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Graph neural networks (GNNs) are gaining popularity for processing graph-structured data. In real-world scenarios, graph data within the same dataset can vary significantly in scale. This variability leads to depth-sensitivity, where the optimal depth of GNN layers depends on the scale of the graph data. Empirically, fewer layers are sufficient for message passing in smaller graphs, while larger graphs typically require deeper networks to capture long-range dependencies and global features. However, existing methods generally use a fixed number of GNN layers to generate representations for all graphs, overlooking the depth-sensitivity issue in graph structure data. To address this challenge, we propose the depth adaptive mixture of expert (DA-MoE) method, which incorporates two main improvements to GNN backbone: \textbf{1)} DA-MoE employs different GNN layers, each considered an expert with its own parameters. Such a design allows the model to flexibly aggregate information at different scales, effectively addressing the depth-sensitivity issue in graph data. \textbf{2)} DA-MoE utilizes GNN to capture the structural information instead of the linear projections in the gating network. Thus, the gating network enables the model to capture complex patterns and dependencies within the data. By leveraging these improvements, each expert in DA-MoE specifically learns distinct graph patterns at different scales. Furthermore, comprehensive experiments on the TU dataset and open graph benchmark (OGB) have shown that DA-MoE consistently surpasses existing baselines on various tasks, including graph, node, and link-level analyses. The code are available at \url{https://github.com/Celin-Yao/DA-MoE}., Comment: 8pages

Molecular optimization (MO) is a crucial stage in drug discovery in which task-oriented generated molecules are optimized to meet practical industrial requirements. Existing mainstream MO approaches primarily utilize external property predictors to guide iterative property optimization. However, learning all molecular samples in the vast chemical space is unrealistic for predictors. As a result, errors and noise are inevitably introduced during property prediction due to the nature of approximation. This leads to discrepancy accumulation, generalization reduction and suboptimal molecular candidates. In this paper, we propose a text-guided multi-property molecular optimization method utilizing transformer-based diffusion language model (TransDLM). TransDLM leverages standardized chemical nomenclature as semantic representations of molecules and implicitly embeds property requirements into textual descriptions, thereby preventing error propagation during diffusion process. Guided by physically and chemically detailed textual descriptions, TransDLM samples and optimizes encoded source molecules, retaining core scaffolds of source molecules and ensuring structural similarities. Moreover, TransDLM enables simultaneous sampling of multiple molecules, making it ideal for scalable, efficient large-scale optimization through distributed computation on web platforms. Furthermore, our approach surpasses state-of-the-art methods in optimizing molecular structural similarity and enhancing chemical properties on the benchmark dataset. The code is available at: https://anonymous.4open.science/r/TransDLM-A901.

In this work, we conduct a three-coupled-channel analysis of the $Z_c(3900)$ structure, focusing on the $D\bar{D}^*$, $J/\psi \pi$, and $\rho \eta_c$ channels, based on the one-boson exchange model. Drawing from previous study on the exotic state $T_{cc}$, we only utilize one more parameter to construct the interactions between the channels. Our model successfully reproduces the experimental line shapes of the invariant mass distribution at $\sqrt{s} = 4.23$ and $4.26$ GeV for the three channels. Additionally, the finite-volume energy levels in our model show agreement with current LQCD conclusion. Detailed analysis suggests that the $Z_c(3900)$ peaks in the $\pi J/\psi$ and $\rho \eta_c$ distributions primarily arise from the triangle loop involving the $D_1 \bar{D} D^*$ intermediate system. In the $D\bar{D}^*$ distribution, the threshold peak is generated by the cascade decay mechanism enhanced by a triangle diagram. Moreover, we find a virtual pole located far from the threshold, indicating that $Z_c(3900)$ peaks are not associated with physical pole. We conclude that the $Z_c(3900)$ peaks are predominantly caused by the threshold cusp.

We calculate the correlation functions for the $\bar K^0 p, \pi^+ \Sigma^0, \pi^0 \Sigma^+, \pi^+ \Lambda$, and $\eta \Sigma^+$ states, which in the chiral unitary approach predict an excited $\Sigma^*(1/2^-)$ state at the $\bar K N$ threshold, recently observed by the Belle collaboration. Once this is done, we tackle the inverse problem of seeing how much information one can obtain from these correlation functions. With the resampling method, one can determine the scattering parameters of all the channels with relative precision by means of the analysis in a general framework, and find a clear cusp-like structure corresponding to the $\Sigma^*(1/2^-)$ in the different amplitudes at the $\bar{K}N$ threshold., Comment: 10 pages, 4 figures, 7 tables

The $DDK$ system has gain increasing attention in recent research due to its potential to contain a three-hadron bound state. This article utilizes an extension of the Non-Relativistic Effective Field Theory (NREFT) and the finite volume particle-dimer framework to derive Lorentz-invariant quantization conditions for the $DDK$ three-body system. Using current model input conditions, the finite volume energy spectrum of the $DDK$ three-body system was calculated. This new calculation incorporates relativistic kinematics, allowing it to be applicable across a broader energy range starting from the threshold. In this work, we present a comprehensive \( O(p^{2}) \) calculation. The spurious pole is effectively subtracted within the framework of relativistic kinematics. The spectra in the moving frame are also obtained. These analyses provide a broader testing ground for future lattice simulations. They are expected to reveal more detailed properties of the $DDK$ system and other three-hadron systems., Comment: 20 pages, 7 figures

Molecular optimization is a crucial aspect of drug discovery, aimed at refining molecular structures to enhance drug efficacy and minimize side effects, ultimately accelerating the overall drug development process. Many target-based molecular optimization methods have been proposed, significantly advancing drug discovery. These methods primarily on understanding the specific drug target structures or their hypothesized roles in combating diseases. However, challenges such as a limited number of available targets and a difficulty capturing clear structures hinder innovative drug development. In contrast, phenotypic drug discovery (PDD) does not depend on clear target structures and can identify hits with novel and unbiased polypharmacology signatures. As a result, PDD-based molecular optimization can reduce potential safety risks while optimizing phenotypic activity, thereby increasing the likelihood of clinical success. Therefore, we propose a fragment-masked molecular optimization method based on PDD (FMOP). FMOP employs a regression-free diffusion model to conditionally optimize the molecular masked regions without training, effectively generating new molecules with similar scaffolds. On the large-scale drug response dataset GDSCv2, we optimize the potential molecules across all 945 cell lines. The overall experiments demonstrate that the in-silico optimization success rate reaches 94.4%, with an average efficacy increase of 5.3%. Additionally, we conduct extensive ablation and visualization experiments, confirming that FMOP is an effective and robust molecular optimization method. The code is available at:https://anonymous.4open.science/r/FMOP-98C2., Comment: 11 pages, 5 figures, 2 tables

The glass state problem stems from the failure described in terms of one-electron theory or atoms (molecules) as independent particles. In 2005, de Gennes proposed that the way to explain the glass transition in simple terms was to construct the cluster model of molecules in contact with all existing glass models and to refine the picture of the mean-field hard-sphere molecules (HSMs) in contact with each other. In the process of refining this picture, we discovered the two-electron quantum theory derived from the second solution of de Gennes n = 0, where the clustered contact of the two HSMs along the z-axis is the sequential emergence of the 16 z-direction interface excited quantum states of their coupled electron pair, the two HSMs suddenly overlap by 0.27% to form a magic-interface two-dimensional vector. The two coupled electron orbitals synchronously escaped the two HSMs 16 times, tangent to the magic interface 16 times, and 16 parallel repulsive electron pairs with an interval of 5.9987{\deg}, which is a clustered boson interaction between the two HSMs. This is the common origin of boson peaks in the glass state and electron pairing in the high-temperature superconductivity. Therefore, the collective behavior of electrons in the two-electron theory can unify the glass transition and the high-temperature superconducting transition. This paper is not only a complete theoretical statement on glass transition, but also a new interpretation of the theory of high-temperature superconductivity, which provides a new theoretical perspective in the search for room-temperature superconducting materials., Comment: 29 pages, 11 figures, 1 table, Supplemental materials

Convenient and consistent phase convention is important in the construction of the hadronic Lagrangian. However, the importance of phase convention has been overlooked for a long time and the sources of different conventions are never explicitly addressed. This obscure situation can cause mistakes and misinterpretations in hadron physics. In this paper, we systematically analyze and compare the flavor $SU_3$ phase conventions from the perspective of the quark model. All sources which could lead to different conventions are pointed out and carefully studied. With the tool of the quark model, we also clarify some misconceptions and demonstrate a consistent way to incorporate different conventions., Comment: 25 pages, 3 figures, submitted to Symmetry

Dynamic scene video deblurring aims to remove undesirable blurry artifacts captured during the exposure process. Although previous video deblurring methods have achieved impressive results, they suffer from significant performance drops due to the domain gap between training and testing videos, especially for those captured in real-world scenarios. To address this issue, we propose a domain adaptation scheme based on a blurring model to achieve test-time fine-tuning for deblurring models in unseen domains. Since blurred and sharp pairs are unavailable for fine-tuning during inference, our scheme can generate domain-adaptive training pairs to calibrate a deblurring model for the target domain. First, a Relative Sharpness Detection Module is proposed to identify relatively sharp regions from the blurry input images and regard them as pseudo-sharp images. Next, we utilize a blurring model to produce blurred images based on the pseudo-sharp images extracted during testing. To synthesize blurred images in compliance with the target data distribution, we propose a Domain-adaptive Blur Condition Generation Module to create domain-specific blur conditions for the blurring model. Finally, the generated pseudo-sharp and blurred pairs are used to fine-tune a deblurring model for better performance. Extensive experimental results demonstrate that our approach can significantly improve state-of-the-art video deblurring methods, providing performance gains of up to 7.54dB on various real-world video deblurring datasets. The source code is available at https://github.com/Jin-Ting-He/DADeblur., Comment: ECCV 2024

$P_c(4457)$ has been discovered over five years, but the parity of this particle remains undetermined. In this letter we propose a new interpretation for $P_c(4457)$, which is the state generated from the coupled-channel $\bar{D}^0\Lambda_c^{+}(2595)$ and $\pi^0 P_c(4312)$ since they can exchange an almost on-shell $\Sigma_c^+$. In this scenario, the parity of $P_c(4457)$ will be positive, which is different from the candidate of the bound state of $\bar{D}^*\Sigma_c$. The main decay channel of $P_c(4457)$ in this model is $P_c(4312)\pi$. We propose three processes $\Lambda_b^0 \to J/\psi K_s p \pi^-$, $\Lambda_b^0 \to J/\psi K^- p \pi^0$, and $\Lambda_b^0 \to J/\psi p \pi^- \pi^+ K^-$ to verify $P_c(4457)\to P_c(4312)\pi$., Comment: 6 Pages, 2 figures

Strong empirical and phenomenological indications exist for large sea-quark admixtures in the low-lying excited baryons. Investigating the low-lying excited baryon $\Sigma^*(1/2^-)$ is important to determine the nature of the low-lying excited baryons. We review the experimental and theoretical progress on the studies of the $\Sigma^*(1/2^-)$. Although several candidates have received intensive discussions, such as $\Sigma(1620)$ and $\Sigma(1480)$, their existence needs further confirmation. Following the prediction of the unquenched quark models for the $\Sigma^*(1/2^-)$, many theoretical works suggested the existence of these states in various processes. Future experimental measurements could shed light on the existence of the low-lying excited $\Sigma^*(1/2^-)$ state., Comment: 9 pages, 2 figures, the version of publication in Chinese Physics Letters

Graph contrastive learning (GCL) is a popular method for leaning graph representations by maximizing the consistency of features across augmented views. Traditional GCL methods utilize single-perspective i.e. data or model-perspective) augmentation to generate positive samples, restraining the diversity of positive samples. In addition, these positive samples may be unreliable due to uncontrollable augmentation strategies that potentially alter the semantic information. To address these challenges, this paper proposed a innovative framework termed dual-perspective cross graph contrastive learning (DC-GCL), which incorporates three modifications designed to enhance positive sample diversity and reliability: 1) We propose dual-perspective augmentation strategy that provide the model with more diverse training data, enabling the model effective learning of feature consistency across different views. 2) From the data perspective, we slightly perturb the original graphs using controllable data augmentation, effectively preserving their semantic information. 3) From the model perspective, we enhance the encoder by utilizing more powerful graph transformers instead of graph neural networks. Based on the model's architecture, we propose three pruning-based strategies to slightly perturb the encoder, providing more reliable positive samples. These modifications collectively form the DC-GCL's foundation and provide more diverse and reliable training inputs, offering significant improvements over traditional GCL methods. Extensive experiments on various benchmarks demonstrate that DC-GCL consistently outperforms different baselines on various datasets and tasks., Comment: 12 pages, 5 figures, submitted to IEEE TKDE

Exploring the complex structure of the human brain is crucial for understanding its functionality and diagnosing brain disorders. Thanks to advancements in neuroimaging technology, a novel approach has emerged that involves modeling the human brain as a graph-structured pattern, with different brain regions represented as nodes and the functional relationships among these regions as edges. Moreover, graph neural networks (GNNs) have demonstrated a significant advantage in mining graph-structured data. Developing GNNs to learn brain graph representations for brain disorder analysis has recently gained increasing attention. However, there is a lack of systematic survey work summarizing current research methods in this domain. In this paper, we aim to bridge this gap by reviewing brain graph learning works that utilize GNNs. We first introduce the process of brain graph modeling based on common neuroimaging data. Subsequently, we systematically categorize current works based on the type of brain graph generated and the targeted research problems. To make this research accessible to a broader range of interested researchers, we provide an overview of representative methods and commonly used datasets, along with their implementation sources. Finally, we present our insights on future research directions. The repository of this survey is available at \url{https://github.com/XuexiongLuoMQ/Awesome-Brain-Graph-Learning-with-GNNs}., Comment: 9 pages, 2 figures, IJCAI-2024

Drug-target interaction (DTI) prediction is a critical component of the drug discovery process. In the drug development engineering field, predicting novel drug-target interactions is extremely crucial.However, although existing methods have achieved high accuracy levels in predicting known drugs and drug targets, they fail to utilize global protein information during DTI prediction. This leads to an inability to effectively predict interaction the interactions between novel drugs and their targets. As a result, the cross-field information fusion strategy is employed to acquire local and global protein information. Thus, we propose the siamese drug-target interaction SiamDTI prediction method, which utilizes a double channel network structure for cross-field supervised learning.Experimental results on three benchmark datasets demonstrate that SiamDTI achieves higher accuracy levels than other state-of-the-art (SOTA) methods on novel drugs and targets.Additionally, SiamDTI's performance with known drugs and targets is comparable to that of SOTA approachs. The code is available at https://anonymous.4open.science/r/DDDTI-434D.

Drug response prediction (DRP) is a crucial phase in drug discovery, and the most important metric for its evaluation is the IC50 score. DRP results are heavily dependent on the quality of the generated molecules. Existing molecule generation methods typically employ classifier-based guidance, enabling sampling within the IC50 classification range. However, these methods fail to ensure the sampling space range's effectiveness, generating numerous ineffective molecules. Through experimental and theoretical study, we hypothesize that conditional generation based on the target IC50 score can obtain a more effective sampling space. As a result, we introduce regressor-free guidance molecule generation to ensure sampling within a more effective space and support DRP. Regressor-free guidance combines a diffusion model's score estimation with a regression controller model's gradient based on number labels. To effectively map regression labels between drugs and cell lines, we design a common-sense numerical knowledge graph that constrains the order of text representations. Experimental results on the real-world dataset for the DRP task demonstrate our method's effectiveness in drug discovery. The code is available at:https://anonymous.4open.science/r/RMCD-DBD1., Comment: 22 pages, 7 figures, 9 tables

In this study, the impact of triangle singularity is investigated in the isospin-breaking process $J/\psi \to \Lambda \bar{\Lambda} \pi$. The triangle singularity is found to play a significant role in the process, resulting in the creation of a resonance-like structure around 1.4 GeV in the $\Lambda\pi(\bar{\Lambda}\pi)$ invariant mass spectrum. To amplify the impact of this triangle singularity, the presence of two $\Sigma^*(\frac{1}{2}^-)$ states around 1.4 GeV and 1.6 GeV is essential, yet these states have not been definitively identified in the current baryon spectrum. We recommend that experiments, particularly the Beijing Spectrometer (BESIII) and the future Super Tau-Charm Factory (STCF), to investigate the process $J/\psi \to \Lambda \bar{\Lambda} \pi$ to offer direct evidences for our predicted triangle singularity and additional evidence regarding the $\Sigma(\frac{1}{2}^-)$ states.

Graph Masked Autoencoders (GMAEs) have emerged as a notable self-supervised learning approach for graph-structured data. Existing GMAE models primarily focus on reconstructing node-level information, categorizing them as single-scale GMAEs. This methodology, while effective in certain contexts, tends to overlook the complex hierarchical structures inherent in many real-world graphs. For instance, molecular graphs exhibit a clear hierarchical organization in the form of the atoms-functional groups-molecules structure. Hence, the inability of single-scale GMAE models to incorporate these hierarchical relationships often leads to their inadequate capture of crucial high-level graph information, resulting in a noticeable decline in performance. To address this limitation, we propose Hierarchical Graph Masked AutoEncoders (Hi-GMAE), a novel multi-scale GMAE framework designed to handle the hierarchical structures within graphs. First, Hi-GMAE constructs a multi-scale graph hierarchy through graph pooling, enabling the exploration of graph structures across different granularity levels. To ensure masking uniformity of subgraphs across these scales, we propose a novel coarse-to-fine strategy that initiates masking at the coarsest scale and progressively back-projects the mask to the finer scales. Furthermore, we integrate a gradual recovery strategy with the masking process to mitigate the learning challenges posed by completely masked subgraphs. Diverging from the standard graph neural network (GNN) used in GMAE models, Hi-GMAE modifies its encoder and decoder into hierarchical structures. This entails using GNN at the finer scales for detailed local graph analysis and employing a graph transformer at coarser scales to capture global information. Our experiments on 15 graph datasets consistently demonstrate that Hi-GMAE outperforms 17 state-of-the-art self-supervised competitors., Comment: 10 pages, 6 figures, 3 tables

Motivated by the Belle measurements of the process $\Lambda_c^+\to \eta\pi^+\Lambda$, we investigate this process by considering the contributions from the $\Lambda(1670)$, $a_0(980)$, and $\Sigma(1385)$. In addition, we also consider the predicted low-lying baryon $\Sigma^*(1/2^-)$. Our results involving the $\Sigma^*(1/2^-)$ are favored by fitting to the Belle data of the $\eta\Lambda$ and $\pi^+\Lambda$ invariant mass distributions. Furthermore, we predict the $\eta\pi^+$ invariant mass distribution and the angular distribution $d\Gamma/d{\rm cos}\theta$, which are significantly different depending on whether or not the contribution from the $\Sigma^*(1/2^-)$ is considered. Finally, we show that, with the contribution from the $\Sigma^*(1/2^-)$, the calculated Dalizt plot agrees with the Belle measurements. Future precise measurements of the process $\Lambda_c^+\to \eta\pi^+\Lambda$ could shed further light on the existence of the low-lying $\Sigma^*(1/2^-)$., Comment: 11 pages, 14 figures, comments are welcome

With the continuous accumulation of data from hadron collision experiments, efficient Partial Wave Analysis tools are indispensable for constructing a clear hadron spectrum. Currently, automated computations of scattering amplitudes primarily use the helicity scheme and covariant effective Lagrangian method. The automated calculations under the covariant $L$-$S$ scheme, which is one of the commonly used partial wave analysis schemes, have not been fully realized. In this work, we provide a general algorithm for computing partial wave amplitudes under the covariant $L$-$S$ scheme. This will lay the foundation for automated computation of partial wave amplitudes under the covariant $L$-$S$ scheme., Comment: 6 pages

We propose to manipulate topological polaritons in optomechanical ladders consisting of an optical Su-Schrieffer-Heeger (SSH) chain and a mechanical SSH chain connected through optomechanical (interchain) interactions. We show that the topological phase diagrams are divided into six areas by four boundaries and that there are four topological phases characterized by the Berry phases. We find that a topologically nontrivial phase of the polaritons is generated by the optomechanical interaction between the optical and mechanical SSH chains even though they are both in the topologically trivial phases. Counter-intuitively, six edge states appear in one of the topological phases with only two topological nontrivial bands, and some edge states are localized near but not at the boundaries of an open-boundary ladder. Moreover, a two-dimensional Chern insulator with higher Chern numbers is simulated by introducing proper periodical adiabatic modulations of the driving amplitude and frequency. Our work not only opens a route towards topological polaritons manipulation by optomachanical interactions, but also will exert a far-reaching influence on designing topologically protected polaritonic devices., Comment: 8+15 pages, 3+7 figures

The predictive power of the deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc) with density functional PC-PK1 is demonstrated for superheavy region ($101 \leqslant Z \leqslant 120$) by comparing with available experimental and evaluated data in the AME2020. The DRHBc theory predicts 93 bound nuclei beyond the drip line $N = 258$ in the region of $106 \leqslant Z \leqslant 112$, which form a stability peninsula. The odd-even differences between odd-$N$ and even-$N$ nuclei are remarkable in the stability peninsula; the number of bound odd-$N$ nuclei is less than that of bound even-$N$ nuclei, and the one-neutron separation energy of an odd-$N$ nucleus is smaller than those of its neighboring even-$N$ nuclei due to the blocking effect. The deformation effect is indispensable for the reentrant stability beyond the drip line by significantly affecting the structure of single-particle levels around the Fermi energy. The interplay between deformation and pairing effects affects the position where the odd-$N$ nucleus becomes bound in the stability peninsula. By examining the deformation effect at different orders, it is found that quadrupole deformation makes leading contribution to the appearance of stability peninsula and the effects of hexadecapole and hexacontatetrapole deformations are nonnegligible., Comment: 6 figures

The investigation of hadron interactions within lattice QCD has been facilitated by the well-known quantisation condition, linking scattering phase shifts to finite-volume energies. Additionally, the ability to utilise systems at finite total boosts has been pivotal in smoothly charting the energy-dependent behaviour of these phase shifts. The existing implementations of the quantization condition at finite boosts rely on momentum transformations between rest and moving frames, defined directly in terms of the energy eigenvalues. This energy dependence is unsuitable in the formulation of a Hamiltonian.In this work, we introduce a novel approach to generalise the three-momentum boost prescription, enabling the incorporation of energy-independent finite-volume Hamiltonians within moving frames. We demonstrate the application of our method through numerical comparisons, employing a phenomenological $\pi\pi$ scattering example., Comment: 30 pages, 5 figures

In this paper, we prove some Willmore-type inequalities for closed hypersurfaces in weighted manifolds with nonnegative Bakry-\'Emery Ricci curvature. In particular, we get a sharp Willmore type inequality steady gradient Ricci solitons. We also prove a sharp Willmore-like inequality in shrinking gradient Ricci solitons. Moreover, we characterize the equality cases of our Willmore-type inequalities. These results can be regarded as generalizations of Agostiniani-Fogagnolo-Mazzieri's Willmore-type inequality to weighted manifolds. As applications, we derive some isoperimetric type inequalities under certain existence assumptions of isoperimetric regions in weighted manifolds., Comment: Mini corrected, Corollary 1.3 added

Graph masked autoencoders (GMAE) have emerged as a significant advancement in self-supervised pre-training for graph-structured data. Previous GMAE models primarily utilize a straightforward random masking strategy for nodes or edges during training. However, this strategy fails to consider the varying significance of different nodes within the graph structure. In this paper, we investigate the potential of leveraging the graph's structural composition as a fundamental and unique prior in the masked pre-training process. To this end, we introduce a novel structure-guided masking strategy (i.e., StructMAE), designed to refine the existing GMAE models. StructMAE involves two steps: 1) Structure-based Scoring: Each node is evaluated and assigned a score reflecting its structural significance. Two distinct types of scoring manners are proposed: predefined and learnable scoring. 2) Structure-guided Masking: With the obtained assessment scores, we develop an easy-to-hard masking strategy that gradually increases the structural awareness of the self-supervised reconstruction task. Specifically, the strategy begins with random masking and progresses to masking structure-informative nodes based on the assessment scores. This design gradually and effectively guides the model in learning graph structural information. Furthermore, extensive experiments consistently demonstrate that our StructMAE method outperforms existing state-of-the-art GMAE models in both unsupervised and transfer learning tasks. Codes are available at https://github.com/LiuChuang0059/StructMAE., Comment: 9 pages, 3 Figures. Accepted by IJCAI 2024

Fake news is pervasive on social media, inflicting substantial harm on public discourse and societal well-being. We investigate the explicit structural information and textual features of news pieces by constructing a heterogeneous graph concerning the relations among news topics, entities, and content. Through our study, we reveal that fake news can be effectively detected in terms of the atypical heterogeneous subgraphs centered on them, which encapsulate the essential semantics and intricate relations between news elements. However, suffering from the heterogeneity, exploring such heterogeneous subgraphs remains an open problem. To bridge the gap, this work proposes a heterogeneous subgraph transformer (HeteroSGT) to exploit subgraphs in our constructed heterogeneous graph. In HeteroSGT, we first employ a pre-trained language model to derive both word-level and sentence-level semantics. Then the random walk with restart (RWR) is applied to extract subgraphs centered on each news, which are further fed to our proposed subgraph Transformer to quantify the authenticity. Extensive experiments on five real-world datasets demonstrate the superior performance of HeteroSGT over five baselines. Further case and ablation studies validate our motivation and demonstrate that performance improvement stems from our specially designed components.

Learning-based stereo matching techniques have made significant progress. However, existing methods inevitably lose geometrical structure information during the feature channel generation process, resulting in edge detail mismatches. In this paper, the Motif Cha}nnel Attention Stereo Matching Network (MoCha-Stereo) is designed to address this problem. We provide the Motif Channel Correlation Volume (MCCV) to determine more accurate edge matching costs. MCCV is achieved by projecting motif channels, which capture common geometric structures in feature channels, onto feature maps and cost volumes. In addition, edge variations in %potential feature channels of the reconstruction error map also affect details matching, we propose the Reconstruction Error Motif Penalty (REMP) module to further refine the full-resolution disparity estimation. REMP integrates the frequency information of typical channel features from the reconstruction error. MoCha-Stereo ranks 1st on the KITTI-2015 and KITTI-2012 Reflective leaderboards. Our structure also shows excellent performance in Multi-View Stereo. Code is avaliable at https://github.com/ZYangChen/MoCha-Stereo., Comment: Accepted to CVPR 2024

Ascribed as $\rho\rho$ and $K^* \bar{K}^*$ molecular states, respectively, iso-scalar $f_0(1500)$ and $f_0(1710)$ states are expected to have iso-vector partners, potentially identified as $a_0(1450)$ and $a_0(1710)$. The predicted dominant decay modes for these two $a_0$ resonances are $a_0(1450) \to \omega\pi\pi$ and $a_0(1710) \to \omega\pi\pi,\,\phi\pi\pi$. We estimate cross sections for two-photon production of these four resonances within the hadronic molecular picture, and demonstrate that SuperKEKB's luminosity is sufficient for their observation and more precise parameter measurements.

We derive the modified L\"uscher equation in the presence of the long-range force caused by the exchange of a light particle. It is shown that the use of this equation enables one to circumvent the problems related to the strong partial-wave mixing and the t-channel sub-threshold singularities. It is also demonstrated that the present method is intrinsically linked to the so-called modified effective-range expansion (MERE) in the infinite volume. A detailed comparison with the two recently proposed alternative approaches is provided., Comment: 28 pages, 4 figures

This presentation opens with a brief review of lattice QCD calculations showing the $2s$ radial excitation of the nucleon sits at approximately 2 GeV, well above the Roper resonance position. We then proceed to reconcile this observation with experimental scattering data. While the idea of dressing quark-model states in a coupled-channel analysis to describe scattering data has been around for decades, it's now possible to bring these descriptions to the finite-volume of lattice QCD for confrontation with lattice-QCD calculations. This combination of lattice QCD and experiment demands that we reconsider our preconceived notions about the quark-model and its excitation spectrum. We close with a discussion of an unanticipated resolution to the missing baryon resonances problem., Comment: 8 pages, 2 figures. To appear as a plenary contribution to the proceedings of HADRON 2023, the 20th International Conference on Hadron Spectroscopy and Structure, 5th-9th June 2023, Genova, Italy

Image deblurring aims to remove undesired blurs from an image captured in a dynamic scene. Much research has been dedicated to improving deblurring performance through model architectural designs. However, there is little work on data augmentation for image deblurring. Since continuous motion causes blurred artifacts during image exposure, we aspire to develop a groundbreaking blur augmentation method to generate diverse blurred images by simulating motion trajectories in a continuous space. This paper proposes Implicit Diffusion-based reBLurring AUgmentation (ID-Blau), utilizing a sharp image paired with a controllable blur condition map to produce a corresponding blurred image. We parameterize the blur patterns of a blurred image with their orientations and magnitudes as a pixel-wise blur condition map to simulate motion trajectories and implicitly represent them in a continuous space. By sampling diverse blur conditions, ID-Blau can generate various blurred images unseen in the training set. Experimental results demonstrate that ID-Blau can produce realistic blurred images for training and thus significantly improve performance for state-of-the-art deblurring models. The source code is available at https://github.com/plusgood-steven/ID-Blau., Comment: CVPR 2024