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Tian Liu

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16 papers

2 author rows

EAAI Journal 2026 Journal Article

Dynamic estimation of mean skin temperature during physical exercises using an explainable attention-based neural network: A data-driven alternative to segmental weighting methods

Qing Zhang
Hetian Feng
Li Ding
Tian Liu
Chao Sun
Jing Zhang
Jiachen Nie

Background Mean skin temperature (MST) is a crucial physiological parameter of the human body. Traditionally, MST has been estimated using fixed-weight formulas based on body surface area proportions. However, these static methods neglect physiological changes during exercise and may not generalize well across activity types. Methods We developed ThermoAttention Network (TANet), a deep learning model combining long short-term memory networks with an attention mechanism. The model processes local skin temperature and dynamically assigns weights to eight body segments across resting, weighted hiking, and heavy lifting conditions via a proxy classification task. The attention outputs indicate each segment's contribution to MST estimation, enhancing transparency and user trust. Results TANet achieved 94. 2% classification accuracy on the proxy task. Different exercise conditions had significant effects on local skin temperature. The attention weights (mean values across windows and subjects) revealed physiologically consistent patterns: the hand dominated MST estimation at rest (21. 1%); the upper arm (17. 0%) and forearm (11. 2%) gained importance during hiking; and the chest (15. 3%) and upper arm (15. 6%) during lifting. Conclusions TANet overcomes the limitations of traditional MST formulas by adaptively assigning segment weights, enabling accurate and interpretable assessment of MST during physical activity. This framework advances explainable artificial intelligence (AI) for thermal health monitoring and supports human-centered design of wearable systems.

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IROS Conference 2025 Conference Paper

EANS: Reducing Energy Consumption for UAV with an Environmental Adaptive Navigation Strategy

Tian Liu
Han Liu
Boyang Li 0009
Long Chen 0005
Kai Huang 0001

Unmanned Aerial Vehicles (Uavs) are limited by the onboard energy. Refinement of the navigation strategy directly affects both the flight velocity and the trajectory based on the adjustment of key parameters in the Uavs pipeline, thus reducing energy consumption. However, existing techniques tend to adopt static and conservative strategies in dynamic scenarios, leading to inefficient energy reduction. Dynamically adjusting the navigation strategy requires overcoming the challenges including the task pipeline interdependencies, the environmental-strategy correlations, and the selecting parameters. To solve the aforementioned problems, this paper proposes a method to dynamically adjust the navigation strategy of the Uavs by analyzing its dynamic characteristics and the temporal characteristics of the autonomous navigation pipeline, thereby reducing Uavs energy consumption in response to environmental changes. We compare our method with the baseline through hardware-in-the-loop (HIL) simulation and real-world experiments, showing our method 3. 2X and 2. 6X improvements in mission time, 2. 4X and 1. 6X improvements in energy, respectively.

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IROS Conference 2024 Conference Paper

An Efficient Position Reconfiguration Approach for Maximizing Lifetime of Fixed-wing Swarm Drones

Han Liu
Tian Liu
Mingyue Cui
Yunxiao Shan
Shuai Zhao 0004
Kai Huang 0001

With the development and application of swarm drones, some researchers have tried to replicating the migration patterns of geese in drones swarm formation to extend their lifetime. However, the problem of performing appropriate position reconfiguration based on the battery energy still remains an unsolved issue. This paper proposes an efficient position reconfiguration approach that reduces the energy consumption imbalance of the swarm and prolongs the lifetime. The approach includes: (1) a two-step MIP (mixed-integer programming)-based optimization method. (2) a two-step heuristic algorithm that can run in pseudo-polynomial time and without the need for an optimization solver. The approach provides a complete position reconfiguration solution that determines (i) the number of position reconfiguration; (ii) which drones need to exchange positions in every position reconfiguration; (iii) the length of time to maintain each position before next reconfiguration. Finally, the approach is compared with other three methods in experiments which demonstrate the effectiveness of it.

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JBHI Journal 2023 Journal Article

sEMG-Based End-to-End Continues Prediction of Human Knee Joint Angles Using the Tightly Coupled Convolutional Transformer Model

Tuanjie Liang
Ning Sun
Qiong Wang
Jingyu Bu
Long Li
Yuhao Chen
Menglin Cao
Jin Ma

Wearable exoskeleton robots can promote the rehabilitation of patients with physical dysfunction. And improving human-computer interaction performance is a significant challenge for exoskeleton robots. The traditional feature extraction process based on surface Electromyography(sEMG) is complex and requires manual intervention, making real-time performance difficult to guarantee. In this study, we propose an end-to-end method to predict human knee joint angles based on sEMG signals using a tightly coupled convolutional transformer (TCCT) model. We first collected sEMG signals from 5 healthy subjects. Then, the envelope was extracted from the noise-removed sEMG signal and used as the input to the model. Finally, we developed the TCCT model to predict the knee joint angle after 100 ms. For the prediction performance, we used the Root Mean Square Error(RMSE), Pearson Correlation Coefficient(CC), and Adjustment R 2 as metrics to evaluate the error between the actual knee angle and the predicted knee angle. The results show that the model can predict the human knee angle quickly and accurately. The mean RMSE, Adjustment R 2, and (CC) values of the model are 3. 79°, 0. 96, and 0. 98, respectively, which are better than traditional deep learning models such as Informer (4. 14, 0. 95, 0. 98), CNN (5. 56, 0. 89, 0. 96) and CNN-BiLSTM (3. 97, 0. 95, 0. 98). In addition, the prediction time of our proposed model is only 11. 67 ± 0. 67 ms, which is less than 100 ms. Therefore, the real-time and accuracy of the model can meet the continuous prediction of human knee joint angle in practice.

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YNICL Journal 2022 Journal Article

The longitudinal neural dynamics changes of whole brain connectome during natural recovery from poststroke aphasia

Liming Fan
Chenxi Li
Zi-gang Huang
Jie Zhao
Xiaofeng Wu
Tian Liu
Youjun Li
Jue Wang

Poststroke aphasia is one of the most dramatic functional deficits that results from direct damage of focal brain regions and dysfunction of large-scale brain networks. The reconstruction of language function depends on the hierarchical whole-brain dynamic reorganization. However, investigations into the longitudinal neural changes of large-scale brain networks for poststroke aphasia remain scarce. Here we characterize large-scale brain dynamics in left-frontal-stroke aphasia through energy landscape analysis. Using fMRI during an auditory comprehension task, we find that aphasia patients suffer serious whole-brain dynamics perturbation in the acute and subacute stages after stroke, in which the brains were restricted into two major activity patterns. Following spontaneous recovery process, the brain flexibility improved in the chronic stage. Critically, we demonstrated that the abnormal neural dynamics are correlated with the aberrant brain network coordination. Taken together, the energy landscape analysis exhibited that the acute poststroke aphasia has a constrained, low dimensional brain dynamics, which were replaced by less constrained and high dimensional dynamics at chronic aphasia. Our study provides a new perspective to profoundly understand the pathological mechanisms of poststroke aphasia.

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YNIMG Journal 2015 Journal Article

Age and sex related differences in subcortical brain iron concentrations among healthy adults

Ninni Persson
Jianlin Wu
Qing Zhang
Ting Liu
Jing Shen
Ruyi Bao
Mingfei Ni
Tian Liu

Age and sex can influence brain iron levels. We studied the influence of these variables on deep gray matter magnetic susceptibilities. In 183 healthy volunteers (44. 7±14. 2years, range 20–69, ♀ 49%), in vivo quantitative susceptibility mapping (QSM) at 1. 5T was performed to estimate brain iron accumulation in the following regions of interest (ROIs): caudate nucleus (Cd), putamen (Pt), globus pallidus (Gp), thalamus (Th), pulvinar (Pul), red nucleus (Rn), substantia nigra (Sn) and the cerebellar dentate nuclei (Dn). We gauged the influence of age and sex on magnetic susceptibility by specifying a series of structural equation models. The distributions of susceptibility varied in degree across the structures, conforming to histologic findings (Hallgren and Sourander, 1958), with the highest degree of susceptibility in the Gp and the lowest in the Th. Iron increase correlated across several ROIs, which may reflect an underlying age-related process. Advanced age was associated with a particularly strong linear rise of susceptibility in the striatum. Nonlinear age trends were found in the Rn, where they were the most pronounced, followed by the Pul and Sn, while minimal nonlinear trends were observed for the Pt, Th, and Dn. Moreover, sex related variations were observed, so that women showed lower levels of susceptibility in the Sn after accounting for age. Regional susceptibility of the Pul increased linearly with age in men but exhibited a nonlinear association with age in women with a leveling off starting from midlife. Women expected to be post menopause (+51years) showed lower total magnetic susceptibility in the subcortical gray matter. The current report not only is consistent with previous reports of age related variations of brain iron, but also adds to the current knowledge by reporting age-related changes in less studied, smaller subcortical nuclei. This is the first in-vivo report to show lower total subcortical brain iron levels selectively in women from midlife, compared to men and younger women. These results encourage further assessment of sex differences in brain iron. We anticipate that age and sex are important co-factors to take into account when establishing a baseline level for differentiating pathologic neurodegeneration from healthy aging. The variations in regional susceptibility reported herein should be evaluated further using a longitudinal study design to determine within-person changes in aging.

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TCS Journal 2015 Journal Article

Improved parameterized and exact algorithms for cut problems on trees

Iyad Kanj
Guohui Lin
Tian Liu
Weitian Tong
Ge Xia
Jinhui Xu
Boting Yang
Fenghui Zhang

We study the Multicut on Trees and the Generalized Multiway Cut on Trees problems. For the Multicut on Trees problem, we present a parameterized algorithm that runs in time O ⁎ ( ρ k ), where ρ = 2 + 1 < 1. 554 is the positive root of the polynomial x 4 − 2 x 2 − 1. This improves the current-best algorithm of Chen et al. that runs in time O ⁎ ( 1. 619 k ). For the Generalized Multiway Cut on Trees problem, we show that this problem is solvable in polynomial time if the number of terminal sets is fixed; this answers an open question posed in a recent paper by Liu and Zhang. By reducing the Generalized Multiway Cut on Trees problem to the Multicut on Trees problem, our results give a parameterized algorithm that solves the Generalized Multiway Cut on Trees problem in time O ⁎ ( ρ k ).

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TCS Journal 2014 Journal Article

Approximating the maximum multiple RNA interaction problem

Weitian Tong
Randy Goebel
Tian Liu
Guohui Lin

RNA interactions are fundamental in many cellular processes, where two or more RNA molecules can be involved. Multiple RNA interactions are also believed to be much more complex than pairwise interactions. Recently, multiple RNA interaction prediction has been formulated as a maximization problem. Here we extensively examine this optimization problem under several biologically meaningful interaction models. We present a polynomial time algorithm for the problem when the order of interacting RNAs is known and pseudoknot interactions are allowed; for the general problem without an assumed RNA order, we prove the NP-hardness for both variants (allowing and disallowing pseudoknot interactions), and present a constant ratio approximation algorithm for each of them.

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TCS Journal 2014 Journal Article

Circular convex bipartite graphs: Feedback vertex sets

Tian Liu
Min Lu
Zhao Lu
Ke Xu

A feedback vertex set is a subset of vertices, such that the removal of this subset renders the remaining graph cycle-free. The weight of a feedback vertex set is the sum of weights of its vertices. Finding a minimum weighted feedback vertex set is tractable for convex bipartite graphs, but NP -complete even for unweighted bipartite graphs. In a circular convex (convex, respectively) bipartite graph, there is a circular (linear, respectively) ordering defined on one class of vertices, such that for every vertex in another class, the neighborhood of this vertex is a circular arc (an interval, respectively). The minimum weighted feedback vertex set problem is shown tractable for circular convex bipartite graphs in this paper, by making a Cook reduction (i. e. polynomial time Turing reduction) for this problem from circular convex bipartite graphs to convex bipartite graphs.

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TCS Journal 2013 Journal Article

Feedback vertex sets on restricted bipartite graphs

Wei Jiang
Tian Liu
Chaoyi Wang
Ke Xu

A feedback vertex set (FVS) in a graph is a subset of vertices whose complement induces a forest. Finding a minimum FVS is NP -complete on bipartite graphs, but tractable on convex bipartite graphs and on chordal bipartite graphs. A bipartite graph is called tree convex, if a tree is defined on one part of the vertices, such that for every vertex in the other part, its neighborhood induces a subtree. When the tree is a path, a triad or a star, the bipartite graph is called convex bipartite, triad convex bipartite or star convex bipartite, respectively. We show that: (1) FVS is tractable on triad convex bipartite graphs; (2) FVS is NP -complete on star convex bipartite graphs and on tree convex bipartite graphs where the maximum degree of vertices on the tree is at most three.

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YNIMG Journal 2013 Journal Article

Magnetic susceptibility anisotropy: Cylindrical symmetry from macroscopically ordered anisotropic molecules and accuracy of MRI measurements using few orientations

Cynthia Wisnieff
Tian Liu
Pascal Spincemaille
Shuai Wang
Dong Zhou
Yi Wang

White matter is an essential component of the central nervous system and is of major concern in neurodegenerative diseases such as multiple sclerosis (MS). Recent MRI studies have explored the unique anisotropic magnetic properties of white matter using susceptibility tensor imaging. However, these measurements are inhibited in practice by the large number of different head orientations needed to accurately reconstruct the susceptibility tensor. Adding reasonable constraints reduces the number of model parameters and can help condition the tensor reconstruction from a small number of orientations. The macroscopic magnetic susceptibility is decomposed as a sum of molecular magnetic polarizabilities, demonstrating that macroscopic order in molecular arrangement is essential to the existence of and symmetry in susceptibility anisotropy and cylindrical symmetry is a natural outcome of an ordered molecular arrangement. Noise propagation in the susceptibility tensor reconstruction is analyzed through its condition number, showing that the tensor reconstruction is highly susceptible to the distribution of acquired subject orientations and to the tensor symmetry properties, with a substantial over- or under-estimation of susceptibility anisotropy in fiber directions not favorably oriented with respect to the acquired orientations. It was found that a careful acquisition of three non-coplanar orientations and the use of cylindrical symmetry guided by diffusion tensor imaging allowed reasonable estimation of magnetic susceptibility anisotropy in certain major white matter tracts in the human brain.

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AAAI Conference 2012 Conference Paper

Efficient Optimization of Control Libraries

Debadeepta Dey
Tian Liu
Boris Sofman
James Bagnell

A popular approach to high dimensional control problems in robotics uses a library of candidate “maneuvers” or “trajectories”. The library is either evaluated on a fixed number of candidate choices at runtime (e. g. path set selection for planning) or by iterating through a sequence of feasible choices until success is achieved (e. g. grasp selection). The performance of the library relies heavily on the content and order of the sequence of candidates. We propose a provably efficient method to optimize such libraries, leveraging recent advances in optimizing sub-modular functions of sequences. This approach is demonstrated on two important problems: mobile robot navigation and manipulator grasp set selection. In the first case, performance can be improved by choosing a subset of candidates which optimizes the metric under consideration (cost of traversal). In the second case, performance can be optimized by minimizing the depth in the list that is searched before a successful candidate is found. Our method can be used in both on-line and batch settings with provable performance guarantees, and can be run in an anytime manner to handle real-time constraints.

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YNIMG Journal 2012 Journal Article

Morphology enabled dipole inversion for quantitative susceptibility mapping using structural consistency between the magnitude image and the susceptibility map

Jing Liu
Tian Liu
Ludovic de Rochefort
James Ledoux
Ildar Khalidov
Weiwei Chen
A. John Tsiouris
Cynthia Wisnieff

The magnetic susceptibility of tissue can be determined in gradient echo MRI by deconvolving the local magnetic field with the magnetic field generated by a unit dipole. This Quantitative Susceptibility Mapping (QSM) problem is unfortunately ill-posed. By transforming the problem to the Fourier domain, the susceptibility appears to be undersampled only at points where the dipole kernel is zero, suggesting that a modest amount of additional information may be sufficient for uniquely resolving susceptibility. A Morphology Enabled Dipole Inversion (MEDI) approach is developed that exploits the structural consistency between the susceptibility map and the magnitude image reconstructed from the same gradient echo MRI. Specifically, voxels that are part of edges in the susceptibility map but not in the edges of the magnitude image are considered to be sparse. In this approach an L1 norm minimization is used to express this sparsity property. Numerical simulations and phantom experiments are performed to demonstrate the superiority of this L1 minimization approach over the previous L2 minimization method. Preliminary brain imaging results in healthy subjects and in patients with intracerebral hemorrhages illustrate that QSM is feasible in practice.

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IJCAI Conference 2011 Conference Paper

Large Hinge Width on Sparse Random Hypergraphs

Tian Liu
Xiaxiang Lin
Chaoyi Wang
Kaile Su
Ke Xu

Consider random hypergraphs on n vertices, where each k-element subset of vertices is selected with probability p independently and randomly as a hyperedge. By sparse we mean that the total number of hyperedges is O(n) or O(n ln n). When k = 2, these are exactly the classical Erdö s-Ré nyi random graphs G(n, p). We prove that with high probability, hinge width on these sparse random hypergraphs can grow linearly with the expected number of hyperedges. Some random constraint satisfaction problems such as Model RB and Model RD have satisfiability thresholds on these sparse constraint hypergraphs, thus the large hinge width results provide some theoretical evidence for random instances around satisfiability thresholds to be hard for a standard hinge-decomposition based algorithm. We also conduct experiments on these and other kinds of random graphs with several hundreds vertices, including regular random graphs and power law random graphs. The experimental results also show that hinge width can grow linearly with the number of edges on these different random graphs. These results may be of further interests.

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TCS Journal 2010 Journal Article

On exponential time lower bound of Knapsack under backtracking

Xin Li
Tian Liu

We prove an Ω ( 2 0. 69 n / n ) time lower bound of Knapsack problem under the adaptive priority branching trees (pBT) model. The pBT model is a formal model of algorithms covering backtracking and dynamic programming [M. Alekhnovich, A. Borodin, A. Magen, J. Buresh-Oppenheim, R. Impagliazzo, T. Pitassi, Toward a model for backtracking and dynamic programming, ECCC TR09-038, 2009. Earlier version in Proc 20th IEEE Computational Complexity, 2005, pp. 308–322]. Our result improves the Ω ( 2 0. 5 n / n ) lower bound of M. Alekhovich et al. and the Ω ( 2 0. 66 n / n ) lower bound of Li et al. [X. Li, T. Liu, H. Peng, L. Qian, H. Sun, J. Xu, K. Xu, J. Zhu, Improved exponential time lower bound of Knapsack problem under BT model, in: Proc 4th TAMC 2007, in: LNCS, vol. 4484, 2007, pp. 624–631] through optimized arguments.

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TCS Journal 1993 Journal Article

Exponential-time and subexponential-time sets

Shouwen Tang
Bin Fu
Tian Liu

In this paper, we prove that the symmetric difference of a ⩽P k-parity-hard set for E and a subexponential-time-computable set is still ⩽P k-parity-hard for E. This remains true for ⩽P m-hard set for E since a 1-parity reduction is a many—one reduction. In addition, we show that this property fails to hold for some other types of reductions. We introduce and study the notions of E-complete kernel and E-hard kernel.

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