Tension-induced tunable corrugation in two-phase soft composites and its properties as a band gap structure

This thesis numerically investigates the elastic deformation response of a two-phase soft composite under externally applied concentric tension and its properties as a band gap structure. By carefully designing the inclusion pattern, it is possible to induce corrugations normal to the direction of stretch. By stacking 1D composite fibers to form 2D membranes, these corrugations collectively lead to the formation of membrane channels with shapes and sizes tunable by the level of stretch and enable the interfaces to progressively soften and evolve into non-planar geometries characterized by the nucleation and stable growth of interfacial channels of irregular shapes. It is also possible to modulate the band gap profile of the composite structure as a result of interfacial deformations and the corresponding microstructural evolution. Furthermore, by using specific inclusion patterns in laminated plates, it is possible to create pop-ups and troughs enabling the development of complex 3D geometries from planar construction. The corrugation amplitude increases with the stiffness of inclusion and its eccentricity from the tension axis. The techniques discussed in this thesis provide greater flexibility and controllability in pattern design and have potential applications in providing a novel framework for harnessing controlled damage in the development of targeted acoustic band gaps and optimizing damping properties of composites

Distilling Causal Effect from Miscellaneous Other-Class for Continual Named Entity Recognition

Continual Learning for Named Entity Recognition (CL-NER) aims to learn a growing number of entity types over time from a stream of data. However, simply learning Other-Class in the same way as new entity types amplifies the catastrophic forgetting and leads to a substantial performance drop. The main cause behind this is that Other-Class samples usually contain old entity types, and the old knowledge in these Other-Class samples is not preserved properly. Thanks to the causal inference, we identify that the forgetting is caused by the missing causal effect from the old data. To this end, we propose a unified causal framework to retrieve the causality from both new entity types and Other-Class. Furthermore, we apply curriculum learning to mitigate the impact of label noise and introduce a self-adaptive weight for balancing the causal effects between new entity types and Other-Class. Experimental results on three benchmark datasets show that our method outperforms the state-of-the-art method by a large margin. Moreover, our method can be combined with the existing state-of-the-art methods to improve the performance in CL-NERComment: Accepted by EMNLP202

State-Aware Proximal Pessimistic Algorithms for Offline Reinforcement Learning

Pessimism is of great importance in offline reinforcement learning (RL). One broad category of offline RL algorithms fulfills pessimism by explicit or implicit behavior regularization. However, most of them only consider policy divergence as behavior regularization, ignoring the effect of how the offline state distribution differs with that of the learning policy, which may lead to under-pessimism for some states and over-pessimism for others. Taking account of this problem, we propose a principled algorithmic framework for offline RL, called \emph{State-Aware Proximal Pessimism} (SA-PP). The key idea of SA-PP is leveraging discounted stationary state distribution ratios between the learning policy and the offline dataset to modulate the degree of behavior regularization in a state-wise manner, so that pessimism can be implemented in a more appropriate way. We first provide theoretical justifications on the superiority of SA-PP over previous algorithms, demonstrating that SA-PP produces a lower suboptimality upper bound in a broad range of settings. Furthermore, we propose a new algorithm named \emph{State-Aware Conservative Q-Learning} (SA-CQL), by building SA-PP upon representative CQL algorithm with the help of DualDICE for estimating discounted stationary state distribution ratios. Extensive experiments on standard offline RL benchmark show that SA-CQL outperforms the popular baselines on a large portion of benchmarks and attains the highest average return

Comparative Component Analysis of Exons with Different Splicing Frequencies

Transcriptional isoforms are not just random combinations of exons. What has caused exons to be differentially spliced and whether exons with different splicing frequencies are subjected to divergent regulation by potential elements or splicing signals? Beyond the conventional classification for alternatively spliced exons (ASEs) and constitutively spliced exons (CSEs), we have classified exons from alternatively spliced human genes and their mouse orthologs (12,314 and 5,464, respectively) into four types based on their splicing frequencies. Analysis has indicated that different groups of exons presented divergent compositional and regulatory properties. Interestingly, with the decrease of splicing frequency, exons tend to have greater lengths, higher GC content, and contain more splicing elements and repetitive elements, which seem to imply that the splicing frequency is influenced by such factors. Comparison of non-alternatively spliced (NAS) mouse genes with alternatively spliced human orthologs also suggested that exons with lower splicing frequencies may be newly evolved ones which gained functions with splicing frequencies altered through the evolution. Our findings have revealed for the first time that certain factors may have critical influence on the splicing frequency, suggesting that exons with lower splicing frequencies may originate from old repetitive sequences, with splicing sites altered by mutation, gaining novel functions and become more frequently spliced

Effect of lattice volume and strain on the conductivity of BaCeY-oxide ceramic proton conductors

In-situ electrochemical impedance spectroscopy was used to study the effect of lattice volume and strain on the proton conductivity of the yttrium-doped barium cerate proton conductor by applying the hydrostatic pressure up to 1.25 GPa. An increase from 0.62 eV to 0.73 eV in the activation energy of the bulk conductivity was found with increasing pressure during a unit cell volume change of 0.7%, confirming a previously suggested correlation between lattice volume and proton diffusivity in the crystal lattice. One strategy worth trying in the future development of the ceramic proton conductors could be to expand the lattice and potentially lower the activation energy under tensile strain

Resonant scattering of energetic electrons by unusual low-frequency hiss

Abstract We quantify the resonant scattering effects of the unusual low-frequency dawnside plasmaspheric hiss observed on 30 September 2012 by the Van Allen Probes. In contrast to normal (∼100-2000 Hz) hiss emissions, this unusual hiss event contained most of its wave power at ∼20-200 Hz. Compared to the scattering by normal hiss, the unusual hiss scattering speeds up the loss of ∼50-200 keV electrons and produces more pronounced pancake distributions of ∼50-100 keV electrons. It is demonstrated that such unusual low-frequency hiss, even with a duration of a couple of hours, plays a particularly important role in the decay and loss process of energetic electrons, resulting in shorter electron lifetimes for ∼50-400 keV electrons than normal hiss, and should be carefully incorporated into global modeling of radiation belt electron dynamics during periods of intense injections

Entropy and isokinetic temperature in fast ion transport

ABSTRACT: Ion transport in crystalline solids is an essential process for many electrochemical energy converters such as solid-state batteries and fuel cells. Empirical data have shown that ion transport in crystal lattices obeys the Meyer-Neldel Rule (MNR). For similar, closely related materials, when the material properties are changed by doping or by strain, the measured ionic conductivities showing different activation energies intersect on the Arrhenius plot, at an isokinetic temperature. Therefore, the isokinetic temperature is a critical parameter for improving the ionic conductivity. However, a comprehensive understanding of the fundamental mechanism of MNR in ion transport is lacking. Here the physical significance and applicability of MNR is discussed, that is, of activation entropy-enthalpy compensation, in crystalline fast ionic conductors, and the methods for determining the isokinetic temperature. Lattice vibrations provide the excitation energy for the ions to overcome the activation barrier. The multi-excitation entropy model suggests that isokinetic temperature can be tuned by modulating the excitation phonon frequency. The relationship between isokinetic temperature and isokinetic prefactor can provide information concerning conductivity mechanisms. The need to effectively determine the isokinetic temperature for accelerating the design of new fast ionic conductors with high conductivity is highlighted