arXiv subjects

Jiahui Xu

Publications and source records attributed to Jiahui Xu.

33 records · Page 2Linked to original sources

arXiv2024

Unidirectional Chiral Emission via Twisted Bi-layer Metasurfaces

Controlling and channelling light emissions from unpolarized quantum dots into specific directions with chiral polarization remains a key challenge in modern photonics. Stacked metasurface designs offer a potential compact solution for chirality and directionality engineering. However, experimental observations of directional chiral radiation from resonant metasurfaces with quantum emitters remain obscure. In this paper, we present experimental observations of unidirectional chiral emission from a twisted bi-layer metasurface via multi-dimensional control, including twist angle, interlayer distance, and lateral displacement between the top and bottom layers, as enabled by doublet alignment lithography (DAL). First, maintaining alignment, the metasurface demonstrates a resonant intrinsic optical chirality with near-unity circular dichroism of 0.94 and reflectance difference of 74%, where a high circular dichroism greater than 0.9 persists across a wide range of angles from -11 to 11 degrees. Second, engineered lateral displacement induces a unidirectional chiral resonance, resulting in unidirectional chiral emission from the quantum dots deposited onto the metasurface. Our bi-layer metasurfaces offer a universal compact platform for efficient radiation manipulation over a wide angular range, promising potential applications in miniaturized lasers, grating couplers, and chiral nanoantennas.

physics.optics↗

arXiv2024

Pose2Gaze: Eye-body Coordination during Daily Activities for Gaze Prediction from Full-body Poses

Human eye gaze plays a significant role in many virtual and augmented reality (VR/AR) applications, such as gaze-contingent rendering, gaze-based interaction, or eye-based activity recognition. However, prior works on gaze analysis and prediction have only explored eye-head coordination and were limited to human-object interactions. We first report a comprehensive analysis of eye-body coordination in various human-object and human-human interaction activities based on four public datasets collected in real-world (MoGaze), VR (ADT), as well as AR (GIMO and EgoBody) environments. We show that in human-object interactions, e.g. pick and place, eye gaze exhibits strong correlations with full-body motion while in human-human interactions, e.g. chat and teach, a person's gaze direction is correlated with the body orientation towards the interaction partner. Informed by these analyses we then present Pose2Gaze, a novel eye-body coordination model that uses a convolutional neural network and a spatio-temporal graph convolutional neural network to extract features from head direction and full-body poses, respectively, and then uses a convolutional neural network to predict eye gaze. We compare our method with state-of-the-art methods that predict eye gaze only from head movements and show that Pose2Gaze outperforms these baselines with an average improvement of 24.0% on MoGaze, 10.1% on ADT, 21.3% on GIMO, and 28.6% on EgoBody in mean angular error, respectively. We also show that our method significantly outperforms prior methods in the sample downstream task of eye-based activity recognition. These results underline the significant information content available in eye-body coordination during daily activities and open up a new direction for gaze prediction.

cs.CV↗

arXiv2024

Towards Safe and Reliable Autonomous Driving: Dynamic Occupancy Set Prediction

In the rapidly evolving field of autonomous driving, reliable prediction is pivotal for vehicular safety. However, trajectory predictions often deviate from actual paths, particularly in complex and challenging environments, leading to significant errors. To address this issue, our study introduces a novel method for Dynamic Occupancy Set (DOS) prediction, it effectively combines advanced trajectory prediction networks with a DOS prediction module, overcoming the shortcomings of existing models. It provides a comprehensive and adaptable framework for predicting the potential occupancy sets of traffic participants. The innovative contributions of this study include the development of a novel DOS prediction model specifically tailored for navigating complex scenarios, the introduction of precise DOS mathematical representations, and the formulation of optimized loss functions that collectively advance the safety and efficiency of autonomous systems. Through rigorous validation, our method demonstrates marked improvements over traditional models, establishing a new benchmark for safety and operational efficiency in intelligent transportation systems.

cs.RO↗

arXiv2024

DTP-Net: Learning to Reconstruct EEG signals in Time-Frequency Domain by Multi-scale Feature Reuse

Electroencephalography (EEG) signals are easily corrupted by various artifacts, making artifact removal crucial for improving signal quality in scenarios such as disease diagnosis and brain-computer interface (BCI). In this paper, we present a fully convolutional neural architecture, called DTP-Net, which consists of a Densely Connected Temporal Pyramid (DTP) sandwiched between a pair of learnable time-frequency transformations for end-to-end electroencephalogram (EEG) denoising. The proposed method first transforms a single-channel EEG signal of arbitrary length into the time-frequency domain via an Encoder layer. Then, noises, such as ocular and muscle artifacts, are extracted by DTP in a multi-scale fashion and reduced. Finally, a Decoder layer is employed to reconstruct the artifact-reduced EEG signal. Additionally, we conduct an in-depth analysis of the representation learning behavior of each module in DTP-Net to substantiate its robustness and reliability. Extensive experiments conducted on two public semi-simulated datasets demonstrate the effective artifact removal performance of DTP-Net, which outperforms state-of-art approaches. Experimental results demonstrate cleaner waveforms and significant improvement in Signal-to-Noise Ratio (SNR) and Relative Root Mean Square Error (RRMSE) after denoised by the proposed model. Moreover, the proposed DTP-Net is applied in a specific BCI downstream task, improving the classification accuracy by up to 5.55% compared to that of the raw signals, validating its potential applications in the fields of EEG-based neuroscience and neuro-engineering.

eess.SP↗

arXiv2024

Uncertainty-Aware Prediction and Application in Planning for Autonomous Driving: Definitions, Methods, and Comparison

Autonomous driving systems face the formidable challenge of navigating intricate and dynamic environments with uncertainty. This study presents a unified prediction and planning framework that concurrently models short-term aleatoric uncertainty (SAU), long-term aleatoric uncertainty (LAU), and epistemic uncertainty (EU) to predict and establish a robust foundation for planning in dynamic contexts. The framework uses Gaussian mixture models and deep ensemble methods, to concurrently capture and assess SAU, LAU, and EU, where traditional methods do not integrate these uncertainties simultaneously. Additionally, uncertainty-aware planning is introduced, considering various uncertainties. The study's contributions include comparisons of uncertainty estimations, risk modeling, and planning methods in comparison to existing approaches. The proposed methods were rigorously evaluated using the CommonRoad benchmark and settings with limited perception. These experiments illuminated the advantages and roles of different uncertainty factors in autonomous driving processes. In addition, comparative assessments of various uncertainty modeling strategies underscore the benefits of modeling multiple types of uncertainties, thus enhancing planning accuracy and reliability. The proposed framework facilitates the development of methods for UAP and surpasses existing uncertainty-aware risk models, particularly when considering diverse traffic scenarios. Project page: https://swb19.github.io/UAP/.

cs.RO↗

arXiv2023

Enhancing Black-Box Few-Shot Text Classification with Prompt-Based Data Augmentation

Training or finetuning large-scale language models (LLMs) such as GPT-3 requires substantial computation resources, motivating recent efforts to explore parameter-efficient adaptation to downstream tasks. One practical area of research is to treat these models as black boxes and interact with them through their inference APIs. In this paper, we investigate how to optimize few-shot text classification without accessing the gradients of the LLMs. To achieve this, we treat the black-box model as a feature extractor and train a classifier with the augmented text data. Data augmentation is performed using prompt-based finetuning on an auxiliary language model with a much smaller parameter size than the black-box model. Through extensive experiments on eight text classification datasets, we show that our approach, dubbed BT-Classifier, significantly outperforms state-of-the-art black-box few-shot learners and performs on par with methods that rely on full-model tuning.

cs.CL↗

arXiv2023

Combining X-ray Nano-CT and XANES Techniques for 3D Operando Monitoring of Lithiation Spatial Composition evolution in NMC Electrode

In this study, we present a well-defined methodology for conducting Operando X-ray absorption near-edge structure spectroscopy (XANES) in conjunction with transmission X-ray nano computed tomography (TXM-nanoCT) experiments on the LiNi$_{0.5}$Mn$_{0.3}$Co$_{0.2}$O$_2$ (NMC) cathode electrode. To minimize radiation-induced damage to the sample during charge and discharge cycles and to gain a comprehensive 3D perspective of the (de)lithiation process of the active material, we propose a novel approach that relies on employing only three energy levels, strategically positioned at pre-edge, edge, and post-edge. By adopting this technique, we successfully track the various (de)lithiation states within the three-dimensional space during partial cycling. Furthermore, we are able to extract the nanoscale lithium distribution within individual secondary particles. Our observations reveal the formation of a core-shell structure during lithiation and we also identify that not all surface areas of the particles exhibit activity during the process. Notably, lithium intercalation exhibits a distinct preference, leading to non-uniform lithiation degrees across different electrode locations. The proposed methodology is not limited to the NMC cathode electrode but can be extended to study realistic dedicated electrodes with high active material (AM) density, facilitating exploration and quantification of heterogeneities and inhomogeneous lithiation within such electrodes. This multi-scale insight into the (de)lithiation process and lithiation heterogeneities within the electrodes is expected to provide valuable knowledge for optimizing electrode design and ultimately enhancing electrode performance in the context of material science and battery materials research.

physics.chem-ph↗

arXiv2023

Computational Model for Predicting Particle Fracture During Electrode Calendering

In the context of calling for low carbon emissions, lithium-ion batteries (LIBs) have been widely concerned as a power source for electric vehicles, so the fundamental science behind their manufacturing has attracted much attention in recent years. Calendering is an important step of the LIB electrode manufacturing process, and the changes it brings to the electrode microstructure and mechanical properties are worth studying. In this work, we reported the observed cracking of active material (AM) particles due to calendering pressure under ex situ nano-X-ray tomography experiments. We developed a 3D-resolved discrete element method (DEM) model with bonded connections to physically mimic the calendering process using real AM particle shapes derived from the tomography experiments. The DEM model can well predict the change of the morphology of the dry electrode under pressure, and the changes of the applied pressure and porosity are consistent with the experimental values. At the same time, the model is able to simulate the secondary AM particles cracking by the fracture of the bond under force. Our model is the first of its kind being able to predict the fracture of the secondary particles along the calendering process. This work provides a tool for guidance in the manufacturing of optimized LIB electrodes.

physics.comp-ph↗

arXiv2022

Tri-Functional Metasurface for Phase, Amplitude, and Luminescence Control

In optical anti-counterfeiting, several distinct optically variable devices (OVDs) are often concurrently employed to compensate for the insufficient security level of constituent OVDs. Alternatively, metasurfaces that exhibit multiple optical responses effectively combine multiple OVDs into one, thus significantly enhancing their security and hindering fraudulent replication. This work demonstrates the simultaneous control of three separate optical responses, i.e., phase, amplitude, and luminescence, using anisotropic gap-plasmon metasurfaces. Due to the incorporated geometric anisotropy, the designed structure exhibits distinct responses under x- and y-polarized light, revealing either a color image, or a holographic projection in the far field. Furthermore, inserting upconversion nanoparticles (UCNPs) into the dielectric gaps of the structures, the designed metasurface is able to generate a third luminescent image upon illumination with the near-infrared light. The stochastic distribution of the UCNPs constitutes a unique fingerprint, achieving a physically unclonable function (PUF) layer. Crucially, our triple-mode metasurface requires only readily attainable equipment such as a macro-lens/camera and a laser pointer to read most of the channels, thus paving the way towards highly secure and easy-to-authenticate metasurface-driven OVDs (mOVDs).

physics.optics↗

arXiv2022

An Experimentally-Validated 3D Electrochemical Model Revealing Electrode Manufacturing Parameters Effects on Battery Performance

Electrode manufacturing is at the core of the lithium ion battery (LIB) fabrication process. The electrode microstructure and the electrochemical performance are determined by the adopted manufacturing parameters. However, in view of the strong interdependencies between these parameters, evaluating their influence on the performance is not a trivial task. In this work we present an experimentally validated 3D-resolved electrochemical model of a NMC111-based electrode which reveals how slurry formulation and calendering degree affect the electrode performance. A series of electrodes with different formulations and calendering degrees were fabricated at the experimental level. Corresponding three-dimensional manufacturing models were built based on the same experimental manufacturing parameters to generate the digital counterparts of the experimental electrodes that were then used in the electrochemical model. The results of simulations and experiments were compared individually. Among the manufacturing parameters analyzed, we found that the major factors linking manufacturing parameters and electrode performance are the carbon and binder domain (CBD) distribution within the electrode volume, and the electrostatic potential difference between the electrode and the current collector. A well-connected electronic conductive network throughout the electrode is vital for ensuring full utilization of active material, and it was found that increasing calendering degree is effective in reducing interfacial impedance. This work uncovers, based on a dual modeling/experimental approach, the essence of how electrode manufacturing process takes effect on electrode performance by influencing its microstructure.

physics.chem-ph↗

arXiv2022

Lithium Ion Battery Electrode Manufacturing Model Accounting for 3D Realistic Shapes of Active Material Particles

The demand for lithium ion batteries (LIBs) on the market has gradually risen, with production increasing every year. To meet industrial needs, the development of digital twins designed to optimize LIB manufacturing processes is essential. Here, by using LiNi0.33Co0.33Mn0.33O2 (NMC111) material as an example, we introduce the realistic particles shapes of the active material obtained from X-ray micro-computed tomography into a Coarse-Grained Molecular Dynamic physical model to simulate the slurry and its drying, and into a Discrete Element Method model able to simulate the calendering of the resulting electrode. This model enables to link the manufacturing parameters with the microstructure of the electrodes and to better observe the effect of the former on the heterogeneity of the electrodes. The results of the simulations allow us, among others, to observe the alteration of the electrode heterogeneity during the manufacturing process and the slight deformation of the secondary particles of active material.

physics.chem-ph↗

arXiv2022

Functional Data-Driven Framework for Fast Forecasting of Electrode Slurry Rheology Simulated by Molecular Dynamics

Computational modeling of the manufacturing process of Lithium-Ion Battery (LIB) composite electrodes based on mechanistic approaches, allows predicting the influence of manufacturing parameters on electrode properties. However, ensuring that the calculated properties match well with experimental data, is typically time and resources consuming In this work, we tackled this issue by proposing a functional data-driven framework combining Functional Principal Component Analysis and K-Nearest Neighbors algorithms. This aims first to recover the early numerical values of a mechanistic electrode manufacturing simulation to predict if the observable being calculated is prone to match or not, \textit{i.e} screening step. In a second step it recovers additional numerical values of the ongoing mechanistic simulation iterations to predict the mechanistic simulation result, \textit{i.e} forecasting step. We demonstrated this approach in context of LIB manufacturing through non-equilibrium molecular dynamics (NEMD) simulations, aiming to capture the rheological behavior of electrode slurries. We discuss in full details our novel methodology and we report that the expected mechanistic simulation results can be obtained 11 times faster with respect to running the complete mechanistic simulation, while being accurate enough from an experimental point of view, with a $F1_{score}$ equals to 0.90, and a $R^2_{score}$ equals to 0.96 for the learnings validation. This paves the way towards a powerful tool to drastically reduce the utilization of computational resources while running mechanistic simulations of battery manufacturing electrodes.

physics.chem-ph↗

arXiv2021

Group-Aware Graph Neural Network for Nationwide City Air Quality Forecasting

The problem of air pollution threatens public health. Air quality forecasting can provide the air quality index hours or even days later, which can help the public to prevent air pollution in advance. Previous works focus on citywide air quality forecasting and cannot solve nationwide city forecasting problem, whose difficulties lie in capturing the latent dependencies between geographically distant but highly correlated cities. In this paper, we propose the group-aware graph neural network (GAGNN), a hierarchical model for nationwide city air quality forecasting. The model constructs a city graph and a city group graph to model the spatial and latent dependencies between cities, respectively. GAGNN introduces differentiable grouping network to discover the latent dependencies among cities and generate city groups. Based on the generated city groups, a group correlation encoding module is introduced to learn the correlations between them, which can effectively capture the dependencies between city groups. After the graph construction, GAGNN implements message passing mechanism to model the dependencies between cities and city groups. The evaluation experiments on Chinese city air quality dataset indicate that our GAGNN outperforms existing forecasting models.

cs.LG↗

arXiv2020

Recyclable Waste Identification Using CNN Image Recognition and Gaussian Clustering

Waste recycling is an important way of saving energy and materials in the production process. In general cases recyclable objects are mixed with unrecyclable objects, which raises a need for identification and classification. This paper proposes a convolutional neural network (CNN) model to complete both tasks. The model uses transfer learning from a pretrained Resnet-50 CNN to complete feature extraction. A subsequent fully connected layer for classification was trained on the augmented TrashNet dataset [1]. In the application, sliding-window is used for image segmentation in the pre-classification stage. In the post-classification stage, the labelled sample points are integrated with Gaussian Clustering to locate the object. The resulting model has achieved an overall detection rate of 48.4% in simulation and final classification accuracy of 92.4%.

cs.CV↗

arXiv2019

Uncovering Sociological Effect Heterogeneity using Machine Learning

Individuals do not respond uniformly to treatments, events, or interventions. Sociologists routinely partition samples into subgroups to explore how the effects of treatments vary by covariates like race, gender, and socioeconomic status. In so doing, analysts determine the key subpopulations based on theoretical priors. Data-driven discoveries are also routine, yet the analyses by which sociologists typically go about them are problematic and seldom move us beyond our expectations, and biases, to explore new meaningful subgroups. Emerging machine learning methods allow researchers to explore sources of variation that they may not have previously considered, or envisaged. In this paper, we use causal trees to recursively partition the sample and uncover sources of treatment effect heterogeneity. We use honest estimation, splitting the sample into a training sample to grow the tree and an estimation sample to estimate leaf-specific effects. Assessing a central topic in the social inequality literature, college effects on wages, we compare what we learn from conventional approaches for exploring variation in effects to causal trees. Given our use of observational data, we use leaf-specific matching and sensitivity analyses to address confounding and offer interpretations of effects based on observed and unobserved heterogeneity. We encourage researchers to follow similar practices in their work on variation in sociological effects.

stat.OT↗