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Jin Cao

Publications and source records attributed to Jin Cao.

At least 55 records · Page 3Linked to original sources
arXiv2025

Skin-inspired in-sensor encoding of strain vector using tunable quantum geometry

Human skin provides crucial tactile feedback, allowing us to skillfully perceive various objects by sensing and encoding complex deformations through multiple parameters in each tactile receptor. However, replicating this high-dimensional tactile perception with conventional materials' electronic properties remains a daunting challenge. Here, we present a skin-inspired method to encode strain vectors directly within a sensor. This is achieved by leveraging the strain-tunable quantum properties of electronic bands in the van der Waals topological semimetal Td -WTe2. We observe robust and independent responses from the second-order and third-order nonlinear Hall signals in Td -WTe2 when subjected to variations in both the magnitude and direction of strain. Through rigorous temperature-dependent measurements and scaling law analysis, we establish that these strain responses primarily stem from quantum geometry-related phenomena, including the Berry curvature and Berry-connection polarizability tensor. Furthermore, our study demonstrates that the strain-dependent nonlinear Hall signals can efficiently encode high-dimensional strain information using a single device. This capability enables accurate and comprehensive sensing of complex strain patterns in the embossed character "NJU". Our findings highlight the promising application of topological quantum materials in advancing next-generation, bio-inspired flexible electronics.

cond-mat.mes-hall↗
arXiv2025

Time Series Language Model for Descriptive Caption Generation

The automatic generation of representative natural language descriptions for observable patterns in time series data enhances interpretability, simplifies analysis and increases cross-domain utility of temporal data. While pre-trained foundation models have made considerable progress in natural language processing (NLP) and computer vision (CV), their application to time series analysis has been hindered by data scarcity. Although several large language model (LLM)-based methods have been proposed for time series forecasting, time series captioning is under-explored in the context of LLMs. In this paper, we introduce TSLM, a novel time series language model designed specifically for time series captioning. TSLM operates as an encoder-decoder model, leveraging both text prompts and time series data representations to capture subtle temporal patterns across multiple phases and generate precise textual descriptions of time series inputs. TSLM addresses the data scarcity problem in time series captioning by first leveraging an in-context prompting synthetic data generation, and second denoising the generated data via a novel cross-modal dense retrieval scoring applied to time series-caption pairs. Experimental findings on various time series captioning datasets demonstrate that TSLM outperforms existing state-of-the-art approaches from multiple data modalities by a significant margin.

cs.CL↗
arXiv2024

Chain-of-Restoration: Multi-Task Image Restoration Models are Zero-Shot Step-by-Step Universal Image Restorers

Despite previous image restoration (IR) methods have often concentrated on isolated degradations, recent research has increasingly focused on addressing composite degradations involving a complex combination of multiple isolated degradations. However, current IR methods for composite degradations require building training data that contain an exponential number of possible degradation combinations, which brings in a significant burden. To alleviate this issue, this paper proposes a new task setting, i.e. Universal Image Restoration (UIR). Specifically, UIR doesn't require training on all the degradation combinations but only on a set of degradation bases and then removing any degradation that these bases can potentially compose in a zero-shot manner. Inspired by the Chain-of-Thought that prompts large language models (LLMs) to address problems step-by-step, we propose Chain-of-Restoration (CoR) mechanism, which instructs models to remove unknown composite degradations step-by-step. By integrating a simple Degradation Discriminator into pre-trained multi-task models, CoR facilitates the process where models remove one degradation basis per step, continuing this process until the image is fully restored from the unknown composite degradation. Extensive experiments show that CoR can significantly improve model performance in removing composite degradations, achieving comparable or better results than those state-of-the-art (SoTA) methods trained on all degradations.

cs.CV↗
arXiv2024

HAIR: Hypernetworks-based All-in-One Image Restoration

Image restoration aims to recover a high-quality clean image from its degraded version. Recent progress in image restoration has demonstrated the effectiveness of All-in-One image restoration models in addressing various unknown degradations simultaneously. However, these existing methods typically utilize the same parameters to tackle images with different types of degradation, forcing the model to balance the performance between different tasks and limiting its performance on each task. To alleviate this issue, we propose HAIR, a Hypernetworks-based All-in-One Image Restoration plug-and-play method that generates parameters based on the input image and thus makes the model to adapt to specific degradation dynamically. Specifically, HAIR consists of two main components, i.e., Classifier and Hyper Selecting Net (HSN). The Classifier is a simple image classification network used to generate a Global Information Vector (GIV) that contains the degradation information of the input image, and the HSN is a simple fully-connected neural network that receives the GIV and outputs parameters for the corresponding modules. Extensive experiments demonstrate that HAIR can significantly improve the performance of existing image restoration models in a plug-and-play manner, both in single-task and All-in-One settings. Notably, our proposed model Res-HAIR, which integrates HAIR into the well-known Restormer, can obtain superior or comparable performance compared with current state-of-the-art methods. Moreover, we theoretically demonstrate that to achieve a given small enough error, our proposed HAIR requires fewer parameters in contrast to mainstream embedding-based All-in-One methods. The code is available at https://github.com/toummHus/HAIR.

cs.CV↗
arXiv2024

Credit Card Fraud Detection Using Advanced Transformer Model

With the proliferation of various online and mobile payment systems, credit card fraud has emerged as a significant threat to financial security. This study focuses on innovative applications of the latest Transformer models for more robust and precise fraud detection. To ensure the reliability of the data, we meticulously processed the data sources, balancing the dataset to address the issue of data sparsity significantly. We also selected highly correlated vectors to strengthen the training process.To guarantee the reliability and practicality of the new Transformer model, we conducted performance comparisons with several widely adopted models, including Support Vector Machine (SVM), Random Forest, Neural Network, and Logistic Regression. We rigorously compared these models using metrics such as Precision, Recall, and F1 Score. Through these detailed analyses and comparisons, we present to the readers a highly efficient and powerful anti-fraud mechanism with promising prospects. The results demonstrate that the Transformer model not only excels in traditional applications but also shows great potential in niche areas like fraud detection, offering a substantial advancement in the field.

cs.LG↗
arXiv2024

Advanced Payment Security System:XGBoost, LightGBM and SMOTE Integrated

With the rise of various online and mobile payment systems, transaction fraud has become a significant threat to financial security. This study explores the application of advanced machine learning models, specifically based on XGBoost and LightGBM, for developing a more accurate and robust Payment Security Protection Model. To enhance data reliability, we meticulously processed the data sources and applied SMOTE (Synthetic Minority Over-sampling Technique) to address class imbalance and improve data representation. By selecting highly correlated features, we aimed to strengthen the training process and boost model performance. We conducted thorough performance evaluations of our proposed models, comparing them against traditional methods including Random Forest, Neural Network, and Logistic Regression. Using metrics such as Precision, Recall, and F1 Score, we rigorously assessed their effectiveness. Our detailed analyses and comparisons reveal that the combination of SMOTE with XGBoost and LightGBM offers a highly efficient and powerful mechanism for payment security protection. Moreover, the integration of XGBoost and LightGBM in a Local Ensemble model further demonstrated outstanding performance. After incorporating SMOTE, the new combined model achieved a significant improvement of nearly 6\% over traditional models and around 5\% over its sub-models, showcasing remarkable results.

cs.CR↗
arXiv2024

Training Data Attribution: Was Your Model Secretly Trained On Data Created By Mine?

The emergence of text-to-image models has recently sparked significant interest, but the attendant is a looming shadow of potential infringement by violating the user terms. Specifically, an adversary may exploit data created by a commercial model to train their own without proper authorization. To address such risk, it is crucial to investigate the attribution of a suspicious model's training data by determining whether its training data originates, wholly or partially, from a specific source model. To trace the generated data, existing methods require applying extra watermarks during either the training or inference phases of the source model. However, these methods are impractical for pre-trained models that have been released, especially when model owners lack security expertise. To tackle this challenge, we propose an injection-free training data attribution method for text-to-image models. It can identify whether a suspicious model's training data stems from a source model, without additional modifications on the source model. The crux of our method lies in the inherent memorization characteristic of text-to-image models. Our core insight is that the memorization of the training dataset is passed down through the data generated by the source model to the model trained on that data, making the source model and the infringing model exhibit consistent behaviors on specific samples. Therefore, our approach involves developing algorithms to uncover these distinct samples and using them as inherent watermarks to verify if a suspicious model originates from the source model. Our experiments demonstrate that our method achieves an accuracy of over 80\% in identifying the source of a suspicious model's training data, without interfering the original training or generation process of the source model.

cs.CV↗
arXiv2024

Intrinsic Dynamic Generation of Spin Polarization by Time-Varying Electric Field

Electric control of spin in insulators is desired for low-consumption and ultrafast spintronics, but the underlying mechanism remains largely unexplored. Here, we propose an intrinsic effect of dynamic spin generation driven by time-varying electric field. In the intraband response regime, it can be nicely formulated as a Berry curvature effect and leads to two phenomena that are forbidden in the $dc$ limit: linear spin generation in nonmagnetic insulators and intrinsic N{é}el spin-orbit torque in $\mathcal{PT}$-symmetric antiferromagnetic insulators. These phenomena are driven by the time derivative of field rather than the field itself, and have a quantum origin in the first-order dynamic anomalous spin polarizability. Combined with first-principles calculations, we predict sizable effects driven by terahertz field in nonmagnetic monolayer Bi and in antiferromagnetic even-layer MnBi$_2$Te$_4$, which can be detected in experiment.

cond-mat.mes-hall↗
arXiv2024

Analysis of the Effect of Tilted Corner Cube Reflector Arrays on Lunar Laser Ranging

This paper primarily investigates the effect of the tilt of corner cube reflector (CCR) arrays on lunar laser ranging (LLR). A mathematical model was established to study the random errors caused by the tilt of the CCR arrays. The study found that, ideally, when the laser ranging pulse width is 10 picoseconds or less, it is possible to distinguish from which specific corner cubes within the CCR array each peak in the echo signal originates. Consequently, partial data from the echo can be extracted for signal processing, significantly reducing random errors and improving the single-shot precision of LLR. The distance obtained by extracting part of the echo can be reduced to the center position of the array, thereby providing multiple higher-precision ranging results from each measurement. This not only improves the precision of LLR but also increases the data volume. A simulation experiment based on the 1.2 m laser ranging system at Yunnan Observatories was conducted. By extracting one peak for signal processing, the single-shot precision improved from 32.24 mm to 2.52 mm, validating the theoretical analysis results. Finally, an experimental laser ranging system based on a 53 cm binocular telescope system was established for ground experiments. The experimental results indicated that the echo signal could identify the tilt state of the CCR array. By extracting the peak returned by the central CCR for signal processing, the ranging precision was greatly improved. Through theoretical analyses, simulation experiments, and ground experiments, a solution to reduce the random errors caused by the tilt of the CCR array was provided. This offers an approach to enhance the single-shot precision of future LLR and provides a reference for upgrading ground-based equipment at future laser ranging stations.

astro-ph.IM↗
arXiv2024

Intrinsic Nonlinear Spin Hall Effect and Manipulation of Perpendicular Magnetization

We propose an intrinsic nonlinear spin Hall effect, which enables the generation of collinearly-polarized spin current in a large class of nonmagnetic materials with the corresponding linear response being symmetry-forbidden. This opens a new avenue for field-free switching of perpendicular magnetization, which is required for the next-generation information storage technology. We develop the microscopic theory of this effect, and clarify its quantum origin in band geometric quantities which can be enhanced by topological nodal features. Combined with first-principles calculations, we predict pronounced effects at room temperature in topological metals $\mathrm{PbTaSe_{2}}$ and PdGa. Our work establishes a fundamental nonlinear response in spin transport, and opens the door to exploring spintronic applications based on nonlinear spin Hall effect.

cond-mat.mes-hall↗
arXiv2024

Multi-Scenario Combination Based on Multi-Agent Reinforcement Learning to Optimize the Advertising Recommendation System

This paper explores multi-scenario optimization on large platforms using multi-agent reinforcement learning (MARL). We address this by treating scenarios like search, recommendation, and advertising as a cooperative, partially observable multi-agent decision problem. We introduce the Multi-Agent Recurrent Deterministic Policy Gradient (MARDPG) algorithm, which aligns different scenarios under a shared objective and allows for strategy communication to boost overall performance. Our results show marked improvements in metrics such as click-through rate (CTR), conversion rate, and total sales, confirming our method's efficacy in practical settings.

cs.LG↗
arXiv2024

Rough Set improved Therapy-Based Metaverse Assisting System

Chronic neck and shoulder pain (CNSP) is a major global public health issue. Traditional treatments like physiotherapy and rehabilitation have drawbacks, including high costs, low precision, and user discomfort. This paper presents an interactive system based on Cognitive Therapy Theory (CBT) for CNSP treatment. The system includes a pain detection module using EMG and IMU to monitor pain and optimize data with Rough Set theory, and a cognitive therapy module that processes this data further for CBT-based interventions, including massage and heating therapy. An experimental plan is outlined to evaluate the system's effectiveness and performance. The goal is to create an accessible device for CNSP therapy. Additionally, the paper explores the system's application in a metaverse environment, enhancing treatment immersion and personalization. The metaverse platform simulates treatment environments and responds to real-time patient data, allowing for continuous monitoring and adjustment of treatment plans.

cs.HC↗
arXiv2024

Dog Heart Rate and Blood Oxygen Metaverse Interaction System

This study developed an improved dog heart rate and blood oxygen sensor system using Arduino. Traditional methods face accuracy and reliability issues. Our system integrates advanced computational techniques with hardware-based sensing to enhance measurement precision. An Arduino microcontroller connected to a heart rate and blood oxygen sensor collects raw data, which is preprocessed and filtered to remove noise. Experimental plans include long-term monitoring of multiple dogs and comparative analysis with traditional methods to validate the system's effectiveness, aiming for widespread use in the home or veterinary clinics. Additionally, the system offers dog owners the possibility to interact with their virtual dogs in the metaverse using AR/VR, allowing them to better understand their real dogs' health conditions by observing their virtual health status.

cs.HC↗
arXiv2024

Research on the Application of Computer Vision Based on Deep Learning in Autonomous Driving Technology

This research aims to explore the application of deep learning in autonomous driving computer vision technology and its impact on improving system performance. By using advanced technologies such as convolutional neural networks (CNN), multi-task joint learning methods, and deep reinforcement learning, this article analyzes in detail the application of deep learning in image recognition, real-time target tracking and classification, environment perception and decision support, and path planning and navigation. Application process in key areas. Research results show that the proposed system has an accuracy of over 98% in image recognition, target tracking and classification, and also demonstrates efficient performance and practicality in environmental perception and decision support, path planning and navigation. The conclusion points out that deep learning technology can significantly improve the accuracy and real-time response capabilities of autonomous driving systems. Although there are still challenges in environmental perception and decision support, with the advancement of technology, it is expected to achieve wider applications and greater capabilities in the future. potential.

cs.CV↗
arXiv2024

Predict Click-Through Rates with Deep Interest Network Model in E-commerce Advertising

This paper proposes new methods to enhance click-through rate (CTR) prediction models using the Deep Interest Network (DIN) model, specifically applied to the advertising system of Alibaba's Taobao platform. Unlike traditional deep learning approaches, this research focuses on localized user behavior activation for tailored ad targeting by leveraging extensive user behavior data. Compared to traditional models, this method demonstrates superior ability to handle diverse and dynamic user data, thereby improving the efficiency of ad systems and increasing revenue.

cs.IR↗
arXiv2024

Optimizing Search Advertising Strategies: Integrating Reinforcement Learning with Generalized Second-Price Auctions for Enhanced Ad Ranking and Bidding

This paper explores the integration of strategic optimization methods in search advertising, focusing on ad ranking and bidding mechanisms within E-commerce platforms. By employing a combination of reinforcement learning and evolutionary strategies, we propose a dynamic model that adjusts to varying user interactions and optimizes the balance between advertiser cost, user relevance, and platform revenue. Our results suggest significant improvements in ad placement accuracy and cost efficiency, demonstrating the model's applicability in real-world scenarios.

cs.LG↗
arXiv2024

Infrared Image Super-Resolution via Lightweight Information Split Network

Single image super-resolution (SR) is an established pixel-level vision task aimed at reconstructing a high-resolution image from its degraded low-resolution counterpart. Despite the notable advancements achieved by leveraging deep neural networks for SR, most existing deep learning architectures feature an extensive number of layers, leading to high computational complexity and substantial memory demands. These issues become particularly pronounced in the context of infrared image SR, where infrared devices often have stringent storage and computational constraints. To mitigate these challenges, we introduce a novel, efficient, and precise single infrared image SR model, termed the Lightweight Information Split Network (LISN). The LISN comprises four main components: shallow feature extraction, deep feature extraction, dense feature fusion, and high-resolution infrared image reconstruction. A key innovation within this model is the introduction of the Lightweight Information Split Block (LISB) for deep feature extraction. The LISB employs a sequential process to extract hierarchical features, which are then aggregated based on the relevance of the features under consideration. By integrating channel splitting and shift operations, the LISB successfully strikes an optimal balance between enhanced SR performance and a lightweight framework. Comprehensive experimental evaluations reveal that the proposed LISN achieves superior performance over contemporary state-of-the-art methods in terms of both SR quality and model complexity, affirming its efficacy for practical deployment in resource-constrained infrared imaging applications.

eess.IV↗