arXiv subjects

Miao Yu

Publications and source records attributed to Miao Yu.

At least 73 records · Page 4Linked to original sources

arXiv2022

A Monte Carlo Method for Rayleigh Scattering in Liquid Detectors

A new Monte Carlo method has been implemented to describe the angular and polarization distributions of anisotropic liquids, like water and linear alkylbenzene, by considering orientational fluctuations of polarizability tensors. The scattered light of anisotropic liquids is depolarized with an angular distribution of $1+(1-ρ_v)/(1+3ρ_v)\cos^2θ$, which is modified by the depolarization ratio $ρ_v$. A standalone experiment has validated the simulation results of LAB. The new method can provide more accurate knowledge on light propagation in large liquid detectors, which is beneficial to the developments of reconstruction for detectors.

physics.ins-det↗

arXiv2022

Directional Acoustic Luneburg Lens Waveguide

This paper investigates the acoustic Luneburg Lens (ALL) as a design framework for guiding acoustic wave propagation. In this study, we propose to develop an acoustic waveguide based on the characteristics of both acoustic wave focusing and collimation of cascaded ALLs. The continuous variation of refractive index of the ALL is achieved by using lattice unit cells with a graded filling ratio. A cascaded ALL waveguide device is fabricated based on the additive manufacturing technique. The experimental results obtained with this device are consistent with the numerical simulations and theoretical calculations.

physics.app-ph↗

arXiv2021

Acoustic jet based on acoustic metamaterial

In this paper, a novel gradient index (GRIN) acoustic metamaterial is proposed based on the concept of optical modified generalized Luneburg lens (MGLL). With the MGLL, double-foci and high energy density between the two foci can be achieved, which enables the realization of an ultra-long acoustic jet between the two foci. Compared to the generalized Luneburg lens with a single focus, which can only achieve short acoustic jet in the near field or ultra-long acoustic jet in the far field, the proposed MGLL can achieve an acoustic jet extending from the near field to the far field. This capability of the MGLL is theoretically and numerically demonstrated. Furthermore, acoustic metamaterial based MGLL is designed with lattice unit cells having a variable filling ratio. Numerical simulation results show that ultra-long acoustic jets can be achieved with a jet length of up to 30λ , covering both the near field and the far field.

physics.app-ph↗

arXiv2021

On-Surface Decarboxylation Coupling Facilitated by Lock-to-Unlock Variation of Molecules upon the Reaction

On-surface synthesis (OSS) involving relatively high energy barriers remains challenging due to a typical dilemma: firm molecular anchor is required to prevent molecular desorption upon the reaction, whereas sufficient lateral mobility is crucial for subsequent coupling and assembly. By locking the molecular precursors on the substrate then unlocking them during the reaction, we present a strategy to address this challenge. High-yield synthesis based on well-defined decarboxylation, intermediate transition and hexamerization is demonstrated, resulting in an extended and ordered network exclusively composed of the newly-synthesized macrocyclic compound. Thanks to the steric hindrance of its maleimide group, we attain a preferential selection of the coupling. This work unlocks a promising path to enrich the reaction types and improve the coupling selectivity hence the structual homogeneity of the final product for OSS.

physics.chem-ph↗

arXiv2021

Subsurface Carbon-Induced Local Charge of Copper for On-Surface Displacement Reaction

Transition metal carbides have sparked unprecedented enthusiasm as high-performance catalysts in recent years. Still, the catalytic properties of copper (Cu) carbide remain unexplored. By introducing subsurface carbon (C) to Cu(111), displacement reaction of proton in carboxyl acid group with single Cu atom is demonstrated at the atomic scale and room temperature. Its occurrence is attributed to the C-doping induced local charge of surface Cu atoms (up to +0.30 e/atom), which accelerates the rate of on-surface deprotonation via reduction of the corresponding energy barrier, thus enabling the instant displacement of a proton with a Cu atom when the molecules land on the surface. Such well-defined and robust Cu$^{δ+}$ surface based on the subsurface C doping offers a novel catalytic platform for on-surface synthesis.

cond-mat.other↗

arXiv2021

Online Testing of Subgroup Treatment Effects Based on Value Difference

Online A/B testing plays a critical role in the high-tech industry to guide product development and accelerate innovation. It performs a null hypothesis statistical test to determine which variant is better. However, a typical A/B test presents two problems: (i) a fixed-horizon framework inflates the false-positive errors under continuous monitoring; (ii) the homogeneous effects assumption fails to identify a subgroup with a beneficial treatment effect. In this paper, we propose a sequential test for subgroup treatment effects based on value difference, named SUBTLE, to address these two problems simultaneously. The SUBTLE allows the experimenters to "peek" at the results during the experiment without harming the statistical guarantees. It assumes heterogeneous treatment effects and aims to test if some subgroup of the population will benefit from the investigative treatment. If the testing result indicates the existence of such a subgroup, a subgroup will be identified using a readily available estimated optimal treatment rule. We examine the empirical performance of our proposed test on both simulations and a real dataset. The results show that the SUBTLE has high detection power with controlled type I error at any time, is more robust to noise covariates, and can achieve early stopping compared with the corresponding fixed-horizon test.

stat.ME↗

arXiv2021

Detecting and Counting Oysters

Oysters are an essential species in the Chesapeake Bay living ecosystem. Oysters are filter feeders and considered the vacuum cleaners of the Chesapeake Bay that can considerably improve the Bay's water quality. Many oyster restoration programs have been initiated in the past decades and continued to date. Advancements in robotics and artificial intelligence have opened new opportunities for aquaculture. Drone-like ROVs with high maneuverability are getting more affordable and, if equipped with proper sensory devices, can monitor the oysters. This work presents our efforts for videography of the Chesapeake bay bottom using an ROV, constructing a database of oysters, implementing Mask R-CNN for detecting oysters, and counting their number in a video by tracking them.

cs.RO↗

arXiv2021

Event vertex and time reconstruction in large volume liquid scintillator detector

Large-volume liquid scintillator detectors with ultra-low background levels have been widely used to study neutrino physics and search for dark matter. Event vertex and event time are not only useful for event selection but also essential for the reconstruction of event energy. In this study, four event vertex and event time reconstruction algorithms using charge and time information collected by photomultiplier tubes were analyzed comprehensively. The effects of photomultiplier tube properties were also investigated. The results indicate that the transit time spread is the main effect degrading the vertex reconstruction, while the effect of dark noise is limited. In addition, when the event is close to the detector boundary, the charge information provides better performance for vertex reconstruction than the time information.

physics.ins-det↗

arXiv2021

Improving the energy uniformity for large liquid scintillator detectors

It is challenging to achieve high precision energy resolution for large liquid scintillator detectors. Energy non-uniformity is one of the main obstacles. To surmount it, a calibration-data driven method was developed previously to reconstruct event energy in the JUNO experiment. In this paper, we investigated the choice of calibration sources thoroughly, optimized the calibration positions and corrected the residual detector azimuthal asymmetry. All these efforts lead to a reduction of the energy non-uniformity near the detector boundary, from about 0.64% to 0.38%. And within the fiducial volume of the detector it is improved from 0.3% to 0.17%. As a result the energy resolution could be further improved.

physics.ins-det↗

arXiv2020

Adaptive Tracking Control of Soft Robots using Integrated Sensing Skin and Recurrent Neural Networks

In this paper, we study integrated estimation and control of soft robots. A significant challenge in deploying closed loop controllers is reliable proprioception via integrated sensing in soft robots. Despite the considerable advances accomplished in fabrication, modelling, and model-based control of soft robots, integrated sensing and estimation is still in its infancy. To that end, this paper introduces a new method of estimating the degree of curvature of a soft robot using a stretchable sensing skin. The skin is a spray-coated piezoresistive sensing layer on a latex membrane. The mapping from the strain signal to the degree of curvature is estimated by using a recurrent neural network. We investigate uni-directional bending as well as bi-directional bending of a single-segment soft robot. Moreover, an adaptive controller is developed to track the degree of curvature of the soft robot in the presence of dynamic uncertainties. Subsequently, using the integrated soft sensing skin, we experimentally demonstrate successful curvature tracking control of the soft robot.

cs.RO↗

arXiv2020

Why Layer-Wise Learning is Hard to Scale-up and a Possible Solution via Accelerated Downsampling

Layer-wise learning, as an alternative to global back-propagation, is easy to interpret, analyze, and it is memory efficient. Recent studies demonstrate that layer-wise learning can achieve state-of-the-art performance in image classification on various datasets. However, previous studies of layer-wise learning are limited to networks with simple hierarchical structures, and the performance decreases severely for deeper networks like ResNet. This paper, for the first time, reveals the fundamental reason that impedes the scale-up of layer-wise learning is due to the relatively poor separability of the feature space in shallow layers. This argument is empirically verified by controlling the intensity of the convolution operation in local layers. We discover that the poorly-separable features from shallow layers are mismatched with the strong supervision constraint throughout the entire network, making the layer-wise learning sensitive to network depth. The paper further proposes a downsampling acceleration approach to weaken the poor learning of shallow layers so as to transfer the learning emphasis to deep feature space where the separability matches better with the supervision restraint. Extensive experiments have been conducted to verify the new finding and demonstrate the advantages of the proposed downsampling acceleration in improving the performance of layer-wise learning.

cs.CV↗

arXiv2020

Broadband Acoustic Collimation and Focusing using Reduced Aberration Acoustic Luneburg Lens

Acoustic Luneburg lens is a symmetric gradient-index lens with a refractive index decreasing radially from the centre to the outer surface. It can be used to manipulate acoustic wave propagation with collimation and focusing capabilities. However, previously studied acoustic Luneburg lens works only at audible frequency ranges from 1 kHz to 7 kHz, or at a single ultrasonic frequency of 40 kHz. In addition, the acoustic Luneburg lens at high frequency is not omnidirectional in the previous researches. Furthermore, there is no realization of simulation and experimental testing of 3D acoustic Luneburg lens until now. In this paper, a practical reduced aberration acoustic Luneburg lens (RAALL) are proposed for broadband and omnidirectional acoustic collimation and focusing with reduced aberrations. Ray tracing technique shows that RAALL can achieve a better acoustic focusing compared with traditional modified acoustic Luneburg lens. Following that, two models of RAALL are designed and fabricated through additive manufacturing technology: a 2D version and a 3D version. Collimation and focusing performances of the ultrasonic waves are theoretically, numerically and experimentally demonstrated for both 2D and 3D lenses, and their broadband and omnidirectional characteristics are verified.

physics.app-ph↗

arXiv2020

Realization of Flattened Structural Luneburg Lens Based on Quasi-Conformal Transformation

Conventional structural Luneburg lens is a symmetric circular gradient-index lens with refractive indices decreasing from the centre along the radial direction. In this paper, a flattened structural Luneburg lens (FSLL) based on structural thickness variations is designed by using the quasi-conformal transformation (QCT) technique. Through numerical simulations and experimental studies, the FSLL is demonstrated to have excellent beam steering performance for the manipulation of flexural wave propagation at desired angles.

physics.app-ph↗

arXiv2020

Bi-functional Structural Luneburg Lens for Broadband Structural Wave Cloaking and Waveguide

In this paper, we explore the concept of structural Luneburg Lens (SLL) as a design framework for performing dynamic structural tailoring to obtain a structural wave cloak and a structural waveguide. The SLL is a graded refractive index lens, which is realized by using a variable thickness structure defined in a thin plate. Due to the thickness variation of the plate, the refractive index decreases radially from the centre to the outer surface of the lens. By taking advantage of the unique capabilities of SLL for flexural wave focusing and collimation, we develop a structural wave cloak and waveguide based on cascaded SLLs. The cascaded SLL design enables the integration of functional devices into thin-walled structures while preserving the structural characteristics. Analytical, numerical, and experimental studies are carried out to characterize the performance of the SLL cloak and the SLL waveguide. The results demonstrate that that these cascaded SLL devices exhibit excellent performance for structural wave cloaking and waveguiding over a broadband operating frequency range.

physics.app-ph↗

arXiv2020

Multi-Functional Variable Thickness Structure for Broadband and Omnidirectional Focusing and Collimation

Luneburg lens is a symmetric gradient-index lens with a refractive index that increases from the outer surface to the center in a radial manner. It has the ability to focus and collimate waves, which makes it useful for energy harvesting, waveguiding and as a component in transducers. An ideal Luneburg lens should be easy to fabricate, has broadband and omnidirectional characteristics, as well as a focal length that can be easily tuned. However, existing structural Luneburg lenses based on phononic crystals can hardly achieve these requirements. Here, we propose an alternative structural Luneburg lens which has a refractive index that varies smoothly with its radial distance as a result of a changing thickness. Theoretical calculations, numerical simulations and experimental measurements of flexural wave propagation through the lens showed that flexural wave focusing can be obtained inside, at the edge and outside of the variable thickness lens for different frequencies and propagation directions. Flexural wave collimation was also demonstrated when a point source was placed at the respective focal points for each lens. Furthermore, it was shown that flexural waves that were focused onto a piezoelectric energy harvester by the Luneburg lens can lead to a significant increase in the harvested voltage compared to that obtained without focusing.

physics.app-ph↗

arXiv2020

A New Framework for Online Testing of Heterogeneous Treatment Effect

We propose a new framework for online testing of heterogeneous treatment effects. The proposed test, named sequential score test (SST), is able to control type I error under continuous monitoring and detect multi-dimensional heterogeneous treatment effects. We provide an online p-value calculation for SST, making it convenient for continuous monitoring, and extend our tests to online multiple testing settings by controlling the false discovery rate. We examine the empirical performance of the proposed tests and compare them with a state-of-art online test, named mSPRT using simulations and a real data. The results show that our proposed test controls type I error at any time, has higher detection power and allows quick inference on online A/B testing.

stat.ME↗

arXiv2019

Ultrasound Beam Steering with Flattened Acoustic Metamaterial Luneburg Lens

We report ultrasound beam steering based on 2D and 3D flattened acoustic metamaterial Luneburg lenses at 40 kHz. The effective properties of the lenses are obtained by using the quasi-conformal transformation (QCT) technique and solving the Laplace equation with Dirichlet and Neumann boundary conditions. A 2D lens and a 3D lens were designed and fabricated. The numerical and experimental results with these lenses demonstrate excellent beam steering performance of ultrasonic waves in both near field and far field.

physics.app-ph↗

arXiv2019

Progressive Pose Attention Transfer for Person Image Generation

This paper proposes a new generative adversarial network for pose transfer, i.e., transferring the pose of a given person to a target pose. The generator of the network comprises a sequence of Pose-Attentional Transfer Blocks that each transfers certain regions it attends to, generating the person image progressively. Compared with those in previous works, our generated person images possess better appearance consistency and shape consistency with the input images, thus significantly more realistic-looking. The efficacy and efficiency of the proposed network are validated both qualitatively and quantitatively on Market-1501 and DeepFashion. Furthermore, the proposed architecture can generate training images for person re-identification, alleviating data insufficiency. Codes and models are available at: https://github.com/tengteng95/Pose-Transfer.git.

cs.CV↗