arXiv subjects

Xiang Chen

Publications and source records attributed to Xiang Chen.

At least 415 records · Page 23Linked to original sources

arXiv2022

TOSE: A Fast Capacity Determination Algorithm Based on Random Matrix Theory

Wireless network capacity is one of the most important performance metrics for wireless communication networks. Future wireless networks will be composed of extremely large number of base stations (BSs) and users, and organized in the form of multiple clusters. Unfortunately, the determination of average cluster capacity for such future wireless networks is difficult, and lacks of both analytical expressions and fast algorithms. In this paper, we propose a fast algorithm TOSE to estimate the average cluster capacity based on the random matrix theory (RMT). It can avoid the exact eigenvalue derivations of large dimensional matrices, which are complicated and inevitable in conventional capacity determination methods. Instead, fast eigenvalue estimations can be realized based on RMT in our TOSE algorithm. In addition, we derive the analytical upper and lower bounds of the average cluster capacity. Our numerical experiments show that TOSE is faster than the conventional Cholesky decomposition method, by at least three orders of magnitude. Besides, TOSE has superior generality, since it is independent of the distributions of BSs and users, and the shape of network areas.

cs.IT↗

arXiv2022

Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset

Event extraction (EE) is crucial to downstream tasks such as new aggregation and event knowledge graph construction. Most existing EE datasets manually define fixed event types and design specific schema for each of them, failing to cover diverse events emerging from the online text. Moreover, news titles, an important source of event mentions, have not gained enough attention in current EE research. In this paper, We present Title2Event, a large-scale sentence-level dataset benchmarking Open Event Extraction without restricting event types. Title2Event contains more than 42,000 news titles in 34 topics collected from Chinese web pages. To the best of our knowledge, it is currently the largest manually-annotated Chinese dataset for open event extraction. We further conduct experiments on Title2Event with different models and show that the characteristics of titles make it challenging for event extraction, addressing the significance of advanced study on this problem. The dataset and baseline codes are available at https://open-event-hub.github.io/title2event.

cs.CL↗

arXiv2022

Robust output regulation of linear system subject to modeled and unmodeled uncertainty

In this paper, a novel robust output regulation control framework is proposed for the system subject to noise, modeled disturbance and unmodeled disturbance to seek tracking performance and robustness simultaneously. The output regulation scheme is utilized in the framework to track the reference in the presence of modeled disturbance, and the effect of unmodeled disturbance is reduced by an $\mathcal{H}_\infty$ compensator. The Kalman filter can be also introduced in the stabilization loop to deal with the white noise. Furthermore, the tracking error in the presence/absence of noise and disturbance is estimated. The effectiveness and performance of our proposed control framework is verified in the numerical example by applying in the Furuta Inverted Pendulum system.

eess.SY↗

arXiv2022

ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select

We study the problem of extracting N-ary relation tuples from scientific articles. This task is challenging because the target knowledge tuples can reside in multiple parts and modalities of the document. Our proposed method ReSel decomposes this task into a two-stage procedure that first retrieves the most relevant paragraph/table and then selects the target entity from the retrieved component. For the high-level retrieval stage, ReSel designs a simple and effective feature set, which captures multi-level lexical and semantic similarities between the query and components. For the low-level selection stage, ReSel designs a cross-modal entity correlation graph along with a multi-view architecture, which models both semantic and document-structural relations between entities. Our experiments on three scientific information extraction datasets show that ReSel outperforms state-of-the-art baselines significantly.

cs.CL↗

arXiv2022

Controlled Ordering of Room-Temperature Magnetic Skyrmions in a Polar Van der Waals Magnet

Control and understanding of ensembles of skyrmions is important for realization of future technologies. In particular, the order-disorder transition associated with the 2D lattice of magnetic skyrmions can have significant implications for transport and other dynamic functionalities. To date, skyrmion ensembles have been primarily studied in bulk crystals, or as isolated skyrmions in thin film devices. Here, we investigate the condensation of the skyrmion phase at room temperature and zero field in a polar, Van der Waals magnet. We demonstrate that we can engineer an ordered skyrmion crystal through structural confinement on the $μ$m scale, showing control over this order-disorder transition on scales relevant for device applications.

cond-mat.mtrl-sci↗

arXiv2022

Complete two-loop electroweak corrections to $e^+e^-\rightarrow HZ$

We compute the complete two-loop electroweak corrections to the Higgsstralung process $e^+e^-\rightarrow HZ$ at the future Higgs factory. The Feynman integrals involved in the computation are decomposed into linear combinations of a minimal set of master integrals taking advantage of the recent developments of integral reduction techniques. The master integrals are then evaluated by differential equations with boundary conditions provided by the auxiliary mass flow method. Our final result for given $\sqrt{s}$ is expressed as a piecewise function defined by several deeply expanded power series, which has high precision and can be further manipulated efficiently. Our calculation presents the first complete two-loop electroweak corrections for processes with four external particles.

hep-ph↗

arXiv2022

LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting

Most NER methods rely on extensive labeled data for model training, which struggles in the low-resource scenarios with limited training data. Existing dominant approaches usually suffer from the challenge that the target domain has different label sets compared with a resource-rich source domain, which can be concluded as class transfer and domain transfer. In this paper, we propose a lightweight tuning paradigm for low-resource NER via pluggable prompting (LightNER). Specifically, we construct the unified learnable verbalizer of entity categories to generate the entity span sequence and entity categories without any label-specific classifiers, thus addressing the class transfer issue. We further propose a pluggable guidance module by incorporating learnable parameters into the self-attention layer as guidance, which can re-modulate the attention and adapt pre-trained weights. Note that we only tune those inserted module with the whole parameter of the pre-trained language model fixed, thus, making our approach lightweight and flexible for low-resource scenarios and can better transfer knowledge across domains. Experimental results show that LightNER can obtain comparable performance in the standard supervised setting and outperform strong baselines in low-resource settings. Code is in https://github.com/zjunlp/DeepKE/tree/main/example/ner/few-shot.

cs.CL↗

arXiv2022

Thermal cycling induced alteration of the stacking order and spin-flip in the room temperature van der Waals magnet Fe$_5$GeTe$_2$

The magnetic properties of the quasi-two-dimensional van der Waals magnet Fe$_{5-δ}$GeTe$_2$ (F5GT), which has a high ferromagnetic ordering temperature $T_{\text{C}}$ $\sim$ 315 K, remains to be better understood. It has been demonstrated that the magnetization of F5GT is sensitive to both the Fe deficiency $δ$ and the thermal cycling history. Here, we investigate the structural and magnetic properties of F5GT with a minimal Fe deficiency ($|δ|$ $\le$ 0.1), utilizing combined x-ray and neutron scattering techniques. Our study reveals that the quenched F5GT single crystals experience an irreversible, first-order transition at $T_{\text{S}}$ $\sim$ 110 K upon first cooling, where the stacking order partly or entirely converts from ABC-stacking to AA-stacking order. Importantly, the magnetic properties, including the magnetic moment direction and the enhanced $T_{\text{C}}$ after the thermal cycling, are intimately related to the alteration of the stacking order. Our work highlights the significant influence of the lattice symmetry to the magnetism in F5GT.

cond-mat.str-el↗

arXiv2022

Multimode theory of electron hole transverse instability

We present Vlasov-Poisson 3-D linear stability analysis of an initially planar electron hole structure, solving for the distribution function by integration along unperturbed orbits. The non-sinusoidal potential perturbation shape (parallel to $B$) is expanded in eigenfunctions of the adiabatic Poisson operator, generalizing the prior assumption of a rigid shift of the equilibrium. We show that the shiftmode is then modified by a second discrete mode plus an integral over a continuum of wave-like modes. A rigorous treatment shows that the continuum can be approximated effectively by a single mode that satisfies the external wave dispersion relation, thus making the perturbation a weighted sum of three modes. We find numerically the solution for the complex instability frequency, and the corresponding three mode amplitudes determining the perturbation eigenmode. This multimode analysis refines the accuracy of the prior single mode results, giving slightly higher growth rates at most parameters, as expected from the extra mode shape freedom. Oscillating modes near stability boundaries have larger mode distortions which help explain PIC simulations that observe instability up to $\sim20$\% beyond the prior shiftmode thresholds, and narrowing of the perturbation. At high magnetic field, the multimode analysis predicts a reduction of the already small growth rate.

physics.plasm-ph↗

arXiv2022

QC-ODKLA: Quantized and Communication-Censored Online Decentralized Kernel Learning via Linearized ADMM

This paper focuses on online kernel learning over a decentralized network. Each agent in the network receives continuous streaming data locally and works collaboratively to learn a nonlinear prediction function that is globally optimal in the reproducing kernel Hilbert space with respect to the total instantaneous costs of all agents. In order to circumvent the curse of dimensionality issue in traditional online kernel learning, we utilize random feature (RF) mapping to convert the non-parametric kernel learning problem into a fixed-length parametric one in the RF space. We then propose a novel learning framework named Online Decentralized Kernel learning via Linearized ADMM (ODKLA) to efficiently solve the online decentralized kernel learning problem. To further improve the communication efficiency, we add the quantization and censoring strategies in the communication stage and develop the Quantized and Communication-censored ODKLA (QC-ODKLA) algorithm. We theoretically prove that both ODKLA and QC-ODKLA can achieve the optimal sublinear regret $\mathcal{O}(\sqrt{T})$ over $T$ time slots. Through numerical experiments, we evaluate the learning effectiveness, communication, and computation efficiencies of the proposed methods.

cs.LG↗

arXiv2022

Antiferromagnetic order in Co-doped Fe$_5$GeTe$_2$ probed by resonant magnetic x-ray scattering

The quasi-two-dimensional van der Waals magnet Fe$_{5-δ}$GeTe$_2$ has emerged as a promising platform for electronic and spintronic functionalities at room temperature, owing to its large ferromagnetic ordering temperature $T_{\text{C}}$ $\sim$ 315 K. Interestingly, by cobalt (Co) substitution of iron in F5GT, $i.e.$ $({\text{Fe}}_{1-x}{\text{Co}}_x)_{5-δ}{\text{GeTe}}_2$ (Co-F5GT), not only can its magnetic transition temperature be further enhanced, but the magnetic and structural ground states can also be tuned. Specifically, an antiferromagnetic (AFM) order is induced beyond the Co doping level $x \ge 0.4$. Here, we investigate the magnetic properties of a Co-F5GT single crystal at $x = 0.45(1)$, by utilizing the element specific, resonant magnetic x-ray scattering technique. Our study reveals an A-type, Ising-like AFM ground state, with a transition temperature $T_{\text{N}}$ $\sim$ 340 K. In addition, our work unveils an important contribution from Co magnetic moments to the magnetic order. The application of the in-plane magnetic fields gradually polarize the spin moments along the field direction, but without inducing incommensurate spin texture(s).

cond-mat.mtrl-sci↗

arXiv2022

SOUL-Net: A Sparse and Low-Rank Unrolling Network for Spectral CT Image Reconstruction

Spectral computed tomography (CT) is an emerging technology, that generates a multienergy attenuation map for the interior of an object and extends the traditional image volume into a 4D form. Compared with traditional CT based on energy-integrating detectors, spectral CT can make full use of spectral information, resulting in high resolution and providing accurate material quantification. Numerous model-based iterative reconstruction methods have been proposed for spectral CT reconstruction. However, these methods usually suffer from difficulties such as laborious parameter selection and expensive computational costs. In addition, due to the image similarity of different energy bins, spectral CT usually implies a strong low-rank prior, which has been widely adopted in current iterative reconstruction models. Singular value thresholding (SVT) is an effective algorithm to solve the low-rank constrained model. However, the SVT method requires manual selection of thresholds, which may lead to suboptimal results. To relieve these problems, in this paper, we propose a Sparse and lOw-rank UnroLling Network for spectral CT image reconstruction (SOUL-Net), that learns the parameters and thresholds in a data-driven manner. Furthermore, a Taylor expansion-based neural network backpropagation method is introduced to improve the numerical stability. The qualitative and quantitative results demonstrate that the proposed method outperforms several representative state-of-the-art algorithms in terms of detail preservation and artifact reduction.

physics.med-ph↗

arXiv2022

Unpaired Deep Image Dehazing Using Contrastive Disentanglement Learning

We offer a practical unpaired learning based image dehazing network from an unpaired set of clear and hazy images. This paper provides a new perspective to treat image dehazing as a two-class separated factor disentanglement task, i.e, the task-relevant factor of clear image reconstruction and the task-irrelevant factor of haze-relevant distribution. To achieve the disentanglement of these two-class factors in deep feature space, contrastive learning is introduced into a CycleGAN framework to learn disentangled representations by guiding the generated images to be associated with latent factors. With such formulation, the proposed contrastive disentangled dehazing method (CDD-GAN) employs negative generators to cooperate with the encoder network to update alternately, so as to produce a queue of challenging negative adversaries. Then these negative adversaries are trained end-to-end together with the backbone representation network to enhance the discriminative information and promote factor disentanglement performance by maximizing the adversarial contrastive loss. During the training, we further show that hard negative examples can suppress the task-irrelevant factors and unpaired clear exemples can enhance the task-relevant factors, in order to better facilitate haze removal and help image restoration. Extensive experiments on both synthetic and real-world datasets demonstrate that our method performs favorably against existing unpaired dehazing baselines.

eess.IV↗

arXiv2022

On the Age of Information for AMP based Grant-Free Random Access

With the rapid development of Internet of Things (IoT), massive devices are deployed, which poses severe challenges on access networks due to limited communication resources. When massive users contend for access, the information freshness gets worse caused by increasing collisions. It could be fatal for information freshness sensing scenarios, such as remote monitoring systems or self-driving systems, in which information freshness plays a critical part. In this paper, by taking the Age of Information (AoI) as the primary performance indicator, the information freshness using AMP-based grant-free scheme is investigated and compared with grant-based scheme. Base on the analysis, a user scheduling strategy with sleep threshold and forcing active threshold is proposed to further reduce average AoI (AAoI). Numerical results reveal that the AMP-based grant-free scheme can provide sufficient access capability with less pilot resources, and it is robust to the fluctuation of the number of active users. That ensures that the AMP-based grant-free scheme can keep the AAoI at a low level. It is also shown that the proposed threshold strategy can effectively improve the information freshness.

cs.IT↗

arXiv2022

Phonon softening and slowing down of charge-density-wave fluctuations in BaNi$_2$As$_2$

BaNi$_2$As$_2$ is a nonmagnetic analogue of the iron pnictide superconductors, and in its tetragonal state exhibits an incommensurate charge-density-wave (IC-CDW) and a sizable elastoresistance. In this work, phonons in BaNi$_2$As$_2$ associated with the IC-CDW and uniform in-plane lattice distortions are investigated using high-resolution inelastic X-ray scattering. The in-plane transverse acoustic phonons reveal no softening at temperatures where the elastoresistance increases strongly, indicating the latter to be electronically driven. Systematic phonon measurements reveal the IC-CDW occurs in two stages upon cooling: underdamped phonons first soften to zero energy well above the IC-CDW ordering temperature, then the resulting quasielastic IC-CDW fluctuations gradually slow down and coalesce into the static IC-CDW order. A possible origin for our observations is the IC-CDW in tetragonal BaNi$_2$As$_2$ being uniaxial, which provides an additional Ising degree of freedom favorable for disordered IC-CDW modulations, and accounts for the elastoresistance through a weak coupling to the lattice.

cond-mat.supr-con↗

arXiv2022

BashExplainer: Retrieval-Augmented Bash Code Comment Generation based on Fine-tuned CodeBERT

Developers use shell commands for many tasks, such as file system management, network control, and process management. Bash is one of the most commonly used shells and plays an important role in Linux system development and maintenance. Due to the language flexibility of Bash code, developers who are not familiar with Bash often have difficulty understanding the purpose and functionality of Bash code. In this study, we study Bash code comment generation problem and proposed an automatic method BashExplainer based on two-stage training strategy. In the first stage, we train a Bash encoder by fine-tuning CodeBERT on our constructed Bash code corpus. In the second stage, we first retrieve the most similar code from the code repository for the target code based on semantic and lexical similarity. Then we use the trained Bash encoder to generate two vector representations. Finally, we fuse these two vector representations via the fusion layer and generate the code comment through the decoder. To show the competitiveness of our proposed method, we construct a high-quality corpus by combining the corpus shared in the previous NL2Bash study and the corpus shared in the NLC2CMD competition. This corpus contains 10,592 Bash codes and corresponding comments. Then we selected ten baselines from previous studies on automatic code comment generation, which cover information retrieval methods, deep learning methods, and hybrid methods.

cs.SE↗

arXiv2022

Domain-Adaptive Text Classification with Structured Knowledge from Unlabeled Data

Domain adaptive text classification is a challenging problem for the large-scale pretrained language models because they often require expensive additional labeled data to adapt to new domains. Existing works usually fails to leverage the implicit relationships among words across domains. In this paper, we propose a novel method, called Domain Adaptation with Structured Knowledge (DASK), to enhance domain adaptation by exploiting word-level semantic relationships. DASK first builds a knowledge graph to capture the relationship between pivot terms (domain-independent words) and non-pivot terms in the target domain. Then during training, DASK injects pivot-related knowledge graph information into source domain texts. For the downstream task, these knowledge-injected texts are fed into a BERT variant capable of processing knowledge-injected textual data. Thanks to the knowledge injection, our model learns domain-invariant features for non-pivots according to their relationships with pivots. DASK ensures the pivots to have domain-invariant behaviors by dynamically inferring via the polarity scores of candidate pivots during training with pseudo-labels. We validate DASK on a wide range of cross-domain sentiment classification tasks and observe up to 2.9% absolute performance improvement over baselines for 20 different domain pairs. Code will be made available at https://github.com/hikaru-nara/DASK.

cs.CL↗

arXiv2022

Pervasive beyond room-temperature ferromagnetism in a doped van der Waals magnet: Ni doped Fe$_5$GeTe$_2$ with $T_{\text{C}}$ up to 478 K

The existence of long range magnetic order in low dimensional magnetic systems, such as the quasi-two-dimensional (2D) van der Waals (vdW) magnets, has attracted intensive studies of new physical phenomena. The vdW Fe$_N$GeTe$_2$ ($N$ = 3, 4, 5; FGT) family is exceptional owing to its vast tunability of magnetic properties. Particularly, a ferromagnetic ordering temperature ($T_{\text{C}}$) above room temperature at $N$ = 5 (F5GT) is observed. Here, our study shows that, by nickel (Ni) substitution of iron (Fe) in F5GT, a record high $T_{\text{C}}$ = 478(6) K is achieved. Importantly, pervasive, beyond-room-temperature ferromagnetism exists in almost the entire doping range of the phase diagram of Ni-F5GT. We argue that this striking observation in Ni-F5GT can be possibly due to several contributing factors, in which the structural alteration enhanced 3D magnetic couplings might be critical for enhancing the ferromagnetic order.

cond-mat.mtrl-sci↗