arXiv subjects

Yue Shi

Publications and source records attributed to Yue Shi.

At least 73 records · Page 4Linked to original sources

arXiv2023

USR: Unsupervised Separated 3D Garment and Human Reconstruction via Geometry and Semantic Consistency

Dressed people reconstruction from images is a popular task with promising applications in the creative media and game industry. However, most existing methods reconstruct the human body and garments as a whole with the supervision of 3D models, which hinders the downstream interaction tasks and requires hard-to-obtain data. To address these issues, we propose an unsupervised separated 3D garments and human reconstruction model (USR), which reconstructs the human body and authentic textured clothes in layers without 3D models. More specifically, our method proposes a generalized surface-aware neural radiance field to learn the mapping between sparse multi-view images and geometries of the dressed people. Based on the full geometry, we introduce a Semantic and Confidence Guided Separation strategy (SCGS) to detect, segment, and reconstruct the clothes layer, leveraging the consistency between 2D semantic and 3D geometry. Moreover, we propose a Geometry Fine-tune Module to smooth edges. Extensive experiments on our dataset show that comparing with state-of-the-art methods, USR achieves improvements on both geometry and appearance reconstruction while supporting generalizing to unseen people in real time. Besides, we also introduce SMPL-D model to show the benefit of the separated modeling of clothes and the human body that allows swapping clothes and virtual try-on.

cs.CV↗

arXiv2022

Spin structure and dynamics of the topological semimetal Co$_{3}$Sn$_{2-x}$In$_{x}$S$_{2}$

The anomalous Hall effect (AHE), typically observed in ferromagnetic (FM) metals with broken time-reversal symmetry, depends on electronic and magnetic properties. In Co$_{3}$Sn$_{2-x}$In$_{x}$S$_{2}$, a giant AHE has been attributed to Berry curvature associated with the FM Weyl semimetal phase, yet recent studies report complicated magnetism. We use neutron scattering to determine the spin dynamics and structures as a function of $x$ and provide a microscopic understanding of the AHE and magnetism interplay. Spin gap and stiffness indicate a contribution from Weyl fermions consistent with the AHE. The magnetic structure evolves from $c$-axis ferromagnetism at $x$ = 0 to a canted antiferromagnetic (AFM) structure with reduced $c$-axis moment and in-plane AFM order at $x$ = 0.12 and further reduced $c$-axis FM moment at $x$ = 0.3. Since noncollinear spins can induce non-zero Berry curvature in real space acting as a fictitious magnetic field, our results revealed another AHE contribution, establishing the impact of magnetism on transport.

cond-mat.str-el↗

arXiv2022

A Supervisory Volt/VAR Control Scheme for Coordinating Voltage Regulators with Smart Inverters on a Distribution System

This paper focuses on the effective use of smart inverters for Volt/Var control (VVC) on a distribution system. New smart inverters offer Var support capability but for their effective use they need to be coordinated with existing Volt/Var schemes. A new VVC scheme is proposed to facilitate such coordination. The proposed scheme decomposes the problem into two levels. The first level uses Load Tap Changer (LTC) and Voltage Regulators (VRs) and coordinates their control with smart inverters to adjust the voltage level on the circuit to keep the voltages along the circuit within the desired range. The second level determines Var support needed from smart inverters to minimize the overall power loss in the circuit. The results of the supervisory control are sent to the devices which have their local controllers. To avoid frequent dispatch, smart inverters are supervised by shifting their Volt/Var characteristics as needed. This allows for the smart inverters to operate close to their optimal control while meeting the limited communication requirements on a distribution system. A case study using the IEEE 34 bus system shows the effectiveness of this supervisory control scheme compared to traditional volt/var schemes.

eess.SY↗

arXiv2022

Continuous Raman sideband cooling beyond the Lamb-Dicke Regime in a trapped ion chain

We report continuous Raman sideband cooling (CRSC) of a long ion chain to the motional ground state beyond the Lamb-Dicke (LD) regime. By driving multiple sideband transitions simultaneously, we show that nearly all axial modes of a 24-ion chain are cooled to the ground state, with an LD parameter as large as $η= 1.3$, spanning a frequency bandwidth of 4 MHz. Compared to traditional ground-state cooling methods such as pulsed sideband cooling or electromagnetic-induced-transparency (EIT) cooling, our method offers two key advantages: robustness to timing errors; and an ultra-wide bandwidth unlimited by the number of ions. This technique contributes as a crucial step for large-scale quantum information processing with linear ion chains and higher dimensions alike, and can be readily generalized to other atomic and molecular systems.

quant-ph↗

arXiv2021

A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-resolution

Realistic hyperspectral image (HSI) super-resolution (SR) techniques aim to generate a high-resolution (HR) HSI with higher spectral and spatial fidelity from its low-resolution (LR) counterpart. The generative adversarial network (GAN) has proven to be an effective deep learning framework for image super-resolution. However, the optimisation process of existing GAN-based models frequently suffers from the problem of mode collapse, leading to the limited capacity of spectral-spatial invariant reconstruction. This may cause the spectral-spatial distortion on the generated HSI, especially with a large upscaling factor. To alleviate the problem of mode collapse, this work has proposed a novel GAN model coupled with a latent encoder (LE-GAN), which can map the generated spectral-spatial features from the image space to the latent space and produce a coupling component to regularise the generated samples. Essentially, we treat an HSI as a high-dimensional manifold embedded in a latent space. Thus, the optimisation of GAN models is converted to the problem of learning the distributions of high-resolution HSI samples in the latent space, making the distributions of the generated super-resolution HSIs closer to those of their original high-resolution counterparts. We have conducted experimental evaluations on the model performance of super-resolution and its capability in alleviating mode collapse. The proposed approach has been tested and validated based on two real HSI datasets with different sensors (i.e. AVIRIS and UHD-185) for various upscaling factors and added noise levels, and compared with the state-of-the-art super-resolution models (i.e. HyCoNet, LTTR, BAGAN, SR- GAN, WGAN).

eess.IV↗

arXiv2021

Solving the three-dimensional Skyrme Hartree-Fock-Bogoliubov problem using the mixed-basis method

Background: The symmetry-unrestricted Hartree-Fock-Bogoliubov (HFB) simulation is important for describing various quantum many-body systems. However, the HFB problem in Cartesian coordinate space is numerically challenging. Purpose: For describing ground states without imposing axial symmetry and looking ahead to future extension for dynamics with full time dependence, we present a numerically efficient implementation of the three-dimensional (3D) HFB code. Methods: We develop a 3D Skyrme HFB code based on the mixed-basis representation (HFBmix) which consists of two harmonic-oscillator (HO) bases in the x- and y-directions, and finite-difference (FD) basis in the z-direction in solving the nuclear 3D HFB problem. Results: The results show very well agreement among all the three codes (HFBmix, HO3D, and hfodd). Especially for the HF calculations, the differences in total energies are on the order of a few keV for the lightest O and Mg nuclei. The HFBmix is applied to spherical, prolate, and triaxial systems, and gives the same quadrupole moments for the deformed nuclei as those of the HO-based calculations. Feasibility of the HFBmix is demonstrated in the fission isomer and barrier calculations of 240Pu. Conclusions: The HFBmix is useful for solving the nuclear 3D HFB problem for its numerical efficiency. Future work will include the analysis of deformed drip-line systems and systematic potential-energy surface calculation for fission-path analysis as well as the time-dependent extension of the HFBmix code for dynamics calculations.

nucl-th↗

arXiv2021

A Novel CropdocNet for Automated Potato Late Blight Disease Detection from the Unmanned Aerial Vehicle-based Hyperspectral Imagery

Late blight disease is one of the most destructive diseases in potato crop, leading to serious yield losses globally. Accurate diagnosis of the disease at early stage is critical for precision disease control and management. Current farm practices in crop disease diagnosis are based on manual visual inspection, which is costly, time consuming, subject to individual bias. Recent advances in imaging sensors (e.g. RGB, multiple spectral and hyperspectral cameras), remote sensing and machine learning offer the opportunity to address this challenge. Particularly, hyperspectral imagery (HSI) combining with machine learning/deep learning approaches is preferable for accurately identifying specific plant diseases because the HSI consists of a wide range of high-quality reflectance information beyond human vision, capable of capturing both spectral-spatial information. The proposed method considers the potential disease specific reflectance radiation variance caused by the canopy structural diversity, introduces the multiple capsule layers to model the hierarchical structure of the spectral-spatial disease attributes with the encapsulated features to represent the various classes and the rotation invariance of the disease attributes in the feature space. We have evaluated the proposed method with the real UAV-based HSI data under the controlled field conditions. The effectiveness of the hierarchical features has been quantitatively assessed and compared with the existing representative machine learning/deep learning methods. The experiment results show that the proposed model significantly improves the accuracy performance when considering hierarchical-structure of spectral-spatial features, comparing to the existing methods only using spectral, or spatial or spectral-spatial features without consider hierarchical-structure of spectral-spatial features.

cs.CV↗

arXiv2021

An algorithm for Egyptian fraction representations with restricted denominators

A unit fraction representation of a rational number $r$ is a finite sum of reciprocals of positive integers that equals $r$. Of particular interest is the case when all denominators in the representation are distinct, resulting in an Egyptian fraction representation of $r$. Common algorithms for computing Egyptian fraction representations of a given rational number tend to result in extremely large denominators and cannot be adapted to restrictions on the allowed denominators. We describe an algorithm for finding all unit fraction representations of a given rational number using denominators from a given finite multiset of positive integers. The freely available algorithm, implemented in Scheme, is particularly well suited to computing dense Egyptian fraction representations, where the allowed denominators have a prescribed maximum.

math.NT↗

arXiv2021

Strongly anisotropic antiferromagnetic coupling in EuFe2As2 revealed by stress detwinning

Of all parent compounds of iron-based high-temperature superconductors, EuFe2As2 exhibits by far the largest magnetostructural coupling due to the sizable biquadratic interaction between Eu and Fe moments. While the coupling between Eu antiferromagnetic order and Fe structural/antiferromagnetic domains enables rapid field detwinning, this prevents simple magnetometry measurements from extracting the critical fields of the Eu metamagnetic transition. Here we measure these critical fields by combining x-ray magnetic circular dichroism spectroscopy with in-situ tunable uniaxial stress and applied magnetic field. The combination of two tuning knobs allows us to separate the stress-detwinning of structural domains from the field-induced reorientation of Eu moments. Intriguingly, we find a spin-flip transition which can only result from a strongly anisotropic interaction between Eu planes. We argue that this anisotropic exchange is a consequence of the strong anisotropy in the magnetically ordered Fe layer, which presents a new form of higher-order coupling between Eu and Fe magnetism.

cond-mat.supr-con↗

arXiv2021

A Biologically Interpretable Two-stage Deep Neural Network (BIT-DNN) For Vegetation Recognition From Hyperspectral Imagery

Spectral-spatial based deep learning models have recently proven to be effective in hyperspectral image (HSI) classification for various earth monitoring applications such as land cover classification and agricultural monitoring. However, due to the nature of "black-box" model representation, how to explain and interpret the learning process and the model decision, especially for vegetation classification, remains an open challenge. This study proposes a novel interpretable deep learning model -- a biologically interpretable two-stage deep neural network (BIT-DNN), by incorporating the prior-knowledge (i.e. biophysical and biochemical attributes and their hierarchical structures of target entities) based spectral-spatial feature transformation into the proposed framework, capable of achieving both high accuracy and interpretability on HSI based classification tasks. The proposed model introduces a two-stage feature learning process: in the first stage, an enhanced interpretable feature block extracts the low-level spectral features associated with the biophysical and biochemical attributes of target entities; and in the second stage, an interpretable capsule block extracts and encapsulates the high-level joint spectral-spatial features representing the hierarchical structure of biophysical and biochemical attributes of these target entities, which provides the model an improved performance on classification and intrinsic interpretability with reduced computational complexity. We have tested and evaluated the model using four real HSI datasets for four separate tasks (i.e. plant species classification, land cover classification, urban scene recognition, and crop disease recognition tasks). The proposed model has been compared with five state-of-the-art deep learning models.

cs.CV↗

arXiv2021

Robust Remanufacturing Planning with Parameter Uncertainty

We consider the problem of remanufacturing planning in the presence of statistical estimation errors. Determining the optimal remanufacturing timing, first and foremost, requires modeling of the state transitions of a system. The estimation of these probabilities, however, often suffers from data inadequacy and is far from accurate, resulting in serious degradation in performance. To mitigate the impacts of the uncertainty in transition probabilities, we develop a novel data-driven modeling framework for remanufacturing planning in which decision makers can remain robust with respect to statistical estimation errors. We model the remanufacturing planning problem as a robust Markov decision process, and construct ambiguity sets that contain the true transition probability distributions with high confidence. We further establish structural properties of optimal robust policies and insights for remanufacturing planning. A computational study on the NASA turbofan engine shows that our data-driven decision framework consistently yields better worst-case performances and higher reliability of the performance guarantee

math.OC↗

arXiv2020

Rapid production of defect-free beryllium ion Coulomb crystals

Trapped atomic ions find wide applications ranging from precision measurement to quantum information science and quantum computing. Among the different atomic species employed, beryllium ions are widely used due to its light mass and convenient atomic structure. However, the ion loading process requires a high temperature for sufficient vapor pressure, generating undesirable gas load for the background vacuum and limiting the lifetime of a long ion chain. Here, we demonstrate a simple method to rapidly produce pure linear chains of beryllium ions with pulsed laser ablation, serving as a starting point for large-scale quantum information processing. Our method is fast compared to thermal ovens; reduces the vacuum load to only 10^(-12) Torr level; yields a short recovery time of a few seconds; and also eliminates the need for a deep ultraviolet laser for photo-ionization. In addition, we apply feedback control to obtain defect-free ion arrays with desirable lengths.

physics.atom-ph↗

arXiv2020

Quantum Oscillations in the Field-induced Ferromagnetic State of MnBi$_{2-x}$Sb$_{x}$Te$_{4}$

The intrinsic antiferromagnetic topological insulator MnBi$_{2}$Te$_{4}$ undergoes a metamagnetic transition in a c-axis magnetic field. It has been predicted that ferromagnetic MnBi$_{2}$Te$_{4}$ is an ideal Weyl semimetal with a single pair of Weyl nodes. Here we report measurements of quantum oscillations detected in the field-induced ferromagnetic phase of MnBi$_{2-x}$Sb$_{x}$Te$_{4}$, where Sb substitution tunes the majority carriers from electrons to holes. Single frequency Shubnikov-de Haas oscillations were observed in a wide range of Sb concentrations (0.54 $\leq$ x $\leq$ 1.21). The evolution of the oscillation frequency and the effective mass shows reasonable agreement with the Weyl semimetal band-structure of ferromagnetic MnBi$_{2}$Te$_{4}$ predicted by density functional calculations. Intriguingly, the quantum oscillation frequency shows a strong temperature dependence, indicating that the electronic structure sensitively depends on magnetism.

cond-mat.mtrl-sci↗

arXiv2020

Fast Object Detection with Latticed Multi-Scale Feature Fusion

Scale variance is one of the crucial challenges in multi-scale object detection. Early approaches address this problem by exploiting the image and feature pyramid, which raises suboptimal results with computation burden and constrains from inherent network structures. Pioneering works also propose multi-scale (i.e., multi-level and multi-branch) feature fusions to remedy the issue and have achieved encouraging progress. However, existing fusions still have certain limitations such as feature scale inconsistency, ignorance of level-wise semantic transformation, and coarse granularity. In this work, we present a novel module, the Fluff block, to alleviate drawbacks of current multi-scale fusion methods and facilitate multi-scale object detection. Specifically, Fluff leverages both multi-level and multi-branch schemes with dilated convolutions to have rapid, effective and finer-grained feature fusions. Furthermore, we integrate Fluff to SSD as FluffNet, a powerful real-time single-stage detector for multi-scale object detection. Empirical results on MS COCO and PASCAL VOC have demonstrated that FluffNet obtains remarkable efficiency with state-of-the-art accuracy. Additionally, we indicate the great generality of the Fluff block by showing how to embed it to other widely-used detectors as well.

cs.CV↗

arXiv2020

An implementation of nuclear time-dependent density-functional theory and its application to the nuclear isovector electric dipole resonance

Following a previous paper [Y. Shi, Phys. Rev. C 98, 014329(2018)], we present an extension of the density-functional theory to allow for dynamic calculations based on the obtained static Hartree-Fock results. We perform extensive benchmark calculations, by comparing the calculated results with that of an existing code Sky3D. To perform linear-response calculations using the TDDFT method, comparisons have been made with the finite-amplitude quasiparticle random-phase approximation (FAM-QRPA) method. We plan to apply the TDDFT method to a systematic description of the IVD resonances in the Zr, Mo, and Ru isotopes. The strengths of IVD resonances are calculated using two complementary methods: TDDFT and FAM-QRPA methods. For the TDDFT results, additional benchmark calculations have been performed using the well-tested code Sky3D. In these three models, the important ingredients which have major influence on the results, such as time-odd potentials, boundary conditions, smoothing procedures, spurious peaks etc., have been carefully examined. The current TDDFT and the Sky3D codes yield almost identical response functions once both codes use the same time-odd mean fields and absorbing boundary conditions. The strengths of the IVD resonances calculated using the TDDFT and FAM-QRPA methods agree reasonably well with the same position of the giant dipole resonance. Upon seeing a reasonable accuracy offered by the implemented code, we perform systematic TDDFT calculations for spherical Zr and Mo isotopes near $N=50$, where experimental data exist. For neutron-rich Zr, Mo, and Ru isotopes where shape evolution exist we predict the photoabsorption cross sections based on oblate and triaxial minima.

nucl-th↗

arXiv2020

Nematic Fluctuations in an Orbital Selective Superconductor Fe$_{1+y}$Te$_{1-x}$Se$_{x}$

We present a systematic study of the nematic fluctuations in the iron chalcogenide superconductor Fe$_{1+y}$Te$_{1-x}$Se$_{x}$ ($0 \leq x \leq 0.53$) using the elastoresistivity technique. Near $x = 0$, in proximity to the double-stripe magnetic order of Fe$_{1+y}$Te, a diverging $B_{1g}$ nematic susceptibility is observed. Upon increasing $x$, despite the absence of magnetic order, the $B_{2g}$ nematic susceptibility increases and becomes dominant, closely following the strength of the $(π, π)$ spin fluctuations. Over a wide range of compositions ($0.17 \leq x \leq 0.53$), while the $B_{2g}$ nematic susceptibility follows a Curie temperature dependence (with zero Weiss temperature) at low temperatures, it shows deviations from Curie-Weiss behavior for temperatures higher than $50K$. This is the opposite of what is observed in typical iron pnictides, where Curie-Weiss deviations are seen at low temperatures. We attribute this unusual temperature dependence to a loss of coherence of the $d_{xy}$ orbital, which is supported by our theoretical calculations. Our results highlight the importance of orbital differentiation on the nematic properties of iron-based materials.

cond-mat.supr-con↗

arXiv2019

A cranked self-consistent mean-field description of the triaxially deformed rotational bands in 138Nd

The survey of different configurations near Fermi surface of 138Nd results in 12 lowest configurations, at both positive- and negative-deformations. These are calculated to be the energetically lowest configurations. The results show that, for both EDFs, the rotational states based on positive-minimum, which is at gamma~35, are lower than the respective configurations with negative-deformation. The general trends of the spin-versus-omega curve, and the energy-versus-spin curve reproduce well those of the experimental data. Further, for the observed bands `T1-T8', the calculated results using SLy4L allows the configurations of the observed bands to be assigned. The calculations predict transitional quadrupole moments, which can be used to compare with future experimental data. The current cranked self-consistent mean-field calculations of the near-yrast high-spin rotational bands in 138Nd reproduce well the experimental data. The results suggest that the experimentally observed bands can be assigned to the calculated bands with various configurations at the positive-deformation. The predictions of the current calculations are complementary to that of the well-know macroscopic-microscopic calculations, both of which await future experiment to verify.

nucl-th↗

arXiv2018

A Coordinated Volt-Var Control Scheme for Distribution Systems with High DER Penetration

In this paper, a new Volt/Var Control (VVC) scheme is proposed to facilitate the coordination between the conventional VVC devices and the new smart PV inverters to provide an effective voltage control on a system with high PV penetration. The proposed scheme decomposes the problem into two levels. The first level uses Load Tap Changer (LTC) and Voltage Regulators (VRs) to adjust the voltage level on the circuit to keep the voltages along the circuit within the desired range. The second level determines Var support needed from smart inverters to smooth the fast voltage variations while providing effective power factor correction to keep the power losses at minimum. The case study shows that the proposed VVC method is very effective in maintaining acceptable voltages on the system under various operating conditions while meeting the operational constrains. The results also show the computational efficiency of the method.

eess.SP↗