arXiv subjects

Peng Xu

Publications and source records attributed to Peng Xu.

At least 307 records · Page 17Linked to original sources

arXiv2021

Turing: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interface

A natural language database interface (NLDB) can democratize data-driven insights for non-technical users. However, existing Text-to-SQL semantic parsers cannot achieve high enough accuracy in the cross-database setting to allow good usability in practice. This work presents Turing, a NLDB system toward bridging this gap. The cross-domain semantic parser of Turing with our novel value prediction method achieves $75.1\%$ execution accuracy, and $78.3\%$ top-5 beam execution accuracy on the Spider validation set. To benefit from the higher beam accuracy, we design an interactive system where the SQL hypotheses in the beam are explained step-by-step in natural language, with their differences highlighted. The user can then compare and judge the hypotheses to select which one reflects their intention if any. The English explanations of SQL queries in Turing are produced by our high-precision natural language generation system based on synchronous grammars.

cs.CL↗

arXiv2021

X2Parser: Cross-Lingual and Cross-Domain Framework for Task-Oriented Compositional Semantic Parsing

Task-oriented compositional semantic parsing (TCSP) handles complex nested user queries and serves as an essential component of virtual assistants. Current TCSP models rely on numerous training data to achieve decent performance but fail to generalize to low-resource target languages or domains. In this paper, we present X2Parser, a transferable Cross-lingual and Cross-domain Parser for TCSP. Unlike previous models that learn to generate the hierarchical representations for nested intents and slots, we propose to predict flattened intents and slots representations separately and cast both prediction tasks into sequence labeling problems. After that, we further propose a fertility-based slot predictor that first learns to dynamically detect the number of labels for each token, and then predicts the slot types. Experimental results illustrate that our model can significantly outperform existing strong baselines in cross-lingual and cross-domain settings, and our model can also achieve a good generalization ability on target languages of target domains. Furthermore, our model tackles the problem in an efficient non-autoregressive way that reduces the latency by up to 66% compared to the generative model.

cs.CL↗

arXiv2021

BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue Modeling

Task-oriented dialogue (ToD) benchmarks provide an important avenue to measure progress and develop better conversational agents. However, existing datasets for end-to-end ToD modeling are limited to a single language, hindering the development of robust end-to-end ToD systems for multilingual countries and regions. Here we introduce BiToD, the first bilingual multi-domain dataset for end-to-end task-oriented dialogue modeling. BiToD contains over 7k multi-domain dialogues (144k utterances) with a large and realistic bilingual knowledge base. It serves as an effective benchmark for evaluating bilingual ToD systems and cross-lingual transfer learning approaches. We provide state-of-the-art baselines under three evaluation settings (monolingual, bilingual, and cross-lingual). The analysis of our baselines in different settings highlights 1) the effectiveness of training a bilingual ToD system compared to two independent monolingual ToD systems, and 2) the potential of leveraging a bilingual knowledge base and cross-lingual transfer learning to improve the system performance under low resource condition.

cs.CL↗

arXiv2021

Optimizing Deeper Transformers on Small Datasets

It is a common belief that training deep transformers from scratch requires large datasets. Consequently, for small datasets, people usually use shallow and simple additional layers on top of pre-trained models during fine-tuning. This work shows that this does not always need to be the case: with proper initialization and optimization, the benefits of very deep transformers can carry over to challenging tasks with small datasets, including Text-to-SQL semantic parsing and logical reading comprehension. In particular, we successfully train $48$ layers of transformers, comprising $24$ fine-tuned layers from pre-trained RoBERTa and $24$ relation-aware layers trained from scratch. With fewer training steps and no task-specific pre-training, we obtain the state-of-the-art performance on the challenging cross-domain Text-to-SQL parsing benchmark Spider. We achieve this by deriving a novel Data-dependent Transformer Fixed-update initialization scheme (DT-Fixup), inspired by the prior T-Fixup work. Further error analysis shows that increasing depth can help improve generalization on small datasets for hard cases that require reasoning and structural understanding.

cs.CL↗

arXiv2021

Alternative LISA-TAIJI networks

The space-borne gravitational wave (GW) detectors, LISA and TAIJI, are planned to be launched in the 2030s. The dual detectors with comparable sensitivities will form a network observing GW with significant advantages. In this work, we investigate the three possible LISA-TAIJI networks for the different location and orientation compositions of LISA orbit ($+60^\circ$ inclination and trailing the Earth by $20^\circ$) and alternative TAIJI orbit configurations including TAIJIp ($+60^\circ$ inclination and leading the Earth by $20^\circ$), TAIJIc ($+60^\circ$ inclination and co-located with LISA), TAIJIm ($-60^\circ$ inclination and leading the Earth by $20^\circ$). In the three LISA-TAIJI configurations, the LISA-TAIJIm network shows the best performance on the sky localization and polarization determination for the massive binary system due to their better complementary antenna pattern, and LISA-TAIJIc could achieve the best cross-correlation and observe the stochastic GW background with an optimal sensitivity.

gr-qc↗

arXiv2021

Effect of the selective localization of carbon nanotubes and phase domain in immiscible blends on tunable microwave dielectric properties

In recent years, the immiscible polymer blend system has attracted much attention as the matrix of nanocomposites. Herein, from the perspective of dynamics, the control of the carbon nanotubes (CNTs) migration aided with the interface of polystyrene (PS) and poly(methyl methacrylate) (PMMA) blends was achieved through a facile melt mixing method. Thus, we revealed a comprehensive relationship between several typical CNTs migrating scenarios and the microwave dielectric properties of their nanocomposites. Based on the unique morphologies and phase domain structures of the immiscible matrix, we further investigated the multiple microwave dielectric relaxation processes and shed new light on the relation between relaxation peak position and the phase domain size distribution. Moreover, by integrating the CNTs interface localization control with the matrix co-continuous structure construction, we found that the interface promotes double percolation effect to achieve conductive percolation at low CNTs loading (~1.06 vol%). Overall, the present study provides a unique nanocomposite material design symphonizing both functional fillers dispersion and location as well as the matrix architecture optimization for microwave applications.

physics.app-ph↗

arXiv2021

Multi-Graph Transformer for Free-Hand Sketch Recognition

Learning meaningful representations of free-hand sketches remains a challenging task given the signal sparsity and the high-level abstraction of sketches. Existing techniques have focused on exploiting either the static nature of sketches with Convolutional Neural Networks (CNNs) or the temporal sequential property with Recurrent Neural Networks (RNNs). In this work, we propose a new representation of sketches as multiple sparsely connected graphs. We design a novel Graph Neural Network (GNN), the Multi-Graph Transformer (MGT), for learning representations of sketches from multiple graphs which simultaneously capture global and local geometric stroke structures, as well as temporal information. We report extensive numerical experiments on a sketch recognition task to demonstrate the performance of the proposed approach. Particularly, MGT applied on 414k sketches from Google QuickDraw: (i) achieves small recognition gap to the CNN-based performance upper bound (72.80% vs. 74.22%), and (ii) outperforms all RNN-based models by a significant margin. To the best of our knowledge, this is the first work proposing to represent sketches as graphs and apply GNNs for sketch recognition. Code and trained models are available at https://github.com/PengBoXiangShang/multigraph_transformer.

cs.CV↗

arXiv2021

Revealing and controlling nuclear dynamics following inner-shell photoionization of N2

In this work, we apply the Monte Carlo wave packet method to study the ultrafast nuclear dynamics following inner-shell photoionization of N2 exposed to an ultrashort intense X-ray pulse. The photon energy of the X-ray pulse is large enough to remove a 1s electron from the N atom in N2. The intermediate state in N+2 is highly excited so that autoionization takes place from this state to the dissociative or non-dissociative electronic states of ungerade and gerade symmetries in N++2. The possible vibrational resonances allowed by the non-dissociative states prevents a direct extraction of the nuclear kinetic release (KER) spectrum from the nuclear wave packets in N++2. Therefore, we propose a hybrid technique by combining the advantages of two energy analysis strategies to obtain the final nuclear KER spectrum of the process. A femtosecond IR probe pulse, which couples the electronic states in N++2 together, is applied to achieve a time-resolved imaging and controlling of the ultrafast dynamics that takes place during double ionization of N2. The influence of the laser parameters including the peak intensity, pulse duration and pump-probe delay, on the nuclear dynamics is also investigated.

physics.atom-ph↗

arXiv2021

On the criteria of large cavitation bubbles in a tube during a transient process

Extreme cavitation scenarios such as water column separations in hydraulic systems during transient processes caused by large cavitation bubbles can lead to catastrophic destruction. In the present paper, we study the onset criteria and dynamics of large cavitation bubbles in a tube. A new cavitation number $Ca_2 = {l^*}^{-1} Ca_0$ is proposed to describe the maximum length $L_{\max}$ of the cavitation bubble, where $l^*$ is a non-dimensional length of the water column indicating its slenderness, and $Ca_0$ is the classic cavitation number. Combined with the onset criteria for acceleration-induced cavitation ($Ca_1<1$, Pan et al. (2017)), we show that the occurrence of large cylindrical cavitation bubbles requires both $Ca_2<1$ and $Ca_1<1$ simultaneously. We also establish a Rayleigh-type model for the dynamics of large cavitation bubbles in a tube. The bubbles collapse at a finite end speed, and the time from the maximum bubble size to collapse is $T_c=\sqrt{2}\sqrt{lL_{\max}}\sqrt{\fracρ{p_\infty}}$, where $l$ is the length of the water column, $L_{\max}$ is the maximum bubble length, $ρ$ is the liquid density, and $p_{\infty}$ is the reference pressure in the far field. The analytical results are validated against systematic experiments using a modified 'tube-arrest' apparatus, which can decouple acceleration and velocity. The results in the current work can guide design and operation of hydraulic systems encountering transient processes.

physics.flu-dyn↗

arXiv2021

The Twelvefold Way of Non-Sequential Lossless Compression

Many information sources are not just sequences of distinguishable symbols but rather have invariances governed by alternative counting paradigms such as permutations, combinations, and partitions. We consider an entire classification of these invariances called the twelvefold way in enumerative combinatorics and develop a method to characterize lossless compression limits. Explicit computations for all twelve settings are carried out for i.i.d. uniform and Bernoulli distributions. Comparisons among settings provide quantitative insight.

cs.IT↗

arXiv2020

Influence of ground-Rydberg coherence in two-qubit gate based on Rydberg blockade

For neutral atom qubits, the two-qubit gate is typically realized via the Rydberg blockade effect, which hints about the special status of the Rydberg level besides the regular qubit register states. Here, we carry out experimental and theoretical studies to reveal how the ground-Rydberg coherence of the control qubit atom affects the process of two-qubit Controlled-Z ($C_Z$) gate, such as the commonly used ground-Rydberg $π$-gap-$π$ pulse sequence originally proposed in Phys. Rev. Lett. \textbf{85}, 2208 (2000). We measure the decoherence of the control qubit atom after the $π$-gap-$π$ pulses and make a direct comparison with the typical decoherence time $τ_{gr}$ extracted from Ramsey fringes of the ground-Rydberg transition. In particular, we observe that the control qubit atom subject to such pulse sequences experiences a process which is essentially similar to the ground-Rydberg Ramsey interference. Furthermore, we build a straightforward theoretical model to link the decoherence process of control qubit subject to $C_Z$ gate $π$-gap-$π$ pulse sequence and the $τ_{gr}$, and also analyze the typical origins of decoherence effects. Finally, we discuss the $C_Z$ gate fidelity loss due to the limits imposed by the ground-Rydberg coherence properties and prospective for improving fidelity with new gate protocols.

quant-ph↗

arXiv2020

Unsupervised Regionalization of Particle-resolved Aerosol Mixing State Indices on the Global Scale

The aerosol mixing state significantly affects the climate and health impacts of atmospheric aerosol particles. Simplified aerosol mixing state assumptions, common in Earth System models, can introduce errors in the prediction of these aerosol impacts. The aerosol mixing state index, a metric to quantify aerosol mixing state, is a convenient measure for quantifying these errors. Global estimates of aerosol mixing state indices have recently become available via supervised learning models, but require regionalization to ease spatiotemporal analysis. Here we developed a simple but effective unsupervised learning approach to regionalize predictions of global aerosol mixing state indices. We used the monthly average of aerosol mixing state indices global distribution as the input data. Grid cells were then clustered into regions by the k-means algorithm without explicit spatial information as input. This approach resulted in eleven regions over the globe with specific spatial aggregation patterns. Each region exhibited a unique distribution of mixing state indices and aerosol compositions, showing the effectiveness of the unsupervised regionalization approach. This study defines "aerosol mixing state zones" that could be useful for atmospheric science research.

cs.LG↗

arXiv2020

Efficient preparation of 2D defect-free atom arrays with near-fewest sorting-atom moves

Sorting atoms stochastically loaded in optical tweezer arrays via an auxiliary mobile tweezer is an efficient approach to preparing intermediate-scale defect-free atom arrays in arbitrary geometries. However, high filling fraction of atom-by-atom assemblers is impeded by redundant sorting moves with imperfect atom transport, especially for scaling the system size to larger atom numbers. Here, we propose a new sorting algorithm (heuristic cluster algorithm, HCA) which provides near-fewest moves in our tailored atom assembler scheme and experimentally demonstrate a $5\times6$ defect-free atom array with 98.4(7)$\%$ filling fraction for one rearrangement cycle. The feature of HCA that the number of moves $N_{m}\approx N$ ($N$ is the number of defect sites to be filled) makes the filling fraction uniform as the size of atom assembler enlarged. Our method is essential to scale hundreds of assembled atoms for bottom-up quantum computation, quantum simulation and precision measurement.

quant-ph↗

arXiv2020

Ergodic Secrecy Rate of RIS-Assisted Communication Systems in the Presence of Discrete Phase Shifts and Multiple Eavesdroppers

This letter investigates the ergodic secrecy rate (ESR) of a reconfigurable intelligent surface (RIS)-assisted communication system in the presence of discrete phase shifts and multiple eavesdroppers (Eves). In particular, a closed-form approximation of the ESR is derived for both non-colluding and colluding Eves. The analytical results are shown to be accurate when the number of reflecting elements of the RIS $N$ is large. Asymptotic analysis is provided to investigate the impact of $N$ on the ESR, and it is proved that the ESR scales with $\log_2 N$ for both non-colluding and colluding Eves. Numerical results are provided to verify the analytical results and the obtained scaling laws.

cs.IT↗

arXiv2020

Zero-Shot Terrain Generalization for Visual Locomotion Policies

Legged robots have unparalleled mobility on unstructured terrains. However, it remains an open challenge to design locomotion controllers that can operate in a large variety of environments. In this paper, we address this challenge of automatically learning locomotion controllers that can generalize to a diverse collection of terrains often encountered in the real world. We frame this challenge as a multi-task reinforcement learning problem and define each task as a type of terrain that the robot needs to traverse. We propose an end-to-end learning approach that makes direct use of the raw exteroceptive inputs gathered from a simulated 3D LiDAR sensor, thus circumventing the need for ground-truth heightmaps or preprocessing of perception information. As a result, the learned controller demonstrates excellent zero-shot generalization capabilities and can navigate 13 different environments, including stairs, rugged land, cluttered offices, and indoor spaces with humans.

cs.RO↗

arXiv2020

Learning to Walk in the Real World with Minimal Human Effort

Reliable and stable locomotion has been one of the most fundamental challenges for legged robots. Deep reinforcement learning (deep RL) has emerged as a promising method for developing such control policies autonomously. In this paper, we develop a system for learning legged locomotion policies with deep RL in the real world with minimal human effort. The key difficulties for on-robot learning systems are automatic data collection and safety. We overcome these two challenges by developing a multi-task learning procedure and a safety-constrained RL framework. We tested our system on the task of learning to walk on three different terrains: flat ground, a soft mattress, and a doormat with crevices. Our system can automatically and efficiently learn locomotion skills on a Minitaur robot with little human intervention. The supplemental video can be found at: \url{https://youtu.be/cwyiq6dCgOc}.

cs.RO↗

arXiv2020

Switchable next-nearest-neighbor coupling for controlled two-qubit operations

In a superconducting quantum processor with nearest neighbor coupling, the dispersive interaction between adjacent qubits can result in an effective next-nearest-neighbor coupling whose strength depends on the state of the intermediary qubit. Here, we theoretically explore the possibility of engineering this next-nearest-neighbor coupling to implement controlled two-qubit operations where the intermediary qubit controls the operation on the next-nearest neighbor pair of qubits. In particular, in a system comprising two types of superconducting qubits with anharmonicities of opposite-sign arranged in an -A-B-A- pattern, where the unwanted static ZZ coupling between adjacent qubits could be heavily suppressed, a switchable coupling between the next-nearest-neighbor qubits can be achieved via the intermediary qubit, the qubit state of which functions as an on/off switch for this coupling. Therefore, depending on the adopted activating scheme, various controlled two-qubit operations such as controlled-iSWAP gate can be realized, potentially enabling circuit depth reductions as to a standard decomposition approach for implementing generic quantum algorithms.

quant-ph↗

arXiv2020

MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models

Existing pre-trained large language models have shown unparalleled generative capabilities. However, they are not controllable. In this paper, we propose MEGATRON-CNTRL, a novel framework that uses large-scale language models and adds control to text generation by incorporating an external knowledge base. Our framework consists of a keyword predictor, a knowledge retriever, a contextual knowledge ranker, and a conditional text generator. As we do not have access to ground-truth supervision for the knowledge ranker, we make use of weak supervision from sentence embedding. The empirical results show that our model generates more fluent, consistent, and coherent stories with less repetition and higher diversity compared to prior work on the ROC story dataset. We showcase the controllability of our model by replacing the keywords used to generate stories and re-running the generation process. Human evaluation results show that 77.5% of these stories are successfully controlled by the new keywords. Furthermore, by scaling our model from 124 million to 8.3 billion parameters we demonstrate that larger models improve both the quality of generation (from 74.5% to 93.0% for consistency) and controllability (from 77.5% to 91.5%).

cs.CL↗