arXiv subjects

Ying Chen

Publications and source records attributed to Ying Chen.

At least 181 records · Page 10Linked to original sources

arXiv2024

SurvMamba: State Space Model with Multi-grained Multi-modal Interaction for Survival Prediction

Multi-modal learning that combines pathological images with genomic data has significantly enhanced the accuracy of survival prediction. Nevertheless, existing methods have not fully utilized the inherent hierarchical structure within both whole slide images (WSIs) and transcriptomic data, from which better intra-modal representations and inter-modal integration could be derived. Moreover, many existing studies attempt to improve multi-modal representations through attention mechanisms, which inevitably lead to high complexity when processing high-dimensional WSIs and transcriptomic data. Recently, a structured state space model named Mamba emerged as a promising approach for its superior performance in modeling long sequences with low complexity. In this study, we propose Mamba with multi-grained multi-modal interaction (SurvMamba) for survival prediction. SurvMamba is implemented with a Hierarchical Interaction Mamba (HIM) module that facilitates efficient intra-modal interactions at different granularities, thereby capturing more detailed local features as well as rich global representations. In addition, an Interaction Fusion Mamba (IFM) module is used for cascaded inter-modal interactive fusion, yielding more comprehensive features for survival prediction. Comprehensive evaluations on five TCGA datasets demonstrate that SurvMamba outperforms other existing methods in terms of performance and computational cost.

cs.CV↗

arXiv2024

Advancing Myopia To Holism: Fully Contrastive Language-Image Pre-training

In rapidly evolving field of vision-language models (VLMs), contrastive language-image pre-training (CLIP) has made significant strides, becoming foundation for various downstream tasks. However, relying on one-to-one (image, text) contrastive paradigm to learn alignment from large-scale messy web data, CLIP faces a serious myopic dilemma, resulting in biases towards monotonous short texts and shallow visual expressivity. To overcome these issues, this paper advances CLIP into one novel holistic paradigm, by updating both diverse data and alignment optimization. To obtain colorful data with low cost, we use image-to-text captioning to generate multi-texts for each image, from multiple perspectives, granularities, and hierarchies. Two gadgets are proposed to encourage textual diversity. To match such (image, multi-texts) pairs, we modify the CLIP image encoder into multi-branch, and propose multi-to-multi contrastive optimization for image-text part-to-part matching. As a result, diverse visual embeddings are learned for each image, bringing good interpretability and generalization. Extensive experiments and ablations across over ten benchmarks indicate that our holistic CLIP significantly outperforms existing myopic CLIP, including image-text retrieval, open-vocabulary classification, and dense visual tasks.

cs.CV↗

arXiv2024

Haldane phase, field-induced magnetic ordering and Tomonaga-Luttinger liquid behavior in a spin-one chain compound NiC$_2$O$_4$$\cdot$2NH$_3$

We performed single-crystal magnetic susceptibility and $^1$H NMR measurements on a quasi-1D, spin-1 antiferromagnet NiC$_2$O$_4$$\cdot$2NH$_3$, with temperature down to 100 mK and with field up to 26 T. With field applied along the chain direction (crystalline $b$ direction), a spin gap is determined at low fields. Our susceptibility and spin-lattice relaxation measurements reveal a Haldane phase at low field, with an intrachain exchange coupling $J$ $\approx$ 35 K and an easy-plane single-ion anisotropy of 17 K. A field-induced antiferromagnetic (AFM) ordering emerges at fields of 2.1 T, which sets a three-dimensional (3D) quantum critical point (QCP). The high-temperature spin-lattice relaxation rates $1/T_1$ resolves an onset of Tomonaga-Luttinger liquid behavior at field above $3.5$ T, which characterizes a hidden 1D QCP.

cond-mat.str-el↗

arXiv2024

OphCLIP: Hierarchical Retrieval-Augmented Learning for Ophthalmic Surgical Video-Language Pretraining

Surgical practice involves complex visual interpretation, procedural skills, and advanced medical knowledge, making surgical vision-language pretraining (VLP) particularly challenging due to this complexity and the limited availability of annotated data. To address the gap, we propose OphCLIP, a hierarchical retrieval-augmented vision-language pretraining framework specifically designed for ophthalmic surgical workflow understanding. OphCLIP leverages the OphVL dataset we constructed, a large-scale and comprehensive collection of over 375K hierarchically structured video-text pairs with tens of thousands of different combinations of attributes (surgeries, phases/operations/actions, instruments, medications, as well as more advanced aspects like the causes of eye diseases, surgical objectives, and postoperative recovery recommendations, etc). These hierarchical video-text correspondences enable OphCLIP to learn both fine-grained and long-term visual representations by aligning short video clips with detailed narrative descriptions and full videos with structured titles, capturing intricate surgical details and high-level procedural insights, respectively. Our OphCLIP also designs a retrieval-augmented pretraining framework to leverage the underexplored large-scale silent surgical procedure videos, automatically retrieving semantically relevant content to enhance the representation learning of narrative videos. Evaluation across 11 datasets for phase recognition and multi-instrument identification shows OphCLIP's robust generalization and superior performance.

cs.CV↗

arXiv2024

Causal Inference in Finance: An Expertise-Driven Model for Instrument Variables Identification and Interpretation

Instrumental Variable (IV) provides a source of treatment randomization that is conditionally independent of the outcomes, responding to the challenges of counterfactual and confounding biases. In finance, IV construction typically relies on pre-designed synthetic IVs, with effectiveness measured by specific algorithms. This classic paradigm cannot be generalized to address broader issues that require more and specific IVs. Therefore, we propose an expertise-driven model (ETE-FinCa) to optimize the source of expertise, instantiate IVs by the expertise concept, and interpret the cause-effect relationship by integrating concept with real economic data. The results show that the feature selection based on causal knowledge graphs improves the classification performance than others, with up to a 11.7% increase in accuracy and a 23.0% increase in F1-score. Furthermore, the high-quality IVs we defined can identify causal relationships between the treatment and outcome variables in the Two-Stage Least Squares Regression model with statistical significance.

econ.GN↗

arXiv2024

ST-Align: A Multimodal Foundation Model for Image-Gene Alignment in Spatial Transcriptomics

Spatial transcriptomics (ST) provides high-resolution pathological images and whole-transcriptomic expression profiles at individual spots across whole-slide scales. This setting makes it an ideal data source to develop multimodal foundation models. Although recent studies attempted to fine-tune visual encoders with trainable gene encoders based on spot-level, the absence of a wider slide perspective and spatial intrinsic relationships limits their ability to capture ST-specific insights effectively. Here, we introduce ST-Align, the first foundation model designed for ST that deeply aligns image-gene pairs by incorporating spatial context, effectively bridging pathological imaging with genomic features. We design a novel pretraining framework with a three-target alignment strategy for ST-Align, enabling (1) multi-scale alignment across image-gene pairs, capturing both spot- and niche-level contexts for a comprehensive perspective, and (2) cross-level alignment of multimodal insights, connecting localized cellular characteristics and broader tissue architecture. Additionally, ST-Align employs specialized encoders tailored to distinct ST contexts, followed by an Attention-Based Fusion Network (ABFN) for enhanced multimodal fusion, effectively merging domain-shared knowledge with ST-specific insights from both pathological and genomic data. We pre-trained ST-Align on 1.3 million spot-niche pairs and evaluated its performance through two downstream tasks across six datasets, demonstrating superior zero-shot and few-shot capabilities. ST-Align highlights the potential for reducing the cost of ST and providing valuable insights into the distinction of critical compositions within human tissue.

cs.CV↗

arXiv2024

SegBook: A Simple Baseline and Cookbook for Volumetric Medical Image Segmentation

Computed Tomography (CT) is one of the most popular modalities for medical imaging. By far, CT images have contributed to the largest publicly available datasets for volumetric medical segmentation tasks, covering full-body anatomical structures. Large amounts of full-body CT images provide the opportunity to pre-train powerful models, e.g., STU-Net pre-trained in a supervised fashion, to segment numerous anatomical structures. However, it remains unclear in which conditions these pre-trained models can be transferred to various downstream medical segmentation tasks, particularly segmenting the other modalities and diverse targets. To address this problem, a large-scale benchmark for comprehensive evaluation is crucial for finding these conditions. Thus, we collected 87 public datasets varying in modality, target, and sample size to evaluate the transfer ability of full-body CT pre-trained models. We then employed a representative model, STU-Net with multiple model scales, to conduct transfer learning across modalities and targets. Our experimental results show that (1) there may be a bottleneck effect concerning the dataset size in fine-tuning, with more improvement on both small- and large-scale datasets than medium-size ones. (2) Models pre-trained on full-body CT demonstrate effective modality transfer, adapting well to other modalities such as MRI. (3) Pre-training on the full-body CT not only supports strong performance in structure detection but also shows efficacy in lesion detection, showcasing adaptability across target tasks. We hope that this large-scale open evaluation of transfer learning can direct future research in volumetric medical image segmentation.

eess.IV↗

arXiv2024

Application of an ANN and LSTM-based Ensemble Model for Stock Market Prediction

Stock trading has always been a key economic indicator in modern society and a primary source of profit for financial giants such as investment banks, quantitative trading firms, and hedge funds. Discovering the underlying patterns within the seemingly volatile yet intrinsically structured economic activities has become a central focus of research for many companies. Our study leverages widely-used modern financial forecasting algorithms, including LSTM, ANN, CNN, and BiLSTM. We begin by comparing the predictive performance of these well-known algorithms on our stock market data, utilizing metrics such as R2, MAE, MSE, RMSE for detailed evaluation. Based on the performance of these models, we then aim to combine their strengths while mitigating their weaknesses, striving to construct a powerful hybrid model that overcomes the performance limitations of individual models.Through rigorous experimentation and exploration, we ultimately developed an LSTM+ANN model that breaks through prior performance bottlenecks, achieving promising and exciting results.

cs.CE↗

arXiv2024

Use QUDA for lattice QCD calculation with Python

We developed PyQUDA, a Python wrapper for QUDA written in Cython, designed to facilitate lattice QCD calculations using the Python programming language. PyQUDA leverages the optimized linear algebra capabilities of NumPy/CuPy/PyTorch, along with the highly optimized lattice QCD operations provided by QUDA to accelerate research. This integration simplifies the process of writing calculation codes, enabling researchers to build more complex Python packages like EasyDistillation for specific physics objectives. PyQUDA supports a range of lattice QCD operations, including hybrid Monte Carlo (HMC) with N-flavor clover/HISQ fermions and inversion for the Wilson/clover/HISQ fermion action with the multigrid solver. It also includes utility functions for reading lattice QCD data stored in Chroma, MILC, and $χ$QCD formats. Type hints are supported by stub files and multi-GPU support is provided through mpi4py.

hep-lat↗

arXiv2024

Universal enveloping algebras of weighted differential Poisson algebras

The $λ$-differential operators and modified $λ$-differential operators are generalizations of classical differential operators. This paper introduces the notions of $λ$-differential Poisson ($λ$-DP for short) algebras and modified $λ$-differential Poisson ($λ$-mDP for short) algebras as generalizations of differential Poisson algebras. The $λ$-DP algebra is proved to be closed under tensor product, and a $λ$-DP algebra structure is provided on the cohomology algebra of the $λ$-DP algebra. These conclusions are also applied to $λ$-mDP algebras and their modules. Finally, the universal enveloping algebras of $λ$-DP algebras are generalized by constructing a $\mathcal{P}$-triple. Three isomorphisms among opposite algebras, tensor algebras and the universal enveloping algebras of $λ$-DP algebras are obtained.

math-ph↗

arXiv2024

Charm physics with overlap fermions on 2+1-flavor domain wall fermion configurations

Decay constants of pseudoscalar mesons $D$, $D_s$, $η_c$ and vector mesons $D^*$, $D_s^*$, $J/ψ$ are determined from $N_f=2+1$ lattice QCD at a lattice spacing $a\sim0.08$ fm. For vector mesons, the decay constants defined by tensor currents are given in the $\overline{\rm MS}$ scheme at $2$ GeV. The calculation is performed on domain wall fermion configurations generated by the RBC-UKQCD Collaborations and the overlap fermion action is used for the valence quarks. Comparing the current results with our previous ones at a coarser lattice spacing $a\sim0.11$ fm gives us a better understanding of the discretization error. We obtain $f_{D_s^*}^T(\overline{\rm MS},\text{2 GeV})/f_{D_s^*}=0.907(20)$ with a better precision than our previous result. Combining our $f_{D_s^*}=277(11)$ MeV with the total width of $D_s^*$ determined in a recent work gives a branching fraction $4.26(52)\times10^{-5}$ for $D_s^*$ leptonic decay.

hep-lat↗

arXiv2024

MetaSegNet: Metadata-collaborative Vision-Language Representation Learning for Semantic Segmentation of Remote Sensing Images

Semantic segmentation of remote sensing images plays a vital role in a wide range of Earth Observation applications, such as land use land cover mapping, environment monitoring, and sustainable development. Driven by rapid developments in artificial intelligence, deep learning (DL) has emerged as the mainstream for semantic segmentation and has achieved many breakthroughs in the field of remote sensing. However, most DL-based methods focus on unimodal visual data while ignoring rich multimodal information involved in the real world. Non-visual data, such as text, can gather extra knowledge from the real world, which can strengthen the interpretability, reliability, and generalization of visual models. Inspired by this, we propose a novel metadata-collaborative segmentation network (MetaSegNet) that applies vision-language representation learning for semantic segmentation of remote sensing images. Unlike the common model structure that only uses unimodal visual data, we extract the key characteristic (e.g. the climate zone) from freely available remote sensing image metadata and transfer it into geographic text prompts via the generic ChatGPT. Then, we construct an image encoder, a text encoder, and a crossmodal attention fusion subnetwork to extract the image and text feature and apply image-text interaction. Benefiting from such a design, the proposed MetaSegNet not only demonstrates superior generalization in zero-shot testing but also achieves competitive accuracy with the state-of-the-art semantic segmentation methods on the large-scale OpenEarthMap dataset (70.4% mIoU) and the Potsdam dataset (93.3% mean F1 score) as well as the LoveDA dataset (52.0% mIoU).

cs.CV↗

arXiv2024

A unified fourth-order Bhatnagar-Gross-Krook lattice Boltzmann model for high-dimensional linear hyperbolic equations

In this work, we first develop a unified Bhatnagar-Gross-Krook lattice Boltzmann (BGK-LB) model for the $d$($d\geq 1$)-dimensional linear hyperbolic equation (L-HE), where the natural moments and the D$d$Q$(2d^2+1)$ [($2d^2+1$) discrete velocities in $d$-dimensional space] lattice structure are considered. Subsequently, at the acoustic scaling, we conduct an accuracy analysis on the developed BGK-LB model by the direct Taylor expansion (DTE) method, and present the second- and third-order moments of the equilibrium distribution functions (EDFs) to ensure that the BGK-LB model can be fourth-order consistent with the L-HE. And on this basis, when considering the Dirichlet boundary condition, the fourth-order full-way and half-way boundary schemes are proposed to approximate the unknown distribution functions to ensure that the BGK-LB model can be overall fourth-order accurate. Thereafter, based on the kinetic entropy theory, we derive the conditions that the fourth-order moments of the EDFs should satisfy to ensure the microscopic entropy stability of the BGK-LB model. In addition, with the aid of the von Neumann stability analysis, we also discuss the $L^2$ stability of the BGK-LB model and numerically plot the stability regions. In particular, from a numerical perspective, we find that the region of microscopic entropy stability is identical to that of $L^2$ stability. Finally, we carry out some numerical experiments to test the accuracy and stability of the BGK-LB model, and the numerical results are in agreement with our theoretical analysis. In addition, we compare the developed full-way and half-way boundary schemes for the Dirichlet boundary condition, which shows that the full-way boundary scheme is more stable.

math.NA↗

arXiv2024

Control System Design and Experiments for Autonomous Underwater Helicopter Docking Procedure Based on Acoustic-inertial-optical Guidance

A control system structure for the underwater docking procedure of an Autonomous Underwater Helicopter (AUH) is proposed in this paper, which utilizes acoustic-inertial-optical guidance. Unlike conventional Autonomous Underwater Vehicles (AUVs), the maneuverability requirements for AUHs are more stringent during the docking procedure, requiring it to remain stationary or have minimal horizontal movement while moving vertically. The docking procedure is divided into two stages: Homing and Landing, each stage utilizing different guidance methods. Additionally, a segmented aligning strategy operating at various altitudes and a linear velocity decision are both adopted in Landing stage. Due to the unique structure of the Subsea Docking System (SDS), the AUH is required to dock onto the SDS in a fixed orientation with specific attitude and altitude. Therefore, a particular criterion is proposed to determine whether the AUH has successfully docked onto the SDS. Furthermore, the effectiveness and robustness of the proposed control method in AUH's docking procedure are demonstrated through pool experiments and sea trials.

cs.RO↗

arXiv2024

An Improved ESO-Based Line-of-Sight Guidance Law for Path Following of Underactuated Autonomous Underwater Helicopter With Nonlinear Tracking Differentiator and Anti-saturation Controller

This paper presents an Improved Extended-state-observer based Line-of-Sight (IELOS) guidance law for path following of underactuated Autonomous Underwater helicopter (AUH) utilizing a nonlinear tracking differentiator and anti-saturation controller. Due to the high mobility of the AUH, the classical reduced-order Extended-State-Observer (ESO) struggles to accurately track the sideslip angle, especially when rapid variation occurs. By incorporating the nonlinear tracking differentiator and anti-saturation controller, the IELOS guidance law can precisely track sideslip angle and mitigate propeller thrust buffet compared to the classical Extended-state-observer based Line-of-Sight (ELOS) guidance law. The performance of ESO is significantly influenced by the bandwidth, with the Improved Extended-State-Observer (IESO) proving effective at low bandwidths where the classical ESO falls short. The paper establishes the input-to-state stability of the closed-loop system. Subsequently, simulation and pool experimental results are showcased to validate the effectiveness of the IELOS guidance law, which outperforms both the Line-of-Sight (LOS) and Adaptive Line-of-Sight (ALOS) guidance laws in terms of performance.

eess.SY↗

arXiv2024

Toward Tiny and High-quality Facial Makeup with Data Amplify Learning

Contemporary makeup approaches primarily hinge on unpaired learning paradigms, yet they grapple with the challenges of inaccurate supervision (e.g., face misalignment) and sophisticated facial prompts (including face parsing, and landmark detection). These challenges prohibit low-cost deployment of facial makeup models, especially on mobile devices. To solve above problems, we propose a brand-new learning paradigm, termed "Data Amplify Learning (DAL)," alongside a compact makeup model named "TinyBeauty." The core idea of DAL lies in employing a Diffusion-based Data Amplifier (DDA) to "amplify" limited images for the model training, thereby enabling accurate pixel-to-pixel supervision with merely a handful of annotations. Two pivotal innovations in DDA facilitate the above training approach: (1) A Residual Diffusion Model (RDM) is designed to generate high-fidelity detail and circumvent the detail vanishing problem in the vanilla diffusion models; (2) A Fine-Grained Makeup Module (FGMM) is proposed to achieve precise makeup control and combination while retaining face identity. Coupled with DAL, TinyBeauty necessitates merely 80K parameters to achieve a state-of-the-art performance without intricate face prompts. Meanwhile, TinyBeauty achieves a remarkable inference speed of up to 460 fps on the iPhone 13. Extensive experiments show that DAL can produce highly competitive makeup models using only 5 image pairs.

cs.CV↗

arXiv2024

Self-similarity Prior Distillation for Unsupervised Remote Physiological Measurement

Remote photoplethysmography (rPPG) is a noninvasive technique that aims to capture subtle variations in facial pixels caused by changes in blood volume resulting from cardiac activities. Most existing unsupervised methods for rPPG tasks focus on the contrastive learning between samples while neglecting the inherent self-similar prior in physiological signals. In this paper, we propose a Self-Similarity Prior Distillation (SSPD) framework for unsupervised rPPG estimation, which capitalizes on the intrinsic self-similarity of cardiac activities. Specifically, we first introduce a physical-prior embedded augmentation technique to mitigate the effect of various types of noise. Then, we tailor a self-similarity-aware network to extract more reliable self-similar physiological features. Finally, we develop a hierarchical self-distillation paradigm to assist the network in disentangling self-similar physiological patterns from facial videos. Comprehensive experiments demonstrate that the unsupervised SSPD framework achieves comparable or even superior performance compared to the state-of-the-art supervised methods. Meanwhile, SSPD maintains the lowest inference time and computation cost among end-to-end models.

cs.CV↗

arXiv2024

The radiative decay of scalar glueball from lattice QCD

We perform the first lattice QCD study on the radiative decay of the scalar glueball to the vector meson $ϕ$ in the quenched approximation. The calculations are carried out on three gauge ensembles with different lattice spacings, which enable us to do the continuum extrapolation. We first revisit the radiative $J/ψ$ decay into the scalar glueball $G$ and obtain the partial decay width $Γ(J/ψ\to γG)=0.578(86)~\text{keV}$ and the branching fraction $\text{Br}(J/ψ\to γG) = 6.2(9)\times 10^{-3}$. We then extend the similar calculation to the process $G\to γϕ$ and get the partial decay width $Γ(G \to γϕ)= 0.074(47)~\text{keV}$, which implies that the combined branching fraction of $J/ψ\toγG\to γγϕ$ is as small as $\mathcal{O}(10^{-9})$ such that this process is hardly detected by the BESIII experiment even with the large $J/ψ$ sample of $\mathcal{O}(10^{10})$. With the vector meson dominance model, the two-photon decay width of the scalar glueball is estimated to be $Γ(G\toγγ)=0.53(46)~\text{eV}$, which results in a large stickiness $S(G)\sim \mathcal{O}(10^4)$ of the scalar glueball by assuming the stickiness of $f_2(1270)$ to be one.

hep-lat↗