Mirror energy difference and the structure of loosely bound proton-rich nuclei around A = 20

The properties of loosely bound proton-rich nuclei around A = 20 are investigated within the framework of nuclear shell model. In these nuclei, the strength of the effective interactions involving the loosely bound proton s1=2 orbit are significantly reduced in comparison with those in their mirror nuclei. We evaluate the reduction of the effective interaction by calculating the monopole-baseduniversal interaction (VMU) in the Woods-Saxon basis. The shell-model Hamiltonian in the sd shell, such as USD, can thus be modified to reproduce the binding energies and energy levels of the weakly bound proton-rich nuclei around A = 20. The effect of the reduction of the effective interaction on the structure and decay properties of these nuclei is also discussed.Comment: accepted by Physical Review

C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder

Representation learning assumes that real-world data is generated by a few semantically meaningful generative factors (i.e., sources of variation) and aims to discover them in the latent space. These factors are expected to be causally disentangled, meaning that distinct factors are encoded into separate latent variables, and changes in one factor will not affect the values of the others. Compared to statistical independence, causal disentanglement allows more controllable data generation, improved robustness, and better generalization. However, most existing work assumes unconfoundedness in the discovery process, that there are no common causes to the generative factors and thus obtain only statistical independence. In this paper, we recognize the importance of modeling confounders in discovering causal generative factors. Unfortunately, such factors are not identifiable without proper inductive bias. We fill the gap by introducing a framework entitled Confounded-Disentanglement (C-Disentanglement), the first framework that explicitly introduces the inductive bias of confounder via labels from domain expertise. In addition, we accordingly propose an approach to sufficiently identify the causally disentangled factors under any inductive bias of the confounder. We conduct extensive experiments on both synthetic and real-world datasets. Our method demonstrates competitive results compared to various SOTA baselines in obtaining causally disentangled features and downstream tasks under domain shifts.Comment: accepted to Neurips 202

Homeotic transformation induced by protein transduction

One of the most fundamental features of living organisms is that cells are separated from their external environment by a thin, but highly complex plasma membrane constituted of a lipid bilayer. Although, the lipid bilayer is only a few nanometers in width, it is impermeable to most molecules apart from small hydrophobic ones. The ability of small molecules to diffuse through a lipid bilayer is related to their lipid solubility. Hydrophilic macromolecular Antennapedia homeodomain peptide has been shown to be able to translocate from extracellular space into the cytoplasm of cells in a receptor-independent manner. Its third α-helix domain, designated as “Penetratin”, was proposed to be the functional transduction domain that is responsible for the translocation, and it is widely used for intracellular delivery of various exogenous proteins. Although Penetratin has been regarded to be the only element conferring the capacity of its parent polypeptide to penetrate through the plasma membrane, we found that the complete Antennapedia homeodomain exhibits an appreciably higher level of translocation efficiency as compared to Penetratin. Pharmacological analysis demonstrated that macropinocytic endocytosis plays a significant role underlying the process of the homeodomain internalization, and this is consistent with the observation that internalized polypeptide co-localizes with a fluid phase dye. Our studies identify macropinocytosis as a major mechanism by which Antennapedia homeodomain obtains the access to the interior of cells. In the process of macropinocytosis, signaling from the plasma membrane is required for actin remodeling to generate mechanical deformation forces; the interaction between positively charged Antennapedia homeodomain and negatively charged extracellular heparan sulfate could trigger the signaling cascade for fluid phase endocytosis. This would presumably explain why positively charged peptides, polymers, and liposomes are able to penetrate cells. As a fluid phase macropinocytosis provides cells with a way to non-selectively internalize large quantities of solute, it represents an effective means for drug delivery into cells. Both of “Penetratin” and Antennapedia homeodomain exploit macropinocytosis to a certain extent, the comparison between them may advance our understanding of the mechanisms triggering macropinocytotic endocytosis

Reliability analysis of condition monitoring network of wind turbine blade based on wireless sensor networks

This paper proposes a reliability analysis method for the condition monitoring network of wind turbine blade based on wireless sensor networks. Two critical factors, which play significant roles in the reliability evaluation of the monitoring network, are focused on, that is, the reliability of sensor nodes and the reliability of communication links. First, with the established reliability models for sensor nodes and communication links, the method of establishing reliability simulation model of monitoring network is presented based on Monte Carlo method. Second, according to the analysis of the intra-cluster reliabilities of the tree topology and the mesh topology, the topology selection principle of the sensor network for a single blade is proposed. Finally, the influence of maintenance cycle and communication interference on the overall reliability of the monitoring network is illustrated and the proper maintenance cycle is achieved. The solution to communication interference is put forward with the data retransmission measure. The overall reliability of the network is improved effectively by adopting the one-time data retransmission measure. Our work is expected to provide the guidance in theory and technology for constructing the high-performance condition monitoring and control system for wind turbine blades.</p

DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization

Visual reinforcement learning (RL) has shown promise in continuous control tasks. Despite its progress, current algorithms are still unsatisfactory in virtually every aspect of the performance such as sample efficiency, asymptotic performance, and their robustness to the choice of random seeds. In this paper, we identify a major shortcoming in existing visual RL methods that is the agents often exhibit sustained inactivity during early training, thereby limiting their ability to explore effectively. Expanding upon this crucial observation, we additionally unveil a significant correlation between the agents' inclination towards motorically inactive exploration and the absence of neuronal activity within their policy networks. To quantify this inactivity, we adopt dormant ratio as a metric to measure inactivity in the RL agent's network. Empirically, we also recognize that the dormant ratio can act as a standalone indicator of an agent's activity level, regardless of the received reward signals. Leveraging the aforementioned insights, we introduce DrM, a method that uses three core mechanisms to guide agents' exploration-exploitation trade-offs by actively minimizing the dormant ratio. Experiments demonstrate that DrM achieves significant improvements in sample efficiency and asymptotic performance with no broken seeds (76 seeds in total) across three continuous control benchmark environments, including DeepMind Control Suite, MetaWorld, and Adroit. Most importantly, DrM is the first model-free algorithm that consistently solves tasks in both the Dog and Manipulator domains from the DeepMind Control Suite as well as three dexterous hand manipulation tasks without demonstrations in Adroit, all based on pixel observations