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Shuo Zhang

Publications and source records attributed to Shuo Zhang.

At least 235 records · Page 13Linked to original sources
arXiv2022

Ring That Bell: A Corpus and Method for Multimodal Metaphor Detection in Videos

We present the first openly available multimodal metaphor annotated corpus. The corpus consists of videos including audio and subtitles that have been annotated by experts. Furthermore, we present a method for detecting metaphors in the new dataset based on the textual content of the videos. The method achieves a high F1-score (62\%) for metaphorical labels. We also experiment with other modalities and multimodal methods; however, these methods did not out-perform the text-based model. In our error analysis, we do identify that there are cases where video could help in disambiguating metaphors, however, the visual cues are too subtle for our model to capture. The data is available on Zenodo.

cs.MM↗
arXiv2022

A Scheme to fabricate magnetic graphene-like cobalt nitride CoN4monolayer proposed by first-principles calculations

We propose a scheme to fabricate the cobalt nitride CoN4 monolayer, a magnetic graphene-like two-dimensional material, in which all Co and N atoms are in a plane. Under the pressure above 40 GPa, the bulk CoN4 is stabilized in a triclinic phase. With the pressure decreasing, the triclinic phase of CoN4 is transformed into an orthorhombic phase, and the latter is a layered compound with large interlayer spacing. At ambient condition, the weak interlayer couplings are so small that single CoN4 layer can be exfoliated by the mechanical method.

cond-mat.mtrl-sci↗
arXiv2022

Sideband Cooling of a Trapped Ion in Strong Sideband Coupling Regime

Conventional theoretical studies on the ground-state laser cooling of a trapped ion have mostly focused on the weak sideband coupling (WSC) regime, where the cooling rate is inverse proportional to the linewidth of the excited state. In a recent work~[New J. Phys. 23, 023018 (2021)], we proposed a theoretical framework to study the ground state cooling of a trapped ion in the strong sideband coupling (SSC) regime, under the assumption of a vanishing carrier transition. Here we extend this analysis to more general situations with nonvanishing carrier transitions, where we show that by properly tuning the coupling lasers a cooling rate proportional to the linewidth can be achieved. Our theoretical predictions closely agree with the corresponding exact solutions in the SSC regime, which provide an important theoretical guidance for sideband cooling experiments.

quant-ph↗
arXiv2022

A mixed element scheme of Helmholtz transmission eigenvalue problem for anisotropic media

In this paper, we study the Helmholtz transmission eigenvalue problem for inhomogeneous anisotropic media with the index of refraction $n(x)\equiv 1$ in two and three dimension. Starting with a nonlinear fourth order formulation established by Cakoni, Colton and Haddar [2009], by introducing some auxiliary variables, we present an equivalent mixed formulation for this problem, followed up with the finite element discretization. Using the proposed scheme, we rigorously show that the optimal convergence rate for the transmission eigenvalues both on convex and nonconvex domains can be expected. Moreover, by this scheme, we will obtain a sparse generalized eigenvalue problem whose size is so demanding even with a coarse mesh that its smallest few real eigenvalues fail to be solved by the shift and invert method. We partially overcome this critical issue by deflating the almost all of the $\infty$ eigenvalue of huge multiplicity, resulting in a drastic reduction of the matrix size without deteriorating the sparsity. Extensive numerical examples are reported to demonstrate the effectiveness and efficiency of the proposed scheme.

math.NA↗
arXiv2022

M Subdwarf Research III. Spectroscopic Diagnostics for Breaking Parameter Degeneracy

To understand the parameter degeneracy of M subdwarf spectra at low resolution, we assemble a large number of spectral features in the wavelength range of 0.6-2.5 μm with band strength quantified by narrowband indices. Based on the index trends of BT-Settl model sequences, we illustrate how the main atmospheric parameters (Teff, log g, [M/H], and [alpha/Fe]) affect each spectral feature differently. Furthermore, we propose a four-step process to determine the four parameters sequentially, which extends the basic idea proposed by Jao et al. Each step contains several spectral features that break the degeneracy effect when determining a specific stellar parameter. Finally, the feasibility of each spectroscopic diagnostic with different spectral qualities is investigated. The result is resolution-independent down to R~200.

astro-ph.SR↗
arXiv2022

Realistic Data Augmentation Framework for Enhancing Tabular Reasoning

Existing approaches to constructing training data for Natural Language Inference (NLI) tasks, such as for semi-structured table reasoning, are either via crowdsourcing or fully automatic methods. However, the former is expensive and time-consuming and thus limits scale, and the latter often produces naive examples that may lack complex reasoning. This paper develops a realistic semi-automated framework for data augmentation for tabular inference. Instead of manually generating a hypothesis for each table, our methodology generates hypothesis templates transferable to similar tables. In addition, our framework entails the creation of rational counterfactual tables based on human written logical constraints and premise paraphrasing. For our case study, we use the InfoTabs, which is an entity-centric tabular inference dataset. We observed that our framework could generate human-like tabular inference examples, which could benefit training data augmentation, especially in the scenario with limited supervision.

cs.CL↗
arXiv2022

Enhancing Tabular Reasoning with Pattern Exploiting Training

Recent methods based on pre-trained language models have exhibited superior performance over tabular tasks (e.g., tabular NLI), despite showing inherent problems such as not using the right evidence and inconsistent predictions across inputs while reasoning over the tabular data. In this work, we utilize Pattern-Exploiting Training (PET) (i.e., strategic MLM) on pre-trained language models to strengthen these tabular reasoning models' pre-existing knowledge and reasoning abilities. Our upgraded model exhibits a superior understanding of knowledge facts and tabular reasoning compared to current baselines. Additionally, we demonstrate that such models are more effective for underlying downstream tasks of tabular inference on InfoTabs. Furthermore, we show our model's robustness against adversarial sets generated through various character and word level perturbations.

cs.CL↗
arXiv2022

Two-dimensional binary transition metal nitride $M$N$_4$ ($M$ = V, Cr, Mn, Fe, Co) with a graphene-like structure and strong magnetic properties

Binary transition metal nitride with a graphene-like structure and strong magnetic properties is rare. Based on the first-principles calculations, we design two kinds of $M$N$_4$ ($M$ =transition metal) monolayers, which are transition metal nitrides with a planar structure, made up of $M$N$_4$ units aligned in the rhombic and square patterns. The two structural lattices have robust stability and good compatibility with different metal atoms, and the underlying mechanism is the combination of $sp^2$ hybridization, coordinate bond, and $π$ conjugation. With the metal atom changing from V, Cr, Mn, Fe to Co, the total charge of $M$N$_4$ system increases by one electron in turn, which results in continuous adjustability of the electronic and magnetic properties. The planar ligand field is another feature of the two $M$N$_4$ lattices, which brings about the special splitting of five suborbitals of 3$d$ metal atom and gives rise to strong magnetism. Moreover, room-temperature ferromagnetism in square-CoN$_4$ monolayer with the Curie temperatures of 321 K is determined by solving the Heisenberg model combined with Monte Carlo method.

cond-mat.mtrl-sci↗
arXiv2022

Evaluating the Resilience of Variational Quantum Algorithms to Leakage Noise

As we are entering the era of constructing practical quantum computers, suppressing the inevitable noise to accomplish reliable computational tasks will be the primary goal. Leakage noise, as the amplitude population leaking outside the qubit subspace, is a particularly damaging source of error that error correction approaches cannot handle. However, the impact of this noise on the performance of variational quantum algorithms (VQAs), a type of near-term quantum algorithms that is naturally resistant to a variety of noises, is yet unknown. Here, {we consider a typical scenario with the widely used hardware-efficient ansatz and the emergence of leakage in two-qubit gates}, observing that leakage noise generally reduces the expressive power of VQAs. Furthermore, we benchmark the influence of leakage noise on VQAs in real-world learning tasks. Results show that, both for data fitting and data classification, leakage noise generally has a negative impact on the training process and final outcomes. Our findings give strong evidence that VQAs are vulnerable to leakage noise in most cases, implying that leakage noise must be effectively suppressed in order to achieve practical quantum computing applications, whether for near-term quantum algorithms and long-term error-correcting quantum computing.

quant-ph↗
arXiv2022

Prediction of single-atom-thick transition metal nitride CrN$_4$ with a square-planar network and high-temperature ferromagnetism

Single-atom-thick two-dimensional materials such as graphene usually have a hexagonal lattice while the square-planar lattice is uncommon in the family of two-dimensional materials. Here, we demonstrate that single-atom-thick transition metal nitride CrN$_4$ monolayer is a stable free-standing layer with a square-planar network. The stability of square-planar geometry is ascribed to the combination of N=N double bond, Cr-N coordination bond, and $π$-d conjugation, in which the double $π$-d conjugation is rarely reported in previous studies. This mechanism is entirely different from that of the reported two-dimensional materials, leading to lower formation energy and more robust stability compared to the synthesized g-C$_3$N$_4$ monolayer. On the other hand, CrN$_4$ layer has a ferromagnetic ground state, in which the ferromagnetic coupling between two Cr atoms is mediated by electrons of the half-filled large $π$ orbitals from $π$-d conjugation. The high-temperature ferromagnetism in CrN$_4$ monolayer is confirmed by solving the Heisenberg model with Monte Carlo method.

cond-mat.mtrl-sci↗
arXiv2022

Low-degree robust Hellinger-Reissner finite element schemes for planar linear elasticity with symmetric stress tensors

In this paper, we study the construction of low-degree robust finite element schemes for planar linear elasticity on general triangulations. Firstly, we present a low-degree nonconforming Helling-Reissner finite element scheme. For the stress tensor space, the piecewise polynomial shape function space is $$ {\rm span}\left\{\left(\begin{array}{cc}1&0\\ 0 & 0\end{array}\right),\left(\begin{array}{cc}0&1\\ 1 & 0\end{array}\right),\left(\begin{array}{cc}0&0\\ 0 & 1\end{array}\right),\left(\begin{array}{cc}0&x\\ x & 0\end{array}\right),\left(\begin{array}{cc}0&y\\ y & 0\end{array}\right),\left(\begin{array}{cc}0&x^2-y^2\\ x^2-y^2 & 0\end{array}\right)\right\}, $$ the dimension of the total space is asymptotically 8 times of the number of vertices, and the supports of the basis functions are each a patch of an edge. The piecewise rigid body space is used for the displacement. Robust error estimations in $\mathbb{L}^2$ and broken $\mathbf{H}({\rm div})$ norms are presented. Secondly, we investigate the theoretical construction of schemes with lowest-degree polynomial shape function spaces. Specifically, a Hellinger-Reissner finite element scheme is constructed, with the local shape function space for the stress tensor being 5-dimensional which is of the lowest degree for the local approximation of $\mathbf{H}({\rm div};\mathbb{S})$, and the space for the displacement is piecewise constants. Robust error estimations in $\mathbb{L}^2$ and broken $\mathbf{H}({\rm div})$ norms are presented for regular solutions and data.

math.NA↗
arXiv2022

DuMLP-Pin: A Dual-MLP-dot-product Permutation-invariant Network for Set Feature Extraction

Existing permutation-invariant methods can be divided into two categories according to the aggregation scope, i.e. global aggregation and local one. Although the global aggregation methods, e. g., PointNet and Deep Sets, get involved in simpler structures, their performance is poorer than the local aggregation ones like PointNet++ and Point Transformer. It remains an open problem whether there exists a global aggregation method with a simple structure, competitive performance, and even much fewer parameters. In this paper, we propose a novel global aggregation permutation-invariant network based on dual MLP dot-product, called DuMLP-Pin, which is capable of being employed to extract features for set inputs, including unordered or unstructured pixel, attribute, and point cloud data sets. We strictly prove that any permutation-invariant function implemented by DuMLP-Pin can be decomposed into two or more permutation-equivariant ones in a dot-product way as the cardinality of the given input set is greater than a threshold. We also show that the DuMLP-Pin can be viewed as Deep Sets with strong constraints under certain conditions. The performance of DuMLP-Pin is evaluated on several different tasks with diverse data sets. The experimental results demonstrate that our DuMLP-Pin achieves the best results on the two classification problems for pixel sets and attribute sets. On both the point cloud classification and the part segmentation, the accuracy of DuMLP-Pin is very close to the so-far best-performing local aggregation method with only a 1-2% difference, while the number of required parameters is significantly reduced by more than 85% in classification and 69% in segmentation, respectively. The code is publicly available on https://github.com/JaronTHU/DuMLP-Pin.

cs.CV↗
arXiv2022

The Photon Ring in M87*

We report measurements of the gravitationally lensed secondary image -- the first in an infinite series of so-called "photon rings" -- around the supermassive black hole M87* via simultaneous modeling and imaging of the 2017 Event Horizon Telescope (EHT) observations. The inferred ring size remains constant across the seven days of the 2017 EHT observing campaign and is consistent with theoretical expectations, providing clear evidence that such measurements probe spacetime and a striking confirmation of the models underlying the first set of EHT results. The residual diffuse emission evolves on timescales comparable to one week. We are able to detect with high significance a southwestern extension consistent with that expected from the base of a jet that is rapidly rotating in the clockwise direction. This result adds further support to the identification of the jet in M87* with a black hole spin-driven outflow, launched via the Blandford-Znajek process. We present three revised estimates for the mass of M87* based on identifying the modeled thin ring component with the bright ringlike features seen in simulated images, one of which is only weakly sensitive to the astrophysics of the emission region. All three estimates agree with each other and previously reported values. Our strongest mass constraint combines information from both the ring and the diffuse emission region, which together imply a mass-to-distance ratio of $4.20^{+0.12}_{-0.06}~μ{\rm as}$ and a corresponding black hole mass of $(7.13\pm0.39)\times10^9M_\odot$, where the error on the latter is now dominated by the systematic uncertainty arising from the uncertain distance to M87*.

astro-ph.HE↗
arXiv2022

Active Learning on a Programmable Photonic Quantum Processor

Training a quantum machine learning model generally requires a large labeled dataset, which incurs high labeling and computational costs. To reduce such costs, a selective training strategy, called active learning (AL), chooses only a subset of the original dataset to learn while maintaining the trained model's performance. Here, we design and implement two AL-enpowered variational quantum classifiers, to investigate the potential applications and effectiveness of AL in quantum machine learning. Firstly, we build a programmable free-space photonic quantum processor, which enables the programmed implementation of various hybrid quantum-classical computing algorithms. Then, we code the designed variational quantum classifier with AL into the quantum processor, and execute comparative tests for the classifiers with and without the AL strategy. The results validate the great advantage of AL in quantum machine learning, as it saves at most $85\%$ labeling efforts and $91.6\%$ percent computational efforts compared to the training without AL on a data classification task. Our results inspire AL's further applications in large-scale quantum machine learning to drastically reduce training data and speed up training, underpinning the exploration of practical quantum advantages in quantum physics or real-world applications.

quant-ph↗
arXiv2022

A primal finite element scheme of the Hodge Laplace problem

In this paper, a unified family, for any $n\geqslant 2$ and $1\leqslant k\leqslant n-1$, of nonconforming finite element schemes are presented for the primal weak formulation of the $n$-dimensional Hodge-Laplace equation on $HΛ^k\cap H^*_0Λ^k$ and on the simplicial subdivisions of the domain. The finite element scheme possesses an $\mathcal{O}(h)$-order convergence rate for sufficiently regular data, and an $\mathcal{O}(h^s)$-order rate on any $s$-regular domain, $0<s\leqslant 1$, no matter what topology the domain has.

math.NA↗
arXiv2022

Parameter-Parallel Distributed Variational Quantum Algorithm

Variational quantum algorithms (VQAs) have emerged as a promising near-term technique to explore practical quantum advantage on noisy intermediate-scale quantum (NISQ) devices. However, the inefficient parameter training process due to the incompatibility with backpropagation and the cost of a large number of measurements, posing a great challenge to the large-scale development of VQAs. Here, we propose a parameter-parallel distributed variational quantum algorithm (PPD-VQA), to accelerate the training process by parameter-parallel training with multiple quantum processors. To maintain the high performance of PPD-VQA in the realistic noise scenarios, a alternate training strategy is proposed to alleviate the acceleration attenuation caused by noise differences among multiple quantum processors, which is an unavoidable common problem of distributed VQA. Besides, the gradient compression is also employed to overcome the potential communication bottlenecks. The achieved results suggest that the PPD-VQA could provide a practical solution for coordinating multiple quantum processors to handle large-scale real-word applications.

quant-ph↗
arXiv2022

PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training

Deep learning based semi-supervised learning (SSL) methods have achieved strong performance in medical image segmentation, which can alleviate doctors' expensive annotation by utilizing a large amount of unlabeled data. Unlike most existing semi-supervised learning methods, adversarial training based methods distinguish samples from different sources by learning the data distribution of the segmentation map, leading the segmenter to generate more accurate predictions. We argue that the current performance restrictions for such approaches are the problems of feature extraction and learning preference. In this paper, we propose a new semi-supervised adversarial method called Patch Confidence Adversarial Training (PCA) for medical image segmentation. Rather than single scalar classification results or pixel-level confidence maps, our proposed discriminator creates patch confidence maps and classifies them at the scale of the patches. The prediction of unlabeled data learns the pixel structure and context information in each patch to get enough gradient feedback, which aids the discriminator in convergent to an optimal state and improves semi-supervised segmentation performance. Furthermore, at the discriminator's input, we supplement semantic information constraints on images, making it simpler for unlabeled data to fit the expected data distribution. Extensive experiments on the Automated Cardiac Diagnosis Challenge (ACDC) 2017 dataset and the Brain Tumor Segmentation (BraTS) 2019 challenge dataset show that our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation.

eess.IV↗