arXiv subjects

Xiao-Bo Jin

Publications and source records attributed to Xiao-Bo Jin.

18 recordsLinked to original sources

arXiv2026

Probing Heavy-Quark Spin Symmetry in Double $J/ψ$ Hadroproduction

We perform the first complete $\mathcal{O}(α_s^5)$ analysis of the prompt hadroproduction of $J/ψ$ pairs with large transverse momenta $p_T^{ψψ}$ in the nonrelativistic-QCD factorization framework, including all possible Fock state combinations $c\bar{c}(m)+c\bar{c}(n)$, with $m,n={}^3S_1^{[1,8]},{}^1S_0^{[8]},{}^3P_J^{[1,8]}$. We observe that CMS and ATLAS data constrain a specific linear combination of the long-distance matrix elements (LDMEs) $\langle\mathcal{O}^{J/ψ}({}^1S_0^{[8]})\rangle$ and $\langle\mathcal{O}^{J/ψ}({}^3P_0^{[8]})\rangle$. In conjunction with two other combinations fixed by single prompt $J/ψ$ hadroproduction, we gain a new LDME set, which turns out to be largely compatible with the world data of prompt $J/ψ$ yield and polarization and to probe heavy-quark spin symmetry, by which agreement is established with LHCb data of prompt $η_c$ yield. Our $\mathcal{O}(α_s^5)$ predictions also nicely agree with CMS and ATLAS data in the lowest bins of $J/ψ$ pair invariant mass $m^{ψψ}$, beyond leading-order kinematics.

hep-ph↗

arXiv2026

Exclusive process $γγ\rightarrow J/ψ+γ$ production in ultraperipheral proton and nuclear collisions at the HL-LHC and FCC

We present a next-to-leading-order (NLO) analysis of exclusive $J/ψ+γ$ production via photon-photon fusion in ultraperipheral collisions at the High-Luminosity Large Hadron Collider (HL-LHC) and the Future Circular Collider (FCC). The study is performed within the NRQCD factorization framework for proton-proton, proton-nucleus, and nucleus-nucleus collisions, with nuclear species spanning a wide range of nuclear charges (O, Ca, Ar, Kr, Xe, and Pb), enabling a systematic investigation of nuclear effects on the production cross sections. The photon fluxes are modeled using an electric-dipole form factor, with the impact-parameter dependence strictly enforced to ensure the exclusivity of the process. We present predictions for total cross sections and kinematic distributions at leading order and NLO. With a transverse momentum cut of $p_T > 2\,\mathrm{GeV}$, the NLO corrections reduce the cross section by approximately $35\%$ at the central scale. The associated theoretical uncertainties are systematically estimated via renormalization scale variations. Despite this suppression, the cross sections remain sizable and indicate that exclusive $J/ψ+ γ$ production serves as a sensitive probe of photon-induced quarkonium production mechanisms. We thus present the corresponding event yields predicted for the HL-LHC and the FCC.

hep-ph↗

arXiv2025

Hyperbolic Structured Classification for Robust Single Positive Multi-label Learning

Single Positive Multi-Label Learning (SPMLL) addresses the challenging scenario where each training sample is annotated with only one positive label despite potentially belonging to multiple categories, making it difficult to capture complex label relationships and hierarchical structures. While existing methods implicitly model label relationships through distance-based similarity, lacking explicit geometric definitions for different relationship types. To address these limitations, we propose the first hyperbolic classification framework for SPMLL that represents each label as a hyperbolic ball rather than a point or vector, enabling rich inter-label relationship modeling through geometric ball interactions. Our ball-based approach naturally captures multiple relationship types simultaneously: inclusion for hierarchical structures, overlap for co-occurrence patterns, and separation for semantic independence. Further, we introduce two key component innovations: a temperature-adaptive hyperbolic ball classifier and a physics-inspired double-well regularization that guides balls toward meaningful configurations. To validate our approach, extensive experiments on four benchmark datasets (MS-COCO, PASCAL VOC, NUS-WIDE, CUB-200-2011) demonstrate competitive performance with superior interpretability compared to existing methods. Furthermore, statistical analysis reveals strong correlation between learned embeddings and real-world co-occurrence patterns, establishing hyperbolic geometry as a more robust paradigm for structured classification under incomplete supervision.

cs.CV↗

arXiv2025

The Demon is in Ambiguity: Revisiting Situation Recognition with Single Positive Multi-Label Learning

Context recognition (SR) is a fundamental task in computer vision that aims to extract structured semantic summaries from images by identifying key events and their associated entities. Specifically, given an input image, the model must first classify the main visual events (verb classification), then identify the participating entities and their semantic roles (semantic role labeling), and finally localize these entities in the image (semantic role localization). Existing methods treat verb classification as a single-label problem, but we show through a comprehensive analysis that this formulation fails to address the inherent ambiguity in visual event recognition, as multiple verb categories may reasonably describe the same image. This paper makes three key contributions: First, we reveal through empirical analysis that verb classification is inherently a multi-label problem due to the ubiquitous semantic overlap between verb categories. Second, given the impracticality of fully annotating large-scale datasets with multiple labels, we propose to reformulate verb classification as a single positive multi-label learning (SPMLL) problem - a novel perspective in SR research. Third, we design a comprehensive multi-label evaluation benchmark for SR that is carefully designed to fairly evaluate model performance in a multi-label setting. To address the challenges of SPMLL, we futher develop the Graph Enhanced Verb Multilayer Perceptron (GE-VerbMLP), which combines graph neural networks to capture label correlations and adversarial training to optimize decision boundaries. Extensive experiments on real-world datasets show that our approach achieves more than 3\% MAP improvement while remaining competitive on traditional top-1 and top-5 accuracy metrics.

cs.CV↗

arXiv2025

Next-to-leading-order QCD corrections to double prompt $J/ψ$ hadroproduction

Within the framework of nonrelativistic-QCD (NRQCD) factorization, we perform a comprehensive investigation of the color singlet (CS) contribution to prompt $J/ψ$ pair production at the CERN Large Hadron Collider at next-to-leading order (NLO) in $α_s$. Specifically, we compare our NLO predictions with measurements from the LHCb, CMS, and ATLAS Collaborations. We find that the CS contribution itself can well describe the LHCb data in most of the experimental bins, except in the regions where the fixed-order calculation is spoiled by the emission of soft or hard-collinear gluons. In the CMS and ATLAS cases, however, the CS predictions greatly undershoot both the measured total and differential cross sections, despite sizable $K$ factors, of 2--3, for the total cross sections.

hep-ph↗

arXiv2024

F-SE-LSTM: A Time Series Anomaly Detection Method with Frequency Domain Information

With the development of society, time series anomaly detection plays an important role in network and IoT services. However, most existing anomaly detection methods directly analyze time series in the time domain and cannot distinguish some relatively hidden anomaly sequences. We attempt to analyze the impact of frequency on time series from a frequency domain perspective, thus proposing a new time series anomaly detection method called F-SE-LSTM. This method utilizes two sliding windows and fast Fourier transform (FFT) to construct a frequency matrix. Simultaneously, Squeeze-and-Excitation Networks (SENet) and Long Short-Term Memory (LSTM) are employed to extract frequency-related features within and between periods. Through comparative experiments on multiple datasets such as Yahoo Webscope S5 and Numenta Anomaly Benchmark, the results demonstrate that the frequency matrix constructed by F-SE-LSTM exhibits better discriminative ability than ordinary time domain and frequency domain data. Furthermore, F-SE-LSTM outperforms existing state-of-the-art deep learning anomaly detection methods in terms of anomaly detection capability and execution efficiency.

cs.AI↗

arXiv2024

Next-to-leading-order relativistic and QCD corrections to prompt $\boldsymbol{J/ψ}$ pair photoproduction at future $\boldsymbol{e^+e^-}$ colliders

Within the framework of nonrelativistic-QCD factorization, we calculate both the next-to-leading-order relativistic and QCD corrections to prompt $J/ψ$ pair production, with feeddown from $ψ(2S)$ mesons, via photon-photon collisions at future $e^+e^-$ colliders including the Future Circular Lepton Collider (FCC-ee), the Circular Electron Positron Collider (CEPC), and the Compact Linear Collider (CLIC). We present total cross sections and distributions in single $J/ψ$ transverse momentum and rapidity, and in $J/ψ$ pair invariant mass. The relativistic and QCD corrections both turn out to be large and negative. Yet, the production rates are large enough for useful experimental studies.

hep-ph↗

arXiv2024

Relativistic corrections to prompt double charmonium hadroproduction near threshold

We calculate the relativistic corrections to prompt $J/ψ$ pair and $J/ψ+ψ(2S)$ hadroproduction through the color-singlet channel within the framework of nonrelativistic QCD (NRQCD) factorization. The short-distance coefficients are obtained by matching full-QCD and NRQCD calculations at the partonic level, in which both squared amplitude and phase space are expanded in $v^2$. We find that such an expansion of the phase space spoils the convergence of NRQCD factorization near the production threshold. To fix this problem, we propose to modify the matching between full QCD and NRQCD by adopting the physical phase space. In this modified approach, the theoretical uncertainties due to the choice of charm quark mass $m_c$ are largely reduced and the overall agreement of our predictions with LHC data is significantly improved, both as for total and differential cross sections.

hep-ph↗

arXiv2023

Context Does Matter: End-to-end Panoptic Narrative Grounding with Deformable Attention Refined Matching Network

Panoramic Narrative Grounding (PNG) is an emerging visual grounding task that aims to segment visual objects in images based on dense narrative captions. The current state-of-the-art methods first refine the representation of phrase by aggregating the most similar $k$ image pixels, and then match the refined text representations with the pixels of the image feature map to generate segmentation results. However, simply aggregating sampled image features ignores the contextual information, which can lead to phrase-to-pixel mis-match. In this paper, we propose a novel learning framework called Deformable Attention Refined Matching Network (DRMN), whose main idea is to bring deformable attention in the iterative process of feature learning to incorporate essential context information of different scales of pixels. DRMN iteratively re-encodes pixels with the deformable attention network after updating the feature representation of the top-$k$ most similar pixels. As such, DRMN can lead to accurate yet discriminative pixel representations, purify the top-$k$ most similar pixels, and consequently alleviate the phrase-to-pixel mis-match substantially.Experimental results show that our novel design significantly improves the matching results between text phrases and image pixels. Concretely, DRMN achieves new state-of-the-art performance on the PNG benchmark with an average recall improvement 3.5%. The codes are available in: https://github.com/JaMesLiMers/DRMN.

cs.CV↗

arXiv2022

Color-octet contributions for $J/ψ$ inclusive production at B factories in soft gluon factorization

We have studied color-octet contributions for $J/ψ$ inclusive production at B factories, i.e., $e^+e^-\to J/ψ(^3P_J^{[8]},^1S_0^{[8]}) + X_{\mathrm{non}-c\bar c}$, using the soft gluon factorization (SGF) approach, in which the $J/ψ$ energy spectrum is expressed in a form of perturbatively calculable short-distance hard parts convoluted with one-dimensional soft gluon distributions (SGDs). The series of velocity corrections originated from kinematic effect can be naturally resummed in this approach. Short-distance hard parts have been calculated analytically to next-to-leading order in $α_s$. Renormalization group equations for SGDs have been derived and solved, which resums Sudakov logarithms originated from soft gluon emissions. Our final result gives a upper bound for color-octet matrix elements consistent with that extracted from hadron colliders. This may relieve the well-known universality problem in the NRQCD factorization. As a comparison, we also analytically calculated short-distance hard parts in the NRQCD factorization, with Sudakov logarithms resummed by using soft collinear effective theory. The comparison shows that velocity corrections from kinematic effect, which have been resummed in SGF, are significant for phenomenological study. Furthermore, it is found that Sudakov logarithms originated from soft gluon emissions are very important, while it is not the case for Sudakov logarithms originated from jet function. Therefore, the partial Sudakov resummation in SGF has already captured the main physics.

hep-ph↗

arXiv2021

Fragmentation function of $g\to Q\bar{Q}(^3S_1^{[8]})$ in soft gluon factorization and threshold resummation

We study the fragmentation function of the gluon to color-octet $^3S_1$ heavy quark-antiquark pair using the soft gluon factorization (SGF) approach, which expresses the fragmentation function in a form of perturbative short-distance hard part convoluted with one-dimensional color-octet $^3S_1$ soft gluon distribution (SGD). The short distance hard part is calculated to next-to-leading order in $α_s$ and a renormalization group equation for the SGD is derived. By solving the renormalization group equation, threshold logarithms are resummed to all orders in perturbation theory. The comparison with gluon fragmentation function calculated in NRQCD factorization approach indicates that the SGF formula resums a series of velocity corrections in NRQCD which are important for phenomenological study.

hep-ph↗

arXiv2018

Beyond Attributes: Adversarial Erasing Embedding Network for Zero-shot Learning

In this paper, an adversarial erasing embedding network with the guidance of high-order attributes (AEEN-HOA) is proposed for going further to solve the challenging ZSL/GZSL task. AEEN-HOA consists of two branches, i.e., the upper stream is capable of erasing some initially discovered regions, then the high-order attribute supervision is incorporated to characterize the relationship between the class attributes. Meanwhile, the bottom stream is trained by taking the current background regions to train the same attribute. As far as we know, it is the first time of introducing the erasing operations into the ZSL task. In addition, we first propose a class attribute activation map for the visualization of ZSL output, which shows the relationship between class attribute feature and attention map. Experiments on four standard benchmark datasets demonstrate the superiority of AEEN-HOA framework.

cs.CV↗

arXiv2018

Stochastic Conjugate Gradient Algorithm with Variance Reduction

Conjugate gradient (CG) methods are a class of important methods for solving linear equations and nonlinear optimization problems. In this paper, we propose a new stochastic CG algorithm with variance reduction and we prove its linear convergence with the Fletcher and Reeves method for strongly convex and smooth functions. We experimentally demonstrate that the CG with variance reduction algorithm converges faster than its counterparts for four learning models, which may be convex, nonconvex or nonsmooth. In addition, its area under the curve performance on six large-scale data sets is comparable to that of the LIBLINEAR solver for the L2-regularized L2-loss but with a significant improvement in computational efficiency

cs.LG↗

arXiv2016

Ranking Entity Based on Both of Word Frequency and Word Sematic Features

Entity search is a new application meeting either precise or vague requirements from the search engines users. Baidu Cup 2016 Challenge just provided such a chance to tackle the problem of the entity search. We achieved the first place with the average MAP scores on 4 tasks including movie, tvShow, celebrity and restaurant. In this paper, we propose a series of similarity features based on both of the word frequency features and the word semantic features and describe our ranking architecture and experiment details.

cs.IR↗

arXiv2014

Combination of Multiple Bipartite Ranking for Web Content Quality Evaluation

Web content quality estimation is crucial to various web content processing applications. Our previous work applied Bagging + C4.5 to achive the best results on the ECML/PKDD Discovery Challenge 2010, which is the comibination of many point-wise rankinig models. In this paper, we combine multiple pair-wise bipartite ranking learner to solve the multi-partite ranking problems for the web quality estimation. In encoding stage, we present the ternary encoding and the binary coding extending each rank value to $L - 1$ (L is the number of the different ranking value). For the decoding, we discuss the combination of multiple ranking results from multiple bipartite ranking models with the predefined weighting and the adaptive weighting. The experiments on ECML/PKDD 2010 Discovery Challenge datasets show that \textit{binary coding} + \textit{predefined weighting} yields the highest performance in all four combinations and furthermore it is better than the best results reported in ECML/PKDD 2010 Discovery Challenge competition.

cs.IR↗

arXiv2013

Qualitative detection of oil adulteration with machine learning approaches

The study focused on the machine learning analysis approaches to identify the adulteration of 9 kinds of edible oil qualitatively and answered the following three questions: Is the oil sample adulterant? How does it constitute? What is the main ingredient of the adulteration oil? After extracting the high-performance liquid chromatography (HPLC) data on triglyceride from 370 oil samples, we applied the adaptive boosting with multi-class Hamming loss (AdaBoost.MH) to distinguish the oil adulteration in contrast with the support vector machine (SVM). Further, we regarded the adulterant oil and the pure oil samples as ones with multiple labels and with only one label, respectively. Then multi-label AdaBoost.MH and multi-label learning vector quantization (ML-LVQ) model were built to determine the ingredients and their relative ratio in the adulteration oil. The experimental results on six measures show that ML-LVQ achieves better performance than multi-label AdaBoost.MH.

cs.CE↗

arXiv2013

Evaluating Web Content Quality via Multi-scale Features

Web content quality measurement is crucial to various web content processing applications. This paper will explore multi-scale features which may affect the quality of a host, and develop automatic statistical methods to evaluate the Web content quality. The extracted properties include statistical content features, page and host level link features and TFIDF features. The experiments on ECML/PKDD 2010 Discovery Challenge data set show that the algorithm is effective and feasible for the quality tasks of multiple languages, and the multi-scale features have different identification ability and provide good complement to each other for most tasks.

cs.IR↗

arXiv2013

Linear NDCG and Pair-wise Loss

Linear NDCG is used for measuring the performance of the Web content quality assessment in ECML/PKDD Discovery Challenge 2010. In this paper, we will prove that the DCG error equals a new pair-wise loss.

cs.LG↗