individual charged parts of the pore system contributed to ICR in an additive way due to the additive effect on the ion concentration regulation along the pore axis. With various combinations of fully/partially charged surface_inner and surface_tip, diverse ICR ratios from ~2 to ~170 can be achieved. Our findings shed light on the mechanism of ionic current rectification in ultra-short conical nanopores, and provide a useful guide to the design and modification of ultra-short conical nanopores in ionic circuits and nanofluidic sensors.

physics.chem-ph↗

arXiv2020

NMR study of the spin correlations in the $S=1$ armchair chain Ni$_2$NbBO$_6$

We report our nuclear magnetic resonance (NMR) study on the structurally spin chain compound Ni$_2$NbBO$_6$ with complex magnetic coupling. The antiferromagnetic transition is monitored by the line splitting resulting from the staggered internal hyperfine field. The magnetic coupling configuration proposed by the first-principle density functional theory (DFT) is supported by our NMR spectral analysis. For the spin dynamics, a prominent peak at $T\sim35$ K well above the Néel temperature ($T_N\sim20$ K at $μ_0H=10$ T) is observed from the spin-lattice relaxation data. As compared with the dc-susceptibility, this behavior indicates a antiferromagnetic coupling with the typical energy scale of $\sim3$ meV. Thus, the Ni$_2$NbBO$_6$ compound can be viewed as strongly ferromagnetically coupled armchair spin chains along the crystalline $b$-axis. These facts place strong constraints to the theoretical model for this compound.

cond-mat.str-el↗

arXiv2020

NMR study of the spin excitations in the frustrated antiferromagnet Yb(BaBO$_3$)$_3$ with a triangular lattice

In this paper, we study the spin excitation properties of the frustrated triangular-lattice antiferromagnet Yb(BaBO$_3$)$_3$ with nuclear magnetic resonance. From the spectral analysis, neither magnetic ordering nor spin freezing is observed with temperature down to $T=0.26$ K, far below its Curie-Weiss temperature $|θ_w|\sim2.3$ K. From the nuclear relaxation measurement, precise temperature-independent spin-lattice relaxation rates are observed at low temperatures under a weak magnetic field, indicating the gapless spin excitations. Further increasing the field intensity, we observe a spin excitation gap with the gap size proportional to the field intensity. These phenomena suggest a very unusual strongly correlated quantum disordered phase, and the implications for the quantum spin liquid state are further discussed.

cond-mat.str-el↗

arXiv2020

Multi-head Monotonic Chunkwise Attention For Online Speech Recognition

The attention mechanism of the Listen, Attend and Spell (LAS) model requires the whole input sequence to calculate the attention context and thus is not suitable for online speech recognition. To deal with this problem, we propose multi-head monotonic chunk-wise attention (MTH-MoChA), an improved version of MoChA. MTH-MoChA splits the input sequence into small chunks and computes multi-head attentions over the chunks. We also explore useful training strategies such as LSTM pooling, minimum world error rate training and SpecAugment to further improve the performance of MTH-MoChA. Experiments on AISHELL-1 data show that the proposed model, along with the training strategies, improve the character error rate (CER) of MoChA from 8.96% to 7.68% on test set. On another 18000 hours in-car speech data set, MTH-MoChA obtains 7.28% CER, which is significantly better than a state-of-the-art hybrid system.

cs.CL↗

arXiv2020

Generation of the Squeezed State with an Arbitrary Complex Amplitude Distribution

The squeezed state is important in quantum metrology and quantum information. The most effective generation tool known is the optical parametric oscillator (OPO). Currently, only the squeezed states of lower-order spatial modes can be generated by an OPO. However, the squeezed states of higher-order complex spatial modes are more useful for applications such as quantum metrology, quantum imaging and quantum information. A major challenge for future applications is efficient generation. Here, we use cascaded phase-only spatial light modulators to modulate the amplitude and phase of the incident fundamental mode squeezed state. This efficiently generates a series of squeezed higher-order Hermite-Gauss modes and a squeezed arbitrary complex amplitude distributed mode. The method may yield new applications in biophotonics, quantum metrology and quantum information processing.

quant-ph↗

arXiv2020

Network-Based Prediction of the 2019-nCoV Epidemic Outbreak in the Chinese Province Hubei

At the moment of writing (12 February, 2020), the future evolution of the 2019-nCoV virus is unclear. Predictions of the further course of the epidemic are decisive to deploy targeted disease control measures. We consider a network-based model to describe the 2019-nCoV epidemic in the Hubei province. The network is composed of the cities in Hubei and their interactions (e.g., traffic flow). However, the precise interactions between cities is unknown and must be inferred from observing the epidemic. We propose a network-based method to predict the future prevalence of the 2019-nCoV virus in every city. Our results indicate that network-based modelling is beneficial for an accurate forecast of the epidemic outbreak.

physics.soc-ph↗

arXiv2019

Novel magnetic field tuning of quantum spin excitations in a weakly coupled $S=$1/2 Heisenberg spin chain as seen from NMR

We report our NMR study of the spin excitations in the quasi-one dimensional (1D) $S=1/2$ quantum magnet CH$_3$NH$_3$Cu(HCOO)$_3$ lying in the 1D-3D dimensional crossover regime with Dzyaloshinskii-Moriya(DM) interactions. Above $T_N$, the spinon excitation is observed from the constant $1/T_1$ at low temperatures contributed from the staggered spin susceptibility. At low temperatures well below $T_N$, the $1/T_1$ begins to flatten out under weak magnetic fields. With the increasing field intensity, $1/T_1$ tends to show a power-law temperature dependence gradually, with the index increasing from zero to $\sim3$, and finally $\sim5$, which are the respective typical characteristic for the dominating two-magnon Raman process and three-magnon scattering contributions to the nuclear relaxation in a conventional 3D magnet. A possible physical mechanism for this novel magnetic field tuning of quantum spin excitations related with the enhanced effective staggered field created by the DM interactions under magnetic field is discussed.

cond-mat.str-el↗

arXiv2019

Underexposed Image Correction via Hybrid Priors Navigated Deep Propagation

Enhancing visual qualities for underexposed images is an extensively concerned task that plays important roles in various areas of multimedia and computer vision. Most existing methods often fail to generate high-quality results with appropriate luminance and abundant details. To address these issues, we in this work develop a novel framework, integrating both knowledge from physical principles and implicit distributions from data to solve the underexposed image correction task. More concretely, we propose a new perspective to formulate this task as an energy-inspired model with advanced hybrid priors. A propagation procedure navigated by the hybrid priors is well designed for simultaneously propagating the reflectance and illumination toward desired results. We conduct extensive experiments to verify the necessity of integrating both underlying principles (i.e., with knowledge) and distributions (i.e., from data) as navigated deep propagation. Plenty of experimental results of underexposed image correction demonstrate that our proposed method performs favorably against the state-of-the-art methods on both subjective and objective assessments. Additionally, we execute the task of face detection to further verify the naturalness and practical value of underexposed image correction. What's more, we employ our method to single image haze removal whose experimental results further demonstrate its superiorities.

cs.CV↗

arXiv2018

Task Embedded Coordinate Update: A Realizable Framework for Multivariate Non-convex Optimization

We in this paper propose a realizable framework TECU, which embeds task-specific strategies into update schemes of coordinate descent, for optimizing multivariate non-convex problems with coupled objective functions. On one hand, TECU is capable of improving algorithm efficiencies through embedding productive numerical algorithms, for optimizing univariate sub-problems with nice properties. From the other side, it also augments probabilities to receive desired results, by embedding advanced techniques in optimizations of realistic tasks. Integrating both numerical algorithms and advanced techniques together, TECU is proposed in a unified framework for solving a class of non-convex problems. Although the task embedded strategies bring inaccuracies in sub-problem optimizations, we provide a realizable criterion to control the errors, meanwhile, to ensure robust performances with rigid theoretical analyses. By respectively embedding ADMM and a residual-type CNN in our algorithm framework, the experimental results verify both efficiency and effectiveness of embedding task-oriented strategies in coordinate descent for solving practical problems.

cs.LG↗

arXiv2018

Learning Converged Propagations with Deep Prior Ensemble for Image Enhancement

Enhancing visual qualities of images plays very important roles in various vision and learning applications. In the past few years, both knowledge-driven maximum a posterior (MAP) with prior modelings and fully data-dependent convolutional neural network (CNN) techniques have been investigated to address specific enhancement tasks. In this paper, by exploiting the advantages of these two types of mechanisms within a complementary propagation perspective, we propose a unified framework, named deep prior ensemble (DPE), for solving various image enhancement tasks. Specifically, we first establish the basic propagation scheme based on the fundamental image modeling cues and then introduce residual CNNs to help predicting the propagation direction at each stage. By designing prior projections to perform feedback control, we theoretically prove that even with experience-inspired CNNs, DPE is definitely converged and the output will always satisfy our fundamental task constraints. The main advantage against conventional optimization-based MAP approaches is that our descent directions are learned from collected training data, thus are much more robust to unwanted local minimums. While, compared with existing CNN type networks, which are often designed in heuristic manners without theoretical guarantees, DPE is able to gain advantages from rich task cues investigated on the bases of domain knowledges. Therefore, DPE actually provides a generic ensemble methodology to integrate both knowledge and data-based cues for different image enhancement tasks. More importantly, our theoretical investigations verify that the feedforward propagations of DPE are properly controlled toward our desired solution. Experimental results demonstrate that the proposed DPE outperforms state-of-the-arts on a variety of image enhancement tasks in terms of both quantitative measure and visual perception quality.

cs.CV↗

arXiv2017

Measurement of $D^{*}$-meson triggered correlations in p+p collisions at RHIC

We report the preliminary results of the azimuthal correlations between $D^{*\pm}$ mesons and charged hadrons ($D^{*}$-h) measured by the STAR experiment in proton+proton collisions at $\sqrt{s}$ = 500 GeV. Results at mid-rapidity in the transverse-momentum range 8 $\le$ $p^{D^{*}}_{T}$ $\le$ 20 GeV/$c$ are compared with light hadron triggered correlations (h-h) and PYTHIA predictions. We also present an exploratory study of azimuthal correlations between $D^{*+}$ and $D^{*-}$ mesons in p+p collisions. The prospects of measuring heavy-flavor triggered correlations in heavy-ion collisions at RHIC energies are also discussed.

nucl-ex↗

arXiv2017

Higher Order Mode Entanglement in a Type II Optical Parametric Oscillator

Nonclassical beams in high order spatial modes have attracted much interest but they exhibit much less squeezing and entanglement than the fundamental spatial modes, limiting their applications. We experimentally demonstrate the relation between pump modes and entanglement of first-order HG modes (HG10 entangled states) in a type II OPO and show that the maximum entanglement of high order spatial modes can be obtained by optimizing the pump spatial mode. To our knowledge, this is the first time to report this. Utilizing the optimal pump mode, the HG10 mode threshold can be reached easily without HG00 oscillation and HG10 entanglement is enhanced by 53.5% over HG00 pumping. The technique is broadly applicable to entanglement generation in high order modes.

quant-ph↗

arXiv2016

Symplectic Geometric Algorithm for Quaternion Kinematical Differential Equation

Solving quaternion kinematical differential equations is one of the most significant problems in the automation, navigation, aerospace and aeronautics literatures. Most existing approaches for this problem neither preserve the norm of quaternions nor avoid errors accumulated in the sense of long term time. We present symplectic geometric algorithms to deal with the quaternion kinematical differential equation by modeling its time-invariant and time-varying versions with Hamiltonian systems by adopting a three-step strategy. Firstly, a generalized Euler's formula for the autonomous quaternion kinematical differential equation are proved and used to construct symplectic single-step transition operators via the centered implicit Euler scheme for autonomous Hamiltonian system. Secondly, the symplecitiy, orthogonality and invertibility of the symplectic transition operators are proved rigorously. Finally, the main results obtained are generalized to design symplectic geometric algorithm for the time-varying quaternion kinematical differential equation which is a non-autonomous and nonlinear Hamiltonian system essentially. Our novel algorithms have simple algorithmic structures and low time complexity of computation, which are easy to be implemented with real-time techniques. The correctness and efficiencies of the proposed algorithms are verified and validated via numerical simulations.

eess.SY↗