Xiaodong Li

AAAI Conference 2026 Conference Paper

DegVoC: Revisiting Neural Vocoder from a Degradation Perspective

• Andong Li
• Tong Lei
• Lingling Dai
• Kai Li
• Rilin Chen
• Meng Yu
• Xiaodong Li
• Dong Yu

Existing neural vocoders have demonstrated promising performance by leveraging Mel-spectrum as an acoustic feature for conditional audio generation. Nonetheless, they remain constrained by an inherent ``performance-cost'' dilemma that significantly hinders the development of this field. This paper revisits this foundational task from a novel degradation perspective, where Mel-spectrum is regarded as a special signal degradation process from the target spectrum. Drawing inspiration from traditional sparse signal recovery problems, we propose DegVoC, a GAN-based neural vocoder with a two-step solution procedure. First, by exploiting degradation priors, we attempt to retrieve the initial spectral structure from Mel-domain representations as an initial solution via a simple linear transformation. Based on that, we introduce a deep prior solver that accounts for the heterogeneous distribution of sub-bands in the time-frequency domain. A convolution-style attention module with a large kernel size is specially devised for efficient inter-frame and inter-band contextual modeling. With 3.89 M parameters and substantially reduced inference complexity, DegVoC achieves state-of-the-art performance across objective and subjective evaluations, outperforming existing GAN-, DDPM- and flow-matching-based baselines.

AAAI Conference 2026 Conference Paper

GOMPSNR: Reflourish the Signal-to-Noise Ratio Metric for Audio Generation Tasks

• Lingling Dai
• Andong Li
• Cheng Chi
• Yifan Liang
• Xiaodong Li
• Chengshi Zheng

In the field of audio generation, signal-to-noise ratio (SNR) has long served as an objective metric for evaluating audio quality. Nevertheless, recent studies have shown that SNR and its variants are not always highly correlated with human perception, prompting us to raise the questions: Why does SNR fail in measuring audio quality? And how to improve its reliability as an objective metric? In this paper, we identify the inadequate measurement of phase distance as a pivotal factor and propose to reformulate SNR with specially designed phase-distance terms, yielding an improved metric named GOMPSNR. We further extend the newly proposed formulation to derive two novel categories of loss function, corresponding to magnitude-guided phase refinement and joint magnitude-phase optimization, respectively. Besides, extensive experiments are conducted for an optimal combination of different loss functions. Experimental results on advanced neural vocoders demonstrate that our proposed GOMPSNR exhibits more reliable error measurement than SNR. Meanwhile, our proposed loss functions yield substantial improvements in model performance, and our well-chosen combination of different loss functions further optimizes the overall model capability.

EAAI Journal 2026 Journal Article

GRHP: Graph-Fused Hierarchical Planning for Embodied Long-Horizon Robotic Task

• Xiaodong Li
• Guohui Tian
• Yongcheng Cui
• Xuyang Shao
• Zhiwei Wang

Embodied Long-Horizon task planning is crucial for robots to perform complex household tasks. Existing methods typically rely on Vision Language Models (VLMs) to generate high-level semantic planning, but face critical limitations. First, the plans often fail to be grounded in the physical environment due to an inability to perceive spatial layouts and object relationships. Second, the high-level plans lack direct executability, as they cannot be readily mapped to the robot’s atomic actions. To overcome these challenges, we present Graph-Fused Hierarchical Planning (GRHP), a novel framework for Long-Horizon task planning. GRHP employs a unified dual-graph perception structure, where a scene graph captures spatial context and relationships, and a task graph models action dependencies and intent. Environmental constraints are directly integrated into task planning through explicit cross-graph fusion. Furthermore, GRHP features a hierarchical planning architecture that strategically decouples the planning process. It leverages large models for efficient high-level semantic planning, while small models handle precise action generation. This decomposition ensures that high-level instruction translates directly into executable low-level actions. Extensive experiments on GATP (Graph-Aware Task Planning), an enhanced version of the challenging ALFRED benchmark, demonstrate the effectiveness of GRHP. Qualitative analysis corroborates the significant contributions of scene perception, task modeling, and the hierarchical design for agent performance. Our code is available at: https: //github. com/LL00qw/GRHP.

AAAI Conference 2026 Conference Paper

SLD-L2S: Hierarchical Subspace Latent Diffusion for High-Fidelity Lip to Speech Synthesis

• Yifan Liang
• Andong Li
• Kang Yang
• Guochen Yu
• Fangkun Liu
• Lingling Dai
• Xiaodong Li
• Chengshi Zheng

Although lip-to-speech synthesis (L2S) has achieved significant progress in recent years, current state-of-the-art methods typically rely on intermediate representations such as mel-spectrograms or discrete self-supervised learning (SSL) tokens. The potential of latent diffusion models (LDMs) in this task remains largely unexplored. In this paper, we introduce SLD-L2S, a novel L2S framework built upon a hierarchical subspace latent diffusion model. Our method aims to directly map visual lip movements to the continuous latent space of a pre-trained neural audio codec, thereby avoiding the information loss inherent in traditional intermediate representations. The core of our method is a hierarchical architecture that processes visual representations through multiple parallel subspaces, initiated by a subspace decomposition module. To efficiently enhance interactions within and between these subspaces, we design the diffusion convolution block (DiCB) as our network backbone. Furthermore, we employ a reparameterized flow matching technique to directly generate the target latent vectors. This enables a principled inclusion of speech language model (SLM) and semantic losses during training, moving beyond conventional flow matching objectives and improving synthesized speech quality. Our experiments show that SLD-L2S achieves state-of-the-art generation quality on multiple benchmark datasets, surpassing existing methods in both objective and subjective evaluations.

TMLR Journal 2025 Journal Article

Learning Linear Polytree Structural Equation Model

• Xingmei Lou
• Yu Hu
• Xiaodong Li

We are interested in the problem of learning the directed acyclic graph (DAG) when data are generated from a linear structural equation model (SEM) and the causal structure can be characterized by a polytree. Under the Gaussian polytree models, we study sufficient conditions on the sample sizes for the well-known Chow-Liu algorithm to exactly recover both the skeleton and the equivalence class of the polytree, which is uniquely represented by a CPDAG. On the other hand, necessary conditions on the required sample sizes for both skeleton and CPDAG recovery are also derived in terms of information-theoretic lower bounds, which match the respective sufficient conditions and thereby give a sharp characterization of the difficulty of these tasks. We also consider the problem of inverse correlation matrix estimation under the linear polytree models, and establish the estimation error bound in terms of the dimension and the total number of v-structures. We also consider an extension of group linear polytree models, in which each node represents a group of variables. Our theoretical findings are illustrated by comprehensive numerical simulations, and experiments on benchmark data also demonstrate the robustness of polytree learning when the true graphical structures can only be approximated by polytrees.

IJCAI Conference 2025 Conference Paper

Learning Neural Vocoder from Range-Null Space Decomposition

• Andong Li
• Tong Lei
• Zhihang Sun
• Rilin Chen
• Erwei Yin
• Xiaodong Li
• Chengshi Zheng

Despite the rapid development of neural vocoders in recent years, they usually suffer from some intrinsic challenges like opaque modeling, and parameter-performance trade-off. In this study, we propose an innovative time-frequency (T-F) domain-based neural vocoder to resolve the above-mentioned challenges. To be specific, we bridge the connection between the classical signal range-null decomposition (RND) theory and vocoder task, and the reconstruction of target spectrogram can be decomposed into the superimposition between the range-space and null-space, where the former is enabled by a linear domain shift from the original mel-scale domain to the target linear-scale domain, and the latter is instantiated via a learnable network for further spectral detail generation. Accordingly, we propose a novel dual-path framework, where the spectrum is hierarchically encoded/decoded, and the cross- and narrow-band modules are elaborately devised for efficient sub-band and sequential modeling. Comprehensive experiments are conducted on the LJSpeech and LibriTTS benchmarks. Quantitative and qualitative results show that while enjoying lightweight network parameters, the proposed approach yields state-of-the-art performance among existing advanced methods. Our code and the pretrained model weights are available at https: //github. com/Andong-Li-speech/RNDVoC.

IJCAI Conference 2025 Conference Paper

Optimize Battery Control: A Multi-Objective Evolutionary Ensemble Reinforcement Learning Approach

• Jingwei Hu
• Kai Xie
• Zheng Fang
• Xiaodong Li
• Junchi Yan
• Zhihong Zhang

The Dynamically Reconfigurable Battery (DRB) systems, which use high-speed power electronic switches to dynamically adjust battery interconnections in real-time, are critical to the performance of the battery pack. Traditional battery management strategies often fail to address multi-objective optimization, leading to imbalanced performance and inadequate energy utilization. To enhance decision-making across multiple objectives, an Evolutionary Ensemble Reinforcement Learning (EERL) framework is proposed in this paper. This framework incorporates evolutionary algorithms to associate ensemble learning, thus improving reinforcement learning (RL) performance. It decomposes a complex objective into multiple sub-objectives, each optimized independently, while incorporating diverse performance metrics into the correlation stage to derive the Pareto optimal solution. The EERL can efficiently mitigate potential adverse effects such as short circuits, disconnections, and reverse charging, thereby effectively reducing capacity differences among various batteries. Simulations and real-world testing demonstrate that the proposed approach overcomes the issue of local optima entrapment in multi-objective optimization scenarios. In a real-world system, an 11. 08 % increase in energy efficiency is observed compared to existing approaches.

AAAI Conference 2025 Conference Paper

Sharper Error Bounds in Late Fusion Multi-view Clustering with Eigenvalue Proportion Optimization

• Liang Du
• Henghui Jiang
• Xiaodong Li
• Yiqing Guo
• Yan Chen
• Feijiang Li
• Peng Zhou
• Yuhua Qian

Multi-view clustering (MVC) aims to integrate complementary information from multiple views to enhance clustering performance. Late Fusion Multi-View Clustering (LFMVC) has shown promise by synthesizing diverse clustering results into a unified consensus. However, current LFMVC methods struggle with noisy and redundant partitions and often fail to capture high-order correlations across views. To address these limitations, we present a novel theoretical framework for analyzing the generalization error bounds of multiple kernel k-means, leveraging local Rademacher complexity and principal eigenvalue proportions. Our analysis establishes a convergence rate of O(1/n), significantly improving upon the existing rate in the order of O(sqrt(k/n)). Building on this insight, we propose a low-pass graph filtering strategy within a multiple linear K-means framework to mitigate noise and redundancy, further refining the principal eigenvalue proportion and enhancing clustering accuracy. Experimental results on benchmark datasets confirm that our approach outperforms state-of-the-art methods in clustering performance and robustness.

IROS Conference 2024 Conference Paper

Transformer-Based Relationship Inference Model for Household Object Organization by Integrating Graph Topology and Ontology

• Xiaodong Li
• Guohui Tian
• Yongcheng Cui
• Yu Gu

In domestic environments, the conventional organization of objects by service robots often relies on the inherent properties of each object, such as placing fragile bowls in enclosed cupboards. However, this approach tends to overlook the importance of the orderly arrangement of objects, neglecting the specific placement order of bowls within the cabinet. In practice, effective object organization necessitates consideration of both individual properties and the relationships defined by these properties. In this paper, we have constructed a specialized dataset encompassing the ontological properties of household objects along with their relationships. Furthermore, we have introduced a graph-based model to explicitly represent these relationships and proposed a novel feature extraction technique that integrates the Graph Attention Network (GAT) with the BERT model to predict the relationships among objects. Subsequently, we utilized the Transformer framework to train a model, enabling it to infer relationships between objects. Experimental validation demonstrates the effectiveness of our approach in accurately predicting relationships between household objects, thus facilitating their orderly organization. Our approach significantly augments the object organization capabilities for service robots by accurately predicting the relationships among household objects. Our code is available at: https://github.com/Li-XD-Pro/Household-Object-Organization

EAAI Journal 2023 Journal Article

Automatic meter error detection with a data-driven approach

• Ruimin Chu
• Li Chik
• Jeffrey Chan
• Kurt Gutzmann
• Xiaodong Li

Meter error is one of the main contributing factors to unexpected fuel losses or gains in storage tanks at service stations. Although fuel dispensers are expected to be calibrated to standard accuracy periodically to ensure fair and reliable trade in the fuel market, some fuel retailers are unable to keep up with the standards. The current industry practice relies on onsite inspection to identify the issue, which leads to a cost burden if inspections are scheduled too frequently. To the best of our knowledge, there is no previously reported research tailored to the remote meter error detection problem. In this paper, we propose a novel framework for remote and automatic meter error detection via a data-driven approach based on inventory data and fuel transaction data. Specifically, we propose to use mean shift change point detection methods, including statistical-based as well as deep learning-based methods (LSTM-VAE, VAE, Kernel learning), to approach the problem. We present results on our data sets containing both real-world and simulated meter error data, and further evaluate these methods on several widely-used benchmark datasets, to assess their validity, advantages and limitations. The obtained results show that LSTM-VAE outperforms other models in most of the settings for the meter error dataset and the benchmark datasets.

AAAI Conference 2022 Conference Paper

Enhancing Column Generation by a Machine-Learning-Based Pricing Heuristic for Graph Coloring

• Yunzhuang Shen
• Yuan Sun
• Xiaodong Li
• Andrew Eberhard
• Andreas Ernst

Column Generation (CG) is an effective method for solving large-scale optimization problems. CG starts by solving a subproblem with a subset of columns (i. e. , variables) and gradually includes new columns that can improve the solution of the current subproblem. The new columns are generated as needed by repeatedly solving a pricing problem, which is often NPhard and is a bottleneck of the CG approach. To tackle this, we propose a Machine-Learning-based Pricing Heuristic (MLPH) that can generate many high-quality columns efficiently. In each iteration of CG, our MLPH leverages an ML model to predict the optimal solution of the pricing problem, which is then used to guide a sampling method to efficiently generate multiple high-quality columns. Using the graph coloring problem, we empirically show that MLPH significantly enhances CG as compared to six state-of-the-art methods, and the improvement in CG can lead to substantially better performance of the branch-and-price exact method.

JMLR Journal 2022 Journal Article

Nonconvex Matrix Completion with Linearly Parameterized Factors

• Ji Chen
• Xiaodong Li
• Zongming Ma

Techniques of matrix completion aim to impute a large portion of missing entries in a data matrix through a small portion of observed ones. In practice, prior information and special structures are usually employed in order to improve the accuracy of matrix completion. In this paper, we propose a unified nonconvex optimization framework for matrix completion with linearly parameterized factors. In particular, by introducing a condition referred to as Correlated Parametric Factorization, we conduct a unified geometric analysis for the nonconvex objective by establishing uniform upper bounds for low-rank estimation resulting from any local minimizer. Perhaps surprisingly, the condition of Correlated Parametric Factorization holds for important examples including subspace-constrained matrix completion and skew-symmetric matrix completion. The effectiveness of our unified nonconvex optimization method is also empirically illustrated by extensive numerical simulations. [abs] [ pdf ][ bib ] &copy JMLR 2022. ( edit, beta )

IJCAI Conference 2022 Conference Paper

Taylor, Can You Hear Me Now? A Taylor-Unfolding Framework for Monaural Speech Enhancement

• Andong Li
• Shan You
• Guochen Yu
• Chengshi Zheng
• Xiaodong Li

While the deep learning techniques promote the rapid development of the speech enhancement (SE) community, most schemes only pursue the performance in a black-box manner and lack adequate model interpretability. Inspired by Taylor's approximation theory, we propose an interpretable decoupling-style SE framework, which disentangles the complex spectrum recovery into two separate optimization problems i. e. , magnitude and complex residual estimation. Specifically, serving as the 0th-order term in Taylor's series, a filter network is delicately devised to suppress the noise component only in the magnitude domain and obtain a coarse spectrum. To refine the phase distribution, we estimate the sparse complex residual, which is defined as the difference between target and coarse spectra, and measures the phase gap. In this study, we formulate the residual component as the combination of various high-order Taylor terms and propose a lightweight trainable module to replace the complicated derivative operator between adjacent terms. Finally, following Taylor's formula, we can reconstruct the target spectrum by the superimposition between 0th-order and high-order terms. Experimental results on two benchmark datasets show that our framework achieves state-of-the-art performance over previous competing baselines in various evaluation metrics. The source code is available at https: //github. com/Andong-Li-speech/TaylorSENet.

AAAI Conference 2022 Conference Paper

Unbiased IoU for Spherical Image Object Detection

• Feng Dai
• Bin Chen
• Hang Xu
• Yike Ma
• Xiaodong Li
• Bailan Feng
• Peng Yuan
• Chenggang Yan

As one of the fundamental components of object detection, intersection-over-union (IoU) calculations between two bounding boxes play an important role in samples selection, NMS operation and evaluation of object detection algorithms. This procedure is well-defined and solved for planar images, while it is challenging for spherical ones. Some existing methods utilize planar bounding boxes to represent spherical objects. However, they are biased due to the distortions of spherical objects. Others use spherical rectangles as unbiased representations, but they adopt excessive approximate algorithms when computing the IoU. In this paper, we propose an unbiased IoU as a novel evaluation criterion for spherical image object detection, which is based on the unbiased representations and utilize unbiased analytical method for IoU calculation. This is the first time that the absolutely accurate IoU calculation is applied to the evaluation criterion, thus object detection algorithms can be correctly evaluated for spherical images. With the unbiased representation and calculation, we also present Spherical CenterNet, an anchor free object detection algorithm for spherical images. The experiments show that our unbiased IoU gives accurate results and the proposed Spherical CenterNet achieves better performance on one real-world and two synthetic spherical object detection datasets than existing methods.

AAAI Conference 2020 Conference Paper

Revisiting Probability Distribution Assumptions for Information Theoretic Feature Selection

• Yuan Sun
• Wei Wang
• Michael Kirley
• Xiaodong Li
• Jeffrey Chan

Feature selection has been shown to be beneficial for many data mining and machine learning tasks, especially for big data analytics. Mutual Information (MI) is a well-known information-theoretic approach used to evaluate the relevance of feature subsets and class labels. However, estimating highdimensional MI poses significant challenges. Consequently, a great deal of research has focused on using low-order MI approximations or computing a lower bound on MI called Variational Information (VI). These methods often require certain assumptions made on the probability distributions of features such that these distributions are realistic yet tractable to compute. In this paper, we reveal two sets of distribution assumptions underlying many MI and VI based methods: Feature Independence Distribution and Geometric Mean Distribution. We systematically analyze their strengths and weaknesses and propose a logical extension called Arithmetic Mean Distribution, which leads to an unbiased and normalised estimation of probability densities. We conduct detailed empirical studies across a suite of 29 real-world classification problems and illustrate improved prediction accuracy of our methods based on the identification of more informative features, thus providing support for our theoretical findings.

JBHI Journal 2019 Journal Article

HerGePred: Heterogeneous Network Embedding Representation for Disease Gene Prediction

• Kuo Yang
• Ruyu Wang
• Guangming Liu
• Zixin Shu
• Ning Wang
• Runshun Zhang
• Jian Yu
• Jianxin Chen

The discovery of disease-causing genes is a critical step towards understanding the nature of a disease and determining a possible cure for it. In recent years, many computational methods to identify disease genes have been proposed. However, making full use of disease-related (e. g. , symptoms) and gene-related (e. g. , gene ontology and protein-protein interactions) information to improve the performance of disease gene prediction is still an issue. Here, we develop a heterogeneous disease-gene-related network (HDGN) embedding representation framework for disease gene prediction (called HerGePred). Based on this framework, a low-dimensional vector representation (LVR) of the nodes in the HDGN can be obtained. Then, we propose two specific algorithms, namely, an LVR-based similarity prediction and a random walk with restart on a reconstructed heterogeneous disease-gene network (RWRDGN), to predict disease genes with high performance. First, to validate the rationality of the framework, we analyze the similarity-based overlap distribution of disease pairs and design an experiment for disease-gene association recovery, the results of which revealed that the LVR of nodes performs well at preserving the local and global network structure of the HDGN. Then, we apply tenfold cross validation and external validation to compare our methods with other well-known disease gene prediction algorithms. The experimental results show that the RW-RDGN performs better than the state-of-the-art algorithm. The prediction results of disease candidate genes are essential for molecular mechanism investigation and experimental validation. The source codes of HerGePred and experimental data are available at https://github.com/yangkuoone/HerGePred.

JMLR Journal 2019 Journal Article

Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCA

• Ji Chen
• Xiaodong Li

This work studies low-rank approximation of a positive semidefinite matrix from partial entries via nonconvex optimization. We characterized how well local-minimum based low-rank factorization approximates a fixed positive semidefinite matrix without any assumptions on the rank-matching, the condition number or eigenspace incoherence parameter. Furthermore, under certain assumptions on rank-matching and well-boundedness of condition numbers and eigenspace incoherence parameters, a corollary of our main theorem improves the state-of-the-art sampling rate results for nonconvex matrix completion with no spurious local minima in Ge et al. (2016, 2017). In addition, we have investigated when the proposed nonconvex optimization results in accurate low-rank approximations even in presence of large condition numbers, large incoherence parameters, or rank mismatching. We also propose to apply the nonconvex optimization to memory-efficient kernel PCA. Compared to the well-known Nyström methods, numerical experiments indicate that the proposed nonconvex optimization approach yields more stable results in both low-rank approximation and clustering. [abs] [ pdf ][ bib ] &copy JMLR 2019. ( edit, beta )

AAAI Conference 2018 Conference Paper

Predicting Aesthetic Score Distribution Through Cumulative Jensen-Shannon Divergence

• Xin Jin
• Le Wu
• Xiaodong Li
• Siyu Chen
• Siwei Peng
• Jingying Chi
• Shiming Ge
• Chenggen Song

Aesthetic quality prediction is a challenging task in the computer vision community because of the complex interplay with semantic contents and photographic technologies. Recent studies on the powerful deep learning based aesthetic quality assessment usually use a binary high-low label or a numerical score to represent the aesthetic quality. However the scalar representation cannot describe well the underlying varieties of the human perception of aesthetics. In this work, we propose to predict the aesthetic score distribution (i. e. , a score distribution vector of the ordinal basic human ratings) using Deep Convolutional Neural Network (DCNN). Conventional DCNNs which aim to minimize the difference between the predicted scalar numbers or vectors and the ground truth cannot be directly used for the ordinal basic rating distribution. Thus, a novel CNN based on the Cumulative distribution with Jensen-Shannon divergence (CJS-CNN) is presented to predict the aesthetic score distribution of human ratings, with a new reliability-sensitive learning method based on the kurtosis of the score distribution, which eliminates the requirement of the original full data of human ratings (without normalization). Experimental results on large scale aesthetic dataset demonstrate the effectiveness of our introduced CJS-CNN in this task.

YNICL Journal 2016 Journal Article

Effects of outcome on the covariance between risk level and brain activity in adolescents with internet gaming disorder

• Xin Qi
• Yongxin Yang
• Shouping Dai
• Peihong Gao
• Xin Du
• Yang Zhang
• Guijin Du
• Xiaodong Li

Individuals with internet gaming disorder (IGD) often have impaired risky decision-making abilities, and IGD-related functional changes have been observed during neuroimaging studies of decision-making tasks. However, it is still unclear how feedback (outcomes of decision-making) affects the subsequent risky decision-making in individuals with IGD. In this study, twenty-four adolescents with IGD and 24 healthy controls (HCs) were recruited and underwent functional magnetic resonance imaging while performing the balloon analog risk task (BART) to evaluate the effects of prior outcomes on brain activity during subsequent risky decision-making in adolescents with IGD. The covariance between risk level and activation of the bilateral ventral medial prefrontal cortex, left inferior frontal cortex, right ventral striatum (VS), left hippocampus/parahippocampus, right inferior occipital gyrus/fusiform gyrus and right inferior temporal gyrus demonstrated interaction effects of group by outcome (P <0. 05, AlphaSim correction). The regions with interactive effects were defined as ROI, and ROI-based intergroup comparisons showed that the covariance between risk level and brain activation was significantly greater in adolescents with IGD compared with HCs after a negative outcome occurred (P <0. 05). Our results indicated that negative outcomes affected the covariance between risk level and activation of the brain regions related to value estimation (prefrontal cortex), anticipation of rewards (VS), and emotional-related learning (hippocampus/parahippocampus), which may be one of the underlying neural mechanisms of disadvantageous risky decision-making in adolescents with IGD.

IS Journal 2015 Journal Article

Does Summarization Help Stock Prediction? A News Impact Analysis

• Xiaodong Li
• Haoran Xie
• Yangqiu Song
• Shanfeng Zhu
• Qing Li
• Fu Lee Wang

The authors study the problem of how news summarization can help stock price prediction, proposing a generic stock price prediction framework to enable the use of different external signals to predict stock prices. Experiments were conducted on five years of Hong Kong Stock Exchange data, with news reported by Finet; evaluations were performed at individual stock, sector index, and market index levels. The authors' results show that prediction based on news article summarization can effectively outperform prediction based on full-length articles on both validation and independent testing sets.

IROS Conference 2011 Conference Paper

Multipoint sliding probe methods for in situ electrical transport property characterization of individual nanostructures

• Zheng Fan
• Xinyong Tao
• Xiaodong Li
• Lixin Dong

Sliding probe methods are designed for the in situ electrical property characterization of individual one-dimensional (1D) nanostructures by eliminating the contact resistance between the fixed-end support and the specimen. The key to achieve a high resolution is to keep a constant resistance between the other end of the specimen contacting to the sliding probe. To achieve this objective, we have developed several important techniques including multipoint continuous sliding, flexible probes, and specimen-shape adapting based on nanorobotic manipulation inside a transmission electron microscope (TEM). With a copper-nanowire-tipped probe, we have shown that a flexible probe facilitates the contact force control. The adapting of the shape of a probe tip is significant for keeping a constant contact area between the probe and the specimen. This can be implemented by using a soft probe or a tip with a shape resembling the profile of the specimen. Here we show that by flowing copper from a nanotube probe against the specimen, it is possible to make a well adapted shape of the tip to the specimen after the copper cooled down. By avoiding stick-slip motion and controlling the contact force and area, it will be possible to keep a constant contact resistance between the sliding probe and the specimen, hence significantly improve the measurement resolution. Sliding probe methods are an in situ technique characterized by higher resolution and simplicity in setup as compared with conventional two- and four-terminal methods, respectively. Furthermore, it is superior for local property characterization, which is of particular interest for hetero-structured nanomaterials and defect detection.

YNIMG Journal 2000 Journal Article

A comparative study of global optimization approaches to neuromagnetic source localization

• Tianzi Jiang
• Xiaodong Li
• Frithjof Kruggel