High Dimensional Model Selection and Validation: A Comparison Study

Model selection is a challenging issue in high dimensional statistical analysis, and many approaches have been proposed in recent years. In this thesis, we compare the performance of three penalized logistic regression approaches (Ridge, Lasso, and Elastic Net) and three information criteria (AIC, BIC, and EBIC) on binary response variable in high dimensional situation through extensive simulation study. The models are built and selected on the training datasets, and their performance are evaluated through AUC on the validation datasets. We also display the comparison results on two real datasets (Arcene Data and University Retention Data). The performance differences among those approaches are discussed at the end

The Nelson-Seiberg theorem generalized with nonpolynomial superpotentials

The Nelson-Seiberg theorem relates R-symmetries to F-term supersymmetry breaking, and provides a guiding rule for new physics model building beyond the Standard Model. A revision of the theorem gives a necessary and sufficient condition to supersymmetry breaking in models with polynomial superpotentials. This work revisits the theorem to include models with nonpolynomial superpotentials. With a generic R-symmetric superpotential, a singularity at the origin of the field space implies both R-symmetry breaking and supersymmetry breaking. We give a generalized necessary and sufficient condition for supersymmetry breaking which applies to both perturbative and nonperturbative models.Comment: 10 pages, discussions on D-terms, runaway models and existence of vacua added, Advances in High Energy Physics accepted versio

Crystal structure of azilsartan methyl ester ethyl acetate hemisolvate

We gratefully acknowledge financial support from the NSFC (No. 21002009), the Scientific and Technological Project of Jiangsu Province (BY2014037–01), the Major Program for Natural Science Research of Jiangsu Colleges and Universities (12 K J A150002, 14 K J A150002) and the Qing Lan Project of Jiangsu Province.Peer reviewe

The substructure and halo population of the Double Cluster hh and χ\chi Persei

In order to study the stellar population and possible substructures in the outskirts of Double Cluster $h$ and $\chi$ Persei, we investigate using the GAIA DR2 data a sky area of about 7.5 degrees in radius around the Double Cluster cores. We identify member stars using various criteria, including their kinematics (viz, proper motion), individual parallaxes, as well as photometric properties. A total of 2186 member stars in the parameter space were identified as members. Based on the spatial distribution of the member stars, we find an extended halo structure of $h$ and $\chi$ Persei, about 6 - 8 times larger than their core radii. We report the discovery of filamentary substructures extending to about 200 pc away from the Double Cluster. The tangential velocities of these distant substructures suggest that they are more likely to be the remnants of primordial structures, instead of a tidally disrupted stream from the cluster cores. Moreover, the internal kinematic analysis indicates that halo stars seems to be experiencing a dynamic stretching in the RA direction, while the impact of the core components is relatively negligible. This work also suggests that the physical scale and internal motions of young massive star clusters may be more complex than previously thought.Comment: 9 pagges, 9 figures, Accecpted to A&

Improving PTM Site Prediction by Coupling of Multi-Granularity Structure and Multi-Scale Sequence Representation

Protein post-translational modification (PTM) site prediction is a fundamental task in bioinformatics. Several computational methods have been developed to predict PTM sites. However, existing methods ignore the structure information and merely utilize protein sequences. Furthermore, designing a more fine-grained structure representation learning method is urgently needed as PTM is a biological event that occurs at the atom granularity. In this paper, we propose a PTM site prediction method by Coupling of Multi-Granularity structure and Multi-Scale sequence representation, PTM-CMGMS for brevity. Specifically, multigranularity structure-aware representation learning is designed to learn neighborhood structure representations at the amino acid, atom, and whole protein granularity from AlphaFold predicted structures, followed by utilizing contrastive learning to optimize the structure representations.Additionally, multi-scale sequence representation learning is used to extract context sequence information, and motif generated by aligning all context sequences of PTM sites assists the prediction. Extensive experiments on three datasets show that PTM-CMGMS outperforms the state-of-the-art methods