arXiv subjects

Ji Xu

Publications and source records attributed to Ji Xu.

At least 55 records · Page 3Linked to original sources

arXiv2024

The production of charmonium pentaquark from b-baryon and B-meson decay: SU(3) analysis

In this paper, we study the production of charmonium pentaquark $c \bar c q q q$ from bottom baryon and B-meson decays under the flavor SU(3) symmetry. Decay amplitudes for various processes are parametrized in terms of the SU(3) irreducible nonperturbative amplitudes. A number of relations between decay widths have been deduced. Moreover, the strong decays of pentaquark is also taken into account. These results can be tested in future measurements at LHCb, Belle II and CEPC. Once a few decay branching fractions have been measured, our work could provide hints for exploring new decay channels or new pentaquark states.

hep-ph↗

arXiv2024

Logarithmic moments of B-meson quasidistribution amplitude

It was demonstrated that the lattice simulation of $B$-meson light-cone distribution amplitude (LCDA) is feasible via the quasi-distribution amplitude (quasi-DA) in large momentum effective theory (LaMET). The structures of logarithmic moments (LMs) of $B$-meson quasi-DA is explored in this work. The one-loop results indicate mixing in the matching: the $n$-th LM would be not only factorized into the $n$-th LM of LCDA, but also other moments with different power, accompanied by short distance coefficients. These results supply the understanding of the matching in LaMET and may provide guidance to the lattice study of LMs or other parameters of $B$-meson LCDA.

hep-ph↗

arXiv2023

Direct reduction of iron-ore with hydrogen in fluidized beds: A coarse-grained CFD-DEM-IBM study

Hydrogen metallurgy technology uses hydrogen as the reducing agent instead of carbon reduction, which is one of the important ways to reduce carbon dioxide emissions and ensure the green and sustainable development of iron and steel industry. Due to the advantages of high gas-solid contact efficiency and outstanding mass and heat transfer, direct reduction of iron ore in fluidized beds has attracted much attention. In this study, a coarse-grained CFD-DEM-IBM solver based on hybrid CPU-GPU computing is developed to simulate the direct reduction process of two kinds of iron ore with hydrogen in fluidized beds, where an unreacted shrinking core model based on multiple reaction paths is used to model the reduction reactions, a coarse-grained model and multiple GPUs enable the significant acceleration of particle computation, and the immersed boundary method (IBM) enables the use of simple mesh even in complex geometries of reactors. The predicted results of particle reduction degree are in good agreement with the experimental values, which proves the correctness of the CFD-DEM-IBM solver. In addition, the effects of reaction kinetic parameters and operating temperature on particle reduction degree are also investigated. Present study provides a method for digital design, optimization and scale-up of ironmaking reactors.

physics.flu-dyn↗

arXiv2023

Accessing the subleading-twist $B$-meson light-cone distribution amplitude with Large-Momentum Effective Theory

Within the framework of large momentum effective theory (LaMET), we propose a hard-collinear factorization formula to extract the subleading-twist $B$-meson light-cone distribution amplitude (LCDA) from the quasidistribution amplitude calculated on the lattice. The one-loop matching coefficient and an integro-differential equation governing the evolution of the quasidistribution amplitude are derived. Our results could be useful either in evaluating the subleading-twist $B$-meson LCDA on the lattice or in the understanding the feasibility of LaMET on higher-twist distributions.

hep-ph↗

arXiv2023

Towards Designing Optimal Sensing Matrices for Generalized Linear Inverse Problems

We consider an inverse problem $\mathbf{y}= f(\mathbf{Ax})$, where $\mathbf{x}\in\mathbb{R}^n$ is the signal of interest, $\mathbf{A}$ is the sensing matrix, $f$ is a nonlinear function and $\mathbf{y} \in \mathbb{R}^m$ is the measurement vector. In many applications, we have some level of freedom to design the sensing matrix $\mathbf{A}$, and in such circumstances we could optimize $\mathbf{A}$ to achieve better reconstruction performance. As a first step towards optimal design, it is important to understand the impact of the sensing matrix on the difficulty of recovering $\mathbf{x}$ from $\mathbf{y}$. In this paper, we study the performance of one of the most successful recovery methods, i.e., the expectation propagation (EP) algorithm. We define a notion of spikiness for the spectrum of $\bmmathbfA}$ and show the importance of this measure for the performance of EP. We show that whether a spikier spectrum can hurt or help the recovery performance depends on $f$. Based on our framework, we are able to show that, in phase-retrieval problems, matrices with spikier spectrums are better for EP, while in 1-bit compressed sensing problems, less spiky spectrums lead to better performance. Our results unify and substantially generalize existing results that compare Gaussian and orthogonal matrices, and provide a platform towards designing optimal sensing systems.

cs.IT↗

arXiv2023

Nuclear effects in extracting $\sin^2θ_W$ and a probe for short-range correlations

We investigate the neutral-current neutrino-nucleon deep inelastic scattering with particular emphasis on short-range correlation and EMC effect, as well as their impact on the weak-mixing angle $\sin^2θ_W$ determination. The ratios of structure function $F_{2(NC)}^{A}$ and $x F_{3(NC)}^{A}$ are presented where the nuclei $A$ are chosen as carbon, iron and lead. One kind of universal modification function is proposed which would provide a nontrivial test of SRC universality on the platform of neutrino-nucleon DIS. In addition, we study the impact of ``SRC-driven'' nuclear effects on the extraction of $\sin^2θ_W$ which is naturally associated with the renowned NuTeV anomaly. The results indicate that these effects may account for a substantial fraction of the NuTeV anomaly and considerably affect the value of extracted $\sin^2θ_W$.

hep-ph↗

arXiv2023

Advancing underwater acoustic target recognition via adaptive data pruning and smoothness-inducing regularization

Underwater acoustic recognition for ship-radiated signals has high practical application value due to the ability to recognize non-line-of-sight targets. However, due to the difficulty of data acquisition, the collected signals are scarce in quantity and mainly composed of mechanical periodic noise. According to the experiments, we observe that the repeatability of periodic signals leads to a double-descent phenomenon, which indicates a significant local bias toward repeated samples. To address this issue, we propose a strategy based on cross-entropy to prune excessively similar segments in training data. Furthermore, to compensate for the reduction of training data, we generate noisy samples and apply smoothness-inducing regularization based on KL divergence to mitigate overfitting. Experiments show that our proposed data pruning and regularization strategy can bring stable benefits and our framework significantly outperforms the state-of-the-art in low-resource scenarios.

cs.LG↗

arXiv2023

Nucleons as modified Ising models

In this paper, we propose a map which connects nucleons bound in nuclei and Ising spins in Ising model. This proposal is based on the fact that the description of states of nucleons and Ising spins could share the same type of observables. We present a nuclear model as a correspondence to an explicit modified Ising model and qualitatively confirm the correctness of this map by simulation on a two-dimensional square lattice. This map would help us understand the profound connections between different physical systems.

hep-ph↗

arXiv2023

Determinate Node Selection for Semi-supervised Classification Oriented Graph Convolutional Networks

Graph Convolutional Networks (GCNs) have been proved successful in the field of semi-supervised node classification by extracting structural information from graph data. However, the random selection of labeled nodes used by GCNs may lead to unstable generalization performance of GCNs. In this paper, we propose an efficient method for the deterministic selection of labeled nodes: the Determinate Node Selection (DNS) algorithm. The DNS algorithm identifies two categories of representative nodes in the graph: typical nodes and divergent nodes. These labeled nodes are selected by exploring the structure of the graph and determining the ability of the nodes to represent the distribution of data within the graph. The DNS algorithm can be applied quite simply on a wide range of semi-supervised graph neural network models for node classification tasks. Through extensive experimentation, we have demonstrated that the incorporation of the DNS algorithm leads to a remarkable improvement in the average accuracy of the model and a significant decrease in the standard deviation, as compared to the original method.

cs.LG↗

arXiv2023

Symmetry breaking of Pancharatnam-Berry phase using non-axisymmetric meta-atoms

The Pancharatnam-Berry (PB) phase in metasurfaces obeys the symmetry restriction, according to which the PB phases of two orthogonal circularly polarized waves are the same but with opposite signs. Here, we reveal a general mechanism to break the axisymmetry of meta-atoms in order to break the PB-phase symmetry restriction. As a proof of concept, we designed a novel meta-atom with a QR-code structure and successfully demonstrated circular-polarization multiplexing metasurface holography. This study provides a fundamentally new understanding of the PB phase and opens a path for arbitrary wavefront engineering using asymmetric electromagnetic structures.

physics.optics↗

arXiv2022

Perturbative unitarity and NEC violation in genesis cosmology

Explorations of the violation of null energy condition (NEC) in cosmology could enrich our understanding of the very early universe and the related gravity theories. Although a fully stable NEC violation can be realized in the ``beyond Horndeski'' theory, it remains an open question whether a violation of the NEC is allowed by some fundamental properties of UV-complete theories or the consistency requirements of effective field theory (EFT). We investigate the tree-level perturbative unitarity for stable NEC violations in the contexts of both Galileon and ``beyond Horndeski'' genesis cosmology, in which the universe is asymptotically Minkowskian in the past. We find that the constraints of perturbative unitarity imply that we may need some unknown new physics below the cut-off scale of the EFT other than that represented by the ``beyond Horndeski'' operators.

gr-qc↗

arXiv2022

Semi-supervised Learning with Deterministic Labeling and Large Margin Projection

The centrality and diversity of the labeled data are very influential to the performance of semi-supervised learning (SSL), but most SSL models select the labeled data randomly. This study first construct a leading forest that forms a partially ordered topological space in an unsupervised way, and select a group of most representative samples to label with one shot (differs from active learning essentially) using property of homeomorphism. Then a kernelized large margin metric is efficiently learned for the selected data to classify the remaining unlabeled sample. Optimal leading forest (OLF) has been observed to have the advantage of revealing the difference evolution along a path within a subtree. Therefore, we formulate an optimization problem based on OLF to select the samples. Also with OLF, the multiple local metrics learning is facilitated to address multi-modal and mix-modal problem in SSL, especially when the number of class is large. Attribute to this novel design, stableness and accuracy of the performance is significantly improved when compared with the state-of-the-art graph SSL methods. The extensive experimental studies have shown that the proposed method achieved encouraging accuracy and efficiency. Code has been made available at https://github.com/alanxuji/DeLaLA.

cs.AI↗

arXiv2022

Matching the B-meson quasidistribution amplitude in the RI/MOM scheme

Within the framework of large momentum effective theory (LaMET), the light-cone distribution amplitude of $B$-meson in heavy-quark effective theory (HQET) can be extracted from lattice calculations of quasidistribution amplitude through hard-collinear factorization formula. This quasiquantity can be renormalized in a regularization-independent momentum subtraction scheme (RI/MOM). In this work, we derive the matching coefficient which connects the renormalized quasiditribution amplitude in the RI/MOM scheme and standard LCDA in the $\overline{\textrm{MS}}$ scheme at one-loop accuracy. Our numerical analysis approves of the feasibility of RI/MOM scheme for renormalizing $B$-meson quasidistribution amplitude. These results will be crucial for exploring the partonic structure of heavy-quark hadrons.

hep-ph↗

arXiv2022

Searching for tetraquark through weak decays of b-baryons

After the experimental observation of charged and neutral $Z_c(3900)$, it is of prime importance to pursue searches for additional production modes of these exotic states. In this work, we investigate the possibility to study the $Z_c(3900)$ through weak decays of b-baryons at the LHCb. Decay amplitudes for various processes have been parametrized in terms of the SU(3) irreducible nonperturbative amplitudes. A number of relations for the partial decay widths are deduced from these results. The decay widths of $Λ_b^0\to Λ^0 Z_c^0(3900)$, $Ξ_b^- \to Σ^- Z_{c}^0(3900)$ and $Ξ_b^0 \to Λ^0 Z_{c}^0(3900)$ have also been calculated from which some relevant partial decay widths of b-baryons could be estimated. The results presented in this paper can be tested experimentally at hadron colliders in the future.

hep-ph↗

arXiv2022

Inverse moment of the $B$-meson quasi distribution amplitude

We perform a study on the structure of inverse moment (IM) of quasi distributions, by taking $B$-meson quasi distribution amplitude (quasi-DA) as an example. Based on a one-loop calculation, we derive the renormalization group equation and velocity evolution equation for the first IM of quasi-DA. We find that, in the large velocity limit, the first IM of $B$-meson quasi-DA can be factorized into IM as well as logarithmic moments of light-cone distribution amplitude (LCDA), accompanied by short distance coefficients. Our results can be useful either in understanding the patterns of perturbative matching in Large Momentum Effective Theory or evaluating inverse moment of $B$-meson LCDA on the lattice.

hep-ph↗

arXiv2022

On the proliferation of support vectors in high dimensions

The support vector machine (SVM) is a well-established classification method whose name refers to the particular training examples, called support vectors, that determine the maximum margin separating hyperplane. The SVM classifier is known to enjoy good generalization properties when the number of support vectors is small compared to the number of training examples. However, recent research has shown that in sufficiently high-dimensional linear classification problems, the SVM can generalize well despite a proliferation of support vectors where all training examples are support vectors. In this paper, we identify new deterministic equivalences for this phenomenon of support vector proliferation, and use them to (1) substantially broaden the conditions under which the phenomenon occurs in high-dimensional settings, and (2) prove a nearly matching converse result.

math.ST↗

arXiv2022

Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational(RAMC) Speech Dataset

This paper introduces a high-quality rich annotated Mandarin conversational (RAMC) speech dataset called MagicData-RAMC. The MagicData-RAMC corpus contains 180 hours of conversational speech data recorded from native speakers of Mandarin Chinese over mobile phones with a sampling rate of 16 kHz. The dialogs in MagicData-RAMC are classified into 15 diversified domains and tagged with topic labels, ranging from science and technology to ordinary life. Accurate transcription and precise speaker voice activity timestamps are manually labeled for each sample. Speakers' detailed information is also provided. As a Mandarin speech dataset designed for dialog scenarios with high quality and rich annotations, MagicData-RAMC enriches the data diversity in the Mandarin speech community and allows extensive research on a series of speech-related tasks, including automatic speech recognition, speaker diarization, topic detection, keyword search, text-to-speech, etc. We also conduct several relevant tasks and provide experimental results to help evaluate the dataset.

cs.CL↗

arXiv2022

Improving CTC-based speech recognition via knowledge transferring from pre-trained language models

Recently, end-to-end automatic speech recognition models based on connectionist temporal classification (CTC) have achieved impressive results, especially when fine-tuned from wav2vec2.0 models. Due to the conditional independence assumption, CTC-based models are always weaker than attention-based encoder-decoder models and require the assistance of external language models (LMs). To solve this issue, we propose two knowledge transferring methods that leverage pre-trained LMs, such as BERT and GPT2, to improve CTC-based models. The first method is based on representation learning, in which the CTC-based models use the representation produced by BERT as an auxiliary learning target. The second method is based on joint classification learning, which combines GPT2 for text modeling with a hybrid CTC/attention architecture. Experiment on AISHELL-1 corpus yields a character error rate (CER) of 4.2% on the test set. When compared to the vanilla CTC-based models fine-tuned from the wav2vec2.0 models, our knowledge transferring method reduces CER by 16.1% relatively without external LMs.

cs.CL↗