arXiv subjects

Xia Zhu

Publications and source records attributed to Xia Zhu.

At least 19 recordsLinked to original sources

arXiv2026

A Wireless Volumetric Metamaterial Resonator for Breast MRI

High-performance breast magnetic resonance imaging (MRI) remains limited by the availability, geometry, and cost of dedicated receive coils. In contrast, spine coils are widely installed in MR scanners globally but provide insufficient sensitivity for anterior breast tissue. Here, we report a wireless volumetric metamaterial resonator (VMR) that converts a standard built-in spine coil into a scalable breast MRI receiver platform without modifying the scanner hardware. The VMR consists of axially stacked coaxial resonant rings that support a co-rotating collective mode, producing a centrally concentrated RF magnetic field within the enclosed imaging volume. Leveraging distributed capacitance in the coaxial cable, gap-engineered geometric tuning, and PIN-diode-based self-detuning, the VMR enables size scalability, electric-field confinement, and transmit-receive compatibility, as validated through bench measurements and 3.0 T MRI experiments. Three VMR prototypes spanning small, medium, and large breast-equivalent volumes were designed and evaluated using electromagnetic simulations, bench measurements, and phantom MRI experiments. Compared with a commercial 16-channel breast coil, the VMR-augmented spine coil achieved more than 4-fold higher central SNR and reduced spatial non-uniformity from approximately 70% to 20% within the target imaging region. Under SENSE parallel imaging at acceleration factors of 2-4, the VMR-augmented spine coil maintained an average 4-fold higher central SNR than the commercial coil for three sizes. These results establish the VMR as a passive, scalable hardware strategy for improving breast MRI performance using existing clinical scanner infrastructure.

physics.med-ph↗

arXiv2026

A Wireless Reconfigurable Metasurface for Enhanced Parallel Magnetic Resonance Imaging

Modern magnetic resonance imaging (MRI) relies on application-specific multi-channel receive coils to achieve high performance, but these coils are typically costly, rigid, and difficult to generalize across anatomies. Recent wireless, low-cost metamaterials offer improved signal-to-noise ratio (SNR) but remain anatomy-dependent, are prone to destructive inter-element interference, and lack demonstrated compatibility with parallel imaging. Herein, a wireless, reconfigurable coaxial loop metasurface (CLM) is introduced as a platform for localized SNR enhancement that can operate either as a standalone element or as an insertable add-on alongside existing clinical receive systems. Through its coaxial architecture and shared current pathways, the CLM establishes a collective in-phase resonant mode that enforces phase-coherent current distributions across all loops, resulting in consistently constructive interference. Benchmarking on a 3.0 T MR system using an 8-loop CLM shows SNR enhancements of up to 14.8-fold and 14.02-fold in the sagittal and axial planes, relative to the birdcage coil (BC). As an add-on to a clinical posterior receive array, it further demonstrates up to 2.9-fold SNR enhancement and compatibility with parallel imaging across ex vivo and in vivo settings. The proposed CLM paves the way toward a new class of reconfigurable and insertable MRI hardware for flexible and system-compatible signal enhancement.

physics.med-ph↗

arXiv2026

ENEC: A Lossless AI Model Compression Method Enabling Fast Inference on Ascend NPUs

The rapid scaling of Large Language Models presents significant challenges for their deployment and inference, particularly on resource-constrained specialized AI hardware accelerators such as Huawei's Ascend NPUs, where weight data transfer has become a critical performance bottleneck. While lossless compression can preserve model accuracy and reduce data volume, existing lossless compression algorithms exhibit extremely low throughput when ported to the Ascend NPU architecture. In this paper, we propose ENEC, a novel lossless compression method specifically customized for AI model weights and optimized for Ascend Neural Processing Units. ENEC adopts a block-based fixed-length encoding scheme and incorporates a series of NPU-specific optimizations: bit-width quantization with hierarchical halving bit-packing, vectorized branch-free integer transformation, and dependency-decoupled intra-segment scan for efficient prefix-sum computation. Experimental results demonstrate that ENEC outperforms existing state-of-the-art NPU compressors in both compression ratio and throughput. Compared to leading GPU solutions, ENEC achieves a 3.43X higher throughput than DietGPU and a 1.12X better compression ratio than nvCOMP. By reducing weight transmission overhead, ENEC significantly improves end-to-end inference performance, achieving up to a 6.3X speedup. On Ascend NPUs, ENEC is the first open-source lossless compression algorithm for model weights that achieves performance comparable to state-of-the-art GPU compressors, offering an effective solution for deploying large-scale AI models.

cs.AR↗

arXiv2026

LiteCache: A Query Similarity-Driven, GPU-Centric KVCache Subsystem for Efficient LLM Inference

During LLM inference, KVCache memory usage grows linearly with sequence length and batch size and often exceeds GPU capacity. Recent proposals offload KV states to host memory and reduce transfers using top-k attention. But their CPU-centric management of the on-GPU cache and CPU-GPU data movement incurs high overhead and fragments the bulk GPU execution that CUDA Graph relies on. To close this gap, we observe that adjacent queries within the same attention head exhibit strong directional similarity and retrieve highly overlapping top-k KV states. This insight enables a simple head granularity cache algorithm, QSAC, in which each head reuses its previously cached KV states whenever the current query is sufficiently similar to the prior one. QSAC further simplifies cache management primitives and cuts CPU involvement almost entirely. We develop LiteCache, a KVCache subsystem that incorporates QSAC. LiteCache introduces a GPU-centric synchronization controller and speculative sparse prefetching, enabling fully overlapped data movement and computation. These mechanisms produce a stable and predictable execution pattern that remains compatible with the bulk execution mode required by CUDA Graphs. Evaluation on two widely-used LLMs indicates that LiteCache achieves comparable accuracy to baselines, while sharply minimizing CPU overhead, fully utilizing PCIe bandwidth, thus improving decoding throughput by 10.7-224.2% on both H100 and A40 GPUs and easily supporting sequence lengths beyond 1M. We opensource LiteCache at https://anonymous.4open.science/r/LiteCache-888D.

cs.LG↗

arXiv2026

Circularly polarized metamaterial cage for homogeneous signal-to-noise ratio enhancement in magnetic resonance imaging

The signal-to-noise ratio (SNR) in magnetic resonance imaging (MRI) governs the quality of signal detection and directly impacts the clarity and reliability of the acquired images. Recent advances in metamaterials have enabled lightweight solutions with selective magnetic responses, offering a route to locally boost SNR in targeted anatomical regions but often with compromised field homogeneity. Here, a wireless metamaterial cage constructed from coaxial cables is engineered for homogeneous SNR enhancement at 3.0 T. With its cylindrical geometry and electromagnetic architecture, the device supports circularly polarized resonance through engineered phase-shifted currents, enabling selective and omnidirectional interaction with the rotating B_1^- field to achieve uniform magnetic field distribution. Integrated with the body coil, the device yields a 32-fold SNR enhancement while maintaining comparable homogeneity to the body coil alone, exhibiting only 12.07% variation within the region of interest (ROI). Benchmarking against a state-of-the-art 16-channel extremity coil further shows that the metacage achieves at least 1.94-fold and 2.24-fold higher SNR in axial and coronal planes, respectively, and exhibits substantially lower SNR variation (12.07% compared to 54.83% for the extremity coil). The results establish the metacage as a compelling platform for next-generation wireless MRI technologies.

physics.med-ph↗

arXiv2025

A Fast Solver-Free Algorithm for Traffic Engineering in Large-Scale Data Center Network

Rapid growth of data center networks (DCNs) poses significant challenges for large-scale traffic engineering (TE). Existing acceleration strategies, which rely on commercial solvers or deep learning, face scalability issues and struggle with degrading performance or long computational time. Unlike existing algorithms adopting parallel strategies, we propose Sequential Source-Destination Optimization (SSDO), a sequential solver-free algorithm for TE. SSDO decomposes the problem into subproblems, each focused on adjusting the split ratios for a specific source-destination (SD) demand while keeping others fixed. To enhance the efficiency of subproblem optimization, we design a Balanced Binary Search Method (BBSM), which identifies the most balanced split ratios among multiple solutions that minimize Maximum Link Utilization (MLU). SSDO dynamically updates the sequence of SDs based on real-time utilization, which accelerates convergence and enhances solution quality. We evaluate SSDO on Meta DCNs and two wide-area networks. In a Meta topology, SSDO achieves a 65\% and 60\% reduction in normalized MLU compared to TEAL and POP, two state-of-the-art TE acceleration methods, while delivering a $12\times$ speedup over POP. These results demonstrate the superior performance of SSDO in large-scale TE.

cs.NI↗

arXiv2025

ATRO: A Fast Algorithm for Topology Engineering of Reconfigurable Datacenter Networks

Reconfigurable data center networks (DCNs) enhance traditional architectures with optical circuit switches (OCSs), enabling dynamic reconfiguration of inter-pod links, i.e., the logical topology. Optimizing this topology is crucial for adapting to traffic dynamics but is challenging due to its combinatorial nature. The complexity increases further when demands can be distributed across multiple paths, requiring joint optimization of topology and routing. We propose Alternating Topology and Routing Optimization (ATRO), a unified framework that supports both one-hop topology optimization (where traffic is routed via direct paths) and multi-hop joint optimization (where routing is also optimized). Although these settings differ in constraints, both are combinatorially hard and challenge solver-based methods. ATRO addresses both cases efficiently: in the one-hop case, it guarantees the global optimum via an accelerated binary search; in the multi-hop case, it alternates between topology and routing updates, with routing steps optionally accelerated by existing traffic engineering (TE) methods. ATRO supports warm-starting and improves solution quality monotonically across iterations. ATRO remains competitive even when paired with solver-free TE methods, forming a fully solver-free optimization pipeline that still outperforms prior approaches in runtime and maximum link utilization across diverse workloads.

cs.NI↗

arXiv2024

Maintaining Adversarial Robustness in Continuous Learning

Adversarial robustness is essential for security and reliability of machine learning systems. However, adversarial robustness enhanced by defense algorithms is easily erased as the neural network's weights update to learn new tasks. To address this vulnerability, it is essential to improve the capability of neural networks in terms of robust continual learning. Specially, we propose a novel gradient projection technique that effectively stabilizes sample gradients from previous data by orthogonally projecting back-propagation gradients onto a crucial subspace before using them for weight updates. This technique can maintaining robustness by collaborating with a class of defense algorithms through sample gradient smoothing. The experimental results on four benchmarks including Split-CIFAR100 and Split-miniImageNet, demonstrate that the superiority of the proposed approach in mitigating rapidly degradation of robustness during continual learning even when facing strong adversarial attacks.

cs.LG↗

arXiv2024

A robust near-field body area network based on coaxially-shielded textile metamaterial

A body area network (BAN) involving wearable sensors populated around the human body can continuously monitor physiological signals, finding applications in personal healthcare and athletic evaluation. Existing near-field communication (NFC)-enabled BAN solutions, while facilitating reliable and secure interconnection among battery-free sensors, face challenges such as limited spectral stability against external interference. Here we demonstrate a textile metamaterial featuring a coaxially-shielded internal structure designed to mitigate interference from extraneous loadings. The metamaterial can be patterned onto clothing to form a scalable, customizable network, enabling communication between NFC-enabled devices and developed battery-free textile NFC sensing nodes placed within the network. Proof of concept demonstration shows the metamaterial's robustness against mechanical deformation and exposure to lossy, conductive saline solutions, underscoring its potential applications in wet environments, particularly in athletic activities involving water or significant perspiration, offering insights for the future development of radio frequency components for a robust BAN at the system level.

physics.app-ph↗

arXiv2023

Wireless, Customizable Coaxially-shielded Coils for Magnetic Resonance Imaging

Anatomy-specific RF receive coil arrays routinely adopted in magnetic resonance imaging (MRI) for signal acquisition, are commonly burdened by their bulky, fixed, and rigid configurations, which may impose patient discomfort, bothersome positioning, and suboptimal sensitivity in certain situations. Herein, leveraging coaxial cables' inherent flexibility and electric field confining property, for the first time, we present wireless, ultra-lightweight, coaxially-shielded MRI coils achieving a signal-to-noise ratio (SNR) comparable to or surpassing that of commercially available cutting-edge receive coil arrays with the potential for improved patient comfort, ease of implementation, and significantly reduced costs. The proposed coils demonstrate versatility by functioning both independently in form-fitting configurations, closely adapting to relatively small anatomical sites, and collectively by inductively coupling together as metamaterials, allowing for extension of the field-of-view of their coverage to encompass larger anatomical regions without compromising coil sensitivity. The wireless, coaxially-shielded MRI coils reported herein pave the way toward next generation MRI coils.

physics.med-ph↗

arXiv2023

Wearable Coaxially-shielded Metamaterial for Magnetic Resonance Imaging

Recent advancements in metamaterials have yielded the possibility of a wireless solution to improve signal-to-noise ratio (SNR) in magnetic resonance imaging (MRI). Unlike traditional closely packed local coil arrays with rigid designs and numerous components, these lightweight, cost-effective metamaterials eliminate the need for radio frequency (RF) cabling, baluns, adapters, and interfaces. However, their clinical adoption has been limited by their low sensitivity, bulky physical footprint, and limited, specific use cases. Herein, we introduce a wearable metamaterial developed using commercially available coaxial cable, designed for a 3.0 T MRI system. This metamaterial inherits the coaxially-shielded structure of its constituent coaxial cable, effectively containing the electric field within the cable, thereby mitigating the electric coupling to its loading while ensuring safer clinical adoption, lower signal loss, and resistance to frequency shifts. Weighing only 50g, the metamaterial maximizes its sensitivity by conforming to the anatomical region of interest. MRI images acquired using this metamaterial with various pulse sequences demonstrate an up to 2-fold SNR enhancement when compared to a state-of-the-art 16-channel knee coil. This work introduces a novel paradigm for constructing metamaterials in the MRI environment, paving the way for the development of next-generation wireless MRI technology.

physics.med-ph↗

arXiv2023

Computational-design Enabled Wearable and Tunable Metamaterials via Freeform Auxetics for Magnetic Resonance Imaging

Metamaterials hold significant promise for enhancing the imaging capabilities of MRI machines as an additive technology, due to their unique ability to enhance local magnetic fields. However, despite their potential, the metamaterials reported in the context of MRI applications have often been impractical. This impracticality arises from their predominantly flat configurations and their susceptibility to shifts in resonance frequencies, preventing them from realizing their optimal performance. Here, we introduce a computational method for designing wearable and tunable metamaterials via freeform auxetics. The proposed computational-design tools yield an approach to solving the complex circle packing problems in an interactive and efficient manner, thus facilitating the development of deployable metamaterials configured in freeform shapes. With such tools, the developed metamaterials may readily conform to a patient's kneecap, ankle, head, or any part of the body in need of imaging, and while ensuring an optimal resonance frequency, thereby paving the way for the widespread adoption of metamaterials in clinical MRI applications.

physics.med-ph↗

arXiv2023

Conformal Metamaterials with Active Tunability and Self-adaptivity for Magnetic Resonance Imaging

Ongoing effort has been devoted to applying metamaterials to boost the imaging performance of magnetic resonance imaging owing to their unique capacity for electromagnetic field confinement and enhancement. However, there are still major obstacles to widespread clinical adoption of conventional metamaterials due to several notable restrictions, namely: their typically bulky and rigid structures, deviations in their optimal resonance frequency, and their inevitable interference with the transmission RF field in MRI. Herein, we address these restrictions and report a conformal, smart metamaterial, which may not only be readily tuned to achieve the desired, precise frequency match with MRI by a controlling circuit, but is also capable of selectively amplifying the magnetic field during the RF reception phase by sensing the excitation signal strength passively, thereby remaining off during the RF transmission phase and thereby ensuring its optimal performance when applied to MRI as an additive technology. By addressing a host of current technological challenges, the metamaterial presented herein paves the way toward the wide-ranging utilization of metamaterials in clinical MRI, thereby translating this promising technology to the MRI bedside.

physics.med-ph↗

arXiv2023

Interpretable System Identification and Long-term Prediction on Time-Series Data

Time-series prediction has drawn considerable attention during the past decades fueled by the emerging advances of deep learning methods. However, most neural network based methods lack interpretability and fail in extracting the hidden mechanism of the targeted physical system. To overcome these shortcomings, an interpretable sparse system identification method without any prior knowledge is proposed in this study. This method adopts the Fourier transform to reduces the irrelevant items in the dictionary matrix, instead of indiscriminate usage of polynomial functions in most system identification methods. It shows an interpretable system representation and greatly reduces computing cost. With the adoption of $l_1$ norm in regularizing the parameter matrix, a sparse description of the system model can be achieved. Moreover, Three data sets including the water conservancy data, global temperature data and financial data are used to test the performance of the proposed method. Although no prior knowledge was known about the physical background, experimental results show that our method can achieve long-term prediction regardless of the noise and incompleteness in the original data more accurately than the widely-used baseline data-driven methods. This study may provide some insight into time-series prediction investigations, and suggests that an white-box system identification method may extract the easily overlooked yet inherent periodical features and may beat neural-network based black-box methods on long-term prediction tasks.

cs.LG↗

arXiv2021

Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting

Multivariate time-series forecasting plays a crucial role in many real-world applications. It is a challenging problem as one needs to consider both intra-series temporal correlations and inter-series correlations simultaneously. Recently, there have been multiple works trying to capture both correlations, but most, if not all of them only capture temporal correlations in the time domain and resort to pre-defined priors as inter-series relationships. In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of multivariate time-series forecasting. StemGNN captures inter-series correlations and temporal dependencies \textit{jointly} in the \textit{spectral domain}. It combines Graph Fourier Transform (GFT) which models inter-series correlations and Discrete Fourier Transform (DFT) which models temporal dependencies in an end-to-end framework. After passing through GFT and DFT, the spectral representations hold clear patterns and can be predicted effectively by convolution and sequential learning modules. Moreover, StemGNN learns inter-series correlations automatically from the data without using pre-defined priors. We conduct extensive experiments on ten real-world datasets to demonstrate the effectiveness of StemGNN. Code is available at https://github.com/microsoft/StemGNN/

cs.LG↗

arXiv2020

MFPP: Morphological Fragmental Perturbation Pyramid for Black-Box Model Explanations

Deep neural networks (DNNs) have recently been applied and used in many advanced and diverse tasks, such as medical diagnosis, automatic driving, etc. Due to the lack of transparency of the deep models, DNNs are often criticized for their prediction that cannot be explainable by human. In this paper, we propose a novel Morphological Fragmental Perturbation Pyramid (MFPP) method to solve the Explainable AI problem. In particular, we focus on the black-box scheme, which can identify the input area that is responsible for the output of the DNN without having to understand the internal architecture of the DNN. In the MFPP method, we divide the input image into multi-scale fragments and randomly mask out fragments as perturbation to generate a saliency map, which indicates the significance of each pixel for the prediction result of the black box model. Compared with the existing input sampling perturbation method, the pyramid structure fragment has proved to be more effective. It can better explore the morphological information of the input image to match its semantic information, and does not need any value inside the DNN. We qualitatively and quantitatively prove that MFPP meets and exceeds the performance of state-of-the-art (SOTA) black-box interpretation method on multiple DNN models and datasets.

cs.CV↗

arXiv2020

Channel Pruning via Optimal Thresholding

Structured pruning, especially channel pruning is widely used for the reduced computational cost and the compatibility with off-the-shelf hardware devices. Among existing works, weights are typically removed using a predefined global threshold, or a threshold computed from a predefined metric. The predefined global threshold based designs ignore the variation among different layers and weights distribution, therefore, they may often result in sub-optimal performance caused by over-pruning or under-pruning. In this paper, we present a simple yet effective method, termed Optimal Thresholding (OT), to prune channels with layer dependent thresholds that optimally separate important from negligible channels. By using OT, most negligible or unimportant channels are pruned to achieve high sparsity while minimizing performance degradation. Since most important weights are preserved, the pruned model can be further fine-tuned and quickly converge with very few iterations. Our method demonstrates superior performance, especially when compared to the state-of-the-art designs at high levels of sparsity. On CIFAR-100, a pruned and fine-tuned DenseNet-121 by using OT achieves 75.99% accuracy with only 1.46e8 FLOPs and 0.71M parameters.

cs.CV↗

arXiv2020

Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis

With the wide adoption of functional magnetic resonance imaging (fMRI) by cognitive neuroscience researchers, large volumes of brain imaging data have been accumulated in recent years. Aggregating these data to derive scientific insights often faces the challenge that fMRI data are high-dimensional, heterogeneous across people, and noisy. These challenges demand the development of computational tools that are tailored both for the neuroscience questions and for the properties of the data. We review a few recently developed algorithms in various domains of fMRI research: fMRI in naturalistic tasks, analyzing full-brain functional connectivity, pattern classification, inferring representational similarity and modeling structured residuals. These algorithms all tackle the challenges in fMRI similarly: they start by making clear statements of assumptions about neural data and existing domain knowledge, incorporating those assumptions and domain knowledge into probabilistic graphical models, and using those models to estimate properties of interest or latent structures in the data. Such approaches can avoid erroneous findings, reduce the impact of noise, better utilize known properties of the data, and better aggregate data across groups of subjects. With these successful cases, we advocate wider adoption of explicit model construction in cognitive neuroscience. Although we focus on fMRI, the principle illustrated here is generally applicable to brain data of other modalities.

stat.AP↗