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Yang Shen

Publications and source records attributed to Yang Shen.

At least 109 records · Page 6Linked to original sources
arXiv2023

Experimental Access to Observing Decay from Extremely Long-Lived Metastable Electronic States via Penning Trap Spectrometry

Long-lived ionic quantum states known as metastable electronic states in highly-charged ions (HCIs) are of great interest in fundamental physics. Especially, it generates transitions with very narrow natural linewidth which is a promising candidate for use in the next generation HCI atomic clocks to reach an accuracy below $10^{-19}$. A recent experiment reported in [Nature,581(7806) 2020], used Penning trap mass spectrometry to measure the energy of an extremely long-lived metastable electronic state, thus opening doors to search for HCI clock transitions. Building upon prior research, this study introduces an experimental proposal with the goal of measuring lifetimes of the metastable states beyond seconds. Our approach employs a sequential pulse-and-phase measurement scheme, allowing for direct observations of the decay processes from metastable electronic states through single-ion mass spectrometry in a Penning trap. This measurement poses a significant challenge to conventional techniques like fluorescence detection. To demonstrate the effectiveness of this method, we conducted a comprehensive simulation under real experimental conditions, yielding promising results in a specific scenario. Two suitable candidates are proposed for testing this method, and the state-of-the-art MCDHF theory are employed for accurate energy levels and transition rate calculations. Some future prospects in the experimental determinations of a wide range of energy and lifetimes of long-lived metastable electronic states, probing hyperfine and magnetic quenching effects on high-order forbidden transitions and search for highly quality HCI clock transitions are discussed.

physics.atom-ph↗
arXiv2023

FMRT: Learning Accurate Feature Matching with Reconciliatory Transformer

Local Feature Matching, an essential component of several computer vision tasks (e.g., structure from motion and visual localization), has been effectively settled by Transformer-based methods. However, these methods only integrate long-range context information among keypoints with a fixed receptive field, which constrains the network from reconciling the importance of features with different receptive fields to realize complete image perception, hence limiting the matching accuracy. In addition, these methods utilize a conventional handcrafted encoding approach to integrate the positional information of keypoints into the visual descriptors, which limits the capability of the network to extract reliable positional encoding message. In this study, we propose Feature Matching with Reconciliatory Transformer (FMRT), a novel Transformer-based detector-free method that reconciles different features with multiple receptive fields adaptively and utilizes parallel networks to realize reliable positional encoding. Specifically, FMRT proposes a dedicated Reconciliatory Transformer (RecFormer) that consists of a Global Perception Attention Layer (GPAL) to extract visual descriptors with different receptive fields and integrate global context information under various scales, Perception Weight Layer (PWL) to measure the importance of various receptive fields adaptively, and Local Perception Feed-forward Network (LPFFN) to extract deep aggregated multi-scale local feature representation. Extensive experiments demonstrate that FMRT yields extraordinary performance on multiple benchmarks, including pose estimation, visual localization, homography estimation, and image matching.

cs.CV↗
arXiv2023

Reexamining doped two-legged Hubbard ladders

We revisit the ground state of the Hubbard model on 2-legged ladders in this work. We perform DMRG calculation on large system sizes with large kept states and perform extrapolation of DMRG results with truncation errors in the converged region. We find the superconducting correlation exponent $K_{sc}$ extracted from the pair-pair correlation is very sensitive to the position of the reference bond, reflecting a huge boundary effect on it. By systematically removing the effects from boundary conditions, finite sizes, and truncation errors in DMRG, we obtain the most accurate value of $K_{sc}$ and $K_ρ$ so far with DMRG. With these exponents, we confirm that the 2-legged Hubbard model is in the Luther-Emery liquid phase with $K_{sc} \cdot K_ρ= 1$ from tiny doping near half-filling to $1/8$ hole doping. When the doping is increased to $δ\gtrapprox 1/6$, the behaviors of charge, pairing, and spin correlations don't change qualitatively, but the relationship $K_{sc} \cdot K_ρ= 1$ is likely to be violated. With the further increase of the doping to $δ= 1/3$, the quasi long-ranged charge correlation turns to a true long-ranged charge order and the spin gap is closed, while the pair-pair correlation still decays algebraically. Our work provides a standard way to analyze the correlation functions when studying systems with open boundary conditions.

cond-mat.str-el↗
arXiv2023

Learning-based Control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy

The growing demand for accurate control in varying and unknown environments has sparked a corresponding increase in the requirements for power supply components, including permanent magnet synchronous motors (PMSMs). To infer the unknown part of the system, machine learning techniques are widely employed, especially Gaussian process regression (GPR) due to its flexibility of continuous system modeling and its guaranteed performance. For practical implementation, distributed GPR is adopted to alleviate the high computational complexity. However, the study of distributed GPR from a control perspective remains an open problem. In this paper, a control-aware optimal aggregation strategy of distributed GPR for PMSMs is proposed based on the Lyapunov stability theory. This strategy exclusively leverages the posterior mean, thereby obviating the need for computationally intensive calculations associated with posterior variance in alternative approaches. Moreover, the straightforward calculation process of our proposed strategy lends itself to seamless implementation in high-frequency PMSM control. The effectiveness of the proposed strategy is demonstrated in the simulations.

eess.SY↗
arXiv2023

Half-filled stripe to N$\acute e$el antiferromagnetism transition in the $t'$-Hubbard model on honeycomb lattice

We study the ground state of the doped Hubbard model on honeycomb lattice with both nearest ($t$) and next-nearest neighboring hoppings ($t'$) in the small doping and strongly interacting region. Previous study on the model without $t'$ showed the ground state is a half-filled stripe. We employ density matrix renormalization group and extrapolate the results with truncation errors in the converged region. In the $t' < 0$ side, we find the half-filled stripe phase at $t' = 0$ is stable against the frustration of $t'$ until a critical point $-0.4 < t'_c < -0.3$, beyond which the ground state switches to anti-ferromagnetic N$\acute e$el phase with charge modulation. With further increase of $t'$ to $-0.7$, the ground state becomes paramagnetic. In the $t' > 0$ side, the half-filled stripe stretches to $t' \approx 0.7$. We don't find obvious enhancement of pairing for the range of $t'$ studied. We study width-4 cylinders in this work but the results for spin, charge, and pairing correlation agree qualitatively for periodic and anti-periodic boundary conditions in the half-filled stripe and anti-ferromagnetic N$\acute e$el phases, suggesting the results are likely to be representative for true two-dimensional systems. The half-filled stripe to anti-ferromagnetic N$\acute e$el phase transition can be realized on real materials or ultra-cold atom platform.

cond-mat.str-el↗
arXiv2023

Two-Temperature Principle for Electrothermal Performance Evaluation of GaN HEMTs

We present a comprehensive investigation of self-heating in gallium nitride (GaN) high-electron-mobility transistors (HEMTs) through technology computer-aided design (TCAD) simulations and phonon Monte Carlo (MC) simulations. With microscopic phonon-based electrothermal simulations, we scrutinize both the temperature profiles and electrothermal coupling effect within GaN HEMTs. Two metrics, maximum channel temperature ($T_\text{max}$) and equivalent channel temperature ($T_\text{eq}$), are introduced to measure the reliability and electrical performance degradation of the device, respectively. The influence of bias-dependent heat generation and phonon ballistic transport on the two indicators is thoroughly examined.

physics.app-ph↗
arXiv2023

Optical vortices enabled by structural vortices

The structural symmetry of solids plays an important role in defining their linear and nonlinear optical properties. The quest for versatile, cost-effective, large-scale, and defect-free approaches and materials platforms for tailoring structural and optical properties on demand has been underway for decades. We experimentally demonstrate a bottom-up self-assembly-based organic engineered material comprised of synthesized molecules with large dipole moments that are crystallized into a spherulite structure. The molecules align in an azimuthal direction, resulting in a vortex polarity with spontaneously broken symmetry leading to strong optical anisotropy and nonlinear optical responses. These unique polarization properties of the judiciously designed organic spherulite combined with the symmetry of structured optical beams enable a plethora of new linear and nonlinear light-matter interactions, including the generation of optical vortex beams with complex spin states and on-demand topological charges at the fundamental, doubled, and tripled frequencies. The results of this work are likely to enable numerous applications in areas such as high-dimensional quantum information processing, with large capacity and high security. The demonstrated spherulite crystals facilitate stand-alone micro-scale devices that rely on the unique micro-scale spontaneous vortex polarity that is likely to enable future applications for high-dimensional quantum information processing, spatiotemporal optical vortices, and a novel platform for optical manipulation and trapping.

physics.optics↗
arXiv2023

Equiangular Basis Vectors

We propose Equiangular Basis Vectors (EBVs) for classification tasks. In deep neural networks, models usually end with a k-way fully connected layer with softmax to handle different classification tasks. The learning objective of these methods can be summarized as mapping the learned feature representations to the samples' label space. While in metric learning approaches, the main objective is to learn a transformation function that maps training data points from the original space to a new space where similar points are closer while dissimilar points become farther apart. Different from previous methods, our EBVs generate normalized vector embeddings as "predefined classifiers" which are required to not only be with the equal status between each other, but also be as orthogonal as possible. By minimizing the spherical distance of the embedding of an input between its categorical EBV in training, the predictions can be obtained by identifying the categorical EBV with the smallest distance during inference. Various experiments on the ImageNet-1K dataset and other downstream tasks demonstrate that our method outperforms the general fully connected classifier while it does not introduce huge additional computation compared with classical metric learning methods. Our EBVs won the first place in the 2022 DIGIX Global AI Challenge, and our code is open-source and available at https://github.com/NJUST-VIPGroup/Equiangular-Basis-Vectors.

cs.CV↗
arXiv2023

Comparative study of charge order in undoped infinite-layer nickelate superconductors

To understand the microscopic mechanism of the charge order observed in the parent compound of the infinite-layer nickelate superconductors, we consider a minimal three-legged model consisting of a two-legged Hubbard ladder for the Ni $3d_{x^2-y^2}$ electrons and a free conduction electron chain from the rare-earths. With highly accurate density matrix renormalization group calculations, when the chemical potential difference is adjusted to make the Hubbard ladder with $1/3$ hole doping, we find a long-range charge order with period $3$ in the ground state of the model, while the spin excitation has a small energy gap. Moreover, the electron pair-pair correlation has a quasi-long-range behavior, indicating an instability of superconductivity even at half-filling. As a comparison, the same method is applied to a pure two-legged Hubbard model with $1/3$ hole doping in which the period-3 charge order is a quasi-long range one. The difference between them demonstrates that the free electron chain of the three-legged ladder plays the role of a charge reservoir and enhances the charge order in the undoped infinite-layer nickelates.

cond-mat.str-el↗
arXiv2023

Detection of quantum signals free of classical noise via quantum correlation

Extracting useful signals is key to both classical and quantum technologies. Conventional noise filtering methods rely on different patterns of signal and noise in frequency or time domains, thus limiting their scope of application, especially in quantum sensing. Here, we propose a signal-nature-based (not signal-pattern-based) approach which singles out a quantum signal from its classical noise background by employing the intrinsic quantum nature of the system. We design a novel protocol to extract the Quantum Correlation signal and use it to single out the signal of a remote nuclear spin from its overwhelming classical noise backgrounds, which is impossible to be accomplished by conventional filter methods. Our work demonstrates the quantum/classical nature as a new degree of freedom in quantum sensing. The further generalization of this quantum nature-based method opens a new direction in quantum research.

quant-ph↗
arXiv2023

Observation of ballistic-diffusive thermal transport in GaN transistors using thermoreflectance thermal imaging

To develop effective thermal management strategies for GaN transistors, it is essential to accurately predict the device junction temperature. Since the width of the heat generation in the devices is comparable to phonon mean free paths of GaN, phonon ballistic transport exists and can significantly affect the heat transport process, which necessitates a thorough understanding of the influence of the phonon ballistic effects in GaN transistors. In this paper, the ballistic-diffusive phonon transport in GaN-on-SiC devices is examined by measuring the hotspot temperature using the thermoreflectance thermal imaging TTI combined with the hybrid phonon Monte Carlo-diffusion simulations. A series of Au heaters are fabricated on the top of the GaN layer to quantitatively mimic the different heat source distributions during device operation. The experimental and simulation results show a good consistency and both indicate that the phonon ballistic effects can significantly increase the hotspot temperature. With the size of the heat source decreasing, the errors of Fourier's law-based predictions increase, which emphasizes the necessity to carefully consider the phonon ballistic transport in device thermal simulations.

cond-mat.mes-hall↗
arXiv2023

Delving Deep into Simplicity Bias for Long-Tailed Image Recognition

Simplicity Bias (SB) is a phenomenon that deep neural networks tend to rely favorably on simpler predictive patterns but ignore some complex features when applied to supervised discriminative tasks. In this work, we investigate SB in long-tailed image recognition and find the tail classes suffer more severely from SB, which harms the generalization performance of such underrepresented classes. We empirically report that self-supervised learning (SSL) can mitigate SB and perform in complementary to the supervised counterpart by enriching the features extracted from tail samples and consequently taking better advantage of such rare samples. However, standard SSL methods are designed without explicitly considering the inherent data distribution in terms of classes and may not be optimal for long-tailed distributed data. To address this limitation, we propose a novel SSL method tailored to imbalanced data. It leverages SSL by triple diverse levels, i.e., holistic-, partial-, and augmented-level, to enhance the learning of predictive complex patterns, which provides the potential to overcome the severe SB on tail data. Both quantitative and qualitative experimental results on five long-tailed benchmark datasets show our method can effectively mitigate SB and significantly outperform the competing state-of-the-arts.

cs.CV↗
arXiv2022

The Cheeger constants of random Belyi surfaces

Brooks and Makover developed a combinatorial model of random hyperbolic surfaces by gluing certain hyperbolic ideal triangles. In this paper we show that for any $ε>0$, as the number of ideal triangles goes to infinity, a generic hyperbolic surface in Brooks-Makover's model has Cheeger constant less than $\frac{3}{2π}+ε$.

math.DG↗
arXiv2022

Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative

This paper targets at improving the generalizability of hypergraph neural networks in the low-label regime, through applying the contrastive learning approach from images/graphs (we refer to it as HyperGCL). We focus on the following question: How to construct contrastive views for hypergraphs via augmentations? We provide the solutions in two folds. First, guided by domain knowledge, we fabricate two schemes to augment hyperedges with higher-order relations encoded, and adopt three vertex augmentation strategies from graph-structured data. Second, in search of more effective views in a data-driven manner, we for the first time propose a hypergraph generative model to generate augmented views, and then an end-to-end differentiable pipeline to jointly learn hypergraph augmentations and model parameters. Our technical innovations are reflected in designing both fabricated and generative augmentations of hypergraphs. The experimental findings include: (i) Among fabricated augmentations in HyperGCL, augmenting hyperedges provides the most numerical gains, implying that higher-order information in structures is usually more downstream-relevant; (ii) Generative augmentations do better in preserving higher-order information to further benefit generalizability; (iii) HyperGCL also boosts robustness and fairness in hypergraph representation learning. Codes are released at https://github.com/weitianxin/HyperGCL.

cs.LG↗
arXiv2022

SEMICON: A Learning-to-hash Solution for Large-scale Fine-grained Image Retrieval

In this paper, we propose Suppression-Enhancing Mask based attention and Interactive Channel transformatiON (SEMICON) to learn binary hash codes for dealing with large-scale fine-grained image retrieval tasks. In SEMICON, we first develop a suppression-enhancing mask (SEM) based attention to dynamically localize discriminative image regions. More importantly, different from existing attention mechanism simply erasing previous discriminative regions, our SEM is developed to restrain such regions and then discover other complementary regions by considering the relation between activated regions in a stage-by-stage fashion. In each stage, the interactive channel transformation (ICON) module is afterwards designed to exploit correlations across channels of attended activation tensors. Since channels could generally correspond to the parts of fine-grained objects, the part correlation can be also modeled accordingly, which further improves fine-grained retrieval accuracy. Moreover, to be computational economy, ICON is realized by an efficient two-step process. Finally, the hash learning of our SEMICON consists of both global- and local-level branches for better representing fine-grained objects and then generating binary hash codes explicitly corresponding to multiple levels. Experiments on five benchmark fine-grained datasets show our superiority over competing methods.

cs.CV↗
arXiv2022

Microscopic study of optically-stable, coherent color centers in diamond generated by high-temperature annealing

Single color centers in solid have emerged as promising physical platforms for quantum information science. Creating these centers with excellent quantum properties is a key foundation for further technological developments. In particular, the microscopic understanding of the spin bath environments is the key to engineer color centers for quantum control. In this work, we propose and demonstrate a distinct high-temperature annealing (HTA) approach for creating high-quality nitrogen vacancy (NV) centers in implantation-free diamonds. Simultaneously using the created NV centers as probes for their local environment we verify that no damage was microscopically induced by the HTA. Nearly all single NV centers created in ultra-low-nitrogen-concentration membranes possess stable and Fourier-transform-limited optical spectra. Furthermore, HTA strongly reduces noise sources naturally grown in ensemble samples, and leads to more than three-fold improvements of decoherence time and sensitivity. We also verify that the vacancy activation and defect reformation, especially H3 and P1 centers, can explain the reconfiguration between spin baths and color centers. This novel approach will become a powerful tool in vacancy-based quantum technology.

cond-mat.mes-hall↗
arXiv2022

Cone-constrained Monotone Mean-Variance Portfolio Selection Under Diffusion Models

We consider monotone mean-variance (MMV) portfolio selection problems with a conic convex constraint under diffusion models, and their counterpart problems under mean-variance (MV) preferences. We obtain the precommitted optimal strategies to both problems in closed form and find that they coincide, without and with the presence of the conic constraint. This result generalizes the equivalence between MMV and MV preferences from non-constrained cases to a specific constrained case. A comparison analysis reveals that the orthogonality property under the conic convex set is a key to ensuring the equivalence result.

q-fin.PM↗
arXiv2022

Thermal spreading resistance of GaN HEMTs with heat source heating studied by hybrid Monte Carlo-diffusion simulations

Exact assessment of thermal spreading resistance is of great importance to the thermal management of electronic devices, especially when completely considering the heat conduction process from the nanoscale heat source to the macroscopic scale heat sink. The existing simulation methods are either based on convectional Fourier's law or limited to small system sizes, making it difficult to accurately and efficiently study the cross-scale heat transfer. In this paper, a hybrid phonon Monte Carlo-diffusion method that couples phonon Monte Carlo (MC) method with Fourier's law by dividing the computational domain is adopted to analyze thermal spreading resistance in ballistic-diffusive regime. Compared with phonon MC simulation, the junction temperature of the hybrid method has the same precision, while the time costs could be reduced up to 2 orders of magnitude at most. Furthermore, the simulation results indicate that the heating scheme has a remarkable impact on phonon transport. The thermal resistance of the heat source (HS) scheme can be larger than that of the heat flux (HF) scheme, which is opposite from the prediction of Fourier's law. In the HS scheme, the enhanced phonon-boundary scattering counteracts the broadening of the heat source, leading to a stronger ballistic effect as the heat source thickness decreases. The conclusion is verified by a one-dimensional thermal resistance model. This work has opened up an opportunity for the fast and extensive thermal modeling of cross-scale heat transfer in electronic devices and highlighted the influence of heating schemes.

cond-mat.mtrl-sci↗