arXiv subjects

Di Zhou

Publications and source records attributed to Di Zhou.

At least 37 records · Page 2Linked to original sources

arXiv2025

ReXGroundingCT: A 3D Chest CT Dataset for Segmentation of Findings from Free-Text Reports

We introduce ReXGroundingCT, the first publicly available dataset linking free-text findings to pixel-level 3D segmentations in chest CT scans. The dataset includes 3,142 non-contrast chest CT scans paired with standardized radiology reports from CT-RATE. Construction followed a structured three-stage pipeline. First, GPT-4 was used to extract and standardize findings, descriptors, and metadata from reports originally written in Turkish and machine-translated into English. Second, GPT-4o-mini categorized each finding into a hierarchical ontology of lung and pleural abnormalities. Third, 3D annotations were produced for all CT volumes: the training set was quality-assured by board-certified radiologists, and the validation and test sets were fully annotated by board-certified radiologists. Additionally, a complementary chain-of-thought dataset was created to provide step-by-step hierarchical anatomical reasoning for localizing findings within the CT volume, using GPT-4o and localization coordinates derived from organ segmentation models. ReXGroundingCT contains 16,301 annotated entities across 8,028 text-to-3D-segmentation pairs, covering diverse radiological patterns from 3,142 non-contrast CT scans. About 79% of findings are focal abnormalities and 21% are non-focal. The dataset includes a public validation set of 50 cases and a private test set of 100 cases, both annotated by board-certified radiologists. The dataset establishes a foundation for enabling free-text finding segmentation and grounded radiology report generation in CT imaging. Model performance on the private test set is hosted on a public leaderboard at https://rexrank.ai/ReXGroundingCT. The dataset is available at https://huggingface.co/datasets/rajpurkarlab/ReXGroundingCT.

eess.IV↗

arXiv2025

Interlayer coupling enhanced superconductivity near 100 K in La$_{3-x}$Nd$_x$Ni$_2$O$_7$

Systematically controlling the superconducting transition temperature ($T_\text{c}$) in the bilayer Ruddlesden-Popper nickelate La$_3$Ni$_2$O$_7$ remains a significant challenge. Here, we address this by synthesizing high-quality polycrystalline La$_{3-x}$Nd$_x$Ni$_2$O$_7$ ($0 \leq x \leq 2.4$) with record-level rare-earth substitution. Nd doping compresses the lattice, particularly along the $c$ axis, enhances the spin density wave transition temperature, and elevates the pressure required for the orthorhombic-to-tetragonal structural transition. Superconductivity is observed across all doping levels under high pressures, with the onset $T_\text{c}$ rising to $\sim$93~K for $x = 2.1$ and $2.4$ from the electronic transport measurement. Using the radio-frequency transmission technique, newly applied to nickelate superconductors, we detect signatures of superconductivity at $98 \pm 2$~K in the $x=2.4$ compound, pushing the $T_\text{c}$ frontier further. We identify a universal linear relationship where $T_\text{c}$ decreases with the $c$-axis lattice parameter at a rate of approximately $-28$~K/Å, demonstrating that enhanced interlayer magnetic exchange coupling is the dominant mechanism for superconducting pairing. Our work establishes the critical role of magnetism and provides a unified structural descriptor for elevating $T_\text{c}$ in bilayer nickelates.

cond-mat.supr-con↗

arXiv2025

3C Resources Joint Allocation for Time-Deterministic Remote Sensing Image Backhaul in the Space-Ground Integrated Network

Low-Earth-orbit (LEO) satellites assist observation satellites (OSs) to compress and backhaul more time-determined images (TDI) has become a new paradigm, which is used to enhance the timeout caused by the limited computing resources of OSs. However, how to capture the time-varying and dynamic characteristics of multi-dimensional resources is challenging for efficient collaborative scheduling. Motivated by this factor, we design a highly succinct multi-dimensional resource time-expanded graph (MDR-TEG) modell. Specifically, by employing a slots division mechanism and introducing an external virtual node, the time-varying communication, caching, and computing (3C) resources are depicted in low complexity by the link weights within, between, and outside the slots. Based on the MDR-TEG, the maximizing successful transmission ratio of TDI (MSTR-TDI) is modeled as a mixed integer linear programming (MILP) problem. Which further relaxed decomposed into two tractable sub-problems: maximizing the successful transmission rate of images (MSTRI) and ensuring the timeliness problem (ETP). Subsequently, an efficient subgradient of relaxation computing constraint (SRCC) algorithm is proposed. The upper and lower bounds of MSTR-TDI are obtained by solving the two subproblems and the dual problem (DP), and the direction of the next iteration is obtained by feedback. Furthermore, arranging the sending sequences of images to improve the quality of the solution. The approximate optimal solution of MSTR-TDI is eventually obtained through repeated iterations. The simulation results verify the superiority of the proposed MDR-TEG model and the effectiveness of the SRCC.

eess.SY↗

arXiv2025

Physics-informed Neural-operator Predictive Control for Drag Reduction in Turbulent Flows

Assessing turbulence control effects for wall friction numerically is a significant challenge since it requires expensive simulations of turbulent fluid dynamics. We instead propose an efficient deep reinforcement learning (RL) framework for modeling and control of turbulent flows. It is model-based RL for predictive control (PC), where both the policy and the observer models for turbulence control are learned jointly using Physics Informed Neural Operators (PINO), which are discretization invariant and can capture fine scales in turbulent flows accurately. Our PINO-PC outperforms prior model-free reinforcement learning methods in various challenging scenarios where the flows are of high Reynolds numbers and unseen, i.e., not provided during model training. We find that PINO-PC achieves a drag reduction of 39.0\% under a bulk-velocity Reynolds number of 15,000, outperforming previous fluid control methods by more than 32\%.

cs.LG↗

arXiv2025

Topological edge states and amplitude-dependent delocalization in quasiperiodic elliptically geared lattices

We present a class of mechanical lattices based on elliptical gears with quasiperiodic modulation and geometric nonlinearity, capable of exhibiting topologically protected modes and amplitude-driven transitions. Starting from a one-dimensional chain of modulated elliptical gears, we demonstrate the emergence of localized edge states arising from quasiperiodic variation in the gears' moments of inertia, analogous to the topological edge modes of the Aubry-Andre-Harper model. Under increasing excitation amplitude, the system undergoes a nonlinear transition, where edge localization breaks down and energy delocalizes into the bulk. By coupling multiple such chains with varying modulation phase, we construct a two-dimensional lattice in which the phase acts as a synthetic dimension. This structure supports topological wave propagation along the synthetic dimension. Nonlinearity again induces a breakdown of topological states, leading to complex, amplitude-dependent wave propagation. We further propose a numerical continuation approach to analyzing the periodic orbits and their linear stability, effectively discovering the boundary of the basin of bounded motion and detecting the occurrence of delocalization under certain excitation amplitudes. Our results reveal that elliptical geared systems offer a passive, amplitude-dependent platform for exploring topological phenomena and synthetic dimensionality in mechanical metamaterials.

physics.app-ph↗

arXiv2025

SmartFlow: A CFD-solver-agnostic deep reinforcement learning framework for computational fluid dynamics on HPC platforms

Deep reinforcement learning (DRL) is emerging as a powerful tool for fluid-dynamics research, encompassing active flow control, autonomous navigation, turbulence modeling and discovery of novel numerical schemes. We introduce SmartFlow, a CFD-solver-agnostic framework for both single- and multi-agent DRL algorithms that can easily integrate with MPI-parallel CPU and GPU-accelerated solvers. Built on Relexi and SmartSOD2D, SmartFlow uses the SmartSim infrastructure library and our newly developed SmartRedis-MPI library to enable asynchronous, low-latency, in-memory communication between CFD solvers and Python-based DRL algorithms. SmartFlow leverages PyTorch's Stable-Baselines3 for training, which provides a modular, Gym-like environment API. We demonstrate its versatility via three case studies: single-agent synthetic-jet control for drag reduction in a cylinder flow simulated by the high-order FLEXI solver, multi-agent cylinder wake control using the GPU-accelerated spectral-element code SOD2D, and multi-agent wall-model learning for large-eddy simulation with the finite-difference solver CaLES. SmartFlow's CFD-solver-agnostic design and seamless HPC integration is promising to accelerate RL-driven fluid-mechanics studies.

physics.flu-dyn↗

arXiv2025

Real-Time Communication-Aware Ride-Sharing Route Planning for Urban Air Mobility: A Multi-Source Hybrid Attention Reinforcement Learning Approach

Urban Air Mobility (UAM) systems are rapidly emerging as promising solutions to alleviate urban congestion, with path planning becoming a key focus area. Unlike ground transportation, UAM trajectory planning has to prioritize communication quality for accurate location tracking in constantly changing environments to ensure safety. Meanwhile, a UAM system, serving as an air taxi, requires adaptive planning to respond to real-time passenger requests, especially in ride-sharing scenarios where passenger demands are unpredictable and dynamic. However, conventional trajectory planning strategies based on predefined routes lack the flexibility to meet varied passenger ride demands. To address these challenges, this work first proposes constructing a radio map to evaluate the communication quality of urban airspace. Building on this, we introduce a novel Multi-Source Hybrid Attention Reinforcement Learning (MSHA-RL) framework for the challenge of effectively focusing on passengers and UAM locations, which arises from the significant dimensional disparity between the representations. This model first generates the alignment among diverse data sources with large gap dimensions before employing hybrid attention to balance global and local insights, thereby facilitating responsive, real-time path planning. Extensive experimental results demonstrate that the approach enables communication-compliant trajectory planning, reducing travel time and enhancing operational efficiency while prioritizing passenger safety.

cs.RO↗

arXiv2025

Space-ground Fluid AI for 6G Edge Intelligence

Edge artificial intelligence (AI) and space-ground integrated networks (SGINs) are two main usage scenarios of the sixth-generation (6G) mobile networks. Edge AI supports pervasive low-latency AI services to users, whereas SGINs provide digital services to spatial, aerial, maritime, and ground users. This article advocates the integration of the two technologies by extending edge AI to space, thereby delivering AI services to every corner of the planet. Beyond a simple combination, our novel framework, called space-ground fluid AI, leverages the predictive mobility of satellites to facilitate fluid horizontal and vertical task/model migration in the networks. This ensures non-disruptive AI service provisioning in spite of the high mobility of satellite servers. The aim of the article is to introduce the (space-ground) fluid AI technology. First, we outline the network architecture and unique characteristics of fluid AI. Then, we delve into three key components of fluid AI, i.e., fluid learning, fluid inference, and fluid model downloading. They share the common feature of coping with satellite mobility via inter-satellite and space-ground cooperation to support AI services. Finally, we discuss the considerations for the real-world deployment of fluid AI and identify further research opportunities.

cs.NI↗

arXiv2025

Tunable Magneto-optical Kerr effect in two-dimensional non-collinear antiferromagnetic material HfFeCl6

With the development of two-dimensional (2D) magnetic materials, magneto-optical Kerr effect (MOKE) is widely used to measure ferromagnetism in 2D systems. Although this effect is usually inactive in antiferromagnets (AFM), recent theoretical studies have demonstrated that the presence of MOKE relies on the symmetry of the system and antiferromagnets with noncollinear magnetic order can also induce a significant MOKE signal even without a net magnetization. However, this phenomenon is rarely studied in 2D systems due to a scarcity of appropriate materials hosting noncollinear AFM order. Here, based on first-principles calculations, we investigate the HfFeCl6 monolayer with noncollinear Y-AFM ground states, which simultaneously breaks the time-reversal (T) and time-inversion (TI) symmetry, activating the MOKE even though with zero net magnetic moment. In addition, four different MOKE spectra can be obtained in the four permutation states of spin chirality and crystal chirality. The MOKE spectra are switchable when reversing both crystal and spin chirality. Our study provides a material platform to explore the MOKE effect and can potentially be used for electrical readout of AFM states.

cond-mat.mtrl-sci↗

arXiv2025

Coexisting Euler and Stiefel-Whitney Topological Phases in Elastic Metamaterials

The study of topological band theory in classical structures has led to the development of novel topological metamaterials with intriguing properties. While single-gap topologies are well understood, recent novel multi-gap phases have garnished increasing interest. These novel phases are characterized by invariants, such as the Euler and second Stiefel-Whitney classes, which involve Bloch eigen-subspaces of multiple bands and can change by braiding in momentum space non-Abelian charged band degeneracies belonging to adjacent energy gaps. Here, we theoretically predict and experimentally demonstrate that two of such topological phases can coexist within a single system using vectorial elastic waves. The inherent coupling between different polarization modes enables non-Abelian braiding of nodal points of multiple energy band gaps and results in coexisting Euler and Stiefel-Whitney topological insulator phases. We furthermore unveil the central role played by the topologically stable Goldstone modes' degeneracy. Our findings represent the first realization of hybrid phases in vectorial fields exhibiting topologically nontrivial Goldstone modes, paving the way for bifunctional applications that leverage the coexistence of topological edge and corner states.

cond-mat.mtrl-sci↗

arXiv2025

Transmission of radio-frequency waves and nuclear magnetic resonance in lanthanum superhydrides

The discovery of near-room temperature superconductivity in the lanthanum hydride LaH$_{10}$ has revolutionized this field of research. However, the need to use diamond anvils for the synthesis of such superconductors severely limits the number of experimental techniques to study these materials. Nuclear magnetic resonance (NMR) is one of the key methods for probing spin systems of superconductors. In this work, we show how $^1$H NMR measurements can be realized in diamond anvil cells to study high-temperature superconductivity in lanthanum polyhydrides at pressures up to 165 GPa. In the newly discovered superhydride LaH$_{12}$, we observed a pronounced suppression of the $^1$H NMR signal intensity below $\textit{T$_{c}$(onset)}$ = 260 K in a magnetic field of 7 T, corresponding to the screening of the radio-frequency pulses. Below the critical temperature of superconductivity, all $^1$H NMR characteristics, including the spin-lattice relaxation rate $\textit{1/T$_{1}$T}$, demonstrate pronounced features, evidencing the bulk nature of the superconducting transition. In zero field, the radio-frequency signal transmission through the LaH$_{12}$ sample shows a pronounced drop below $\textit{T$_{c}$(onset)}$ = 267 K, confirming the superconducting nature of the transition. A description of the $\textit{1/T$_{1}$T}$ data with an exponential form allows the estimation of the superconducting gap $\textit{$Δ$(0)}$ lying between 427 and 671 K (corresponding to 36.8 to 57.8 meV), and the ratio $\textit{R$_Δ$ = 2$Δ$(0)/k$_B$T$_c$}$ between 3.76 and 5.16 in the synthesized hydride sample.

cond-mat.supr-con↗

arXiv2025

Ternary superhydrides under pressure of Anderson's theorem: Near-record superconductivity in (La,Sc)H$_{12}$

Lanthanum-hydrogen system and its derivatives remain among the most promising for achieving room-temperature superconductivity. In this study, we examined the formation of ternary lanthanum-scandium superhydrides at pressures up to 220 GPa. The primary product of the LaSc alloy's reaction with hydrogen is newly discovered cubic (La,Sc)H$_{12}$, demonstrating clear superconducting transition in all six channels of the van der Pauw scheme below 244-248 K. In this compound with an unusually large unit cell volume, virtually no magnetoresistance was observed in fields up to 68 Tesla. Synthesized samples of (La,Sc)H$_{12}$ demonstrate pronounced superconducting diode and SQUID-like effects at a record high temperature of 233 K. Furthermore, our analysis revealed the formation of lower hexagonal polyhydrides (La,Sc)H$_{6-7}$, which could potentially account for the anomaly in electrical resistance observed near 274 K. This anomaly between 265-290 K also appears in the radio-frequency transmission measurements and may be of a superconducting nature.

cond-mat.supr-con↗

arXiv2025

Structure of an axisymmetric turbulent boundary layer under adverse pressure gradient: a large-eddy simulation study

The spatial characteristics and structure of an axisymmetric turbulent boundary layer under strong adverse pressure gradient and weak transverse curvature are investigated using incompressible large-eddy simulation. The boundary layer is on a $20^{\circ}$ tail cone of a body of revolution at a length-based Reynolds number of $1.9\times10^6$. The simulation results are in agreement with the experimental measurements of Balantrapu et al. (J. Fluid Mech., vol. 929, 2021) and significantly expand the experimental results with new flow-field details and physical insights. The mean streamwise velocity profiles exhibit a shortened logarithmic region and a longer wake region compared with planar boundary layers at zero pressure gradient. With the embedded-shear-layer scaling, self-similarity is observed for the mean velocity and all three components of turbulence intensity. The azimuthal-wavenumber spectra of streamwise velocity fluctuations possess two peaks in the wall-normal direction, an inner peak at the wavelength of approximately 100 wall units and an outer peak in the wake region with growing strength, wavelength and distance to the wall in the downstream direction. Two-point correlations of streamwise velocity fluctuations show significant downstream growth and elongation of turbulence structures with increasing inclination angle. However, relative to the boundary-layer thickness, the correlation structures decrease in size in the downstream direction. The distributions of streamwise and wall-normal integral lengths across the boundary-layer thickness resemble those of zero-pressure-gradient planar boundary layers, whereas the azimuthal integral length deviates from the planar boundary-layer behavior at downstream stations.

physics.flu-dyn↗

arXiv2024

Quadrupole topological behavior of elastic waves in two-dimensional square lattices with nonsymmorphic symmetries

We investigate a novel higher-order topological behavior in elastic lattices characterized by nonsymmorphic symmetries. In the theoretical spring-mass lattice, altering the vertex mass allows for fine-tuning of the topological features within the bandgap. We analyze the quadrupole topological behavior in square lattices with nonsymmorphic symmetries using nested Wannier bands. Beyond second-order topological metamaterials, a single-phase topological configuration promotes energy localization at the corners due to a non-zero relative quadrupole moment. Our findings are validated through experimental observations of higher-order topological corner states, which show excellent agreement with simulated results and theoretical predictions. Additionally, the elastic lattices in the self-similar system exhibit fractal higher-order topological behaviors, revealing numerous topological edge and corner states. The self-similar lattice also demonstrates enhanced energy localization, with the number of topological states showing a linear correlation to the corner dimension. This study provides new insights into elastic higher-order topological insulators and inspires innovative strategies for simulating topological elastic materials.

physics.app-ph↗

arXiv2024

Floquet Chern Vector Topological Insulators in Three Dimensions

We theoretically and numerically investigate Chern vector insulators and topological surface states in a three-dimensional lattice, based on phase-delayed temporal-periodic interactions within the tight-binding model. These Floquet interactions break time-reversal symmetry, effectively inducing a gauge field analogous to magnetic flux. This gauge field results in Chern numbers in all spatial dimensions, collectively forming the Chern vector. This vector characterizes the topological phases and signifies the emergence of robust surface states. Numerically, we observe these states propagating unidirectionally without backscattering on all open surfaces of the three-dimensional system. Our work paves the way for breaking time-reversal symmetry and realizing three-dimensional Chern vector topological insulators using temporal-periodic Floquet techniques.

cond-mat.mtrl-sci↗

arXiv2024

Superconducting memory and trapped magnetic flux in ternary lanthanum polyhydrides

Superconducting memory is a promising technology for data storage because of its speed, high energy efficiency, non-volatility, and compatibility with quantum computing devices. However, the need for cryogenic temperatures makes superconducting memory an extremely expensive and specialized device. Ternary lanthanum polyhydrides, due to their high critical temperatures of 240-250 K, represent a convenient platform for studying effects associated with superconductivity in disordered granular systems. In this work, we investigate a trapped magnetic flux and memory effects in recently discovered lanthanum-neodymium (La,Nd)H$_{10}$ and lanthanum-scandium (La,Sc)H$_{12}$ superhydrides at a pressure of 175-196 GPa. We use a steady magnetic field of a few Tesla (T) and strong pulsed fields up to 68 T to create the trapped flux state in the compressed superhydrides. We find a clockwise hysteresis of magnetoresistance in cerium CeH$_{9-10}$ and lanthanum-cerium (La,Ce)H$_{10+x}$ polyhydrides, a characteristic feature of granular superconductors. A study of the current-voltage characteristics and voltage-temperature curves of the samples with frozen magnetic flux indicates a significant memory effect in La-Sc polyhydrides already at 225-230 K.

cond-mat.supr-con↗

arXiv2024

Observation of the Josephson effect in superhydrides: DC SQUID based on (La,Ce)H$_{10+x}$ with operating temperature of 179 K

Among known materials, hydride superconductors have the highest critical temperatures and are very promising as a basis for electronic sensors. Superconducting quantum interference devices (SQUID), due to its unique sensitivity to magnetic fields, are the most important applications of superconductors in microelectronics. In this work, we describe a direct current SQUID made of lanthanum-cerium superhydride (La, Ce)H$_{10+x}$ at a pressure of 148 GPa, with an operating temperature of 179 K and a bias current of about 2 mA. When placing (La, Ce)H$_{10+x}$ in a modulated magnetic field (0.1-0.005 Hz, 5 G), we observed the generation of higher harmonics up to 18$ν$$_0$ and a periodic dependence of the sample resistance on the magnetic flux density R ${\propto}$ sin($π$$Φ$/$Φ$$_0$). We demonstrate that the (La, Ce)H$_{10+x}$ SQUID with a size of about 6 $μ$m, operates in the mode of low thermal fluctuations and can be used to detect magnetic fields below 0.1 G. Our findings pave the road to more advanced applications of the Josephson effect and SQUIDs made of hydride superconductors.

cond-mat.supr-con↗

arXiv2024

Fully-Polarized Topological Isostatic Metamaterials in Three Dimensions

Topological surface states are unique to topological materials and are immune to disturbances. In isostatic lattices, mechanical topological floppy modes exhibit softness depending on the polarization relative to the terminating surface. However, in three dimensions, the polarization of topological floppy modes is disrupted by the ubiquitous mechanical Weyl lines. Here, we demonstrate, both theoretically and experimentally, the fully-polarized topological mechanical phases free of Weyl lines. Floppy modes emerge exclusively on a particular surface of the three-dimensional isostatic structure, leading to the strongly asymmetric stiffness between opposing boundaries. Additionally, uniform soft strains can reversibly shift the lattice configuration to Weyl phases, reducing the stiffness contrast to a trivially comparable level. Our work demonstrates the fully-polarized topological mechanical phases in three dimensions, and paves the way towards engineering soft and adaptive metamaterials.

cond-mat.soft↗