arXiv subjects

Dawei Liu

Publications and source records attributed to Dawei Liu.

33 records · Page 2Linked to original sources

arXiv2025

Spatiotemporal Topological Combs for Robust High-Dimensional Information Transmission

Sculpting light across its independent degrees of freedom-from orbital angular momentum to the discrete wavelengths of optical frequency combs-has unlocked vast communication bandwidth by enabling massively parallel information channels. However, the Shannon-Hartley theorem sets a hard limit by tying channel capacity to the trade-off between SNR and rate, a central challenge in communication. Inspired by lock-in amplification in electronics, we encode data on THz optical burst carriers so the signal resides beyond the conventional noise band, yielding exceptional robustness. By leveraging a programmable all-degree-of-freedom (All-DoF) modulator, we generate a spatiotemporal topological comb (ST-Comb) that structures light into a vast, highentropy state space for high-dimensional information encoding. Crucially, we find that the associated topological winding number is preserved under diverse perturbations, ensuring stable information encoding and retrieval. This paradigm illustrates how structured light can simultaneously expand channel dimensionality and maintain robustness, charting a pathway to chip-scale, reconfigurable photonic platforms for the PHz era, while also opening previously inaccessible regimes of light-matter interaction.

physics.optics↗

arXiv2025

Polarization-Sensitive Au-TiO2 Nanopillars for Tailored Photocatalytic Activity

Plasmonic metasurfaces play a crucial role in resonance-driven photocatalytic reactions by effectively enhancing reactivity via localized surface plasmon resonances. Catalytic activity can be selectively modulated by tuning the strength of plasmonic resonances through two primary non-thermal mechanisms: near-field enhancement and hot carrier injection, which govern the population of energetic carrier excited or injected into unoccupied molecular orbitals. We developed a set of polarization-sensitive metasurfaces consisting of elliptical Au-TiO2 nanopillars, specifically designed to plasmonically modulate the reactivity of a model reaction: the photocatalytic degradation of methylene blue. Surface-enhanced Raman spectroscopy reveals a polarization-dependent reaction yield in real-time, modulating from 4.7 (transverse electric polarization) to 9.98 (transverse magnetic polarization) in 10 s period, as quantified by the integrated area of the 480 cm-1 Raman peak and correlated with enhanced absorption at 633 nm. The single metasurface configuration enables continuous tuning of photocatalytic reactivity via active control of plasmonic resonance strength, as evidenced by the positive correlation between measured absorption and product yield. This dynamic approach provides a route to selectively enhance or suppress resonance-driven reactions, which can be further leveraged to achieve selectivity in multibranch reactions, guiding product yields toward desired outcomes.

physics.optics↗

arXiv2025

Multiple Object Stitching for Unsupervised Representation Learning

Contrastive learning for single object centric images has achieved remarkable progress on unsupervised representation, but suffering inferior performance on the widespread images with multiple objects. In this paper, we propose a simple but effective method, Multiple Object Stitching (MOS), to refine the unsupervised representation for multi-object images. Specifically, we construct the multi-object images by stitching the single object centric ones, where the objects in the synthesized multi-object images are predetermined. Hence, compared to the existing contrastive methods, our method provides additional object correspondences between multi-object images without human annotations. In this manner, our method pays more attention to the representations of each object in multi-object image, thus providing more detailed representations for complicated downstream tasks, such as object detection and semantic segmentation. Experimental results on ImageNet, CIFAR and COCO datasets demonstrate that our proposed method achieves the leading unsupervised representation performance on both single object centric images and multi-object ones. The source code is available at https://github.com/visresearch/MultipleObjectStitching.

cs.CV↗

arXiv2025

Liquid Metal-Exfoliated SnO$_2$-Based Mixed-dimensional Heterostructures for Visible-to-Near-Infrared Photodetection

Ultra-thin two-dimensional (2D) materials have gained significant attention for making next-generation optoelectronic devices. Here, we report a large-area heterojunction photodetector fabricated using a liquid metal-printed 2D $\text{SnO}_2$ layer transferred onto CdTe thin films. The resulting device demonstrates efficient broadband light sensing from visible to near-infrared wavelengths, with enhanced detectivity and faster photo response than bare CdTe photodetectors. Significantly, the device shows a nearly $10^5$-fold increase in current than the dark current level when illuminated with a 780 nm laser and achieves a specific detectivity of around $10^{12} \, \text{Jones}$, nearly two orders of magnitude higher than a device with pure CdTe thin film. Additionally, temperature-dependent optoelectronic testing shows that the device maintains a stable response up to $140^\circ \text{C}$ and generates distinctive photocurrent at temperatures up to $80^\circ \text{C}$, demonstrating its thermal stability. Using band structure analysis, density functional theory (DFT) calculations, and photocurrent mapping, the formation of a $p$-$n$ junction is indicated, contributing to the enhanced photo response attributed to the efficient carrier separation by the built-in potential in the hetero-junction and the superior electron mobility of 2D $\text{SnO}_2$. Our results highlight the effectiveness of integrating liquid metal-exfoliated 2D materials for enhanced photodetector performance.

cond-mat.mtrl-sci↗

arXiv2024

Noise Suppression for CRP Gathers Based on Self2Self with Dropout

Noise suppression in seismic data processing is a crucial research focus for enhancing subsequent imaging and reservoir prediction. Deep learning has shown promise in computer vision and holds significant potential for seismic data processing. However, supervised learning, which relies on clean labels to train network prediction models, faces challenges due to the unavailability of clean labels for seismic exploration data. In contrast, self-supervised learning substitutes traditional supervised learning with surrogate tasks by different auxiliary means, exploiting internal input data information. Inspired by Self2Self with Dropout, this paper presents a self-supervised learning-based noise suppression method called Self-Supervised Deep Convolutional Networks (SSDCN), specifically designed for Common Reflection Point (CRP) gathers. We utilize pairs of Bernoulli-sampled instances of the input noisy image as surrogate tasks to leverage its inherent structure. Furthermore, SSDCN incorporates geological knowledge through the normal moveout correction technique, which capitalizes on the approximately horizontal behavior and strong self-similarity observed in useful signal events within CRP gathers. By exploiting the discrepancy in self-similarity between the useful signals and noise in CRP gathers, SSDCN effectively extracts self-similarity features during training iterations, prioritizing the extraction of useful signals to achieve noise suppression. Experimental results on synthetic and actual CRP gathers demonstrate that SSDCN achieves high-fidelity noise suppression.

physics.geo-ph↗

arXiv2024

A Survey on Image-text Multimodal Models

With the significant advancements of Large Language Models (LLMs) in the field of Natural Language Processing (NLP), the development of image-text multimodal models has garnered widespread attention. Current surveys on image-text multimodal models mainly focus on representative models or application domains, but lack a review on how general technical models influence the development of domain-specific models, which is crucial for domain researchers. Based on this, this paper first reviews the technological evolution of image-text multimodal models, from early explorations of feature space to visual language encoding structures, and then to the latest large model architectures. Next, from the perspective of technological evolution, we explain how the development of general image-text multimodal technologies promotes the progress of multimodal technologies in the biomedical field, as well as the importance and complexity of specific datasets in the biomedical domain. Then, centered on the tasks of image-text multimodal models, we analyze their common components and challenges. After that, we summarize the architecture, components, and data of general image-text multimodal models, and introduce the applications and improvements of image-text multimodal models in the biomedical field. Finally, we categorize the challenges faced in the development and application of general models into external factors and intrinsic factors, further refining them into 2 external factors and 5 intrinsic factors, and propose targeted solutions, providing guidance for future research directions. For more details and data, please visit our GitHub page: \url{https://github.com/i2vec/A-survey-on-image-text-multimodal-models}.

cs.CL↗

arXiv2023

Inter-Instance Similarity Modeling for Contrastive Learning

The existing contrastive learning methods widely adopt one-hot instance discrimination as pretext task for self-supervised learning, which inevitably neglects rich inter-instance similarities among natural images, then leading to potential representation degeneration. In this paper, we propose a novel image mix method, PatchMix, for contrastive learning in Vision Transformer (ViT), to model inter-instance similarities among images. Following the nature of ViT, we randomly mix multiple images from mini-batch in patch level to construct mixed image patch sequences for ViT. Compared to the existing sample mix methods, our PatchMix can flexibly and efficiently mix more than two images and simulate more complicated similarity relations among natural images. In this manner, our contrastive framework can significantly reduce the gap between contrastive objective and ground truth in reality. Experimental results demonstrate that our proposed method significantly outperforms the previous state-of-the-art on both ImageNet-1K and CIFAR datasets, e.g., 3.0% linear accuracy improvement on ImageNet-1K and 8.7% kNN accuracy improvement on CIFAR100. Moreover, our method achieves the leading transfer performance on downstream tasks, object detection and instance segmentation on COCO dataset. The code is available at https://github.com/visresearch/patchmix

cs.CV↗

arXiv2022

Nash Equilibrium Seeking Over Directed Graphs

In this paper, we aim to develop distributed continuous-time algorithms over directed graphs to seek the Nash equilibrium in a noncooperative game. Motivated by the recent consensus-based designs, we present a distributed algorithm with a proportional gain for weight-balanced directed graphs. By further embedding a distributed estimator of the left eigenvector associated with zero eigenvalue of the graph Laplacian, we extend it to the case with arbitrary strongly connected directed graphs having possible unbalanced weights. In both cases, the Nash equilibrium is proven to be exactly reached with an exponential convergence rate. An example is given to illustrate the validity of the theoretical results.

math.OC↗

arXiv2021

Quality control, data cleaning, imputation

This chapter addresses important steps during the quality assurance and control of RWD, with particular emphasis on the identification and handling of missing values. A gentle introduction is provided on common statistical and machine learning methods for imputation. We discuss the main strengths and weaknesses of each method, and compare their performance in a literature review. We motivate why the imputation of RWD may require additional efforts to avoid bias, and highlight recent advances that account for informative missingness and repeated observations. Finally, we introduce alternative methods to address incomplete data without the need for imputation.

stat.ME↗

arXiv2020

Machine-learning-based methods for output only structural modal identification

In this study, we propose a machine-learning-based approach to identify the modal parameters of the output-only data for structural health monitoring (SHM) that makes full use of the characteristic of independence of modal responses and the principle of machine learning. By taking advantage of the independence feature of each mode, we use the principle of unsupervised learning, making the training process of the deep neural network becomes the process of modal separation. A self-coding deep neural network is designed to identify the structural modal parameters from the vibration data of structures. The mixture signals, that is, the structural response data, are used as the input of the neural network. Then we use a complex loss function to restrict the training process of the neural network, making the output of the third layer the modal responses we want, and the weights of the last two layers are mode shapes. The deep neural network is essentially a nonlinear objective function optimization problem. A novel loss function is proposed to constrain the independent feature with consideration of uncorrelation and non-Gaussianity to restrict the designed neural network to obtain the structural modal parameters. A numerical example of a simple structure and an example of actual SHM data from a cable-stayed bridge are presented to illustrate the modal parameter identification ability of the proposed approach. The results show the approach's good capability in blindly extracting modal information from system responses.

cs.LG↗

arXiv2019

Semi-Unsupervised Lifelong Learning for Sentiment Classification: Less Manual Data Annotation and More Self-Studying

Lifelong machine learning is a novel machine learning paradigm which can continually accumulate knowledge during learning. The knowledge extracting and reusing abilities enable the lifelong machine learning to solve the related problems. The traditional approaches like Naïve Bayes and some neural network based approaches only aim to achieve the best performance upon a single task. Unlike them, the lifelong machine learning in this paper focuses on how to accumulate knowledge during learning and leverage them for further tasks. Meanwhile, the demand for labelled data for training also is significantly decreased with the knowledge reusing. This paper suggests that the aim of the lifelong learning is to use less labelled data and computational cost to achieve the performance as well as or even better than the supervised learning.

cs.CL↗

arXiv2017

Complementary Normalized Compressive Ghost Imaging with Entangled Photons

We demonstrate a compressive normalized ghost imaging system with entangled photons employing complementary compressive imaging (CCI) technique. The quantum ghost image reconstruction was achieved at only 19.53% sampling ratio of raster scanning. With the special +1/-1 type sensing matrix and appropriate optimal algorithm, the photon utilization efficiency and robustness of the imaging system is enhanced significantly. Our results reveal the great potential of CCI technique applied in quantum imaging and other quantum optics field such as quantum charactering and quantum state tomography to use the information loaded in each photon with high efficiency.

quant-ph↗

arXiv2015

Neutrino Physics with JUNO

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purpose underground liquid scintillator detector, was proposed with the determination of the neutrino mass hierarchy as a primary physics goal. It is also capable of observing neutrinos from terrestrial and extra-terrestrial sources, including supernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos, atmospheric neutrinos, solar neutrinos, as well as exotic searches such as nucleon decays, dark matter, sterile neutrinos, etc. We present the physics motivations and the anticipated performance of the JUNO detector for various proposed measurements. By detecting reactor antineutrinos from two power plants at 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4 sigma significance with six years of running. The measurement of antineutrino spectrum will also lead to the precise determination of three out of the six oscillation parameters to an accuracy of better than 1\%. Neutrino burst from a typical core-collapse supernova at 10 kpc would lead to ~5000 inverse-beta-decay events and ~2000 all-flavor neutrino-proton elastic scattering events in JUNO. Detection of DSNB would provide valuable information on the cosmic star-formation rate and the average core-collapsed neutrino energy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400 events per year, significantly improving the statistics of existing geoneutrino samples. The JUNO detector is sensitive to several exotic searches, e.g. proton decay via the $p\to K^++\barν$ decay channel. The JUNO detector will provide a unique facility to address many outstanding crucial questions in particle and astrophysics. It holds the great potential for further advancing our quest to understanding the fundamental properties of neutrinos, one of the building blocks of our Universe.

physics.ins-det↗

arXiv2015

Waterproofed Photomultiplier Tube Assemblies for the Daya Bay Reactor Neutrino Experiment

In the Daya Bay Reactor Neutrino Experiment 960 20-cm-diameter waterproof photomultiplier tubes are used to instrument three water pools as Cherenkov detectors for detecting cosmic-ray muons. Of these 960 photomultiplier tubes, 341 are recycled from the MACRO experiment. A systematic program was undertaken to refurbish them as waterproof assemblies. In the context of passing the water leakage check, a success rate better than 97% was achieved. Details of the design, fabrication, testing, operation, and performance of these waterproofed photomultiplier-tube assemblies are presented.

physics.ins-det↗

arXiv2011

Dependence of Neutrino Mixing Angles and CP-violating Phase on Mixing Matrix Parametrizations

We consider various neutrino mixing matrix parametrizations and the dependence of the mixing angles and CP-violating phase on the different parametrizations. The transformations of neutrino mixing parameters between various parametrizations are presented. Although the $θ_{13}$ mixing angle is determined to be small in the conventional parametrization, we note that in several other parametrizations the values of $θ_{13}$ are quite large. Should the value of $θ_{13}$ turn out to be tiny in the conventional parametrization, this study suggests that other alternative mixing matrix representations would be more suitable for dertermining the value of the CP-violating phase.

hep-ph↗