arXiv subjects

Jin Li

Publications and source records attributed to Jin Li.

At least 145 records · Page 8Linked to original sources

arXiv2024

Optimization of Nuclear Mass Models Using Algorithms and Neural Networks

Taking into account nucleon-nucleon gravitational interaction, higher-order terms of symmetry energy, pairing interaction, and neural network corrections, a new BW4 mass model has been developed, which more accurately reflects the contributions of various terms to the binding energy. A novel hybrid algorithm and neural network correction method has been implemented to optimize the discrepancy between theoretical and experimental results, significantly improving the model's binding energy predictions (reduced to around 350 keV). At the same time, the theoretical accuracy near magic nuclei has been marginally enhanced, effectively capturing the special interaction effects around magic nuclei and showing good agreement with experimental data.

nucl-th↗

arXiv2024

Holographic Einstein Rings of AdS Black Holes in Horndeski Theory

By utilizing the AdS/CFT correspondence and wave optics techniques, we conducted an extensive study of the imaging properties of holographic Einstein rings in the context of Anti-de Sitter (AdS) black holes (BHs) in Horndeski theory. Our results indicate that the optical characteristics of these holographic Einstein rings are significantly influenced by the observer's position, the physical parameters of the BH, the nature of the wave source, and the configuration of the optical system. Specifically, when the observer is positioned at the north pole of the AdS boundary, the holographic image prominently displays a ring structure aligning with the BH's photon sphere. We thoroughly analyzed how various physical parameters -- including the observation position, event horizon radius, temperature, and the parameter $γ$ in Horndeski theory -- affect the holographic Einstein rings. These parameters play a crucial role in determining the rings' radius and brightness, with variations potentially causing the ring structures to deform or even transform into bright spots. Furthermore, our comparative analysis between wave optics and geometric optics reveals a strong agreement in predicting the positions and brightnesses of both the photon ring and the Einstein ring. This research offers new insights into the spacetime geometry of BHs in Horndeski theory and proposes a promising framework for exploring the gravitational duals of strongly coupled systems.

gr-qc↗

arXiv2024

Fourth-order compact finite difference schemes for solving biharmonic equations with Dirichlet boundary conditions

In this study, we propose a genuine fourth-order compact finite difference scheme for solving biharmonic equations with Dirichlet boundary conditions in both two and three dimensions. In the 2D case, we build upon the high-order compact (HOC) schemes for flux-type boundary conditions originally developed by Zhilin Li and Kejia Pan [SIAM J. Sci. Comput., 45 (2023), pp. A646-A674] to construct a high order compact discretization for coupled boundary conditions. When considering the 3D case, we modify carefully designed undetermined coefficient methods of Li and Pan to derive the finite difference approximations of coupled boundary conditions. The resultant FD discretization maintains the global fourth order convergence and compactness. Unlike the very popular Stephenson method, the number of unknows do not increase with dimensions. Besides, it is noteworthy that the condition number of the coefficient matrix increases at a rate of $O(h^{-2})$ in both 2D and 3D. We also validate the performance of the proposed genuine HOC methods through nontrivial examples.

math.NA↗

arXiv2024

Progressive Residual Extraction based Pre-training for Speech Representation Learning

Self-supervised learning (SSL) has garnered significant attention in speech processing, excelling in linguistic tasks such as speech recognition. However, jointly improving the performance of pre-trained models on various downstream tasks, each requiring different speech information, poses significant challenges. To this purpose, we propose a progressive residual extraction based self-supervised learning method, named ProgRE. Specifically, we introduce two lightweight and specialized task modules into an encoder-style SSL backbone to enhance its ability to extract pitch variation and speaker information from speech. Furthermore, to prevent the interference of reinforced pitch variation and speaker information with irrelevant content information learning, we residually remove the information extracted by these two modules from the main branch. The main branch is then trained using HuBERT's speech masking prediction to ensure the performance of the Transformer's deep-layer features on content tasks. In this way, we can progressively extract pitch variation, speaker, and content representations from the input speech. Finally, we can combine multiple representations with diverse speech information using different layer weights to obtain task-specific representations for various downstream tasks. Experimental results indicate that our proposed method achieves joint performance improvements on various tasks, such as speaker identification, speech recognition, emotion recognition, speech enhancement, and voice conversion, compared to excellent SSL methods such as wav2vec2.0, HuBERT, and WavLM.

eess.AS↗

arXiv2024

Privacy Amplification via Shuffling: Unified, Simplified, and Tightened

The shuffle model of differential privacy provides promising privacy-utility balances in decentralized, privacy-preserving data analysis. However, the current analyses of privacy amplification via shuffling lack both tightness and generality. To address this issue, we propose the \emph{variation-ratio reduction} as a comprehensive framework for privacy amplification in both single-message and multi-message shuffle protocols. It leverages two new parameterizations: the total variation bounds of local messages and the probability ratio bounds of blanket messages, to determine indistinguishability levels. Our theoretical results demonstrate that our framework provides tighter bounds, especially for local randomizers with extremal probability design, where our bounds are exactly tight. Additionally, variation-ratio reduction complements parallel composition in the shuffle model, yielding enhanced privacy accounting for popular sampling-based randomizers employed in statistical queries (e.g., range queries, marginal queries, and frequent itemset mining). Empirical findings demonstrate that our numerical amplification bounds surpass existing ones, conserving up to $30\%$ of the budget for single-message protocols, $75\%$ for multi-message ones, and a striking $75\%$-$95\%$ for parallel composition. Our bounds also result in a remarkably efficient $\tilde{O}(n)$ algorithm that numerically amplifies privacy in less than $10$ seconds for $n=10^8$ users.

cs.CR↗

arXiv2024

Shadows, Quasinormal Modes, and Optical Appearances of Black Holes in Horndeski Theory

This work describes the motion of photons in black hole (BH) spacetimes within the framework of Horndeski theory. We focus on the shadows, quasinormal modes (QNMs) and optical appearances of BHs surrounded by geometrically thin accretion disks. The QNMs of BHs are calculated by the WKB method and the eikonal limit, respectively. Using Event Horizon Telescope (EHT) observations of $\mathrm{M} 87^*$ and $\mathrm{Sgr} \mathrm{A}^*$, we can constrain the parameter in Horndeski theory to a small range. Based on the constraint, we obtain the frequency ranges of the fundamental modes for $\mathrm{M} 87^*$ and $\mathrm{Sgr} \mathrm{A}^*$ in Horndeski theory. By exploring the optical appearances of BHs, we find that for the current resolution of the EHT, it primarily captures direct emission. This work advances our understanding of the observational characteristics of BHs in Horndeski theory and constrains Horndeski theory by EHT observations of $\mathrm{M} 87^*$ and $\mathrm{Sgr} \mathrm{A}^*$.

gr-qc↗

arXiv2024

Effectiveness Study of Calibration and Correction Algorithms on the Prototype of the POLAR-2/LPD Detector

Gaseous X-ray polarimetry refers to a class of detectors used for measuring the polarization of soft X-rays. The systematic effects of such detectors introduce residual modulation, leading to systematic biases in the polarization detection results of the source. This paper discusses the systematic effects and their calibration and correction using the Gas Microchannel Plate-Pixel Detector (GMPD) prototype for POLAR-2/Low-Energy X-ray Polarization Detector (LPD). Additionally, we propose an algorithm that combines parameterization with Monte Carlo simulation and Bayesian iteration to eliminate residual modulation. The residual modulation after data correction at different energy points has been reduced to below 1%, and a good linear relationship is observed between the polarization degree and modulation degree. The improvement in modulation degree after correction ranges from 2% to 15%, and the results exceed those of the Imaging X-Ray Polarimetry Explorer (IXPE) above 5 keV.

astro-ph.IM↗

arXiv2024

PointSCNet: Point Cloud Structure and Correlation Learning Based on Space Filling Curve-Guided Sampling

Geometrical structures and the internal local region relationship, such as symmetry, regular array, junction, etc., are essential for understanding a 3D shape. This paper proposes a point cloud feature extraction network named PointSCNet, to capture the geometrical structure information and local region correlation information of a point cloud. The PointSCNet consists of three main modules: the space-filling curve-guided sampling module, the information fusion module, and the channel-spatial attention module. The space-filling curve-guided sampling module uses Z-order curve coding to sample points that contain geometrical correlation. The information fusion module uses a correlation tensor and a set of skip connections to fuse the structure and correlation information. The channel-spatial attention module enhances the representation of key points and crucial feature channels to refine the network. The proposed PointSCNet is evaluated on shape classification and part segmentation tasks. The experimental results demonstrate that the PointSCNet outperforms or is on par with state-of-the-art methods by learning the structure and correlation of point clouds effectively.

cs.CV↗

arXiv2024

ParamReL: Learning Parameter Space Representation via Progressively Encoding Bayesian Flow Networks

The recently proposed Bayesian Flow Networks~(BFNs) show great potential in modeling parameter spaces, offering a unified strategy for handling continuous, discretized, and discrete data. However, BFNs cannot learn high-level semantic representation from the parameter space since {common encoders, which encode data into one static representation, cannot capture semantic changes in parameters.} This motivates a new direction: learning semantic representations hidden in the parameter spaces to characterize mixed-typed noisy data. {Accordingly, we propose a representation learning framework named ParamReL, which operates in the parameter space to obtain parameter-wise latent semantics that exhibit progressive structures. Specifically, ParamReL proposes a \emph{self-}encoder to learn latent semantics directly from parameters, rather than from observations. The encoder is then integrated into BFNs, enabling representation learning with various formats of observations. Mutual information terms further promote the disentanglement of latent semantics and capture meaningful semantics simultaneously.} We illustrate {conditional generation and reconstruction} in ParamReL via expanding BFNs, and extensive {quantitative} experimental results demonstrate the {superior effectiveness} of ParamReL in learning parameter representation.

cs.LG↗

arXiv2024

Are You Copying My Prompt? Protecting the Copyright of Vision Prompt for VPaaS via Watermark

Visual Prompt Learning (VPL) differs from traditional fine-tuning methods in reducing significant resource consumption by avoiding updating pre-trained model parameters. Instead, it focuses on learning an input perturbation, a visual prompt, added to downstream task data for making predictions. Since learning generalizable prompts requires expert design and creation, which is technically demanding and time-consuming in the optimization process, developers of Visual Prompts as a Service (VPaaS) have emerged. These developers profit by providing well-crafted prompts to authorized customers. However, a significant drawback is that prompts can be easily copied and redistributed, threatening the intellectual property of VPaaS developers. Hence, there is an urgent need for technology to protect the rights of VPaaS developers. To this end, we present a method named \textbf{WVPrompt} that employs visual prompt watermarking in a black-box way. WVPrompt consists of two parts: prompt watermarking and prompt verification. Specifically, it utilizes a poison-only backdoor attack method to embed a watermark into the prompt and then employs a hypothesis-testing approach for remote verification of prompt ownership. Extensive experiments have been conducted on three well-known benchmark datasets using three popular pre-trained models: RN50, BIT-M, and Instagram. The experimental results demonstrate that WVPrompt is efficient, harmless, and robust to various adversarial operations.

cs.CR↗

arXiv2024

Comparison and application of different post-Newtonian models for inspiralling stellar-mass binary black holes with space-based GW detectors

Space-based gravitational wave (GW) detectors are expected to detect the stellar-mass binary black holes (SBBHs) inspiralling in the low-frequency band, which exist in several years before the merger. Accurate GW waveforms in the inspiral phase are crucial for the detection and analysis of those SBBHs. In our study, based on post-Newtonian (PN) models, we investigate the differences in the detection, accuracy requirement, and parameter estimation of SBBHs in the cases of LISA, Taiji, and their joint detection. We find that low-order PN models are sufficient for simulating low-mass ($\le 50\ \mathrm{M}_\odot$) SBBHs population. Moreover, for detectable SBBHs in space-based GW detectors, over 90% of the GW signals from low-order PN models meet accuracy requirement. Additionally, different PN models do not exhibit significant differences in Bayesian inference. Our research provides a comprehensive reference for balancing computational resources and the desired accuracy of GW waveform generation. It highlights the suitability of low-order PN models for simulating SBBHs and emphasizes their potential in the detection and parameter estimation of SBBHs.

gr-qc↗

arXiv2024

Incremental Learning with Concept Drift Detection and Prototype-based Embeddings for Graph Stream Classification

Data stream mining aims at extracting meaningful knowledge from continually evolving data streams, addressing the challenges posed by nonstationary environments, particularly, concept drift which refers to a change in the underlying data distribution over time. Graph structures offer a powerful modelling tool to represent complex systems, such as, critical infrastructure systems and social networks. Learning from graph streams becomes a necessity to understand the dynamics of graph structures and to facilitate informed decision-making. This work introduces a novel method for graph stream classification which operates under the general setting where a data generating process produces graphs with varying nodes and edges over time. The method uses incremental learning for continual model adaptation, selecting representative graphs (prototypes) for each class, and creating graph embeddings. Additionally, it incorporates a loss-based concept drift detection mechanism to recalculate graph prototypes when drift is detected.

cs.LG↗

arXiv2024

$\operatorname{SL}(n)$ contravariant function-valued valuations on polytopes

We present a complete classification of $\operatorname{SL}(n)$ contravariant, $C(\mathbb{R}^n\setminus\{o\})$-valued valuations on polytopes, without any additional assumptions.It extends the previous results of the second author [Int. Math. Res. Not. 2020] which have a good connection with the $L_p$ and Orlicz Brunn-Minkowski theory. Additionally, our results deduce a complete classification of $\operatorname{SL}(n)$ contravariant symmetric-tensor-valued valuations on polytopes.

math.MG↗

arXiv2024

Open-Vocabulary Scene Text Recognition via Pseudo-Image Labeling and Margin Loss

Scene text recognition is an important and challenging task in computer vision. However, most prior works focus on recognizing pre-defined words, while there are various out-of-vocabulary (OOV) words in real-world applications. In this paper, we propose a novel open-vocabulary text recognition framework, Pseudo-OCR, to recognize OOV words. The key challenge in this task is the lack of OOV training data. To solve this problem, we first propose a pseudo label generation module that leverages character detection and image inpainting to produce substantial pseudo OOV training data from real-world images. Unlike previous synthetic data, our pseudo OOV data contains real characters and backgrounds to simulate real-world applications. Secondly, to reduce noises in pseudo data, we present a semantic checking mechanism to filter semantically meaningful data. Thirdly, we introduce a quality-aware margin loss to boost the training with pseudo data. Our loss includes a margin-based part to enhance the classification ability, and a quality-aware part to penalize low-quality samples in both real and pseudo data. Extensive experiments demonstrate that our approach outperforms the state-of-the-art on eight datasets and achieves the first rank in the ICDAR2022 challenge.

cs.CV↗

arXiv2024

Unsupervised Incremental Learning with Dual Concept Drift Detection for Identifying Anomalous Sequences

In the contemporary digital landscape, the continuous generation of extensive streaming data across diverse domains has become pervasive. Yet, a significant portion of this data remains unlabeled, posing a challenge in identifying infrequent events such as anomalies. This challenge is further amplified in non-stationary environments, where the performance of models can degrade over time due to concept drift. To address these challenges, this paper introduces a new method referred to as VAE4AS (Variational Autoencoder for Anomalous Sequences). VAE4AS integrates incremental learning with dual drift detection mechanisms, employing both a statistical test and a distance-based test. The anomaly detection is facilitated by a Variational Autoencoder. To gauge the effectiveness of VAE4AS, a comprehensive experimental study is conducted using real-world and synthetic datasets characterized by anomalous rates below 10\% and recurrent drift. The results show that the proposed method surpasses both robust baselines and state-of-the-art techniques, providing compelling evidence for their efficacy in effectively addressing some of the challenges associated with anomalous sequence detection in non-stationary streaming data.

cs.CE↗

arXiv2024

Integrated Optical Vortex Microcomb

The explorations of physical degrees of freedom with infinite dimensionalities, such as orbital angular momentum and frequency of light, have profoundly reshaped the landscape of modern optics with representative photonic functional devices including optical vortex emitters and frequency combs. In nanophotonics, whispering gallery mode microresonators naturally support orbital angular momentum of light and have been demonstrated as on-chip emitters of monochromatic optical vortices. On the other hand, whispering gallery mode microresonators serve as a highly efficient nonlinear optical platform for producing light at different frequencies - i.e., microcombs. Here, we interlace the optical vortices and microcombs by demonstrating an optical vortex comb on an III-V integrated nonlinear microresonator. The angular-grating-dressed nonlinear microring simultaneously emits spatiotemporal light springs consisting of 50 orbital angular momentum modes that are each spectrally addressed to the frequency components (longitudinal whispering gallery modes) of the generated microcomb. We further experimentally generate optical pulses with time-varying orbital angular momenta by carefully introducing a specific intermodal phase relation to spatiotemporal light springs. This work may immediately boost the development of integrated nonlinear/quantum photonics for exploring fundamental optical physics and advancing photonic quantum technology.

physics.optics↗

arXiv2024

ContriMix: Scalable stain color augmentation for domain generalization without domain labels in digital pathology

Differences in staining and imaging procedures can cause significant color variations in histopathology images, leading to poor generalization when deploying deep-learning models trained from a different data source. Various color augmentation methods have been proposed to generate synthetic images during training to make models more robust, eliminating the need for stain normalization during test time. Many color augmentation methods leverage domain labels to generate synthetic images. This approach causes three significant challenges to scaling such a model. Firstly, incorporating data from a new domain into deep-learning models trained on existing domain labels is not straightforward. Secondly, dependency on domain labels prevents the use of pathology images without domain labels to improve model performance. Finally, implementation of these methods becomes complicated when multiple domain labels (e.g., patient identification, medical center, etc) are associated with a single image. We introduce ContriMix, a novel domain label free stain color augmentation method based on DRIT++, a style-transfer method. Contrimix leverages sample stain color variation within a training minibatch and random mixing to extract content and attribute information from pathology images. This information can be used by a trained ContriMix model to create synthetic images to improve the performance of existing classifiers. ContriMix outperforms competing methods on the Camelyon17-WILDS dataset. Its performance is consistent across different slides in the test set while being robust to the color variation from rare substances in pathology images. We make our code and trained ContriMix models available for research use. The code for ContriMix can be found at https://gitlab.com/huutan86/contrimix

eess.IV↗

arXiv2024

From CLIP to DINO: Visual Encoders Shout in Multi-modal Large Language Models

Multi-modal Large Language Models (MLLMs) have made significant strides in expanding the capabilities of Large Language Models (LLMs) through the incorporation of visual perception interfaces. Despite the emergence of exciting applications and the availability of diverse instruction tuning data, existing approaches often rely on CLIP or its variants as the visual branch, and merely extract features from the deep layers. However, these methods lack a comprehensive analysis of the visual encoders in MLLMs. In this paper, we conduct an extensive investigation into the effectiveness of different vision encoders within MLLMs. Our findings reveal that the shallow layer features of CLIP offer particular advantages for fine-grained tasks such as grounding and region understanding. Surprisingly, the vision-only model DINO, which is not pretrained with text-image alignment, demonstrates promising performance as a visual branch within MLLMs. By simply equipping it with an MLP layer for alignment, DINO surpasses CLIP in fine-grained related perception tasks. Building upon these observations, we propose a simple yet effective feature merging strategy, named COMM, that integrates CLIP and DINO with Multi-level features Merging, to enhance the visual capabilities of MLLMs. We evaluate COMM through comprehensive experiments on a wide range of benchmarks, including image captioning, visual question answering, visual grounding, and object hallucination. Experimental results demonstrate the superior performance of COMM compared to existing methods, showcasing its enhanced visual capabilities within MLLMs.

cs.CV↗