arXiv subjects

Ji Xu

Publications and source records attributed to Ji Xu.

At least 73 records · Page 4Linked to original sources

arXiv2022

Distribution Amplitudes of $K^*$ and $ϕ$ at Physical Pion Mass from Lattice QCD

We present the first lattice QCD calculation of the distribution amplitudes of longitudinally and transversely polarized vector mesons $K^*$ and $ϕ$ using large momentum effective theory. We use the clover fermion action on three ensembles with 2+1+1 flavors of highly improved staggered quarks (HISQ) action, generated by MILC collaboration, at physical pion mass and \{0.06, 0.09, 0.12\} fm lattice spacings, and choose three different hadron momenta $P_z=\{1.29, 1.72, 2.15\}$ GeV. The resulting lattice matrix elements are nonperturbatively renormalized in a hybrid scheme proposed recently. An extrapolation to the continuum and infinite momentum limit is carried out. We find that while the longitudinal distribution amplitudes tend to be close to the asymptotic form, the transverse ones deviate rather significantly from the asymptotic form. Our final results provide crucial {\it ab initio} theory inputs for analyzing pertinent exclusive processes.

hep-lat↗

arXiv2022

Pion and Kaon Distribution Amplitudes from Lattice QCD

We present the state-of-the-art lattice QCD calculation of the pion and kaon light-cone distribution amplitudes (DAs) using large-momentum effective theory. The calculation is done at three lattice spacings $a\approx\{0.06,0.09,0.12\}$ fm and physical pion and kaon masses, with the meson momenta $P_z = \{1.29,1.72,2.15\}$ GeV. The result is non-perturbatively renormalized in a recently proposed hybrid scheme with self renormalization, and extrapolated to the continuum as well as the infinite momentum limit. We find a significant deviation of the pion and kaon DAs from the asymptotic form, and a large $SU(3)$ flavor breaking effect in the kaon DA.

hep-lat↗

arXiv2021

Deciphering Weak Decays of Triply Heavy Baryons by SU(3) Analysis

Baryons with three heavy quarks are the last missing pieces of the lowest-lying baryon multiplets in the quark model after the discovery of doubly heavy baryons. In this work, we study nonleptonic weak decays of triply heavy baryons $Ω_{ccc}^{++}$, $Ω_{bbb}^{-}$, $Ω_{ccb}^{+}$, and $Ω_{cbb}^{0}$. 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 can be deduced from these results that can be examined in future. Some decay channels and cascade decay modes which likely to be used to reconstruct the triply heavy baryons have been also listed.

hep-ph↗

arXiv2021

Nuclear effects in neutrino-nucleus DIS and probe for short range correlation

We investigate charged-current neutrino-nucleus deep inelastic scattering with particular interests in relationship of short range correlation (SRC) and EMC effect. The structure functions $F^A_2(x,Q^2)$, $x F^A_3(x,Q^2)$ and ratios of differential cross sections are presented where the nuclei $A$ are chosen to be carbon, iron and lead. We propose a kind of universal modification functions which would provide a nontrivial test of SRC universality on the platform of neutrino-nucleus scattering and improve our understanding of nucleon structure substantially.

hep-ph↗

arXiv2021

Weak decays of doubly heavy baryons: four-body nonleptonic decay channels

The LHCb Collaboration announced the observation of doubly charmed baryon through $Ξ_{c c}^{++} \rightarrow Λ_{c}^{+} K^{-} π^{+} π^{+}$ in 2017. Since then, a series of studies of doubly heavy baryons have been presented. $Ξ_{cc}^{++}$ was discovered through nonleptonic four-body decay mode, and experimental data has indicated that the decay modes of $Ξ_{c c}^{++}$ are not saturated by two and three-body intermediate states. In this work, we analyze the four-body weak decays of doubly heavy baryons $Ξ_{cc}^{++}, Ξ_{cc}^+$, and $Ω_{cc}^+$. Decay amplitudes for various channels are parametrized in terms of SU(3) irreducible amplitudes. We point out that branching fractions for Cabibbo-allowed processes $Ξ_{cc}^{+}\toΛ_c^+π^+ π^0 K^-$, $Ω_{cc}^{+}\toΛ_c^+π^+ \overline K^0 K^-$ would be helpful to search for $Ξ_{cc}^+$ and $Ω_{cc}^+$ in future measurements at experimental facilities like LHC, Belle II, and CEPC.

hep-ph↗

arXiv2021

Consistent Risk Estimation in Moderately High-Dimensional Linear Regression

Risk estimation is at the core of many learning systems. The importance of this problem has motivated researchers to propose different schemes, such as cross validation, generalized cross validation, and Bootstrap. The theoretical properties of such estimates have been extensively studied in the low-dimensional settings, where the number of predictors $p$ is much smaller than the number of observations $n$. However, a unifying methodology accompanied with a rigorous theory is lacking in high-dimensional settings. This paper studies the problem of risk estimation under the moderately high-dimensional asymptotic setting $n,p \rightarrow \infty$ and $n/p \rightarrow δ>1$ ($δ$ is a fixed number), and proves the consistency of three risk estimates that have been successful in numerical studies, i.e., leave-one-out cross validation (LOOCV), approximate leave-one-out (ALO), and approximate message passing (AMP)-based techniques. A corner stone of our analysis is a bound that we obtain on the discrepancy of the `residuals' obtained from AMP and LOOCV. This connection not only enables us to obtain a more refined information on the estimates of AMP, ALO, and LOOCV, but also offers an upper bound on the convergence rate of each estimate.

math.ST↗

arXiv2020

When Does Preconditioning Help or Hurt Generalization?

While second order optimizers such as natural gradient descent (NGD) often speed up optimization, their effect on generalization has been called into question. This work presents a more nuanced view on how the \textit{implicit bias} of first- and second-order methods affects the comparison of generalization properties. We provide an exact asymptotic bias-variance decomposition of the generalization error of overparameterized ridgeless regression under a general class of preconditioner $\boldsymbol{P}$, and consider the inverse population Fisher information matrix (used in NGD) as a particular example. We determine the optimal $\boldsymbol{P}$ for both the bias and variance, and find that the relative generalization performance of different optimizers depends on the label noise and the "shape" of the signal (true parameters): when the labels are noisy, the model is misspecified, or the signal is misaligned with the features, NGD can achieve lower risk; conversely, GD generalizes better than NGD under clean labels, a well-specified model, or aligned signal. Based on this analysis, we discuss several approaches to manage the bias-variance tradeoff, and the potential benefit of interpolating between GD and NGD. We then extend our analysis to regression in the reproducing kernel Hilbert space and demonstrate that preconditioned GD can decrease the population risk faster than GD. Lastly, we empirically compare the generalization error of first- and second-order optimizers in neural network experiments, and observe robust trends matching our theoretical analysis.

stat.ML↗

arXiv2020

Analysis of $B_c \to τν_τ$ at CEPC

The precise determination of the $B_c \to τν_τ$ branching ratio provides an advantageous opportunity for understanding the electroweak structure of the Standard Model, measuring the CKM matrix element $|V_{cb}|$ and probing new physics models. In this paper, we discuss the potential of measuring the processes of $B_c \to τν_τ$ with $τ$ decaying leptonically at the proposed Circular Electron Positron Collider (CEPC). We conclude that during the $Z$ pole operation, the channel signal can achieve five $σ$ significance with $\sim 10^9$ $Z$ decays, and the signal strength accuracies for $B_c \to τν_τ$ can reach around 1% level at the nominal CEPC $Z$ pole statistics of one trillion $Z$ decays assuming the total $B_c \to τν_τ$ yield is $3.6 \times 10^6$. Our theoretical analysis indicates the accuracy could provide a strong constraint on the general effective Hamiltonian for the $b \to cτν$ transition. If the total $B_c$ yield can be determined to $\mathcal{O}(1\%)$ level of accuracy in the future, these results also imply $|V_{cb}|$ could be measured up to $\mathcal{O}(1\%)$ level of accuracy.

hep-ex↗

arXiv2020

On the Optimal Weighted $\ell_2$ Regularization in Overparameterized Linear Regression

We consider the linear model $\mathbf{y} = \mathbf{X} \mathbfβ_\star + \mathbfε$ with $\mathbf{X}\in \mathbb{R}^{n\times p}$ in the overparameterized regime $p>n$. We estimate $\mathbfβ_\star$ via generalized (weighted) ridge regression: $\hat{\mathbfβ}_λ= \left(\mathbf{X}^T\mathbf{X} + λ\mathbfΣ_w\right)^\dagger \mathbf{X}^T\mathbf{y}$, where $\mathbfΣ_w$ is the weighting matrix. Under a random design setting with general data covariance $\mathbfΣ_x$ and anisotropic prior on the true coefficients $\mathbb{E}\mathbfβ_\star\mathbfβ_\star^T = \mathbfΣ_β$, we provide an exact characterization of the prediction risk $\mathbb{E}(y-\mathbf{x}^T\hat{\mathbfβ}_λ)^2$ in the proportional asymptotic limit $p/n\rightarrow γ\in (1,\infty)$. Our general setup leads to a number of interesting findings. We outline precise conditions that decide the sign of the optimal setting $λ_{\rm opt}$ for the ridge parameter $λ$ and confirm the implicit $\ell_2$ regularization effect of overparameterization, which theoretically justifies the surprising empirical observation that $λ_{\rm opt}$ can be negative in the overparameterized regime. We also characterize the double descent phenomenon for principal component regression (PCR) when both $\mathbf{X}$ and $\mathbfβ_\star$ are anisotropic. Finally, we determine the optimal weighting matrix $\mathbfΣ_w$ for both the ridgeless ($λ\to 0$) and optimally regularized ($λ= λ_{\rm opt}$) case, and demonstrate the advantage of the weighted objective over standard ridge regression and PCR.

stat.ML↗

arXiv2020

Two models of double descent for weak features

The "double descent" risk curve was proposed to qualitatively describe the out-of-sample prediction accuracy of variably-parameterized machine learning models. This article provides a precise mathematical analysis for the shape of this curve in two simple data models with the least squares/least norm predictor. Specifically, it is shown that the risk peaks when the number of features $p$ is close to the sample size $n$, but also that the risk decreases towards its minimum as $p$ increases beyond $n$. This behavior is contrasted with that of "prescient" models that select features in an a priori optimal order.

cs.LG↗

arXiv2020

Spectral Method for Phase Retrieval: an Expectation Propagation Perspective

Phase retrieval refers to the problem of recovering a signal $\mathbf{x}_{\star}\in\mathbb{C}^n$ from its phaseless measurements $y_i=|\mathbf{a}_i^{\mathrm{H}}\mathbf{x}_{\star}|$, where $\{\mathbf{a}_i\}_{i=1}^m$ are the measurement vectors. Many popular phase retrieval algorithms are based on the following two-step procedure: (i) initialize the algorithm based on a spectral method, (ii) refine the initial estimate by a local search algorithm (e.g., gradient descent). The quality of the spectral initialization step can have a major impact on the performance of the overall algorithm. In this paper, we focus on the model where the measurement matrix $\mathbf{A}=[\mathbf{a}_1,\ldots,\mathbf{a}_m]^{\mathrm{H}}$ has orthonormal columns, and study the spectral initialization under the asymptotic setting $m,n\to\infty$ with $m/n\toδ\in(1,\infty)$. We use the expectation propagation framework to characterize the performance of spectral initialization for Haar distributed matrices. Our numerical results confirm that the predictions of the EP method are accurate for not-only Haar distributed matrices, but also for realistic Fourier based models (e.g. the coded diffraction model). The main findings of this paper are the following: (1) There exists a threshold on $δ$ (denoted as $δ_{\mathrm{weak}}$) below which the spectral method cannot produce a meaningful estimate. We show that $δ_{\mathrm{weak}}=2$ for the column-orthonormal model. In contrast, previous results by Mondelli and Montanari show that $δ_{\mathrm{weak}}=1$ for the i.i.d. Gaussian model. (2) The optimal design for the spectral method coincides with that for the i.i.d. Gaussian model, where the latter was recently introduced by Luo, Alghamdi and Lu.

cs.IT↗

arXiv2020

$B$-meson light-cone distribution amplitude from the Euclidean quantity

A new method for the model-independent determination of the light-cone distribution amplitude (LCDA) of the $B$-meson in heavy quark effective theory (HQET) is proposed by combining the large momentum effective theory (LaMET) and the numerical simulation technique on the Euclidean lattice. We demonstrate the autonomous scale dependence of the non-local quasi-HQET operator with the aid of the auxiliary field approach, and further determine the perturbative matching coefficient entering the hard-collinear factorization formula for the $B$-meson quasi-distribution amplitude at the one-loop accuracy. These results will be crucial to explore the partonic structure of heavy-quark hadrons in the static limit and to improve the theory description of exclusive $B$-meson decay amplitudes based upon perturbative QCD factorization theorems.

hep-ph↗

arXiv2020

The Multi-granularity in Graph Revealed by a Generalized Leading Tree

There are hierarchical characteristics in the network and how to effectively reveal the hierarchical characteristics in the network is a problem in the research of network structure. If a node is assigned to the community to which it belongs, how to assign the community to a higher level of community to which it belongs is a problem. In this paper, the density of data points is investigated based on the clustering task. By forming the density of data points, the hierarchical difference of data points is constructed. In combination with the distance between data points, a density-based leading tree can be constructed. But in a graph structure, it is a problem to build a lead tree that reveals the hierarchical relationships of the nodes on the graph. Based on the method of tree formation based on density, this paper extends the model of leading tree to the hierarchical structure of graph nodes, discusses the importance of graph nodes, and forms a leading tree that can reveal the hierarchical structure of graph nodes and the dependency of community. Experiments were carried out on real data sets, and a tree structure was formed in the experiment. This graph leading tree can well reveal the hierarchical relationships in the graph structure.

cs.SI↗

arXiv2019

Sub-threshold $J/ψ$ and $Υ$ Production in $γA$ Collisions

We study sub-threshold heavy quarkonium ($J/ψ$ and $Υ$) photo-productions in $γA$ collisions as an independent test of the universality of the nucleon-nucleon short range correlation (SRC) in nuclear scattering processes. Just below the $γp$ threshold, the cross section is dominated by the mean field contribution of nucleons inside the nucleus. The SRC contributions start to dominate at lower photon energies, depending on the fraction of the SRC pairs in the target nucleus. We give an estimate of the cross sections in the sub-threshold region both for $J/ψ$ and $Υ$. This may be helpful for future measurements at JLab as well as at the Electron-Ion Collider in the U.S., and especially in China.

hep-ph↗

arXiv2019

On the number of variables to use in principal component regression

We study least squares linear regression over $N$ uncorrelated Gaussian features that are selected in order of decreasing variance. When the number of selected features $p$ is at most the sample size $n$, the estimator under consideration coincides with the principal component regression estimator; when $p>n$, the estimator is the least $\ell_2$ norm solution over the selected features. We give an average-case analysis of the out-of-sample prediction error as $p,n,N \to \infty$ with $p/N \to α$ and $n/N \to β$, for some constants $α\in [0,1]$ and $β\in (0,1)$. In this average-case setting, the prediction error exhibits a "double descent" shape as a function of $p$. We also establish conditions under which the minimum risk is achieved in the interpolating ($p>n$) regime.

math.ST↗

arXiv2019

Gluonic Probe for the Short Range Correlation in Nucleus

We investigate the gluonic probe to the nucleon-nucleon short range correlation (SRC) in nucleus through heavy flavor production in deep inelastic scattering (DIS). The relevant EMC effects of $F_2^{c\bar c}$ structure function will provide a universality test of the SRCs which have been extensively studied in the quark-channel. These SRCs can also be studied through the sub-threshold production of heavy flavor in $eA$ collisions at the intermediate energy electron-ion collider, including open Charm and $J/ψ$ ($Υ$) production.

hep-ph↗

arXiv2019

Matching Quasi Generalized Parton Distributions in the RI/MOM scheme

Within the framework of large momentum effective theory (LaMET), genenaralized parton distributions (GPDs) can be extracted from lattice calculations of quasi-GPDs through a perturbative matching relation, up to power corrections that are suppressed by the hadron momentum. In this paper, we focus on isovector quark GPDs, including the unpolarized, longitudinally and transversely polarized cases, and present the one-loop matching that connects the quasi-GPDs renormalized in a regularization-independent momentum subtraction (RI/MOM) scheme to the GPDs in MS scheme. We find that the matching coefficient is independent of the momentum transfer squared. As a consequence, the matching for the quasi-GPD with zero skewness is the same as that for the quasi-PDF. Our results provide a crucial input for the determination of quark GPDs from lattice QCD using LaMET.

hep-ph↗

arXiv2019

Learning to Reconstruct and Understand Indoor Scenes from Sparse Views

This paper proposes a new method for simultaneous 3D reconstruction and semantic segmentation of indoor scenes. Unlike existing methods that require recording a video using a color camera and/or a depth camera, our method only needs a small number of (e.g., 3-5) color images from uncalibrated sparse views as input, which greatly simplifies data acquisition and extends applicable scenarios. Since different views have limited overlaps, our method allows a single image as input to discern the depth and semantic information of the scene. The key issue is how to recover relatively accurate depth from single images and reconstruct a 3D scene by fusing very few depth maps. To address this problem, we first design an iterative deep architecture, IterNet, that estimates depth and semantic segmentation alternately, so that they benefit each other. To deal with the little overlap and non-rigid transformation between views, we further propose a joint global and local registration method to reconstruct a 3D scene with semantic information from sparse views. We also make available a new indoor synthetic dataset simultaneously providing photorealistic high-resolution RGB images, accurate depth maps and pixel-level semantic labels for thousands of complex layouts, useful for training and evaluation. Experimental results on public datasets and our dataset demonstrate that our method achieves more accurate depth estimation, smaller semantic segmentation errors and better 3D reconstruction results, compared with state-of-the-art methods.

cs.CV↗