Self-poling effect on Mn-doped Pb(Mg1/3Nb2/3)O3-PbTiO3 single crystals synthesized by solid-state single crystal growth method

Department of Materials Science and EngineeringPiezoelectric single crystal such as PZN-PT [Pb(Zn1/3Nb2/3)O3-PbTiO3] and PMN-PT [Pb(Mg1/3-Nb2/3)O3-PbTiO3] which have magnificent piezoelectric properties, have been used for various applications, e.g., sensors, transducers and actuators. For piezoelectric applications, ferroelectric materials are usually employed because of their high performance after poling process. Poling process that forces ferroelectric domains to alignotherwise, randomly oriented, is essential in making a ferroelectric into a piezoelectric. It is typically performed at an elevated temperature by applying a certain amount of a unipolar electric field for some time since domain alignment is a time-dependent thermally activated process. However, induced piezoelectric properties generally disappear when ferroelectric material is heated up to Curie temperature (Tc) where aligned dipoles scatter. Because the synthesis of common ferroelectric materials is processed at high temperature, ferroelectric materials must be poled for piezoelectric application. In this paper, ferroelectric PMN-PT single crystals with doping Mn for inducing self-poling effect will be discussed. The Mn-doped PMN-PT exhibits a high piezoelectric response without any poling process. Moreover, high piezoelectric properties are re-induced after heating above TC with self-poling on cooling process. The defect-dipoles which is caused by Mn ions generate internal bias fields (Ei) which give forces aligning dipoles of PMN-PT to have spontaneous polarization. The Mn-PMN-PT crystal which is able to be self-poled has its own preferred poling direction. So opposite DC-poling can enhance piezoelectric and dielectric performances like AC-poling which is highly interested by ferroelectric single crystal society. The mechanism presented in this paper can offers a new perspective for enhancing the dielectric and piezoelectric properties of doped ferroelectric single crystals.clos

Comparing Sample-wise Learnability Across Deep Neural Network Models

Estimating the relative importance of each sample in a training set has important practical and theoretical value, such as in importance sampling or curriculum learning. This kind of focus on individual samples invokes the concept of sample-wise learnability: How easy is it to correctly learn each sample (cf. PAC learnability)? In this paper, we approach the sample-wise learnability problem within a deep learning context. We propose a measure of the learnability of a sample with a given deep neural network (DNN) model. The basic idea is to train the given model on the training set, and for each sample, aggregate the hits and misses over the entire training epochs. Our experiments show that the sample-wise learnability measure collected this way is highly linearly correlated across different DNN models (ResNet-20, VGG-16, and MobileNet), suggesting that such a measure can provide deep general insights on the data's properties. We expect our method to help develop better curricula for training, and help us better understand the data itself.Comment: Accepted to AAAI 2019 Student Abstrac

Hypercore Decomposition for Non-Fragile Hyperedges: Concepts, Algorithms, Observations, and Applications

Hypergraphs are a powerful abstraction for modeling high-order relations, which are ubiquitous in many fields. A hypergraph consists of nodes and hyperedges (i.e., subsets of nodes); and there have been a number of attempts to extend the notion of $k$-cores, which proved useful with numerous applications for pairwise graphs, to hypergraphs. However, the previous extensions are based on an unrealistic assumption that hyperedges are fragile, i.e., a high-order relation becomes obsolete as soon as a single member leaves it. In this work, we propose a new substructure model, called ($k$, $t$)-hypercore, based on the assumption that high-order relations remain as long as at least $t$ fraction of the members remain. Specifically, it is defined as the maximal subhypergraph where (1) every node has degree at least $k$ in it and (2) at least $t$ fraction of the nodes remain in every hyperedge. We first prove that, given $t$ (or $k$), finding the ($k$, $t$)-hypercore for every possible $k$ (or $t$) can be computed in time linear w.r.t the sum of the sizes of hyperedges. Then, we demonstrate that real-world hypergraphs from the same domain share similar ($k$, $t$)-hypercore structures, which capture different perspectives depending on $t$. Lastly, we show the successful applications of our model in identifying influential nodes, dense substructures, and vulnerability in hypergraphs.Comment: 24 pages, 14 figure