Collisional interaction limits between dark matters and baryons in `cooling flow' clusters

Presuming weak collisional interactions to exchange the kinetic energy between dark matter and baryonic matter in a galaxy cluster, we re-examine the effectiveness of this process in several `cooling flow' galaxy clusters using available X-ray observations and infer an upper limit on the heavy dark matter particle (DMP)$-$proton cross section $\sigma_{\rm xp}$. With a relative collisional velocity $V-$dependent power-law form of $\sigma_{\rm xp}=\sigma_0(V/10^3 {\rm km s^{-1}})^a$ where $a\leq 0$, our inferred upper limit is \sigma_0/m_{\rm x}\lsim 2\times10^{-25} {\rm cm}^2 {\rm GeV}^{-1} with $m_{\rm x}$ being the DMP mass. Based on a simple stability analysis of the thermal energy balance equation, we argue that the mechanism of DMP$-$baryon collisional interactions is unlikely to be a stable nongravitational heating source of intracluster medium (ICM) in inner core regions of `cooling flow' galaxy clusters.Comment: 8 pages, 2 figures, MNRAS accepte

Memanti­nium chloride 0.1-hydrate

The crystal structure of the title compound, C12H22N+·Cl−·0.1H2O, consists of (3,5-dimethyl-1-adamantyl)ammonium chloride (memanti­nium chloride) and uncoordinated water mol­ecules. The four six-membered rings of the memanti­nium cation assume typical chair conformations. The Cl− counter-anion links with the memanti­nium cation via N—H⋯Cl hydrogen bonding, forming channels where the disordered crystal water molecules are located. The O atom of the water mol­ecule is located on a threefold rotation axis, its two H atoms symmetrically distributed over six sites; the water mol­ecule links with the Cl− anions via O—H⋯Cl hydrogen bonding

Does Differential Privacy Prevent Backdoor Attacks in Practice?

Differential Privacy (DP) was originally developed to protect privacy. However, it has recently been utilized to secure machine learning (ML) models from poisoning attacks, with DP-SGD receiving substantial attention. Nevertheless, a thorough investigation is required to assess the effectiveness of different DP techniques in preventing backdoor attacks in practice. In this paper, we investigate the effectiveness of DP-SGD and, for the first time in literature, examine PATE in the context of backdoor attacks. We also explore the role of different components of DP algorithms in defending against backdoor attacks and will show that PATE is effective against these attacks due to the bagging structure of the teacher models it employs. Our experiments reveal that hyperparameters and the number of backdoors in the training dataset impact the success of DP algorithms. Additionally, we propose Label-DP as a faster and more accurate alternative to DP-SGD and PATE. We conclude that while Label-DP algorithms generally offer weaker privacy protection, accurate hyper-parameter tuning can make them more effective than DP methods in defending against backdoor attacks while maintaining model accuracy

ASIAM-HGNN: Automatic Selection and Interpretable Aggregation of Meta-Path Instances for Heterogeneous Graph Neural Network

In heterogeneous information network (HIN)-based applications, the existing methods usually use Heterogeneous Graph Neural Networks (HGNN) to handle some complex tasks. However, these methods still have some shortcomings: 1) they manually pre-select some meta-paths and thus some important ones are missing, while the missing ones still contains the information and features of the node in the entire graph structure; and 2) they have no high interpretability since they do not consider the logical sequences in an HIN. In order to deal with them, we propose ASIAM-HGNN: a heterogeneous graph neural network combined with the automatic selection and interpretable aggregation of meta-path instances. Our model can automatically filter important meta paths for each node, while preserving the logical sequence between nodes, so as to solve the problems existing in other models. A group of experiments are conducted on real-world datasets, and the results demonstrate that the models learned by our method have a better performance in most of task scenarios