Photoproduction of ηc\eta_c in NRQCD

We present a calculation for the photoproduction of $\eta_c$ under the framework of NRQCD factorization formalism. We find a quite unique feature that the color-singlet contribution to this process vanishes at not only the leading order but also the next to leading order perturbative QCD calculations and that the dominant contribution comes from the color-octet ${}^1S_0^{(8)}$ subprocess. The nonperturbative color-octet matrix element of ${}^1S_0^{(8)}$ of $\eta_c$ is related to that of ${}^3S_1^{(8)}$ of $J/\psi$ by the heavy quark spin symmetry, and the latter can be determined from the direct production of $J/\psi$ at large transverse momentum at the Fermilib Tevatron. We then conclude that the measurement of this process may clarify the existing conflict between the color-octet prediction and the experimental result on the $J/\psi$ photoprodution.Comment: 11 pages, revtex, 4 ps figure

Stable Learning via Sample Reweighting

We consider the problem of learning linear prediction models with model misspecification bias. In such case, the collinearity among input variables may inflate the error of parameter estimation, resulting in instability of prediction results when training and test distributions do not match. In this paper we theoretically analyze this fundamental problem and propose a sample reweighting method that reduces collinearity among input variables. Our method can be seen as a pretreatment of data to improve the condition of design matrix, and it can then be combined with any standard learning method for parameter estimation and variable selection. Empirical studies on both simulation and real datasets demonstrate the effectiveness of our method in terms of more stable performance across different distributed data.Comment: Accepted as poster paper at AAAI202

Inelastic electroproduction of ηc\eta_c at ep colliders

Using the nonrelativistic QCD factorization formalism, we calculate the electroproduction cross sections of $\eta_c$ in ep collisions, including the contribution from both the transverse photon and the longitudinal photon. For this process the color-singlet contribution vanishes up to the next to leading order perturbative QCD calculations. The dominant contribution comes from the color-octet ${}^1S_0^{(8)}$ subprocess. The nonperturbative color-octet matrix element of ${}^1S_0^{(8)}$ of $\eta_c$ is related to that of ${}^3S_1^{(8)}$ of $J/\psi$ by heavy quark spin symmetry, and the latter can be determined from the direct production of $J/\psi$ at large transverse momentum at the Fermilab Tevatron. The measurement of this process at DESY HERA can be viewed as another independent test for the color-octet production mechanismComment: 17 pages, 5 figures, final version to appear in Phys. Rev.

Qualitative analysis of housing demand using Google trends data

Big data analytics often refer to the breakdown of huge amounts of data into a more readable and useful format. This study utilises Google Trends big data as a proxy for an analysis of housing demand. We employ a qualitative method (fuzzy set/Qualitative Comparative Analysis, fsQCA), instead of a quantitative method, for our estimate and forecast. The empirical results show that fsQCA successfully forecasts seasonal time series, even though the dataset is small in size. Our findings fill the gap in the qualitative and time series forecasting literature, and the forecasting procedure herein also offers a good standard for industry

Stable Prediction with Model Misspecification and Agnostic Distribution Shift

For many machine learning algorithms, two main assumptions are required to guarantee performance. One is that the test data are drawn from the same distribution as the training data, and the other is that the model is correctly specified. In real applications, however, we often have little prior knowledge on the test data and on the underlying true model. Under model misspecification, agnostic distribution shift between training and test data leads to inaccuracy of parameter estimation and instability of prediction across unknown test data. To address these problems, we propose a novel Decorrelated Weighting Regression (DWR) algorithm which jointly optimizes a variable decorrelation regularizer and a weighted regression model. The variable decorrelation regularizer estimates a weight for each sample such that variables are decorrelated on the weighted training data. Then, these weights are used in the weighted regression to improve the accuracy of estimation on the effect of each variable, thus help to improve the stability of prediction across unknown test data. Extensive experiments clearly demonstrate that our DWR algorithm can significantly improve the accuracy of parameter estimation and stability of prediction with model misspecification and agnostic distribution shift

Hierarchical Topological Ordering with Conditional Independence Test for Limited Time Series

Learning directed acyclic graphs (DAGs) to identify causal relations underlying observational data is crucial but also poses significant challenges. Recently, topology-based methods have emerged as a two-step approach to discovering DAGs by first learning the topological ordering of variables and then eliminating redundant edges, while ensuring that the graph remains acyclic. However, one limitation is that these methods would generate numerous spurious edges that require subsequent pruning. To overcome this limitation, in this paper, we propose an improvement to topology-based methods by introducing limited time series data, consisting of only two cross-sectional records that need not be adjacent in time and are subject to flexible timing. By incorporating conditional instrumental variables as exogenous interventions, we aim to identify descendant nodes for each variable. Following this line, we propose a hierarchical topological ordering algorithm with conditional independence test (HT-CIT), which enables the efficient learning of sparse DAGs with a smaller search space compared to other popular approaches. The HT-CIT algorithm greatly reduces the number of edges that need to be pruned. Empirical results from synthetic and real-world datasets demonstrate the superiority of the proposed HT-CIT algorithm

MEDOE: A Multi-Expert Decoder and Output Ensemble Framework for Long-tailed Semantic Segmentation

Long-tailed distribution of semantic categories, which has been often ignored in conventional methods, causes unsatisfactory performance in semantic segmentation on tail categories. In this paper, we focus on the problem of long-tailed semantic segmentation. Although some long-tailed recognition methods (e.g., re-sampling/re-weighting) have been proposed in other problems, they can probably compromise crucial contextual information and are thus hardly adaptable to the problem of long-tailed semantic segmentation. To address this issue, we propose MEDOE, a novel framework for long-tailed semantic segmentation via contextual information ensemble-and-grouping. The proposed two-sage framework comprises a multi-expert decoder (MED) and a multi-expert output ensemble (MOE). Specifically, the MED includes several "experts". Based on the pixel frequency distribution, each expert takes the dataset masked according to the specific categories as input and generates contextual information self-adaptively for classification; The MOE adopts learnable decision weights for the ensemble of the experts' outputs. As a model-agnostic framework, our MEDOE can be flexibly and efficiently coupled with various popular deep neural networks (e.g., DeepLabv3+, OCRNet, and PSPNet) to improve their performance in long-tailed semantic segmentation. Experimental results show that the proposed framework outperforms the current methods on both Cityscapes and ADE20K datasets by up to 1.78% in mIoU and 5.89% in mAcc.Comment: 18 pages, 9 figure