arXiv subjects

Rui Liu

Publications and source records attributed to Rui Liu.

At least 307 records · Page 17Linked to original sources

arXiv2023

Observation of Kondo lattice and Kondo-enhanced anomalous Hall effect in an itinerant ferromagnet

The interplay between Kondo screening and magnetic interactions is central to comprehending the intricate phases in heavy-fermion compounds. However, the role of the itinerant magnetic order, which is driven by the conducting (c) electrons, has been largely uncharted in the context of heavy-fermion systems due to the scarcity of material candidates. Here we demonstrate the coexistence of the coherent Kondo screening and d-orbital ferromagnetism in material system La$_{1-x}$Ce$_x$Co$_2$As$_2$, through comprehensive thermodynamic and electrical transport measurements. Additionally, using angle-resolved photoemission spectroscopy (ARPES), we further observe the f-orbit-dominated bands near the Fermi level ($E_f$) and signatures of the f-c hybridization below the magnetic transition temperature, providing strong evidence of Kondo lattice state in the presence of ferromagnetic order. Remarkably, by changing the ratio of Ce/La, we observe a substantial enhancement of the anomalous Hall effect (AHE) in the Kondo lattice regime. The value of the Hall conductivity quantitatively matches with the first-principle calculation that optimized with our ARPES results and can be attributed to the large Berry curvature (BC) density engendered by the topological nodal rings composed of the Ce-4f and Co-3d orbitals at $E_f$. Our findings point to the realization of a new platform for exploring correlation-driven topological responses in a novel Kondo lattice environment.

cond-mat.str-el↗

arXiv2023

Spatiotemporal information conversion machine for time-series prediction

Making predictions in a robust way is a difficult task only based on the observed data of a nonlinear system. In this work, a neural network computing framework, the spatiotemporal information conversion machine (STICM), was developed to efficiently and accurately render a multistep-ahead prediction of a time series by employing a spatial-temporal information (STI) transformation. STICM combines the advantages of both the STI equation and the temporal convolutional network, which maps the high-dimensional/spatial data to the future temporal values of a target variable, thus naturally providing the prediction of the target variable. From the observed variables, the STICM also infers the causal factors of the target variable in the sense of Granger causality, which are in turn selected as effective spatial information to improve the prediction robustness of time-series. The STICM was successfully applied to both benchmark systems and real-world datasets, all of which show superior and robust performance in multistep-ahead prediction, even when the data were perturbed by noise. From both theoretical and computational viewpoints, the STICM has great potential in practical applications in artificial intelligence (AI) or as a model-free method based only on the observed data, and also opens a new way to explore the observed high-dimensional data in a dynamical manner for machine learning.

cs.LG↗

arXiv2022

Influence of magnetic reconnection on the eruptive catastrophes of coronal magnetic flux ropes

Large-scale solar eruptive activities have a close relationship with coronal magnetic flux ropes. Previous numerical studies have found that the equilibrium of a coronal flux rope system could be disrupted if the axial magnetic flux of the rope exceeds a critical value, so that the catastrophe occurs, initiating the flux rope to erupt. Further studies discovered that the catastrophe does not necessarily exist: the flux rope system with certain photospheric flux distributions could be non-catastrophic. It is noteworthy that most previous numerical studies are under the ideal magnetohydrodynamic (MHD) condition, so that it is still elusive whether there is the catastrophe associated with the critical axial flux if magnetic reconnection is included in the flux rope system. In this paper, we carried out numerical simulations to investigate the evolutions of coronal magnetic rope systems under the ideal MHD and the resistive condition. Under the ideal MHD condition, our simulation results demonstrate that the flux rope systems with either too compact or too weak photospheric magnetic source regions are non-catastrophic versus varying axial flux of the rope, and thus no eruption could be initiated; if there is magnetic reconnection in the rope system, however, those flux rope systems could change to be capable of erupting via the catastrophe associated with increasing axial flux. Therefore, magnetic reconnection could significantly influence the catastrophic behaviors of flux rope system. It should be both the magnetic topology and the local physical parameters related to magnetic reconnection that determine whether the increasing axial flux is able to cause flux rope eruptions.

astro-ph.SR↗

arXiv2022

TegFormer: Topic-to-Essay Generation with Good Topic Coverage and High Text Coherence

Creating an essay based on a few given topics is a challenging NLP task. Although several effective methods for this problem, topic-to-essay generation, have appeared recently, there is still much room for improvement, especially in terms of the coverage of the given topics and the coherence of the generated text. In this paper, we propose a novel approach called TegFormer which utilizes the Transformer architecture where the encoder is enriched with domain-specific contexts while the decoder is enhanced by a large-scale pre-trained language model. Specifically, a \emph{Topic-Extension} layer capturing the interaction between the given topics and their domain-specific contexts is plugged into the encoder. Since the given topics are usually concise and sparse, such an additional layer can bring more topic-related semantics in to facilitate the subsequent natural language generation. Moreover, an \emph{Embedding-Fusion} module that combines the domain-specific word embeddings learnt from the given corpus and the general-purpose word embeddings provided by a GPT-2 model pre-trained on massive text data is integrated into the decoder. Since GPT-2 is at a much larger scale, it contains a lot more implicit linguistic knowledge which would help the decoder to produce more grammatical and readable text. Extensive experiments have shown that the pieces of text generated by TegFormer have better topic coverage and higher text coherence than those from SOTA topic-to-essay techniques, according to automatic and human evaluations. As revealed by ablation studies, both the Topic-Extension layer and the Embedding-Fusion module contribute substantially to TegFormer's performance advantage.

cs.CL↗

arXiv2022

Federated Graph Neural Networks: Overview, Techniques and Challenges

With its capability to deal with graph data, which is widely found in practical applications, graph neural networks (GNNs) have attracted significant research attention in recent years. As societies become increasingly concerned with the need for data privacy protection, GNNs face the need to adapt to this new normal. Besides, as clients in Federated Learning (FL) may have relationships, more powerful tools are required to utilize such implicit information to boost performance. This has led to the rapid development of the emerging research field of federated graph neural networks (FedGNNs). This promising interdisciplinary field is highly challenging for interested researchers to grasp. The lack of an insightful survey on this topic further exacerbates the entry difficulty. In this paper, we bridge this gap by offering a comprehensive survey of this emerging field. We propose a 2-dimensional taxonomy of the FedGNNs literature: 1) the main taxonomy provides a clear perspective on the integration of GNNs and FL by analyzing how GNNs enhance FL training as well as how FL assists GNNs training, and 2) the auxiliary taxonomy provides a view on how FedGNNs deal with heterogeneity across FL clients. Through discussions of key ideas, challenges, and limitations of existing works, we envision future research directions that can help build more robust, explainable, efficient, fair, inductive, and comprehensive FedGNNs.

cs.DC↗

arXiv2022

MnTTS2: An Open-Source Multi-Speaker Mongolian Text-to-Speech Synthesis Dataset

Text-to-Speech (TTS) synthesis for low-resource languages is an attractive research issue in academia and industry nowadays. Mongolian is the official language of the Inner Mongolia Autonomous Region and a representative low-resource language spoken by over 10 million people worldwide. However, there is a relative lack of open-source datasets for Mongolian TTS. Therefore, we make public an open-source multi-speaker Mongolian TTS dataset, named MnTTS2, for the benefit of related researchers. In this work, we prepare the transcription from various topics and invite three professional Mongolian announcers to form a three-speaker TTS dataset, in which each announcer records 10 hours of speeches in Mongolian, resulting 30 hours in total. Furthermore, we build the baseline system based on the state-of-the-art FastSpeech2 model and HiFi-GAN vocoder. The experimental results suggest that the constructed MnTTS2 dataset is sufficient to build robust multi-speaker TTS models for real-world applications. The MnTTS2 dataset, training recipe, and pretrained models are released at: \url{https://github.com/ssmlkl/MnTTS2}

eess.AS↗

arXiv2022

Embedding Learning in Hybrid Quantum-Classical Neural Networks

Quantum embedding learning is an important step in the application of quantum machine learning to classical data. In this paper we propose a quantum few-shot embedding learning paradigm, which learns embeddings useful for training downstream quantum machine learning tasks. Crucially, we identify the circuit bypass problem in hybrid neural networks, where learned classical parameters do not utilize the Hilbert space efficiently. We observe that the few-shot learned embeddings generalize to unseen classes and suffer less from the circuit bypass problem compared with other approaches.

quant-ph↗

arXiv2022

Investigating pre-eruptive magnetic properties at the footprints of erupting magnetic flux ropes

It is well established that solar eruptions are powered by free magnetic energy stored in current-carrying magnetic field in the corona. It has also been generally accepted that magnetic flux ropes (MFRs) are a critical component of many coronal mass ejections (CMEs). What remains controversial is whether MFRs are present well before the eruption. Our aim is to identify progenitors of MFRs, and investigate pre-eruptive magnetic properties associated with these progenitors. Here we analyze 28 MFRs erupting within 45 deg from the disk center from 2010 to 2015. All MFRs'feet are well identified by conjugate coronal dimmings. We then calculate magnetic properties at the feet of the MFRs, prior to their eruptions, using Helioseismic and Magnetic Imager (HMI) vector magnetograms. Our results show that only 8 erupting MFRs are associated with significant non-neutralized electric currents, 4 of which also exhibit pre-eruptive dimmings at the foot-prints. Twist and current distributions are asymmetric at the two feet of these MFRs. The presence of pre-eruption dimmings associated with non-neutralized currents suggests the pre-existing MFRs. Furthermore, evolution of conjugate dimmings and electric currents within the foot-prints can provide clues about the internal structure of MFRs and their formation mechanism.

astro-ph.SR↗

arXiv2022

Effects of supra-arcade downflows interacting with the post-flare arcade

Supra-arcade downflows (SADs) are dark voids descending through plasma above the post-flare arcade. Although they are generally viewed as byproducts of flare reconnections in the corona, the nature of SADs is under debate. Here we investigated six distinct episodes of SADs observed in the post-maximum phase of an M-class flare of April 11, 2013. Differential emission measure analysis revealed that SAD cases occurring close to the flare maximum contain an enhanced hot plasma component at 5--7~MK whereas those occurring later exhibited a depression in hot plasma at 7--12~MK compared to the ambient supra-arcade plasma. On-disk location of the flare enabled us to examine in detail the interaction of SADs with the post-flare arcade, whose effects include 1) transverse oscillations of period $\sim\,$160~s in the supra-arcade rays in the wake of voids, 2) footpoint brightening in 1700{~Å} whose peak is delayed by 22-46~s with respect to the SAD's arrival at the top of the arcade, and 3) EUV intensity perturbations expanding and propagating with a speed $\sim\,$400 km~s$^{-1}$. On the other hand, due to line-of-sight confusion in the optically thin corona, the ribbon enhancement following the interaction produces an illusion of plasma rebound at the top of the arcade, where the interaction fails to yield significant plasma heating. These effects indicate that the interaction mainly generates MHD waves propagating toward the surface, which may further produce quasi-periodic brightening at flare ribbons, therefore contributing to quasi-periodic flare gradual phase emission in EUV.

astro-ph.SR↗

arXiv2022

Frequency Spectrum Augmentation Consistency for Domain Adaptive Object Detection

Domain adaptive object detection (DAOD) aims to improve the generalization ability of detectors when the training and test data are from different domains. Considering the significant domain gap, some typical methods, e.g., CycleGAN-based methods, adopt the intermediate domain to bridge the source and target domains progressively. However, the CycleGAN-based intermediate domain lacks the pix- or instance-level supervision for object detection, which leads to semantic differences. To address this problem, in this paper, we introduce a Frequency Spectrum Augmentation Consistency (FSAC) framework with four different low-frequency filter operations. In this way, we can obtain a series of augmented data as the intermediate domain. Concretely, we propose a two-stage optimization framework. In the first stage, we utilize all the original and augmented source data to train an object detector. In the second stage, augmented source and target data with pseudo labels are adopted to perform the self-training for prediction consistency. And a teacher model optimized using Mean Teacher is used to further revise the pseudo labels. In the experiment, we evaluate our method on the single- and compound- target DAOD separately, which demonstrate the effectiveness of our method.

cs.CV↗

arXiv2022

Quantifying syntax similarity with a polynomial representation of dependency trees

We introduce a graph polynomial that distinguishes tree structures to represent dependency grammar and a measure based on the polynomial representation to quantify syntax similarity. The polynomial encodes accurate and comprehensive information about the dependency structure and dependency relations of words in a sentence. We apply the polynomial-based methods to analyze sentences in the Parallel Universal Dependencies treebanks. Specifically, we compare the syntax of sentences and their translations in different languages, and we perform a syntactic typology study of available languages in the Parallel Universal Dependencies treebanks. We also demonstrate and discuss the potential of the methods in measuring syntax diversity of corpora.

cs.CL↗

arXiv2022

Formation and Eruption of Hot Channels during an M6.5 Class Solar Flare

We investigate the formation and eruption of hot channels associated with the M6.5 class flare (SOL2015-06-22T18:23) occurring in NOAA AR 12371 on 2015 June 22. Two flare precursors are observed before the flare main phase. Observations in 94 Å and 131 Å by SDO/AIA have revealed the early morphology of the first hot channel as a group of hot loops, which is termed as seed hot channel. A few seed hot channels are formed above the polarity inversion line (PIL) and the formation is associated with footpoint brightenings' parallel motion along the PIL, which proceeds into the early stage of the flare main phase. During this process, seed hot channels build up and rise slowly, being accelerated at the peak of the second precursor. They merge in the process of acceleration forming a larger hot channel, which then forms an "inverted γ" shape kinking structure. Before the flare peak, the second kinking hot channel with negative crossing appears near the first kinking hot channel that has erupted. The eruption of these two hot channels produce two peaks on the main flare's GOES light curve. The footpoint brightenings' propagation along the PIL indicate that the first kinking hot channel may be formed due to zipper reconnection. The occurrence of merging between seed hot channels observed by AIA is supported by the extrapolated nonlinear force-free field models. The observed writhing motion of the first kinking hot channel may be driven by the Lorentz force.

astro-ph.SR↗

arXiv2022

Exploiting modality-invariant feature for robust multimodal emotion recognition with missing modalities

Multimodal emotion recognition leverages complementary information across modalities to gain performance. However, we cannot guarantee that the data of all modalities are always present in practice. In the studies to predict the missing data across modalities, the inherent difference between heterogeneous modalities, namely the modality gap, presents a challenge. To address this, we propose to use invariant features for a missing modality imagination network (IF-MMIN) which includes two novel mechanisms: 1) an invariant feature learning strategy that is based on the central moment discrepancy (CMD) distance under the full-modality scenario; 2) an invariant feature based imagination module (IF-IM) to alleviate the modality gap during the missing modalities prediction, thus improving the robustness of multimodal joint representation. Comprehensive experiments on the benchmark dataset IEMOCAP demonstrate that the proposed model outperforms all baselines and invariantly improves the overall emotion recognition performance under uncertain missing-modality conditions. We release the code at: https://github.com/ZhuoYulang/IF-MMIN.

cs.CV↗

arXiv2022

FCTalker: Fine and Coarse Grained Context Modeling for Expressive Conversational Speech Synthesis

Conversational Text-to-Speech (TTS) aims to synthesis an utterance with the right linguistic and affective prosody in a conversational context. The correlation between the current utterance and the dialogue history at the utterance level was used to improve the expressiveness of synthesized speech. However, the fine-grained information in the dialogue history at the word level also has an important impact on the prosodic expression of an utterance, which has not been well studied in the prior work. Therefore, we propose a novel expressive conversational TTS model, termed as FCTalker, that learn the fine and coarse grained context dependency at the same time during speech generation. Specifically, the FCTalker includes fine and coarse grained encoders to exploit the word and utterance-level context dependency. To model the word-level dependencies between an utterance and its dialogue history, the fine-grained dialogue encoder is built on top of a dialogue BERT model. The experimental results show that the proposed method outperforms all baselines and generates more expressive speech that is contextually appropriate. We release the source code at: https://github.com/walker-hyf/FCTalker.

cs.CL↗

arXiv2022

Explicit Intensity Control for Accented Text-to-speech

Accented text-to-speech (TTS) synthesis seeks to generate speech with an accent (L2) as a variant of the standard version (L1). How to control the intensity of accent in the process of TTS is a very interesting research direction, and has attracted more and more attention. Recent work design a speaker-adversarial loss to disentangle the speaker and accent information, and then adjust the loss weight to control the accent intensity. However, such a control method lacks interpretability, and there is no direct correlation between the controlling factor and natural accent intensity. To this end, this paper propose a new intuitive and explicit accent intensity control scheme for accented TTS. Specifically, we first extract the posterior probability, called as ``goodness of pronunciation (GoP)'' from the L1 speech recognition model to quantify the phoneme accent intensity for accented speech, then design a FastSpeech2 based TTS model, named Ai-TTS, to take the accent intensity expression into account during speech generation. Experiments show that the our method outperforms the baseline model in terms of accent rendering and intensity control.

cs.SD↗

arXiv2022

Solar Ring Mission: Building a Panorama of the Sun and Inner-heliosphere

Solar Ring (SOR) is a proposed space science mission to monitor and study the Sun and inner heliosphere from a full 360° perspective in the ecliptic plane. It will deploy three 120°-separated spacecraft on the 1-AU orbit. The first spacecraft, S1, locates 30° upstream of the Earth, the second, S2, 90° downstream, and the third, S3, completes the configuration. This design with necessary science instruments, e.g., the Doppler-velocity and vector magnetic field imager, wide-angle coronagraph, and in-situ instruments, will allow us to establish many unprecedented capabilities: (1) provide simultaneous Doppler-velocity observations of the whole solar surface to understand the deep interior, (2) provide vector magnetograms of the whole photosphere - the inner boundary of the solar atmosphere and heliosphere, (3) provide the information of the whole lifetime evolution of solar featured structures, and (4) provide the whole view of solar transients and space weather in the inner heliosphere. With these capabilities, Solar Ring mission aims to address outstanding questions about the origin of solar cycle, the origin of solar eruptions and the origin of extreme space weather events. The successful accomplishment of the mission will construct a panorama of the Sun and inner-heliosphere, and therefore advance our understanding of the star and the space environment that holds our life.

astro-ph.SR↗

arXiv2022

Paradoxical constitutive law between donor O-H bond length and its stretching frequency in water dimer

The constitutive laws of hydrogen bonds (H-bonds) are central to understanding microphysical processes not precisely observed, especially in terms of structural properties. Previous experiences in water H-bonding revealed that as the intermolecular O...O distance shortens, the O-H stretching frequency redshifts; thus, an elongated O-H bond length can be empirically inferred, which is described as the constitutive law under the cooperative effect. Here, using the high-precision CCSD(T) method, we report a violation of the conventional constitutive law in water dimer. That is, when the variation in the O...O distance changes from stretching by 0.06 to contracting by -0.15 angstrom compared to the equilibrium position, the donor O-H bond length decreases from 0.9724 to 0.9717 angstrom, and the O-H stretching frequency redshifts from 3715 to 3708 cm-1. Our work highlights that the O-H bond length decreases simultaneously with its stretching frequency, which is clearly inconsistent with the previously recognized constitutive law.

physics.chem-ph↗

arXiv2022

A Deep Investigation of RNN and Self-attention for the Cyrillic-Traditional Mongolian Bidirectional Conversion

Cyrillic and Traditional Mongolian are the two main members of the Mongolian writing system. The Cyrillic-Traditional Mongolian Bidirectional Conversion (CTMBC) task includes two conversion processes, including Cyrillic Mongolian to Traditional Mongolian (C2T) and Traditional Mongolian to Cyrillic Mongolian conversions (T2C). Previous researchers adopted the traditional joint sequence model, since the CTMBC task is a natural Sequence-to-Sequence (Seq2Seq) modeling problem. Recent studies have shown that Recurrent Neural Network (RNN) and Self-attention (or Transformer) based encoder-decoder models have shown significant improvement in machine translation tasks between some major languages, such as Mandarin, English, French, etc. However, an open problem remains as to whether the CTMBC quality can be improved by utilizing the RNN and Transformer models. To answer this question, this paper investigates the utility of these two powerful techniques for CTMBC task combined with agglutinative characteristics of Mongolian language. We build the encoder-decoder based CTMBC model based on RNN and Transformer respectively and compare the different network configurations deeply. The experimental results show that both RNN and Transformer models outperform the traditional joint sequence model, where the Transformer achieves the best performance. Compared with the joint sequence baseline, the word error rate (WER) of the Transformer for C2T and T2C decreased by 5.72\% and 5.06\% respectively.

cs.CL↗