arXiv subjects

Ji Xu

Publications and source records attributed to Ji Xu.

At least 37 records · Page 2Linked to original sources

arXiv2025

Heavy meson lightcone distribution amplitudes from Lattice QCD

Lightcone distribution amplitudes (LCDAs) within the framework of heavy quark effective theory (HQET) play a crucial role in the theoretical description of weak decays of heavy bottom mesons. However, the first-principle determination of HQET LCDAs faces significant theoretical challenges. In this presentation, we introduce a practical approach to address these obstacles. This makes sequential use of effective field theories. Leveraging the newly-generated lattice ensembles, we present a pioneering lattice calculation, offering new insights into LCDAs for heavy mesons. Additionally, we discuss the impact of these results on the heavy-to-light form factors and briefly give potential future directions in this field.

hep-lat↗

arXiv2025

Searching for doubly charmed baryon from $\overline{B}_c$ meson decays

In this paper, we study the production of doubly charmed baryon from anti-bottom charmed meson. Using the effective Lagrangian approach, we discuss triangle diagrams in hadronic level to get access to the branching ratios of $\overline{B}_c\to \mathcal{B}_{ccq}+\mathcal{B}_{\bar c\bar q\bar q}$. It seems that the specific process $\overline {B}_c \to Ξ_{cc}^{++} \, \overline Ξ_{\bar c}^{'-}$ occupies the largest possibility in the order of $6.68\times 10^{-6}$. In addition, although the production of undiscovered $Ω_{cc}^+$ is Cabibbo suppressed in $\overline B_c\to Ω_{cc}^+ \, \overline Ξ_{\bar c}^0$, it's branching ratio can still reach $10^{-7}$ level. These results are excepted to be fairly valuable supports for future experiments.

hep-ph↗

arXiv2025

Calculation of heavy meson light-cone distribution amplitudes from lattice QCD

We develop an approach for calculating heavy quark effective theory (HQET) light-cone distribution amplitudes (LCDAs) by employing a sequential effective theory methodology. The theoretical foundation of the framework is established, elucidating how the quasi distribution amplitudes (quasi DAs) with three scales can be utilized to compute HQET LCDAs. We provide theoretical support for this approach by demonstrating the rationale behind devising a hierarchical ordering for the three involved scales, discussing the factorization at each step, clarifying the underlying reason for obtaining HQET LCDAs in the final phase, and addressing potential theoretical challenges. The lattice QCD simulation aspect is explored in detail, and the computations of quasi DAs are presented. We employ three fitting strategies to handle contributions from excited states and extract the bare matrix elements. For renormalization purposes, we apply hybrid renormalization schemes at short and long distance separations. To mitigate long-distance perturbations, we perform an extrapolation in $λ= z\cdot P^z$ and assess the stability against various parameters. After two-step matching, our results for HQET LCDAs are found in agreement with existing model parametrizations. The potential phenomenological implications of the results are discussed, shedding light on how these findings could impact our understanding of the strong interaction dynamics and physics beyond the standard model. It should be noted, however, that systematic uncertainties have not been accounted for yet.

hep-lat↗

arXiv2025

Linear relation between short range correlation and EMC effect of gluons in nuclei

We explore the EMC effect of gluons through heavy quark production in deep inelastic scattering (DIS) and the nucleon-nucleon short range correlation (SRC) from sub-threshold photoproduction of the $J/ψ$. Applying an effective field theory analysis, we scrutinize the gluon parton distribution functions in nuclear environment and propose a linear relation between the slope of reduced cross section ratio, which reflects the magnitude of gluon EMC effect, and the $J/ψ$ production cross section at the photon energy $E_γ= 7.0\,\textrm{GeV}$ in the nucleus rest frame, a characteristic representative of SRC. This finding is supported by a numerical calculation using the available phenomenological results for gluon nuclear parton distribution functions (nPDFs). Examining this linear relation by the forthcoming experimental data on electron-ion collider allows to decipher nuclear effects of gluon distributions and enhance our comprehension of the substructure in bound nuclei.

hep-ph↗

arXiv2024

Mass renormalization group of heavy meson light-cone distribution amplitude in QCD

The heavy meson light-cone distribution amplitude (LCDA), as defined in full QCD, plays a key role in the collinear factorization for exclusive heavy meson production and in lattice computations of the LCDA within heavy-quark effective theory (HQET). In addition to its dependence on the renormalization scale, the QCD LCDA also evolves with the heavy quark mass. We derive a partial differential equation to characterize the mass evolution of the heavy meson QCD LCDA, examining the heavy quark mass dependence through its solution. Our results link the internal structure of heavy mesons across different quark masses, offering significant implications for lattice calculations and enabling the extrapolation of results from lower to higher quark masses.

hep-ph↗

arXiv2024

DEMONet: Underwater Acoustic Target Recognition based on Multi-Expert Network and Cross-Temporal Variational Autoencoder

Building a robust underwater acoustic recognition system in real-world scenarios is challenging due to the complex underwater environment and the dynamic motion states of targets. A promising optimization approach is to leverage the intrinsic physical characteristics of targets, which remain invariable regardless of environmental conditions, to provide robust insights. However, our study reveals that while physical characteristics exhibit robust properties, they may lack class-specific discriminative patterns. Consequently, directly incorporating physical characteristics into model training can potentially introduce unintended inductive biases, leading to performance degradation. To utilize the benefits of physical characteristics while mitigating possible detrimental effects, we propose DEMONet in this study, which utilizes the detection of envelope modulation on noise (DEMON) to provide robust insights into the shaft frequency or blade counts of targets. DEMONet is a multi-expert network that allocates various underwater signals to their best-matched expert layer based on DEMON spectra for fine-grained signal processing. Thereinto, DEMON spectra are solely responsible for providing implicit physical characteristics without establishing a mapping relationship with the target category. Furthermore, to mitigate noise and spurious modulation spectra in DEMON features, we introduce a cross-temporal alignment strategy and employ a variational autoencoder (VAE) to reconstruct noise-resistant DEMON spectra to replace the raw DEMON features. The effectiveness of the proposed DEMONet with cross-temporal VAE was primarily evaluated on the DeepShip dataset and our proprietary datasets. Experimental results demonstrated that our approach could achieve state-of-the-art performance on both datasets.

cs.SD↗

arXiv2024

Advancing Robust Underwater Acoustic Target Recognition through Multi-task Learning and Multi-Gate Mixture-of-Experts

Underwater acoustic target recognition has emerged as a prominent research area within the field of underwater acoustics. However, the current availability of authentic underwater acoustic signal recordings remains limited, which hinders data-driven acoustic recognition models from learning robust patterns of targets from a limited set of intricate underwater signals, thereby compromising their stability in practical applications. To overcome these limitations, this study proposes a recognition framework called M3 (Multi-task, Multi-gate, Multi-expert) to enhance the model's ability to capture robust patterns by making it aware of the inherent properties of targets. In this framework, an auxiliary task that focuses on target properties, such as estimating target size, is designed. The auxiliary task then shares parameters with the recognition task to realize multi-task learning. This paradigm allows the model to concentrate on shared information across tasks and identify robust patterns of targets in a regularized manner, thereby enhancing the model's generalization ability. Moreover, M3 incorporates multi-expert and multi-gate mechanisms, allowing for the allocation of distinct parameter spaces to various underwater signals. This enables the model to process intricate signal patterns in a fine-grained and differentiated manner. To evaluate the effectiveness of M3, extensive experiments were implemented on the ShipsEar underwater ship-radiated noise dataset. The results substantiate that M3 has the ability to outperform the most advanced single-task recognition models, thereby achieving the state-of-the-art performance.

cs.SD↗

arXiv2024

Adversarial multi-task underwater acoustic target recognition: towards robustness against various influential factors

Underwater acoustic target recognition based on passive sonar faces numerous challenges in practical maritime applications. One of the main challenges lies in the susceptibility of signal characteristics to diverse environmental conditions and data acquisition configurations, which can lead to instability in recognition systems. While significant efforts have been dedicated to addressing these influential factors in other domains of underwater acoustics, they are often neglected in the field of underwater acoustic target recognition. To overcome this limitation, this study designs auxiliary tasks that model influential factors (e.g., source range, water column depth, or wind speed) based on available annotations and adopts a multi-task framework to connect these factors to the recognition task. Furthermore, we integrate an adversarial learning mechanism into the multi-task framework to prompt the model to extract representations that are robust against influential factors. Through extensive experiments and analyses on the ShipsEar dataset, our proposed adversarial multi-task model demonstrates its capacity to effectively model the influential factors and achieve state-of-the-art performance on the 12-class recognition task.

cs.SD↗

arXiv2024

Long-Tailed Classification Based on Coarse-Grained Leading Forest and Multi-Center Loss

Long-tailed (LT) classification is an unavoidable and challenging problem in the real world. Most existing long-tailed classification methods focus only on solving the class-wise imbalance while ignoring the attribute-wise imbalance. The deviation of a classification model is caused by both class-wise and attribute-wise imbalance. Due to the fact that attributes are implicit in most datasets and the combination of attributes is complex, attribute-wise imbalance is more difficult to handle. For this purpose, we proposed a novel long-tailed classification framework, aiming to build a multi-granularity classification model by means of invariant feature learning. This method first unsupervisedly constructs Coarse-Grained forest (CLF) to better characterize the distribution of attributes within a class. Depending on the distribution of attributes, one can customize suitable sampling strategies to construct different imbalanced datasets. We then introduce multi-center loss (MCL) that aims to gradually eliminate confusing attributes during feature learning process. The proposed framework does not necessarily couple to a specific LT classification model structure and can be integrated with any existing LT method as an independent component. Extensive experiments show that our approach achieves state-of-the-art performance on both existing benchmarks ImageNet-GLT and MSCOCO-GLT and can improve the performance of existing LT methods. Our codes are available on GitHub: \url{https://github.com/jinyery/cognisance}

cs.CV↗

arXiv2024

Study of lepton flavor universality and angular distributions in $D\to K^*_{J} (\to Kπ)\ellν_{\ell}$

The decay of the D meson into multibody final states is a complex process that provides valuable insights into the fundamental interactions within the Standard Model of particle physics. This study focuses on the decay cascade $D^{+}\to K^{*}_{J} \ell^{+}ν\to K^{\pm}π^{\mp} \ell^{+}ν$ where the $K^*_J$ resonance encompasses the $K^*(892),K^{*}(1410),K^{*}_0(1430)$ states. We employ the helicity amplitude technique to derive the angular distributions for the decay chain, enabling the extraction of one-dimensional and two-dimensional distributions. Utilizing form factors for the $D\to K^*$ transition derived from the quark model, we calculate the differential and integrated partial decay widths, explicitly considering the electron and muon masses. Decay branching fractions are calculated, the ratios of the branching fractions are found to be $\frac{\mathcal{B}r(D^{+}\to K^{*}(892)(\to K ^{-}π^{+}) \, μ^{+}ν_μ)}{\mathcal{B}r(D^{+}\to K^{*}(892)(\to K ^{-}π^{+}) \, e^{+}ν_{e})} = 0.975$, $\frac{\mathcal{B}r(D^{+}\to K^{*}(1410)(\to K ^{-}π^{+}) \, μ^{+}ν_μ)}{\mathcal{B}r(D^{+}\to K^{*}(1410)(\to K ^{-}π^{+}) \, e^{+}ν_{e})} = 0.714$ and $\frac{\mathcal{B}r(D^{+}\to K^{*}_{0}(1430)(\to K ^{+}π^{-}) \, μ^{+}ν_μ)}{\mathcal{B}r(D^{+}\to K^{*}_{0}(1430)(\to K ^{+}π^{-}) \, e^{+}ν_{e})} = 0.774$. Results in this work will serve a calibration for the study of $c \to s $ in $D$ meson decays in future and provide useful information towards the understanding of the properties of the $K^{*}$ meson, as well as $K π$ system.

hep-ph↗

arXiv2024

Faithful Density-Peaks Clustering via Matrix Computations on MPI Parallelization System

Density peaks clustering (DP) has the ability of detecting clusters of arbitrary shape and clustering non-Euclidean space data, but its quadratic complexity in both computing and storage makes it difficult to scale for big data. Various approaches have been proposed in this regard, including MapReduce based distribution computing, multi-core parallelism, presentation transformation (e.g., kd-tree, Z-value), granular computing, and so forth. However, most of these existing methods face two limitations. One is their target datasets are mostly constrained to be in Euclidian space, the other is they emphasize only on local neighbors while ignoring global data distribution due to restriction to cut-off kernel when computing density. To address the two issues, we present a faithful and parallel DP method that makes use of two types of vector-like distance matrices and an inverse leading-node-finding policy. The method is implemented on a message passing interface (MPI) system. Extensive experiments showed that our method is capable of clustering non-Euclidean data such as in community detection, while outperforming the state-of-the-art counterpart methods in accuracy when clustering large Euclidean data. Our code is publicly available at https://github.com/alanxuji/FaithPDP.

cs.LG↗

arXiv2024

A critical comparison of the implementation of granular pressure gradient term in Euler-Euler simulation of gas-solid flows

Numerical solution of Euler-Euler model using different in-house, open source and commercial software can generate significantly different results, even when the governing equations and the initial and boundary conditions are exactly same. Unfortunately, the underlying reasons have not been identified yet. In this article, three methods for calculating the granular pressure gradient term are presented for two-fluid model of gas-solid flows and implemented implicitly or explicitly into the solver in OpenFOAM: Method I assumes that the granular pressure gradient is equal to the elastic modulus plus the solid concentration gradient; Method II directly calculates the gradient using a difference scheme; Method III, which is proposed in this work, calculates the gradient as the sum of two partial derivatives: one related to the solid volume fraction and the other related to the granular energy. Obviously, only Methods II and III are consistent with kinetic theory of granular flow. It was found that the difference between all methods is small for bubbling fluidization. While for circulating fluidization, both methods II and III are capable of capturing non-uniform structures and producing superior results over Method I. The contradictory conclusions made from the simulation of different fluidization regimes is due to the different contribution of the term related to the granular energy gradient. Present study concludes that the implementation method of granular pressure gradient may have a significant impact on hydrodynamics and is probably a key factor contributing to the observed differences between different simulation software.

physics.flu-dyn↗

arXiv2024

Center-to-face momentum interpolation and face-to-center flux reconstruction in Euler-Euler simulation of gas-solid flows

In order to resolve the pressure checkerboard field problem with collocated grid, it is essential to employ the momentum interpolation method when formulating the pressure equation, and the flux reconstruction method when updating the cell-centered velocity fields. In this study, we first derive a momentum interpolation method for Euler-Euler simulation of gas-solid flows, which is independent of the time step, the transient term discretization scheme, the under-relaxation factor and the shape of grid; a complete first-order flux reconstruction method is then proposed to update the cell-centered velocities. Their effectiveness are proved by simulating the hydrodynamics of solids settlement, gas-solid fixed bed, bubbling fluidized bed and circulating fluidized bed riser, and then comparing the simulation results to the theoretically known solutions. Their superiority over the standard solver of OpenFOAM in suppressing the high-frequency oscillations and enhancing the smoothness and accuracy is also proved. Finally, the difficulty in fully eliminating the high-frequency oscillations is attributed to the insufficiency of current methods in handling the situations where the independent variables undergo abrupt change.

physics.flu-dyn↗

arXiv2024

Unraveling Complex Data Diversity in Underwater Acoustic Target Recognition through Convolution-based Mixture of Experts

Underwater acoustic target recognition is a difficult task owing to the intricate nature of underwater acoustic signals. The complex underwater environments, unpredictable transmission channels, and dynamic motion states greatly impact the real-world underwater acoustic signals, and may even obscure the intrinsic characteristics related to targets. Consequently, the data distribution of underwater acoustic signals exhibits high intra-class diversity, thereby compromising the accuracy and robustness of recognition systems.To address these issues, this work proposes a convolution-based mixture of experts (CMoE) that recognizes underwater targets in a fine-grained manner. The proposed technique introduces multiple expert layers as independent learners, along with a routing layer that determines the assignment of experts according to the characteristics of inputs. This design allows the model to utilize independent parameter spaces, facilitating the learning of complex underwater signals with high intra-class diversity. Furthermore, this work optimizes the CMoE structure by balancing regularization and an optional residual module. To validate the efficacy of our proposed techniques, we conducted detailed experiments and visualization analyses on three underwater acoustic databases across several acoustic features. The experimental results demonstrate that our CMoE consistently achieves significant performance improvements, delivering superior recognition accuracy when compared to existing advanced methods.

cs.SD↗

arXiv2024

Underwater Acoustic Target Recognition based on Smoothness-inducing Regularization and Spectrogram-based Data Augmentation

Underwater acoustic target recognition is a challenging task owing to the intricate underwater environments and limited data availability. Insufficient data can hinder the ability of recognition systems to support complex modeling, thus impeding their advancement. To improve the generalization capacity of recognition models, techniques such as data augmentation have been employed to simulate underwater signals and diversify data distribution. However, the complexity of underwater environments can cause the simulated signals to deviate from real scenarios, resulting in biased models that are misguided by non-true data. In this study, we propose two strategies to enhance the generalization ability of models in the case of limited data while avoiding the risk of performance degradation. First, as an alternative to traditional data augmentation, we utilize smoothness-inducing regularization, which only incorporates simulated signals in the regularization term. Additionally, we propose a specialized spectrogram-based data augmentation strategy, namely local masking and replicating (LMR), to capture inter-class relationships. Our experiments and visualization analysis demonstrate the superiority of our proposed strategies.

cs.SD↗

arXiv2024

Guiding the underwater acoustic target recognition with interpretable contrastive learning

Recognizing underwater targets from acoustic signals is a challenging task owing to the intricate ocean environments and variable underwater channels. While deep learning-based systems have become the mainstream approach for underwater acoustic target recognition, they have faced criticism for their lack of interpretability and weak generalization performance in practical applications. In this work, we apply the class activation mapping (CAM) to generate visual explanations for the predictions of a spectrogram-based recognition system. CAM can help to understand the behavior of recognition models by highlighting the regions of the input features that contribute the most to the prediction. Our explorations reveal that recognition models tend to focus on the low-frequency line spectrum and high-frequency periodic modulation information of underwater signals. Based on the observation, we propose an interpretable contrastive learning (ICL) strategy that employs two encoders to learn from acoustic features with different emphases (line spectrum and modulation information). By imposing constraints between encoders, the proposed strategy can enhance the generalization performance of the recognition system. Our experiments demonstrate that the proposed contrastive learning approach can improve the recognition accuracy and bring significant improvements across various underwater databases.

cs.SD↗

arXiv2024

Adaptive ship-radiated noise recognition with learnable fine-grained wavelet transform

Analyzing the ocean acoustic environment is a tricky task. Background noise and variable channel transmission environment make it complicated to implement accurate ship-radiated noise recognition. Existing recognition systems are weak in addressing the variable underwater environment, thus leading to disappointing performance in practical application. In order to keep the recognition system robust in various underwater environments, this work proposes an adaptive generalized recognition system - AGNet (Adaptive Generalized Network). By converting fixed wavelet parameters into fine-grained learnable parameters, AGNet learns the characteristics of underwater sound at different frequencies. Its flexible and fine-grained design is conducive to capturing more background acoustic information (e.g., background noise, underwater transmission channel). To utilize the implicit information in wavelet spectrograms, AGNet adopts the convolutional neural network with parallel convolution attention modules as the classifier. Experiments reveal that our AGNet outperforms all baseline methods on several underwater acoustic datasets, and AGNet could benefit more from transfer learning. Moreover, AGNet shows robust performance against various interference factors.

eess.AS↗

arXiv2024

Underwater-Art: Expanding Information Perspectives With Text Templates For Underwater Acoustic Target Recognition

Underwater acoustic target recognition is an intractable task due to the complex acoustic source characteristics and sound propagation patterns. Limited by insufficient data and narrow information perspective, recognition models based on deep learning seem far from satisfactory in practical underwater scenarios. Although underwater acoustic signals are severely influenced by distance, channel depth, or other factors, annotations of relevant information are often non-uniform, incomplete, and hard to use. In our work, we propose to implement Underwater Acoustic Recognition based on Templates made up of rich relevant information (hereinafter called "UART"). We design templates to integrate relevant information from different perspectives into descriptive natural language. UART adopts an audio-spectrogram-text tri-modal contrastive learning framework, which endows UART with the ability to guide the learning of acoustic representations by descriptive natural language. Our experiments reveal that UART has better recognition capability and generalization performance than traditional paradigms. Furthermore, the pre-trained UART model could provide superior prior knowledge for the recognition model in the scenario without any auxiliary annotation.

cs.SD↗