arXiv subjects

Zhendong Zhang

Publications and source records attributed to Zhendong Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Develop and Optimize 5DCT Imaging Simulation and Reconstruction Methods

Purpose: To develop and optimize a 5DCT (3D + cardiac phase + respiratory phase) imaging simulation and reconstruction pipeline, and to compare two sinogram-space interpolation methods for reconstructing images at arbitrary combinations of cardiac and respiratory phase. Methods: Helical CT projections were simulated from the 4D XCAT phantom across a range of cardiac and respiratory motion states, with Poisson and electronic noise added. Ground-truth-matched volumes were generated at 5 cardiac phases and 10 respiratory amplitudes (50 total phase combinations). Because acquired projections are sparsely and unevenly distributed across this joint phase space, each target slice was reconstructed by interpolating rebinned sinogram rows to the target cardiac phase and respiratory amplitude, using either 2D scattered barycentric interpolation or 2D scattered local linear interpolation with a circular kernel for cardiac phase. Reconstructed volumes were compared to phantom ground truth using mean absolute error (MAE), and to conventional respiratory-gated 4DCT (r4DCT) reconstructed from the same simulated data. Results: Both interpolation methods eliminated the severe axial misalignment artifacts present when helical projections were reconstructed without phase-space interpolation. Local linear interpolation achieved lower MAE than barycentric interpolation across most tested conditions, with the largest improvement at low pitch. The 5DCT pipeline also produced respiratory-only volumes with fewer residual cardiac-motion artifacts than conventional r4DCT reconstructed from the same projection data, including at standard clinical pitch (0.1). Conclusions: 5DCT reconstruction using sinogram-space interpolation is feasible and can jointly resolve cardiac and respiratory motion with better accuracy than conventional 4DCT reconstruction.

physics.med-ph↗

arXiv2026

Matter-wave Induced Transparency

Electromagnetically induced transparency suppresses optical absorption through destructive interference, playing a central role in light-matter interaction and quantum information science. We report matter-wave induced transparency, where atomic collisional interactions induce transmission through a lossy molecular potential for the incident atomic scattering waves. Using cesium Bose-Einstein condensates and modulation-induced Feshbach resonances, we realize a three-level atom-molecule coupled system with unprecedented flexibility. Under the dark state condition, a narrow and tunable transparency window appears within a broad dissipative collisional resonance. The transparency window linewidth is controlled by modulation-induced coupling. And scattering pathways are selectable via multifrequency Floquet modulation. These results establish an interference-based route for exploring programmable nonequilibrium and non-Hermitian physics, steering quantum chemistry and precision measurements.

quant-ph↗

arXiv2025

Tunable Molecular Interactions Near an Atomic Feshbach Resonance: Stability and Collapse of a Molecular Bose-Einstein Condensate

Understanding and controlling interactions of ultracold molecules is a cornerstone of quantum chemistry. While the laboratory creation of degenerate molecular gases comprised of bosonic atoms has unlocked powerful new platforms for quantum simulation, progress is limited by the absence of a robust theoretical framework for characterizing inter-molecular interactions. This is in stark contrast to the situation for Fermi gases. In this Letter, we present such a framework providing universal expressions for these molecular scattering lengths as functions of experimentally measurable quantities. Our discoveries are crucial for understanding molecular condensate formation. Calculations of the compressibility reveal that a sign change in such molecular scattering lengths is directly correlated with the instability of these condensates. These results offer fresh insight with broad applications for atomic, molecular, and condensed matter physics, as well as quantum chemistry.

cond-mat.quant-gas↗

arXiv2025

Observation of modulation-induced Feshbach resonance

In this work, we observe a novel resonant mechanism, namely the modulation-induced Feshbach resonance. By applying a far-detuned laser to the cesium D2 transition with intensity modulation, we periodically shake the energy levels of atomic collisional states. This periodic shaking connects the free-scattering states to shallow molecular states. At specific frequencies, we observe significant atom loss, which corresponds to the resonant coupling between these two types of states. This precisely corresponds to a form of Feshbach resonance, yet in the frequency domain rather than the magnetic-field domain. Using this method, we can directly scan the energy spectrum of molecular bound states without synthesizing any molecules. In addition to these bound states, we can also probe the molecular states embedded in the continuum, which are typically very difficult to detect by the conventional methods based on molecular synthesis. Moreover, by using a far-detuned laser instead of a magnetic field coil, it enables spatially dependent control over atomic interactions, coupling multiple levels simultaneously, and inducing new Feshbach resonances for those atoms that do not have conventional magnetic resonances. Therefore, we believe that this new resonant mechanism offers new opportunities for controlling atomic and molecular interactions in quantum simulations.

quant-ph↗

arXiv2025

Sailor: Automating Distributed Training over Dynamic, Heterogeneous, and Geo-distributed Clusters

The high GPU demand of ML training makes it hard to allocate large homogeneous clusters of high-end GPUs in a single availability zone. Leveraging heterogeneous GPUs available within and across zones can improve throughput at a reasonable cost. However, training ML models on heterogeneous resources introduces significant challenges, such as stragglers and a large search space of possible job configurations. Current systems lack support for efficiently training models on heterogeneous resources. We present Sailor, a system that automates distributed training over heterogeneous, geo-distributed, and dynamically available resources. Sailor combines an efficient search space exploration algorithm, accurate runtime and memory footprint simulation, and a distributed training framework that supports different types of heterogeneity to optimize training throughput and cost.

cs.DC↗

arXiv2025

Universal coherent atom-molecule oscillations in the dynamics of the unitary Bose gas near a narrow Feshbach resonance

Quench experiments on a unitary Bose gas around a broad Feshbach resonance have led to the discovery of universal dynamics. This universality is manifested in the measured atomic momentum distributions where, asymptotically, a quasi-equilibrated metastable state is found in which both the momentum distribution and the time scales are determined by the particle density. In this paper we present counterpart studies but for the case of a very narrow Feshbach resonance of $^{133}$Cs atoms having a width of 8.3 mG. In dramatic contrast to the behavior reported earlier, a rapid quench of an atomic condensate to unitarity is observed to ultimately lead to coherent oscillations involving dynamically produced condensed and non-condensed molecules and atoms. The same characteristic frequency, determined by the Feshbach coupling, is observed in all types of particles. To understand these quench dynamics and how these different particle species are created, we develop a beyond Hartree-Fock-Bogoliubov dynamical framework including a new type of cross correlation between atoms and molecules. This leads to a quantitative consistency with the measured frequency. Our results, which can be applied to the general class of bosonic superfluids associated with narrow Feshbach resonances, establish a new paradigm for universal dynamics dominated by quantum many-body interactions.

cond-mat.quant-gas↗

arXiv2025

Acceleration Multiple Heads Decoding for LLM via Dynamic Tree Attention

Multiple heads decoding accelerates the inference of Large Language Models (LLMs) by predicting next several tokens simultaneously. It generates and verifies multiple candidate sequences in parallel via tree attention with a fixed structure. In this paper, we replace the fixed tree attention with dynamic tree attention on multiple head decoding, specifically in the context of MEDUSA. We propose a simple and low complexity strategy to generate candidates and construct the dynamic tree structure. Preliminary experiments show that the proposed method improves the decoding efficiency of multiple head decoding for LLMs while maintaining the generation quality. This result demonstrates the potential for improvement of multiple head decoding in candidate generation.

cs.CV↗

arXiv2025

A Vessel Bifurcation Landmark Pair Dataset for Abdominal CT Deformable Image Registration (DIR) Validation

Deformable image registration (DIR) is an enabling technology in many diagnostic and therapeutic tasks. Despite this, DIR algorithms have limited clinical use, largely due to a lack of benchmark datasets for quality assurance during development. To support future algorithm development, here we introduce our first-of-its-kind abdominal CT DIR benchmark dataset, comprising large numbers of highly accurate landmark pairs on matching blood vessel bifurcations. Abdominal CT image pairs of 30 patients were acquired from several public repositories as well as the authors' institution with IRB approval. The two CTs of each pair were originally acquired for the same patient on different days. An image processing workflow was developed and applied to each image pair: 1) Abdominal organs were segmented with a deep learning model, and image intensity within organ masks was overwritten. 2) Matching image patches were manually identified between two CTs of each image pair 3) Vessel bifurcation landmarks were labeled on one image of each image patch pair. 4) Image patches were deformably registered, and landmarks were projected onto the second image. 5) Landmark pair locations were refined manually or with an automated process. This workflow resulted in 1895 total landmark pairs, or 63 per case on average. Estimates of the landmark pair accuracy using digital phantoms were 0.7+/-1.2mm. The data is published in Zenodo at https://doi.org/10.5281/zenodo.14362785. Instructions for use can be found at https://github.com/deshanyang/Abdominal-DIR-QA. This dataset is a first-of-its-kind for abdominal DIR validation. The number, accuracy, and distribution of landmark pairs will allow for robust validation of DIR algorithms with precision beyond what is currently available.

cs.CV↗

arXiv2025

Flash Window Attention: speedup the attention computation for Swin Transformer

To address the high resolution of image pixels, the Swin Transformer introduces window attention. This mechanism divides an image into non-overlapping windows and restricts attention computation to within each window, significantly enhancing computational efficiency. To further optimize this process, one might consider replacing standard attention with flash attention, which has proven to be more efficient in language models. However, a direct substitution is ineffective. Flash attention is designed for long sequences, whereas window attention deals with shorter sequences but must handle numerous of them in parallel. In this report, we present an optimized solution called Flash Window Attention, tailored specifically for window attention. Flash Window Attention improves attention computation efficiency by up to 300% and enhances end-to-end runtime efficiency by up to 30%. Our code is available online.

cs.CV↗

arXiv2024

Stability and Dynamics of Atom-Molecule Superfluids Near a Narrow Feshbach Resonance

The recent observations of a stable molecular condensate emerging from a condensate of bosonic atoms and related "super-chemical" dynamics have raised an intriguing set of questions. Here we provide a microscopic understanding of this unexpected stability and dynamics in atom-molecule superfluids; we show one essential element behind these phenomena is an extremely narrow Feshbach resonance in $^{133}$Cs at 19.849G. Comparing theory and experiment we demonstrate how this narrow resonance enables the dynamical creation of a large closed-channel molecular fraction superfluid, appearing in the vicinity of unitarity. Theoretically the observed superchemistry (\textit{i.e.}, Bose enhanced reactions of atoms and molecules), is found to be assisted by the formation of Cooper-like pairs of bosonic atoms that have opposite momenta. Importantly, this narrow resonance opens the possibility to explore the quantum critical point of a molecular Bose superfluid and related phenomena which would not be possible near a more typically broad Feshbach resonance.

cond-mat.quant-gas↗

arXiv2024

A comprehensive liver CT landmark pair dataset for evaluating deformable image registration algorithms

Purpose: Evaluating deformable image registration (DIR) algorithms is vital for enhancing algorithm performance and gaining clinical acceptance. However, there's a notable lack of dependable DIR benchmark datasets for assessing DIR performance except for lung images. To address this gap, we aim to introduce our comprehensive liver computed tomography (CT) DIR landmark dataset library. Acquisition and Validation Methods: Thirty CT liver image pairs were acquired from several publicly available image archives as well as authors' institutions under institutional review board approval. The images were processed with a semi-automatic procedure to generate landmark pairs: 1) for each case, liver vessels were automatically segmented on one image; 2) landmarks were automatically detected at vessel bifurcations; 3) corresponding landmarks in the second image were placed using the deformable image registration method; 4) manual validation was applied to reject outliers and confirm the landmarks' positional accuracy. This workflow resulted in an average of ~68 landmark pairs per image pair, in a total of 2028 landmarks for all 30 cases. The general landmarking accuracy of this procedure was evaluated using digital phantoms. Estimates of the mean and standard deviation of landmark pair target registration errors (TRE) on digital phantoms were 0.64 and 0.40 mm. 99% of landmark pairs had TREs below 2 mm. Data Format and Usage Notes: All data are publicly available at Zenodo. Instructions for using our data and MATLAB code can be found on our GitHub page. Potential Applications: The landmark dataset generated in this work is the first collection of large-scale liver CT DIR landmarks prepared on real patient images. This dataset can provide researchers with a dense set of ground truth benchmarks for the quantitative evaluation of DIR algorithms within the liver.

physics.med-ph↗

arXiv2023

AdaptGear: Accelerating GNN Training via Adaptive Subgraph-Level Kernels on GPUs

Graph neural networks (GNNs) are powerful tools for exploring and learning from graph structures and features. As such, achieving high-performance execution for GNNs becomes crucially important. Prior works have proposed to explore the sparsity (i.e., low density) in the input graph to accelerate GNNs, which uses the full-graph-level or block-level sparsity format. We show that they fail to balance the sparsity benefit and kernel execution efficiency. In this paper, we propose a novel system, referred to as AdaptGear, that addresses the challenge of optimizing GNNs performance by leveraging kernels tailored to the density characteristics at the subgraph level. Meanwhile, we also propose a method that dynamically chooses the optimal set of kernels for a given input graph. Our evaluation shows that AdaptGear can achieve a significant performance improvement, up to $6.49 \times$ ($1.87 \times$ on average), over the state-of-the-art works on two mainstream NVIDIA GPUs across various datasets.

cs.DC↗

arXiv2022

Many-body Chemical Reactions in a Quantum Degenerate Gas

Chemical reactions in the quantum degenerate regime are described by mixing of matterwave fields. Quantum coherence and bosonic enhancement are two unique features of many-body reactions involving bosonic reactants and products. Such collective reactions of chemicals, dubbed "super-chemistry", is an elusive goal in quantum chemistry research. Here we report the observation of coherent and collective reactive coupling between Bose condensed atoms and molecules near a Feshbach resonance. Starting from an atomic condensate, the reaction begins with a rapid formation of molecules, followed by oscillations of their populations in the equilibration process. Faster oscillations are observed in samples with higher densities, indicating bosonic enhancement. We present a quantum field model which describes the dynamics well and identifies three-body recombination as the dominant reaction process. Our findings exemplify the highly sought-after quantum many-body chemistry and offer a new paradigm for the control of quantum chemical reactions.

physics.atom-ph↗

arXiv2022

Dynamical preparation of an atomic condensate in a Hofstadter band

The creation of a Hamiltonian in the quantum regime which has non-trivial topological features is a central goal of the cold-atom community, enabling widespread exploration of novel phases of quantum matter. A general scheme to synthesize such Hamiltonians is based on dynamical modulation of optical lattices which thereby generate vector potentials. At the same time the modulation can lead to heating and serious difficulties with equilibration. Here we show that these challenges can be overcome by demonstrating how a Hofstadter Bose-Einstein condensate (BEC) can be dynamically realized, using experimental protocols. From Gross-Pitaevskii simulations our study reveals a complex, multistage evolution; this includes a chaotic intermediate "heating" stage followed by a spontaneous reentrance to the BEC. The observed behavior is reminiscent of evolution in cosmological models.

cond-mat.quant-gas↗

arXiv2021

Learning Multi-Layered GBDT Via Back Propagation

Deep neural networks are able to learn multi-layered representation via back propagation (BP). Although the gradient boosting decision tree (GBDT) is effective for modeling tabular data, it is non-differentiable with respect to its input, thus suffering from learning multi-layered representation. In this paper, we propose a framework of learning multi-layered GBDT via BP. We approximate the gradient of GBDT based on linear regression. Specifically, we use linear regression to replace the constant value at each leaf ignoring the contribution of individual samples to the tree structure. In this way, we estimate the gradient for intermediate representations, which facilitates BP for multi-layered GBDT. Experiments show the effectiveness of the proposed method in terms of performance and representation ability. To the best of our knowledge, this is the first work of optimizing multi-layered GBDT via BP. This work provides a new possibility of exploring deep tree based learning and combining GBDT with neural networks.

cs.LG↗

arXiv2021

Frequency Pooling: Shift-Equivalent and Anti-Aliasing Downsampling

Convolution utilizes a shift-equivalent prior of images, thus leading to great success in image processing tasks. However, commonly used poolings in convolutional neural networks (CNNs), such as max-pooling, average-pooling, and strided-convolution, are not shift-equivalent. Thus, the shift-equivalence of CNNs is destroyed when convolutions and poolings are stacked. Moreover, anti-aliasing is another essential property of poolings from the perspective of signal processing. However, recent poolings are neither shift-equivalent nor anti-aliasing. To address this issue, we propose a new pooling method that is shift-equivalent and anti-aliasing, named frequency pooling. Frequency pooling first transforms the features into the frequency domain, and then removes the frequency components beyond the Nyquist frequency. Finally, it transforms the features back to the spatial domain. We prove that frequency pooling is shift-equivalent and anti-aliasing based on the property of Fourier transform and Nyquist frequency. Experiments on image classification show that frequency pooling improves accuracy and robustness with respect to the shifts of CNNs.

cs.CV↗

arXiv2021

Dynamics of domain walls in a Bose-Einstein condensate driven by density-dependent gauge field

Dynamical coupling between matter and gauge fields underlies the emergence of many exotic particle-like excitations in condensed matter and high energy physics. An important stepping stone to simulate this physics in atomic quantum gases relies on the synthesis of density-dependent gauge fields. Here we demonstrate deterministic formation of domain walls in a stable Bose-Einstein condensate with a synthetic gauge field that depends on the atomic density. The gauge field is created by simultaneous modulations of the optical lattice potential and interatomic interactions, and results in domains of atoms condensed into two different momenta. Modeling the domain walls as elementary excitations, we find that the domain walls respond to synthetic electric field with a charge-to-mass ratio larger than and opposite to that of the bare atoms. Our work offers promising prospects to simulate the dynamics and interactions of novel excitations in quantum systems with dynamical gauge fields.

cond-mat.quant-gas↗

arXiv2020

Atomic Bose-Einstein condensate to molecular Bose-Einstein condensate transition

Preparation of molecular quantum gas promises novel applications including quantum control of chemical reactions, precision measurements, quantum simulation and quantum information processing. Experimental preparation of colder and denser molecular samples, however, is frequently hindered by fast inelastic collisions that heat and deplete the population. Here we report the formation of two-dimensional Bose-Einstein condensates (BECs) of spinning $g-$wave molecules by inducing pairing interactions in an atomic condensate. The trap geometry and the low temperature of the molecules help reducing inelastic loss to ensure thermal equilibrium. We determine the molecular scattering length to be $+220(30)$~Bohr and investigate the unpairing dynamics in the strong coupling regime. Our work confirms the long-sought transition between atomic and molecular condensates, the bosonic analog of the BEC-BCS (Bardeen-Cooper-Schieffer superfluid) crossover in a Fermi gas.

physics.atom-ph↗