arXiv subjects

Xiao Jiang

Publications and source records attributed to Xiao Jiang.

At least 37 records · Page 2Linked to original sources

arXiv2024

Strategies for CT Reconstruction using Diffusion Posterior Sampling with a Nonlinear Model

Diffusion Posterior Sampling(DPS) methodology is a novel framework that permits nonlinear CT reconstruction by integrating a diffusion prior and an analytic physical system model, allowing for one-time training for different applications. However, baseline DPS can struggle with large variability, hallucinations, and slow reconstruction. This work introduces a number of strategies designed to enhance the stability and efficiency of DPS CT reconstruction. Specifically, jumpstart sampling allows one to skip many reverse time steps, significantly reducing the reconstruction time as well as the sampling variability. Additionally, the likelihood update is modified to simplify the Jacobian computation and improve data consistency more efficiently. Finally, a hyperparameter sweep is conducted to investigate the effects of parameter tuning and to optimize the overall reconstruction performance. Simulation studies demonstrated that the proposed DPS technique achieves up to 46.72% PSNR and 51.50% SSIM enhancement in a low-mAs setting, and an over 31.43% variability reduction in a sparse-view setting. Moreover, reconstruction time is sped up from >23.5 s/slice to <1.5 s/slice. In a physical data study, the proposed DPS exhibits robustness on an anthropomorphic phantom reconstruction which does not strictly follow the prior distribution. Quantitative analysis demonstrates that the proposed DPS can accommodate various dose levels and number of views. With 10% dose, only a 5.60% and 4.84% reduction of PSNR and SSIM was observed for the proposed approach. Both simulation and phantom studies demonstrate that the proposed method can significantly improve reconstruction accuracy and reduce computational costs, greatly enhancing the practicality of DPS CT reconstruction.

eess.IV↗

arXiv2024

Enhancing the Prediction of Glass Dynamics by Incorporating the Direction of Deviation from Equilibrium Positions

Elucidating the intricate relationship between the structure and dynamics in the context of the glass transition has been a persistent challenge. Machine learning (ML) has emerged as a pivotal tool, offering novel pathways to predict dynamic behaviors from structural descriptors. Notably, recent research has highlighted that the distance between the initial particle positions between the equilibrium positions substantially enhances the prediction of glassy dynamics. However, these methodologies have been limited in their ability to capture the directional aspects of these deviations from the equilibrium positions, which are crucial for a comprehensive understanding of the complex particle interactions within the cage dynamics. Therefore, this paper introduces a novel structural parameter: the vectorial displacement of particles from their initial configuration to their equilibrium positions. Recognizing the inadequacy of current ML models in effectively handling such vectorial parameters, we have developed an Equivariance-Constrained Invariant Graph Neural Network (EIGNN). This innovative model not only bolsters the descriptive capacity of conventional rotation-invariant models but also streamlines the computational demands associated with rotation-equivariant graph neural networks. Our rigorous experimental validation on 3D glassy system from GlassBench dataset has yielded compelling evidence that the EIGNN model significantly enhance the correlation between structural representation and dynamic properties.

cond-mat.soft↗

arXiv2024

CT Reconstruction using Diffusion Posterior Sampling conditioned on a Nonlinear Measurement Model

Diffusion models have been demonstrated as powerful deep learning tools for image generation in CT reconstruction and restoration. Recently, diffusion posterior sampling, where a score-based diffusion prior is combined with a likelihood model, has been used to produce high quality CT images given low-quality measurements. This technique is attractive since it permits a one-time, unsupervised training of a CT prior; which can then be incorporated with an arbitrary data model. However, current methods rely on a linear model of x-ray CT physics to reconstruct or restore images. While it is common to linearize the transmission tomography reconstruction problem, this is an approximation to the true and inherently nonlinear forward model. We propose a new method that solves the inverse problem of nonlinear CT image reconstruction via diffusion posterior sampling. We implement a traditional unconditional diffusion model by training a prior score function estimator, and apply Bayes rule to combine this prior with a measurement likelihood score function derived from the nonlinear physical model to arrive at a posterior score function that can be used to sample the reverse-time diffusion process. This plug-and-play method allows incorporation of a diffusion-based prior with generalized nonlinear CT image reconstruction into multiple CT system designs with different forward models, without the need for any additional training. We develop the algorithm that performs this reconstruction, including an ordered-subsets variant for accelerated processing and demonstrate the technique in both fully sampled low dose data and sparse-view geometries using a single unsupervised training of the prior.

physics.med-ph↗

arXiv2024

Characterization of dim light response in DVS pixel: Discontinuity of event triggering time

Dynamic Vision Sensors (DVS) have recently generated great interest because of the advantages of wide dynamic range and low latency compared with conventional frame-based cameras. However, the complicated behaviors in dim light conditions are still not clear, restricting the applications of DVS. In this paper, we analyze the typical DVS circuit, and find that there exists discontinuity of event triggering time. In dim light conditions, the discontinuity becomes prominent. We point out that the discontinuity depends exclusively on the changing speed of light intensity. Experimental results on real event data validate the analysis and the existence of discontinuity that reveals the non-first-order behaviors of DVS in dim light conditions.

cs.CV↗

arXiv2024

CT Material Decomposition using Spectral Diffusion Posterior Sampling

In this work, we introduce a new deep learning approach based on diffusion posterior sampling (DPS) to perform material decomposition from spectral CT measurements. This approach combines sophisticated prior knowledge from unsupervised training with a rigorous physical model of the measurements. A faster and more stable variant is proposed that uses a jumpstarted process to reduce the number of time steps required in the reverse process and a gradient approximation to reduce the computational cost. Performance is investigated for two spectral CT systems: dual-kVp and dual-layer detector CT. On both systems, DPS achieves high Structure Similarity Index Metric Measure(SSIM) with only 10% of iterations as used in the model-based material decomposition(MBMD). Jumpstarted DPS (JSDPS) further reduces computational time by over 85% and achieves the highest accuracy, the lowest uncertainty, and the lowest computational costs compared to classic DPS and MBMD. The results demonstrate the potential of JSDPS for providing relatively fast and accurate material decomposition based on spectral CT data.

eess.IV↗

arXiv2024

Towards interpreting the thermally activated $β$ dynamics in metallic glass with the structural constraint neural network

Unraveling the structural factors influencing the dynamics of amorphous solids is crucial. While deep learning aids in navigating these complexities, transparency issues persist. Inspired by the successful application of prototype neural networks in the field of image analysis, this study introduces a new machine-learning approach to tackle the interpretability challenges faced in glassy research. Distinguishing from traditional machine learning models that only predict dynamics from the structural input, the adapted neural network additionally tries to learn structural prototypes under various dynamic patterns in the training phase. Such learned structural constraints can serve as a breakthrough in explaining how structural differences impact dynamics. We further use the proposed model to explore the correlation between the local structure and activation energy in the CuZr metallic glass. Building upon this interpretable model, we demonstrated significant structural differences among particles with distinct activation energies. The insights gained from this analysis serve as a data-driven solution for unraveling the origins of the structural heterogeneity in amorphous alloys, offering a valuable contribution to the understanding the amorphous materials.

cond-mat.soft↗

arXiv2023

A Joint Processing Strategy for Image Quality Improvement in 3D Digital Subtraction Angiography

Three-dimensional digital subtraction angiography (3D-DSA) is a widely adopted technique for clinical evaluation of contrast-enhanced vasculatures. The distribution of a contrast agent such as iodine is often estimated via temporal subtraction. Advancements in spectral imaging technologies such as photon counting detectors offer new opportunities to improve DSA image quality. In this work, we propose a novel joint processing strategy to achieve an iodine image using two-bin spectral measurements from a photon counting detector acquired both the pre- and post-contrast injection. Simulation studies were performed using a digital phantom with iodine-enhanced vessels. The proposed method was compared with temporal subtraction and conventional spectral imaging using just the post-contrast measurements. Imaging performance was evaluated in terms of noise-resolution tradeoffs. Preliminary findings have shown measurably improved image quality given by joint processing, reducing noise by 40% and 70% compared to temporal subtraction and conventional spectral imaging, respectively.

physics.med-ph↗

arXiv2023

A multinode quantum network over a metropolitan area

Towards realizing the future quantum internet, a pivotal milestone entails the transition from two-node proof-of-principle experiments conducted in laboratories to comprehensive, multi-node setups on large scales. Here, we report on the debut implementation of a multi-node entanglement-based quantum network over a metropolitan area. We equipped three quantum nodes with atomic quantum memories and their telecom interfaces, and combined them into a scalable phase-stabilized architecture through a server node. We demonstrated heralded entanglement generation between two quantum nodes situated 12.5 km apart, and the storage of entanglement exceeding the round-trip communication time. We also showed the concurrent entanglement generation on three links. Our work provides a metropolitan-scale testbed for the evaluation and exploration of multi-node quantum network protocols and starts a new stage of quantum internet research.

quant-ph↗

arXiv2023

Gaussian Boson Sampling with Pseudo-Photon-Number Resolving Detectors and Quantum Computational Advantage

We report new Gaussian boson sampling experiments with pseudo-photon-number-resolving detection, which register up to 255 photon-click events. We consider partial photon distinguishability and develop a more complete model for the characterization of the noisy Gaussian boson sampling. In the quantum computational advantage regime, we use Bayesian tests and correlation function analysis to validate the samples against all current classical mockups. Estimating with the best classical algorithms to date, generating a single ideal sample from the same distribution on the supercomputer Frontier would take ~ 600 years using exact methods, whereas our quantum computer, Jiuzhang 3.0, takes only 1.27 us to produce a sample. Generating the hardest sample from the experiment using an exact algorithm would take Frontier ~ 3.1*10^10 years.

quant-ph↗

arXiv2023

Extracting the Quantum Geometric Tensor of an Optical Raman Lattice by Bloch State Tomography

In Hilbert space, the geometry of the quantum state is identified by the quantum geometric tensor (QGT), whose imaginary part is the Berry curvature and real part is the quantum metric tensor. Here, we propose and experimentally implement a complete Bloch state tomography to directly measure eigenfunction of an optical Raman lattice for ultracold atoms. Through the measured eigenfunction, the distribution of the complete QGT in the Brillouin zone is reconstructed, with which the topological invariants are extracted by the Berry curvature and the distances of quantum states in momentum space are measured by the quantum metric tensor. Further, we experimentally test a predicted inequality between the Berry curvature and quantum metric tensor, which reveals a deep connection between topology and geometry.

cond-mat.quant-gas↗

arXiv2023

Geometry-enhanced graph neural network for learning the smoothness of glassy dynamics from static structure

Understanding the dynamic processes of the glassy system continues to be challenging. Recent advances have shown the power of graph neural networks (GNNs) for determining the correlation between structure and dynamics in the glassy system. These methods treat the glassy system as a topological graph. However, the inherent "smoothness" property of the dynamics on the graph (variation of dynamics over the graph) is ignored, resulting in a deteriorated performance of GNN for dynamic predictions over various time scales. In this paper, we first present an experimental investigation assessing the smoothness patterns of particle dynamics from the graph perspective. The results indicate that the long-time dynamics exhibit a smoother property on the graph, while the short-time dynamics reveal a distinctly non-smooth pattern. To establish the relationship between the static structure and dynamics with different smoothness patterns, we propose a novel geometry-enhanced graph neural network (Geo-GNN) model. The Geo-GNN architecture consists of a geometry feature encoder that incorporates rotation-invariant distances and angles to enhance geometric feature learning and a geometry-enhanced aggregation block that can adaptively learn the underlying smoothness patterns during message passing. Experimental results demonstrate that our method is able to capture the inherent smoothness pattern of particles and outperforms state-of-the-art baselines in predicting the dynamics across all time scales. A subsequent study has revealed that geometric features are critical for the model to accurately capture smoothness patterns in dynamics. Our research not only refines the method for predicting glassy dynamics but also provides a new lens through which to investigate the issue of causality regarding dynamical heterogeneity in glasses.

cond-mat.dis-nn↗

arXiv2023

Giant Bulk Electro-photovoltaic Effect in Hetero-nodal-line Systems

Realization of giant and continuously tunable second-order photocurrent is desired for many nonlinear optical (NLO) and optoelectronic applications, which remains to be a great challenge. Here, based on a simple two-band model, we propose a concept of bulk electro-photovoltaic effect, that is, an out-of-plane external electric-field ($E_{ext}$) can continuously tune in-plane shift current along with its sign flip in a hetero-nodal-line (HNL) system. While strong linear optical transition around the nodal-loop may potentially generate giant shift current, an $E_{ext}$ can effectively control the radius of nodal-loop, which can continuously modulate the shift-vector components inside and outside nodal-loop holding opposite signs. This concept has been demonstrated in the HNL HSnN/MoS$_2$ system using first-principles calculations. The HSnN/MoS$_2$ hetero-bilayer not only can produce giant shift current with 1~2 magnitude order larger than other reported systems, but also can realize a giant bulk electro-photovoltaic effect. Our finding opens new routes to create and manipulate giant NLO responses in 2D materials.

cond-mat.mtrl-sci↗

arXiv2023

Solving Graph Problems Using Gaussian Boson Sampling

Gaussian boson sampling (GBS) is not only a feasible protocol for demonstrating quantum computational advantage, but also mathematically associated with certain graph-related and quantum chemistry problems. In particular, it is proposed that the generated samples from the GBS could be harnessed to enhance the classical stochastic algorithms in searching some graph features. Here, we use Jiuzhang, a noisy intermediate-scale quantum computer, to solve graph problems. The samples are generated from a 144-mode fully-connected photonic processor, with photon-click up to 80 in the quantum computational advantage regime. We investigate the open question of whether the GBS enhancement over the classical stochastic algorithms persists -- and how it scales -- with an increasing system size on noisy quantum devices in the computationally interesting regime. We experimentally observe the presence of GBS enhancement with large photon-click number and a robustness of the enhancement under certain noise. Our work is a step toward testing real-world problems using the existing noisy intermediate-scale quantum computers, and hopes to stimulate the development of more efficient classical and quantum-inspired algorithms.

quant-ph↗

arXiv2023

Unusual Nonlinear Optical Responses in Layered Ferroelectric Niobium Oxide Dihalides: Origin and Manipulation

Realization of large and highly tunable second-order nonlinear optical (NLO) responses, e.g., second-harmonic generation (SHG) and bulk photovoltaic effect (BPVE), is critical for developing modern optical and optoelectronic devices. Very recently, the two-dimensional van der Waals ferroelectric NbOX2 (X = Cl, Br or I) are discovered to exhibit unusually large and anisotropic SHG. However, the physical origin and possible tunability of NLO responses in NbOX2 remain to be unclear. In this article, we reveal that the large SHG in NbOCl2 is dominated by the synergy between large transition dipole moment and band-nesting-induced large intensity of electron-hole pairs. Remarkably, the NbOCl2 can exhibit dramatically different strain-dependent BPVE under different polarized light, originating from the interesting light-polarization-dependent orbital transition. Importantly, we successfully achieve a reversible ferroelectric-to-antiferroelectric phase transition via controlling ambient temperature or external pressure, accompanied by the greatly tunable NLO responses. Furthermore, we discover that the evolutions of SHG and BPVE in NbOX2 with variable X obey different rules. Our study provides a deep understanding on the novel NLO physics in NbOX2 and establishes great external-field tunability for device applications.

cond-mat.mtrl-sci↗

arXiv2023

DisPad: Flexible On-Body Displacement of Fabric Sensors for Robust Joint-Motion Tracking

The last few decades have witnessed an emerging trend of wearable soft sensors; however, there are important signal-processing challenges for soft sensors that still limit their practical deployment. They are error-prone when displaced, resulting in significant deviations from their ideal sensor output. In this work, we propose a novel prototype that integrates an elbow pad with a sparse network of soft sensors. Our prototype is fully bio-compatible, stretchable, and wearable. We develop a learning-based method to predict the elbow orientation angle and achieve an average tracking error of 9.82 degrees for single-user multi-motion experiments. With transfer learning, our method achieves the average tracking errors of 10.98 degrees and 11.81 degrees across different motion types and users, respectively. Our core contributions lie in a solution that realizes robust and stable human joint motion tracking across different device displacements.

cs.HC↗

arXiv2022

Nonlocal optical conductivity of Fermi surface nesting materials

We investigate the nonlocal optical conductivity of Fermi surface nesting materials which support charge density waves or spin density waves. The nonlocal optical conductivity contains information of correlations in electron fluids which could not be accessed by standard optical probes. Half metal emerges from doping a charge density wave and similarly spin-valley half metal emerges from doping a spin density wave. Based on the parabolic band approximation, we find the Drude peak is shifted to higher frequency and splits into two peaks in the nonlocal optical conductivity. We attribute this to the two Fermi velocities in the half-metal or spin-valley half metal states.

cond-mat.str-el↗

arXiv2022

Functional building blocks for scalable multipartite entanglement in optical lattices

Featuring excellent coherence and operated parallelly, ultracold atoms in optical lattices form a competitive candidate for quantum computation. For this, a massive number of parallel entangled atom pairs have been realized in superlattices. However, the more formidable challenge is to scale-up and detect multipartite entanglement due to the lack of manipulations over local atomic spins in retro-reflected bichromatic superlattices. Here we developed a new architecture based on a cross-angle spin-dependent superlattice for implementing layers of quantum gates over moderately-separated atoms incorporated with a quantum gas microscope for single-atom manipulation. We created and verified functional building blocks for scalable multipartite entanglement by connecting Bell pairs to one-dimensional 10-atom chains and two-dimensional plaquettes of $2\times4$ atoms. This offers a new platform towards scalable quantum computation and simulation.

cond-mat.quant-gas↗

arXiv2022

Engineering Interlayer Hybridization in Energy Space via Dipolar Overlayers

The interlayer hybridization (IH) of van der Waals (vdW) materials is thought to be mostly associated with the unignorable interlayer overlaps of wavefunctions ($t$) in real space. Here, we develop a more fundamental understanding of IH by introducing a new physical quantity, the IH admixture ratio $α$. Consequently, an exotic strategy of IH engineering in energy space can be proposed, i.e., instead of changing t as commonly used, $α$ can be effectively tuned in energy space by changing the onsite energy difference ($2Δ$) between neighboring-layer states. In practice, this is feasible via reshaping the electrostatic potential of the surface by deposing a dipolar overlayer, e.g., crystalline ice. Our first-principles calculations unveil that IH engineering via adjusting $2Δ$ can greatly tune interlayer optical transitions in transition-metal dichalcogenide bilayers, switch different types of Dirac surface states in Bi$_2$Se$_3$ thin films, and control magnetic phase transition of charge density waves in 1H/1T-TaS$_2$ bilayers, opening new opportunities to govern the fundamental optoelectronic, topological, and magnetic properties of vdW systems beyond the traditional interlayer-distance or twisting engineering.

cond-mat.mtrl-sci↗