arXiv subjects

Wei Liang

Publications and source records attributed to Wei Liang.

At least 91 records · Page 5Linked to original sources

arXiv2022

Recent Advances in Laser Self-Injection Locking to High-$Q$ Microresonators

The stabilization and manipulation of laser frequency by means of an external cavity are nearly ubiquitously used in fundamental research and laser applications. While most of the laser light transmits through the cavity, in the presence of some back-scattered light from the cavity to the laser, the self-injection locking effect can take place, which locks the laser emission frequency to the cavity mode of similar frequency. The self-injection locking leads to dramatic reduction of laser linewidth and noise. Using this approach, a common semiconductor laser locked to an ultrahigh-$Q$ microresonator can obtain sub-hertz linewidth, on par with state-of-the-art fiber lasers. Therefore it paves the way to manufacture high-performance semiconductor lasers with reduced footprint and cost. Moreover, with high laser power, the optical nonlinearity of the microresonator drastically changes the laser dynamics, offering routes for simultaneous pulse and frequency comb generation in the same microresonator. Particularly, integrated photonics technology, enabling components fabricated via semiconductor CMOS process, has brought increasing and extending interest to laser manufacturing using this method. In this article, we present a comprehensive tutorial on analytical and numerical methods of laser self-injection locking, as well a review of most recent theoretical and experimental achievements.

physics.optics↗

arXiv2022

Compact sub-Hz Linewidth Laser Enabled by Self Injection Lock To a Sub-mL FP Cavity

Narrow linewidth laser(NLL) of high frequency stability and small form factor is essential to enable applications in long range sensing, quantum information and atomic clocks. Various high performance NLL have been demonstrated by Pound-Drever Hall(PDH) lock or self injection lock(SIL) of a seed laser to a vaccum-stabilized FP cavity of ultrahigh quality factor(Q). However they are often complicated lab setups due to the sophisticated stabilizing system and locking electronics. Here we report a compact NLL of 68mL volume, realized by SIL of a diode laser to a miniature FP cavity of 7.7x108 Q and 0.5mL volume, bypassing table-size vaccum, thermal and vibration isolation. We characterized the NLL with a self-delayed heterodyne system, the Lorentzian linewidth reaches 60mHz, and the integrated linewidth is ~80Hz. The frequency noise performance exceeds that of commercial NLLs and the best reported hybrid-integrated NLL realized by SIL to high Q on-chip ring resonators. Our work marks a major step toward a field-deployable NLL of superior performance utilizing ultra-high Q FP cavity.

physics.optics↗

arXiv2022

Automated turnkey microcomb for low-noise microwave synthesis

Microresonator-based optical frequency comb (microcomb) has the potential to revolutionize the accuracy of frequency synthesizer in radar and communication applications. However, fundamental limit exists for low noise microcomb generation, especially in low size, weight, power and cost (SWaP-C) package. Here we resolve this limit, by the demonstration of an automated turnkey microcomb, operating close to its low quantum-limited phase noise, within a compact setup size of 85 mm * 90 mm * 25 mm. High quality factor fiber Fabry-Perot resonator (FFPR), with Q up to 4.0 * 10^9, is the key for both low quantum noise and pump noise limit, in the diode-pump case in a self-injection locking scheme. Low phase noise of -80 and -105 dBc/Hz at 100 Hz, -106 and -125 dBc/Hz at 1 kHz, -133 and -148 dBc/Hz at 10 kHz is achieved at 10.1 GHz and 1.7 GHz repetition frequencies, respectively. With the simultaneous automated turnkey, low-noise and direct-diode-pump capability, our microcomb is ready to be used as a low-noise frequency synthesizer with low SWaP-C and thus field deployability.

physics.optics↗

arXiv2022

Towards Versatile Embodied Navigation

With the emergence of varied visual navigation tasks (e.g, image-/object-/audio-goal and vision-language navigation) that specify the target in different ways, the community has made appealing advances in training specialized agents capable of handling individual navigation tasks well. Given plenty of embodied navigation tasks and task-specific solutions, we address a more fundamental question: can we learn a single powerful agent that masters not one but multiple navigation tasks concurrently? First, we propose VXN, a large-scale 3D dataset that instantiates four classic navigation tasks in standardized, continuous, and audiovisual-rich environments. Second, we propose Vienna, a versatile embodied navigation agent that simultaneously learns to perform the four navigation tasks with one model. Building upon a full-attentive architecture, Vienna formulates various navigation tasks as a unified, parse-and-query procedure: the target description, augmented with four task embeddings, is comprehensively interpreted into a set of diversified goal vectors, which are refined as the navigation progresses, and used as queries to retrieve supportive context from episodic history for decision making. This enables the reuse of knowledge across navigation tasks with varying input domains/modalities. We empirically demonstrate that, compared with learning each visual navigation task individually, our multitask agent achieves comparable or even better performance with reduced complexity.

cs.CV↗

arXiv2022

HUMANISE: Language-conditioned Human Motion Generation in 3D Scenes

Learning to generate diverse scene-aware and goal-oriented human motions in 3D scenes remains challenging due to the mediocre characteristics of the existing datasets on Human-Scene Interaction (HSI); they only have limited scale/quality and lack semantics. To fill in the gap, we propose a large-scale and semantic-rich synthetic HSI dataset, denoted as HUMANISE, by aligning the captured human motion sequences with various 3D indoor scenes. We automatically annotate the aligned motions with language descriptions that depict the action and the unique interacting objects in the scene; e.g., sit on the armchair near the desk. HUMANISE thus enables a new generation task, language-conditioned human motion generation in 3D scenes. The proposed task is challenging as it requires joint modeling of the 3D scene, human motion, and natural language. To tackle this task, we present a novel scene-and-language conditioned generative model that can produce 3D human motions of the desirable action interacting with the specified objects. Our experiments demonstrate that our model generates diverse and semantically consistent human motions in 3D scenes.

cs.CV↗

arXiv2022

LGC-Net: A Lightweight Gyroscope Calibration Network for Efficient Attitude Estimation

This paper presents a lightweight, efficient calibration neural network model for denoising low-cost microelectromechanical system (MEMS) gyroscope and estimating the attitude of a robot in real-time. The key idea is extracting local and global features from the time window of inertial measurement units (IMU) measurements to regress the output compensation components for the gyroscope dynamically. Following a carefully deduced mathematical calibration model, LGC-Net leverages the depthwise separable convolution to capture the sectional features and reduce the network model parameters. The Large kernel attention is designed to learn the long-range dependencies and feature representation better. The proposed algorithm is evaluated in the EuRoC and TUM-VI datasets and achieves state-of-the-art on the (unseen) test sequences with a more lightweight model structure. The estimated orientation with our LGC-Net is comparable with the top-ranked visual-inertial odometry systems, although it does not adopt vision sensors. We make our method open-source at: https://github.com/huazai665/LGC-Net

cs.RO↗

arXiv2022

An Efficient Target Detection and Recognition Method in Aerial Remote-sensing Images Based on Multiangle Regions-of-Interest

Recently, deep learning technology have been extensively used in the field of image recognition. However, its main application is the recognition and detection of ordinary pictures and common scenes. It is challenging to effectively and expediently analyze remote-sensing images obtained by the image acquisition systems on unmanned aerial vehicles (UAVs), which includes the identification of the target and calculation of its position. Aerial remote sensing images have different shooting angles and methods compared with ordinary pictures or images, which makes remote-sensing images play an irreplaceable role in some areas. In this study, a new target detection and recognition method in remote-sensing images is proposed based on deep convolution neural network (CNN) for the provision of multilevel information of images in combination with a region proposal network used to generate multiangle regions-of-interest. The proposed method generated results that were much more accurate and precise than those obtained with traditional ways. This demonstrated that the model proposed herein displays tremendous applicability potential in remote-sensing image recognition.

cs.CV↗

arXiv2022

Confidence Band Estimation for Survival Random Forests

Survival random forest is a popular machine learning tool for modeling censored survival data. However, there is currently no statistically valid and computationally feasible approach for estimating its confidence band. This paper proposes an unbiased confidence band estimation by extending recent developments in infinite-order incomplete U-statistics. The idea is to estimate the variance-covariance matrix of the cumulative hazard function prediction on a grid of time points. We then generate the confidence band by viewing the cumulative hazard function estimation as a Gaussian process whose distribution can be approximated through simulation. This approach is computationally easy to implement when the subsampling size of a tree is no larger than half of the total training sample size. Numerical studies show that our proposed method accurately estimates the confidence band and achieves desired coverage rate. We apply this method to veterans' administration lung cancer data.

stat.ME↗

arXiv2022

Counterfactual Cycle-Consistent Learning for Instruction Following and Generation in Vision-Language Navigation

Since the rise of vision-language navigation (VLN), great progress has been made in instruction following -- building a follower to navigate environments under the guidance of instructions. However, far less attention has been paid to the inverse task: instruction generation -- learning a speaker~to generate grounded descriptions for navigation routes. Existing VLN methods train a speaker independently and often treat it as a data augmentation tool to strengthen the follower while ignoring rich cross-task relations. Here we describe an approach that learns the two tasks simultaneously and exploits their intrinsic correlations to boost the training of each: the follower judges whether the speaker-created instruction explains the original navigation route correctly, and vice versa. Without the need of aligned instruction-path pairs, such cycle-consistent learning scheme is complementary to task-specific training targets defined on labeled data, and can also be applied over unlabeled paths (sampled without paired instructions). Another agent, called~creator is added to generate counterfactual environments. It greatly changes current scenes yet leaves novel items -- which are vital for the execution of original instructions -- unchanged. Thus more informative training scenes are synthesized and the three agents compose a powerful VLN learning system. Extensive experiments on a standard benchmark show that our approach improves the performance of various follower models and produces accurate navigation instructions.

cs.CV↗

arXiv2021

Strong decays of the low-lying doubly bottom baryons

In this work, we adopt the $^3P_0$ model to investigate the strong decays of the low-lying doubly bottom baryons in the $j-j$ coupling scheme systematically. In this scheme, we construct the formulism of $^3P_0$ model under the spectator assumption, and then the heavy diquark symmetry is preserved automatically. Our results show that some of the $λ$-mode $Ξ_{bb}(1P)$ and $Ω_{bb}(1P)$ states are narrow, which have good potentials to be observed by future experiments. For the low-lying $ρ$-mode and $ρ$-$λ$ hybrid states, the Okubo-Zweig-Iizuka-allowed strong decays are highly suppressed and they should be extremely narrow. Future experiments can test our phenomenological predictions at the quark level.

hep-ph↗

arXiv2021

PEARL: Parallelized Expert-Assisted Reinforcement Learning for Scene Rearrangement Planning

Scene Rearrangement Planning (SRP) is an interior task proposed recently. The previous work defines the action space of this task with handcrafted coarse-grained actions that are inflexible to be used for transforming scene arrangement and intractable to be deployed in practice. Additionally, this new task lacks realistic indoor scene rearrangement data to feed popular data-hungry learning approaches and meet the needs of quantitative evaluation. To address these problems, we propose a fine-grained action definition for SRP and introduce a large-scale scene rearrangement dataset. We also propose a novel learning paradigm to efficiently train an agent through self-playing, without any prior knowledge. The agent trained via our paradigm achieves superior performance on the introduced dataset compared to the baseline agents. We provide a detailed analysis of the design of our approach in our experiments.

cs.AI↗

arXiv2021

Strong decays of the low-lying bottom strange baryons

In this work, the strong decay behaviors of the $λ-$mode low-lying $Ξ_{b}$ and $Ξ_{b}^\prime$ baryons are investigated within the $^3P_0$ model. Our results suggest that all of the low-lying $\bar 3_F$ states $Ξ_b(2S)$, $Ξ_b(1P)$, and $Ξ_b(1D)$ have small decay widths of less than 20 MeV, and these states have good potentials to be observed in the $Ξ_b^\prime π$ and $Ξ_b^{\prime *} π$ invariant masses. Further, most of the $6_F$ multiplets are relatively narrow and may decay into the $Ξ_b π$, $Ξ_b^\prime π$, $Ξ_b^{\prime *} π$, and $Λ_b \bar K$ final states. Considering the masses and strong decay behaviors, we can assign the newly observed $Ξ_{b}(6100)$ resonance as the $Ξ_b(1P)$ state with $J^P=3/2^-$ and interpret the $Ξ_{b}(6227)$ structure as the $J^P=3/2^-$ $Ξ_b^\prime(1P)$ state with a proper mixing angle. We expect that our predictions for these excited bottom strange baryons will provide helpful information for future experimental research.

hep-ph↗

arXiv2021

Structured Scene Memory for Vision-Language Navigation

Recently, numerous algorithms have been developed to tackle the problem of vision-language navigation (VLN), i.e., entailing an agent to navigate 3D environments through following linguistic instructions. However, current VLN agents simply store their past experiences/observations as latent states in recurrent networks, failing to capture environment layouts and make long-term planning. To address these limitations, we propose a crucial architecture, called Structured Scene Memory (SSM). It is compartmentalized enough to accurately memorize the percepts during navigation. It also serves as a structured scene representation, which captures and disentangles visual and geometric cues in the environment. SSM has a collect-read controller that adaptively collects information for supporting current decision making and mimics iterative algorithms for long-range reasoning. As SSM provides a complete action space, i.e., all the navigable places on the map, a frontier-exploration based navigation decision making strategy is introduced to enable efficient and global planning. Experiment results on two VLN datasets (i.e., R2R and R4R) show that our method achieves state-of-the-art performance on several metrics.

cs.CV↗

arXiv2020

Empirical Likelihood-Based Estimation and Inference in Randomized Controlled Trials with High-Dimensional Covariates

In this paper, we propose a data-adaptive empirical likelihood-based approach for treatment effect estimation and inference, which overcomes the obstacle of the traditional empirical likelihood-based approaches in the high-dimensional setting by adopting penalized regression and machine learning methods to model the covariate-outcome relationship. In particular, we show that our procedure successfully recovers the true variance of Zhang's treatment effect estimator (Zhang, 2018) by utilizing a data-splitting technique. Our proposed estimator is proved to be asymptotically normal and semiparametric efficient under mild regularity conditions. Simulation studies indicate that our estimator is more efficient than the estimator proposed by Wager et al. (2016) when random forests are employed to model the covariate-outcome relationship. Moreover, when multiple machine learning models are imposed, our estimator is at least as efficient as any regular estimator with a single machine learning model. We compare our method to existing ones using the ACTG175 data and the GSE118657 data, and confirm the outstanding performance of our approach.

stat.ME↗

arXiv2020

Empirical Likelihood Weighted Estimation of Average Treatment Effects

There has been growing attention on how to effectively and objectively use covariate information when the primary goal is to estimate the average treatment effect (ATE) in randomized clinical trials (RCTs). In this paper, we propose an effective weighting approach to extract covariate information based on the empirical likelihood (EL) method. The resulting two-sample empirical likelihood weighted (ELW) estimator includes two classes of weights, which are obtained from a constrained empirical likelihood estimation procedure, where the covariate information is effectively incorporated into the form of general estimating equations. Furthermore, this ELW approach separates the estimation of ATE from the analysis of the covariate-outcome relationship, which implies that our approach maintains objectivity. In theory, we show that the proposed ELW estimator is semiparametric efficient. We extend our estimator to tackle the scenarios where the outcomes are missing at random (MAR), and prove the double robustness and multiple robustness properties of our estimator. Furthermore, we derive the semiparametric efficiency bound of all regular and asymptotically linear semiparametric ATE estimators under MAR mechanism and prove that our proposed estimator attains this bound. We conduct simulations to make comparisons with other existing estimators, which confirm the efficiency and multiple robustness property of our proposed ELW estimator. An application to the AIDS Clinical Trials Group Protocol 175 (ACTG 175) data is conducted.

stat.ME↗

arXiv2020

Active Visual Information Gathering for Vision-Language Navigation

Vision-language navigation (VLN) is the task of entailing an agent to carry out navigational instructions inside photo-realistic environments. One of the key challenges in VLN is how to conduct a robust navigation by mitigating the uncertainty caused by ambiguous instructions and insufficient observation of the environment. Agents trained by current approaches typically suffer from this and would consequently struggle to avoid random and inefficient actions at every step. In contrast, when humans face such a challenge, they can still maintain robust navigation by actively exploring the surroundings to gather more information and thus make more confident navigation decisions. This work draws inspiration from human navigation behavior and endows an agent with an active information gathering ability for a more intelligent vision-language navigation policy. To achieve this, we propose an end-to-end framework for learning an exploration policy that decides i) when and where to explore, ii) what information is worth gathering during exploration, and iii) how to adjust the navigation decision after the exploration. The experimental results show promising exploration strategies emerged from training, which leads to significant boost in navigation performance. On the R2R challenge leaderboard, our agent gets promising results all three VLN settings, i.e., single run, pre-exploration, and beam search.

cs.CV↗

arXiv2020

Robust Encoder-Decoder Learning Framework towards Offline Handwritten Mathematical Expression Recognition Based on Multi-Scale Deep Neural Network

Offline handwritten mathematical expression recognition is a challenging task, because handwritten mathematical expressions mainly have two problems in the process of recognition. On one hand, it is how to correctly recognize different mathematical symbols. On the other hand, it is how to correctly recognize the two-dimensional structure existing in mathematical expressions. Inspired by recent work in deep learning, a new neural network model that combines a Multi-Scale convolutional neural network (CNN) with an Attention recurrent neural network (RNN) is proposed to identify two-dimensional handwritten mathematical expressions as one-dimensional LaTeX sequences. As a result, the model proposed in the present work has achieved a WER error of 25.715% and ExpRate of 28.216%.

cs.CV↗

arXiv2020

The newly observed $Λ_b(6072)^0$ structure and its $ρ-$mode nonstrange partners

Inspired by the newly observed $Λ_b(6072)$ structure, we investigate its strong decay behaviors under various assignments within the $^3P_0$ model. Compared with the mass and total decay width, our results suggest that the $Λ_b(6072)$ can be regarded as the lowest $ρ-$mode excitation in $Λ_b$ family. Then, the strong decays of $ρ-$mode nonstrange partners for the $Λ_b(6072)$ are calculated. It is found that the $J^P=5/2^-$ $Λ_b$ and $Λ_c$ states are relatively narrow, and mainly decay into the $Σ_b^{(*)} π$ and $Σ_c^{(*)} π$ final states, respectively. These two states have good potentials to be observed in future experiments, which may help us to distinguish the three-quark model and diquark model.

hep-ph↗