arXiv subjects

Chenxing Wang

Publications and source records attributed to Chenxing Wang.

18 recordsLinked to original sources

arXiv2026

HeadRank: Decoding-Free Passage Reranking via Preference-Aligned Attention Heads

Decoding-free reranking methods that read relevance signals directly from LLM attention weights offer significant latency advantages over autoregressive approaches, yet suffer from attention score homogenization: middle-context documents receive near-identical scores, destroying the fine-grained distinctions required for ranking. We propose HeadRank, a framework that lifts preference optimization from discrete token space into the continuous attention domain through entropy-regularized head selection, hard adjacent-level preference pairs, and a distribution regularizer that jointly sharpen discriminability in the homogenized middle zone. Depth truncation at the deepest selected layer further reduces inference to $\mathcal{O}(1)$ forward passes. Across 14 benchmarks on three Qwen3 scales (0.6B--4B) using only 211 training queries, HeadRank achieves the highest average NDCG@10 at every scale, outperforming both generative and decoding-free baselines on the majority of benchmarks with 100\% formatting success. At 4B, 57.4\% of relevant middle-zone documents reach the top quartile versus 14.2\% for irrelevant ones -- a 43-percentage-point selectivity gap that demonstrates the effectiveness of attention-space preference alignment for listwise reranking.

cs.IR↗

arXiv2026

When & How to Write for Personalized Demand-aware Query Rewriting in Video Search

In video search systems, user historical behaviors provide rich context for identifying search intent and resolving ambiguity. However, traditional methods utilizing implicit history features often suffer from signal dilution and delayed feedback. To address these challenges, we propose WeWrite, a novel Personalized Demand-aware Query Rewriting framework. Specifically, WeWrite tackles three key challenges: (1) When to Write: An automated posterior-based mining strategy extracts high-quality samples from user logs, identifying scenarios where personalization is strictly necessary; (2) How to Write: A hybrid training paradigm combines Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization (GRPO) to align the LLM's output style with the retrieval system; (3) Deployment: A parallel "Fake Recall" architecture ensures low latency. Online A/B testing on a large-scale video platform demonstrates that WeWrite improves the Click-Through Video Volume (VV$>$10s) by 1.07% and reduces the Query Reformulation Rate by 2.97%.

cs.IR↗

arXiv2025

TCAD Simulation of Novel Multi-Spacer HK/MG 28nm Planar MOSFET for Sub-threshold Swing and DIBL Optimization

This study optimizes 28 nm planar MOSFET technology to reduce device leakage current and enhance switching speed. The specific aims are to decrease subthreshold swing (S.S.) and mitigate drain induced barrier lowering (DIBL) effect. Silvaco TCAD software is used for process (Athena) and device (Atlas) simulations. For the further development of MOSFET technology, we implemented our device (planar 28 nm n-MOSFET) with high-k metal-gate (HK/MG), lightly doped drain (LDD), multiple spacers (mult-spacers), and silicide. Simulation validation shows improvements over other 28 nm devices, with lower static power consumption and notable optimizations in both S.S. (69.8 mV/dec) and DIBL effect (30.5 mV/V).

physics.app-ph↗

arXiv2025

Intrinsic-feature-guided 3D Object Detection

LiDAR-based 3D object detection is essential for autonomous driving systems. However, LiDAR point clouds may appear to have sparsity, uneven distribution, and incomplete structures, significantly limiting the detection performance. In road driving environments, target objects referring to vehicles, pedestrians and cyclists are well-suited for enhancing representation through the complete template guidance, considering their grid and topological structures. Therefore, this paper presents an intrinsic-feature-guided 3D object detection method based on a template-assisted feature enhancement module, which extracts intrinsic features from relatively generalized templates and provides rich structural information for foreground objects. Furthermore, a proposal-level contrastive learning mechanism is designed to enhance the feature differences between foreground and background objects. The proposed modules can act as plug-and-play components and improve the performance of multiple existing methods. Extensive experiments illustrate that the proposed method achieves the highly competitive detection results. Code will be available at https://github.com/zhangwanjingjj/IfgNet.git.

cs.CV↗

arXiv2025

Detail-aware multi-view stereo network for depth estimation

Multi-view stereo methods have achieved great success for depth estimation based on the coarse-to-fine depth learning frameworks, however, the existing methods perform poorly in recovering the depth of object boundaries and detail regions. To address these issues, we propose a detail-aware multi-view stereo network (DA-MVSNet) with a coarse-to-fine framework. The geometric depth clues hidden in the coarse stage are utilized to maintain the geometric structural relationships between object surfaces and enhance the expressive capability of image features. In addition, an image synthesis loss is employed to constrain the gradient flow for detailed regions and further strengthen the supervision of object boundaries and texture-rich areas. Finally, we propose an adaptive depth interval adjustment strategy to improve the accuracy of object reconstruction. Extensive experiments on the DTU and Tanks & Temples datasets demonstrate that our method achieves competitive results. The code is available at https://github.com/wsmtht520-/DAMVSNet.

cs.CV↗

arXiv2024

Taylor Modeling and Comparative Research Containing Aspect-Ratio Dependent Optimization of Three-Dimensional Hk Superjunction MOSFETs

This paper presents a comprehensive study on aspect-ratio dependent optimization for specific on-resistance of three-dimensional high-k superjunction MOSFETs. The research introduces a Taylor modeling method, overcoming the computational limitations of the Bessel method. It also employs the Chynoweth model for more accurate breakdown voltage determination. The study provides a comparative analysis of four different superjunction structures, across five aspects: electric field, impact ionization integral, aspect ratio dependent optimization, charge imbalance effect and temperature. The findings offer valuable insights for the manufacturing guidance of superjunction structure selection

physics.app-ph↗

arXiv2024

Design and Process Analysis of a Split-Gate Trench Power MOSFET with Bottom-Trench Hk-Pillar Superjunction for Enhanced Performance

In this paper, we propose a simulation-based novel Split-Gate Trench MOSFET structure with an optimized fabrication process to enhance power efficiency, switching speed, and thermal stability for high-performance semiconductor applications. Integrating high-k pillars with superjunction structures beneath the split gate enhancing breakdown performance by reducing critical field intensity by up to 35%, the device achieves a 15% improvement in Figures of Merit (FOMs) for BV2/Ron,sp. Dynamic testing reveals approximately a 25% reduction in both input and output capacitance, as well as gate-to-drain charge (QGD). This reduction, coupled with an approximately 40% improvement in Baliga's High-Frequency Figure of Merit (BHFFOM) and over 20% increase in the New High-Frequency Figure of Merit (NHFFOM), underscores the design's suitability for high-speed, high-efficiency power electronics. Simulations examining the effects of high-k pillar depth indicate that an optimal depth of 3.5 um achieves a balanced performance between BV and Ron,sp. The influence of high-k materials on BT-Hk-SJ MOSFET performance was investigated by comparing hafnium dioxide (HfO2), nitride, and oxynitride. Among these, HfO2 demonstrated optimal performance across static, dynamic, and diode characteristics due to its high dielectric constant, while material choice had minimal impact, with variations kept within 5%.

physics.app-ph↗

arXiv2024

Calibration of 3D Single-pixel Imaging Systems with a Calibration Field

3D single-pixel imaging (SPI) is a promising imaging technique that can be ffexibly applied to various wavebands. The main challenge in 3D SPI is that the calibration usually requires a large number of standard points as references, which are tricky to capture using single-pixel detectors. Conventional solutions involve sophisticated device deployment and cumbersome operations, resulting in hundreds of images needed for calibration. In our work, we construct a Calibration Field (CaliF) to efffciently generate the standard points from one single image. A high accuracy of the CaliF is guaranteed by the technique of deep learning and digital twin. We perform experiments with our new method to verify its validity and accuracy. We believe our work holds great potential in 3D SPI systems or even general imaging systems.

eess.IV↗

arXiv2024

Towards Effective Fusion and Forecasting of Multimodal Spatio-temporal Data for Smart Mobility

With the rapid development of location based services, multimodal spatio-temporal (ST) data including trajectories, transportation modes, traffic flow and social check-ins are being collected for deep learning based methods. These deep learning based methods learn ST correlations to support the downstream tasks in the fields such as smart mobility, smart city and other intelligent transportation systems. Despite their effectiveness, ST data fusion and forecasting methods face practical challenges in real-world scenarios. First, forecasting performance for ST data-insufficient area is inferior, making it necessary to transfer meta knowledge from heterogeneous area to enhance the sparse representations. Second, it is nontrivial to accurately forecast in multi-transportation-mode scenarios due to the fine-grained ST features of similar transportation modes, making it necessary to distinguish and measure the ST correlations to alleviate the influence caused by entangled ST features. At last, partial data modalities (e.g., transportation mode) are lost due to privacy or technical issues in certain scenarios, making it necessary to effectively fuse the multimodal sparse ST features and enrich the ST representations. To tackle these challenges, our research work aim to develop effective fusion and forecasting methods for multimodal ST data in smart mobility scenario. In this paper, we will introduce our recent works that investigates the challenges in terms of various real-world applications and establish the open challenges in this field for future work.

cs.LG↗

arXiv2024

Kinematics-based 3D Human-Object Interaction Reconstruction from Single View

Reconstructing 3D human-object interaction (HOI) from single-view RGB images is challenging due to the absence of depth information and potential occlusions. Existing methods simply predict the body poses merely rely on network training on some indoor datasets, which cannot guarantee the rationality of the results if some body parts are invisible due to occlusions that appear easily. Inspired by the end-effector localization task in robotics, we propose a kinematics-based method that can drive the joints of human body to the human-object contact regions accurately. After an improved forward kinematics algorithm is proposed, the Multi-Layer Perceptron is introduced into the solution of inverse kinematics process to determine the poses of joints, which achieves precise results than the commonly-used numerical methods in robotics. Besides, a Contact Region Recognition Network (CRRNet) is also proposed to robustly determine the contact regions using a single-view video. Experimental results demonstrate that our method outperforms the state-of-the-art on benchmark BEHAVE. Additionally, our approach shows good portability and can be seamlessly integrated into other methods for optimizations.

cs.CV↗

arXiv2022

3D Single-pixel imaging with active sampling patterns and learning based reconstruction

Single-pixel imaging (SPI) is significant for applications constrained by transmission bandwidth or lighting band, where 3D SPI can be further realized through capturing signals carrying depth. Sampling strategy and reconstruction algorithm are the key issues of SPI. Traditionally, random patterns are often adopted for sampling, but this blindly passive strategy requires a high sampling rate, and even so, it is difficult to develop a reconstruction algorithm that can maintain higher accuracy and robustness. In this paper, an active strategy is proposed to perform sampling with targeted scanning by designed patterns, from which the spatial information can be easily reordered well. Then, deep learning methods are introduced further to achieve 3D reconstruction, and the ability of deep learning to reconstruct desired information under low sampling rates are analyzed. Abundant experiments verify that our method improves the precision of SPI even if the sampling rate is very low, which has the potential to be extended flexibly in similar systems according to practical needs.

eess.IV↗

arXiv2022

Fine-Grained Trajectory-based Travel Time Estimation for Multi-city Scenarios Based on Deep Meta-Learning

Travel Time Estimation (TTE) is indispensable in intelligent transportation system (ITS). It is significant to achieve the fine-grained Trajectory-based Travel Time Estimation (TTTE) for multi-city scenarios, namely to accurately estimate travel time of the given trajectory for multiple city scenarios. However, it faces great challenges due to complex factors including dynamic temporal dependencies and fine-grained spatial dependencies. To tackle these challenges, we propose a meta learning based framework, MetaTTE, to continuously provide accurate travel time estimation over time by leveraging well-designed deep neural network model called DED, which consists of Data preprocessing module and Encoder-Decoder network module. By introducing meta learning techniques, the generalization ability of MetaTTE is enhanced using small amount of examples, which opens up new opportunities to increase the potential of achieving consistent performance on TTTE when traffic conditions and road networks change over time in the future. The DED model adopts an encoder-decoder network to capture fine-grained spatial and temporal representations. Extensive experiments on two real-world datasets are conducted to confirm that our MetaTTE outperforms six state-of-art baselines, and improve 29.35% and 25.93% accuracy than the best baseline on Chengdu and Porto datasets, respectively.

cs.AI↗

arXiv2022

Spatio-Temporal meets Wavelet: Disentangled Traffic Flow Forecasting via Efficient Spectral Graph Attention Network

Traffic forecasting is crucial for public safety and resource optimization, yet is very challenging due to three aspects: i) current existing works mostly exploit intricate temporal patterns (e.g., the short-term thunderstorm and long-term daily trends) within a single method, which fail to accurately capture spatio-temporal dependencies under different schemas; ii) the under-exploration of the graph positional encoding limit the extraction of spatial information in the commonly used full graph attention network; iii) the quadratic complexity of the full graph attention introduces heavy computational needs. To achieve the effective traffic flow forecasting, we propose an efficient spectral graph attention network with disentangled traffic sequences. Specifically, the discrete wavelet transform is leveraged to obtain the low- and high-frequency components of traffic sequences, and a dual-channel encoder is elaborately designed to accurately capture the spatio-temporal dependencies under long- and short-term schemas of the low- and high-frequency components. Moreover, a novel wavelet-based graph positional encoding and a query sampling strategy are introduced in our spectral graph attention to effectively guide message passing and efficiently calculate the attention. Extensive experiments on four real-world datasets show the superiority of our model, i.e., the higher traffic forecasting precision with lower computational cost.

cs.LG↗

arXiv2021

CDGNet: A Cross-Time Dynamic Graph-based Deep Learning Model for Traffic Forecasting

Traffic forecasting is important in intelligent transportation systems of webs and beneficial to traffic safety, yet is very challenging because of the complex and dynamic spatio-temporal dependencies in real-world traffic systems. Prior methods use the pre-defined or learnable static graph to extract spatial correlations. However, the static graph-based methods fail to mine the evolution of the traffic network. Researchers subsequently generate the dynamic graph for each time slice to reflect the changes of spatial correlations, but they follow the paradigm of independently modeling spatio-temporal dependencies, ignoring the cross-time spatial influence. In this paper, we propose a novel cross-time dynamic graph-based deep learning model, named CDGNet, for traffic forecasting. The model is able to effectively capture the cross-time spatial dependence between each time slice and its historical time slices by utilizing the cross-time dynamic graph. Meanwhile, we design a gating mechanism to sparse the cross-time dynamic graph, which conforms to the sparse spatial correlations in the real world. Besides, we propose a novel encoder-decoder architecture to incorporate the cross-time dynamic graph-based GCN for multi-step traffic forecasting. Experimental results on three real-world public traffic datasets demonstrate that CDGNet outperforms the state-of-the-art baselines. We additionally provide a qualitative study to analyze the effectiveness of our architecture.

cs.LG↗

arXiv2021

STJLA: A Multi-Context Aware Spatio-Temporal Joint Linear Attention Network for Traffic Forecasting

Traffic prediction has gradually attracted the attention of researchers because of the increase in traffic big data. Therefore, how to mine the complex spatio-temporal correlations in traffic data to predict traffic conditions more accurately become a difficult problem. Previous works combined graph convolution networks (GCNs) and self-attention mechanism with deep time series models (e.g. recurrent neural networks) to capture the spatio-temporal correlations separately, ignoring the relationships across time and space. Besides, GCNs are limited by over-smoothing issue and self-attention is limited by quadratic problem, result in GCNs lack global representation capabilities, and self-attention inefficiently capture the global spatial dependence. In this paper, we propose a novel deep learning model for traffic forecasting, named Multi-Context Aware Spatio-Temporal Joint Linear Attention (STJLA), which applies linear attention to the spatio-temporal joint graph to capture global dependence between all spatio-temporal nodes efficiently. More specifically, STJLA utilizes static structural context and dynamic semantic context to improve model performance. The static structure context based on node2vec and one-hot encoding enriches the spatio-temporal position information. Furthermore, the multi-head diffusion convolution network based dynamic spatial context enhances the local spatial perception ability, and the GRU based dynamic temporal context stabilizes sequence position information of the linear attention, respectively. Experiments on two real-world traffic datasets, England and PEMSD7, demonstrate that our STJLA can achieve up to 9.83% and 3.08% accuracy improvement in MAE measure over state-of-the-art baselines.

cs.LG↗

arXiv2021

DMGCRN: Dynamic Multi-Graph Convolution Recurrent Network for Traffic Forecasting

Traffic forecasting is a problem of intelligent transportation systems (ITS) and crucial for individuals and public agencies. Therefore, researches pay great attention to deal with the complex spatio-temporal dependencies of traffic system for accurate forecasting. However, there are two challenges: 1) Most traffic forecasting studies mainly focus on modeling correlations of neighboring sensors and ignore correlations of remote sensors, e.g., business districts with similar spatio-temporal patterns; 2) Prior methods which use static adjacency matrix in graph convolutional networks (GCNs) are not enough to reflect the dynamic spatial dependence in traffic system. Moreover, fine-grained methods which use self-attention to model dynamic correlations of all sensors ignore hierarchical information in road networks and have quadratic computational complexity. In this paper, we propose a novel dynamic multi-graph convolution recurrent network (DMGCRN) to tackle above issues, which can model the spatial correlations of distance, the spatial correlations of structure, and the temporal correlations simultaneously. We not only use the distance-based graph to capture spatial information from nodes are close in distance but also construct a novel latent graph which encoded the structure correlations among roads to capture spatial information from nodes are similar in structure. Furthermore, we divide the neighbors of each sensor into coarse-grained regions, and dynamically assign different weights to each region at different times. Meanwhile, we integrate the dynamic multi-graph convolution network into the gated recurrent unit (GRU) to capture temporal dependence. Extensive experiments on three real-world traffic datasets demonstrate that our proposed algorithm outperforms state-of-the-art baselines.

cs.LG↗

arXiv2021

Single-shot fringe projection profilometry based on Deep Learning and Computer Graphics

Multiple works have applied deep learning to fringe projection profilometry (FPP) in recent years. However, to obtain a large amount of data from actual systems for training is still a tricky problem, and moreover, the network design and optimization still worth exploring. In this paper, we introduce computer graphics to build virtual FPP systems in order to generate the desired datasets conveniently and simply. The way of constructing a virtual FPP system is described in detail firstly, and then some key factors to set the virtual FPP system much close to the reality are analyzed. With the aim of accurately estimating the depth image from only one fringe image, we also design a new loss function to enhance the quality of the overall and detailed information restored. And two representative networks, U-Net and pix2pix, are compared in multiple aspects. The real experiments prove the good accuracy and generalization of the network trained by the data from our virtual systems and the designed loss, implying the potential of our method for applications.

eess.IV↗

arXiv2017

Signed Node Relevance Measurements

In this paper, we perform the initial and comprehensive study on the problem of measuring node relevance on signed social networks. We design numerous relevance measurements for signed social networks from both local and global perspectives and investigate the connection between signed relevance measurements, balance theory and signed network properties. Experimental results are conducted to study the effects of signed relevance measurements with four real-world datasets on signed network analysis tasks.

cs.SI↗