arXiv subjects

Yuanpeng Zhang

Publications and source records attributed to Yuanpeng Zhang.

15 recordsLinked to original sources

arXiv2026

Anomalous Hall Response Induced by Correlated Disorder in the Breathing Kagome Lattice Mn$_{3}$Sn

Macroscopic transport tensors are generally constrained by the average crystallographic and magnetic symmetries of a material. In the kagome antiferromagnetic Weyl semimetals Mn$_{3+δ}X$ ($X=$~Sn or Ge), previous studies showed that the anomalous Hall conductivity $σ_{yx}$ is forbidden by the average \hexsg{} structure and coplanar inverse-triangular magnetic order. Here we report that nearly stoichiometric Mn$_3$Sn nevertheless exhibits a finite $σ_{yx}$ with large hysteresis, together with enhanced $σ_{zx}$ and $σ_{yz}$, in the inverse-triangular phase below $T_{\mathrm{N1}}\approx 440~\mathrm{K}$, whereas all AHE components vanish in the amplitude-modulated conical phase below $T_{\mathrm{N2}}\approx 280~\mathrm{K}$. Total scattering and magnetic pair distribution function analysis reveal correlated orthorhombic distortions and noncoplanar Mn moments. First-principles calculations show that this coupled lattice-spin distortion activates the average symmetry forbidden $σ_{yx}$ within the inverse-triangular phase. Its disappearance below $T_{\mathrm{N2}}$ indicates that the correlated disorder must cooperate with a long-range inverse-triangular antiferromagnetic order capable of supporting Berry curvature. Our results establish correlated disorder as an active symmetry-breaking degree of freedom that enables topological transport inaccessible from the Bragg-average structure alone.

cond-mat.str-el↗

arXiv2026

CODA: Algorithm-Hardware Co-design for Edge Video Diffusion via NMP-Enabled Compute-Cache Operator Disaggregation

Deploying Video Diffusion Models (VDMs) on edge devices is appealing for localized and privacy-preserving generation, but their iterative Transformer-based denoising remains too slow for practical local inference. Cross-Timestep Caching (CTC) has emerged as a promising direction for reducing redundant computation, reusing activations across adjacent denoising steps rather than modifying model weights, while largely preserving generation fidelity. However, on memory-constrained edge GPUs, CTC requires a massive cache footprint that quickly exceeds on-device VRAM and forces the cache into host memory. More fundamentally, cache operators remain tightly interleaved and chain-dependent with native compute operators, so naive near-memory offloading still incurs repeated PCIe exchanges for residual and fusion computations, turning cache reuse into a communication- and serialization-bound execution flow. We therefore propose CODA, an algorithm-hardware co-designed architecture centered on Compute-Cache Operator Disaggregation. CODA separates dense compute paths and memory-bound cache paths across the xPU and a lightweight DIMM-side near-memory engine, reorganizes fragmented cache activity into hardware-friendly coalesced segments, and exploits Classifier-Free Guidance (CFG) branch independence to overlap xPU compute with cache-side execution. Experiments show that CODA achieves up to 1.80x end-to-end speedup and 1.74x higher energy efficiency, while preserving competitive generation quality compared with a state-of-the-art caching algorithm.

cs.AR↗

arXiv2026

Isolation-aware Scheduling Framework for DNN-based End-to-End Autonomous Driving System on Tile-based Accelerators

Level-4+ autonomous driving systems (ADS) must run dozens of heterogeneous deep neural networks (DNNs) as end-to-end (E2E) pipelines under a strict latency constraint (<=100 ms), even as execution time varies by up to 3.3x. Cost rules out dedicating isolated hardware to each function in mass-produced ADS, so these DNNs must be densely colocated on a single chip, which introduces shared-resource contention. Tile-based accelerators expose two scheduling opportunities that conventional ADS schedulers do not exploit. First, they provide a tunable degree of parallelism (DoP): assigning more tiles raises DoP and can shorten DNN execution time. Second, they provide hardware-native isolation: tiles can be physically partitioned among co-located DNNs. But using this flexibility is expensive: changing a task's DoP triggers a stop-migrate-restart reallocation of its weights and intermediate features. At ADS task rates of 10-240 Hz, these stalls accumulate along E2E chains and threaten deadlines. Reservation-based schedulers fix DoP and leave this flexibility unused; work-conserving schedulers exploit it but assume reallocation is cheap and treat deadlines as independent. We present ADS-Tile that combines configurable isolation and elastic reservation into a spatio-temporal isolation-sharing space that bounds where and when reallocation occurs; a probabilistic latency model and a DAG-aware runtime scheduler then use this space to decide task colocation and DoP under shared E2E deadlines. On an industry- and academia- derived ADS benchmark, ADS-Tile uses up to 32% fewer tiles than the work-conserving baseline in deadline-critical settings and cuts reallocation-induced wasted processing capacity from 17%-44% to below 1.2%. Controlled spatio-temporal sharing improves resource efficiency and latency predictability for tile-based ADS.

cs.AR↗

arXiv2026

AccelCIM: Systematic Dataflow Exploration for SRAM Compute-in-Memory Accelerator

SRAM-based compute-in-memory (CIM) offers high computational density and energy efficiency for deep neural network (DNN) accelerators, but its limited capacity causes on/off-chip data movement overhead for large DNN models. Existing CIM accelerator studies typically assume that DNN models fit entirely on-chip, leaving efficient dataflow design largely untapped. This paper introduces AccelCIM, a systematic dataflow exploration framework for SRAM CIM accelerator, which addresses two key limitations of prior work. (1) It formulates a systematic dataflow design space spanning CIM macro configurations and macro-array organizations. (2) It introduces rigorous design evaluation using cycle-accurate architectural simulation and post-layout PPA analysis. We conduct an extensive design space exploration and apply AccelCIM to representative LLM applications, providing practical insights for the principled design of CIM accelerators.

cs.AR↗

arXiv2025

A Novel Attention-Augmented Wavelet YOLO System for Real-time Brain Vessel Segmentation on Transcranial Color-coded Doppler

The Circle of Willis (CoW), vital for ensuring consistent blood flow to the brain, is closely linked to ischemic stroke. Accurate assessment of the CoW is important for identifying individuals at risk and guiding appropriate clinical management. Among existing imaging methods, Transcranial Color-coded Doppler (TCCD) offers unique advantages due to its radiation-free nature, affordability, and accessibility. However, reliable TCCD assessments depend heavily on operator expertise for identifying anatomical landmarks and performing accurate angle correction, which limits its widespread adoption. To address this challenge, we propose an AI-powered, real-time CoW auto-segmentation system capable of efficiently capturing cerebral arteries. No prior studies have explored AI-driven cerebrovascular segmentation using TCCD. In this work, we introduce a novel Attention-Augmented Wavelet YOLO (AAW-YOLO) network tailored for TCCD data, designed to provide real-time guidance for brain vessel segmentation in the CoW. We prospectively collected TCCD data comprising 738 annotated frames and 3,419 labeled artery instances to establish a high-quality dataset for model training and evaluation. The proposed AAW-YOLO demonstrated strong performance in segmenting both ipsilateral and contralateral CoW vessels, achieving an average Dice score of 0.901, IoU of 0.823, precision of 0.882, recall of 0.926, and mAP of 0.953, with a per-frame inference speed of 14.199 ms. This system offers a practical solution to reduce reliance on operator experience in TCCD-based cerebrovascular screening, with potential applications in routine clinical workflows and resource-constrained settings. Future research will explore bilateral modeling and larger-scale validation.

eess.IV↗

arXiv2025

AIM: Software and Hardware Co-design for Architecture-level IR-drop Mitigation in High-performance PIM

SRAM Processing-in-Memory (PIM) has emerged as the most promising implementation for high-performance PIM, delivering superior computing density, energy efficiency, and computational precision. However, the pursuit of higher performance necessitates more complex circuit designs and increased operating frequencies, which exacerbate IR-drop issues. Severe IR-drop can significantly degrade chip performance and even threaten reliability. Conventional circuit-level IR-drop mitigation methods, such as back-end optimizations, are resource-intensive and often compromise power, performance, and area (PPA). To address these challenges, we propose AIM, comprehensive software and hardware co-design for architecture-level IR-drop mitigation in high-performance PIM. Initially, leveraging the bit-serial and in-situ dataflow processing properties of PIM, we introduce Rtog and HR, which establish a direct correlation between PIM workloads and IR-drop. Building on this foundation, we propose LHR and WDS, enabling extensive exploration of architecture-level IR-drop mitigation while maintaining computational accuracy through software optimization. Subsequently, we develop IR-Booster, a dynamic adjustment mechanism that integrates software-level HR information with hardware-based IR-drop monitoring to adapt the V-f pairs of the PIM macro, achieving enhanced energy efficiency and performance. Finally, we propose the HR-aware task mapping method, bridging software and hardware designs to achieve optimal improvement. Post-layout simulation results on a 7nm 256-TOPS PIM chip demonstrate that AIM achieves up to 69.2% IR-drop mitigation, resulting in 2.29x energy efficiency improvement and 1.152x speedup.

cs.AR↗

arXiv2025

Exploring the Complex Landscape of Entropy Stabilized Oxides

Entropy-stabilized oxides (ESOs), driven by high configurational entropy, have gained phenomenological research interest due to their potential for tailoring structure property relationships. However, the chemical short range ordering (SRO) and its interplay with local lattice distortion (LD) remain to be explored, although they could diminish the configurational entropy and potentially impact structure property relationships. A combination of experimental and theoretical approaches are employed to investigate the SRO and LD in the prototype ESO, Mg0.2Co0.2Ni0.2Cu0.2Zn0.2O, generally referred to as J14. We demonstrate that the efficiency and accuracy of density functional theory (DFT) relaxed special quasirandom structures (SQS) enhances the analysis of the local structure of J14, unveiling the unique local cationic environments. Importantly, this joint experimental and computational approach sheds light on the understanding of local structure and structure property relationships in J14, demonstrating the necessity for further research into other high entropy and compositionally complex materials.

cond-mat.mtrl-sci↗

arXiv2025

DRIMV_TSK: An Interpretable Surgical Evaluation Model for Incomplete Multi-View Rectal Cancer Data

A reliable evaluation of surgical difficulty can improve the success of the treatment for rectal cancer and the current evaluation method is based on clinical data. However, more data about rectal cancer can be collected with the development of technology. Meanwhile, with the development of artificial intelligence, its application in rectal cancer treatment is becoming possible. In this paper, a multi-view rectal cancer dataset is first constructed to give a more comprehensive view of patients, including the high-resolution MRI image view, pressed-fat MRI image view, and clinical data view. Then, an interpretable incomplete multi-view surgical evaluation model is proposed, considering that it is hard to obtain extensive and complete patient data in real application scenarios. Specifically, a dual representation incomplete multi-view learning model is first proposed to extract the common information between views and specific information in each view. In this model, the missing view imputation is integrated into representation learning, and second-order similarity constraint is also introduced to improve the cooperative learning between these two parts. Then, based on the imputed multi-view data and the learned dual representation, a multi-view surgical evaluation model with the TSK fuzzy system is proposed. In the proposed model, a cooperative learning mechanism is constructed to explore the consistent information between views, and Shannon entropy is also introduced to adapt the view weight. On the MVRC dataset, we compared it with several advanced algorithms and DRIMV_TSK obtained the best results.

cs.LG↗

arXiv2025

A planning tool for neutron powder diffraction experiments

We introduce a computer program to simulate the results of neutron powder-diffraction experiments at the High Flux Isotope Reactor at Oak Ridge National Laboratory. The program is freely available as a web application at http://addie.ornl.gov/hfirestimate, and is designed to be straightforward to use for researchers who are new to neutron diffraction. The input includes the crystal structure of the proposed sample, the sample mass, and the instrument configuration. The results include a plot of the simulated data -- including realistic estimates of background and the error bars due to counting statistics -- and suggestions of how to resolve potential problems with the experiment. Here, we explain the design and implementation of this program and demonstrate its performance using comparisons of simulated and experimental data. We hope that this program will enable researchers to plan neutron-scattering experiments more effectively, increasing the likelihood of successful experiments and improving the productivity of neutron-diffraction research.

physics.ins-det↗

arXiv2025

Multi-Omics Fusion with Soft Labeling for Enhanced Prediction of Distant Metastasis in Nasopharyngeal Carcinoma Patients after Radiotherapy

Omics fusion has emerged as a crucial preprocessing approach in the field of medical image processing, providing significant assistance to several studies. One of the challenges encountered in the integration of omics data is the presence of unpredictability arising from disparities in data sources and medical imaging equipment. In order to overcome this challenge and facilitate the integration of their joint application to specific medical objectives, this study aims to develop a fusion methodology that mitigates the disparities inherent in omics data. The utilization of the multi-kernel late-fusion method has gained significant popularity as an effective strategy for addressing this particular challenge. An efficient representation of the data may be achieved by utilizing a suitable single-kernel function to map the inherent features and afterward merging them in a space with a high number of dimensions. This approach effectively addresses the differences noted before. The inflexibility of label fitting poses a constraint on the use of multi-kernel late-fusion methods in complex nasopharyngeal carcinoma (NPC) datasets, hence affecting the efficacy of general classifiers in dealing with high-dimensional characteristics. This innovative methodology aims to increase the disparity between the two cohorts, hence providing a more flexible structure for the allocation of labels. The examination of the NPC-ContraParotid dataset demonstrates the model's robustness and efficacy, indicating its potential as a valuable tool for predicting distant metastases in patients with nasopharyngeal carcinoma (NPC).

q-bio.QM↗

arXiv2024

Simultaneous development of antiferromagnetism and local symmetry breaking in a kagome magnet (Co$_{0.45}$Fe$_{0.55}$)Sn

CoSn and FeSn, two kagome-lattice metals, have recently attracted significant attention as hosts of electronic flat bands and emergent physical properties. However, current understandings of their physical properties are limited to the knowledge of the average crystal structure. Here, we report the Fe-doping induced co-emergence of the antiferromagentic (AFM) order and local symmetry breaking in (Co0.45Fe0.55)Sn. Rietveld analysis on the neutron and synchrotron x-ray diffraction data indicates A-type antiferromagnetic order with the moment pointing perpendicular to the kagome layers, associated with the anomaly in the MSn(1)2Sn(2)4 (M = Co/Fe) octahedral distortion and the lattice constant c. Reverse Monte Carlo (RMC) modeling of the synchrotron x-ray total scattering results captured the subtle local orthorhombic distortion involving off-axis displacements of Sn2. Our results indicate that the stable hexagonal lattice above TN becomes unstable once the A-type AFM order is formed below TN. We argue that the local symmetry breaking has a magnetic origin and is driven by the out-of-plane magnetic exchange coupling. Our study provides comprehensive information on the crystal structure in both long-range scale and local scale, unveiling unique coupling between AFM order, octahedral distortion, and hidden local symmetry breaking.

cond-mat.str-el↗

arXiv2024

Local distortion driven magnetic phase switching in pyrochlore $Yb_2(Ti_{1-x}Sn_x)_2O_7$

While it is commonly accepted that the disorder induced by magnetic ion doping in quantum magnets usually generates a rugged free-energy landscape resulting in slow or glassy spin dynamics, the disorder/distortion effects associated with non-magnetic ion sites doping are still illusive. Here, using AC susceptibility measurements, we show that the mixture of Sn/Ti on the non-magnetic ion sites of pyrochlore $Yb_2(Ti_{1-x}Sn_x)_2O_7$ induces an antiferromagnetic ground state despite both parent compounds, $Yb_2Ti_2O_7$, and $Yb_2Sn_2O_7$, order ferromagnetically. Local structure studies through neutron total scattering reveals the local distortion in the non-magnetic ion sites and its strong correlation with the magnetic phase switching. Our study, for the first time, demonstrates the local distortion as induced by the non-magnetic ion site mixture could be a new path to achieve magnetic phase switching, which has been traditionally obtained by external stimuli such as temperature, magnetic field, pressure, strain, light, etc.

cond-mat.str-el↗

arXiv2022

Multi-view Contrastive Learning with Additive Margin for Adaptive Nasopharyngeal Carcinoma Radiotherapy Prediction

The prediction of adaptive radiation therapy (ART) prior to radiation therapy (RT) for nasopharyngeal carcinoma (NPC) patients is important to reduce toxicity and prolong the survival of patients. Currently, due to the complex tumor micro-environment, a single type of high-resolution image can provide only limited information. Meanwhile, the traditional softmax-based loss is insufficient for quantifying the discriminative power of a model. To overcome these challenges, we propose a supervised multi-view contrastive learning method with an additive margin (MMCon). For each patient, four medical images are considered to form multi-view positive pairs, which can provide additional information and enhance the representation of medical images. In addition, the embedding space is learned by means of contrastive learning. NPC samples from the same patient or with similar labels will remain close in the embedding space, while NPC samples with different labels will be far apart. To improve the discriminative ability of the loss function, we incorporate a margin into the contrastive learning. Experimental result show this new learning objective can be used to find an embedding space that exhibits superior discrimination ability for NPC images.

cs.CV↗

arXiv2022

Hidden local symmetry breaking in a kagome-lattice magnetic Weyl semimetal

Exploring the relationship between intriguing physical properties and structural complexity is a central topic in studying modern functional materials. Co$_{3}$Sn$_{2}$S$_{2}$, a new discovered kagome-lattice magnetic Weyl semimetal, has triggered intense interest owing to the intimate coupling between topological semimetallic states and peculiar magnetic properties. However, the origins of the magnetic phase separation and spin glass state below $T_{C}$ in this ordered compound are two unresolved yet important puzzles in understanding its magnetism. Here, we report the discovery of local symmetry breaking surprisingly co-emerges with the onset of ferromagnetic order in Co$_{3}$Sn$_{2}$S$_{2}$, by a combined use of neutron total scattering and half polarized neutron diffraction. The mismatch of local and average symmetries occurs below $T_{C}$, indicating that Co$_{3}$Sn$_{2}$S$_{2}$ evolves to an intrinsically lattice disordered system when the ferromagnetic order is established. The local symmetry breaking with intrinsic lattice disorder provides new understandings to the puzzling magnetic properties. Our density function theory calculation indicates that the local symmetry breaking is expected to reorient local ferromagnetic moments, unveiling the existence of the ferromagnetic instability associated with the lattice instability. Furthermore, DFT calculation unveils that the local symmetry breaking could affect the Weyl property by breaking mirror plane. Our findings highlight the fundamentally important role that the local symmetry breaking plays in advancing our understanding on the magnetic and topological properties in Co$_{3}$Sn$_{2}$S$_{2}$, which may draw the attention to explore the overlooked local symmetry breaking in Co$_{3}$Sn$_{2}$S$_{2}$, its derivatives, and more broadly in other topological Dirac/Weyl semimetals and kagome-lattice magnets.

cond-mat.str-el↗

arXiv2015

Spectroscopic super-resolution fluorescence cell imaging using ultra-small Ge quantum dots

In single molecule localisation super-resolution microscopy the need for repeated image capture limits the imaging speed, while the size of fluorescence probes limits the possible theoretical localisation resolution. Here, we demonstrated a spectral imaging based super-resolution approach by separating the overlapping diffraction spots into several detectors during a single scanning period and taking advantage of the size-dependent emission wavelength in nanoparticles. This approach has been tested using off-the-shelf quantum dots (Qdot) and in-house novel ultra-small (~3 nm) Ge QDs. Furthermore, we developed a method-specific Gaussian fitting and maximum likelihood estimation based on a Matlab algorithm for fast QDs localisation. We demonstrate that this methodology results in ~ 40 nm localisation resolution using commercial QDs and ~12 nm localisation resolution using Ge QDs. Using a standard scanning confocal microscope we achieved data acquisition rate of 1.6 seconds/frame. However, we show that this approach has a potential to deliver data acquisition rates on ms scale thus providing super-resolution in live systems.

physics.optics↗