arXiv subjects

Xiaoqing Liu

Publications and source records attributed to Xiaoqing Liu.

At least 37 records · Page 2Linked to original sources

arXiv2025

Enabling Continuous THz Band Coverage via Precise Electron Beam Tailoring in Free-electron Lasers

High-power, continuously tunable narrowband terahertz (THz) sources are essential for advancing nonlinear optics, THz-driven material dynamics, and ultrafast spectroscopy. Conventional techniques typically impose a trade-off between pulse energy and frequency tunability. Here, we introduce a novel free-electron laser approach that overcomes these limitations by pre-modulating a relativistic electron beam with a frequency-beating laser pulse and leveraging bunch compression along with collective effects to enhance microbunching. Experimental results demonstrate that this technique generates narrowband THz emission with continuous frequency tunability from 7.8 to 30.8THz, achieving pulse energies up to 385μJ while maintaining spectral bandwidths between 7.7% and 14.7%. Moreover, the method exhibits exceptional robustness and scalability, highlighting its unique ability to bridge the long-standing THz gap and offering a promising solution for diverse cutting-edge scientific applications.

physics.acc-ph↗

arXiv2025

Solute Co-Segregation Mechanisms at Low-Angle Grain Boundaries in Magnesium: A Combined Atomic-Scale Experimental and Modeling Study

Solute segregation at low-angle grain boundaries (LAGBs) critically affects the microstructure and mechanical properties of magnesium (Mg) alloys. In modern alloys containing multiple substitutional elements, understanding solute-solute interactions at microstructural defects becomes essential for alloy design. This study investigates the co-segregation mechanisms of calcium (Ca), zinc (Zn), and aluminum (Al) at a LAGB in a dilute AZX010 Mg alloy by combining atomic-scale experimental and modeling techniques. Three-dimensional atom probe tomography (3D-APT) revealed significant segregation of Ca, Zn, and Al at the LAGB, with Ca forming linear segregation patterns along dislocation arrays characteristic of the LAGB. Clustering analysis showed increased Ca-Ca pairs at the boundary, indicating synergistic solute interactions. Atomistic simulations and elastic dipole calculations demonstrated that larger Ca atoms prefer tensile regions around dislocations, while smaller Zn and Al atoms favor compressive areas. These simulations also found that Ca-Ca co-segregation near dislocation cores is energetically more favorable than other solute pairings, explaining the enhanced Ca clustering observed experimentally. Thermodynamic modeling incorporating calculated segregation energies and solute-solute interactions accurately predicted solute concentrations at the LAGB, aligning with experimental data. The findings emphasize the importance of solute interactions at dislocation cores in Mg alloys, offering insights for improving mechanical performance through targeted alloying and grain boundary engineering.

cond-mat.mtrl-sci↗

arXiv2024

Scalable Crystal Structure Relaxation Using an Iteration-Free Deep Generative Model with Uncertainty Quantification

In computational molecular and materials science, determining equilibrium structures is the crucial first step for accurate subsequent property calculations. However, the recent discovery of millions of new crystals and complex twisted structures has challenged traditional computational methods, both ab initio and machine-learning-based, due to their computationally intensive iterative processes. To address these scalability issues, here we introduce DeepRelax, a deep generative model capable of performing geometric crystal structure relaxation rapidly and without iterations. DeepRelax learns the equilibrium structural distribution, enabling it to predict relaxed structures directly from their unrelaxed ones. The ability to perform structural relaxation at the millisecond level per structure, combined with the scalability of parallel processing, makes DeepRelax particularly useful for large-scale virtual screening. We demonstrate DeepRelax's reliability and robustness by applying it to five diverse databases, including oxides, Materials Project, two-dimensional materials, van der Waals crystals, and crystals with point defects. DeepRelax consistently shows high accuracy and efficiency, validated by density functional theory calculations. Finally, we enhance its trustworthiness by integrating uncertainty quantification. This work significantly accelerates computational workflows, offering a robust and trustworthy machine-learning method for material discovery and advancing the application of AI for science. Code for DeepRelax is available at https://github.com/Shen-Group/DeepRelax.

cond-mat.mtrl-sci↗

arXiv2024

SDR-Former: A Siamese Dual-Resolution Transformer for Liver Lesion Classification Using 3D Multi-Phase Imaging

Automated classification of liver lesions in multi-phase CT and MR scans is of clinical significance but challenging. This study proposes a novel Siamese Dual-Resolution Transformer (SDR-Former) framework, specifically designed for liver lesion classification in 3D multi-phase CT and MR imaging with varying phase counts. The proposed SDR-Former utilizes a streamlined Siamese Neural Network (SNN) to process multi-phase imaging inputs, possessing robust feature representations while maintaining computational efficiency. The weight-sharing feature of the SNN is further enriched by a hybrid Dual-Resolution Transformer (DR-Former), comprising a 3D Convolutional Neural Network (CNN) and a tailored 3D Transformer for processing high- and low-resolution images, respectively. This hybrid sub-architecture excels in capturing detailed local features and understanding global contextual information, thereby, boosting the SNN's feature extraction capabilities. Additionally, a novel Adaptive Phase Selection Module (APSM) is introduced, promoting phase-specific intercommunication and dynamically adjusting each phase's influence on the diagnostic outcome. The proposed SDR-Former framework has been validated through comprehensive experiments on two clinical datasets: a three-phase CT dataset and an eight-phase MR dataset. The experimental results affirm the efficacy of the proposed framework. To support the scientific community, we are releasing our extensive multi-phase MR dataset for liver lesion analysis to the public. This pioneering dataset, being the first publicly available multi-phase MR dataset in this field, also underpins the MICCAI LLD-MMRI Challenge. The dataset is accessible at:https://bit.ly/3IyYlgN.

eess.IV↗

arXiv2023

Generate to Understand for Representation

In recent years, a significant number of high-quality pretrained models have emerged, greatly impacting Natural Language Understanding (NLU), Natural Language Generation (NLG), and Text Representation tasks. Traditionally, these models are pretrained on custom domain corpora and finetuned for specific tasks, resulting in high costs related to GPU usage and labor. Unfortunately, recent trends in language modeling have shifted towards enhancing performance through scaling, further exacerbating the associated costs. Introducing GUR: a pretraining framework that combines language modeling and contrastive learning objectives in a single training step. We select similar text pairs based on their Longest Common Substring (LCS) from raw unlabeled documents and train the model using masked language modeling and unsupervised contrastive learning. The resulting model, GUR, achieves impressive results without any labeled training data, outperforming all other pretrained baselines as a retriever at the recall benchmark in a zero-shot setting. Additionally, GUR maintains its language modeling ability, as demonstrated in our ablation experiment. Our code is available at \url{https://github.com/laohur/GUR}.

cs.CL↗

arXiv2023

Mixing Data Augmentation with Preserving Foreground Regions in Medical Image Segmentation

The development of medical image segmentation using deep learning can significantly support doctors' diagnoses. Deep learning needs large amounts of data for training, which also requires data augmentation to extend diversity for preventing overfitting. However, the existing methods for data augmentation of medical image segmentation are mainly based on models which need to update parameters and cost extra computing resources. We proposed data augmentation methods designed to train a high accuracy deep learning network for medical image segmentation. The proposed data augmentation approaches are called KeepMask and KeepMix, which can create medical images by better identifying the boundary of the organ with no more parameters. Our methods achieved better performance and obtained more precise boundaries for medical image segmentation on datasets. The dice coefficient of our methods achieved 94.15% (3.04% higher than baseline) on CHAOS and 74.70% (5.25% higher than baseline) on MSD spleen with Unet.

eess.IV↗

arXiv2023

Cluster Entropy: Active Domain Adaptation in Pathological Image Segmentation

The domain shift in pathological segmentation is an important problem, where a network trained by a source domain (collected at a specific hospital) does not work well in the target domain (from different hospitals) due to the different image features. Due to the problems of class imbalance and different class prior of pathology, typical unsupervised domain adaptation methods do not work well by aligning the distribution of source domain and target domain. In this paper, we propose a cluster entropy for selecting an effective whole slide image (WSI) that is used for semi-supervised domain adaptation. This approach can measure how the image features of the WSI cover the entire distribution of the target domain by calculating the entropy of each cluster and can significantly improve the performance of domain adaptation. Our approach achieved competitive results against the prior arts on datasets collected from two hospitals.

cs.CV↗

arXiv2022

Compound Domain Generalization via Meta-Knowledge Encoding

Domain generalization (DG) aims to improve the generalization performance for an unseen target domain by using the knowledge of multiple seen source domains. Mainstream DG methods typically assume that the domain label of each source sample is known a priori, which is challenged to be satisfied in many real-world applications. In this paper, we study a practical problem of compound DG, which relaxes the discrete domain assumption to the mixed source domains setting. On the other hand, current DG algorithms prioritize the focus on semantic invariance across domains (one-vs-one), while paying less attention to the holistic semantic structure (many-vs-many). Such holistic semantic structure, referred to as meta-knowledge here, is crucial for learning generalizable representations. To this end, we present Compound Domain Generalization via Meta-Knowledge Encoding (COMEN), a general approach to automatically discover and model latent domains in two steps. Firstly, we introduce Style-induced Domain-specific Normalization (SDNorm) to re-normalize the multi-modal underlying distributions, thereby dividing the mixture of source domains into latent clusters. Secondly, we harness the prototype representations, the centroids of classes, to perform relational modeling in the embedding space with two parallel and complementary modules, which explicitly encode the semantic structure for the out-of-distribution generalization. Experiments on four standard DG benchmarks reveal that COMEN exceeds the state-of-the-art performance without the need of domain supervision.

cs.CV↗

arXiv2021

Identification of Pediatric Respiratory Diseases Using Fine-grained Diagnosis System

Respiratory diseases, including asthma, bronchitis, pneumonia, and upper respiratory tract infection (RTI), are among the most common diseases in clinics. The similarities among the symptoms of these diseases precludes prompt diagnosis upon the patients' arrival. In pediatrics, the patients' limited ability in expressing their situation makes precise diagnosis even harder. This becomes worse in primary hospitals, where the lack of medical imaging devices and the doctors' limited experience further increase the difficulty of distinguishing among similar diseases. In this paper, a pediatric fine-grained diagnosis-assistant system is proposed to provide prompt and precise diagnosis using solely clinical notes upon admission, which would assist clinicians without changing the diagnostic process. The proposed system consists of two stages: a test result structuralization stage and a disease identification stage. The first stage structuralizes test results by extracting relevant numerical values from clinical notes, and the disease identification stage provides a diagnosis based on text-form clinical notes and the structured data obtained from the first stage. A novel deep learning algorithm was developed for the disease identification stage, where techniques including adaptive feature infusion and multi-modal attentive fusion were introduced to fuse structured and text data together. Clinical notes from over 12000 patients with respiratory diseases were used to train a deep learning model, and clinical notes from a non-overlapping set of about 1800 patients were used to evaluate the performance of the trained model. The average precisions (AP) for pneumonia, RTI, bronchitis and asthma are 0.878, 0.857, 0.714, and 0.825, respectively, achieving a mean AP (mAP) of 0.819.

cs.AI↗

arXiv2021

A Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation

Instance level detection and segmentation of thoracic diseases or abnormalities are crucial for automatic diagnosis in chest X-ray images. Leveraging on constant structure and disease relations extracted from domain knowledge, we propose a structure-aware relation network (SAR-Net) extending Mask R-CNN. The SAR-Net consists of three relation modules: 1. the anatomical structure relation module encoding spatial relations between diseases and anatomical parts. 2. the contextual relation module aggregating clues based on query-key pair of disease RoI and lung fields. 3. the disease relation module propagating co-occurrence and causal relations into disease proposals. Towards making a practical system, we also provide ChestX-Det, a chest X-Ray dataset with instance-level annotations (boxes and masks). ChestX-Det is a subset of the public dataset NIH ChestX-ray14. It contains ~3500 images of 13 common disease categories labeled by three board-certified radiologists. We evaluate our SAR-Net on it and another dataset DR-Private. Experimental results show that it can enhance the strong baseline of Mask R-CNN with significant improvements. The ChestX-Det is released at https://github.com/Deepwise-AILab/ChestX-Det-Dataset.

eess.IV↗

arXiv2021

The effects of ionic liquids on the thermodynamics of H2 activation by frustrated Lewis pairs: a density functional theory study

Nowadays, hydrogen activation by frustrated Lewis pairs (FLPs) and their applications have been demonstrated to be one of emerge research topics in the field of catalysis. Previous studies have shown that the thermodynamics of these reaction is determined by electronic structures of FLPs and solvents. Herein, we investigated the systems consisting of typical FLPs and ionic liquids (ILs), which are well known by their large number of types and excellent solvent effects. The density functional theory (DFT) calculations were performed to study the thermodynamics for H2 activation by both inter- and intra-molecular FLPs, as well as the individual components. The results show that the computed overall Gibbs free energies in ILs are more negative than that computed in toluene. Through the thermodynamics partitioning, we find that ILs favor the H-H cleavage elemental step, while disfavored the elemental steps of proton attachment, hydride attachment and zwitterionic stabilization. Moreover, the results show that these effects are strongly dependent on the type of FLPs, where intra-molecular FLPs are more effected compared to the inter-molecular FLPs.

physics.chem-ph↗

arXiv2021

A molecular dynamics simulation study on the frustrated Lewis pairs in ionic liquids

Steric hindered frustrated Lewis pairs (FLPs) have been shown to activate hydrogen molecules, and their reactivity is strongly determined by the geometric parameters of the Lewis acid s and bases. A recent experimental study showed that ionic liquids (ILs) could largely improve the effective configuration of FLPs. However, the detailed mechanistic profile is still unclear. Herein, we performed a molecular dynamics (MD) simulations, aimi ng to reveal the effects of ILs on the structures of FLPs, and to present a rule for selecting more efficient reaction media. For this purpose, mixture systems were adopt consisting of the ILs [Cnmim][NTf2] (n= 6, 10, 14), and the typical FLP (tBu)3P/B(C6F5)3 . Radial distribution function (RDF) results show that toluene competes with (tBu)3P to interact with B(C6F5)3 , resulting in a relatively low effective (tBu)3P/B(C6F5)3 complex. [Cnmim][NTf2] is more intended to form a solvated shell surrounding the (tBu)3P/B(C6F5)3 , which increases the amount of effective FLPs. Spatial distribution function (SDF) results show that toluene formed a continuum solvation shell, which hinders the interactions of (tBu)3P and B(C6F5)3 , while [Cnmim][NTf2] leave a relatively large empty space, which is accessible by (tBu3)P molecules, resulting in a higher probability of Lewis acids and bases interactions. Lastly, we find that the longer alkyl chain length of[Cnmim] cations, the higher probability of effective FLPs.

physics.chem-ph↗

arXiv2020

Adaptive noise imitation for image denoising

The effectiveness of existing denoising algorithms typically relies on accurate pre-defined noise statistics or plenty of paired data, which limits their practicality. In this work, we focus on denoising in the more common case where noise statistics and paired data are unavailable. Considering that denoising CNNs require supervision, we develop a new \textbf{adaptive noise imitation (ADANI)} algorithm that can synthesize noisy data from naturally noisy images. To produce realistic noise, a noise generator takes unpaired noisy/clean images as input, where the noisy image is a guide for noise generation. By imposing explicit constraints on the type, level and gradient of noise, the output noise of ADANI will be similar to the guided noise, while keeping the original clean background of the image. Coupling the noisy data output from ADANI with the corresponding ground-truth, a denoising CNN is then trained in a fully-supervised manner. Experiments show that the noisy data produced by ADANI are visually and statistically similar to real ones so that the denoising CNN in our method is competitive to other networks trained with external paired data.

eess.IV↗

arXiv2020

GASNet: Weakly-supervised Framework for COVID-19 Lesion Segmentation

Segmentation of infected areas in chest CT volumes is of great significance for further diagnosis and treatment of COVID-19 patients. Due to the complex shapes and varied appearances of lesions, a large number of voxel-level labeled samples are generally required to train a lesion segmentation network, which is a main bottleneck for developing deep learning based medical image segmentation algorithms. In this paper, we propose a weakly-supervised lesion segmentation framework by embedding the Generative Adversarial training process into the Segmentation Network, which is called GASNet. GASNet is optimized to segment the lesion areas of a COVID-19 CT by the segmenter, and to replace the abnormal appearance with a generated normal appearance by the generator, so that the restored CT volumes are indistinguishable from healthy CT volumes by the discriminator. GASNet is supervised by chest CT volumes of many healthy and COVID-19 subjects without voxel-level annotations. Experiments on three public databases show that when using as few as one voxel-level labeled sample, the performance of GASNet is comparable to fully-supervised segmentation algorithms trained on dozens of voxel-level labeled samples.

eess.IV↗

arXiv2019

MMFNet: A Multi-modality MRI Fusion Network for Segmentation of Nasopharyngeal Carcinoma

Segmentation of nasopharyngeal carcinoma (NPC) from Magnetic Resonance Images (MRI) is a crucial prerequisite for NPC radiotherapy. However, manually segmenting of NPC is time-consuming and labor-intensive. Additionally, single-modality MRI generally cannot provide enough information for its accurate delineation. Therefore, a multi-modality MRI fusion network (MMFNet) based on three modalities of MRI (T1, T2 and contrast-enhanced T1) is proposed to complete accurate segmentation of NPC. The backbone of MMFNet is designed as a multi-encoder-based network, consisting of several encoders to capture modality-specific features and one single decoder to fuse them and obtain high-level features for NPC segmentation. A fusion block is presented to effectively fuse features from multi-modality MRI. It firstly recalibrates low-level features captured from modality-specific encoders to highlight both informative features and regions of interest, then fuses weighted features by a residual fusion block to keep balance between fused ones and high-level features from decoder. Moreover, a training strategy named self-transfer, which utilizes pre-trained modality-specific encoders to initialize multi-encoder-based network, is proposed to make full mining of information from different modalities of MRI. The proposed method based on multi-modality MRI can effectively segment NPC and its advantages are validated by extensive experiments.

cs.CV↗

arXiv2019

Image Encryption Algorithm Based on Facebook Social Network

Facebook is the online social networks (OSNs) platform with the largest number of users in the world today, information protection based on Facebook social network platform have important practical significance. Since the information users share on social networks is often based on images, this paper proposes a more secure image encryption algorithm based on Facebook social network platform to ensure the loss of information as much as possible. When the sender encrypts the image for uploading, it can first resist the third party's attack on the encrypted image and prevent the image data from leaking, simultaneously processed by some unknown processing such as compression and filtering of the image on the Facebook platform, the receiver can still decrypt the corresponding image data.

cs.MM↗

arXiv2017

A Co(II)-based Molecular Magnet with a 6 T Coercive Field

Hard magnets with high coercivity, such as Nd2Fe14B and SmCo5 alloys, can maintain magnetisation under a high reverse external magnetic field and have therefore become irreplaceable parts in many practical applications. Molecular magnets are promising alternatives, owing to their precise and designable chemical structures, tuneable functionalities and controllable synthetic methods. Here, we demonstrate that an unusually large coercive field can be achieved in a single-chain magnet. Systematic characterisations, including magnetic susceptibility, heat capacity and neutron diffraction measurements, show that the observed giant coercive field originates from the spin dynamics along the one-dimensional chain of the compound because of the strong exchange coupling between Co(II) centres and radicals.

physics.chem-ph↗

arXiv2016

Chromatic Effect for THz Generation in a Novel Wave-front Tilt Scheme

Deriving single or few cycle terahertz pulse (THz) by intense femtosecond laser through cascaded optical rectification in electro-optic crystals is a crucial technique in cutting-edge time-resolved spectroscopy to characterize micro-scale structures and ultrafast dynamics. In the past decade, lithium niobate (LN) crystal implementation of wave-front tilt scheme has been prevalently used, while painstaking efforts have been invested in order to achieve higher THz conversion efficiency. In this research we developed a brand new type of LN crystal possessing dual-face-cut and Brewster coupling, and conducted experimental and simulative investigation systematically to optimize the multi-dimensionally entangled parameters in THz generation, predicting the extreme conversion efficiency of 10% is potentially promising at the THz absorption coefficient of 0.5cm-1. More remarkably, we first discovered that the chirp of the driving laser pulse plays a decisive role in the wave-front tilt scheme, and the THz generation efficiency could be enhanced tremendously by applying an appropriate chirp.

physics.optics↗