arXiv subjects

Yue Sun

Publications and source records attributed to Yue Sun.

At least 145 records · Page 8Linked to original sources

arXiv2023

Large upper critical fields and dimensionality crossover of superconductivity in infinite-layer nickelate La$_{0.8}$Sr$_{0.2}$NiO$_{2}$

The recently emerging superconductivity in infinite-layer nickelates, with isostructure and isoelectron of cuprates, provides a new platform to explore the pairing mechanism of high-temperature superconductors. In this work, we studied the upper critical field ($H_{\rm{c2}}$) of a high-quality La$_{0.8}$Sr$_{0.2}$NiO$_{2}$ thin film with superconducting transition temperature, $T_{\rm{c}}$ = 18.8 K, using high magnetic field up to 56 T. A very large $H_{\rm{c2}}$, $\sim$ 40 T for $H$ $\Arrowvert$ $c$ and $\sim$ 52 T for $H$ $\Arrowvert$ $ab$, was confirmed, which suggests that infinite-layer nickelates also have great application potential. The anisotropy of $H_{\rm{c2}}$ monotonically decreases from $\sim$ 10 near $T_{\rm{c}}$ to $\sim$ 1.5 at 2 K. Angle dependence of $H_{\rm{c2}}$ confirms the crossover of superconductivity from two-dimensional (2D) to three-dimensional (3D) as the temperature decreases. We discussed that the interstitial orbital effect causes the weakening of anisotropy. The observed abnormal upturning of $H_{\rm{c2}}$ at low temperatures is found to be a universal behavior independent of film quality and rare earth elements. Therefore, it should not be the Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state due to the fact that it is in the dirty limit and insensitive to disorder.

cond-mat.supr-con↗

arXiv2023

Dynamic Sparse Training via Balancing the Exploration-Exploitation Trade-off

Over-parameterization of deep neural networks (DNNs) has shown high prediction accuracy for many applications. Although effective, the large number of parameters hinders its popularity on resource-limited devices and has an outsize environmental impact. Sparse training (using a fixed number of nonzero weights in each iteration) could significantly mitigate the training costs by reducing the model size. However, existing sparse training methods mainly use either random-based or greedy-based drop-and-grow strategies, resulting in local minimal and low accuracy. In this work, we consider the dynamic sparse training as a sparse connectivity search problem and design an exploitation and exploration acquisition function to escape from local optima and saddle points. We further design an acquisition function and provide the theoretical guarantees for the proposed method and clarify its convergence property. Experimental results show that sparse models (up to 98\% sparsity) obtained by our proposed method outperform the SOTA sparse training methods on a wide variety of deep learning tasks. On VGG-19 / CIFAR-100, ResNet-50 / CIFAR-10, ResNet-50 / CIFAR-100, our method has even higher accuracy than dense models. On ResNet-50 / ImageNet, the proposed method has up to 8.2\% accuracy improvement compared to SOTA sparse training methods.

cs.LG↗

arXiv2023

Neurogenesis Dynamics-inspired Spiking Neural Network Training Acceleration

Biologically inspired Spiking Neural Networks (SNNs) have attracted significant attention for their ability to provide extremely energy-efficient machine intelligence through event-driven operation and sparse activities. As artificial intelligence (AI) becomes ever more democratized, there is an increasing need to execute SNN models on edge devices. Existing works adopt weight pruning to reduce SNN model size and accelerate inference. However, these methods mainly focus on how to obtain a sparse model for efficient inference, rather than training efficiency. To overcome these drawbacks, in this paper, we propose a Neurogenesis Dynamics-inspired Spiking Neural Network training acceleration framework, NDSNN. Our framework is computational efficient and trains a model from scratch with dynamic sparsity without sacrificing model fidelity. Specifically, we design a new drop-and-grow strategy with decreasing number of non-zero weights, to maintain extreme high sparsity and high accuracy. We evaluate NDSNN using VGG-16 and ResNet-19 on CIFAR-10, CIFAR-100 and TinyImageNet. Experimental results show that NDSNN achieves up to 20.52\% improvement in accuracy on Tiny-ImageNet using ResNet-19 (with a sparsity of 99\%) as compared to other SOTA methods (e.g., Lottery Ticket Hypothesis (LTH), SET-SNN, RigL-SNN). In addition, the training cost of NDSNN is only 40.89\% of the LTH training cost on ResNet-19 and 31.35\% of the LTH training cost on VGG-16 on CIFAR-10.

cs.NE↗

arXiv2023

Anomalous second magnetization peak in 12442-type RbCa$_2$Fe$_4$As$_4$F$_2$ superconductors

The second magnetization peak (SMP) appears in most superconductors and is crucial for the understanding of vortex physics as well as the application. Although it is well known that the SMP is related to the type and quantity of disorder/defects, the mechanism has not been universally understood. In this work, we selected three stoichiometric superconducting RbCa$_2$Fe$_4$As$_4$F$_2$ single crystals with identical superconducting critical temperature $T_c$ $\sim$ 31 K and similar self-field critical current density $J_c$, but with different amounts of disorder/defects, to study the SMP effect. It is found that only the sample S2 with a moderate disorder/defects shows a significant SMP effect. The evolution of the normalized pinning force density $f_p$ demonstrates that the dominant pinning mechanism changes from weak pinning at low temperatures to strong pinning at high temperatures. The microstructure study for sample S2 reveals some expanded Ca$_2$F$_2$ layers and dislocation defects in RbFe$_2$As$_2$ layers. The normalized magnetic relaxation results indicate that the SMP is strongly associated with the elastic to plastic (E-P) vortex transition. As temperature increases, the SMP gradually evolves into a step-like shape and then becomes a sharp peak near the irreversibility field similar to what is usually observed in low-temperature superconductors. Our findings connect the low field SMP of high-temperature superconductors and the high field peak of low-temperature superconductors, revealing the possible universal origin related to the E-P phase transition.

cond-mat.supr-con↗

arXiv2023

Significant enhancement of critical current density in H+-intercalated FeSe single crystal

Superconducting transition temperature (Tc) and critical current density (Jc) are two key factors that are not only crucial for probing high-temperature superconducting mechanisms, but also for practical applications. The simple crystal structure of FeSe is very favorable for the fabrication of thin films and wires, but its application is limited by the relatively low Tc and small Jc. A previous study has found that the Tc of FeSe can be significantly enhanced over 40 K by using the protonation method. Here, we present a systematic study of Jc and vortex properties of H+-intercalated FeSe (Hx-FeSe) single crystals. The value of Jc for Hx-FeSe single crystal is significantly enhanced, exceeding 1.3*10^6 A/cm2 at 4 K, which is more than two orders of magnitude larger than 1.1*10^4 A/cm2 of pristine FeSe. The vortex pinning mechanism of Hx-FeSe is found to be surface pinning, which is different from the dominant strong point-like pinning in pristine FeSe. Moreover, the systematic study of the vortex phase transition and the underlying mechanism provides a wealth of information for the vortex phase diagram of Hx-FeSe single crystal. Our results confirm that the introduction of H+ intercalations into FeSe not only enhances the Tc, but also significantly increases the value of Jc, which is favorable for practical applications.

cond-mat.supr-con↗

arXiv2023

Around the world in 60 words: A generative vocabulary test for online research

Conducting experiments with diverse participants in their native languages can uncover insights into culture, cognition, and language that may not be revealed otherwise. However, conducting these experiments online makes it difficult to validate self-reported language proficiency. Furthermore, existing proficiency tests are small and cover only a few languages. We present an automated pipeline to generate vocabulary tests using text from Wikipedia. Our pipeline samples rare nouns and creates pseudowords with the same low-level statistics. Six behavioral experiments (N=236) in six countries and eight languages show that (a) our test can distinguish between native speakers of closely related languages, (b) the test is reliable ($r=0.82$), and (c) performance strongly correlates with existing tests (LexTale) and self-reports. We further show that test accuracy is negatively correlated with the linguistic distance between the tested and the native language. Our test, available in eight languages, can easily be extended to other languages.

cs.CL↗

arXiv2022

Generative methods for Urban design and rapid solution space exploration

Rapid population growth and climate change drive urban renewal and urbanization at massive scales. New computational methods are needed to better support urban designers in developing sustainable, resilient, and livable urban environments. Urban design space exploration and multi-objective optimization of masterplans can be used to expedite planning while achieving better design outcomes by incorporating generative parametric modeling considering different stakeholder requirements and simulation-based performance feedback. However, a lack of generalizable and integrative methods for urban form generation that can be coupled with simulation and various design performance analysis constrain the extensibility of workflows. This research introduces an implementation of a tensor-field-based generative urban modeling toolkit that facilitates rapid design space exploration and multi-objective optimization by integrating with Rhino/Grasshopper ecosystem and its urban analysis and environmental performance simulation tools. Our tensor-field modeling method provides users with a generalized way to encode contextual constraints such as waterfront edges, terrain, view-axis, existing streets, landmarks, and non-geometric design inputs such as network directionality, desired densities of streets, amenities, buildings, and people as forces that modelers can weigh. This allows users to generate many, diverse urban fabric configurations that resemble real-world cities with very few model inputs. We present a case study to demonstrate the proposed framework's flexibility and applicability and show how modelers can identify design and environmental performance synergies that would be hard to find otherwise

stat.AP↗

arXiv2022

Spin wavepacket propagation in quasi-2D antiferromagnets

Antiferromagnets are attractive platforms for the propagation of information via spin waves, offering advantages over ferromagnets in speed of response and immunity to external fields. A recent study of the quasi-2D antiferromagnet CrSBr reported that spin wavepackets propagate with group velocities that are orders of magnitude higher than expected from the magnon dispersion obtained by inelastic neutron scattering [1,2]. Here we show that the anomalous magnitude and anisotropy of the group velocity, v_g, are naturally explained by considering the long-range magnetic dipole-dipole interaction. We also demonstrate that v_g can be tuned over orders of magnitude by applying an external magnetic field or varying the sample thickness. Beyond spin wavepacket propagation, the dipolar interaction creates the possibility of non-equilibrium Bose-Einstein condensation in antiferromagnets, previously thought to be a property unique to ferromagnets.

cond-mat.str-el↗

arXiv2022

Spin wavepackets in the Kagome ferromagnet Fe$_3$Sn$_2$: propagation and precursors

The propagation of spin waves in magnetically ordered systems has emerged as a potential means to shuttle quantum information over large distances. Conventionally, the arrival time of a spin wavepacket at a distance, $d$, is assumed to be determined by its group velocity, $v_g$. He we report time-resolved optical measurements of wavepacket propagation in the Kagome ferromagnet Fe$_3$Sn$_2$ that demonstrate the arrival of spin information at times significantly less than $d/v_g$. We show that this spin wave "precursor" originates from the interaction of light with the unusual spectrum of magnetostatic modes in Fe$_3$Sn$_2$. Related effects may have far-reaching consequences toward realizing long-range, ultrafast spin wave transport in both ferromagnetic and antiferromagnetic systems.

cond-mat.mtrl-sci↗

arXiv2022

Elastocaloric signatures of symmetric and antisymmetric strain-tuning of quadrupolar and magnetic phases in DyB2C2

The adiabatic elastocaloric effect measures the temperature change of given systems with strain and probes the entropic landscape in the temperature-strain space. In this study we demonstrate that the DC bias strain-dependence of AC elastocaloric effect can be used to decompose the latter into contributions from symmetric (rotation-symmetry-preserving) and antisymmetric (rotation-symmetry-breaking) strains, using a tetragonal f-electron system DyB2C2--whose antiferroquadrupolar order locally breaks four-fold rotational site symmetries while globally remaining tetragonal--as a showcase example. We capture the strain evolution of the quadrupolar and magnetic phase transitions in the system using both singularities in the elastocaloric coefficient and its jump at the transitions, and the latter we show follows a modified Ehrenfest relation. We find that antisymmetric strain couples to the underlying order parameter in a bi-quadratic manner in the antiferroquadrupolar (AFQ) phase but in a linear-quadratic manner in the canted antiferromagnetic (CAFM) phase; the contrast is attributed to a preserved (broken) tetragonal symmetry in the AFQ (CAFM) phase, respectively. The broken tetragonal symmetry in the CAFM phase is further supported by elastocaloric strain-hysteresis and observation of two sets of domains with mutually perpendicular principal axes in optical birefringence. Additionally, when the quadrupolar moments are ordered in a staggered fashion, we uncover an elastocaloric response that reflects a quadratic increase of entropy with antisymmetric strain, analogous to the role magnetic field plays for Ising antiferromagnets by promoting pseudospin flips. Our results show that AC elastocaloric effect is a compact and incisive thermodynamic probe into the coupling between electronic degrees of freedom and strain, which can potentially be applied to broader classes of quantum materials.

cond-mat.str-el↗

arXiv2022

3D Reconstruction of Multiple Objects by mmWave Radar on UAV

In this paper, we explore the feasibility of utilizing a mmWave radar sensor installed on a UAV to reconstruct the 3D shapes of multiple objects in a space. The UAV hovers at various locations in the space, and its onboard radar senor collects raw radar data via scanning the space with Synthetic Aperture Radar (SAR) operation. The radar data is sent to a deep neural network model, which outputs the point cloud reconstruction of the multiple objects in the space. We evaluate two different models. Model 1 is our recently proposed 3DRIMR/R2P model, and Model 2 is formed by adding a segmentation stage in the processing pipeline of Model 1. Our experiments have demonstrated that both models are promising in solving the multiple object reconstruction problem. We also show that Model 2, despite producing denser and smoother point clouds, can lead to higher reconstruction loss or even loss of objects. In addition, we find that both models are robust to the highly noisy radar data obtained by unstable SAR operation due to the instability or vibration of a small UAV hovering at its intended scanning point. Our exploratory study has shown a promising direction of applying mmWave radar sensing in 3D object reconstruction.

cs.CV↗

arXiv2022

Spin-carrier coupling induced ferromagnetism and giant resistivity peak in EuCd$_2$P$_2$

EuCd$_2$P$_2$ is notable for its unconventional transport: upon cooling the metallic resistivity changes slope and begins to increase, ultimately 100-fold, before returning to its metallic value. Surprisingly, this giant peak occurs at 18K, well above the Néel temperature ($T_N$) of 11.5K. Using a suite of sensitive probes of magnetism, including resonant x-ray scattering and magneto-optical polarimetry, we have discovered that ferromagnetic order onsets above $T_N$ in the temperature range of the resistivity peak. The observation of inverted hysteresis in this regime shows that ferromagnetism is promoted by coupling of localized spins and itinerant carriers. The resulting carrier localization is confirmed by optical conductivity measurements.

cond-mat.str-el↗

arXiv2022

R2P: A Deep Learning Model from mmWave Radar to Point Cloud

Recent research has shown the effectiveness of mmWave radar sensing for object detection in low visibility environments, which makes it an ideal technique in autonomous navigation systems. In this paper, we introduce Radar to Point Cloud (R2P), a deep learning model that generates smooth, dense, and highly accurate point cloud representation of a 3D object with fine geometry details, based on rough and sparse point clouds with incorrect points obtained from mmWave radar. These input point clouds are converted from the 2D depth images that are generated from raw mmWave radar sensor data, characterized by inconsistency, and orientation and shape errors. R2P utilizes an architecture of two sequential deep learning encoder-decoder blocks to extract the essential features of those radar-based input point clouds of an object when observed from multiple viewpoints, and to ensure the internal consistency of a generated output point cloud and its accurate and detailed shape reconstruction of the original object. We implement R2P to replace Stage 2 of our recently proposed 3DRIMR (3D Reconstruction and Imaging via mmWave Radar) system. Our experiments demonstrate the significant performance improvement of R2P over the popular existing methods such as PointNet, PCN, and the original 3DRIMR design.

cs.CV↗

arXiv2022

A Survey of Quantum Computing for Finance

Quantum computers are expected to surpass the computational capabilities of classical computers during this decade and have transformative impact on numerous industry sectors, particularly finance. In fact, finance is estimated to be the first industry sector to benefit from quantum computing, not only in the medium and long terms, but even in the short term. This survey paper presents a comprehensive summary of the state of the art of quantum computing for financial applications, with particular emphasis on stochastic modeling, optimization, and machine learning, describing how these solutions, adapted to work on a quantum computer, can potentially help to solve financial problems, such as derivative pricing, risk modeling, portfolio optimization, natural language processing, and fraud detection, more efficiently and accurately. We also discuss the feasibility of these algorithms on near-term quantum computers with various hardware implementations and demonstrate how they relate to a wide range of use cases in finance. We hope this article will not only serve as a reference for academic researchers and industry practitioners but also inspire new ideas for future research.

quant-ph↗

arXiv2022

Large deviations principle for stationary solutions of stochastic differential equations with multiplicative noise

We study the large deviations principle (LDP) for stationary solutions of a class of stochastic differential equations (SDE) in infinite time intervals by the weak convergence approach, and then establish the LDP for the invariant measures of the SDE by the contraction principle. We further point out the equivalence of the rate function of the LDP for invariant measures induced by the LDP for stationary solutions and the rate function defined by quasi-potential. This fact gives another view of the quasi-potential introduced by Freidlin and Wentzell.

math.PR↗

arXiv2022

Characterization of GaN-based HEMTs Down to 4.2 K for Cryogenic Applications

The cryogenic performance of GaN-based HEMTs (high-electron-mobility transistors) is systematically investigated by the direct current (DC) and low-frequency noise (LFN) characteristics within the temperature (T) range from 300 K to 4.2 K. The important electrical merits of the device, including drain saturation current (IDsat), on-resistance (RON), transductance, subthreshold swing (SS), gate leakage current, and Schottky barrier height, are comprehensively characterized and their temperature-dependent behavior was statistically analyzed. In addition, the LFN of the device shows an evident behavior of 1/f noise from 10 Hz to 10 kHz in the measured temperature range and can be significantly reduced at cryogenic temperature. These results are of great importance to motivate further studies into the GaN-based cryo-devices and systems.

physics.app-ph↗

arXiv2022

Escaping High-order Saddles in Policy Optimization for Linear Quadratic Gaussian (LQG) Control

First order policy optimization has been widely used in reinforcement learning. It guarantees to find the optimal policy for the state-feedback linear quadratic regulator (LQR). However, the performance of policy optimization remains unclear for the linear quadratic Gaussian (LQG) control where the LQG cost has spurious suboptimal stationary points. In this paper, we introduce a novel perturbed policy gradient (PGD) method to escape a large class of bad stationary points (including high-order saddles). In particular, based on the specific structure of LQG, we introduce a novel reparameterization procedure which converts the iterate from a high-order saddle to a strict saddle, from which standard random perturbations in PGD can escape efficiently. We further characterize the high-order saddles that can be escaped by our algorithm.

math.OC↗

arXiv2022

System Identification via Nuclear Norm Regularization

This paper studies the problem of identifying low-order linear systems via Hankel nuclear norm regularization. Hankel regularization encourages the low-rankness of the Hankel matrix, which maps to the low-orderness of the system. We provide novel statistical analysis for this regularization and carefully contrast it with the unregularized ordinary least-squares (OLS) estimator. Our analysis leads to new bounds on estimating the impulse response and the Hankel matrix associated with the linear system. We first design an input excitation and show that Hankel regularization enables one to recover the system using optimal number of observations in the true system order and achieve strong statistical estimation rates. Surprisingly, we demonstrate that the input design indeed matters, by showing that intuitive choices such as i.i.d. Gaussian input leads to provably sub-optimal sample complexity. To better understand the benefits of regularization, we also revisit the OLS estimator. Besides refining existing bounds, we experimentally identify when regularized approach improves over OLS: (1) For low-order systems with slow impulse-response decay, OLS method performs poorly in terms of sample complexity, (2) Hankel matrix returned by regularization has a more clear singular value gap that ease identification of the system order, (3) Hankel regularization is less sensitive to hyperparameter choice. Finally, we establish model selection guarantees through a joint train-validation procedure where we tune the regularization parameter for near-optimal estimation.

stat.ML↗