arXiv subjects

Jun Wen

Publications and source records attributed to Jun Wen.

At least 19 recordsLinked to original sources

arXiv2026

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields

Compressible physical fields are widely present in the real physical world, but current artificial intelligence lacks an understanding mechanism for the non-differentiable features in compressible physical fields. Addressing the limitations of existing deep learning architectures in handling global non-differentiable features, we propose the Inverse Low-Dimensional Manifold reconstruction framework (ILDM). This framework couples the Non-differentiable Approximation Function (NAF) for capturing non-differentiable features in compressible flows with the Smooth Fluid Reconstruction (SFR) module tailored for smooth fluid regions. Extensive evaluations across 1D and 2D benchmarks, including Riemann problems and double Mach reflection, demonstrate that ILDM significantly outperforms cPINN and R-adaptive DeepONet. Specifically, ILDM achieves superior localization of non-differentiable interfaces and maintains robust super-resolution performance even with low-resolution inputs, establishing a physically consistent and scalable paradigm for data-driven fluid dynamics.

physics.comp-ph↗

arXiv2026

A Free Sphere Reverses the Rebound Direction of a Near-Wall Cavitation Bubble

A near-wall cavitation bubble is generally expected to acquire a wallward Kelvin-impulse bias and to rebound or jet toward the wall. Here we show that this canonical direction can be reversed by a wall-supported free sphere. High-speed imaging reveals a transition from away-from-wall to wallward rebound as the initial bubble--sphere separation is increased. By reconstructing the Kelvin impulse on a closed bubble boundary that includes both the visible free interface and the bubble-side contact closure, we find that the reversal is not governed primarily by the instantaneous velocity of the sphere. Instead, sphere displacement creates a contact closure on which the bubble-source contribution supplies an away-from-wall impulse. This contact-source impulse competes with a wallward background formed by the wall-image source and the quadrupolar component of the sphere-induced field. The resulting balance yields a calibrated geometric criterion, $\mathcal{M}_K$, and, in the comparable-size bubble--sphere regime, reduces to a contact number $a_z z_b/R_K^2$. These results identify a contact-geometric mechanism by which a movable particle can redirect the first-cycle jet and rebound bias of a near-wall cavitation bubble.

physics.flu-dyn↗

arXiv2026

RADAR: Redundancy-Aware Diffusion for Multi-Agent Communication Structure Generation

Compared with individual agents, large language model based multi-agent systems have shown great capabilities consistently across diverse tasks, including code generation, mathematical reasoning, and planning, etc. Despite their impressive performance, the effectiveness and robustness of these systems heavily rely on their communication topology, which is often fixed or generated in a single step. This restricts fine-grained structural exploration and flexible composition, resulting in excessive token utilization on simple tasks while limiting capability on complicated tasks. To mitigate this challenge, we introduce RADAR, a redundancy-aware and query-adaptive generative framework that actively reduce communication overhead. Motivated by recent progress in conditional discrete graph diffusion models, we formulate communication topology design as a step-by-step generation process, guided by the effective size of the graph. Comprehensive experiments on six benchmarks demonstrate that RADAR consistently outperforms recent baselines, achieving higher accuracy, lower token consumption, and greater robustness across diverse scenarios. Our code and data are available at https://github.com/cszhangzhen/RADAR.

cs.AI↗

arXiv2025

HyperADRs: A Hierarchical Hypergraph Framework for Drug-Gene-ADR Prediction

Adverse drug reactions (ADRs) are a major barrier to safe and effective pharmacotherapy and increasingly reflect higher order interactions between drugs, genetic background, and clinical phenotypes. Existing graph based approaches usually predict ADRs as properties of drugs or drug pairs, leaving the causal gene implicit and limiting their value for pharmacogenomic decision making. We introduce HyperADRs, a hierarchical hypergraph framework that predicts ADR risk at the level of drug-gene-ADR triads. Starting from curated pharmacogenomic annotations in PharmGKB and the pharmacogenomics subdatabase of DrugBank, we construct high confidence triplets and integrate them with auxiliary molecular, functional, and disease relations from precision-medicine-oriented knowledge graphs. Drugs, genes, and ADR concepts are embedded with modality appropriate pretrained models (UniMol, ESM2, SapBERT) and propagated through a hypergraph convolutional network. A FiLM based, query conditioned contrastive learning module learns context specific representations so that, given any two entities, the model retrieves the correct third entity against many candidates. To improve robustness and interpretability, we propose a nine category ADR macro system scheme that reduces large heterogeneous "other" bins while aligning with organ system reasoning in clinical pharmacology. Across drug-, gene-, and ADR-held-out evaluations on PharmGKB, HyperADRs matches or exceeds strong baselines on ranking based metrics. When trained on PharmGKB and tested on unseen DrugBank triplets, HyperADRs maintains its ranking advantage, indicating that the learned representations capture transferable biological mechanisms and can support mechanistically grounded pharmacogenomic hypothesis generation.

q-bio.QM↗

arXiv2025

SynCell: Contextualized Drug Synergy Prediction

Drug synergy is profoundly influenced by cellular context, as variations in protein interaction landscapes and pathway activities across cell types reshape how drugs act in combination. Most existing models overlook this heterogeneity, relying on static or bulk-level protein-protein interaction (PPI) networks that ignore cell-specific molecular wiring. The availability of large-scale transcriptomic data now enables the reconstruction of cell-line-resolved interactomes, offering a new foundation for contextualized drug synergy modeling. Here we present SynCell, a Contextualized Drug Synergy framework that integrates drug-protein, protein-protein, and protein-cell line relations within a unified graph architecture. SynCell leverages cell-line-specific PPI networks to embed the molecular context in which drugs act, and employs graph convolutional learning to model how pharmacological effects propagate through cell-specific signaling networks. This formulation treats synergy prediction as a cell-line-contextualized drug-drug interaction problem. Across the large-scale DrugCombDB benchmark, SynCell consistently outperforms state-of-the-art baselines - including DeepSynergy, HypergraphSynergy, HERMES, BAITSAO, DTF, and NHP - particularly in predicting synergies involving unseen drugs or novel cell lines. When benchmarked against these seven methods, SynCell demonstrates substantial gains in generalization and biological interpretability, confirming that contextualizing PPIs with cell-line resolution is indispensable for accurate synergy prediction.

q-bio.QM↗

arXiv2025

Traj-CoA: Patient Trajectory Modeling via Chain-of-Agents for Lung Cancer Risk Prediction

Large language models (LLMs) offer a generalizable approach for modeling patient trajectories, but suffer from the long and noisy nature of electronic health records (EHR) data in temporal reasoning. To address these challenges, we introduce Traj-CoA, a multi-agent system involving chain-of-agents for patient trajectory modeling. Traj-CoA employs a chain of worker agents to process EHR data in manageable chunks sequentially, distilling critical events into a shared long-term memory module, EHRMem, to reduce noise and preserve a comprehensive timeline. A final manager agent synthesizes the worker agents' summary and the extracted timeline in EHRMem to make predictions. In a zero-shot one-year lung cancer risk prediction task based on five-year EHR data, Traj-CoA outperforms baselines of four categories. Analysis reveals that Traj-CoA exhibits clinically aligned temporal reasoning, establishing it as a promisingly robust and generalizable approach for modeling complex patient trajectories. Implementation of Traj-CoA is available on https://github.com/zengsihang/Traj-CoA.

cs.AI↗

arXiv2025

Benchmarking and Mitigating Sycophancy in Medical Vision Language Models

Visual language models (VLMs) have the potential to transform medical workflows. However, the deployment is limited by sycophancy. Despite this serious threat to patient safety, a systematic benchmark remains lacking. This paper addresses this gap by introducing a Medical benchmark that applies multiple templates to VLMs in a hierarchical medical visual question answering task. We find that current VLMs are highly susceptible to visual cues, with failure rates showing a correlation to model size or overall accuracy. we discover that perceived authority and user mimicry are powerful triggers, suggesting a bias mechanism independent of visual data. To overcome this, we propose a Visual Information Purification for Evidence based Responses (VIPER) strategy that proactively filters out non-evidence-based social cues, thereby reinforcing evidence based reasoning. VIPER reduces sycophancy while maintaining interpretability and consistently outperforms baseline methods, laying the necessary foundation for the robust and secure integration of VLMs.

cs.CV↗

arXiv2025

TrajSurv: Learning Continuous Latent Trajectories from Electronic Health Records for Trustworthy Survival Prediction

Trustworthy survival prediction is essential for clinical decision making. Longitudinal electronic health records (EHRs) provide a uniquely powerful opportunity for the prediction. However, it is challenging to accurately model the continuous clinical progression of patients underlying the irregularly sampled clinical features and to transparently link the progression to survival outcomes. To address these challenges, we develop TrajSurv, a model that learns continuous latent trajectories from longitudinal EHR data for trustworthy survival prediction. TrajSurv employs a neural controlled differential equation (NCDE) to extract continuous-time latent states from the irregularly sampled data, forming continuous latent trajectories. To ensure the latent trajectories reflect the clinical progression, TrajSurv aligns the latent state space with patient state space through a time-aware contrastive learning approach. To transparently link clinical progression to the survival outcome, TrajSurv uses latent trajectories in a two-step divide-and-conquer interpretation process. First, it explains how the changes in clinical features translate into the latent trajectory's evolution using a learned vector field. Second, it clusters these latent trajectories to identify key clinical progression patterns associated with different survival outcomes. Evaluations on two real-world medical datasets, MIMIC-III and eICU, show TrajSurv's competitive accuracy and superior transparency over existing deep learning methods.

cs.LG↗

arXiv2025

A continuous variable quantum battery with wireless and remote charging

Quantum battery has become one of the hot issues at the research frontiers of quantum physics recently. Charging power, extractable work and wireless charging over long-distance are three important aspects of interest. Non-contact electromagnetic interaction provides an important avenue for wireless charging. In this paper, we design a wireless and remote charging scheme based on the quantized Hamiltonian of two coupled LC circuits, and focus on the charging dynamics of a continuous variable quantum battery. It is found that the quantum entanglement, which is regarded as a significant quantum resource, is not a prerequisite for charging the battery and extracting useful work. On the contrary, all of the energy in the battery could be converted into useful work in the absence of the entanglement for our model. The often overlooked counter-rotating wave coupling in the interacting Hamiltonian is helpful for extracting more useful work from the charger. If the coupling between the battery and the charger is designed to contain both the rotating and counter-rotating wave couplings, the extractable work can be obtained even if the charger is prepared in thermal states, while such effect cannot be achieved in the presence of a rotating or counter-rotating wave coupling alone. Finally, the effect of the system's parameters on the extractable work is discussed, which will provide a reference for full use of the thermal state energy.

quant-ph↗

arXiv2025

Distributed Satellites Dynamic Allocation for Grids with Time Windows: A Potential Game Approach

The allocation of tasks to a large number of distributed satellites is a difficult problem owing to dynamic changes in massive tasks and the complex matching of tasks to satellites. To reduce the complexity of the problem, tasks that are geographically close can be divided into a predefined grid with a specific time window and processed together. The problem then becomes a dynamic grid with time-window allocation problem (DGAP). To ensure consistent visibility between satellites and grids, the timeline of the DGAP is partitioned into several decision-making stages that are determined by dynamic changes in the time window. Subsequently, the DGAP can be resolved progressively adopting the potential game approach in the single-stage DGAP (sDGAP). First, to solve the discontinuity in the goal of the sDGAP, we approximate the goal by a smooth exponential sum function that we regard as the global utility function. Second, a potential game theoretic framework is constructed by decomposing this global utility function into the local utility functions of individuals. We prove that each Nash equilibrium of the proposed potential game is the optimal solution of the sDGAP. Third, to solve the potential game, a distributed algorithm, referred to as the selective time-variant better reply process (SeTVBRP) algorithm, is proposed and its convergence is proved. The SeTVBRP algorithm is an improved algorithm based on the better reply process algorithm, where two improvement methods (i.e., the selective action method and time-variant parameter method) are introduced. Through factor analysis, we demonstrate the effectiveness of the two improvement methods for the sDGAP. Last, numerical results show that the proposed algorithm outperforms existing learning algorithms and is effective in solving the DGAP.

cs.GT↗

arXiv2025

Dicke-Ising quantum battery of an ion chain driven by a mechanical oscillator

A scheme for implementing quantum batteries in a realizable and controllable platform based on a trapped ion chain driven by a mechanical oscillator is proposed. The effects of the hopping interaction between the two-level ions and the coupling interaction between the ions and the external mechanical oscillator on the charging process of the battery are investigated. The importance of the counter-rotating wave terms in the system's Hamiltonian, which are often ignored, is analyzed, and it is found that the charging energy and the ergotropy of the battery are dramatically affected by the counter-rotating wave terms. The quantum phase transition of the two-level system is restrained by the counter-rotating wave terms due to the destruction of the quantum coherence. Lastly, the power-law dependence of the charging process on the distance between the ions is discussed. Our theoretical analysis provides a solid foundation for the development of a practical quantum battery.

quant-ph↗

arXiv2025

HODDI: A Dataset of High-Order Drug-Drug Interactions for Computational Pharmacovigilance

Drug-side effect research is vital for understanding adverse reactions arising in complex multi-drug therapies. However, the scarcity of higher-order datasets that capture the combinatorial effects of multiple drugs severely limits progress in this field. Existing resources such as TWOSIDES primarily focus on pairwise interactions. To fill this critical gap, we introduce HODDI, the first Higher-Order Drug-Drug Interaction Dataset, constructed from U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) records spanning the past decade, to advance computational pharmacovigilance. HODDI contains 109,744 records involving 2,506 unique drugs and 4,569 unique side effects, specifically curated to capture multi-drug interactions and their collective impact on adverse effects. Comprehensive statistical analyses demonstrate HODDI's extensive coverage and robust analytical metrics, making it a valuable resource for studying higher-order drug relationships. Evaluating HODDI with multiple models, we found that simple Multi-Layer Perceptron (MLP) can outperform graph models, while hypergraph models demonstrate superior performance in capturing complex multi-drug interactions, further validating HODDI's effectiveness. Our findings highlight the inherent value of higher-order information in drug-side effect prediction and position HODDI as a benchmark dataset for advancing research in pharmacovigilance, drug safety, and personalized medicine. The dataset and codes are available at https://github.com/TIML-Group/HODDI.

cs.LG↗

arXiv2024

Heterogeneous Entity Representation for Medicinal Synergy Prediction

Medicinal synergy prediction is a powerful tool in drug discovery and development that harnesses the principles of combination therapy to enhance therapeutic outcomes by improving efficacy, reducing toxicity, and preventing drug resistance. While a myriad of computational methods has emerged for predicting synergistic drug combinations, a large portion of them may overlook the intricate, yet critical relationships between various entities in drug interaction networks, such as drugs, cell lines, and diseases. These relationships are complex and multidimensional, requiring sophisticated modeling to capture nuanced interplay that can significantly influence therapeutic efficacy. We introduce a salient deep hypergraph learning method, namely, Heterogeneous Entity Representation for MEdicinal Synergy prediction (HERMES), to predict anti-cancer drug synergy. HERMES integrates heterogeneous data sources, encompassing drug, cell line, and disease information, to provide a comprehensive understanding of the interactions involved. By leveraging advanced hypergraph neural networks with gated residual mechanisms, HERMES can effectively learn complex relationships/interactions within the data. Our results show HERMES demonstrates state-of-the-art performance, particularly in forecasting new drug combinations, significantly surpassing previous methods. This advancement underscores the potential of HERMES to facilitate more effective and precise drug combination predictions, thereby enhancing the development of novel therapeutic strategies.

cs.CE↗

arXiv2024

Multiple Dirichlet series associated with quadrics

We define a multiple Dirichlet series associated with quadrics which is the zero locus of a quadratic form. This multiple Dirichlet series is linked to a Shintani zeta function associated with a prehomogeneous vector space. To obtain the functional equations we construct a filtration of the quadratic space and define the parabolic group actions, and then apply a non-abelian Poisson summation formula which sums over all lower dimensional quadrics along with the original quadrics. We show the group of functional equations is isomorphic to a finite Weyl group of type A3.

math.NT↗

arXiv2023

LATTE: Label-efficient Incident Phenotyping from Longitudinal Electronic Health Records

Electronic health record (EHR) data are increasingly used to support real-world evidence (RWE) studies. Yet its ability to generate reliable RWE is limited by the lack of readily available precise information on the timing of clinical events such as the onset time of heart failure. We propose a LAbel-efficienT incidenT phEnotyping (LATTE) algorithm to accurately annotate the timing of clinical events from longitudinal EHR data. By leveraging the pre-trained semantic embedding vectors from large-scale EHR data as prior knowledge, LATTE selects predictive EHR features in a concept re-weighting module by mining their relationship to the target event and compresses their information into longitudinal visit embeddings through a visit attention learning network. LATTE employs a recurrent neural network to capture the sequential dependency between the target event and visit embeddings before/after it. To improve label efficiency, LATTE constructs highly informative longitudinal silver-standard labels from large-scale unlabeled patients to perform unsupervised pre-training and semi-supervised joint training. Finally, LATTE enhances cross-site portability via contrastive representation learning. LATTE is evaluated on three analyses: the onset of type-2 diabetes, heart failure, and the onset and relapses of multiple sclerosis. We use various evaluation metrics present in the literature including the $ABC_{gain}$, the proportion of reduction in the area between the observed event indicator and the predicted cumulative incidences in reference to the prediction per incident prevalence. LATTE consistently achieves substantial improvement over benchmark methods such as SAMGEP and RETAIN in all settings.

cs.AI↗

arXiv2023

Discriminative Radial Domain Adaptation

Domain adaptation methods reduce domain shift typically by learning domain-invariant features. Most existing methods are built on distribution matching, e.g., adversarial domain adaptation, which tends to corrupt feature discriminability. In this paper, we propose Discriminative Radial Domain Adaptation (DRDA) which bridges source and target domains via a shared radial structure. It's motivated by the observation that as the model is trained to be progressively discriminative, features of different categories expand outwards in different directions, forming a radial structure. We show that transferring such an inherently discriminative structure would enable to enhance feature transferability and discriminability simultaneously. Specifically, we represent each domain with a global anchor and each category a local anchor to form a radial structure and reduce domain shift via structure matching. It consists of two parts, namely isometric transformation to align the structure globally and local refinement to match each category. To enhance the discriminability of the structure, we further encourage samples to cluster close to the corresponding local anchors based on optimal-transport assignment. Extensively experimenting on multiple benchmarks, our method is shown to consistently outperforms state-of-the-art approaches on varied tasks, including the typical unsupervised domain adaptation, multi-source domain adaptation, domain-agnostic learning, and domain generalization.

cs.LG↗

arXiv2022

Harnessing electronic health records for real-world evidence

While randomized controlled trials (RCTs) are the gold-standard for establishing the efficacy and safety of a medical treatment, real-world evidence (RWE) generated from real-world data (RWD) has been vital in post-approval monitoring and is being promoted for the regulatory process of experimental therapies. An emerging source of RWD is electronic health records (EHRs), which contain detailed information on patient care in both structured (e. g., diagnosis codes) and unstructured (e. g., clinical notes, images) form. Despite the granularity of the data available in EHRs, critical variables required to reliably assess the relationship between a treatment and clinical outcome can be challenging to extract. We provide an integrated data curation and modeling pipeline leveraging recent advances in natural language processing, computational phenotyping, modeling techniques with noisy data to address this fundamental challenge and accelerate the reliable use of EHRs for RWE, as well as the creation of digital twins. The proposed pipeline is highly automated for the task and includes guidance for deployment. Examples are also drawn from existing literature on EHR emulation of RCT and accompanied by our own studies with Mass General Brigham (MGB) EHR.

stat.AP↗

arXiv2021

Contrast-reconstruction Representation Learning for Self-supervised Skeleton-based Action Recognition

Skeleton-based action recognition is widely used in varied areas, e.g., surveillance and human-machine interaction. Existing models are mainly learned in a supervised manner, thus heavily depending on large-scale labeled data which could be infeasible when labels are prohibitively expensive. In this paper, we propose a novel Contrast-Reconstruction Representation Learning network (CRRL) that simultaneously captures postures and motion dynamics for unsupervised skeleton-based action recognition. It mainly consists of three parts: Sequence Reconstructor, Contrastive Motion Learner, and Information Fuser. The Sequence Reconstructor learns representation from skeleton coordinate sequence via reconstruction, thus the learned representation tends to focus on trivial postural coordinates and be hesitant in motion learning. To enhance the learning of motions, the Contrastive Motion Learner performs contrastive learning between the representations learned from coordinate sequence and additional velocity sequence, respectively. Finally, in the Information Fuser, we explore varied strategies to combine the Sequence Reconstructor and Contrastive Motion Learner, and propose to capture postures and motions simultaneously via a knowledge-distillation based fusion strategy that transfers the motion learning from the Contrastive Motion Learner to the Sequence Reconstructor. Experimental results on several benchmarks, i.e., NTU RGB+D 60, NTU RGB+D 120, CMU mocap, and NW-UCLA, demonstrate the promise of the proposed CRRL method by far outperforming state-of-the-art approaches.

cs.CV↗