Selected work

Representative Papers

Bayesian Event-Based Model for Disease Subtype and Stage Inference

Dec 03, 2025

Existing models for chronic disease subtyping and progression staging suffer from limited robustness due to model misspecification. Method: We propose a Bayesian Event Sequence Model (BESM) that jointly infers disease subtypes, individualized event ordering, and patient-stage assignments within a Bayesian framework—balancing robustness with biological interpretability. By incorporating structured priors and rigorous uncertainty quantification, BESM improves tolerance to model misspecification relative to the widely used SuStaIn model. Contribution/Results: On synthetic data, BESM consistently outperforms SuStaIn in both subtype classification accuracy and event sequence recovery fidelity. In a real-world Alzheimer’s disease cohort, BESM-derived subtypes and progression pathways align more closely with established neuropathological consensus—yielding biologically plausible staging trajectories and mechanistic insights. Thus, BESM provides a robust, interpretable tool for precision staging and pathophysiological dissection of neurodegenerative disorders.

1 citationsRead paper

Spatio-Temporal Scheduling for Robust and Efficient Multi-Transmitter Wireless Power Transfer

Aug 10, 2026

This work addresses the challenge of achieving both efficiency and robustness in multi-user wireless power transfer under time-varying channels, where conventional single-transmitter time-division scheduling falls short. The authors propose a spatiotemporal joint scheduling approach that leverages coordinated multi-transmitter beamforming to simultaneously optimize temporal and spatial resource allocation. By incorporating a nonlinear rectenna model and strategically exploiting inter-cluster interference as a performance-enhancing factor, the method effectively adapts to dynamic channel conditions. Experimental results demonstrate that the proposed scheme significantly improves both the efficiency and stability of energy delivery, particularly in complex propagation environments characterized by shadow fading and other channel impairments.

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Diagnosing High-Performance BFT Consensus via Mixture Modeling of Block Time Distributions

Aug 03, 2026

This work addresses the challenge of diagnosing the impact of network dynamics and deployment heterogeneity in high-performance Byzantine Fault Tolerant (BFT) consensus systems, where block time distributions often exhibit complex multimodal characteristics. The study introduces, for the first time, a mixture distribution model into BFT performance analysis within a quorum multicast framework, decomposing inter-block intervals into components that reflect distinct network conditions. By integrating core distribution fitting with tail asymptotic analysis, the approach enables fine-grained characterization of validator deployment heterogeneity and communication path diversity. Empirical evaluation on the Hyperliquid and Aptos mainnets reveals that Hyperliquid exhibits a unimodal block time distribution, whereas Aptos displays pronounced multimodality; furthermore, consensus protocol upgrades induce significant distributional shifts, thereby validating the method’s diagnostic efficacy and analytical precision.

0 citationsRead paper

Scalable Training of Continuous-Time Spiking Neural Networks with Differentiable Spike-Time Discretization

Jul 16, 2026

This work addresses the prohibitive memory overhead of deep continuous-time spiking neural networks (SNNs), which arises from the precise tracking of spike timings and hinders scalability. To overcome this limitation, the authors propose a Differentiable Spike Time Discretization (DSTD) framework that maps irregular presynaptic spikes onto fixed time steps as differentiable weighted events, accurately approximating continuous membrane potential dynamics while drastically reducing memory consumption. By integrating the leaky integrate-and-fire (LIF) neuron model, time-to-first-spike (TTFS) encoding, and a temporal regularization mechanism inspired by synchronous firing chains, the approach effectively mitigates neuronal death and enables pipeline-like training. Experiments demonstrate successful training of a 9-layer CIFAR-10 and a 20-layer Fashion-MNIST convolutional SNN on a single GPU, achieving approximately 100× lower peak memory usage and 20× faster training speed.

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On the Correctness of Software Merge

Jul 08, 2026

This work addresses the lack of rigorous correctness criteria in existing three-way merge tools, which often produce syntactically invalid outputs or lose edits. It formally defines merge correctness for software artifacts based on two syntactic properties—parsability and generality—and leverages the categorical notion of pushout to precisely characterize merge semantics. Guided by this formalism, the authors develop a structured merge tool that achieves zero syntactic errors across 43,774 Java file merge scenarios, substantially outperforming mainstream tools such as Git. Experimental comparisons against developer-performed manual merges and cases involving refactorings further validate the approach’s effectiveness and delineate its practical applicability boundaries.

0 citationsRead paper

Recent publications

Latest Papers

Spatio-Temporal Scheduling for Robust and Efficient Multi-Transmitter Wireless Power Transfer

Aug 10, 2026

This work addresses the challenge of achieving both efficiency and robustness in multi-user wireless power transfer under time-varying channels, where conventional single-transmitter time-division scheduling falls short. The authors propose a spatiotemporal joint scheduling approach that leverages coordinated multi-transmitter beamforming to simultaneously optimize temporal and spatial resource allocation. By incorporating a nonlinear rectenna model and strategically exploiting inter-cluster interference as a performance-enhancing factor, the method effectively adapts to dynamic channel conditions. Experimental results demonstrate that the proposed scheme significantly improves both the efficiency and stability of energy delivery, particularly in complex propagation environments characterized by shadow fading and other channel impairments.

0 citationsRead paper

Diagnosing High-Performance BFT Consensus via Mixture Modeling of Block Time Distributions

Aug 03, 2026

This work addresses the challenge of diagnosing the impact of network dynamics and deployment heterogeneity in high-performance Byzantine Fault Tolerant (BFT) consensus systems, where block time distributions often exhibit complex multimodal characteristics. The study introduces, for the first time, a mixture distribution model into BFT performance analysis within a quorum multicast framework, decomposing inter-block intervals into components that reflect distinct network conditions. By integrating core distribution fitting with tail asymptotic analysis, the approach enables fine-grained characterization of validator deployment heterogeneity and communication path diversity. Empirical evaluation on the Hyperliquid and Aptos mainnets reveals that Hyperliquid exhibits a unimodal block time distribution, whereas Aptos displays pronounced multimodality; furthermore, consensus protocol upgrades induce significant distributional shifts, thereby validating the method’s diagnostic efficacy and analytical precision.

0 citationsRead paper

Scalable Training of Continuous-Time Spiking Neural Networks with Differentiable Spike-Time Discretization

Jul 16, 2026

This work addresses the prohibitive memory overhead of deep continuous-time spiking neural networks (SNNs), which arises from the precise tracking of spike timings and hinders scalability. To overcome this limitation, the authors propose a Differentiable Spike Time Discretization (DSTD) framework that maps irregular presynaptic spikes onto fixed time steps as differentiable weighted events, accurately approximating continuous membrane potential dynamics while drastically reducing memory consumption. By integrating the leaky integrate-and-fire (LIF) neuron model, time-to-first-spike (TTFS) encoding, and a temporal regularization mechanism inspired by synchronous firing chains, the approach effectively mitigates neuronal death and enables pipeline-like training. Experiments demonstrate successful training of a 9-layer CIFAR-10 and a 20-layer Fashion-MNIST convolutional SNN on a single GPU, achieving approximately 100× lower peak memory usage and 20× faster training speed.

0 citationsRead paper

On the Correctness of Software Merge

Jul 08, 2026

This work addresses the lack of rigorous correctness criteria in existing three-way merge tools, which often produce syntactically invalid outputs or lose edits. It formally defines merge correctness for software artifacts based on two syntactic properties—parsability and generality—and leverages the categorical notion of pushout to precisely characterize merge semantics. Guided by this formalism, the authors develop a structured merge tool that achieves zero syntactic errors across 43,774 Java file merge scenarios, substantially outperforming mainstream tools such as Git. Experimental comparisons against developer-performed manual merges and cases involving refactorings further validate the approach’s effectiveness and delineate its practical applicability boundaries.

0 citationsRead paper

Microcosmos: Reimagining Artificial Life for the GPU Era

Jul 03, 2026

Existing approaches struggle to support large-scale artificial life evolution while preserving physical realism. This work proposes a GPU-optimized, differentiable simulation engine that embeds elastic filament chains within a two-dimensional viscous fluid, uniquely integrating end-to-end differentiable fluid dynamics, neuroevolution, and quality-diversity search for the first time. The resulting framework ensures physical consistency, full differentiability, and linear scalability, enabling efficient generation of diverse swimming and chemotactic behaviors. By combining these capabilities, the system establishes a novel paradigm for conducting large-scale open-ended evolutionary experiments with strong grounding in real-world physics.

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