arXiv subjects

Hantao Zhang

Publications and source records attributed to Hantao Zhang.

At least 37 records · Page 2Linked to original sources

arXiv2025

Voltage-Controlled Magnetoelectric Devices for Neuromorphic Diffusion Process

Stochastic diffusion processes are pervasive in nature, from the seemingly erratic Brownian motion to the complex interactions of synaptically-coupled spiking neurons. Recently, drawing inspiration from Langevin dynamics, neuromorphic diffusion models were proposed and have become one of the major breakthroughs in the field of generative artificial intelligence. Unlike discriminative models that have been well developed to tackle classification or regression tasks, diffusion models as well as other generative models such as ChatGPT aim at creating content based upon contexts learned. However, the more complex algorithms of these models result in high computational costs using today's technologies, creating a bottleneck in their efficiency, and impeding further development. Here, we develop a spintronic voltage-controlled magnetoelectric memory hardware for the neuromorphic diffusion process. The in-memory computing capability of our spintronic devices goes beyond current Von Neumann architecture, where memory and computing units are separated. Together with the non-volatility of magnetic memory, we can achieve high-speed and low-cost computing, which is desirable for the increasing scale of generative models in the current era. We experimentally demonstrate that the hardware-based true random diffusion process can be implemented for image generation and achieve comparable image quality to software-based training as measured by the Frechet inception distance (FID) score, achieving ~10^3 better energy-per-bit-per-area over traditional hardware.

cs.NE↗

arXiv2025

CAFusion: Controllable Anatomical Synthesis of Perirectal Lymph Nodes via SDF-guided Diffusion

Lesion synthesis methods have made significant progress in generating large-scale synthetic datasets. However, existing approaches predominantly focus on texture synthesis and often fail to accurately model masks for anatomically complex lesions. Additionally, these methods typically lack precise control over the synthesis process. For example, perirectal lymph nodes, which range in diameter from 1 mm to 10 mm, exhibit irregular and intricate contours that are challenging for current techniques to replicate faithfully. To address these limitations, we introduce CAFusion, a novel approach for synthesizing perirectal lymph nodes. By leveraging Signed Distance Functions (SDF), CAFusion generates highly realistic 3D anatomical structures. Furthermore, it offers flexible control over both anatomical and textural features by decoupling the generation of morphological attributes (such as shape, size, and position) from textural characteristics, including signal intensity. Experimental results demonstrate that our synthetic data substantially improve segmentation performance, achieving a 6.45% increase in the Dice coefficient. In the visual Turing test, experienced radiologists found it challenging to distinguish between synthetic and real lesions, highlighting the high degree of realism and anatomical accuracy achieved by our approach. These findings validate the effectiveness of our method in generating high-quality synthetic lesions for advancing medical image processing applications.

eess.IV↗

arXiv2025

DiffAtlas: GenAI-fying Atlas Segmentation via Image-Mask Diffusion

Accurate medical image segmentation is crucial for precise anatomical delineation. Deep learning models like U-Net have shown great success but depend heavily on large datasets and struggle with domain shifts, complex structures, and limited training samples. Recent studies have explored diffusion models for segmentation by iteratively refining masks. However, these methods still retain the conventional image-to-mask mapping, making them highly sensitive to input data, which hampers stability and generalization. In contrast, we introduce DiffAtlas, a novel generative framework that models both images and masks through diffusion during training, effectively ``GenAI-fying'' atlas-based segmentation. During testing, the model is guided to generate a specific target image-mask pair, from which the corresponding mask is obtained. DiffAtlas retains the robustness of the atlas paradigm while overcoming its scalability and domain-specific limitations. Extensive experiments on CT and MRI across same-domain, cross-modality, varying-domain, and different data-scale settings using the MMWHS and TotalSegmentator datasets demonstrate that our approach outperforms existing methods, particularly in limited-data and zero-shot modality segmentation. Code is available at https://github.com/M3DV/DiffAtlas.

cs.CV↗

arXiv2024

Interpolating neural network: A novel unification of machine learning and interpolation theory

Artificial intelligence (AI) has revolutionized software development, shifting from task-specific codes (Software 1.0) to neural network-based approaches (Software 2.0). However, applying this transition in engineering software presents challenges, including low surrogate model accuracy, the curse of dimensionality in inverse design, and rising complexity in physical simulations. We introduce an interpolating neural network (INN), grounded in interpolation theory and tensor decomposition, to realize Engineering Software 2.0 by advancing data training, partial differential equation solving, and parameter calibration. INN offers orders of magnitude fewer trainable/solvable parameters for comparable model accuracy than traditional multi-layer perceptron (MLP) or physics-informed neural networks (PINN). Demonstrated in metal additive manufacturing, INN rapidly constructs an accurate surrogate model of Laser Powder Bed Fusion (L-PBF) heat transfer simulation, achieving sub-10-micrometer resolution for a 10 mm path in under 15 minutes on a single GPU. This makes a transformative step forward across all domains essential to engineering software.

cs.LG↗

arXiv2024

Self-Attention Limits Working Memory Capacity of Transformer-Based Models

Recent work on Transformer-based large language models (LLMs) has revealed striking limits in their working memory capacity, similar to what has been found in human behavioral studies. Specifically, these models' performance drops significantly on N-back tasks as N increases. However, there is still a lack of mechanistic interpretability as to why this phenomenon would arise. Inspired by the executive attention theory from behavioral sciences, we hypothesize that the self-attention mechanism within Transformer-based models might be responsible for their working memory capacity limits. To test this hypothesis, we train vanilla decoder-only transformers to perform N-back tasks and find that attention scores gradually aggregate to the N-back positions over training, suggesting that the model masters the task by learning a strategy to pay attention to the relationship between the current position and the N-back position. Critically, we find that the total entropy of the attention score matrix increases as N increases, suggesting that the dispersion of attention scores might be the cause of the capacity limit observed in N-back tasks. Our findings thus offer insights into the shared role of attention in both human and artificial intelligence. Moreover, the limitations of the self-attention mechanism revealed in the current study could inform future efforts to design more powerful model architectures with enhanced working memory capacity and cognitive capabilities.

cs.CL↗

arXiv2024

LeFusion: Controllable Pathology Synthesis via Lesion-Focused Diffusion Models

Patient data from real-world clinical practice often suffers from data scarcity and long-tail imbalances, leading to biased outcomes or algorithmic unfairness. This study addresses these challenges by generating lesion-containing image-segmentation pairs from lesion-free images. Previous efforts in medical imaging synthesis have struggled with separating lesion information from background, resulting in low-quality backgrounds and limited control over the synthetic output. Inspired by diffusion-based image inpainting, we propose LeFusion, a lesion-focused diffusion model. By redesigning the diffusion learning objectives to focus on lesion areas, we simplify the learning process and improve control over the output while preserving high-fidelity backgrounds by integrating forward-diffused background contexts into the reverse diffusion process. Additionally, we tackle two major challenges in lesion texture synthesis: 1) multi-peak and 2) multi-class lesions. We introduce two effective strategies: histogram-based texture control and multi-channel decomposition, enabling the controlled generation of high-quality lesions in difficult scenarios. Furthermore, we incorporate lesion mask diffusion, allowing control over lesion size, location, and boundary, thus increasing lesion diversity. Validated on 3D cardiac lesion MRI and lung nodule CT datasets, LeFusion-generated data significantly improves the performance of state-of-the-art segmentation models, including nnUNet and SwinUNETR. Code and model are available at https://github.com/M3DV/LeFusion.

eess.IV↗

arXiv2024

Quantum computing for extracting nuclear resonances

Quantum computing has been increasingly applied in nuclear physics. In this work, we combine quantum computing with the complex scaling method to address the resonance problem. Due to the non-Hermiticity introduced by complex scaling, standard quantum computing cannot solve for complex eigenvalues directly. Therefore, it is necessary to embed the non-Hermitian operator into a larger dimensional unitary operator. Additionally, for the case of two basis vectors, we improve the traditional direct measurement method and optimize the quantum circuit. Ultimately, using the $α+α$ system as an example, we obtain the complex eigenenergies from the quantum computer that are consistent with those obtained from direct Hamiltonian diagonalization.

nucl-th↗

arXiv2024

LN-Gen: Rectal Lymph Nodes Generation via Anatomical Features

Accurate segmentation of rectal lymph nodes is crucial for the staging and treatment planning of rectal cancer. However, the complexity of the surrounding anatomical structures and the scarcity of annotated data pose significant challenges. This study introduces a novel lymph node synthesis technique aimed at generating diverse and realistic synthetic rectal lymph node samples to mitigate the reliance on manual annotation. Unlike direct diffusion methods, which often produce masks that are discontinuous and of suboptimal quality, our approach leverages an implicit SDF-based method for mask generation, ensuring the production of continuous, stable, and morphologically diverse masks. Experimental results demonstrate that our synthetic data significantly improves segmentation performance. Our work highlights the potential of diffusion model for accurately synthesizing structurally complex lesions, such as lymph nodes in rectal cancer, alleviating the challenge of limited annotated data in this field and aiding in advancements in rectal cancer diagnosis and treatment.

eess.IV↗

arXiv2024

Medical MLLM is Vulnerable: Cross-Modality Jailbreak and Mismatched Attacks on Medical Multimodal Large Language Models

Security concerns related to Large Language Models (LLMs) have been extensively explored, yet the safety implications for Multimodal Large Language Models (MLLMs), particularly in medical contexts (MedMLLMs), remain insufficiently studied. This paper delves into the underexplored security vulnerabilities of MedMLLMs, especially when deployed in clinical environments where the accuracy and relevance of question-and-answer interactions are critically tested against complex medical challenges. By combining existing clinical medical data with atypical natural phenomena, we define the mismatched malicious attack (2M-attack) and introduce its optimized version, known as the optimized mismatched malicious attack (O2M-attack or 2M-optimization). Using the voluminous 3MAD dataset that we construct, which covers a wide range of medical image modalities and harmful medical scenarios, we conduct a comprehensive analysis and propose the MCM optimization method, which significantly enhances the attack success rate on MedMLLMs. Evaluations with this dataset and attack methods, including white-box attacks on LLaVA-Med and transfer attacks (black-box) on four other SOTA models, indicate that even MedMLLMs designed with enhanced security features remain vulnerable to security breaches. Our work underscores the urgent need for a concerted effort to implement robust security measures and enhance the safety and efficacy of open-source MedMLLMs, particularly given the potential severity of jailbreak attacks and other malicious or clinically significant exploits in medical settings. Our code is available at https://github.com/dirtycomputer/O2M_attack.

cs.CR↗

arXiv2024

Large spin-orbit torque in a-plane $α$-Fe$_{2}$O$_{3}$/Pt bilayers

Realization of efficient spin-orbit torque switching of the Néel vector in insulating antiferromagnets is a challenge, often complicated by spurious effects. Quantifying the spin-orbit torques in antiferromagnet/heavy metal heterostructures is an important first step towards this goal. Here, we employ magneto-optic techniques to study damping-like spin-orbit torque (DL-SOT) in a-plane $α$-Fe$_2$O$_3$ (hematite) with a Pt spin-orbit overlayer. We find that the DL-SOT efficiency is two orders of magnitude larger than reported in c- and r-plane hematite/Pt using harmonic Hall techniques. The large magnitude of DL-SOT is supported by direct imaging of current-induced motion of antiferromagnetic domains that happens at moderate current densities. Our study introduces a new method for quantifying spin-orbit torque in antiferromagnets with a small canted moment and identifies a-plane $α$-Fe$_2$O$_3$ as a promising candidate to realize efficient SOT switching.

cond-mat.mtrl-sci↗

arXiv2024

Meply: A Large-scale Dataset and Baseline Evaluations for Metastatic Perirectal Lymph Node Detection and Segmentation

Accurate segmentation of metastatic lymph nodes in rectal cancer is crucial for the staging and treatment of rectal cancer. However, existing segmentation approaches face challenges due to the absence of pixel-level annotated datasets tailored for lymph nodes around the rectum. Additionally, metastatic lymph nodes are characterized by their relatively small size, irregular shapes, and lower contrast compared to the background, further complicating the segmentation task. To address these challenges, we present the first large-scale perirectal metastatic lymph node CT image dataset called Meply, which encompasses pixel-level annotations of 269 patients diagnosed with rectal cancer. Furthermore, we introduce a novel lymph-node segmentation model named CoSAM. The CoSAM utilizes sequence-based detection to guide the segmentation of metastatic lymph nodes in rectal cancer, contributing to improved localization performance for the segmentation model. It comprises three key components: sequence-based detection module, segmentation module, and collaborative convergence unit. To evaluate the effectiveness of CoSAM, we systematically compare its performance with several popular segmentation methods using the Meply dataset. Our code and dataset will be publicly available at: https://github.com/kanydao/CoSAM.

cs.CV↗

arXiv2023

CARE: A Large Scale CT Image Dataset and Clinical Applicable Benchmark Model for Rectal Cancer Segmentation

Rectal cancer segmentation of CT image plays a crucial role in timely clinical diagnosis, radiotherapy treatment, and follow-up. Although current segmentation methods have shown promise in delineating cancerous tissues, they still encounter challenges in achieving high segmentation precision. These obstacles arise from the intricate anatomical structures of the rectum and the difficulties in performing differential diagnosis of rectal cancer. Additionally, a major obstacle is the lack of a large-scale, finely annotated CT image dataset for rectal cancer segmentation. To address these issues, this work introduces a novel large scale rectal cancer CT image dataset CARE with pixel-level annotations for both normal and cancerous rectum, which serves as a valuable resource for algorithm research and clinical application development. Moreover, we propose a novel medical cancer lesion segmentation benchmark model named U-SAM. The model is specifically designed to tackle the challenges posed by the intricate anatomical structures of abdominal organs by incorporating prompt information. U-SAM contains three key components: promptable information (e.g., points) to aid in target area localization, a convolution module for capturing low-level lesion details, and skip-connections to preserve and recover spatial information during the encoding-decoding process. To evaluate the effectiveness of U-SAM, we systematically compare its performance with several popular segmentation methods on the CARE dataset. The generalization of the model is further verified on the WORD dataset. Extensive experiments demonstrate that the proposed U-SAM outperforms state-of-the-art methods on these two datasets. These experiments can serve as the baseline for future research and clinical application development.

eess.IV↗

arXiv2022

Theory of Harmonic Hall Responses of Spin-Torque Driven Antiferromagnets

Harmonic analysis is a powerful tool to characterize and quantify current-induced torques acting on magnetic materials, but so far it remains an open question in studying antiferromagnets. Here we formulate a general theory of harmonic Hall responses of collinear antiferromagnets driven by current-induced torques including both field-like and damping-like components. By scanning a magnetic field of variable strength in three orthogonal planes, we are able to distinguish the contributions from field-like torque, damping-like torque, and concomitant thermal effects by analyzing the second harmonic signals in the Hall voltage. The analytical expressions of the first and second harmonics as functions of the magnetic field direction and strength are confirmed by numerical simulations with good agreement. We demonstrate our predictions in two prototype antiferromagnets, $α-$Fe$_{2}$O$_{3}$ and NiO, providing direct and general guidance to current and future experiments.

cond-mat.mes-hall↗

arXiv2022

Holey Schröder Designs of Type $\bf 3^n u^1$

A holey Schröder design of type $h_1^{n_1}h_4^{n_2}\cdots h^{n_k}_k$ (HSD$(h_1^{n_1}h_4^{n_2}\cdots h^{n_k}_k))$ is equivalent to a frame idempotent Schröder quasigroup (FISQ$(h_1^{n_1}h_4^{n_2}\cdots h^{n_k}_k))$ of order $n$ with $n_i$ missing subquasigroups (holes) of order $h_i, 1 \le i \le k$, which are disjoint and spanning (i.e., $\sum_{1\le i \le k}n_ih_i = n$). The existence of HSD$(h^nu^1)$ for $h=1, 2, 4$ has been known. In this paper, we consider the existence of HSD$(3^nu^1)$ and show that for $0\le u \le 15$, an HSD$(3^nu^1)$ exists if and only if $n(n + 2u -1) \equiv 0~(mod~4)$, $n\ge 4$ and $n\ge 1+2u/3$. For $0 \le u \le n$, an HSD$(3^nu^1)$ exists if and only if $n(n + 2u -1) \equiv 0~(mod~4)$ and $n \ge 4$, with possible exceptions of $n = 29, 43$. We have also found six new HSDs of type $(4^nu^1)$.

math.CO↗

arXiv2022

A Perspective on Magnon Spin Nernst Effect in Antiferromagnets

Magnon excitations in antiferromagnetic materials and their physical implications enable novel device concepts not available in ferromagnets, emerging as a new area of active research. A unique characteristic of antiferromagnetic magnons is the coexistence of opposite spin polarization, which mimics the electron spin in a variety of transport phenomena. Among them, the most prominent spin-contrasting phenomenon is the magnon spin Nernst effect (SNE), which refers to the generation of transverse pure magnon spin current through a longitudinal temperature gradient. We introduce selected recent progress in the study of magnon SNE in collinear antiferromagnets with a focus on its underlying physical mechanism entailing profound topological features of the magnon band structures. By reviewing how the magnon SNE has inspired and enriched the exploration of topological magnons, we offer our perspectives on this emerging frontier that holds potential in future spintronic nano-technology.

cond-mat.mes-hall↗

arXiv2022

Manipulating Ferrimagnets by Fields and Currents

Ferrimagnets (FIMs) can function as high-frequency antiferromagnets while being easy to detect as ferromagnets, offering unique opportunities for ultrafast device applications. While the physical behavior of FIMs near the compensation point has been widely studied, there lacks a generic understanding of FIMs where the ratio of sublattice spins can vary freely between the ferromagnetic and antiferromagnetic limits. Here we investigate the physical properties of a model two-sublattice FIM manipulated by static magnetic fields and current-induced torques. By continuously varying the ratio of sublattice spins, we clarify how the dynamical chiral modes in an FIM are intrinsically connected to their ferro- and antiferromagnetic counterparts, which reveals unique features not visible near the compensation point. In particular, we find that current-induced torques can trigger spontaneous oscillation of the terahertz exchange mode. Compared with its realization in antiferromagnets, a spin-torque oscillator using FIMs not only has a reduced threshold current density but also can be self-stabilized, obviating the need for dynamic feedback.

cond-mat.mes-hall↗

arXiv2021

Quantifying Spin-Orbit Torques in Antiferromagnet/Heavy Metal Heterostructures

The effect of spin currents on the magnetic order of insulating antiferromagnets (AFMs) is of fundamental interest and can enable new applications. Toward this goal, characterizing the spin-orbit torques (SOT) associated with AFM/heavy metal (HM) interfaces is important. Here we report the full angular dependence of the harmonic Hall voltages in a predominantly easy-plane AFM, epitaxial c-axis oriented $α$-Fe$_2$O$_3$ films, with an interface to Pt. By modeling the harmonic Hall signals together with the $α$-Fe$_2$O$_3$ magnetic parameters, we determine the amplitudes of field-like and damping-like SOT. Out-of-plane field scans are shown to be essential to determining the damping-like component of the torques. In contrast to ferromagnetic/heavy metal heterostructures, our results demonstrate that the field-like torques are significantly larger than the damping-like torques, which we correlate with the presence of a large imaginary component of the interface spin-mixing conductance. Our work demonstrates a direct way of characterizing SOT in AFM/HM heterostructures.

cond-mat.mes-hall↗

arXiv2021

Spin Nernst Effect of Antiferromagnetic Magnons in the Presence of Spin Diffusion

Magnon spin Nernst effect was recently proposed as an intrinsic effect in antiferromagnets, where spin diffusion and boundary spin transmission have been ignored. However, diffusion processes are essential to convert a bulk spin current into boundary spin accumulation, which determines the spin injection rate into detectors through imperfect transmission. We formulate a diffusive theory to describe the detection of magnon spin Nernst effect with boundary conditions reflecting real device geometry. Thanks to the spin diffusion effect, the output signals in both electronic and optical detection grow rapidly with an increasing system size in the transverse dimension, which eventually saturate. Counterintuitively, the measurable signals are even functions of magnetic field, yielding optical detection more favorable than electronic detection.

cond-mat.mes-hall↗