arXiv subjects

Yue Sun

Publications and source records attributed to Yue Sun.

At least 109 records · Page 6Linked to original sources

arXiv2025

Critical Current Density and AC Magnetic Susceptibility of High-quality FeTe$_{0.5}$Se$_{0.5}$ Superconducting Tapes

Iron telluride-selenium superconducting materials, known for their non-toxicity, ease of preparation, simple structure, and high upper critical fields, have attracted much research interest in practical application. In this work, we conducted electrical transport measurements, magneto-optical imaging, and AC magnetic susceptibility measurements on FeTe$_{0.5}$Se$_{0.5}$ superconducting long tapes fabricated via reel-to-reel pulsed laser deposition. Our transport measurements revealed a high critical current density that remains relatively stable even with increasing external magnetic fields, reaching over $1\times 10^5$ A/cm$^2$ at 8 K and 9 T. The calculated pinning force density indicates that normal point pinning is the primary mechanism in these tapes. The magneto-optical images demonstrated that the tapes show homogeneous superconductivity and uniform distribution of critical current density. The AC magnetic susceptibility measurements also confirmed their strong flux pinning nature of withstanding high magnetic field. Based on these characteristics, FeTe$_{0.5}$Se$_{0.5}$ superconducting tapes show promising prospects for applications under high magnetic fields.

cond-mat.supr-con↗

arXiv2025

Effects of disorder on the quantum transport properties in topologically nontrivial metal PbTaSe$_{2}$

Weak antilocalization (WAL), an increase in the electrical conductivity at low temperatures associated with the suppression of electron localization due to quantum interference effects, is often observed in topological materials. In this study, we report the observation of WAL in topologically nontrivial metal PbTaSe$_{2}$ at low temperatures. In the pristine sample, we identified the presence of WAL, which is attributed to the topologically protected backscattering. In order to investigate the influence of disorder on the WAL, we successively introduced controlled amounts of disorder by H$^{+}$-irradiation. As disorder increases, the dip-like magnetoresistance caused by WAL changes to a linear magnetoresistance(MR), and eventually to a quadratic MR as the electronic system becomes highly localized. This research unveils the significance of disorder in shaping the quantum transport characteristics of topological materials.

cond-mat.mes-hall↗

arXiv2024

URoadNet: Dual Sparse Attentive U-Net for Multiscale Road Network Extraction

The challenges of road network segmentation demand an algorithm capable of adapting to the sparse and irregular shapes, as well as the diverse context, which often leads traditional encoding-decoding methods and simple Transformer embeddings to failure. We introduce a computationally efficient and powerful framework for elegant road-aware segmentation. Our method, called URoadNet, effectively encodes fine-grained local road connectivity and holistic global topological semantics while decoding multiscale road network information. URoadNet offers a novel alternative to the U-Net architecture by integrating connectivity attention, which can exploit intra-road interactions across multi-level sampling features with reduced computational complexity. This local interaction serves as valuable prior information for learning global interactions between road networks and the background through another integrality attention mechanism. The two forms of sparse attention are arranged alternatively and complementarily, and trained jointly, resulting in performance improvements without significant increases in computational complexity. Extensive experiments on various datasets with different resolutions, including Massachusetts, DeepGlobe, SpaceNet, and Large-Scale remote sensing images, demonstrate that URoadNet outperforms state-of-the-art techniques. Our approach represents a significant advancement in the field of road network extraction, providing a computationally feasible solution that achieves high-quality segmentation results.

cs.CV↗

arXiv2024

Intrinsic pinning of FeSe$_1$$_-$$_x$S$_x$ single crystals probed by torque magnetometry

Intrinsic pinning is caused by natural pinning centers that occur because of the modulation of the order parameter or weak superconducting layers. Early work has shown that intrinsic pinning generates a high pinning force and critical current density in some layered oxide superconductors. Studying the intrinsic pinning of superconductors is crucial for both fundamental studies and potential applications. Herein, we use torque magnetometry to study angle-resolved in-plane and out-of-plane magnetic torque for a series of high-quality FeSe$_1$$_-$$_x$S$_x$ single crystals. A fourfold torque signal was observed when the magnetic field was within the \textit{ab} plane. We interpret that this fourfold in-plane irreversible torque is from the intrinsic pinning due to combined effects of gap nodes/minimum and twin domains. Additionally, we attributed the observed out-of-plane torque peaks to intrinsic pinning due to the layered structure.

cond-mat.supr-con↗

arXiv2024

Bulk and surface Dirac states accompanied by two superconducting domes in FeSe-based superconductors

Recent investigations of FeSe-based superconductors have revealed the presence of two superconducting domes, and suggest possible distinct pairing mechanisms. Two superconducting domes are commonly found in unconventional superconductors and exhibit unique normal states and electronic structures. In this study, we conducted electromagnetic transport measurements to establish a complete phase diagram, successfully observing the two superconducting domes in FeSe$_{1-x}$S$_x$ (0 $\le x \le$ 0.25) and FeSe$_{1-x}$Te$_x$ (0 $\le x \le$ 1) superconductors. The normal state resistivity on SC1 shows the strange metal state, with a power exponent approximately equal to 1 ($ρ(T)\propto T^n$ with $n\sim 1$), whereas the exponent on SC2 is less than 1. A bulk Dirac state observed on SC1, completely synchronized with the strange metal behavior, indicating a close relationship between them. While a topological surface Dirac state is witnessed on SC2, and undergoes a sign change near the pure nematic quantum critical point. The evolution of the Dirac states indicates that the appearance of the two superconducting domes may originate from the Fermi surface reconstruction. Our findings highlight distinct Dirac states and normal state resistivity across the two superconducting domes, providing convincing evidence for the existence of the two different pairing mechanisms in FeSe-based superconductors.

cond-mat.supr-con↗

arXiv2024

Multiple magnetic orders discovered in the superconducting state of EuFe$_{2}$(As$_{1-x}$P$_{x}$)$_{2}$

The interplay between superconductivity and magnetism is an important subject in condensed matter physics. EuFe$_{2}$As$_{2}$-based iron pnictides could offer an interesting plateau to study their relationship that has attracted considerable attention. So far, two magnetic phase transitions were observed in EuFe$_{2}$As$_{2}$-based crystal, which were deemed to originate from the itinerant Fe moments ($\sim$ 190 K) and the localized Eu$^{2+}$ moments ($\sim$ 19 K), respectively. Here, we systematically studied the heat capacity for the EuFe$_{2}$(As$_{1-x}$P$_{x}$)$_{2}$ crystals with \textit{x} = 0.21 (optimally doped) and \textit{x} = 0.29 (overdoped). We have found two new magnetic orders in the superconducting state (ranging from 0.4 to 1.2 K) in the optimally doped crystal. As more P was introduced into the As site, one of the magnetic orders becomes absent in the overdoped crystal. Additionally, we observed strong field and orientation dependence in heat capacity. The present findings in EuFe$_{2}$(As$_{1-x}$P$_{x}$)$_{2}$ have detected the new low-temperature magnetic orders, which may originate from the localized Eu$^{2+}$ spins order or the spin reorientation.

cond-mat.supr-con↗

arXiv2024

Decomposition Pipeline for Large-Scale Portfolio Optimization with Applications to Near-Term Quantum Computing

Industrially relevant constrained optimization problems, such as portfolio optimization and portfolio rebalancing, are often intractable or difficult to solve exactly. In this work, we propose and benchmark a decomposition pipeline targeting portfolio optimization and rebalancing problems with constraints. The pipeline decomposes the optimization problem into constrained subproblems, which are then solved separately and aggregated to give a final result. Our pipeline includes three main components: preprocessing of correlation matrices based on random matrix theory, modified spectral clustering based on Newman's algorithm, and risk rebalancing. Our empirical results show that our pipeline consistently decomposes real-world portfolio optimization problems into subproblems with a size reduction of approximately 80%. Since subproblems are then solved independently, our pipeline drastically reduces the total computation time for state-of-the-art solvers. Moreover, by decomposing large problems into several smaller subproblems, the pipeline enables the use of near-term quantum devices as solvers, providing a path toward practical utility of quantum computers in portfolio optimization.

math.OC↗

arXiv2024

UniUSNet: A Promptable Framework for Universal Ultrasound Disease Prediction and Tissue Segmentation

Ultrasound is widely used in clinical practice due to its affordability, portability, and safety. However, current AI research often overlooks combined disease prediction and tissue segmentation. We propose UniUSNet, a universal framework for ultrasound image classification and segmentation. This model handles various ultrasound types, anatomical positions, and input formats, excelling in both segmentation and classification tasks. Trained on a comprehensive dataset with over 9.7K annotations from 7 distinct anatomical positions, our model matches state-of-the-art performance and surpasses single-dataset and ablated models. Zero-shot and fine-tuning experiments show strong generalization and adaptability with minimal fine-tuning. We plan to expand our dataset and refine the prompting mechanism, with model weights and code available at (https://github.com/Zehui-Lin/UniUSNet).

cs.CV↗

arXiv2024

Normal-state resistivity and the depairing current density of BaFe$_2$(As,P)$_2$ nanobridges along the $c$ axis

We report precise measurements to obtain the normal-state resistivity and the depairing current density of BaFe$_2$(As$_{1-x}$P$_x$)$_2$($x\sim0.29-0.32$) nanobridges along the $c$ axis, which are fabricated from single crystals near the optimal doping, by using focused ion beam (FIB) techniques. We obtained both of the $ab$-plane and $c$-axis resistivity ($ρ_{ab}$ and $ρ_c$) in the same part of a specimen, by fabricating the $c$-axis nanobridge in the middle of a narrow bridge extended in $ab$-plane, in spite of the slight deficiency of P dopant due to the additional FIB fabrication. The normal-state resistivity anisotropy agreed with the previous results for bulk samples, showing $ρ_c/ρ_{ab} < 8$ just above the superconducting transition temperature, $T_c$, in the slightly underdoped region and a slight decrease with increasing temperatures. The critical current density obtained in the $c$-axis nanobridges near the optimal doping reaches $\sim$8 MA/cm$^2$ at 0.15$T_c$, corresponding to about 87 % of a depairing limit derived by the Eilenberger equations. An extrapolation to $T=$0 K using the Ginzburg-Landau model suggests that the anisotropy of the depairing current density roughly corresponds to that of the normal-state resistivity. At low temperatures, we also observed a step-like voltage jump before arriving at the depairing limit, suggesting the occurrence of phase-slip phenomena near the depairing processes.

cond-mat.supr-con↗

arXiv2024

Enhancing Solutions for Complex PDEs: Introducing Complementary Convolution and Equivariant Attention in Fourier Neural Operators

Neural operators improve conventional neural networks by expanding their capabilities of functional mappings between different function spaces to solve partial differential equations (PDEs). One of the most notable methods is the Fourier Neural Operator (FNO), which draws inspiration from Green's function method and directly approximates operator kernels in the frequency domain. However, after empirical observation followed by theoretical validation, we demonstrate that the FNO approximates kernels primarily in a relatively low-frequency domain. This suggests a limited capability in solving complex PDEs, particularly those characterized by rapid coefficient changes and oscillations in the solution space. Such cases are crucial in specific scenarios, like atmospheric convection and ocean circulation. To address this challenge, inspired by the translation equivariant of the convolution kernel, we propose a novel hierarchical Fourier neural operator along with convolution-residual layers and attention mechanisms to make them complementary in the frequency domain to solve complex PDEs. We perform experiments on forward and reverse problems of multiscale elliptic equations, Navier-Stokes equations, and other physical scenarios, and find that the proposed method achieves superior performance in these PDE benchmarks, especially for equations characterized by rapid coefficient variations.

cs.LG↗

arXiv2024

Relative Difficulty Distillation for Semantic Segmentation

Current knowledge distillation (KD) methods primarily focus on transferring various structured knowledge and designing corresponding optimization goals to encourage the student network to imitate the output of the teacher network. However, introducing too many additional optimization objectives may lead to unstable training, such as gradient conflicts. Moreover, these methods ignored the guidelines of relative learning difficulty between the teacher and student networks. Inspired by human cognitive science, in this paper, we redefine knowledge from a new perspective -- the student and teacher networks' relative difficulty of samples, and propose a pixel-level KD paradigm for semantic segmentation named Relative Difficulty Distillation (RDD). We propose a two-stage RDD framework: Teacher-Full Evaluated RDD (TFE-RDD) and Teacher-Student Evaluated RDD (TSE-RDD). RDD allows the teacher network to provide effective guidance on learning focus without additional optimization goals, thus avoiding adjusting learning weights for multiple losses. Extensive experimental evaluations using a general distillation loss function on popular datasets such as Cityscapes, CamVid, Pascal VOC, and ADE20k demonstrate the effectiveness of RDD against state-of-the-art KD methods. Additionally, our research showcases that RDD can integrate with existing KD methods to improve their upper performance bound.

cs.CV↗

arXiv2024

FAITH: Frequency-domain Attention In Two Horizons for Time Series Forecasting

Time Series Forecasting plays a crucial role in various fields such as industrial equipment maintenance, meteorology, energy consumption, traffic flow and financial investment. However, despite their considerable advantages over traditional statistical approaches, current deep learning-based predictive models often exhibit a significant deviation between their forecasting outcomes and the ground truth. This discrepancy is largely due to an insufficient emphasis on extracting the sequence's latent information, particularly its global information within the frequency domain and the relationship between different variables. To address this issue, we propose a novel model Frequency-domain Attention In Two Horizons, which decomposes time series into trend and seasonal components using a multi-scale sequence adaptive decomposition and fusion architecture, and processes them separately. FAITH utilizes Frequency Channel feature Extraction Module and Frequency Temporal feature Extraction Module to capture inter-channel relationships and temporal global information in the sequence, significantly improving its ability to handle long-term dependencies and complex patterns. Furthermore, FAITH achieves theoretically linear complexity by modifying the time-frequency domain transformation method, effectively reducing computational costs. Extensive experiments on 6 benchmarks for long-term forecasting and 3 benchmarks for short-term forecasting demonstrate that FAITH outperforms existing models in many fields, such as electricity, weather and traffic, proving its effectiveness and superiority both in long-term and short-term time series forecasting tasks. Our codes and data are available at https://github.com/LRQ577/FAITH.

cs.LG↗

arXiv2024

Localization of scalar field on the brane-world by coupling with gravity

In this paper, we consider a coupling mechanism between the kinetic term and the gravity, in which a coupling function $F(R)$ is introduced into the kinetic term of the five-dimensional scalar field. Based on this scenario, we investigate the localization of scalar fields in three specific braneworld models: the Minkowski brane, the de Sitter brane, and the Anti-de Sitter brane. The brane models considered here are regular with no singularity for scalar curvature. For the Minkowshi brane case, the zero mode can always be localized on the brane, and the massive modes can be localized or quasi-localized on the brane. For the dS$_4$ brane case, two forms of factor $F(R)$ is considered. The zero mode can always be localized, and the massive modes could be quasi-localized on the brane. Besides, with the second coupling factor, the scalar zero mode could be localized on the both sides of the origin of extra dimension, while the massive modes could be quasi-localized on the origin. Lastly, for the AdS$_4$ brane case, the localization of the scalar zero mode requires the consideration of a coupling potential $V(Φ,φ)$, while the massive modes can still be localized on the brane with an infinite number.

hep-th↗

arXiv2024

Evidence of Scaling Advantage for the Quantum Approximate Optimization Algorithm on a Classically Intractable Problem

The quantum approximate optimization algorithm (QAOA) is a leading candidate algorithm for solving optimization problems on quantum computers. However, the potential of QAOA to tackle classically intractable problems remains unclear. Here, we perform an extensive numerical investigation of QAOA on the low autocorrelation binary sequences (LABS) problem, which is classically intractable even for moderately sized instances. We perform noiseless simulations with up to 40 qubits and observe that the runtime of QAOA with fixed parameters scales better than branch-and-bound solvers, which are the state-of-the-art exact solvers for LABS. The combination of QAOA with quantum minimum finding gives the best empirical scaling of any algorithm for the LABS problem. We demonstrate experimental progress in executing QAOA for the LABS problem using an algorithm-specific error detection scheme on Quantinuum trapped-ion processors. Our results provide evidence for the utility of QAOA as an algorithmic component that enables quantum speedups.

quant-ph↗

arXiv2024

Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey

This paper reviews the NTIRE 2024 RAW Image Super-Resolution Challenge, highlighting the proposed solutions and results. New methods for RAW Super-Resolution could be essential in modern Image Signal Processing (ISP) pipelines, however, this problem is not as explored as in the RGB domain. Th goal of this challenge is to upscale RAW Bayer images by 2x, considering unknown degradations such as noise and blur. In the challenge, a total of 230 participants registered, and 45 submitted results during thee challenge period. The performance of the top-5 submissions is reviewed and provided here as a gauge for the current state-of-the-art in RAW Image Super-Resolution.

cs.CV↗

arXiv2024

Incorporating Domain Differential Equations into Graph Convolutional Networks to Lower Generalization Discrepancy

Ensuring both accuracy and robustness in time series prediction is critical to many applications, ranging from urban planning to pandemic management. With sufficient training data where all spatiotemporal patterns are well-represented, existing deep-learning models can make reasonably accurate predictions. However, existing methods fail when the training data are drawn from different circumstances (e.g., traffic patterns on regular days) compared to test data (e.g., traffic patterns after a natural disaster). Such challenges are usually classified under domain generalization. In this work, we show that one way to address this challenge in the context of spatiotemporal prediction is by incorporating domain differential equations into Graph Convolutional Networks (GCNs). We theoretically derive conditions where GCNs incorporating such domain differential equations are robust to mismatched training and testing data compared to baseline domain agnostic models. To support our theory, we propose two domain-differential-equation-informed networks called Reaction-Diffusion Graph Convolutional Network (RDGCN), which incorporates differential equations for traffic speed evolution, and Susceptible-Infectious-Recovered Graph Convolutional Network (SIRGCN), which incorporates a disease propagation model. Both RDGCN and SIRGCN are based on reliable and interpretable domain differential equations that allow the models to generalize to unseen patterns. We experimentally show that RDGCN and SIRGCN are more robust with mismatched testing data than the state-of-the-art deep learning methods.

cs.LG↗

arXiv2024

Robust Conformal Prediction under Distribution Shift via Physics-Informed Structural Causal Model

Uncertainty is critical to reliable decision-making with machine learning. Conformal prediction (CP) handles uncertainty by predicting a set on a test input, hoping the set to cover the true label with at least $(1-α)$ confidence. This coverage can be guaranteed on test data even if the marginal distributions $P_X$ differ between calibration and test datasets. However, as it is common in practice, when the conditional distribution $P_{Y|X}$ is different on calibration and test data, the coverage is not guaranteed and it is essential to measure and minimize the coverage loss under distributional shift at \textit{all} possible confidence levels. To address these issues, we upper bound the coverage difference at all levels using the cumulative density functions of calibration and test conformal scores and Wasserstein distance. Inspired by the invariance of physics across data distributions, we propose a physics-informed structural causal model (PI-SCM) to reduce the upper bound. We validated that PI-SCM can improve coverage robustness along confidence level and test domain on a traffic speed prediction task and an epidemic spread task with multiple real-world datasets.

cs.LG↗

arXiv2024

FourCastNeXt: Optimizing FourCastNet Training for Limited Compute

FourCastNeXt is an optimization of FourCastNet - a global machine learning weather forecasting model - that performs with a comparable level of accuracy and can be trained using around 5% of the original FourCastNet computational requirements. This technical report presents strategies for model optimization that maintain similar performance as measured by the root-mean-square error (RMSE) of the modelled variables. By providing a model with very low comparative training costs, FourCastNeXt makes Neural Earth System Modelling much more accessible to researchers looking to conduct training experiments and ablation studies. FourCastNeXt training and inference code are available at https://github.com/nci/FourCastNeXt

cs.CV↗