arXiv subjects

Chen Wang

Publications and source records attributed to Chen Wang.

At least 613 records · Page 34Linked to original sources

arXiv2021

Reduction of the electroweak correlation in the PDF updating by using the forward-backward asymmetry of Drell-Yan process

We propose a new observable for the measurement of the forward-backward asymmetry $(A_{FB})$ in Drell-Yan lepton production. At hadron colliders, the $A_{FB}$ distribution is sensitive to both the electroweak (EW) fundamental parameter $\sin^2 θ_{W}$, the weak mixing angle, and the parton distribution functions (PDFs). Hence, the determination of $\sin^2 θ_{W}$ and the updating of PDFs by directly using the same $A_{FB}$ spectrum are strongly correlated. This correlation would introduce large bias or uncertainty into both precise measurements of EW and PDF sectors. In this article, we show that the sensitivity of $A_{FB}$ on $\sin^2 θ_{W}$ is dominated by its average value around the $Z$ pole region, while the shape (or gradient) of the $A_{FB}$ spectrum is insensitive to $\sin^2 θ_{W}$ and contains important information on the PDF modeling. Accordingly, a new observable related to the gradient of the spectrum is introduced, and demonstrated to be able to significantly reduce the potential bias on the determination of $\sin^2 θ_{W}$ when updating the PDFs using the same $A_{FB}$ data.

hep-ph↗

arXiv2021

Preventing Handheld Phone Distraction for Drivers by Sensing the Gripping Hand

Handheld phone distraction is the leading cause of traffic accidents. However, few efforts have been devoted to detecting when the phone distraction happens, which is a critical input for taking immediate safety measures. This work proposes a phone-use monitoring system, which detects the start of the driver's handheld phone use and eliminates the distraction at once. Specifically, the proposed system emits periodic ultrasonic pulses to sense if the phone is being held in hand or placed on support surfaces (e.g., seat and cup holder) by capturing the unique signal interference resulted from the contact object's damping, reflection and refraction. We derive the short-time Fourier transform from the microphone data to describe such impacts and develop a CNN-based binary classifier to discriminate the phone use between the handheld and the handsfree status. Additionally, we design an adaptive window-based filter to correct the classification errors and identify each handheld phone distraction instance, including its start, end, and duration. Extensive experiments with fourteen people, three phones and two car models show that our system achieves 99% accuracy of recognizing handheld phone-use instances and 0.76-second median error to estimate the distraction's start time.

cs.HC↗

arXiv2021

Quantum phase transition in the one-dimensional Dicke-Hubbard model with coupled qubits

We study the ground state phase diagram of a one-dimensional two qubits Dicke-Hubbard model with XY qubit-qubit interaction. We use a numerical method combing the cluster mean-field theory and the matrix product state(MPS) to obtain the exact wave function of the ground state. When counter-rotating wave terms(CRTs) in the qubit-cavity coupling are neglected, we observe a rich phase diagram including a quantum phase transition between the Mott-insulating phase and the superfluid phase. This phase transition can be either the first-order or the second-order type depending on whether the total angular momentum changes across the phase diagram. Moreover, we observe two quantum triple points, at which three different phases coexist, with both positive and negative XY interactions. By further considering the effect of CRTs, we find that the main feature in the previous phase diagram, including the existence of quantum triple points, is retained. We also show that CRTs extremely demolish the non-local correlations in the coherent phase.

quant-ph↗

arXiv2021

A low-loss ferrite circulator as a tunable chiral quantum system

Ferrite microwave circulators allow one to control the directional flow of microwave signals and noise, and thus play a crucial role in present-day superconducting quantum technology. They are typically viewed as a black-box, and their internal structure is not specified, let alone used as a resource. In this work, we demonstrate a low-loss waveguide circulator constructed with single-crystalline yttrium iron garnet (YIG) in a 3D cavity, and analyze it as a multi-mode hybrid quantum system with coupled photonic and magnonic excitations. We show the coherent coupling of its chiral internal modes with integrated superconducting niobium cavities, and how this enables tunable non-reciprocal interactions between the intra-cavity photons. We also probe experimentally the effective non-Hermitian dynamics of this system and its effective non-reciprocal eigenmodes. The device platform provides a test bed for implementing non-reciprocal interactions in open-system circuit QED.

quant-ph↗

arXiv2021

Blockchain-based Trustworthy Federated Learning Architecture

Federated learning is an emerging privacy-preserving AI technique where clients (i.e., organisations or devices) train models locally and formulate a global model based on the local model updates without transferring local data externally. However, federated learning systems struggle to achieve trustworthiness and embody responsible AI principles. In particular, federated learning systems face accountability and fairness challenges due to multi-stakeholder involvement and heterogeneity in client data distribution. To enhance the accountability and fairness of federated learning systems, we present a blockchain-based trustworthy federated learning architecture. We first design a smart contract-based data-model provenance registry to enable accountability. Additionally, we propose a weighted fair data sampler algorithm to enhance fairness in training data. We evaluate the proposed approach using a COVID-19 X-ray detection use case. The evaluation results show that the approach is feasible to enable accountability and improve fairness. The proposed algorithm can achieve better performance than the default federated learning setting in terms of the model's generalisation and accuracy.

cs.LG↗

arXiv2021

Cycle-Balanced Representation Learning For Counterfactual Inference

With the widespread accumulation of observational data, researchers obtain a new direction to learn counterfactual effects in many domains (e.g., health care and computational advertising) without Randomized Controlled Trials(RCTs). However, observational data suffer from inherent missing counterfactual outcomes, and distribution discrepancy between treatment and control groups due to behaviour preference. Motivated by recent advances of representation learning in the field of domain adaptation, we propose a novel framework based on Cycle-Balanced REpresentation learning for counterfactual inference (CBRE), to solve above problems. Specifically, we realize a robust balanced representation for different groups using adversarial training, and meanwhile construct an information loop, such that preserve original data properties cyclically, which reduces information loss when transforming data into latent representation space.Experimental results on three real-world datasets demonstrate that CBRE matches/outperforms the state-of-the-art methods, and it has a great potential to be applied to counterfactual inference.

cs.LG↗

arXiv2021

Pseudo Supervised Monocular Depth Estimation with Teacher-Student Network

Despite recent improvement of supervised monocular depth estimation, the lack of high quality pixel-wise ground truth annotations has become a major hurdle for further progress. In this work, we propose a new unsupervised depth estimation method based on pseudo supervision mechanism by training a teacher-student network with knowledge distillation. It strategically integrates the advantages of supervised and unsupervised monocular depth estimation, as well as unsupervised binocular depth estimation. Specifically, the teacher network takes advantage of the effectiveness of binocular depth estimation to produce accurate disparity maps, which are then used as the pseudo ground truth to train the student network for monocular depth estimation. This effectively converts the problem of unsupervised learning to supervised learning. Our extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art on the KITTI benchmark.

cs.CV↗

arXiv2021

Schema Extraction on Semi-structured Data

With the continuous development of NoSQL databases, more and more developers choose to use semi-structured data for development and data management, which puts forward requirements for schema management of semi-structured data stored in NoSQL databases. Schema extraction plays an important role in understanding schemas, optimizing queries, and validating data consistency. Therefore, in this survey we investigate structural methods based on tree and graph and statistical methods based on distributed architecture and machine learning to extract schemas. The schemas obtained by the structural methods are more interpretable, and the statistical methods have better applicability and generalization ability. Moreover, we also investigate tools and systems for schemas extraction. Schema extraction tools are mainly used for spark or NoSQL databases, and are suitable for small datasets or simple application environments. The system mainly focuses on the extraction and management of schemas in large data sets and complex application scenarios. Furthermore, we also compare these techniques to facilitate data managers' choice.

cs.DB↗

arXiv2021

Optimizing Pulsed-Laser Ablation Production of AlCl Molecules for Laser Cooling

Aluminum monochloride (AlCl) has been proposed as a promising candidate for laser cooling to ultracold temperatures, and recent spectroscopy results support this prediction. It is challenging to produce large numbers of AlCl molecules because it is a highly reactive open-shell molecule and must be generated in situ. Here we show that pulsed-laser ablation of stable, non-toxic mixtures of Al with an alkali or alkaline earth chlorides, denoted XCln, can provide a robust and reliable source of cold AlCl molecules. Both the chemical identity of XCln and the Al:XCln molar ratio are varied, and the yield of AlCl is monitored using absorption spectroscopy in a cryogenic gas. For KCl, the production of Al and K atoms was also monitored. We model the AlCl production in the limits of nonequilibrium recombination dominated by first-encounter events. The non-equilibrium model is in agreement with the data and also reproduces the observed trend with different XCln precursors. We find that AlCl production is limited by the solid-state densities of Al and Cl atoms and the recondensation of Al atoms in the ablation plume. We suggest future directions for optimizing the production of cold AlCl molecules using laser ablation.

physics.atom-ph↗

arXiv2021

Deep Fraud Detection on Non-attributed Graph

Fraud detection problems are usually formulated as a machine learning problem on a graph. Recently, Graph Neural Networks (GNNs) have shown solid performance on fraud detection. The successes of most previous methods heavily rely on rich node features and high-fidelity labels. However, labeled data is scarce in large-scale industrial problems, especially for fraud detection where new patterns emerge from time to time. Meanwhile, node features are also limited due to privacy and other constraints. In this paper, two improvements are proposed: 1) We design a graph transformation method capturing the structural information to facilitate GNNs on non-attributed fraud graphs. 2) We propose a novel graph pre-training strategy to leverage more unlabeled data via contrastive learning. Experiments on a large-scale industrial dataset demonstrate the effectiveness of the proposed framework for fraud detection.

cs.LG↗

arXiv2021

Sublinear Time and Space Algorithms for Correlation Clustering via Sparse-Dense Decompositions

We present a new approach for solving (minimum disagreement) correlation clustering that results in sublinear algorithms with highly efficient time and space complexity for this problem. In particular, we obtain the following algorithms for $n$-vertex $(+/-)$-labeled graphs $G$: -- A sublinear-time algorithm that with high probability returns a constant approximation clustering of $G$ in $O(n\log^2{n})$ time assuming access to the adjacency list of the $(+)$-labeled edges of $G$ (this is almost quadratically faster than even reading the input once). Previously, no sublinear-time algorithm was known for this problem with any multiplicative approximation guarantee. -- A semi-streaming algorithm that with high probability returns a constant approximation clustering of $G$ in $O(n\log{n})$ space and a single pass over the edges of the graph $G$ (this memory is almost quadratically smaller than input size). Previously, no single-pass algorithm with $o(n^2)$ space was known for this problem with any approximation guarantee. The main ingredient of our approach is a novel connection to sparse-dense graph decompositions that are used extensively in the graph coloring literature. To our knowledge, this connection is the first application of these decompositions beyond graph coloring, and in particular for the correlation clustering problem, and can be of independent interest.

cs.DS↗

arXiv2021

What Matters in Learning from Offline Human Demonstrations for Robot Manipulation

Imitating human demonstrations is a promising approach to endow robots with various manipulation capabilities. While recent advances have been made in imitation learning and batch (offline) reinforcement learning, a lack of open-source human datasets and reproducible learning methods make assessing the state of the field difficult. In this paper, we conduct an extensive study of six offline learning algorithms for robot manipulation on five simulated and three real-world multi-stage manipulation tasks of varying complexity, and with datasets of varying quality. Our study analyzes the most critical challenges when learning from offline human data for manipulation. Based on the study, we derive a series of lessons including the sensitivity to different algorithmic design choices, the dependence on the quality of the demonstrations, and the variability based on the stopping criteria due to the different objectives in training and evaluation. We also highlight opportunities for learning from human datasets, such as the ability to learn proficient policies on challenging, multi-stage tasks beyond the scope of current reinforcement learning methods, and the ability to easily scale to natural, real-world manipulation scenarios where only raw sensory signals are available. We have open-sourced our datasets and all algorithm implementations to facilitate future research and fair comparisons in learning from human demonstration data. Codebase, datasets, trained models, and more available at https://arise-initiative.github.io/robomimic-web/

cs.RO↗

arXiv2021

Single Person Pose Estimation: A Survey

Human pose estimation in unconstrained images and videos is a fundamental computer vision task. To illustrate the evolutionary path in technique, in this survey we summarize representative human pose methods in a structured taxonomy, with a particular focus on deep learning models and single-person image setting. Specifically, we examine and survey all the components of a typical human pose estimation pipeline, including data augmentation, model architecture and backbone, supervision representation, post-processing, standard datasets, evaluation metrics. To envisage the future directions, we finally discuss the key unsolved problems and potential trends for human pose estimation.

cs.CV↗

arXiv2021

Low-resolution Human Pose Estimation

Human pose estimation has achieved significant progress on images with high imaging resolution. However, low-resolution imagery data bring nontrivial challenges which are still under-studied. To fill this gap, we start with investigating existing methods and reveal that the most dominant heatmap-based methods would suffer more severe model performance degradation from low-resolution, and offset learning is an effective strategy. Established on this observation, in this work we propose a novel Confidence-Aware Learning (CAL) method which further addresses two fundamental limitations of existing offset learning methods: inconsistent training and testing, decoupled heatmap and offset learning. Specifically, CAL selectively weighs the learning of heatmap and offset with respect to ground-truth and most confident prediction, whilst capturing the statistical importance of model output in mini-batch learning manner. Extensive experiments conducted on the COCO benchmark show that our method outperforms significantly the state-of-the-art methods for low-resolution human pose estimation.

cs.CV↗

arXiv2021

A practical guide for building superconducting quantum devices

Quantum computing offers a powerful new paradigm of information processing that has the potential to transform a wide range of industries. In the pursuit of the tantalizing promises of a universal quantum computer, a multitude of new knowledge and expertise has been developed, enabling the construction of novel quantum algorithms as well as increasingly robust quantum hardware. In particular, we have witnessed rapid progress in the circuit quantum electrodynamics (cQED) technology, which has emerged as one of the most promising physical systems that is capable of addressing the key challenges in realizing full-stack quantum computing on a large scale. In this article, we present some of the most crucial building blocks developed by the cQED community in recent years and a précis of the latest achievements towards robust universal quantum computation. More importantly, we aim to provide a synoptic outline of the core techniques that underlie most cQED experiments and offer a practical guide for a novice experimentalist to design, construct, and characterize their first quantum device

quant-ph↗

arXiv2021

Tuning nonequilibrium heat current and two-photon statistics via composite qubit-resonator interaction

Quantum thermal transport and two-photon statistics serve as two representative nonequilibrium features in circuit quantum electrodynamics systems. Here, we investigate quantum heat flow and two-photon correlation function at steady-state in a composite qubit-resonator model, where one qubit shows both transverse and longitudinal couplings to a single-mode optical resonator. With weak qubit-resonator interaction, we unravel two microscopic transport pictures, i.e., cotunneling and cyclic heat exchange processes, corresponding to transverse and longitudinal couplings respectively. At strong qubit-resonator coupling, the heat current exhibits nonmonotonic behavior by increasing qubit-resonator coupling strength, which tightly relies on the scattering processes between the qubit and corresponding thermal bath. Furthermore, the longitudinal coupling is preferred to enhance heat current in strong qubit-resonator coupling regime. For two-photon correlation function, it exhibits an antibunching-to-bunching transition, which is mainly dominated by the modulation of energy gap between the first and second excited eigenstates. Our results are expected to deepen the understanding of nonequilibrium thermal transport and nonclassical photon radiation based on the circuit quantum electrodynamics platform.

quant-ph↗

arXiv2021

A wide-field CO survey towards the California Molecular Filament

We present the survey of $^{12}$CO/$^{13}$CO/C$^{18}$O (J=1-0) toward the California Molecular Cloud (CMC) within the region of 161.75$^{\circ} \leqslant l \leqslant$ 167.75$^{\circ}$,-9.5$^{\circ} \leqslant b \leqslant $-7.5$^{\circ}$, using the Purple Mountain Observatory (PMO) 13.7 m millimeter telescope. Adopting a distance of 470 pc, the mass of the observed molecular cloud estimated from $^{12}$CO, $^{13}$CO, and C$^{18}$O is about 2.59$\times$10$^{4}$ M$_\odot$, 0.85$\times$10$^{4}$ M$_\odot$, and 0.09$\times$10$^{4}$ M$_\odot$, respectively. A large-scale continuous filament extending about 72 pc is revealed from the $^{13}$CO images. A systematic velocity gradient perpendicular to the major axis appears and is measured to be $\sim$ 0.82 km s$^{-1}$ pc$^{-1}$. The kinematics along the filament shows an oscillation pattern with a fragmentation wavelength of $\sim$ 2.3 pc and velocity amplitude of $\sim$ 0.92 km s$^{-1}$, which may be related with core-forming flows. Furthermore, assuming an inclination angle to the plane of the sky of 45$^{\circ}$, the estimated average accretion rate is $\sim$ 101 M$_\odot$ Myr$^{-1}$ for the cluster LkH$α$ 101 and $\sim$ 21 M$_\odot$ Myr$^{-1}$ for the other regions. In the C$^{18}$O observations, the large-scale filament could be resolved into multiple substructures and their dynamics are consistent with the scenario of filament formation from converging flows. Approximately 225 C$^{18}$O cores are extracted, of which 181 are starless cores. Roughly 37$\%$ (67/181) of the starless cores have $α_{\text{vir}}$ less than 1. Twenty outflow candidates are identified along the filament. Our results indicate active early-phase star formation along the large-scale filament in the CMC region.

astro-ph.GA↗

arXiv2021

Improved Measurements of Molecular Cloud Distances Based on Global Search

The principle of the background-eliminated extinction-parallax (BEEP) method is examining the extinction difference between on- and off-cloud regions to reveal the extinction jump caused by molecular clouds, thereby revealing the distance in complex dust environments. The BEEP method requires high-quality images of molecular clouds and high-precision stellar parallaxes and extinction data, which can be provided by the Milky Way Imaging Scroll Painting (MWISP) CO survey and the Gaia DR2 catalog, as well as supplementary AV extinction data. In this work, the BEEP method is further improved (BEEP-II) to measure molecular cloud distances in a global search manner. Applying the BEEP-II method to three regions mapped by the MWISP CO survey, we collectively measured 238 distances for 234 molecular clouds. Compared with previous BEEP results, the BEEP-II method measures distances efficiently, particularly for those molecular clouds with large angular size or in complicated environments, making it suitable for distance measurements of molecular clouds in large samples.

astro-ph.GA↗