arXiv subjects

Ye Tao

Publications and source records attributed to Ye Tao.

At least 37 records · Page 2Linked to original sources

arXiv2025

Clustering-based Feature Representation Learning for Oracle Bone Inscriptions Detection

Oracle Bone Inscriptions (OBIs), play a crucial role in understanding ancient Chinese civilization. The automated detection of OBIs from rubbing images represents a fundamental yet challenging task in digital archaeology, primarily due to various degradation factors including noise and cracks that limit the effectiveness of conventional detection networks. To address these challenges, we propose a novel clustering-based feature space representation learning method. Our approach uniquely leverages the Oracle Bones Character (OBC) font library dataset as prior knowledge to enhance feature extraction in the detection network through clustering-based representation learning. The method incorporates a specialized loss function derived from clustering results to optimize feature representation, which is then integrated into the total network loss. We validate the effectiveness of our method by conducting experiments on two OBIs detection dataset using three mainstream detection frameworks: Faster R-CNN, DETR, and Sparse R-CNN. Through extensive experimentation, all frameworks demonstrate significant performance improvements.

cs.CV↗

arXiv2025

Slice or the Whole Pie? Utility Control for AI Models

Training deep neural networks (DNNs) has become an increasingly resource-intensive task, requiring large volumes of labeled data, substantial computational power, and considerable fine-tuning efforts to achieve optimal performance across diverse use cases. Although pre-trained models offer a useful starting point, adapting them to meet specific user needs often demands extensive customization, and infrastructure overhead. This challenge grows when a single model must support diverse appli-cations with differing requirements for performance. Traditional solutions often involve training multiple model versions to meet varying requirements, which can be inefficient and difficult to maintain. In order to overcome this challenge, we propose NNObfuscator, a novel utility control mechanism that enables AI models to dynamically modify their performance according to predefined conditions. It is different from traditional methods that need separate models for each user. Instead, NNObfuscator allows a single model to be adapted in real time, giving you controlled access to multiple levels of performance. This mechanism enables model owners set up tiered access, ensuring that free-tier users receive a baseline level of performance while premium users benefit from enhanced capabilities. The approach improves resource allocation, reduces unnecessary computation, and supports sustainable business models in AI deployment. To validate our approach, we conducted experiments on multiple tasks, including image classification, semantic segmentation, and text to image generation, using well-established models such as ResNet, DeepLab, VGG16, FCN and Stable Diffusion. Experimental results show that NNObfuscator successfully makes model more adaptable, so that a single trained model can handle a broad range of tasks without requiring a lot of changes.

cs.CV↗

arXiv2025

The bump-on-tail instability excited by energetic electrons in helicon plasma

This work explores for the first time bump-on-tail (BOT) instability excited by energetic electrons in helicon plasma. The Berk-Breizman model that developed for the wave-particle interaction and resulted instability in magnetic fusion is used. Details of the BOT instability are computed referring to typical helicon discharge conditions. Parameter studies are also conducted to reveal the effects of collisionality and energetic drive, to account for high-pressure and high-power senarios respectively. It is found that under the HXHM (high magnetic field helicon experiment) experimental parameters, the disturbed distribution function oscillates explosively at the initial stage of BOT instability excitation, and the wave frequency shift does not appear, i.e., the steady-state solution always exists under this mode. In the process of restoring stability, the exchange of energetic particles and wave energy is concurrent with the change of wave amplitude. As the Krook operator increases (i.e., from 0.1 to 1), the saturation level of the electric field and the instability enhance. Additionally, there have a bigger disturbance for the initial EEDF (electron energy distribution function) in high-power helicon devices, so that the energy exchange between waves and energetic particles is stronger as well. Moreover, BOT instability effects the density and flux of bulk plasma, and the flux increases with the Krook operator. The effect of BOT instability is one order of magnitude larger on rotating plasma than that on stationary plasma.These findings present a full picture of BOT instability in helicon plasma and are valuable to controlling it for efficient and safe applications, e.g., high-power space plasma propulsion and plasma material interactions using helicon source.

physics.plasm-ph↗

arXiv2025

Spatial and temporal evolutions of blue-core helicon discharge driven by planar antenna with concentric rings

The spatial and temporal evolutions of blue-core helicon discharge driven by a planar antenna with four concentric rings are explored on the Linear Experimental Advanced Device (LEAD). The discharge experiences distinct density jumps from E mode to H mode, W mode, and blue-core mode, when RF input power increases. This is similar to previous observations using other typical helicon antennas; however, this special antenna could drive modes of even higher levels for which the blue-core plasma column is actually hollow in radius, i.e. peaking off-axis, which was not presented before. The column shows counterclockwise rotation for blue-core mode and clockwise rotation for non-blue-core mode. The reason could be attributed to the radial electric field differenceses for both modes which reverses the rotation direction via ExB drive. Moreover, the centrifugal instability of blue-core helicon plasma is computed using a two-fluid flowing plasma model. It shows that the instability is strong for small axial wave number but becomes weak for large axial wave number. Perturbed density peaks at radius of 0.045 m, while the equilibrium density gradient peaks at radius of 0.055 m. The coincidence of their radial locations suggests that it is a resistive drift mode driven by density gradient. The blue-core mode weakens once the magnetic field or flow rate exceeds the threshold value. Increasing power further leads to a smoother plasma density gradient. The electron temperature profiles decrease with increased power, and the radial gradient of the electron temperature inside the core is smaller as the magnetic field changes. To our best knowledge, it is the first detailed characterization of blue-core helicon plasma driven by planar antenna, especially in terms of azimuthal rotation and centrifugal instability.

physics.plasm-ph↗

arXiv2025

Matrix Healy Plot: A Practical Tool for Visual Assessment of Matrix-Variate Normality

Matrix-valued data, where each observation is represented as a matrix, frequently arises in various scientific disciplines. Modeling such data often relies on matrix-variate normal distributions, making matrix-variate normality testing crucial for valid statistical inference. Recently, the Distance-Distance (DD) plot has been introduced as a graphical tool for visually assessing matrix-variate normality. However, the Mahalanobis squared distances (MSD) used in the DD plot require vectorizing matrix observations, restricting its applicability to cases where the dimension of the vectorized data does not exceed the sample size. To address this limitation, we propose a novel graphical method called the Matrix Healy (MHealy) plot, an extension of the Healy plot for vector-valued data. This new plot is based on more accurate matrix-based MSD that leverages the inherent structure of matrix data. Consequently, it offers a more reliable visual assessment. Importantly, the MHealy plot eliminates the sample size restriction of the DD plot and hence more applicable to matrix-valued data. Empirical results demonstrate its effectiveness and practicality compared to the DD plot across various scenarios, particularly in cases where the DD plot is not available due to limited sample sizes.

stat.ME↗

arXiv2025

Light-YOLOv8-Flame: A Lightweight High-Performance Flame Detection Algorithm

Fire detection algorithms, particularly those based on computer vision, encounter significant challenges such as high computational costs and delayed response times, which hinder their application in real-time systems. To address these limitations, this paper introduces Light-YOLOv8-Flame, a lightweight flame detection algorithm specifically designed for fast and efficient real-time deployment. The proposed model enhances the YOLOv8 architecture through the substitution of the original C2f module with the FasterNet Block module. This new block combines Partial Convolution (PConv) and Convolution (Conv) layers, reducing both computational complexity and model size. A dataset comprising 7,431 images, representing both flame and non-flame scenarios, was collected and augmented for training purposes. Experimental findings indicate that the modified YOLOv8 model achieves a 0.78% gain in mean average precision (mAP) and a 2.05% boost in recall, while reducing the parameter count by 25.34%, with only a marginal decrease in precision by 0.82%. These findings highlight that Light-YOLOv8-Flame offers enhanced detection performance and speed, making it well-suited for real-time fire detection on resource-constrained devices.

cs.CV↗

arXiv2025

GSV3D: Gaussian Splatting-based Geometric Distillation with Stable Video Diffusion for Single-Image 3D Object Generation

Image-based 3D generation has vast applications in robotics and gaming, where high-quality, diverse outputs and consistent 3D representations are crucial. However, existing methods have limitations: 3D diffusion models are limited by dataset scarcity and the absence of strong pre-trained priors, while 2D diffusion-based approaches struggle with geometric consistency. We propose a method that leverages 2D diffusion models' implicit 3D reasoning ability while ensuring 3D consistency via Gaussian-splatting-based geometric distillation. Specifically, the proposed Gaussian Splatting Decoder enforces 3D consistency by transforming SV3D latent outputs into an explicit 3D representation. Unlike SV3D, which only relies on implicit 2D representations for video generation, Gaussian Splatting explicitly encodes spatial and appearance attributes, enabling multi-view consistency through geometric constraints. These constraints correct view inconsistencies, ensuring robust geometric consistency. As a result, our approach simultaneously generates high-quality, multi-view-consistent images and accurate 3D models, providing a scalable solution for single-image-based 3D generation and bridging the gap between 2D Diffusion diversity and 3D structural coherence. Experimental results demonstrate state-of-the-art multi-view consistency and strong generalization across diverse datasets. The code will be made publicly available upon acceptance.

cs.CV↗

arXiv2024

Direct observation of topological magnon edge states

Magnon Chern insulators (MCIs) exhibit unique topological magnon band structures featuring chiral edge states. Direct observations of the topologically protected magnon edge states have long been pursued. Here, we report the spatially resolved detection of magnon edge states in a two-dimensional ferromagnet with honeycomb lattice (single-layer chromium triiodide). Using scanning tunneling microscopy, we observed magnon-assisted inelastic tunneling conductance and revealed the gapped magnon spectra with enhanced signals at the van Hove singularities. Extra tunneling conductance contributed from the magnon edge states was detected at three different edge configurations. Our work provided direct evidence proving the existence of MCI states down to the single-layer limit, initiating spatially-resolved explorations on exotic properties arising from topological edge states of MCIs.

cond-mat.mes-hall↗

arXiv2024

Zero-Knowledge Proof of Distinct Identity: a Standard-compatible Sybil-resistant Pseudonym Extension for C-ITS

Pseudonyms are widely used in Cooperative Intelligent Transport Systems (C-ITS) to protect the location privacy of vehicles. However, the unlinkability nature of pseudonyms also enables Sybil attacks, where a malicious vehicle can pretend to be multiple vehicles at the same time. In this paper, we propose a novel protocol called zero-knowledge Proof of Distinct Identity (zk-PoDI,) which allows a vehicle to prove that it is not the owner of another pseudonym in the local area, without revealing its actual identity. Zk-PoDI is based on the Diophantine equation and zk-SNARK, and does not rely on any specific pseudonym design or infrastructure assistance. We show that zk-PoDI satisfies all the requirements for a practical Sybil-resistance pseudonym system, and it has low latency, adjustable difficulty, moderate computation overhead, and negligible communication cost. We also discuss the future work of implementing and evaluating zk-PoDI in a realistic city-scale simulation environment.

cs.CR↗

arXiv2024

Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems

Conversational recommender systems (CRS) utilize natural language interactions and dialogue history to infer user preferences and provide accurate recommendations. Due to the limited conversation context and background knowledge, existing CRSs rely on external sources such as knowledge graphs to enrich the context and model entities based on their inter-relations. However, these methods ignore the rich intrinsic information within entities. To address this, we introduce the Knowledge-Enhanced Entity Representation Learning (KERL) framework, which leverages both the knowledge graph and a pre-trained language model to improve the semantic understanding of entities for CRS. In our KERL framework, entity textual descriptions are encoded via a pre-trained language model, while a knowledge graph helps reinforce the representation of these entities. We also employ positional encoding to effectively capture the temporal information of entities in a conversation. The enhanced entity representation is then used to develop a recommender component that fuses both entity and contextual representations for more informed recommendations, as well as a dialogue component that generates informative entity-related information in the response text. A high-quality knowledge graph with aligned entity descriptions is constructed to facilitate our study, namely the Wiki Movie Knowledge Graph (WikiMKG). The experimental results show that KERL achieves state-of-the-art results in both recommendation and response generation tasks.

cs.CL↗

arXiv2024

Revealing mechanism of pore defect formation in laser directed energy deposition of aluminum alloy via in-situ synchrotron X-ray imaging

Laser metal additive manufacturing technology is capable of producing components with complex geometries and compositions that cannot be realized by conventional manufacturing methods. However, a large number of pores generated during the additive manufacturing process greatly affect the mechanical properties of the additively manufactured parts, and the mechanism of such pore generation has not been revealed by direct observation clearly. Here, we report the mechanism of pore generation in the laser direct energy deposition process as revealed by {\it in-situ} high-speed high-resolution synchrotron X-ray imaging. We found that dissolution and re-precipitation of external gases and precipitation of metal vapors are the two main mechanisms of pore formation. We further explored the effects of different process parameters on the generation of pores and optimized the process to suppress pore generation. This work provides important insights into the formation of porosity defects during laser metal additive manufacturing, and can provide guidance for related process optimization.

cond-mat.mtrl-sci↗

arXiv2023

Zero-Knowledge Proof of Traffic: A Deterministic and Privacy-Preserving Cross Verification Mechanism for Cooperative Perception Data

Cooperative perception is crucial for connected automated vehicles in intelligent transportation systems (ITSs); however, ensuring the authenticity of perception data remains a challenge as the vehicles cannot verify events that they do not witness independently. Various studies have been conducted on establishing the authenticity of data, such as trust-based statistical methods and plausibility-based methods. However, these methods are limited as they require prior knowledge such as previous sender behaviors or predefined rules to evaluate the authenticity. To overcome this limitation, this study proposes a novel approach called zero-knowledge Proof of Traffic (zk-PoT), which involves generating cryptographic proofs to the traffic observations. Multiple independent proofs regarding the same vehicle can be deterministically cross-verified by any receivers without relying on ground truth, probabilistic, or plausibility evaluations. Additionally, no private information is compromised during the entire procedure. A full on-board unit software stack that reflects the behavior of zk-PoT is implemented within a specifically designed simulator called Flowsim. A comprehensive experimental analysis is then conducted using synthesized city-scale simulations, which demonstrates that zk-PoT's cross-verification ratio ranges between 80 % to 96 %, and 80 % of the verification is achieved in 2 s, with a protocol overhead of approximately 25 %. Furthermore, the analyses of various attacks indicate that most of the attacks could be prevented, and some, such as collusion attacks, can be mitigated. The proposed approach can be incorporated into existing works, including the European Telecommunications Standards Institute (ETSI) and the International Organization for Standardization (ISO) ITS standards, without disrupting the backward compatibility.

cs.NI↗

arXiv2023

Elastic scattering and total reaction cross sections of $^{6}$Li studied with a microscopic continuum discretized coupled channels model

We present a systematic study of $^{6}$Li elastic scattering and total reaction cross sections at incident energies around the Coulomb barrier within the continuum discretized coupled-channels (CDCC) framework, where $^{6}$Li is treated in an $α$+$d$ two-body model. Collisions with $^{27}$Al, $^{64}$Zn, $^{138}$Ba and $^{208}$Pa are analyzed. The microscopic optical potentials (MOP) based on Skyrme nucleon-nucleon interaction for $α$ and $d$ are adopted in CDCC calculations and satisfactory agreement with the experimental data is obtained without any adjustment on MOPs. For comparison, the $α$ and $d$ global phenomenological optical potentials (GOP) are also used in CDCC analysis and a reduction no less than 50$\%$ on the surface imaginary part of deuteron GOP is required for describing the data. In all cases, the $^6$Li breakup effect is significant and provides repulsive correction to the folding model potential. The reduction on the surface imaginary part of GOP of deuteron reveals a strong suppression of the reaction probability of deuteron as a component of $^{6}$Li as compared with that of a free deuteron. A further investigation is made by taking the $d$ breakup process into account equivalently within the dynamic polarization potential approach and it shows that $d$ behaves like a tightly bound nucleus in $^{6}$Li induced reactions.

nucl-th↗

arXiv2023

An Automata-Theoretic Approach to Synthesizing Binarized Neural Networks

Deep neural networks, (DNNs, a.k.a. NNs), have been widely used in various tasks and have been proven to be successful. However, the accompanied expensive computing and storage costs make the deployments in resource-constrained devices a significant concern. To solve this issue, quantization has emerged as an effective way to reduce the costs of DNNs with little accuracy degradation by quantizing floating-point numbers to low-width fixed-point representations. Quantized neural networks (QNNs) have been developed, with binarized neural networks (BNNs) restricted to binary values as a special case. Another concern about neural networks is their vulnerability and lack of interpretability. Despite the active research on trustworthy of DNNs, few approaches have been proposed to QNNs. To this end, this paper presents an automata-theoretic approach to synthesizing BNNs that meet designated properties. More specifically, we define a temporal logic, called BLTL, as the specification language. We show that each BLTL formula can be transformed into an automaton on finite words. To deal with the state-explosion problem, we provide a tableau-based approach in real implementation. For the synthesis procedure, we utilize SMT solvers to detect the existence of a model (i.e., a BNN) in the construction process. Notably, synthesis provides a way to determine the hyper-parameters of the network before training.Moreover, we experimentally evaluate our approach and demonstrate its effectiveness in improving the individual fairness and local robustness of BNNs while maintaining accuracy to a great extent.

cs.LG↗

arXiv2023

Time-to-Collision-Aware Lane-Change Strategy Based on Potential Field and Cubic Polynomial for Autonomous Vehicles

Making safe and successful lane changes (LCs) is one of the many vitally important functions of autonomous vehicles (AVs) that are needed to ensure safe driving on expressways. Recently, the simplicity and real-time performance of the potential field (PF) method have been leveraged to design decision and planning modules for AVs. However, the LC trajectory planned by the PF method is usually lengthy and takes the ego vehicle laterally parallel and close to the obstacle vehicle, which creates a dangerous situation if the obstacle vehicle suddenly steers. To mitigate this risk, we propose a time-to-collision-aware LC (TTCA-LC) strategy based on the PF and cubic polynomial in which the TTC constraint is imposed in the optimized curve fitting. The proposed approach is evaluated using MATLAB/Simulink under high-speed conditions in a comparative driving scenario. The simulation results indicate that the TTCA-LC method performs better than the conventional PF-based LC (CPF-LC) method in generating shorter, safer, and smoother trajectories. The length of the LC trajectory is shortened by over 27.1\%, and the curvature is reduced by approximately 56.1\% compared with the CPF-LC method.

cs.RO↗

arXiv2023

Continuum-discretized coupled-channel calculations for $^{6}$Li fusion reactions with closed channels

Fusion reactions induced by the weakly bound nucleus $^{6}$Li with targets $^{28}$Si, $^{64}$Ni, $^{144}$Sm and $^{209}$Bi at energies around the Coulomb barrier are investigated within a three-body model where $^{6}$Li is described with an $α+ d$ cluster model. The total fusion (TF) cross sections are calculated with the continuum-discretized coupled-channel (CDCC) method and the complete fusion (CF) cross sections are extracted through the sum-rule model. The calculations demonstrate that (i) for the TF cross section calculations, the continuum states up to 40 MeV are found to be necessary, which corresponds to the inclusion of closed channels for light and medium mass targets, such as $^{28}$Si, $^{59}$Co and $^{144}$Sm, (ii) the converged CDCC results for TF cross section at energies above the Coulomb barrier are almost the same as single channel results in which the continuum coupling effect is neglected, and (iii) the continuum coupling strongly influences partial wave fusion cross sections and the closed channels play a significant role in the improvement of the description of the CF cross sections at energies below the Coulomb barrier for the $^6$Li+$^{28}$Si, $^{59}$Co and $^{144}$Sm systems.

nucl-th↗

arXiv2023

Flowsim: A Modular Simulation Platform for Microscopic Behavior Analysis of City-Scale Connected Autonomous Vehicles

As connected autonomous vehicles (CAVs) become increasingly prevalent, there is a growing need for simulation platforms that can accurately evaluate CAV behavior in large-scale environments. In this paper, we propose Flowsim, a novel simulator specifically designed to meet these requirements. Flowsim offers a modular and extensible architecture that enables the analysis of CAV behaviors in large-scale scenarios. It provides researchers with a customizable platform for studying CAV interactions, evaluating communication and networking protocols, assessing cybersecurity vulnerabilities, optimizing traffic management strategies, and developing and evaluating policies for CAV deployment. Flowsim is implemented in pure Python in approximately 1,500 lines of code, making it highly readable, understandable, and easily modifiable. We verified the functionality and performance of Flowsim via a series of experiments based on realistic traffic scenarios. The results show the effectiveness of Flowsim in providing a flexible and powerful simulation environment for evaluating CAV behavior and data flow. Flowsim is a valuable tool for researchers, policymakers, and industry professionals who are involved in the development, evaluation, and deployment of CAVs. The code of Flowsim is publicly available on GitHub under the MIT license.

cs.NI↗

arXiv2022

zk-PoT: Zero-Knowledge Proof of Traffic for Privacy Enabled Cooperative Perception

Cooperative perception is an essential and widely discussed application of connected automated vehicles. However, the authenticity of perception data is not ensured, because the vehicles cannot independently verify the event they did not see. Many methods, including trust-based (i.e., statistical) approaches and plausibility-based methods, have been proposed to determine data authenticity. However, these methods cannot verify data without a priori knowledge. In this study, a novel approach of constructing a self-proving data from the number plate of target vehicles was proposed. By regarding the pseudonym and number plate as a shared secret and letting multiple vehicles prove they know it independently, the data authenticity problem can be transformed to a cryptography problem that can be solved without trust or plausibility evaluations. Our work can be adapted to the existing works including ETSI/ISO ITS standards while maintaining backward compatibility. Analyses of common attacks and attacks specific to the proposed method reveal that most attacks can be prevented, whereas preventing some other attacks, such as collusion attacks, can be mitigated. Experiments based on realistic data set show that the rate of successful verification can achieve 70\% to 80\% at rush hours.

cs.NI↗