arXiv subjects

Wenjie Liu

Publications and source records attributed to Wenjie Liu.

At least 109 records · Page 6Linked to original sources

arXiv2022

FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition

Error correction is widely used in automatic speech recognition (ASR) to post-process the generated sentence, and can further reduce the word error rate (WER). Although multiple candidates are generated by an ASR system through beam search, current error correction approaches can only correct one sentence at a time, failing to leverage the voting effect from multiple candidates to better detect and correct error tokens. In this work, we propose FastCorrect 2, an error correction model that takes multiple ASR candidates as input for better correction accuracy. FastCorrect 2 adopts non-autoregressive generation for fast inference, which consists of an encoder that processes multiple source sentences and a decoder that generates the target sentence in parallel from the adjusted source sentence, where the adjustment is based on the predicted duration of each source token. However, there are some issues when handling multiple source sentences. First, it is non-trivial to leverage the voting effect from multiple source sentences since they usually vary in length. Thus, we propose a novel alignment algorithm to maximize the degree of token alignment among multiple sentences in terms of token and pronunciation similarity. Second, the decoder can only take one adjusted source sentence as input, while there are multiple source sentences. Thus, we develop a candidate predictor to detect the most suitable candidate for the decoder. Experiments on our inhouse dataset and AISHELL-1 show that FastCorrect 2 can further reduce the WER over the previous correction model with single candidate by 3.2% and 2.6%, demonstrating the effectiveness of leveraging multiple candidates in ASR error correction. FastCorrect 2 achieves better performance than the cascaded re-scoring and correction pipeline and can serve as a unified post-processing module for ASR.

cs.CL↗

arXiv2022

Mask the Correct Tokens: An Embarrassingly Simple Approach for Error Correction

Text error correction aims to correct the errors in text sequences such as those typed by humans or generated by speech recognition models. Previous error correction methods usually take the source (incorrect) sentence as encoder input and generate the target (correct) sentence through the decoder. Since the error rate of the incorrect sentence is usually low (e.g., 10\%), the correction model can only learn to correct on limited error tokens but trivially copy on most tokens (correct tokens), which harms the effective training of error correction. In this paper, we argue that the correct tokens should be better utilized to facilitate effective training and then propose a simple yet effective masking strategy to achieve this goal. Specifically, we randomly mask out a part of the correct tokens in the source sentence and let the model learn to not only correct the original error tokens but also predict the masked tokens based on their context information. Our method enjoys several advantages: 1) it alleviates trivial copy; 2) it leverages effective training signals from correct tokens; 3) it is a plug-and-play module and can be applied to different models and tasks. Experiments on spelling error correction and speech recognition error correction on Mandarin datasets and grammar error correction on English datasets with both autoregressive and non-autoregressive generation models show that our method improves the correction accuracy consistently.

cs.CL↗

arXiv2022

A Novel Sequential Coreset Method for Gradient Descent Algorithms

A wide range of optimization problems arising in machine learning can be solved by gradient descent algorithms, and a central question in this area is how to efficiently compress a large-scale dataset so as to reduce the computational complexity. {\em Coreset} is a popular data compression technique that has been extensively studied before. However, most of existing coreset methods are problem-dependent and cannot be used as a general tool for a broader range of applications. A key obstacle is that they often rely on the pseudo-dimension and total sensitivity bound that can be very high or hard to obtain. In this paper, based on the ''locality'' property of gradient descent algorithms, we propose a new framework, termed ''sequential coreset'', which effectively avoids these obstacles. Moreover, our method is particularly suitable for sparse optimization whence the coreset size can be further reduced to be only poly-logarithmically dependent on the dimension. In practice, the experimental results suggest that our method can save a large amount of running time compared with the baseline algorithms.

cs.LG↗

arXiv2022

UAV-aided Radio Map Construction Exploiting Environment Semantics

This paper constructs a full dimensional (6D) radio map to predict the channel gain between any transmitter location and any receiver location based on received signal strength (RSS) measurements between low-altitude aerial nodes and ground nodes. The main challenge is how to describe the signal strength attenuation due to the blockage from the environment. Conventional interpolation-type approaches fail to exploit the close relation between the radio map and the geometry of the environment. This paper proposes to construct radio maps by first estimating and constructing a multi-class 3D virtual obstacle map that describes the geometry of the environment with radio semantics. Mathematically, a least-squares problem is formulated to jointly estimate the virtual obstacle map and the propagation parameters. This problem is found to have a partial quasiconvexity that leads to the development of an efficient parameter estimation and radio map construction algorithm. Numerical experiments confirm that the proposed method substantially reduces the amount of measurement required for achieving the same radio map accuracy. It is also demonstrated that in a unmanned aerial vehicle (UAV)-aided relay communication scenario, a radio map assisted approach for UAV placement can achieve more than 50% capacity gain.

eess.SP↗

arXiv2022

Correlated topological pumping of interacting bosons assisted by Bloch oscillations

Thouless pumping, not only achieving quantized transport but also immune to moderate disorder, has attracted growing attention in both experiments and theories. Here, we explore how particle-particle interactions affect topological transport in a periodically-modulated and tilted optical lattice. Not limited to wannier states, our scheme ensures a dispersionless quantized transport even for initial Gaussian-like wave packets of interacting bosons which do not uniformly occupy a given band. This is because the tilting potential leads to Bloch oscillations uniformly sampling the Berry curvatures over the entire Brillouin zone. The interplay among on-site potential difference, tunneling rate and interactions contributes to the topological transport of bound and scattering states and the topologically resonant tunnelings. Our study deepens the understanding of correlation effects on topological states, and provides a feasible way for detecting topological properties in interacting systems.

quant-ph↗

arXiv2022

Automatically Finding Optimal Index Structure

Existing learned indexes (e.g., RMI, ALEX, PGM) optimize the internal regressor of each node, not the overall structure such as index height, the size of each layer, etc. In this paper, we share our recent findings that we can achieve significantly faster lookup speed by optimizing the structure as well as internal regressors. Specifically, our approach (called AirIndex) expresses the end-to-end lookup time as a novel objective function, and searches for optimal design decisions using a purpose-built optimizer. In our experiments with state-of-the-art methods, AirIndex achieves 3.3x-7.7x faster lookup for the data stored on local SSD, and 1.4x-3.0x faster lookup for the data on Azure Cloud Storage.

cs.DB↗

arXiv2022

Data-driven Self-triggered Control via Trajectory Prediction

Self-triggered control, a well-documented technique for reducing the communication overhead while ensuring desired system performance, is gaining increasing popularity. However, existing methods for self-triggered control require explicit system models that are assumed perfectly known a priori. An end-to-end control paradigm known as data-driven control learns control laws directly from data, and offers a competing alternative to the routine system identification-then-control method. In this context, the present paper puts forth data-driven self-triggered control schemes for unknown linear systems using data collected offline. Specifically, for output feedback control systems, a data-driven model predictive control (MPC) scheme is proposed, which computes a sequence of control inputs while generating a predicted system trajectory. A data-driven self-triggering law is designed using the predicted trajectory, to determine the next triggering time once a new measurement becomes available. For state feedback control systems, instead of capitalizing on MPC to predict the trajectory, a data-fitting problem using the pre-collected input-state data is solved, whose solution is employed to construct the self-triggering mechanism. Both feasibility and stability are established for the proposed self-triggered controllers, which are validated using numerical examples.

eess.SY↗

arXiv2021

Data-Driven Resilient Predictive Control under Denial-of-Service

The study of resilient control of linear time-invariant (LTI) systems against denial-of-service (DoS) attacks is gaining popularity in emerging cyber-physical applications. In previous works, explicit system models are required to design a predictor-based resilient controller. These models can be either given a priori or obtained through a prior system identification step. Recent research efforts have focused on data-driven control based on pre-collected input-output trajectories (i.e., without explicit system models). In this paper, we take an initial step toward data-driven stabilization of stochastic LTI systems under DoS attacks, and develop a resilient model predictive control (MPC) scheme driven purely by data-dependent conditions. The proposed data-driven control method achieves the same level of resilience as the model-based control method. For example, local input-to-state stability (ISS) is achieved under mild assumptions on the noise and the DoS attacks. To recover global ISS, two modifications are further suggested at the price of reduced resilience against DoS attacks or increased computational complexity. Finally, a numerical example is given to validate the effectiveness of the proposed control method.

eess.SY↗

arXiv2021

Heisenberg-limited Frequency Estimation via Driving through Quantum Phase Transitions

High-precision frequency estimation is an ubiquitous issue in fundamental physics and a critical task in spectroscopy. Here, we propose a quantum Ramsey interferometry to realize high-precision frequency estimation in spin-1 Bose-Einstein condensate via driving the system through quantum phase transitions(QPTs). In our scheme, we combine adiabatically driving the system through QPTs with π/2 pulse to realize the initialization and recombination. Through adjusting the laser frequency under fixed evolution time, one can extract the transition frequency via the lock-in point. The lock-in point can be determined from the pattern of the population measurement. In particular, we find the measurement precision of frequency can approach to the Heisenberg-limited scaling. Moreover, the scheme is robust against detection noise and non-adiabatic effect. Our proposed scheme does not require single-particle resolved detection and is within the reach of current experiment techniques. Our study may point out a new way for high-precision frequency estimation.

quant-ph↗

arXiv2021

Edge states in quantum spin chains: the interplay among interaction, gradient magnetic field and Floquet engineering

We explore the edge defects induced by spin-spin interaction in a finite paradigmatic Heisenberg spin chain. By introducing a gradient magnetic field and a periodically-modulated spin-exchange strength, the resonance between modulation frequency and magnetic field gradient can also induce asymmetric defects at the edges, dubbed as Floquet-Wannier-Zeeman edge defects. The interplay between these two types of edge defects allows us to manipulate the magnon edge states from an isolated band into a continuum one. In the high-frequency regime, we analytically derive effective models to interpret the formation mechanisms for edge states by employing the multiscale perturbation analysis. Our study offers new insights to understand and control the magnon edge states governed by the interplay between edge defects induced by the spin-spin interaction and the Floquet-Wannier-Zeeman manipulation.

quant-ph↗

arXiv2021

In Situ Phase-Transition Crystallization of All-Inorganic Water-Resistant Exciton-Radiative Heteroepitaxial CsPbBr3-CsPb2Br5 Core-Shell Perovskite Nanocrystals

The instability of metal halide perovskites upon exposure to moisture or heat strongly hampers their applications in optoelectronic devices. Here, we report the large-yield synthesis of highly water-resistant total-inorganic green luminescent CsPbBr3/CsPb2Br5 core/shell heteronanocrystals (HNCs) by developing an in situ phase transition approach. It is implemented via water-driven phase transition of the original monoclinic CsPbBr3 nanocrystal and the resultant tetragonal CsPb2Br5 nanoshell has small lattice mismatch with the CsPbBr3 core, which ensures formation of an epitaxial interface for the yielded CsPbBr3/CsPb2Br5 HNCs. These HNCs maintain nearly 100% of the original luminescence intensity after immersion in water for eleven months and the luminescence intensity drops only to 81.3% at 100 °C. The transient luminescence spectroscopy and density functional theory calculation reveal that there are double radiative recombination channels in the core CsPbBr3 nanocrystal, and the electron potential barrier provided by the CsPb2Br5 nanoshell significantly improves the exciton recombination rate. A prototype quasi-white light-emitting device based on these robust CsPbBr3/CsPb2Br5 HNCs is realized, showing their strong competence in solid-state lighting and wide color-gamut displays.

cond-mat.mtrl-sci↗

arXiv2021

Resilient Control under Quantization and Denial-of-Service: Co-designing a Deadbeat Controller and Transmission Protocol

This paper is concerned with the problem of stabilizing continuous-time linear time-invariant systems subject to quantization and Denial-of-Service (DoS) attacks. In this context, two DoS-induced challenges emerge with the design of resilient encoding schemes, namely, the coupling between encoding strategies of different signals, and the synchronization between the encoder and decoder. To address these challenges, a novel structure that is equipped with a deadbeat controller as well as a delicate transmission protocol for the input and output channels, co-designed leveraging the controllability index, is put forward. When both input and output channels are subject to DoS attacks and quantization, the proposed structure is shown able to decouple the encoding schemes for input, output, and estimated output signals. This property is further corroborated by designing encoding schemes as well as conditions that ensure exponential stability of the closed-loop system. On the other hand, when only the output channel is subject to network phenomenon, the proposed structure can achieve exponential stabilization without acknowledgment (ACK) signals, in contrast to existing ACK-based results. Finally, a numerical example is given to demonstrate the practical merits of the proposed approach as well as the theory.

eess.SY↗

arXiv2021

Optimal error estimates for Legendre expansions of singular functions with fractional derivatives of bounded variation

We present a new fractional Taylor formula for singular functions whose Caputo fractional derivatives are of bounded variation. It bridges and ``interpolates" the usual Taylor formulas with two consecutive integer orders. This enables us to obtain an analogous formula for the Legendre expansion coefficient of this type of singular functions, and further derive the optimal (weighted) $L^\infty$-estimates and $L^2$-estimates of the Legendre polynomial approximations. This set of results can enrich the existing theory for $p$ and $hp$ methods for singular problems, and answer some open questions posed in some recent literature.

math.NA↗

arXiv2020

Mobility edge of Stark many-body localization

We investigate many-body localization of interacting spinless fermions in a one-dimensional disordered and tilted lattice. The fermions undergo energy-dependent transitions from ergodic to Stark many-body localization driven by the tilted potential, which are manifested by the appearance of mobility edges between delocalized states and Stark many-body localized states even when the disorder is weak. We can concretely diagnose these transitions rather than crossovers by finite-size scaling of energy-level statistics. Moreover, in the Stark many-body localization, the entanglement entropy obeys the area law scaling, in analogy to that in the conventional many-body localization.

quant-ph↗

arXiv2020

Quantum metrology via chaos in a driven Bose-Josephson system

Entanglement preparation and signal accumulation are essential for quantum parameter estimation, which pose significant challenges to both theories and experiments. Here, we propose how to utilize chaotic dynamics in a periodically driven Bose-Josephson system for achieving a high-precision measurement beyond the standard quantum limit (SQL). Starting from an initial non-entangled state, the chaotic dynamics generates quantum entanglement and simultaneously encodes the parameter to be estimated. By using suitable chaotic dynamics, the ultimate measurement precision of the estimated parameter can beat the SQL. The sub-SQL measurement precision scaling can also be obtained via specific observables, such as population measurements, which can be realized with state-of-art techniques. Our study not only provides new insights for understanding quantum chaos and quantum-classical correspondence, but also is of promising applications in entanglement-enhanced quantum metrology.

quant-ph↗

arXiv2020

Floquet-surface bound states in the continuum in a resonantly driven 1D tilted defect-free lattice

We study the Floquet-surface bound states embedded in the continuum (BICs) and bound states out the continuum (BOCs)in a resonantly driven 1D tilted defect-free lattice. In contrast to fragile single-particle BICs assisted by specially tailored potentials, we find that Floquet-surface BICs, stable against structural perturbations, can exist in a wide range of parameter space. By using a multiple-time-scale asymptotic analysis in the high-frequency limit, the appearance of Floquet-surface bound states can be analytically explained by effective Tamm-type defects at boundaries induced by the resonance between the periodic driving and tilt. The phase boundary of existing Floquet-surface states is also analytically given. Based on the repulsion effect of surface states, we propose to detect transition points and measure the number of Floquet-surface bound states by quantum walk. Our work opens a new door to experimental realization of BICs in quantum system.

cond-mat.quant-gas↗

arXiv2020

Modulation-induced long-range magnon bound states in one-dimensional optical lattices

Ultracold two-level atoms in optical lattices offer an excellent experimental platform to explore magnon excitations [Fukuhara et al 2013 Nat. Phys. 9, 235; Fukuhara et al 2013 Nature 502, 76]. Here, we investigate how gradient magnetic field and periodically modulated tunneling strength affect the two-magnon excitations in these ultracold atomic systems. In the resonant condition where the driving frequency matches and smooths the potential bias, the system gains translational invariance in both space and time in the rotating frame, and thus we can develop a Floquet-Bloch band theory for two magnons. We find a new kind of bound states with relative distance no less than two sites, apart from the conventional bound states with relative distance at one site, which indicates the modulation-induced long-range interaction. We analytically derive an effective Hamiltonian via the many-body perturbation theory for a deeper understanding of such novel bound states and explore the interplay between these two types of bound states. Moreover, we propose to probe modulation-induced bound states via quantum walks. Our study not only provides a scheme to form long-range magnon bound states, but also lays a cornerstone for engineering exotic quantum states in multi-particle Floquet systems.

cond-mat.str-el↗

arXiv2020

A Quantum-based Database Query Scheme for Privacy Preservation in Cloud Environment

Cloud computing is a powerful and popular information technology paradigm that enables data service outsourcing and provides higher-level services with minimal management effort. However, it is still a key challenge to protect data privacy when a user accesses the sensitive cloud data. Privacy-preserving database query allows the user to retrieve a data item from the cloud database without revealing the information of the queried data item, meanwhile limiting user's ability to access other ones. In this study, in order to achieve the privacy preservation and reduce the communication complexity, a quantum-based database query scheme for privacy preservation in cloud environment is developed. Specifically, all the data items of the database are firstly encrypted by different keys for protecting server's privacy, and in order to guarantee the clients' privacy, the server is required to transmit all these encrypted data items to the client with the oblivious transfer strategy. Besides, two oracle operations, a modified Grover iteration, and a special offset encryption mechanism are combined together to ensure that the client can correctly query the desirable data item. Finally, performance evaluation is conducted to validate the correctness, privacy, and efficiency of our proposed scheme.

quant-ph↗