arXiv subjects

Xuewen Chen

Publications and source records attributed to Xuewen Chen.

11 recordsLinked to original sources

arXiv2026

Overcoming noise-agility trade-off in integrated lasers for precision sensing

Lasers that combine narrow linewidths with rapid tunability are critical for applications such as coherent optical ranging, distributed fiber-optic sensing, and precision spectroscopy. Despite significant progress in integrated laser technologies, the concurrent realization of low phase noise and frequency agility on a single integrated platform remains challenging owing to a fundamental architectural trade-off: conventional integrated laser designs typically suppress phase noise via high-$Q$ resonators, yet the extended photon lifetimes inherent to such resonators intrinsically constrain tuning speed. Here, we address this noise-agility trade-off by introducing a laser architecture that achieves ultralow phase noise and ultrafast tunability simultaneously. Rather than relying on ultrahigh-$Q$ resonators for self-injection locking, our design employs strong synthetic feedback within a Pockels-tunable, resonator-enhanced distributed Bragg reflector to suppress phase noise. As a proof of concept, we demonstrate a hybrid integrated laser with a short-term linewidth of 29 Hz, realized using a lithium niobate external cavity with a loaded $Q$ of only 0.62 million. The adoption of a moderate resonator $Q$ relaxes the photon-lifetime constraint on tuning speed, enabling sub-exahertz-per-second tuning rates and a chirp nonlinearity as low as 0.14%. Leveraging this laser, we implement a frequency-modulated continuous-wave LiDAR system that achieves a relative ranging precision of $1.7 \times 10^{-4}$ at a measurement rate of $1\,\text{MSa s}^{-1}$, without requiring complex chirp linearization techniques. We further demonstrate fiber-optic acoustic sensing capable of detecting sub-$\mu\epsilon$ dynamic strain, underscoring the platform's versatility for high-speed precision optical measurements. Our work provides a route toward cost-effective yet high-performance sensing and metrology systems.

physics.optics↗

arXiv2024

Bistability in spatiotemporal mode-locking with dynamic multimode gain

Three-dimensional (3D) dissipative soliton existed in spatiotemporal mode-locked (STML) multimode fiber laser has been demonstrated to be a promising formalism for generating high-energy femtosecond pulses, which unfortunately exhibit diverse spatiotemporal dynamics that have not been fully understood. Completely modeling the STML multimode fiber lasers can shed new light on the underlying physics of the spatiotemporal dynamics and thus better manipulate the generation of high-quality energic femtosecond pulses, which however is still largely unmet. To this end, here we theoretically investigate a dynamic multimode gain model of the STML multimode fiber laser by exploring the multimode rate equation (MMRE) in the framework of generalized multimode nonlinear Schr\"odinger equation. Using this dynamic multimode gain model, the attractor dissection theory is revisited to understand the dominant effects that determine the modal composition of 3D dissipative soliton. Specifically, by varying the numerical aperture of the multimode gain fiber (MMGF), different gain dynamics that correspond to distinct types of gain attractors are observed. As a result, two distinguishing STML operation regimes, respectively governed by the multimode gain effect and spatiotemporal saturable absorption, are identified. In the latter regime, especially, 3D dissipative solitons present bistability that there exist bifurcated solutions with two different linearly polarized (LP) mode compositions. To verify the theoretical findings, the experimental implementation shows that the state of STML can be switched between different LP modes, and confirms the presence of bistability. Particularly, the 3D-soliton shaping mechanism that is governed by the multimode gain effect is testified for the first time, to the best of our knowledge.

physics.optics↗

arXiv2024

Practical GHz single-cavity all-fiber dual-comb laser for high-speed spectroscopy

Dual-comb spectroscopy (DCS) with few-GHz tooth spacing that provides the optimal trade-off between spectral resolution and refresh rate is a powerful tool for measuring and analyzing rapidly evolving transient events. Despite such an exciting opportunity, existing technologies compromise either the spectral resolution or refresh rate, leaving few-GHz DCS with robust design largely unmet for frontier applications. In this work, we demonstrate a novel GHz DCS by exploring the multimode interference-mediated spectral filtering effect in an all-fiber ultrashort cavity configuration. The GHz single-cavity all-fiber dual-comb source is seeded by a dual-wavelength mode-locked fiber laser operating at fundamental repetition rates of about 1.0 GHz differing by 148 kHz, which has an excellent stability in the free-running state that the Allan deviation is only 101.7 mHz for an average time of 1 second. Thanks to the large repetition rate difference between the asynchronous dichromatic pulse trains, the GHz DCS enables a refresh time as short as 6.75 us, making it promising for studying nonrepeatable transient phenomena in real time. To this end, the practicality of the present GHz DCS is validated by successfully capturing the 'shock waves' of balloon and firecracker explosions outdoors. This GHz single-cavity all-fiber dual-comb system promises a noteworthy improvement in acquisition speed and reliability without sacrificing measurement accuracy, anticipated as a practical tool for high-speed applications.

physics.optics↗

arXiv2019

An Actor-Critic-Attention Mechanism for Deep Reinforcement Learning in Multi-view Environments

In reinforcement learning algorithms, leveraging multiple views of the environment can improve the learning of complicated policies. In multi-view environments, due to the fact that the views may frequently suffer from partial observability, their level of importance are often different. In this paper, we propose a deep reinforcement learning method and an attention mechanism in a multi-view environment. Each view can provide various representative information about the environment. Through our attention mechanism, our method generates a single feature representation of environment given its multiple views. It learns a policy to dynamically attend to each view based on its importance in the decision-making process. Through experiments, we show that our method outperforms its state-of-the-art baselines on TORCS racing car simulator and three other complex 3D environments with obstacles. We also provide experimental results to evaluate the performance of our method on noisy conditions and partial observation settings.

cs.LG↗

arXiv2019

Attention-based Deep Reinforcement Learning for Multi-view Environments

In reinforcement learning algorithms, it is a common practice to account for only a single view of the environment to make the desired decisions; however, utilizing multiple views of the environment can help to promote the learning of complicated policies. Since the views may frequently suffer from partial observability, their provided observation can have different levels of importance. In this paper, we present a novel attention-based deep reinforcement learning method in a multi-view environment in which each view can provide various representative information about the environment. Specifically, our method learns a policy to dynamically attend to views of the environment based on their importance in the decision-making process. We evaluate the performance of our method on TORCS racing car simulator and three other complex 3D environments with obstacles.

cs.LG↗

arXiv2018

Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining

In this paper, we develop a reinforcement learning (RL) based system to learn an effective policy for carpooling that maximizes transportation efficiency so that fewer cars are required to fulfill the given amount of trip demand. For this purpose, first, we develop a deep neural network model, called ST-NN (Spatio-Temporal Neural Network), to predict taxi trip time from the raw GPS trip data. Secondly, we develop a carpooling simulation environment for RL training, with the output of ST-NN and using the NYC taxi trip dataset. In order to maximize transportation efficiency and minimize traffic congestion, we choose the effective distance covered by the driver on a carpool trip as the reward. Therefore, the more effective distance a driver achieves over a trip (i.e. to satisfy more trip demand) the higher the efficiency and the less will be the traffic congestion. We compared the performance of RL learned policy to a fixed policy (which always accepts carpool) as a baseline and obtained promising results that are interpretable and demonstrate the advantage of our RL approach. We also compare the performance of ST-NN to that of state-of-the-art travel time estimation methods and observe that ST-NN significantly improves the prediction performance and is more robust to outliers.

cs.LG↗

arXiv2017

A Unified Neural Network Approach for Estimating Travel Time and Distance for a Taxi Trip

In building intelligent transportation systems such as taxi or rideshare services, accurate prediction of travel time and distance is crucial for customer experience and resource management. Using the NYC taxi dataset, which contains taxi trips data collected from GPS-enabled taxis [23], this paper investigates the use of deep neural networks to jointly predict taxi trip time and distance. We propose a model, called ST-NN (Spatio-Temporal Neural Network), which first predicts the travel distance between an origin and a destination GPS coordinate, then combines this prediction with the time of day to predict the travel time. The beauty of ST-NN is that it uses only the raw trips data without requiring further feature engineering and provides a joint estimate of travel time and distance. We compare the performance of ST-NN to that of state-of-the-art travel time estimation methods, and we observe that the proposed approach generalizes better than state-of-the-art methods. We show that ST-NN approach significantly reduces the mean absolute error for both predicted travel time and distance, about 17% for travel time prediction. We also observe that the proposed approach is more robust to outliers present in the dataset by testing the performance of ST-NN on the datasets with and without outliers.

stat.ML↗

arXiv2017

Large-scale Datasets: Faces with Partial Occlusions and Pose Variations in the Wild

Face detection methods have relied on face datasets for training. However, existing face datasets tend to be in small scales for face learning in both constrained and unconstrained environments. In this paper, we first introduce our large-scale image datasets, Large-scale Labeled Face (LSLF) and noisy Large-scale Labeled Non-face (LSLNF). Our LSLF dataset consists of a large number of unconstrained multi-view and partially occluded faces. The faces have many variations in color and grayscale, image quality, image resolution, image illumination, image background, image illusion, human face, cartoon face, facial expression, light and severe partial facial occlusion, make up, gender, age, and race. Many of these faces are partially occluded with accessories such as tattoos, hats, glasses, sunglasses, hands, hair, beards, scarves, microphones, or other objects or persons. The LSLF dataset is currently the largest labeled face image dataset in the literature in terms of the number of labeled images and the number of individuals compared to other existing labeled face image datasets. Second, we introduce our CrowedFaces and CrowedNonFaces image datasets. The crowedFaces and CrowedNonFaces datasets include faces and non-faces images from crowed scenes. These datasets essentially aim for researchers to provide a large number of training examples with many variations for large scale face learning and face recognition tasks.

cs.CV↗

arXiv2017

Learning Deep Networks from Noisy Labels with Dropout Regularization

Large datasets often have unreliable labels-such as those obtained from Amazon's Mechanical Turk or social media platforms-and classifiers trained on mislabeled datasets often exhibit poor performance. We present a simple, effective technique for accounting for label noise when training deep neural networks. We augment a standard deep network with a softmax layer that models the label noise statistics. Then, we train the deep network and noise model jointly via end-to-end stochastic gradient descent on the (perhaps mislabeled) dataset. The augmented model is overdetermined, so in order to encourage the learning of a non-trivial noise model, we apply dropout regularization to the weights of the noise model during training. Numerical experiments on noisy versions of the CIFAR-10 and MNIST datasets show that the proposed dropout technique outperforms state-of-the-art methods.

cs.CV↗

arXiv2016

Strong plasmonic enhancement of biexciton emission: controlled coupling of a single quantum dot to a gold nanocone antenna

Multiexcitonic transitions and emission of several photons per excitation comprise a very attractive feature of semiconductor quantum dots for optoelectronics applications. However, these higher-order radiative processes are usually quenched in colloidal quantum dots by Auger and other non-radiative decay channels. To increase the multiexcitonic quantum efficiency, several groups have explored plasmonic enhancement, so far with moderate results. By controlled positioning of individual quantum dots in the near field of gold nanocone antennas, we enhance the radiative decay rates of monoexcitons and biexcitons by 109 and 100 folds at quantum efficiencies of 60% and 70%, respectively, in very good agreement with the outcome of numerical calculations. We discuss the implications of our work for future fundamental and applied research in nano-optics.

physics.optics↗

arXiv2016

Plasmonic Superradiance of Two Emitters Near Metal Nanorod

Quantum emitters, such as q-dots and dye molecules, in the immediate vicinity of plasmonic nanostructures, resonantly excite surface plasmon-polaritons (SPPs) under incoherent pump. The efficiency in the excitation of SPPs increases as the number of the emitters, because the SPP field synchronizes emission of the coupled emitters, in analogy with the superadiance (SR) of coupled emitters in free space. Using fully quantum mechanical model for two emitters coupled to a single gold nanorod, we predict up to 15% increase in the emission yield of single emitter compared to only one emitter coupled to the nanorod due to plasmonic SR. (XW: I use emission yield because the quantum efficiency is one for emitters in free space and there is no room for enhancement). Such emission enhancement is stationary and should be observable even with strong dissipation and dephasing under incoherent pump. Solid-state quantum emitters with blinking behaviors may be utilized to demonstrate such plasmonic SR emission enhancement. Plasmonic SR may find implications in the excitation of non-radiative modes in plasmonic waveguides; and lowing threshold of plasmonic nanolasers.

quant-ph↗