Yuan Yang

YNICL Journal 2026 Journal Article

Condition-dependent disruption of low-frequency EEG–fMRI coupling reveals delayed hemodynamic timing and motor-network reorganization in chronic stroke

• Parikshat Sirpal
• Nishaal Parmar
• Beni Mulyana
• Hazem H. Refai
• Yuan Yang

Neurovascular coupling (NVC) is frequently disrupted after stroke, yet its temporal alignment and motor-network organization during sensorimotor engagement remain incompletely characterized. We tested whether chronic hemiparetic stroke alters the temporal alignment between EEG low-frequency oscillations (LFOs; 0.1-1.5 Hz) and the fMRI BOLD response during peripheral finger stimulation, and whether these alterations map onto motor-network anatomy and motor impairment. Simultaneous EEG-fMRI was acquired in 13 participants (7 chronic stroke; 6 healthy controls), during block-design peripheral transcutaneous index finger stimulation. EEG was decomposed using empirical mode decomposition to isolate physiologically grounded LFO components and Hilbert amplitude envelopes were incorporated into voxel-wise EEG-informed fMRI models. To evaluate robustness to hemodynamic assumptions, we implemented canonical HRF modeling, a derivative-augmented basis set, and subject-specific HRF estimation. Spatial inference was anchored to the Human Motor Area Template (HMAT). Stroke participants exhibited delayed and attenuated stimulation-evoked BOLD responses relative to controls. Cross-modal timing analyses revealed a distributional shift of peak EEG-BOLD alignment lags in stroke toward smaller and more frequently negative values, consistent with altered temporal alignment in the context of delayed hemodynamics, as opposed to causal inversion. The relative ordering of timing and coupling effects was preserved across HRF modeling strategies. HMAT-constrained analyses demonstrated reduced ipsilesional M1/PMd coupling during paretic stimulation with increased contralesional premotor and SMA recruitment. Importantly, ipsilesional motor-region coupling scaled with upper-extremity Fugl-Meyer scores. Together, these findings demonstrate convergent temporal and spatial reorganization of EEG-BOLD coupling after stroke and support LFO-informed EEG-fMRI as a physiologically interpretable framework for quantifying motor-network neurovascular timing.

NeurIPS Conference 2025 Conference Paper

Advancing Expert Specialization for Better MoE

• Hongcan Guo
• Haolang Lu
• Guoshun Nan
• Bolun Chu
• Jialin Zhuang
• Yuan Yang
• Wenhao Che
• Xinye Cao

Mixture-of-Experts (MoE) models enable efficient scaling of large language models (LLMs) by activating only a subset of experts per input. However, we observe that the commonly used auxiliary load balancing loss often leads to expert overlap and overly uniform routing, which hinders expert specialization and degrades overall performance during post-training. To address this, we propose a simple yet effective solution that introduces two complementary objectives: (1) an orthogonality loss to encourage experts to process distinct types of tokens, and (2) a variance loss to encourage more discriminative routing decisions. Gradient-level analysis demonstrates that these objectives are compatible with the existing auxiliary loss and contribute to optimizing the training process. Experimental results over various model architectures and across multiple benchmarks show that our method significantly enhances expert specialization. Notably, our method improves classic MoE baselines with auxiliary loss by up to 23. 79\%, while also maintaining load balancing in downstream tasks, without any architectural modifications or additional components. We will release our code to contribute to the community.

AIIM Journal 2025 Journal Article

Difficulty-aware coupled contour regression network with IoU loss for efficient IVUS delineation

• Yuan Yang
• Xu Yu
• Wei Yu
• Shengxian Tu
• Su Zhang
• Wei Yang

The lumen and external elastic lamina contour delineation is crucial for quantitative analyses of intravascular ultrasound (IVUS) images. However, the various artifacts in IVUS images pose substantial challenges for accurate delineation. Existing mask-based methods often produce anatomically implausible contours in artifact-affected images, while contour-based methods suffer from the over-smooth problem within the artifact regions. In this paper, we directly regress the contour pairs instead of mask-based segmentation. A coupled contour representation is adopted to learn a low-dimensional contour signature space, where the embedded anatomical prior enables the model to avoid producing unreasonable results. Further, a PIoU loss is proposed to capture the overall shape of the contour points and maximize the similarity between the regressed contours and manually delineated contours with various irregular shapes, alleviating the over-smooth problem. For the images with severe artifacts, a difficulty-aware training strategy is designed for contour regression, which gradually guides the model focus on hard samples and improves contour localization accuracy. We evaluate the proposed framework on a large IVUS dataset, consisting of 7204 frames from 185 pullbacks. The mean Dice similarity coefficients of the method for the lumen and external elastic lamina are 0. 951 and 0. 967, which significantly outperforms other state-of-the-art (SOTA) models. All regressed contours in the test images are anatomically plausible. On the public IVUS-2011 dataset, the proposed method attains comparable performance to the SOTA models with the highest processing speed at 100 fps. The code is available at https: //github. com/SMU-MedicalVision/ContourRegression.

JBHI Journal 2025 Journal Article

KGMAEDDI: Knowledge Graph and Molecular-Graph Masked Autoencoder for Drug-Drug Interaction Prediction

• Yu Li
• Zhu-Hong You
• Yuan Yang
• Cheng-gang Mi

Drug–drug interaction (DDI) prediction is essential for drug development and clinical safety. Early studies mainly relied on large labeled datasets and focused on structural or sequential drug features, often overlooking topological relationships with biomedical entities such as genes, diseases, and pathways. Although recent approaches have leveraged knowledge graphs (KGs), they typically neglect molecular structural information. To address these limitations, we propose KGMAEDDI, a novel framework that integrates molecular structures and semantic knowledge from KGs for DDI prediction. Specifically, KGMAEDDI employs a message-passing neural network to capture intrinsic structural features of drugs and a knowledge-aware attention network to extract semantic-rich representations from KGs. These representations are fused via a reconstruction-driven feature fusion module that combines a masked autoencoder and bi-directional cross-attention. This design enforces mutual reconstruction between modalities, thereby aligning structural and semantic embeddings in a shared latent space. We evaluate KGMAEDDI on the DrugBank dataset under both binary and multi-class settings. Experimental results show that KGMAEDDI consistently outperforms state-of-the-art baselines, validating its effectiveness in modeling complex drug interactions.

AAAI Conference 2024 Conference Paper

TEILP: Time Prediction over Knowledge Graphs via Logical Reasoning

• Siheng Xiong
• Yuan Yang
• Ali Payani
• James C Kerce
• Faramarz Fekri

Conventional embedding-based models approach event time prediction in temporal knowledge graphs (TKGs) as a ranking problem. However, they often fall short in capturing essential temporal relationships such as order and distance. In this paper, we propose TEILP, a logical reasoning framework that naturaly integrates such temporal elements into knowledge graph predictions. We first convert TKGs into a temporal event knowledge graph (TEKG) which has a more explicit representation of time in term of nodes of the graph. The TEKG equips us to develop a differentiable random walk approach to time prediction. Finally, we introduce conditional probability density functions, associated with the logical rules involving the query interval, using which we arrive at the time prediction. We compare TEILP with state-of-the-art methods on five benchmark datasets. We show that our model achieves a significant improvement over baselines while providing interpretable explanations. In particular, we consider several scenarios where training samples are limited, event types are imbalanced, and forecasting the time of future events based on only past events is desired. In all these cases, TEILP outperforms state-of-the-art methods in terms of robustness.

IJCAI Conference 2024 Conference Paper

Temporal Inductive Logic Reasoning over Hypergraphs

• Yuan Yang
• Siheng Xiong
• Ali Payani
• James C. Kerce
• Faramarz Fekri

Inductive logic reasoning is a fundamental task in graph analysis, which aims to generalize patterns from data. This task has been extensively studied for traditional graph representations, such as knowledge graphs (KGs), using techniques like inductive logic programming (ILP). Existing ILP methods assume learning from KGs with static facts and binary relations. Beyond KGs, graph structures are widely present in other applications such as procedural instructions, scene graphs, and program executions. While ILP is beneficial for these applications, applying it to those graphs is nontrivial: they are more complex than KGs, which usually involve timestamps and n-ary relations, effectively a type of hypergraph with temporal events. In this work, we propose temporal inductive logic reasoning (TILR), an ILP method that reasons on temporal hypergraphs. To enable hypergraph reasoning, we introduce the multi-start random B-walk, a novel graph traversal method for hypergraphs. By combining it with a path-consistency algorithm, TILR learns logic rules by generalizing from both temporal and relational data. To address the lack of hypergraph benchmarks, we create and release two temporal hypergraph datasets: YouCook2-HG and nuScenes-HG. Experiments on these benchmarks demonstrate that TILR achieves superior reasoning capability over various strong baselines.

AAAI Conference 2024 Short Paper

Visual Abstract Reasoning in Computational Imagery

• Yuan Yang

Despite current AI’s human-like behavior, super efficiency, and unbelievable ability to handle complex games, we still complain that it shows no sign of creativity, originality, or novelty outside its training set, and that it fails to develop new insights into old experience or establish understanding of new experience. In short, it generates content from its training set, but does not invent content. A fundamental reason for this is that current AI is incapable of abstraction and reasoning in an abstract, generalizable, and systematic way. Think, for instance, of what AI systems we can build if we have a base system that can answer this simple question—when two things are the same. Instead of studying these high-level questions, I put my thesis in the context of visual abstract reasoning (VAR), a task widely used in human intelligence tests. A classical example of this task is Raven’s Progressive Matrices (RPM, see Figure 1), a family of intelligence tests that was designed to measure eductive ability, i.e., the ability to make meaning out of confusion and generate high-level, usually nonverbal, schemata which make it easy to handle complexity. A similar concept to eductive ability is fluid intelligence, or the ability to discriminate and perceive complex relationships when no recourse to answers is stored in memory. Whether eductive ability or fluid intelligence, RPM points to the qualities that have been lacking in AI. To explore these qualities in AI, I propose the following research questions.

JBHI Journal 2023 Journal Article

Coupled Contour Regression for Efficient Delineation of Lumen and External Elastic Lamina in Intravascular Ultrasound Images

• Yuan Yang
• Wei Yu
• Haiyan Du
• Li Ling
• Qianjin Feng
• Shengxian Tu
• Wei Yang

Automatic delineation of the lumen and vessel contours in intravascular ultrasound (IVUS) images is crucial for the subsequent IVUS-based analysis. Existing methods usually address this task through mask-based segmentation, which cannot effectively handle the anatomical plausibility of the lumen and external elastic lamina (EEL) contours and thus limits their performance. In this article, we propose a contour encoding based method called coupled contour regression network (CCRNet) to directly predict the lumen and EEL contour pairs. The lumen and EEL contours are resampled, coupled, and embedded into a low-dimensional space to learn a compact contour representation. Then, we employ a convolutional network backbone to predict the coupled contour signatures and reconstruct the signatures to the object contours by a linear decoder. Assisted by the implicit anatomical prior of the paired lumen and EEL contours in the signature space and contour decoder, CCRNet has the potential to avoid producing unreasonable results. We evaluated our proposed method on a large IVUS dataset consisting of 7204 cross-sectional frames from 185 pullbacks. The CCRNet can rapidly extract the contours at 100 fps. Without any post-processing, all produced contours are anatomically reasonable in the test 19 pullbacks. The mean Dice similarity coefficients of our CCRNet for the lumen and EEL are 0. 940 and 0. 958, which are comparable to the mask-based models. In terms of the contour metric Hausdorff distance, our CCRNet achieves 0. 258 mm for lumen and 0. 268 mm for EEL, which outperforms the mask-based models.

AAAI Conference 2022 Conference Paper

LOGICDEF: An Interpretable Defense Framework against Adversarial Examples via Inductive Scene Graph Reasoning

• Yuan Yang
• James C Kerce
• Faramarz Fekri

Deep vision models have provided new capability across a spectrum of applications in transportation, manufacturing, agriculture, commerce, and security. However, recent studies have demonstrated that these models are vulnerable to adversarial attack, exposing a risk-of-use in critical applications where untrusted parties have access to the data environment or even directly to the sensor inputs. Existing adversarial defense methods are either limited to specific types of attacks or are too complex to be applied to practical vision models. More importantly, these methods rely on techniques that are not interpretable to humans. In this work, we argue that an effective defense should produce an explanation as to why the system is attacked, and by using a representation that is easily readable by a human user, e. g. a logic formalism. To this end, we propose logic adversarial defense (LOGICDEF), a defense framework that utilizes the scene graph of the image to provide a contextual structure for detecting and explaining object classification. Our framework first mines inductive logic rules from the extracted scene graph, and then uses these rules to construct a defense model that alerts the user when the vision model violates the consistency rules. The defense model is interpretable and its robustness is further enhanced by incorporating existing relational commonsense knowledge from projects such as ConceptNet. In order to handle the hierarchical nature of such relational reasoning, we use a curriculum learning approach based on object taxonomy, yielding additional improvements to training and performance.

JBHI Journal 2021 Journal Article

Adaptive Stimulation Profiles Modulation for Foot Drop Correction Using Functional Electrical Stimulation: A Proof of Concept Study

• Yurong Li
• Xu Yang
• Yuezhu Zhou
• Jun Chen
• Min Du
• Yuan Yang

Functional electrical stimulation (FES) provides an effective way for foot drop (FD) correction. To overcome the redundant and blind stimulation problems in the state-of-the-art methods, this study proposes a closed-loop scheme for an adaptive electromyography (EMG)-modulated stimulation profile. The developed method detects real-time angular velocity during walking. It provides feedbacks to a long short-term memory (LSTM) neural network for predicting synchronous tibialis anterior (TA) EMG. Based on the prediction, it modulates the stimulation intensity, taking into account of the subject-specific dead zone and saturation of the electrically evoked activation. The proposed method is tested on ten able-bodied participants and six FD subjects as proof of concept. The experimental results show that the proposed method can successfully induce the dorsiflexion of the ankle joint, and generate an activation pattern similar to a natural gait, with the mean Correlation Coefficient of 0. 9021. Thus, the proposed method has the potential to help patients to retrieve normal gait.

ICLR Conference 2020 Conference Paper

Efficient Probabilistic Logic Reasoning with Graph Neural Networks

• Yuyu Zhang
• Xinshi Chen
• Yuan Yang
• Arun Ramamurthy
• Bo Li
• Yuan (Alan) Qi
• Le Song

Markov Logic Networks (MLNs), which elegantly combine logic rules and probabilistic graphical models, can be used to address many knowledge graph problems. However, inference in MLN is computationally intensive, making the industrial-scale application of MLN very difficult. In recent years, graph neural networks (GNNs) have emerged as efficient and effective tools for large-scale graph problems. Nevertheless, GNNs do not explicitly incorporate prior logic rules into the models, and may require many labeled examples for a target task. In this paper, we explore the combination of MLNs and GNNs, and use graph neural networks for variational inference in MLN. We propose a GNN variant, named ExpressGNN, which strikes a nice balance between the representation power and the simplicity of the model. Our extensive experiments on several benchmark datasets demonstrate that ExpressGNN leads to effective and efficient probabilistic logic reasoning.

ICLR Conference 2020 Conference Paper

Learn to Explain Efficiently via Neural Logic Inductive Learning

• Yuan Yang
• Le Song

The capability of making interpretable and self-explanatory decisions is essential for developing responsible machine learning systems. In this work, we study the learning to explain the problem in the scope of inductive logic programming (ILP). We propose Neural Logic Inductive Learning (NLIL), an efficient differentiable ILP framework that learns first-order logic rules that can explain the patterns in the data. In experiments, compared with the state-of-the-art models, we find NLIL is able to search for rules that are x10 times longer while remaining x3 times faster. We also show that NLIL can scale to large image datasets, i.e. Visual Genome, with 1M entities.

YNIMG Journal 2009 Journal Article

The Positivity Bias of Emotion Memory in Older Adults

• aox Xu
• Renl Zhou
• Yuan Yang