Towards microscopic studies of survival probabilities of compound superheavy nuclei

The microscopic approach of fission rates and neutron emission rates in compound nuclei have been applied to $^{258}$No and $^{286}$Cn. The microscopic framework is based on the finite-temperature Skyrme-Hartree-Fock+BCS calculations, in which the fission barriers and mass parameters are self-consistently temperature dependent. The fission rates from low to high temperatures can be obtained based on the imaginary free energy method. The neutron emission rates are obtained with neutron gases at surfaces. Finally the survival probabilities of superheavy nuclei can be calculated microscopically. The microscopic approach has been compared with the widely used statistical models. Generally, there are still large uncertainties in descriptions of fission rates.Comment: 9 pages,7 figures, accepted for Physica Scripta Special Issu

Microscopic description of neutron emission rates in compound nuclei

The neutron emission rates in thermal excited nuclei are conventionally described by statistical models with a phenomenological level density parameter that depends on excitation energies, deformations and mass regions. In the microscopic view of hot nuclei, the neutron emission rates can be determined by the external neutron gas densities without any free parameters. Therefore the microscopic description of thermal neutron emissions is desirable that can impact several understandings such as survival probabilities of superheavy compound nuclei and neutron emissivity in reactors. To describe the neutron emission rates microscopically, the external thermal neutron gases are self-consistently obtained based on the Finite-Temperature Hartree-Fock-Bogoliubov (FT-HFB) approach. The results are compared with the statistical model to explore the connections between the FT-HFB approach and the statistical model. The Skyrme FT-HFB equation is solved by HFB-AX in deformed coordinate spaces. Based on the FT-HFB approach, the thermal properties and external neutron gas are properly described with the self-consistent gas substraction procedure. Then neutron emission rates can be obtained based on the densities of external neutron gases. The thermal statistical properties of $^{238}$U and $^{258}$U are studied in detail in terms of excitation energies. The thermal neutron emission rates in $^{238, 258}$U and superheavy compound nuclei $_{112}^{278}$Cn and $_{114}^{292}$Fl are calculated, which agree well with the statistical model by adopting an excitation-energy-dependent level density parameter. The coordinate-space FT-HFB approach can provide reliable microscopic descriptions of neutron emission rates in hot nuclei, as well as microscopic constraints on the excitation energy dependence of level density parameters for statistical models.Comment: 6 pages, 5 figures, revised and accepted for PR

Study of weakly-bound odd-A nuclei with quasiparticle blocking

The coordinate-space Hartree-Fock-Bogoliubov (HFB) approach with quasiparticle blocking has been applied to study the odd-A weakly bound nuclei $^{17,19}$B and $^{37}$Mg, in which halo structures have been reported in experiments. The Skyrme nuclear forces SLy4 and UNEDF1 have been adopted in our calculations. The results with and without blocking have been compared to demonstrate the emergence of deformed halo structures due to blocking effects. In our calculations, $^{19}$B and $^{37}$Mg have remarkable features of deformed halos.Comment: 7 pages, 4 figures, 1 tabl

Improving Robustness of Graph Neural Networks with Heterophily-Inspired Designs

Recent studies have exposed that many graph neural networks (GNNs) are sensitive to adversarial attacks, and can suffer from performance loss if the graph structure is intentionally perturbed. A different line of research has shown that many GNN architectures implicitly assume that the underlying graph displays homophily, i.e., connected nodes are more likely to have similar features and class labels, and perform poorly if this assumption is not fulfilled. In this work, we formalize the relation between these two seemingly different issues. We theoretically show that in the standard scenario in which node features exhibit homophily, impactful structural attacks always lead to increased levels of heterophily. Then, inspired by GNN architectures that target heterophily, we present two designs -- (i) separate aggregators for ego- and neighbor-embeddings, and (ii) a reduced scope of aggregation -- that can significantly improve the robustness of GNNs. Our extensive empirical evaluations show that GNNs featuring merely these two designs can achieve significantly improved robustness compared to the best-performing unvaccinated model with 24.99% gain in average performance under targeted attacks, while having smaller computational overhead than existing defense mechanisms. Furthermore, these designs can be readily combined with explicit defense mechanisms to yield state-of-the-art robustness with up to 18.33% increase in performance under attacks compared to the best-performing vaccinated model.Comment: preprint with appendix; 30 pages, 1 figur

CUCL: Codebook for Unsupervised Continual Learning

The focus of this study is on Unsupervised Continual Learning (UCL), as it presents an alternative to Supervised Continual Learning which needs high-quality manual labeled data. The experiments under the UCL paradigm indicate a phenomenon where the results on the first few tasks are suboptimal. This phenomenon can render the model inappropriate for practical applications. To address this issue, after analyzing the phenomenon and identifying the lack of diversity as a vital factor, we propose a method named Codebook for Unsupervised Continual Learning (CUCL) which promotes the model to learn discriminative features to complete the class boundary. Specifically, we first introduce a Product Quantization to inject diversity into the representation and apply a cross quantized contrastive loss between the original representation and the quantized one to capture discriminative information. Then, based on the quantizer, we propose an effective Codebook Rehearsal to address catastrophic forgetting. This study involves conducting extensive experiments on CIFAR100, TinyImageNet, and MiniImageNet benchmark datasets. Our method significantly boosts the performances of supervised and unsupervised methods. For instance, on TinyImageNet, our method led to a relative improvement of 12.76% and 7% when compared with Simsiam and BYOL, respectively.Comment: MM '23: Proceedings of the 31st ACM International Conference on Multimedi

PredT4SE-Stack: Prediction of Bacterial Type IV Secreted Effectors From Protein Sequences Using a Stacked Ensemble Method

Gram-negative bacteria use various secretion systems to deliver their secreted effectors. Among them, type IV secretion system exists widely in a variety of bacterial species, and secretes type IV secreted effectors (T4SEs), which play vital roles in host-pathogen interactions. However, experimental approaches to identify T4SEs are time- and resource-consuming. In the present study, we aim to develop an in silico stacked ensemble method to predict whether a protein is an effector of type IV secretion system or not based on its sequence information. The protein sequences were encoded by the feature of position specific scoring matrix (PSSM)-composition by summing rows that correspond to the same amino acid residues in PSSM profiles. Based on the PSSM-composition features, we develop a stacked ensemble model PredT4SE-Stack to predict T4SEs, which utilized an ensemble of base-classifiers implemented by various machine learning algorithms, such as support vector machine, gradient boosting machine, and extremely randomized trees, to generate outputs for the meta-classifier in the classification system. Our results demonstrated that the framework of PredT4SE-Stack was a feasible and effective way to accurately identify T4SEs based on protein sequence information. The datasets and source code of PredT4SE-Stack are freely available at http://xbioinfo.sjtu.edu.cn/PredT4SE_Stack/index.php

Effect of Catalytic Cylinders on Autothermal Reforming of Methane for Hydrogen Production in a Microchamber Reactor

A new multicylinder microchamber reactor is designed on autothermal reforming of methane for hydrogen production, and its performance and thermal behavior, that is, based on the reaction mechanism, is numerically investigated by varying the cylinder radius, cylinder spacing, and cylinder layout. The results show that larger cylinder radius can promote reforming reaction; the mass fraction of methane decreased from 26% to 21% with cylinder radius from 0.25 mm to 0.75 mm; compact cylinder spacing corresponds to more catalytic surface and the time to steady state is decreased from 40 s to 20 s; alteration of staggered and aligned cylinder layout at constant inlet flow rates does not result in significant difference in reactor performance and it can be neglected. The results provide an indication and optimize performance of reactor; it achieves higher conversion compared with other reforming reactors