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Tianyu Liu

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27 papers

2 author rows

JBHI Journal 2026 Journal Article

Few-Shot Class-Incremental Learning With Dynamic Prototype Refinement for Brain Activity Classification

Lei Cao
Hao Li
Yilin Dong
Tianyu Liu
Jie Li

The brain-computer interface (BCI) system facilitates efficient communication and control, with Electroencephalography (EEG) signals as a vital component. Traditional EEG signal classification, based on static deep-learning models, presents a challenge when new classes of the subject’s brain activity emerge. The goal is to develop a model that can recognize new few-shot classes while preserving its ability to discriminate between existing ones. This scenario is referred to as Few-Shot Class-Incremental Learning (FSCIL). This work introduces IncrementEEG, a novel framework meticulously designed to tackle the distinct challenges of FSCIL in EEG-based brain activity classification, focusing specifically on emotion recognition and steady-state visual evoked potential (SSVEP). Our work analyzes the role of additive angular margin loss in improving the model’s discrimination capabilities. The proposed method is designed to demonstrate robustness in open-world conditions and adaptability to new tasks. Furthermore, we introduce a prototype refinement module comprising a prototype augmentation block and an update block. The prototype augmentation block in the deep feature space preserves the decision boundary for prior tasks, and the prototype update block utilizes a shared embedding space to compute the relation matrix for bootstrapping prototype updates. Extensive experiments conducted across multiple datasets show the superior performance of the IncrementEEG framework compared to state-of-the-art methods. The proposed method advances FSCIL brain activity classification, offering promising potential for applications in Brain-Computer Interface systems.

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YNIMG Journal 2026 Journal Article

The regulations on topological organization of prefrontal-sensorimotor cortical network elicited by tonifying and reducing manipulations of acupuncture: A graph theory analysis study based on fNIRS

Jingya Cao
Yuzhu Qu
Li Chen
Tianyu Liu
Jing Guo
Zilei Tian
Chongkai Luo
Yulai Gong

This study aims to investigate the similarities and differences in the topological organization of prefrontal-sensorimotor cortical network (PFC-SMC) induced by traditional tonifying and reducing manipulations of acupuncture (TRMs), including the tonifying, reducing, and even tonifying-reducing manipulations. Thirty-five healthy participants underwent all three types of TRMs while functional near-infrared spectroscopy data were recorded. To characterize the network properties, graph-theoretical analysis was applied to calculate topological metrics at both global and nodal levels. Based on these metrics, k-means++ clustering analysis was subsequently performed to assess the representational and discriminative capacity of the derived features across different acupuncture manipulation. The results revealed that all three acupuncture manipulations perserved the typical small-world properties of the PFC-SMC network (p < 0. 05, false discovery rate [FDR] corrected). However, further comparison showed that both the even tonifying-reducing manipulation and the reducing manipulation elicited significantly higher clustering coefficient and network efficiency than the tonifying manipulation (pFDR < 0. 05). At the nodal level, the tonifying manipulation showed lower nodal betweenness centrality and nodal degree centrality than the other two acupuncture manipulations (pFDR < 0. 05). Notably, these topological properties enabled reliable differentiation among the three TRMs, as confirmed by the clustering analysis. These findings highlight that different acupuncture manipulations exert distinct modulatory effects on the brain functional network, providing visualized evidence for facilitating the understanding and clinical application of TRMs.

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AIIM Journal 2025 Journal Article

Anatomical prior-based vertebral landmark detection for spinal disorder diagnosis

Yukang Yang
Yu Wang
Tianyu Liu
Miao Wang
Ming Sun
Shiji Song
Wenhui Fan
Gao Huang

As one of fundamental ways to interpret spine images, detection of vertebral landmarks is an informative prerequisite for further diagnosis and management of spine disorders such as scoliosis and fractures. Most existing machine learning-based methods for automatic vertebral landmark detection suffer from overlapping landmarks or abnormally long distances between nearby landmarks against anatomical priors, and thus lack sufficient reliability and interpretability. To tackle the problem, this paper systematically utilizes anatomical prior knowledge in vertebral landmark detection. We explicitly formulate anatomical priors of the spine, related to distances among vertebrae and spatial order within the spine, and integrate these geometrical constraints within training loss, inference procedure, and evaluation metrics. First, we introduce an anatomy-constraint loss to regularize the training process with the aforementioned contextual priors explicitly. Second, we propose a simple-yet-effective anatomy-aided inference procedure by employing sequential prediction rather than a parallel counterpart. Third, we provide novel anatomy-related metrics to quantitatively evaluate to which extent landmark predictions follow the anatomical priors, as is not reflected within the widely-used landmark localization error metric. We employ the localization framework on 1410 anterior–posterior radiographic images. Compared with competitive baseline models, we achieve superior landmark localization accuracy and comparable Cobb angle estimation for scoliosis assessment. Ablation studies demonstrate the effectiveness of designed components on the decrease of localization error and improvement of anatomical plausibility. Additionally, we exhibit effective generalization performance by transferring our detection method onto sagittal 2-D slices of CT scans and boost the performance of downstream compression fracture classification at vertebra-level.

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NeurIPS Conference 2025 Conference Paper

CellVerse: Do Large Language Models Really Understand Cell Biology?

Fan Zhang
Tianyu Liu
Zhihong Zhu
Hao Wu
Haixin Wang
Donghao Zhou
Yefeng Zheng
Kun Wang

Recent studies have demonstrated the feasibility of modeling single-cell data as natural languages and the potential of leveraging powerful large language models (LLMs) for understanding cell biology. However, a comprehensive evaluation of LLMs' performance on language-driven single-cell analysis tasks still remains unexplored. Motivated by this challenge, we introduce CellVerse, a unified language-centric question-answering benchmark that integrates four types of single-cell multi-omics data and encompasses three hierarchical levels of single-cell analysis tasks: cell type annotation (cell-level), drug response prediction (drug-level), and perturbation analysis (gene-level). Going beyond this, we systematically evaluate the performance across 14 open-source and closed-source LLMs ranging 160M $\rightarrow$ 671B on CellVerse. Remarkably, the experimental results reveal: (1) Existing specialist models (C2S-Pythia) fail to make reasonable decisions across all sub-tasks within CellVerse, while generalist models such as Qwen, Llama, GPT, and DeepSeek family models exhibit preliminary understanding capabilities within the realm of cell biology. (2) The performance of current LLMs falls short of expectations and has substantial room for improvement. Notably, in the widely studied drug response prediction task, none of the evaluated LLMs demonstrate significant performance improvement over random guessing. CellVerse offers the first large-scale empirical demonstration that significant challenges still remain in applying LLMs to cell biology. By introducing CellVerse, we lay the foundation for advancing cell biology through natural languages and hope this paradigm could facilitate next-generation single-cell analysis. Project Page: https: //cellverse-cuhk. github. io

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NeurIPS Conference 2025 Conference Paper

Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving

Daoguang Zan
Zhirong Huang
Wei Liu
Hanwu Chen
Shulin Xin
Linhao Zhang
Qi Liu
Li Aoyan

The task of issue resolving aims to modify a codebase to generate a patch that addresses a given issue. However, most existing benchmarks focus almost exclusively on Python, making them insufficient for evaluating Large Language Models (LLMs) across different programming languages. To bridge this gap, we introduce a multilingual issue-resolving benchmark, called Multi-SWE-bench, covering 8 languages of Python, Java, TypeScript, JavaScript, Go, Rust, C, and C++. In particular, this benchmark includes a total of 2, 132 high-quality instances, carefully curated by 68 expert annotators, ensuring a reliable and accurate evaluation of LLMs on the issue-resolving task. Based on human-annotated results, the issues are further classified into three difficulty levels. We evaluate a series of state-of-the-art models on Multi-SWE-bench, utilizing both procedural and agent-based frameworks for issue resolving. Our experiments reveal three key findings: (1) Limited generalization across languages: While existing LLMs perform well on Python issues, their ability to generalize across other languages remains limited; (2) Performance aligned with human-annotated difficulty: LLM-based agents' performance closely aligns with human-assigned difficulty, with resolution rates decreasing as issue complexity rises; and (3) Performance drop on cross-file issues: The performance of current methods significantly deteriorates when handling cross-file issues. These findings highlight the limitations of current LLMs and underscore the need for more robust models capable of handling a broader range of programming languages and complex issue scenarios.

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ICLR Conference 2025 Conference Paper

PEARL: Parallel Speculative Decoding with Adaptive Draft Length

Tianyu Liu
Yun Li
Qitan Lv
Kai Liu 0052
Jianchen Zhu
Winston Hu
Xiao Sun

Speculative decoding (SD), where an extra draft model is employed to provide multiple **draft** tokens first and then the original target model verifies these tokens in parallel, has shown great power for LLM inference acceleration. However, existing SD methods suffer from the mutual waiting problem, i.e., the target model gets stuck when the draft model is *guessing* tokens, and vice versa. This problem is directly incurred by the asynchronous execution of the draft model and the target model, and is exacerbated due to the fixed draft length in speculative decoding. To address these challenges, we propose a conceptually simple, flexible, and general framework to boost speculative decoding, namely **P**arallel sp**E**culative decoding with **A**daptive d**R**aft **L**ength (PEARL). Specifically, PEARL proposes *pre-verify* to verify the first draft token in advance during the drafting phase, and *post-verify* to generate more draft tokens during the verification phase. PEARL parallels the drafting phase and the verification phase via applying the two strategies, and achieves adaptive draft length for different scenarios, which effectively alleviates the mutual waiting problem. Experiments on various text generation benchmarks demonstrate the effectiveness of our PEARL, leading to a superior speedup performance up to **4.43$\times$** and **1.50$\times$**, compared to auto-regressive decoding and vanilla speculative decoding, respectively.

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ICML Conference 2025 Conference Paper

Slimming the Fat-Tail: Morphing-Flow for Adaptive Time Series Modeling

Tianyu Liu
Kai Sun
Fuchun Sun 0001
Yu Luo
Yuanlong Zhang

Temporal sequences, even after stationarization, often exhibit leptokurtic distributions with fat tails and persistent distribution shifts. These properties destabilize feature dynamics, amplify model variance, and hinder model convergence in time series forecasting. To address this, we propose Morphing-Flow (MoF), a framework that combines a spline-based transform layer (Flow) and a test-time-trained method (Morph), which adaptively normalizes non-stationary, fat-tailed distributions while preserving critical extreme features. MoF ensures that inputs remain within a network’s effective activation space—a structured, normal-like distribution—even under distributional drift. Experiments across eight datasets show that MoF achieves state-of-the-art performance: With a simple linear backbone architecture, it matches the performance of state-of-the-art models on datasets such as Electricity and ETTh2. When paired with a patch-based Mamba architecture, MoF outperforms its closest competitor by 6. 3% on average and reduces forecasting errors in fat-tailed datasets such as Exchange by 21. 7%. Moreover, MoF acts as a plug-and-play module, boosting performance in existing models without architectural changes.

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NeurIPS Conference 2025 Conference Paper

SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines

Xeron Du
Yifan Yao
Kaijing Ma
Bingli Wang
Tianyu Zheng
Minghao Liu
Yiming Liang
Xiaolong Jin

Large language models (LLMs) have demonstrated remarkable proficiency in mainstream academic disciplines such as mathematics, physics, and computer science. However, human knowledge encompasses over 200 specialized disciplines, far exceeding the scope of existing benchmarks. The capabilities of LLMs in many of these specialized fields-particularly in light industry, agriculture, and service-oriented disciplines-remain inadequately evaluated. To address this gap, we present SuperGPQA, a comprehensive benchmark that evaluates graduate-level knowledge and reasoning capabilities across 285 disciplines. Our benchmark employs a novel Human-LLM collaborative filtering mechanism to eliminate trivial or ambiguous questions through iterative refinement based on both LLM responses and expert feedback. Our experimental results reveal significant room for improvement in the performance of current state-of-the-art LLMs across diverse knowledge domains (e. g. , the reasoning-focused model Gemini-2. 5-Pro achieved the highest accuracy of 63. 56% on SuperGPQA), highlighting the considerable gap between current model capabilities and artificial general intelligence. Additionally, we present comprehensive insights from our management of a large-scale annotation process, involving over 80 expert annotators and an interactive Human-LLM collaborative system, offering valuable methodological guidance for future research initiatives of comparable scope.

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ICLR Conference 2025 Conference Paper

TorchTitan: One-stop PyTorch native solution for production ready LLM pretraining

Wanchao Liang
Tianyu Liu
Less Wright
Will Constable
Andrew Gu
Chien-Chin Huang
Iris Zhang
Wei Feng

The development of large language models (LLMs) has been instrumental in advancing state-of-the-art natural language processing applications. Training LLMs with billions of parameters and trillions of tokens requires sophisticated distributed systems that enable composing and comparing several state-of-the-art techniques in order to efficiently scale across thousands of accelerators. However, existing solutions are complex, scattered across multiple libraries/repositories, lack interoperability, and are cumbersome to maintain. Thus, curating and empirically comparing training recipes requires non-trivial engineering effort. This paper introduces **TORCHTITAN**$^1$, a PyTorch-native distributed training system that unifies and advances state-of-the-art techniques, streamlining integration and reducing engineering overhead. TORCHTITAN enables seamless application of 4D parallelism in a modular and composable manner, while featuring elastic scaling to adapt to changing computational requirements. The system provides comprehensive logging, efficient checkpointing, and debugging tools, ensuring production-ready training. Moreover, TORCHTITAN incorporates innovative hardware-software co-designed solutions, leveraging cutting-edge features like Float8 training and SymmetricMemory to maximize hardware utilization. As a flexible experimental test bed, TORCHTITAN facilitates the curation and comparison of custom recipes for diverse training contexts. By leveraging TORCHTITAN, we developed optimized training recipes for the Llama 3.1 family and provide actionable guidance on selecting and combining distributed training techniques to maximize training efficiency, based on our hands-on experiences. We thoroughly assess TORCHTITAN on the Llama 3.1 family of LLMs, spanning 8 billion to 405 billion parameters, and showcase its exceptional performance, modular composability, and elastic scalability. By stacking training optimizations, we demonstrate accelerations ranging from 65.08% on Llama 3.1 8B at 128 GPU scale (1D), 12.59% on Llama 3.1 70B at 256 GPU scale (2D), to 30% on Llama 3.1 405B at 512 GPU scale (3D) on NVIDIA H100 GPUs over optimized baselines. We also demonstrate the effectiveness of 4D parallelism in enabling long context training. $^1$ GitHub: [https://github.com/pytorch/torchtitan](https://github.com/pytorch/torchtitan)

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NeurIPS Conference 2024 Conference Paper

A benchmark for prediction of transcriptomic responses to chemical perturbations across cell types

Artur Szałata
Andrew Benz
Robrecht Cannoodt
Mauricio Cortes
Jason Fong
Sunil Kuppasani
Richard Lieberman
Tianyu Liu

Single-cell transcriptomics has revolutionized our understanding of cellular heterogeneity and drug perturbation effects. However, its high cost and the vast chemical space of potential drugs present barriers to experimentally characterizing the effect of chemical perturbations in all the myriad cell types of the human body. To overcome these limitations, several groups have proposed using machine learning methods to directly predict the effect of chemical perturbations either across cell contexts or chemical space. However, advances in this field have been hindered by a lack of well-designed evaluation datasets and benchmarks. To drive innovation in perturbation modeling, the Open Problems Perturbation Prediction (OP3) benchmark introduces a framework for predicting the effects of small molecule perturbations on cell type-specific gene expression. OP3 leverages the Open Problems in Single-cell Analysis benchmarking infrastructure and is enabled by a new single-cell perturbation dataset, encompassing 146 compounds tested on human blood cells. The benchmark includes diverse data representations, evaluation metrics, and winning methods from our "Single-cell perturbation prediction: generalizing experimental interventions to unseen contexts" competition at NeurIPS 2023. We envision that the OP3 benchmark and competition will drive innovation in single-cell perturbation prediction by improving the accessibility, visibility, and feasibility of this challenge, thereby promoting the impact of machine learning in drug discovery.

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TCS Journal 2024 Journal Article

Beyond windability: Approximability of the four-vertex model

Tianyu Liu
Xiongxin Yang

We study the approximability of the four-vertex model, a special case of the six-vertex model. We prove that, despite being NP-hard to approximate in the worst case, the four-vertex model admits a fully polynomial randomized approximation scheme (FPRAS) when the input satisfies certain linear equation system over GF ( 2 ). The FPRAS is given by a Markov chain known as the worm process, whose state space and rapid mixing rely on the solution of the linear equation system. This is the first attempt to design an FPRAS for the six-vertex model with unwindable constraint functions. Additionally, we explore the applications of this technique on planar graphs, providing efficient sampling algorithms.

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JBHI Journal 2024 Journal Article

BTSSPro: Prompt-Guided Multimodal Co-Learning for Breast Cancer Tumor Segmentation and Survival Prediction

Wei Li
Tianyu Liu
Feiyan Feng
Shengpeng Yu
Hong Wang
Yanshen Sun

Early detection significantly enhances patients' survival rates by identifying tumors in their initial stages through medical imaging. However, prevailing methodologies encounter challenges in extracting comprehensive information from diverse modalities, thereby exacerbating semantic disparities and overlooking critical task correlations, consequently compromising the accuracy of prognosis predictions. Moreover, clinical insights emphasize the advantageous sharing of parameters between tumor segmentation and survival prediction for enhanced prognostic accuracy. This paper proposes a novel model, BTSSPro, designed to concurrently address B reast cancer T umor S egmentation and S urvival prediction through a Pro mpt-guided multi-modal co-learning framework. Technologically, our approach involves the extraction of tumor-specific discriminative features utilizing shared dual attention (SDA) blocks, which amalgamate spatial and channel information from breast MR images. Subsequently, we employ a guided fusion module (GFM) to seamlessly integrate the Electronic Health Record (EHR) vector into the extracted tumor-related discriminative feature representations. This integration prompts the model's feature selection to align more closely with real-world scenarios. Finally, a feature harmonic unit (FHU) is introduced to coordinate the transformer encoder and CNN decoder, thus reducing semantic differences. Remarkably, BTSSPro achieved a C-index of 0. 968 and Dice score of 0. 715 on the Breast MRI-NACT-Pilot dataset and a C-index of 0. 807 and Dice score of 0. 791 on the ISPY1 dataset, surpassing the previous state-of-the-art methods.

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NeurIPS Conference 2024 Conference Paper

Semi-supervised Knowledge Transfer Across Multi-omic Single-cell Data

Fan Zhang
Tianyu Liu
Zihao Chen
Xiaojiang Peng
Chong Chen
Xian-Sheng Hua
Xiao Luo
Hongyu Zhao

Knowledge transfer between multi-omic single-cell data aims to effectively transfer cell types from scRNA-seq data to unannotated scATAC-seq data. Several approaches aim to reduce the heterogeneity of multi-omic data while maintaining the discriminability of cell types with extensive annotated data. However, in reality, the cost of collecting both a large amount of labeled scRNA-seq data and scATAC-seq data is expensive. Therefore, this paper explores a practical yet underexplored problem of knowledge transfer across multi-omic single-cell data under cell type scarcity. To address this problem, we propose a semi-supervised knowledge transfer framework named Dual label scArcity elimiNation with Cross-omic multi-samplE Mixup (DANCE). To overcome the label scarcity in scRNA-seq data, we generate pseudo-labels based on optimal transport and merge them into the labeled scRNA-seq data. Moreover, we adopt a divide-and-conquer strategy which divides the scATAC-seq data into source-like and target-specific data. For source-like samples, we employ consistency regularization with random perturbations while for target-specific samples, we select a few candidate labels and progressively eliminate incorrect cell types from the label set for additional supervision. Next, we generate virtual scRNA-seq samples with multi-sample Mixup based on the class-wise similarity to reduce cell heterogeneity. Extensive experiments on many benchmark datasets suggest the superiority of our DANCE over a series of state-of-the-art methods.

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NeurIPS Conference 2023 Conference Paper

Learning Rule-Induced Subgraph Representations for Inductive Relation Prediction

Tianyu Liu
Qitan Lv
Jie Wang
Shuling Yang
Hanzhu Chen

Inductive relation prediction (IRP)---where entities can be different during training and inference---has shown great power for completing evolving knowledge graphs. Existing works mainly focus on using graph neural networks (GNNs) to learn the representation of the subgraph induced from the target link, which can be seen as an implicit rule-mining process to measure the plausibility of the target link. However, these methods are not able to differentiate the target link and other links during message passing, hence the final subgraph representation will contain irrelevant rule information to the target link, which reduces the reasoning performance and severely hinders the applications for real-world scenarios. To tackle this problem, we propose a novel $\textit{single-source edge-wise}$ GNN model to learn the $\textbf{R}$ule-induc$\textbf{E}$d $\textbf{S}$ubgraph represen$\textbf{T}$ations $(\textbf{REST}$), which encodes relevant rules and eliminates irrelevant rules within the subgraph. Specifically, we propose a $\textit{single-source}$ initialization approach to initialize edge features only for the target link, which guarantees the relevance of mined rules and target link. Then we propose several RNN-based functions for $\textit{edge-wise}$ message passing to model the sequential property of mined rules. REST is a simple and effective approach with theoretical support to learn the $\textit{rule-induced subgraph representation}$. Moreover, REST does not need node labeling, which significantly accelerates the subgraph preprocessing time by up to $\textbf{11. 66}\times$. Experiments on inductive relation prediction benchmarks demonstrate the effectiveness of our REST.

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NeurIPS Conference 2023 Conference Paper

MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data

Tianyu Liu
Yuge Wang
Rex Ying
Hongyu Zhao

Discovering genes with similar functions across diverse biomedical contexts poses a significant challenge in gene representation learning due to data heterogeneity. In this study, we resolve this problem by introducing a novel model called Multimodal Similarity Learning Graph Neural Network, which combines Multimodal Machine Learning and Deep Graph Neural Networks to learn gene representations from single-cell sequencing and spatial transcriptomic data. Leveraging 82 training datasets from 10 tissues, three sequencing techniques, and three species, we create informative graph structures for model training and gene representations generation, while incorporating regularization with weighted similarity learning and contrastive learning to learn cross-data gene-gene relationships. This novel design ensures that we can offer gene representations containing functional similarity across different contexts in a joint space. Comprehensive benchmarking analysis shows our model's capacity to effectively capture gene function similarity across multiple modalities, outperforming state-of-the-art methods in gene representation learning by up to $\textbf{100. 4}$%. Moreover, we employ bioinformatics tools in conjunction with gene representations to uncover pathway enrichment, regulation causal networks, and functions of disease-associated genes. Therefore, our model efficiently produces unified gene representations for the analysis of gene functions, tissue functions, diseases, and species evolution.

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STOC Conference 2023 Conference Paper

Parallel Discrete Sampling via Continuous Walks

Nima Anari
Yizhi Huang
Tianyu Liu
Thuy-Duong Vuong
Brian Xu
Katherine Yu

We develop a framework for sampling from discrete distributions µ on the hypercube {± 1} n by sampling from continuous distributions supported on ℝ n obtained by convolution with spherical Gaussians. We show that for well-studied families of discrete distributions µ, the result of the convolution is well-conditioned log-concave, whenever the Gaussian’s variance is above an O (1) threshold. We plug off-the-shelf continuous sampling methods into our framework to obtain novel discrete sampling algorithms. Additionally, we introduce and study a crucial notion of smoothness for discrete distributions that we call transport stability, which we use to control the propagation of error in our framework. We expect transport stability to be of independent interest, as we connect it to constructions of optimally mixing local random walks and concentration inequalities. As our main application, we resolve open questions raised by Anari, Hu, Saberi, and Schild on the parallel sampling of distributions which admit parallel counting. We show that determinantal point processes can be sampled via RNC algorithms, that is in time log( n ) O (1) using n O (1) processors. For a wider class of distributions, we show our framework yields Quasi-RNC sampling, i.e., log( n ) O (1) time using n O (log n ) processors. This wider class includes non-symmetric determinantal point processes and random Eulerian tours in digraphs, the latter nearly resolving another open question raised by prior work.

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IROS Conference 2023 Conference Paper

Tight Collision Probability for UAV Motion Planning in Uncertain Environment

Tianyu Liu
Fu Zhang 0002
Fei Gao 0011
Jia Pan 0001

Operating unmanned aerial vehicles (UAVs) in complex environments that feature dynamic obstacles and external disturbances poses significant challenges, primarily due to the inherent uncertainty in such scenarios. Additionally, inaccurate robot localization and modeling errors further exacerbate these challenges. Recent research on UAV motion planning in static environments has been unable to cope with the rapidly changing surroundings, resulting in trajectories that may not be feasible. Moreover, previous approaches that have addressed dynamic obstacles or external disturbances in isolation are insufficient to handle the complexities of such environments. This paper proposes a reliable motion planning framework for UAVs, integrating various uncertainties into a chance constraint that characterizes the uncertainty in a probabilistic manner. The chance constraint provides a probabilistic safety certificate by calculating the collision probability between the robot's Gaussian-distributed forward reachable set and states of obstacles. To reduce the conservatism of the planned trajectory, we propose a tight upper bound of the collision probability and evaluate it both exactly and approximately. The approximated solution is used to generate motion primitives as a reference trajectory, while the exact solution is leveraged to iteratively optimize the trajectory for better results. Our method is thoroughly tested in simulation and real-world experiments, verifying its reliability and effectiveness in uncertain environments.

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AIIM Journal 2022 Journal Article

A multi-scale keypoint estimation network with self-supervision for spinal curvature assessment of idiopathic scoliosis from the imperfect dataset

Tianyu Liu
Yu Wang
Yukang Yang
Ming Sun
Wenhui Fan
Cody Bunger
Cheng Wu

Idiopathic scoliosis (IS) is a common lifetime disease, which exhibits an obvious deformity of spinal curvature to seriously affect heart and lung function. Accurate radiographic assessment of spinal curvature is vitally important for the clinical diagnosis and treatment planning of idiopathic scoliosis. Deep learning algorithms have been widely adopted to the medical image analysis with the remarkable advancement in computer vision. The automated methods can improve the efficiency of clinical diagnosis to relieve the burden of doctors, which have advantage in dealing with the tedious and repetitive tasks. However, existing methods usually require sufficiently large training datasets with strict annotation, which are costly and laborious especially for medical images. Moreover, the medical images of serious IS always contain the blurry and occlusive parts, which would make the accurate and robust estimation of the spinal curvature more difficult. In this paper, a dot annotation approach is presented to train the spinal curvature assessment model, rather than using strict annotation of IS X-ray images. We develop a multi-scale keypoint estimation network to reduce the requirement for large training datasets, in which the Squeeze-and-Excitation (SE) blocks are incorporated to improve the representational capacity of the model. Then, a self-supervision module is designed to alleviate the blurry and occlusive problem, and we use the two-view radiographic assessments of IS to generate a 3D spinal curvature. Finally, extensive experiments are conducted on a collected clinical dataset, in which we obtain 81. 5 AP and the average E d between the predicted keypoints and the ground truths is 0. 43, making an improvement over the mainstream approaches.

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IJCAI Conference 2022 Conference Paper

Robust Fine-tuning via Perturbation and Interpolation from In-batch Instances

Shoujie Tong
Qingxiu Dong
Damai Dai
Yifan Song
Tianyu Liu
Baobao Chang
Zhifang Sui

Fine-tuning pretrained language models (PLMs) on downstream tasks has become common practice in natural language processing. However, most of the PLMs are vulnerable, e. g. , they are brittle under adversarial attacks or imbalanced data, which hinders the application of the PLMs on some downstream tasks, especially in safe-critical scenarios. In this paper, we propose a simple yet effective fine-tuning method called Match-Tuning to force the PLMs to be more robust. For each instance in a batch, we involve other instances in the same batch to interact with it. To be specific, regarding the instances with other labels as a perturbation, Match-Tuning makes the model more robust to noise at the beginning of training. While nearing the end, Match-Tuning focuses more on performing an interpolation among the instances with the same label for better generalization. Extensive experiments on various tasks in GLUE benchmark show that Match-Tuning consistently outperforms the vanilla fine-tuning by 1. 64 scores. Moreover, Match-Tuning exhibits remarkable robustness to adversarial attacks and data imbalance.

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ICRA Conference 2022 Conference Paper

Star-Convex Constrained Optimization for Visibility Planning with Application to Aerial Inspection

Tianyu Liu
Qianhao Wang
Xingguang Zhong
Zhepei Wang
Chao Xu 0001
Fu Zhang 0002
Fei Gao 0011

The visible capability is critical in many robot applications, such as inspection and surveillance, etc. Without the assurance of the visibility to targets, some tasks end up not being complete or even failing. In this paper, we propose a visibility guaranteed planner by star-convex constrained optimization. The visible space is modeled as star convex polytope (SCP) by nature and is generated by finding the visible points directly on point cloud. By exploiting the properties of the SCP, the visibility constraint is formulated for trajectory optimization. The trajectory is confined in the safe and visible flight corridor which consists of convex polytopes and SCPs. We further make a relaxation to the visibility constraints and transform the constrained trajectory optimization problem into an unconstrained one that can be reliably and efficiently solved. To validate the capability of the proposed planner, we present the practical application in site inspection. The experimental results show that the method is efficient, scalable, and visibility guaranteed, presenting the prospect of application to various other applications in the future.

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AAAI Conference 2021 Conference Paper

Towards Faithfulness in Open Domain Table-to-text Generation from an Entity-centric View

Tianyu Liu
Xin Zheng
Baobao Chang
Zhifang Sui

In open domain table-to-text generation, we notice that the unfaithful generation usually contains hallucinated content which can not be aligned to any input table record. We thus try to evaluate the generation faithfulness with two entity-centric metrics: table record coverage and the ratio of hallucinated entities in text, both of which are shown to have strong agreement with human judgements. Then based on these metrics, we quantitatively analyze the correlation between training data quality and generation fidelity which indicates the potential usage of entity information in faithful generation. Motivated by these findings, we propose two methods for faithful generation: 1) augmented training by incorporating the auxiliary entity information, including both an augmented planbased model and an unsupervised model and 2) training instance selection based on faithfulness ranking. We show these approaches improve generation fidelity in both full dataset setting and few shot learning settings by both automatic and human evaluations.

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AIIM Journal 2019 Journal Article

A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset

Tianyu Liu
Wenhui Fan
Cheng Wu

Background and Objective Cerebral stroke has become a significant global public health issue in recent years. The ideal solution to this concern is to prevent in advance by controlling related metabolic factors. However, it is difficult for medical staff to decide whether special precautions are needed for a potential patient only based on the monitoring of physiological indicators unless they are obviously abnormal. This paper will develop a hybrid machine learning approach to predict cerebral stroke for clinical diagnosis based on the physiological data with incompleteness and class imbalance. Methods Two steps are involved in the whole process. Firstly, random forest regression is adopted to impute missing values before classification. Secondly, an automated hyperparameter optimization(AutoHPO) based on deep neural network(DNN) is applied to stroke prediction on an imbalanced dataset. Results The medical dataset contains 43, 400 records of potential patients which includes 783 occurrences of stroke. The false negative rate from our prediction approach is only 19. 1%, which has reduced by an average of 51. 5% in comparison to other traditional approaches. The false positive rate, accuracy and sensitivity predicted by the proposed approach are respectively 33. 1, 71. 6, and 67. 4%. Conclusion The approach proposed in this paper has effectively reduced the false negative rate with a relatively high overall accuracy, which means a successful decrease in the misdiagnosis rate for stroke prediction. The results are more reliable and valid as the reference in stroke prognosis, and also can be acquired conveniently at a low cost.

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AAAI Conference 2019 Conference Paper

Hierarchical Encoder with Auxiliary Supervision for Neural Table-to-Text Generation: Learning Better Representation for Tables

Tianyu Liu
Fuli Luo
Qiaolin Xia
Shuming Ma
Baobao Chang
Zhifang Sui

Generating natural language descriptions for the structured tables which consist of multiple attribute-value tuples is a convenient way to help people to understand the tables. Most neural table-to-text models are based on the encoder-decoder framework. However, it is hard for a vanilla encoder to learn the accurate semantic representation of a complex table. The challenges are two-fold: firstly, the table-to-text datasets often contain large number of attributes across different domains, thus it is hard for the encoder to incorporate these heterogeneous resources. Secondly, the single encoder also has difficulties in modeling the complex attribute-value structure of the tables. To this end, we first propose a two-level hierarchical encoder with coarse-to-fine attention to handle the attribute-value structure of the tables. Furthermore, to capture the accurate semantic representations of the tables, we propose 3 joint tasks apart from the prime encoder-decoder learning, namely auxiliary sequence labeling task, text autoencoder and multi-labeling classification, as the auxiliary supervisions for the table encoder. We test our models on the widely used dataset WIKIBIO which contains Wikipedia infoboxes and related descriptions. The dataset contains complex tables as well as large number of attributes across different domains. We achieve the state-of-the-art performance on both automatic and human evaluation metrics.

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IJCAI Conference 2019 Conference Paper

Playing Card-Based RTS Games with Deep Reinforcement Learning

Tianyu Liu
Zijie Zheng
Hongchang Li
Kaigui Bian
Lingyang Song

Game AI is of great importance as games are simulations of reality. Recent research on game AI has shown much progress in various kinds of games, such as console games, board games and MOBA games. However, the exploration in RTS games remains a challenge for their huge state space, imperfect information, sparse rewards and various strategies. Besides, the typical card-based RTS games have complex card features and are still lacking solutions. We present a deep model SEAT (selection-attention) to play card-based RTS games. The SEAT model includes two parts, a selection part for card choice and an attention part for card usage, and it learns from scratch via deep reinforcement learning. Comprehensive experiments are performed on Clash Royale, a popular mobile card-based RTS game. Empirical results show that the SEAT model agent makes it to reach a high winning rate against rule-based agents and decision-tree-based agent.

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IJCAI Conference 2018 Conference Paper

Learning to Explain Ambiguous Headlines of Online News

Tianyu Liu
Wei Wei
Xiaojun Wan

With the purpose of attracting clicks, online news publishers and editors use diverse strategies to make their headlines catchy, with a sacrifice of accuracy. Specifically, a considerable portion of news headlines is ambiguous. Such headlines are unclear relative to the content of the story, and largely degrade the reading experience of the audience. In this paper, we focus on dealing with the information gap caused by the ambiguous news headlines. We define a new task of explaining ambiguous headlines with short informative texts, and build a benchmark dataset for evaluation. We address the task by selecting a proper sentence from the news body to resolve the ambiguity in an ambiguous headline. Both feature engineering methods and neural network methods are explored. For feature engineering, we improve a standard SVM classifier with elaborately designed features. For neural networks, we propose an ambiguity-aware neural matching model based on a previous model. Utilizing automatic and manual evaluation metrics, we demonstrate the efficacy and the complementarity of the two methods, and the ambiguity-aware neural matching model achieves the state-of-the-art performance on this challenging task.

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AAAI Conference 2018 Conference Paper

Order-Planning Neural Text Generation From Structured Data

Lei Sha
Lili Mou
Tianyu Liu
Pascal Poupart
Sujian Li
Baobao Chang
Zhifang Sui

Generating texts from structured data (e. g. , a table) is important for various natural language processing tasks such as question answering and dialog systems. In recent studies, researchers use neural language models and encoder-decoder frameworks for table-to-text generation. However, these neural network-based approaches typically do not model the order of content during text generation. When a human writes a summary based on a given table, he or she would probably consider the content order before wording. In this paper, we propose an order-planning text generation model, where order information is explicitly captured by link-based attention. Then a self-adaptive gate combines the link-based attention with traditional content-based attention. We conducted experiments on the WIKIBIO dataset and achieve higher performance than previous methods in terms of BLEU, ROUGE, and NIST scores; we also performed ablation tests to analyze each component of our model. 1

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AAAI Conference 2018 Conference Paper

Table-to-Text Generation by Structure-Aware Seq2seq Learning

Tianyu Liu
Kexiang Wang
Lei Sha
Baobao Chang
Zhifang Sui

Table-to-text generation aims to generate a description for a factual table which can be viewed as a set of ﬁeld-value records. To encode both the content and the structure of a table, we propose a novel structure-aware seq2seq architecture which consists of ﬁeld-gating encoder and description generator with dual attention. In the encoding phase, we update the cell memory of the LSTM unit by a ﬁeld gate and its corresponding ﬁeld value in order to incorporate ﬁeld information into table representation. In the decoding phase, dual attention mechanism which contains word level attention and ﬁeld level attention is proposed to model the semantic relevance between the generated description and the table. We conduct experiments on the WIKIBIO dataset which contains over 700k biographies and corresponding infoboxes from Wikipedia. The attention visualizations and case studies show that our model is capable of generating coherent and informative descriptions based on the comprehensive understanding of both the content and the structure of a table. Automatic evaluations also show our model outperforms the baselines by a great margin. Code for this work is available on https: //github. com/tyliupku/wiki2bio.

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