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Wei Han

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10 papers

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AIIM Journal 2026 Journal Article

Application research of dynamic chaotic sequence generation mechanism in pre-hospital emergency data encryption

Wei Han
Lu Lu
Jingtao Ma
Qin Li
Zhuang Li

Background and objectives In the context of pre-hospital emergency care, the security of patients' physiological data has become increasingly important due to the widespread use of portable and wearable devices. This study aims to explore the application of dynamic chaotic sequence generation mechanisms for data encryption in pre-hospital emergency. Methods In this study, a chaotic encryption method is proposed in which the initial key is generated using characteristic waveforms and iterative counting, and the dynamic key update is performed using the time-varying properties of pulse-wave signals and the sensitivity of chaotic sequences. The algorithm is capable of adapting its complexity to the requisite security level, whilst concomitantly managing energy consumption. The chaotic encryption scheme under scrutiny consists of normalizing the pulse waveform and iteratively applying Tent and Logistic mappings to generate pseudo-random sequences. Results The system has been subjected to rigorous autocorrelation, SEN, NIST (National Institute of Standards and Technology) stochasticity tests, and cryptographic security evaluations. The results of these tests demonstrate that the combined chaotic system effectively mitigates the cyclic effect and enhances stochasticity. The analysis revealed that the cryptographic performance of the 32-bit fixed-point system in image encryption is comparable to that of the floating-point system, ensuring high efficiency and security of encryption. Conclusions This study highlights the potential of dynamic chaotic sequence generation for secure data transmission in emergency medical environments and paves the way for further exploration and optimization in real-time applications.

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NeurIPS Conference 2025 Conference Paper

Efficient Prompt Compression with Evaluator Heads for Long-Context Transformer Inference

Weizhi Fei
Xueyan Niu
XIE GUOQING
Yingqing Liu
Bo Bai
Wei Han

Although applications involving long-context inputs are crucial for the effective utilization of large language models (LLMs), they also result in increased computational costs and reduced performance. To address this challenge, we propose an efficient, training-free prompt compression method that retains key information within compressed prompts. We identify specific attention heads in transformer-based LLMs, which we designate as evaluator heads, that are capable of selecting tokens in long inputs that are most significant for inference. Building on this discovery, we develop EHPC, an Evaluator Head-based Prompt Compression method, which enables LLMs to rapidly "skim through'' input prompts by leveraging only the first few layers with evaluator heads during the pre-filling stage, subsequently passing only the important tokens to the model for inference. EHPC achieves state-of-the-art results across two mainstream benchmarks: prompt compression and long-context inference acceleration. Consequently, it effectively improves performance with the reduced costs associated with commercial API calls compared to prompt compressing methods. We further demonstrate that EHPC attains competitive results compared to key-value cache-based acceleration methods, thereby highlighting its potential to enhance the efficiency of LLMs for long-context tasks.

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AAAI Conference 2025 Conference Paper

Hyperbolic-Constraint Point Cloud Reconstruction from Single RGB-D Images

Wenrui Li
Zhe Yang
Wei Han
Hengyu Man
Xingtao Wang
Xiaopeng Fan

Reconstructing desired objects and scenes has long been a primary goal in 3D computer vision. Single-view point cloud reconstruction has become a popular technique due to its low cost and accurate results. However, single-view reconstruction methods often rely on expensive CAD models and complex geometric priors. Effectively utilizing prior knowledge about the data remains a challenge. In this paper, we introduce hyperbolic space to 3D point cloud reconstruction, enabling the model to represent and understand complex hierarchical structures in point clouds with low distortion. We build upon previous methods by proposing a hyperbolic Chamfer distance and a regularized triplet loss to enhance the relationship between partial and complete point clouds. Additionally, we design adaptive boundary conditions to improve the model's understanding and reconstruction of 3D structures. Our model outperforms most existing models, and ablation studies demonstrate the significance of our model and its components. Experimental results show that our method significantly improves feature extraction capabilities. Our model achieves outstanding performance in 3D reconstruction tasks.

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NeurIPS Conference 2025 Conference Paper

MLR-Bench: Evaluating AI Agents on Open-Ended Machine Learning Research

Hui Chen
Miao Xiong
Yujie Lu
Wei Han
Ailin Deng
Yufei He
Jiaying Wu
Yibo Li

Recent advancements in AI agents have demonstrated their growing potential to drive and support scientific discovery. In this work, we introduce MLR-Bench, a comprehensive benchmark for evaluating AI agents on open-ended machine learning research. MLR-Bench includes three key components: (1) 201 research tasks sourced from NeurIPS, ICLR, and ICML workshops covering diverse ML topics; (2) MLR-Judge, an automated evaluation framework combining LLM-based reviewers with carefully designed review rubrics to assess research quality; and (3) MLR-Agent, a modular agent scaffold capable of completing research tasks through four stages: idea generation, proposal formulation, experimentation, and paper writing. Our framework supports both stepwise assessment across these distinct research stages, and end-to-end evaluation of the final research paper. We then use MLR-Bench to evaluate six frontier LLMs and an advanced coding agent, finding that while LLMs are effective at generating coherent ideas and well-structured papers, current coding agents frequently (e. g. , in 80\% of the cases) produce fabricated or invalidated experimental results—posing a major barrier to scientific reliability. We validate MLR-Judge through human evaluation, showing high agreement with expert reviewers, supporting its potential as a scalable tool for research evaluation. We open-source MLR-Bench to help the community benchmark, diagnose, and improve AI research agents toward trustworthy and transparent scientific discovery.

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AAAI Conference 2025 Conference Paper

Riemann-based Multi-scale Attention Reasoning Network for Text-3D Retrieval

Wenrui Li
Wei Han
Yandu Chen
Yeyu Chai
Yidan Lu
Xingtao Wang
Xiaopeng Fan

Due to the challenges in acquiring paired Text-3D data and the inherent irregularity of 3D data structures, combined representation learning of 3D point clouds and text remains unexplored. In this paper, we propose a novel Riemann-based Multi-scale Attention Reasoning Network (RMARN) for text-3D retrieval. Specifically, the extracted text and point cloud features are refined by their respective Adaptive Feature Refiner (AFR). Furthermore, we introduce the innovative Riemann Local Similarity (RLS) module and the Global Pooling Similarity (GPS) module. However, as 3D point cloud data and text data often possess complex geometric structures in high-dimensional space, the proposed RLS employs a novel Riemann Attention Mechanism to reflect the intrinsic geometric relationships of the data. Without explicitly defining the manifold, RMARN learns the manifold parameters to better represent the distances between text-point cloud samples. To address the challenges of lacking paired text-3D data, we have created the large-scale Text-3D Retrieval dataset T3DR-HIT, which comprises over 3,380 pairs of text and point cloud data. T3DR-HIT contains coarse-grained indoor 3D scenes and fine-grained Chinese artifact scenes, consisting of 1,380 and over 2,000 text-3D pairs, respectively. Experiments on our custom datasets demonstrate the superior performance of the proposed method.

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EAAI Journal 2024 Journal Article

MFFSP: Multi-scale feature fusion scene parsing network for landslides detection based on high-resolution satellite images

Penglei Li
Yi Wang
Tongzhen Si
Kashif Ullah
Wei Han
Lizhe Wang

Fast and efficient landslide detection plays an important role in post-disaster rescue and risk assessment. Existing convolution neural network (CNN) based landslide detection methods are difficult to exploit global long-distance dependencies due to limited receptive fields. Considering that landslide occurrence is susceptible to local and global conditions, we propose a novel multi-scale feature fusion scene parsing (MFFSP) framework to explore information at different scales by coupling CNN with Transformer to learn local and global clues for landslide detection based on satellite data. In the encoder, we design three modules, visual geometry module (VGM), residual learning module (RLM), and Transformer module (TRM) to exploit multi-scale features. Specifically, VGM and RLM are constructed based on convolution operations to explore local features by learning low-level and middle-level information, while TRM is built based on self-attention mechanism to learn long-distance dependencies. In the decoder, TRM and VGM are further extended to motivate the model to mine long-distance dependencies and detailed spatial information by deeply fusing features from multiple scales. To demonstrate the performance of the model, we employ two study areas with four test regions to conduct experiments and compare with seven state-of-the-art deep learning models. Extensive experiments demonstrate that MFFSP greatly outperforms other algorithms. In addition, we conduct numerous ablation experiments, proving that MFFSP fully combines the complementary advantages of CNN and Transformer to mine robust features.

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TMLR Journal 2022 Journal Article

Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

Jiahui Yu
Yuanzhong Xu
Jing Yu Koh
Thang Luong
Gunjan Baid
Zirui Wang
Vijay Vasudevan
Alexander Ku

We present the Pathways Autoregressive Text-to-Image (Parti) model, which generates high-fidelity photorealistic images and supports content-rich synthesis involving complex compositions and world knowledge. Parti treats text-to-image generation as a sequence-to-sequence modeling problem, akin to machine translation, with sequences of image tokens as the target outputs rather than text tokens in another language. This strategy can naturally tap into the rich body of prior work on large language models, which have seen continued advances in capabilities and performance through scaling data and model sizes. Our approach is simple: First, Parti uses a Transformer-based image tokenizer, ViT-VQGAN, to encode images as sequences of discrete tokens. Second, we achieve consistent quality improvements by scaling the encoder-decoder Transformer model up to 20B parameters, with a new state-of-the-art zero-shot FID score of 7.23 and finetuned FID score of 3.22 on MS-COCO. Our detailed analysis on Localized Narratives as well as PartiPrompts (P2), a new holistic benchmark of over 1600 English prompts, demonstrate the effectiveness of Parti across a wide variety of categories and difficulty aspects. We also explore and highlight limitations of our models in order to define and exemplify key areas of focus for further improvements.

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YNICL Journal 2019 Journal Article

Low-rank network signatures in the triple network separate schizophrenia and major depressive disorder

Wei Han
Christian Sorg
Changgang Zheng
Qinli Yang
Xiaosong Zhang
Arvid Ternblom
Cobbinah Bernard Mawuli
Lianli Gao

Brain imaging studies have revealed that functional and structural brain connectivity in the so-called triple network (i.e., default mode network (DMN), salience network (SN) and central executive network (CEN)) are consistently altered in schizophrenia. However, similar changes have also been found in patients with major depressive disorder, prompting the question of specific triple network signatures for the two disorders. In this study, we proposed Supervised Convex Nonnegative Matrix Factorization (SCNMF) to extract distributed multi-modal brain patterns. These patterns distinguish schizophrenia and major depressive disorder in a latent low-dimensional space of the triple brain network. Specifically, 21 patients of schizophrenia and 25 patients of major depressive disorder were assessed by T1-weighted, diffusion-weighted, and resting-state functional MRIs. Individual structural and functional connectivity networks, based on pre-defined regions of the triple network were constructed, respectively. Afterwards, SCNMF was employed to extract the discriminative patterns. Experiments indicate that SCNMF allows extracting the low-rank discriminative patterns between the two disorders, achieving a classification accuracy of 82.6% based on the extracted functional and structural abnormalities with support vector machine. Experimental results show the specific brain patterns for schizophrenia and major depressive disorder that are multi-modal, complex, and distributed in the triple network. Parts of the prefrontal cortex including superior frontal gyri showed variation between patients with schizophrenia and major depression due to structural properties. In terms of functional properties, the middle cingulate cortex, inferior parietal lobule, and cingulate cortex were the most discriminative regions.

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NeurIPS Conference 2017 Conference Paper

Dilated Recurrent Neural Networks

Shiyu Chang
Yang Zhang
Wei Han
Mo Yu
Xiaoxiao Guo
Wei Tan
Xiaodong Cui
Michael Witbrock

Learning with recurrent neural networks (RNNs) on long sequences is a notoriously difficult task. There are three major challenges: 1) complex dependencies, 2) vanishing and exploding gradients, and 3) efficient parallelization. In this paper, we introduce a simple yet effective RNN connection structure, the DilatedRNN, which simultaneously tackles all of these challenges. The proposed architecture is characterized by multi-resolution dilated recurrent skip connections and can be combined flexibly with diverse RNN cells. Moreover, the DilatedRNN reduces the number of parameters needed and enhances training efficiency significantly, while matching state-of-the-art performance (even with standard RNN cells) in tasks involving very long-term dependencies. To provide a theory-based quantification of the architecture's advantages, we introduce a memory capacity measure, the mean recurrent length, which is more suitable for RNNs with long skip connections than existing measures. We rigorously prove the advantages of the DilatedRNN over other recurrent neural architectures. The code for our method is publicly available at https: //github. com/code-terminator/DilatedRNN.

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YNIMG Journal 2012 Journal Article

Resting-state functional connectivity of the vermal and hemispheric subregions of the cerebellum with both the cerebral cortical networks and subcortical structures

Li Sang
Wen Qin
Yong Liu
Wei Han
Yunting Zhang
Tianzi Jiang
Chunshui Yu

The human cerebellum is a heterogeneous structure, and the pattern of resting-state functional connectivity (rsFC) of each subregion has not yet been fully characterized. We aimed to systematically investigate rsFC pattern of each cerebellar subregion in 228 healthy young adults. Voxel-based analysis revealed that several subregions showed similar rsFC patterns, reflecting functional integration; however, different subregions displayed distinct rsFC patterns, representing functional segregation. The same vermal and hemispheric subregions showed either different patterns or different strengths of rsFCs with the cerebrum, and different subregions of lobules VII and VIII displayed different rsFC patterns. Region of interest (ROI)-based analyses also confirmed these findings. Specifically, strong rsFCs were found: between lobules I–VI and vermal VIIb–IX and the visual network; between hemispheric VI, VIIb, VIIIa and the auditory network; between lobules I–VI, VIII and the sensorimotor network; between lobule IX, vermal VIIIb and the default-mode network; between lobule Crus I, hemispheric Crus II and the fronto-parietal network; between hemispheric VIIb, VIII and the task-positive network; between hemispheric VI, VIIb, VIII and the salience network; between most cerebellar subregions and the thalamus; between lobules V, VIIb and the midbrain red nucleus; between hemispheric Crus I, Crus II, vermal VIIIb, IX and the caudate nucleus; between lobules V, VI, VIIb, VIIIa and the pallidum and putamen; and between lobules I–V, hemispheric VIII, IX and the hippocampus and amygdala. These results confirm the existence of both functional integration and segregation among cerebellar subregions and largely improve our understanding of the functional organization of the human cerebellum.

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