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Weijun Chen

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4 papers

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TCS Journal 2026 Journal Article

A unifying approach to probabilistic testing equivalences

Weijun Chen
Yuxi Fu
Huan Long
Hao Wu

Probabilistic concurrent systems are foundational models for modern mobile computing. In this paper, a unifying approach to probabilistic testing equivalences is proposed. With the help of a new distribution-based semantics for probabilistic models and a probabilistic testing framework with respect to process predicates, the internal characterization and the external characterization for testing equivalences are studied. The latter characterization can be viewed as the generalization of the classical fair/should equivalence and may equivalence. These equivalences are shown to be congruences. A thorough comparison between these equivalences and probabilistic bisimilarities is carried out. The techniques introduced in this paper can be easily extended to other probabilistic concurrent models. To showcase this flexibility, a case study is carried out on the pCSP model.

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AAAI Conference 2025 Conference Paper

DHMoE: Diffusion Generated Hierarchical Multi-Granular Expertise for Stock Prediction

Weijun Chen
Yanze Wang

Stock prediction stands as a pivotal research objective within the Fintech. Existing deep learning research revolves around the development and scaling of one individual neural network predictor. However, in the dynamic and noisy landscape of the stock market, reliance solely on a single predictor poses risks of limited adaptability to diverse market conditions and challenges in effectively integrating multi-source information. Besides, top-down teaching and bottom-up hierarchical decision-making paradigms are critical for robust and accurate stock prediction within successful quantitative firms. Nonetheless, there is scarcely any research that integrates this workflow into stock prediction. To this end, we propose Diffusion Generated Hierarchical Mixture-of-Experts (DHMoE) to emulate such workflow in stock prediction. Specifically, DHMoE is crafted as a three-layer tree structure, where each expert functions as a node within the tree and their parameters are generated in a top-down, recursive manner. Recognizing the leading role of the top-level root expert, we harness the robust capabilities of diffusion models for generating and introduce the Diffusion Inverted Transformer (DIT) as the root expert. The DIT is tailored to receive information from various modalities as conditional inputs and allocate parameters to bottom-level experts. These bottom-level experts are responsible for performing predictions specific to their respective input modalities. The prediction results are then synthesized in a bottom-up manner, culminating in the final prediction outcomes. Experiments on three stock trading datasets reveal that DHMoE outperforms state-of-the-art methods in terms of both cumulative and risk-adjusted returns.

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IJCAI Conference 2024 Conference Paper

Automatic De-Biased Temporal-Relational Modeling for Stock Investment Recommendation

Weijun Chen
Shun Li
Xipu Yu
Heyuan Wang
Wei Chen
Tengjiao Wang

Stock investment recommendation is crucial for guiding investment decisions and managing portfolios. Recent studies have demonstrated the potential of temporal-relational models (TRM) to yield excess investment returns. However, in the complicated finance ecosystem, the current TRM suffer from both the intrinsic temporal bias from the low signal-to-noise ratio (SNR) and the relational bias caused by utilizing inappropriate relational topologies and propagation mechanisms. Moreover, the distribution shifts behind macro-market scenarios invalidate the underlying i. i. d. assumption and limit the generalization ability of TRM. In this paper, we pioneer the impact of the above issues on the effective learning of temporal-relational patterns and propose an Automatic De-Biased Temporal-Relational Model (ADB-TRM) for stock recommendation. Specifically, ADB-TRM consists of three main components, i. e. , (i) a meta-learned architecture forms a dual-stage training process, with the inner part ameliorating temporal-relational bias and the outer meta-learner counteracting distribution shifts, (ii) automatic adversarial sample generation guides the model adaptively to alleviate bias and enhance its profiling ability through adversarial training, and (iii) global-local interaction helps seek relative invariant stock embeddings from local and global distribution perspectives to mitigate distribution shifts. Experiments on three datasets from distinct stock markets show that ADB-TRM excels state-of-the-arts over 28. 41% and 9. 53% in terms of cumulative and risk-adjusted returns.

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YNIMG Journal 2021 Journal Article

Diffusion MRI of the infant brain reveals unique asymmetry patterns during the first-half-year of development

Tingting Liu
Fusheng Gao
Weihao Zheng
Yuqing You
Zhiyong Zhao
Ying Lv
Weijun Chen
Hongxi Zhang

The human brain demonstrates anatomical and functional lateralization/asymmetry between the left and right hemispheres, and such asymmetry is known to start from the early age of life. However, how the asymmetry changes with brain development during infancy remained unknown. In this study, we aimed to systematically investigate the spatiotemporal pattern of brain asymmetry in healthy preterm-born infants during the first-half-year of development, using high angular resolution diffusion MRI. Sixty-five healthy preterm-born infants (gestational age between 25.3-36.6 weeks) were scanned with postmenstrual age (PMA) ranging from term-equivalent age (TEA) to 6-months. At the regional level, we performed a region-of-interest-based analysis by segmenting the brain into 63 symmetrical pairs of regions, based on which the laterality index was assessed and correlated with PMA. At the voxel level, we performed a fixel-based analysis of each fiber component between the native and left-right flipped data, separately in TEA-1 month, 1-3 months, and 3-6 months groups. The infant brains demonstrated extensive regions with structural asymmetry during their first half-of-year of life. A distinct central-peripheral asymmetry pattern was observed in mean diffusivity, namely, leftward lateralization in the neocortex and rightward asymmetry in the deep brain regions. Besides, the posterior brain demonstrated a higher lateralization index compared with the anterior brain in all metrics, which is congruent with the brain developmental pattern from caudal to rostral. Regionally, language processing regions showed a rightward asymmetry, while visuospatial processing regions exhibited leftward lateralization in fractional anisotropy, fibre density, and fibre cross-section measurements, and most white matter regions were lateralized to the left in these measurements. The laterality index of several regions (12 out 63) demonstrated significant developmental changes in mean diffusivity. At the fixel level, the fiber cross-section of inferior fronto-occipital fasciculus showed significant leftward asymmetry and the extent of asymmetry increased with PMA. In summary, the results revealed unique spatiotemporal patterns of macro- and micro-structural asymmetry in early life, which dynamically changed with age. These findings may contribute to the understanding of brain development during infancy.

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