arXiv subjects

Jun Shen

Publications and source records attributed to Jun Shen.

At least 55 records · Page 3Linked to original sources

arXiv2025

DECT-based Space-Squeeze Method for Multi-Class Classification of Metastatic Lymph Nodes in Breast Cancer

Background: Accurate assessment of metastatic burden in axillary lymph nodes is crucial for guiding breast cancer treatment decisions, yet conventional imaging modalities struggle to differentiate metastatic burden levels and capture comprehensive lymph node characteristics. This study leverages dual-energy computed tomography (DECT) to exploit spectral-spatial information for improved multi-class classification. Purpose: To develop a noninvasive DECT-based model classifying sentinel lymph nodes into three categories: no metastasis ($N_0$), low metastatic burden ($N_{+(1-2)}$), and heavy metastatic burden ($N_{+(\geq3)}$), thereby aiding therapeutic planning. Methods: We propose a novel space-squeeze method combining two innovations: (1) a channel-wise attention mechanism to compress and recalibrate spectral-spatial features across 11 energy levels, and (2) virtual class injection to sharpen inter-class boundaries and compact intra-class variations in the representation space. Results: Evaluated on 227 biopsy-confirmed cases, our method achieved an average test AUC of 0.86 (95% CI: 0.80-0.91) across three cross-validation folds, outperforming established CNNs (VGG, ResNet, etc). The channel-wise attention and virtual class components individually improved AUC by 5.01% and 5.87%, respectively, demonstrating complementary benefits. Conclusions: The proposed framework enhances diagnostic AUC by effectively integrating DECT's spectral-spatial data and mitigating class ambiguity, offering a promising tool for noninvasive metastatic burden assessment in clinical practice.

eess.IV↗

arXiv2025

Giant Magnetocaloric Effect in Spin Supersolid Candidate Na$_2$BaCo(PO$_4$)$_2$

Supersolid, an exotic quantum state of matter that consists of particles forming an incompressible solid structure while simultaneously showing superfluidity of zero viscosity [1], is one of the long-standing pursuits in fundamental research [2, 3]. Although the initial report of $^4$He supersolid turned out to be an artifact [4], this intriguing quantum matter has inspired enthusiastic investigations into ultracold quantum gases [5-8]. Nevertheless, the realization of supersolidity in condensed matter remains elusive. Here we find evidence for a quantum magnetic analogue of supersolid -- the spin supersolid -- in the recently synthesized triangular-lattice antiferromagnet Na$_2$BaCo(PO$_4$)$_2$ [9]. Notably, a giant magnetocaloric effect related to the spin supersolidity is observed in the demagnetization cooling process, manifesting itself as two prominent valley-like regimes, with the lowest temperature attaining below 100 mK. Not only is there an experimentally determined series of critical fields but the demagnetization cooling profile also shows excellent agreement with the theoretical simulations with an easy-axis Heisenberg model. Neutron diffractions also successfully locate the proposed spin supersolid phases by revealing the coexistence of three-sublattice spin solid order and interlayer incommensurability indicative of the spin superfluidity. Thus, our results indicate a strong entropic effect of the spin supersolid phase in a frustrated quantum magnet and open up a viable and promising avenue for applications in sub-Kelvin refrigeration, especially in the context of persistent concerns about helium shortages [10, 11].

cond-mat.str-el↗

arXiv2025

Interpretable Bilingual Multimodal Large Language Model for Diverse Biomedical Tasks

Several medical Multimodal Large Languange Models (MLLMs) have been developed to address tasks involving visual images with textual instructions across various medical modalities, achieving impressive results. Most current medical generalist models are region-agnostic, treating the entire image as a holistic representation. However, they struggle to identify which specific regions they are focusing on when generating a sentence. To mimic the behavior of doctors, who typically begin by reviewing the entire image before concentrating on specific regions for a thorough evaluation, we aim to enhance the capability of medical MLLMs in understanding anatomical regions within entire medical scans. To achieve it, we first formulate Region-Centric tasks and construct a large-scale dataset, MedRegInstruct, to incorporate regional information into training. Combining our collected dataset with other medical multimodal corpora for training, we propose a Region-Aware medical MLLM, MedRegA, which is the first bilingual generalist medical AI system to simultaneously handle image-level and region-level medical vision-language tasks across a broad range of modalities. Our MedRegA not only enables three region-centric tasks, but also achieves the best performance for visual question answering, report generation and medical image classification over 8 modalities, showcasing significant versatility. Experiments demonstrate that our model can not only accomplish powerful performance across various medical vision-language tasks in bilingual settings, but also recognize and detect structures in multimodal medical scans, boosting the interpretability and user interactivity of medical MLLMs. Our project page is https://medrega.github.io.

cs.CV↗

arXiv2024

Pullback measure attractors and limiting behaviors of McKean-Vlasov stochastic delay lattice systems

We study the long-term behavior of the distribution of the solution process to the non-autonomous McKean-Vlasov stochastic delay lattice system defined on the integer set $\mathbb{Z}$. Specifically, we first establish the well-posedness of solutions for this non-autonomous, distribution-dependent stochastic delay lattice system. Then, we prove the existence and uniqueness of pullback measure attractors for the non-autonomous dynamical system generated by the solution operators, defined in the space of probability measures. Furthermore, as an application of the pullback measure attractor, we prove the ergodicity and exponentially mixing of invariant measures for the system under appropriate conditions. Finally, we establish the upper semi-continuity of these attractors as the distribution-dependent stochastic delay lattice system converges to a distribution-independent system.

math.DS↗

arXiv2024

Extension of the Active-Orbital-Based and Adaptive CC($P$;$Q$) Approaches to Excited Electronic States: Application to Potential Cuts of Water

We report the first study using active-orbital-based and adaptive CC($P$;$Q$) approaches to describe excited electronic states. These CC($P$;$Q$) methodologies are applied, alongside their completely renormalized (CR) coupled-cluster (CC) and equation-of-motion (EOM) CC counterparts, to recover the ground- and excited-state potential cuts of the water molecule along the O-H bond-breaking coordinate obtained in the parent CC/EOMCC calculations with a full treatment of singles, doubles, and triples (CCSDT/EOMCCSDT). We demonstrate that the active-orbital-based and adaptive CC($P$;$Q$) approaches closely approximate the CCSDT/EOMCCSDT data using significantly reduced computational costs while improving the CR-CC and CR-EOMCC energetics in stretched regions of the O-H bond-breaking potentials.

physics.chem-ph↗

arXiv2024

PIORS: Personalized Intelligent Outpatient Reception based on Large Language Model with Multi-Agents Medical Scenario Simulation

In China, receptionist nurses face overwhelming workloads in outpatient settings, limiting their time and attention for each patient and ultimately reducing service quality. In this paper, we present the Personalized Intelligent Outpatient Reception System (PIORS). This system integrates an LLM-based reception nurse and a collaboration between LLM and hospital information system (HIS) into real outpatient reception setting, aiming to deliver personalized, high-quality, and efficient reception services. Additionally, to enhance the performance of LLMs in real-world healthcare scenarios, we propose a medical conversational data generation framework named Service Flow aware Medical Scenario Simulation (SFMSS), aiming to adapt the LLM to the real-world environments and PIORS settings. We evaluate the effectiveness of PIORS and SFMSS through automatic and human assessments involving 15 users and 15 clinical experts. The results demonstrate that PIORS-Nurse outperforms all baselines, including the current state-of-the-art model GPT-4o, and aligns with human preferences and clinical needs. Further details and demo can be found at https://github.com/FudanDISC/PIORS

cs.CL↗

arXiv2024

The X-ray re-brightening of GRB afterglow revisited: a possible signature from activity of the central engine

Long-duration gamma-ray bursts (GRBs) are thought to be from core collapse of massive stars, and a rapidly spinning magnetar or black hole may be formed as the central engine. The extended emission in the prompt emission, flares and plateaus in X-ray afterglow, are proposed to be as the signature of central engine re-activity. However, the directly evidence from observations of identifying the central engines remain an open question. In this paper, we systemically search for long-duration GRBs that consist of bumps in X-ray afterglow detected by Swift/XRT, and find that the peak time of the X-ray bumps exhibit bimodal distribution (defined as early and late bumps) with division line at $t=7190$ s. Although we cannot rule out that such a bimodality arises from selection effects. We proposed that the long-duration GRBs with an early (or late) bumps may be originated from the fall-back accretion onto a new-born magnetar (or black hole). By adopting MCMC method to fit the early (or late) bumps of X-ray afterglow with the fall-back accretion of magnetar (or black hole), it is found that the initial surface magnetic filed and period of magnetars for most early bumps are clustered around $5.88\times10^{13}$ G and $1.04$ ms, respectively. Meanwhile, the derived accretion mass of black hole for late bumps is range of $[4\times10^{-4}, 1.8\times10^{-2}]~M_{\odot}$, and the typical fall-back radius is distributed range of $[1.04, 4.23]\times 10^{11}$ cm which is consistent with the typical radius of a Wolf-Rayet star. However, we also find that the fall-back accretion magnetar model is disfavored by the late bumps, but the fall-back accretion of black hole model can not be ruled out to interpret the early bumps of X-ray afterglow.

astro-ph.HE↗

arXiv2024

Invariant measures, periodic measures and pullback measure attractors of McKean-Vlasov stochastic reaction-diffusion equations on unbounded domains

This paper deals with the long term dynamics of the non-autonomous McKean-Vlasov stochastic reaction-diffusion equations on R^n. We first prove the existence and uniqueness of pullback measure attractors of the non-autonomous dynamical system generated by the solution operators defined in the space of probability measures. We then prove the existence and uniqueness of invariant measures and periodic measures of the equation under further conditions. We finally establish the upper semi-continuity of pullback measure attractors as well as the convergence of invariant measures and periodic measures when the distribution dependent stochastic equations converge to a distribution independent system.

math.PR↗

arXiv2024

Empowering Visual Artists with Tokenized Digital Assets with NFTs

The Non-Fungible Tokens (NFTs) has the transformative impact on the visual arts industry by examining the nexus between empowering art practices and leveraging blockchain technology. First, we establish the context for this study by introducing some basic but critical technological aspects and affordances of the blockchain domain. Second, we revisit the creative practices involved in producing traditional artwork, covering various types, production processes, trading, and monetization methods. Third, we introduce and define the key fundamentals of the blockchain ecosystem, including its structure, consensus algorithms, smart contracts, and digital wallets. Fourth, we narrow the focus to NFTs, detailing their history, mechanics, lifecycle, and standards, as well as their application in the art world. In particular, we outline the key processes for minting and trading NFTs in various marketplaces and discuss the relevant market dynamics and pricing. We also consider major security concerns, such as wash trading, to underscore some of the central cybersecurity issues facing this domain. Finally, we conclude by considering future research directions, emphasizing improvements in user experience, security, and privacy. Through this innovative research overview, which includes input from creative industry and cybersecurity sdomain expertise, we offer some new insights into how NFTs can empower visual artists and reshape the wider copyright industries.

cs.CR↗

arXiv2024

A Knowledge-Inspired Hierarchical Physics-Informed Neural Network for Pipeline Hydraulic Transient Simulation

The high-pressure transportation process of pipeline necessitates an accurate hydraulic transient simulation tool to prevent slack line flow and over-pressure, which can endanger pipeline operations. However, current numerical solution methods often face difficulties in balancing computational efficiency and accuracy. Additionally, few studies attempt to reform physics-informed learning architecture for pipeline transient simulation with magnitude different in outputs and imbalanced gradient in loss function. To address these challenges, a Knowledge-Inspired Hierarchical Physics-Informed Neural Network is proposed for hydraulic transient simulation of multi-product pipelines. The proposed model integrates governing equations, boundary conditions, and initial conditions into the training process to ensure consistency with physical laws. Furthermore, magnitude conversion of outputs and equivalent conversion of governing equations are implemented to enhance the training performance of the neural network. To further address the imbalanced gradient of multiple loss terms with fixed weights, a hierarchical training strategy is designed. Numerical simulations demonstrate that the proposed model outperforms state-of-the-art models and can still produce accurate simulation results under complex hydraulic transient conditions, with mean absolute percentage errors reduced by 87.8\% and 92.7 \% in pressure prediction. Thus, the proposed model can conduct accurate and effective hydraulic transient analysis, ensuring the safe operation of pipelines.

cs.CE↗

arXiv2024

A Prior Embedding-Driven Architecture for Long Distance Blind Iris Recognition

Blind iris images, which result from unknown degradation during the process of iris recognition at long distances, often lead to decreased iris recognition rates. Currently, little existing literature offers a solution to this problem. In response, we propose a prior embedding-driven architecture for long distance blind iris recognition. We first proposed a blind iris image restoration network called Iris-PPRGAN. To effectively restore the texture of the blind iris, Iris-PPRGAN includes a Generative Adversarial Network (GAN) used as a Prior Decoder, and a DNN used as the encoder. To extract iris features more efficiently, we then proposed a robust iris classifier by modifying the bottleneck module of InsightFace, which called Insight-Iris. A low-quality blind iris image is first restored by Iris-PPRGAN, then the restored iris image undergoes recognition via Insight-Iris. Experimental results on the public CASIA-Iris-distance dataset demonstrate that our proposed method significantly superior results to state-of-the-art blind iris restoration methods both quantitatively and qualitatively, Specifically, the recognition rate for long-distance blind iris images reaches 90% after processing with our methods, representing an improvement of approximately ten percentage points compared to images without restoration.

cs.CV↗

arXiv2024

An Exploration Study on Developing Blockchain Systems the Practitioners Perspective

Context: Blockchain-based software (BBS) exploits the concepts and technologies popularized by cryptocurrencies offering decentralized transaction ledgers with immutable content for security-critical and transaction critical systems. Recent research has explored the strategic benefits and technical limitations of BBS in various fields, including cybersecurity, healthcare, education, and financial technologies. Despite growing interest from academia and industry, there is a lack of empirical evidence, leading to an incomplete understanding of the processes, methods, and techniques necessary for systematic BBS development. Objectives: Existing research lacks a consolidated view, particularly empirically driven guidelines based on published evidence and development practices. This study aims to address the gap by consolidating empirical evidence and development practices to derive or leverage existing processes, patterns, and models for designing, implementing, and validating BBS systems. Method: Tied to this knowledge gap, we conducted a two-phase research project. First, a systematic literature review of 58 studies was performed to identify a development process comprising 23 tasks for BBS systems. Second, a survey of 102 blockchain practitioners from 35 countries across six continents was conducted to validate the BBS system development process. Results: Our results revealed a statistically significant difference (p-value <.001) in the importance ratings of 24 out of 26 BBS tasks by our participants. The only two tasks that were not statistically significant were incentive protocol design and granularity design. Conclusion: Our research is among the first to advance understanding on the aspect of development process for blockchain-based systems and helps researchers and practitioners in their quests on challenges and recommendations associated with the development of BBS systems

cs.SE↗

arXiv2024

Vox-UDA: Voxel-wise Unsupervised Domain Adaptation for Cryo-Electron Subtomogram Segmentation with Denoised Pseudo Labeling

Cryo-Electron Tomography (cryo-ET) is a 3D imaging technology facilitating the study of macromolecular structures at near-atomic resolution. Recent volumetric segmentation approaches on cryo-ET images have drawn widespread interest in biological sector. However, existing methods heavily rely on manually labeled data, which requires highly professional skills, thereby hindering the adoption of fully-supervised approaches for cryo-ET images. Some unsupervised domain adaptation (UDA) approaches have been designed to enhance the segmentation network performance using unlabeled data. However, applying these methods directly to cryo-ET images segmentation tasks remains challenging due to two main issues: 1) the source data, usually obtained through simulation, contain a certain level of noise, while the target data, directly collected from raw-data from real-world scenario, have unpredictable noise levels. 2) the source data used for training typically consists of known macromoleculars, while the target domain data are often unknown, causing the model's segmenter to be biased towards these known macromolecules, leading to a domain shift problem. To address these challenges, in this work, we introduce the first voxel-wise unsupervised domain adaptation approach, termed Vox-UDA, specifically for cryo-ET subtomogram segmentation. Vox-UDA incorporates a noise generation module to simulate target-like noises in the source dataset for cross-noise level adaptation. Additionally, we propose a denoised pseudo-labeling strategy based on improved Bilateral Filter to alleviate the domain shift problem. Experimental results on both simulated and real cryo-ET subtomogram datasets demonstrate the superiority of our proposed approach compared to state-of-the-art UDA methods.

cs.CV↗

arXiv2024

DST-GTN: Dynamic Spatio-Temporal Graph Transformer Network for Traffic Forecasting

Accurate traffic forecasting is essential for effective urban planning and congestion management. Deep learning (DL) approaches have gained colossal success in traffic forecasting but still face challenges in capturing the intricacies of traffic dynamics. In this paper, we identify and address this challenges by emphasizing that spatial features are inherently dynamic and change over time. A novel in-depth feature representation, called Dynamic Spatio-Temporal (Dyn-ST) features, is introduced, which encapsulates spatial characteristics across varying times. Moreover, a Dynamic Spatio-Temporal Graph Transformer Network (DST-GTN) is proposed by capturing Dyn-ST features and other dynamic adjacency relations between intersections. The DST-GTN can model dynamic ST relationships between nodes accurately and refine the representation of global and local ST characteristics by adopting adaptive weights in low-pass and all-pass filters, enabling the extraction of Dyn-ST features from traffic time-series data. Through numerical experiments on public datasets, the DST-GTN achieves state-of-the-art performance for a range of traffic forecasting tasks and demonstrates enhanced stability.

cs.AI↗

arXiv2024

EAN-MapNet: Efficient Vectorized HD Map Construction with Anchor Neighborhoods

High-definition (HD) map is crucial for autonomous driving systems. Most existing works design map elements detection heads based on the DETR decoder. However, the initial queries lack explicit incorporation of physical positional information, and vanilla self-attention entails high computational complexity. Therefore, we propose EAN-MapNet for Efficiently constructing HD map using Anchor Neighborhoods. Firstly, we design query units based on the anchor neighborhoods, allowing non-neighborhood central anchors to effectively assist in fitting the neighborhood central anchors to the target points representing map elements. Then, we propose grouped local self-attention (GL-SA) by leveraging the relative instance relationship among the queries. This facilitates direct feature interaction among queries of the same instances, while innovatively employing local queries as intermediaries for interaction among queries from different instances. Consequently, GL-SA significantly reduces the computational complexity of self-attention while ensuring ample feature interaction among queries. On the nuScenes dataset, EAN-MapNet achieves a state-of-the-art performance with 63.0 mAP after training for 24 epochs, surpassing MapTR by 12.7 mAP. Furthermore, it considerably reduces memory consumption by 8198M compared to MapTRv2.

cs.CV↗

arXiv2024

FDNet: Frequency Domain Denoising Network For Cell Segmentation in Astrocytes Derived From Induced Pluripotent Stem Cells

Artificially generated induced pluripotent stem cells (iPSCs) from somatic cells play an important role for disease modeling and drug screening of neurodegenerative diseases. Astrocytes differentiated from iPSCs are important targets to investigate neuronal metabolism. The astrocyte differentiation progress can be monitored through the variations of morphology observed from microscopy images at different differentiation stages, then determined by molecular biology techniques upon maturation. However, the astrocytes usually ``perfectly'' blend into the background and some of them are covered by interference information (i.e., dead cells, media sediments, and cell debris), which makes astrocytes difficult to observe. Due to the lack of annotated datasets, the existing state-of-the-art deep learning approaches cannot be used to address this issue. In this paper, we introduce a new task named astrocyte segmentation with a novel dataset, called IAI704, which contains 704 images and their corresponding pixel-level annotation masks. Moreover, a novel frequency domain denoising network, named FDNet, is proposed for astrocyte segmentation. In detail, our FDNet consists of a contextual information fusion module (CIF), an attention block (AB), and a Fourier transform block (FTB). CIF and AB fuse multi-scale feature embeddings to localize the astrocytes. FTB transforms feature embeddings into the frequency domain and conducts a high-pass filter to eliminate interference information. Experimental results demonstrate the superiority of our proposed FDNet over the state-of-the-art substitutes in astrocyte segmentation, shedding insights for iPSC differentiation progress prediction.

eess.IV↗

arXiv2024

GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative model

Adversarial generative models, such as Generative Adversarial Networks (GANs), are widely applied for generating various types of data, i.e., images, text, and audio. Accordingly, its promising performance has led to the GAN-based adversarial attack methods in the white-box and black-box attack scenarios. The importance of transferable black-box attacks lies in their ability to be effective across different models and settings, more closely aligning with real-world applications. However, it remains challenging to retain the performance in terms of transferable adversarial examples for such methods. Meanwhile, we observe that some enhanced gradient-based transferable adversarial attack algorithms require prolonged time for adversarial sample generation. Thus, in this work, we propose a novel algorithm named GE-AdvGAN to enhance the transferability of adversarial samples whilst improving the algorithm's efficiency. The main approach is via optimising the training process of the generator parameters. With the functional and characteristic similarity analysis, we introduce a novel gradient editing (GE) mechanism and verify its feasibility in generating transferable samples on various models. Moreover, by exploring the frequency domain information to determine the gradient editing direction, GE-AdvGAN can generate highly transferable adversarial samples while minimizing the execution time in comparison to the state-of-the-art transferable adversarial attack algorithms. The performance of GE-AdvGAN is comprehensively evaluated by large-scale experiments on different datasets, which results demonstrate the superiority of our algorithm. The code for our algorithm is available at: https://github.com/LMBTough/GE-advGAN

cs.CV↗

arXiv2023

FairCompass: Operationalising Fairness in Machine Learning

As artificial intelligence (AI) increasingly becomes an integral part of our societal and individual activities, there is a growing imperative to develop responsible AI solutions. Despite a diverse assortment of machine learning fairness solutions is proposed in the literature, there is reportedly a lack of practical implementation of these tools in real-world applications. Industry experts have participated in thorough discussions on the challenges associated with operationalising fairness in the development of machine learning-empowered solutions, in which a shift toward human-centred approaches is promptly advocated to mitigate the limitations of existing techniques. In this work, we propose a human-in-the-loop approach for fairness auditing, presenting a mixed visual analytical system (hereafter referred to as 'FairCompass'), which integrates both subgroup discovery technique and the decision tree-based schema for end users. Moreover, we innovatively integrate an Exploration, Guidance and Informed Analysis loop, to facilitate the use of the Knowledge Generation Model for Visual Analytics in FairCompass. We evaluate the effectiveness of FairCompass for fairness auditing in a real-world scenario, and the findings demonstrate the system's potential for real-world deployability. We anticipate this work will address the current gaps in research for fairness and facilitate the operationalisation of fairness in machine learning systems.

cs.LG↗