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Peng Lin

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10 papers

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EAAI Journal 2026 Journal Article

A new mineral quantification method via experiment-enhanced transfer learning of linear mixed mid-infrared spectra data

Tao Han
Tengfei Yu
Peng Lin
Wen Ma
Zhenhao Xu

Accurate quantitative mineral analysis is fundamental for understanding geological evolution, predicting rock mechanical behavior, and delineating exploration targets. Mid-infrared spectroscopy offers rapid, non-destructive acquisition of mineralogical information. However, the application of mid-infrared spectroscopy in mineral quantification is still hindered by strong spectral nonlinearity caused by intimate mineral mixtures and the challenges of spectral testing in complex geological conditions. To address these limitations, we proposed a new mineral quantification method via experiment-enhanced transfer learning of linear mixed mid-infrared spectra data (MQM-ExpTL). The proposed method integrates submodel internal transfer and comprehensive fine-tuning strategies to construct a deep learning model capable of robustly quantifying protolith and clay minerals while capturing and interpreting non-linear mixing effects. On the independent test set, our method significantly outperformed support vector regression (SVR), partial least squares regression (PLSR), competitive adaptive reweighted sampling-support vector regression (CARS-SVR), competitive adaptive reweighted sampling-partial least squares regression (CARS-PLSR), the standard transfer learning model, and the model without transfer learning across coefficient of determination (R2), mean squared error (MSE), and mean absolute error (MAE). Specifically, our method achieves an overall prediction accuracy of R2 = 0. 98, yielding an improvement of 0. 9% to 32% over the comparative methods. Furthermore, we elucidated the rationale for adopting the validation loss for hyperparameter optimization and examined the differences in model selection when employing various globally optimal model selection criteria. The method provides an efficient quantitative approach to support the application of mid-infrared spectroscopy in remote sensing interpretation, geological identification, and mineral exploration.

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AAAI Conference 2026 Conference Paper

Elite Pattern Reinforcement for Vehicle Routing Problems

Ning Li
Peng Lin
Peng Zhang
Ruichen Tian

Machine learning methods have been increasingly applied to solve Vehicle Routing Problems (VRPs). A high-efficiency approach is to learn solution construction using deep neural networks. However, their tendency toward premature convergence is a critical barrier, severely hindering generalization across diverse distributions and scales. To overcome this, we introduce Elite-Pattern Reinforcement (EPR), a novel strategy designed to create a synergy between the diverse, exploratory nature of reinforcement learning and the high-quality, structured knowledge from classical heuristics. The strategy guides the learning process by reinforcing structural patterns from elite solutions, employing an elite-guided score modulation to integrate this external knowledge. The inherent symmetry of path patterns is also exploited to augment the structural information. This steers the policy away from premature convergence by enabling it to distinguish and favour elite path patterns over inferior ones. Integrating our strategy with four construction methods yields substantial performance improvements on the CVRPLIB and TSPLIB benchmarks. Furthermore, our approach outperforms state-of-the-art learning-based methods, demonstrating superior generalization across diverse distributions and scales.

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AAAI Conference 2026 Conference Paper

S³: Spiking Neurons as an Isolating Segmenter for Brain Signal Decoding

Qian Zheng
Ming Chen
Sha Zhao
Shi Gu
Peng Lin
De Ma
Huajin Tang
Gang Pan

Recent brain decoding studies have primarily emphasized the development of brain decoders, while largely neglecting the segmentation step. Existing methods typically adopt fixed-length segmentation, which might overlook subject- or task-level variability and disrupt temporal patterns within brain signals. To address this gap, we propose S3, which leverages spiking neurons as an isolating segmenter for brain signal decoding. S3 segments brain signals adaptively, considering subject- and task-level variability while preserving intrinsic temporal patterns of brain signals. It exploits the unique reset mechanism of spiking neurons to isolate previous irrelevant temporal patterns during the generation of each segmentation point. To optimize S3 for enhancing task performance in the absence of segmentation labels, we develop an optimization method where segmentation pseudo-labels are created with a stochastic-greedy algorithm to optimize them, while circumventing gradient blockade between S3 and task performance. Experiments on 10 downstream tasks across 13 public datasets demonstrate that S3 consistently outperforms existing methods, validating its effectiveness, generalizability and interpretability.

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EAAI Journal 2025 Journal Article

A Bayesian-physical informed conditional tabular generative adversarial network framework for low-carbon concrete data augmentation and hyperparameter optimization

Shiqi Wang
Peng Xia
Fuyuan Gong
Yuxi Zhao
Peng Lin

Data shortage, unbalanced data distribution and multi-factor coupling mechanism of materials all increase the difficulty of design. This paper proposed a physical constraint-conditional generative adversarial network (PI-CTGAN) to solve the above problems. Firstly, residual layers are added to the generator to enhance model stability. The continuous differentiable function and wasserstein_distance were constructed to embed physical loss functions into the generator, including water-cement ratio, supplementary cementitious materials (SCMs) ratio, and aggregate water absorption ratio. Based on this, Bayesian optimization (BO) was used to optimize the hyperparameters of PI-CTGAN. The results showed that BO effectively optimized the model's hyperparameters, reducing the total of Kolmogorov-Smirnov distribution (K-Stot) of the generated dataset by 27. 9 %. Additionally, applying physical loss to the optimized model can improve the model's data recognition capability, with generation accuracy increasing by 16. 2 %. The influence of physical weight (WPI) and activation functions on data generation quality was compared. Revealing that K-Stot initially decreased and then increased with WPI. The model using the rectified linear unit exhibited the best generation accuracy, with a K-Stot of 0. 37 and anomaly data ratios (water-binder ratio and supplementary cementitious materials/binder ratio) of 11. 67 % and 3. 2 %, respectively. The generated data and the experimental data show statistical similarity and conform to the physical law. By constructing the target dataset and related physical constraints, the proposed PI-CTGAN can effectively solve the issues of multi-source data sets with data shortage and imbalance, thereby providing numerous datasets to guide engineering design.

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AIJ Journal 2025 Journal Article

Local-MIP: Efficient local search for mixed integer programming

Peng Lin
Shaowei Cai
Mengchuan Zou
Jinkun Lin

Mixed Integer Programming (MIP) is a fundamental model in operations research with broad industrial applications. Local search is a powerful methodology for solving complex optimization problems; however, the development of local search algorithms for MIP still needs exploration. In this work, we propose Local-MIP, an efficient local search algorithm tailored for MIP that integrates novel operators and employs a two-mode architecture to adaptively apply operators based on the current solution's feasibility. For the feasible mode, we propose the lift move operator and a corresponding lift process to improve the objective value while maintaining feasibility. For the infeasible mode, we propose the breakthrough move and mixed tight move operators to respectively optimize the objective function and satisfy constraints. To apply operators intelligently, we develop a dynamic weighting scheme that balances the priorities of the objective function and constraints. Furthermore, we propose a two-level scoring function structure that hierarchically selects operations, guiding the search toward high-quality feasible solutions. Experiments are conducted on public benchmarks to compare Local-MIP with state-of-the-art MIP solvers in finding high-quality solutions. The results show that Local-MIP significantly outperforms CPLEX, HiGHS, SCIP, and Feasibility Jump while remaining competitive with the commercial solver Gurobi on challenging problems within short time limits. Moreover, Local-MIP establishes 10 new records on MIPLIB open instances.

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IJCAI Conference 2024 Conference Paper

ParaILP: A Parallel Local Search Framework for Integer Linear Programming with Cooperative Evolution Mechanism

Peng Lin
Mengchuan Zou
Zhihan Chen
Shaowei Cai

The integer linear programming (ILP) problem is a fundamental research topic in operations research, and the local search method is an important class of algorithms for quickly solving many combinatorial optimization problems. With rapidly increasing computing power, parallelism turns out to be a promising approach to enhancing the efficiency of problem-solving. However, rare studies investigate parallel local search algorithms for solving the general ILP problem. We propose the first parallel local search framework (ParaILP) for solving the general ILP problem, based on two novel ideas: a new initialization method named polarity initialization to construct different initial solutions for local search threads and a cooperative evolution mechanism for managing and generating high-quality solutions using information shared by different threads. Extensive experiments demonstrate that ParaILP is significantly better than the state-of-the-art academic parallel solvers FiberSCIP and HiGHS, and is competitive with the state-of-the-art commercial parallel solver Gurobi. Experiments are also conducted to analyze the parallelization scalability and the effectiveness of our techniques.

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IJCAI Conference 2023 Conference Paper

Black-Box Data Poisoning Attacks on Crowdsourcing

Pengpeng Chen
Yongqiang Yang
Dingqi Yang
Hailong Sun
Zhijun Chen
Peng Lin

Understanding the vulnerability of label aggregation against data poisoning attacks is key to ensuring data quality in crowdsourced label collection. State-of-the-art attack mechanisms generally assume full knowledge of the aggregation models while failing to consider the flexibility of malicious workers in selecting which instances to label. Such a setup limits the applicability of the attack mechanisms and impedes further improvement of their success rate. This paper introduces a black-box data poisoning attack framework that finds the optimal strategies for instance selection and labeling to attack unknown label aggregation models in crowdsourcing. We formulate the attack problem on top of a generic formalization of label aggregation models and then introduce a substitution approach that attacks a substitute aggregation model in replacement of the unknown model. Through extensive validation on multiple real-world datasets, we demonstrate the effectiveness of both instance selection and model substitution in improving the success rate of attacks.

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JAIR Journal 2020 Journal Article

Improved High Dimensional Discrete Bayesian Network Inference using Triplet Region Construction

Peng Lin
Martin Neil
Norman Fenton

Performing efficient inference on high dimensional discrete Bayesian Networks (BNs) is challenging. When using exact inference methods the space complexity can grow exponentially with the tree-width, thus making computation intractable. This paper presents a general purpose approximate inference algorithm, based on a new region belief approximation method, called Triplet Region Construction (TRC). TRC reduces the cluster space complexity for factorized models from worst-case exponential to polynomial by performing graph factorization and producing clusters of limited size. Unlike previous generations of region-based algorithms, TRC is guaranteed to converge and effectively addresses the region choice problem that bedevils other region-based algorithms used for BN inference. Our experiments demonstrate that it also achieves significantly more accurate results than competing algorithms.

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NeurIPS Conference 2016 Conference Paper

Infinite Hidden Semi-Markov Modulated Interaction Point Process

matt zhang
Peng Lin
Ting Guo
Yang Wang
Fang Chen

The correlation between events is ubiquitous and important for temporal events modelling. In many cases, the correlation exists between not only events' emitted observations, but also their arrival times. State space models (e. g. , hidden Markov model) and stochastic interaction point process models (e. g. , Hawkes process) have been studied extensively yet separately for the two types of correlations in the past. In this paper, we propose a Bayesian nonparametric approach that considers both types of correlations via unifying and generalizing hidden semi-Markov model and interaction point process model. The proposed approach can simultaneously model both the observations and arrival times of temporal events, and determine the number of latent states from data. A Metropolis-within-particle-Gibbs sampler with ancestor resampling is developed for efficient posterior inference. The approach is tested on both synthetic and real-world data with promising outcomes.

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AAAI Conference 2016 Conference Paper

Interaction Point Processes via Infinite Branching Model

Peng Lin
Bang Zhang
Ting Guo
Yang Wang
Fang Chen

Many natural and social phenomena can be modeled by interaction point processes (IPPs) (Diggle et al. 1994), stochastic point processes considering the interaction between points. In this paper, we propose the inﬁnite branching model (IBM), a Bayesian statistical model that can generalize and extend some popular IPPs, e. g. , Hawkes process (Hawkes 1971; Hawkes and Oakes 1974). It treats IPP as a mixture of basis point processes with the aid of a distance dependent prior over branching structure that describes the relationship between points. The IBM can estimate point event intensity, interaction mechanism and branching structure simultaneously. A generic Metropolis-within-Gibbs sampling method is also developed for model parameter inference. The experiments on synthetic and real-world data demonstrate the superiority of the IBM.

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