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Chen Lin

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15 papers

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JBHI Journal 2026 Journal Article

Harnessing Terminal Signal-Aware Deep Learning for Accurate Multi-Class Secreted Effector Identification

Lesong Wei
Shida He
Quan Zou
Chen Lin

Gram-negative bacterial secreted effectors are translocated through specialized secretion systems to manipulate host cellular processes, and their accurate identification is crucial for understanding bacterial pathogenesis. Recent deep learning methods have significantly advanced this field, yet current approaches primarily rely on global sequence representations, overlooking the biological significance of terminal regions where secretion signals reside. Moreover, severe class imbalance among different secreted effector types remains a critical challenge for multi-class prediction. Here, we propose TermSE, a terminal signal-aware framework for multi-class secreted effector identification. TermSE explicitly captures N-terminal and C-terminal sequence features through convolutional neural networks applied to protein language model embeddings, and integrates them with global sequence representations for multi-view sequence characterization. To address class imbalance, TermSE employs a cosine-normalized classifier combined with weighted sampling to mitigate feature magnitude bias and ensure sufficient learning from minority classes. Extensive experiments demonstrate that TermSE outperforms existing methods in both cross-validation and independent test settings, with robust generalization across varying sequence identity levels. Furthermore, interpretability analysis confirms that TermSE learns to focus on biologically meaningful terminal patterns specific to each secreted effector type. These results highlight the potential of TermSE as an effective and interpretable tool for secreted effector discovery.

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EAAI Journal 2025 Journal Article

Design of an efficient fault-tolerant quantum-computing circuit with quantum neural network learning

Chen Lin
Rucong Xu
Yun Li

Fault-tolerance is key to the practical realization of quantum computation, but the design of an efficient, low-overhead and fault-tolerant error-correction circuit remains a major challenge so far. To help address this issue, we first propose a noise-adaptive dissipative quantum neural network (DQNN) model to mitigate the effects of error propagation for constructing a fault-tolerant quantum circuit. Then, we develop a method for preparing a fault-tolerant auxiliary entangled state based on the DQNN model, reducing computational delays and qubit resource consumption. This method utilizes the adaptability of quantum machine learning to the distribution of noisy inputs and its practicality in noisy intermediate scale quantum devices, thus reducing interaction with classical computers and further optimizing the real-time requirements for active error-correction. By integrating quantum error-correction and quantum neural network learning, this DQNN scheme provides a novel solution for constructing scalable fault-tolerant quantum computation. Compared with existing fault-tolerant methods, the DQNN process requires fewer error-propagation efforts and offers higher fidelity in a noisy environment for error thresholds higher than 1 0 − 4. The effectiveness of this method is verified through experimental simulations using the Qiskit. The code for experiments and model in this paper can be found on GitHub: https: //github. com/Ricardo-Vv/Qiskit_exam/tree/master.

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AAAI Conference 2025 Conference Paper

Enhancing Sequential Recommendation with Global Diffusion

Mingxuan Luo
Yang Li
Chen Lin

Existing sequential recommendation models are mostly based on sequential models, which can be misled by inconsistent items in the local sequence. This study proposes GlobalDiff, a plug-and-play framework to enhance the performance of sequential models by utilizing a diffusion model to restore the global non-sequential data structure of the item universe and compensate for the local sequential context. Several novel techniques are proposed, including training construction, guided reverse approximator, and inference ensemble, to seamlessly integrate the diffusion model with the sequential model. Extensive experiments on various datasets demonstrate that GlobalDiff can enhance advanced sequential models by an average improvement of 9.67%.

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NeurIPS Conference 2024 Conference Paper

Not All Tokens Are What You Need for Pretraining

Zhenghao Lin
Zhibin Gou
Yeyun Gong
Xiao Liu
Yelong Shen
Ruochen Xu
Chen Lin
Yujiu Yang

Previous language model pre-training methods have uniformly applied a next-token prediction loss to all training tokens. Challenging this norm, we posit that ''Not all tokens in a corpus are equally important for language model training''. Our initial analysis examines token-level training dynamics of language model, revealing distinct loss patterns for different tokens. Leveraging these insights, we introduce a new language model called Rho-1. Unlike traditional LMs that learn to predict every next token in a corpus, Rho-1 employs Selective Language Modeling (SLM), which selectively trains on useful tokens that aligned with the desired distribution. This approach involves scoring training tokens using a reference model, and then training the language model with a focused loss on tokens with higher scores. When continual continual pretraining on 15B OpenWebMath corpus, Rho-1 yields an absolute improvement in few-shot accuracy of up to 30% in 9 math tasks. After fine-tuning, Rho-1-1B and 7B achieved state-of-the-art results of 40. 6% and 51. 8% on MATH dataset, respectively - matching DeepSeekMath with only 3% of the pretraining tokens. Furthermore, when continual pretraining on 80B general tokens, Rho-1 achieves 6. 8% average enhancement across 15 diverse tasks, increasing both data efficiency and performance of the language model pre-training.

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AAAI Conference 2023 Conference Paper

Code-Aware Cross-Program Transfer Hyperparameter Optimization

Zijia Wang
Xiangyu He
Kehan Chen
Chen Lin
Jinsong Su

Hyperparameter tuning is an essential task in automatic machine learning and big data management. To accelerate tuning, many recent studies focus on augmenting BO, the primary hyperparameter tuning strategy, by transferring information from other tuning tasks. However, existing studies ignore program similarities in their transfer mechanism, thus they are sub-optimal in cross-program transfer when tuning tasks involve different programs. This paper proposes CaTHPO, a code-aware cross-program transfer hyperparameter optimization framework, which makes three improvements. (1) It learns code-aware program representation in a self-supervised manner to give an off-the-shelf estimate of program similarities. (2) It adjusts the surrogate and AF in BO based on program similarities, thus the hyperparameter search is guided by accumulated information across similar programs. (3) It presents a safe controller to dynamically prune undesirable sample points based on tuning experiences of similar programs. Extensive experiments on tuning various recommendation models and Spark applications have demonstrated that CatHPO can steadily obtain better and more robust hyperparameter performances within fewer samples than state-of-the-art competitors.

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YNIMG Journal 2022 Journal Article

Optimization of fast gray matter acquisition T1 inversion recovery (FGATIR) on 7T MRI for deep brain stimulation targeting

Shengzhen Tao
Xiangzhi Zhou
Erin M. Westerhold
Erik H. Middlebrooks
Chen Lin

Deep brain stimulation (DBS) is an increasingly utilized treatment for multiple neurological disorders. Continued improvements in DBS outcome are, in part, related to increasing ability to directly visualize stimulation targets by MRI. However, it is challenging to image DBS targets with conventional MRI techniques due to limited contrast. Fast Gray Matter Acquisition T1 Inversion Recovery (FGATIR) is a commonly used MRI sequence that improves visualization of several key DBS targets by suppressing white matter (WM) signal to better reveal deep-brain gray matter (GM) structures. Due to increased signal level at high field strength, application of FGATIR on 7T MRI may allow higher spatial resolution and better DBS targeting accuracy. However, successful utilization of FGATIR requires meticulous sequence optimization involving multiple parameters to maximize GM signal while suppressing WM. This is further complicated by the transmit RF field (B1+) inhomogeneity on 7T, which can cause severe contrast degradation. In this work, we introduce a systematic approach to optimize FGATIR and to improve visualization of thalamic DBS targets on 7T. FGATIR optimization is cast into a constrained optimization problem whose objective function and constraints are designed to maximize the GM-WM contrast-to-noise ratio (CNR) while accounting for B1+ inhomogeneity. This approach allows a systematic search for optimal parameters across the multi-dimensional parametric space while limiting the negative effect of B1+ variation. Bloch equation simulations were performed to solve the proposed optimization problem and to compare the sequence derived from this method against the sequence optimized without considering B1+ inhomogeneity. The results showed that this approach can improve GM-WM CNR in the presence of B1+ inhomogeneity, especially in some high relative B1+ areas where several key thalamic DBS targets are located. Additionally, in vivo images were acquired on a clinical 7T MRI to further validate this approach. Severe contrast degradation in the thalamus was observed when B1+ effect was not considered in sequence optimization, while the proposed approach yielded improved image contrast in the thalamus with key DBS targets well-defined. These results demonstrated that the proposed method allowed optimization of FGATIR on 7T to better visualize thalamic DBS targets, which may lead to improved DBS targeting accuracy as well as treatment outcome.

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NeurIPS Conference 2021 Conference Paper

A Continuous Mapping For Augmentation Design

Keyu Tian
Chen Lin
Ser Nam Lim
Wanli Ouyang
Puneet Dokania
Philip Torr

Automated data augmentation (ADA) techniques have played an important role in boosting the performance of deep models. Such techniques mostly aim to optimize a parameterized distribution over a discrete augmentation space. Thus, are restricted by the discretization of the search space which normally is handcrafted. To overcome the limitations, we take the first step to constructing a continuous mapping from $\mathbb{R}^d$ to image transformations (an augmentation space). Using this mapping, we take a novel approach where 1) we pose the ADA as a continuous optimization problem over the parameters of the augmentation distribution; and 2) use Stochastic Gradient Langevin Dynamics to learn and sample augmentations. This allows us to potentially explore the space of infinitely many possible augmentations, which otherwise was not possible due to the discretization of the space. This view of ADA is radically different from the standard discretization based view of ADA, and it opens avenues for utilizing the vast efficient gradient-based algorithms available for continuous optimization problems. Results over multiple benchmarks demonstrate the efficiency improvement of this work compared with previous methods.

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YNICL Journal 2020 Journal Article

Improved detection of focal cortical dysplasia using a novel 3D imaging sequence: Edge-Enhancing Gradient Echo (3D-EDGE) MRI

Erik H. Middlebrooks
Chen Lin
Erin Westerhold
Lela Okromelidze
Prasanna Vibhute
Sanjeet S. Grewal
Vivek Gupta

Epilepsy is a common neurological disorder with focal cortical dysplasia (FCD) being one of the most common lesional causes. Detection of FCD by MRI is a major determinant of surgical outcome. Evolution of MRI sequences and hardware has greatly increased the detection rate of FCD, but these gains have largely been related to the more visible Type IIb FCD, with Type I and IIa remaining elusive. While most sequence improvements have relied on increasing contrast between gray and white matter, we propose a novel imaging approach, 3D Edge-Enhancing Gradient Echo (3D-EDGE), to directly image the gray-white boundary. By acquiring images at an inversion time where gray and white matter have equal signal but opposite phases, voxels with a mixture of gray and white matter (e.g., at the gray-white boundary) will have cancellation of longitudinal magnetization producing a thin area of signal void at the normal boundary. By creating greater sensitivity for minor changes in T1 relaxation, microarchitectural abnormalities present in FCD produce greater contrast than on other common MRI sequences. 3D-EDGE had a significantly greater contrast ratio between lesion and white matter for FCD compared to MP2RAGE (98% vs 17%; p = 0.0006) and FLAIR (98% vs 19%; p = 0.0006), which highlights its potential to improve outcomes in epilepsy. We present a discussion of the framework for 3D-EDGE, optimization strategies, and analysis of a series of FCDs to highlight the benefit of 3D-EDGE in FCD detection compared to commonly used sequences in epilepsy.

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NeurIPS Conference 2020 Conference Paper

Improving Auto-Augment via Augmentation-Wise Weight Sharing

Keyu Tian
Chen Lin
Ming Sun
Luping Zhou
Junjie Yan
Wanli Ouyang

The recent progress on automatically searching augmentation policies has boosted the performance substantially for various tasks. A key component of automatic augmentation search is the evaluation process for a particular augmentation policy, which is utilized to return reward and usually runs thousands of times. A plain evaluation process, which includes full model training and validation, would be time-consuming. To achieve efficiency, many choose to sacrifice evaluation reliability for speed. In this paper, we dive into the dynamics of augmented training of the model. This inspires us to design a powerful and efficient proxy task based on the Augmentation-Wise Weight Sharing (AWS) to form a fast yet accurate evaluation process in an elegant way. Comprehensive analysis verifies the superiority of this approach in terms of effectiveness and efficiency. The augmentation policies found by our method achieve superior accuracies compared with existing auto-augmentation search methods. On CIFAR-10, we achieve a top-1 error rate of 1. 24%, which is currently the best performing single model without extra training data. On ImageNet, we get a top-1 error rate of 20. 36% for ResNet-50, which leads to 3. 34% absolute error rate reduction over the baseline augmentation.

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JBHI Journal 2019 Journal Article

Empirical Mode Decomposition and Monogenic Signal-Based Approach for Quantification of Myocardial Infarction From MR Images

Thi-Thao Tran
Van-Truong Pham
Chen Lin
Hui-Wen Yang
Yung-Hung Wang
Kuo-Kai Shyu
Wen-Yih Issac Tseng
Mao-Yuan Marine Su

Quantification of myocardial infarction on late Gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) images into heterogeneous infarct periphery (or gray zone) and infarct core plays an important role in cardiac diagnosis, especially in identifying patients at high risk of cardiovascular mortality. However, quantification task is challenging due to noise corrupted in cardiac MR images, the contrast variation, and limited resolution of images. In this study, we propose a novel approach for automatic myocardial infarction quantification, termed DEMPOT, which consists of three key parts: Decomposition of image into intrinsic modes, monogenic phase performing on combined dominant modes, and multilevel Otsu thresholding on the phase. In particular, inspired by the Hilbert–Huang transform, we perform the multidimensional ensemble empirical mode decomposition and 2-D generalization of the Hilbert transform known as the Riesz transform on the MR image to obtain the monogenic phase that is robust to noise and contrast variation. Then, a two-stage algorithm using multilevel Otsu thresholding is accomplished on the monogenic phase to automatically quantify the myocardium into healthy, gray zone, and infarct core regions. Experiments on LGE-CMR images with myocardial infarction from 82 patients show the superior performance of the proposed approach in terms of reproducibility, robustness, and effectiveness.

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AAAI Conference 2019 Conference Paper

Non-Compensatory Psychological Models for Recommender Systems

Chen Lin
Xiaolin Shen
Si Chen
Muhua Zhu
Yanghua Xiao

The study of consumer psychology reveals two categories of consumption decision procedures: compensatory rules and non-compensatory rules. Existing recommendation models which are based on latent factor models assume the consumers follow the compensatory rules, i. e. they evaluate an item over multiple aspects and compute a weighted or/and summated score which is used to derive the rating or ranking of the item. However, it has been shown in the literature of consumer behavior that, consumers adopt non-compensatory rules more often than compensatory rules. Our main contribution in this paper is to study the unexplored area of utilizing non-compensatory rules in recommendation models. Our general assumptions are (1) there are K universal hidden aspects. In each evaluation session, only one aspect is chosen as the prominent aspect according to user preference. (2) Evaluations over prominent and non-prominent aspects are non-compensatory. Evaluation is mainly based on item performance on the prominent aspect. For non-prominent aspects the user sets a minimal acceptable threshold. We give a conceptual model for these general assumptions. We show how this conceptual model can be realized in both pointwise rating prediction models and pair-wise ranking prediction models. Experiments on real-world data sets validate that adopting non-compensatory rules improves recommendation performance for both rating and ranking models.

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IJCAI Conference 2018 Conference Paper

Live Face Verification with Multiple Instantialized Local Homographic Parameterization

Chen Lin
Zhouyingcheng Liao
Peng Zhou
Jianguo Hu
Bingbing Ni

State-of-the-art live face verification methods would easily be attacked by recorded facial expression sequence. This work directly addresses this issue via proposing a patch-wise motion parameterization based verification network infrastructure. This method directly explores the underlying subtle motion difference between the facial movements re-captured from a planer screen (e. g. , a pad) and those from a real face; therefore interactive facial expression is no longer required. Furthermore, inspired by the fact that? a fake facial movement sequence MUST contains many patch-wise fake sequences? , we embed our network into a multiple instance learning framework, which further enhance the recall rate of the proposed technique. Extensive experimental results on several face benchmarks well demonstrate the superior performance of our method.

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NeurIPS Conference 2018 Conference Paper

Synaptic Strength For Convolutional Neural Network

Chen Lin
Zhao Zhong
Wu Wei
Junjie Yan

Convolutional Neural Networks(CNNs) are both computation and memory inten-sive which hindered their deployment in mobile devices. Inspired by the relevantconcept in neural science literature, we propose Synaptic Pruning: a data-drivenmethod to prune connections between input and output feature maps with a newlyproposed class of parameters called Synaptic Strength. Synaptic Strength is de-signed to capture the importance of a connection based on the amount of informa-tion it transports. Experiment results show the effectiveness of our approach. OnCIFAR-10, we prune connections for various CNN models with up to96%, whichresults in significant size reduction and computation saving. Further evaluation onImageNet demonstrates that synaptic pruning is able to discover efficient modelswhich is competitive to state-of-the-art compact CNNs such as MobileNet-V2andNasNet-Mobile. Our contribution is summarized as following: (1) We introduceSynaptic Strength, a new class of parameters for CNNs to indicate the importanceof each connections. (2) Our approach can prune various CNNs with high com-pression without compromising accuracy. (3) Further investigation shows, theproposed Synaptic Strength is a better indicator for kernel pruning compared withthe previous approach in both empirical result and theoretical analysis.

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EAAI Journal 2016 Journal Article

Shape collaborative representation with fuzzy energy based active contour model

Van-Truong Pham
Thi-Thao Tran
Kuo-Kai Shyu
Chen Lin
Pa-Chun Wang
Men-Tzung Lo

This paper presents a fuzzy energy-based active contour model for image segmentation with shape prior based on collaborative representation of training shapes. In the paper, a fuzzy energy functional including a data term and a shape prior term is proposed. The data term relies on image information to guide the evolution of the contour. Meanwhile, the shape prior term constrains the evolving contour with respect to the priori shape to handle background clutter and object occlusion. Especially, in this study, the prior shape is represented as the combination of atoms in the shape dictionary based on collaborative representation. In particular, instead of using ℓ 1 -norm regularization as in sparse representation, we utilize ℓ 2 -regularized linear regression scheme which can obtain algebraic solution for the coding coefficients, and significantly reduces the computation time. The proposed model therefore can segment images with background clutter and object occlusion even when the training set includes shapes with large variation. In addition, the proposed shape collaborative representation model also takes less computational time compared to shape sparse representation approach. Experimental results on various images and comparisons with other models show the desired performances of the proposed model.

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YNIMG Journal 2011 Journal Article

Regional reproducibility of pulsed arterial spin labeling perfusion imaging at 3T

Yang Wang
Andrew J. Saykin
Josef Pfeuffer
Chen Lin
Kristine M. Mosier
Li Shen
Sungeun Kim
Gary D. Hutchins

Arterial spin labeling (ASL) is a promising non-invasive magnetic resonance imaging (MRI) technique for measuring regional cerebral blood flow (rCBF) or perfusion in vivo. To evaluate the feasibility of ASL as a biomarker for clinical trials, it is important to examine test-retest reproducibility. We investigated both inter- and intra-session reproducibility of perfusion MRI using a pulsed ASL (PASL) sequence PICORE Q2TIPS with an echo-planar imaging (EPI) readout. Structural MRI regions of interest (ROIs) were extracted individually by automated parcellation and segmentation methods using FreeSurfer. These cortical and subcortical ROIs were used to assess regional perfusion stability. Our results indicated regional variability in grey matter rCBF. Although rCBF measurements were characterized by intersubject variation, our results also indicated relatively less within-subject variability estimated as within-subject standard deviation (SDW) (intersession SDW: 2. 0 to 8. 8; intrasession SDW: 2. 8 to 9. 6) and acceptable reliabilities as measured using intraclass correlation coefficient (ICC) (intersession ICC: 0. 68 to 0. 94; intrasession ICC: 0. 66 to 0. 95) for regional MRI perfusion measurements using the PICORE Q2TIPS technique. Overall, our findings suggest that PASL is a technique with good within and between session reproducibility. Further reproducibility studies in target populations relevant for specific clinical trials of neurovascular related agents will be important and the present results provide a framework for such assessments.

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