Unipolar Resistance Switching in Amorphous High-k dielectrics Based on Correlated Barrier Hopping Theory

We have proposed a kind of nonvolatile resistive switching memory based on amorphous LaLuO3, which has already been established as a promising candidate of high-k gate dielectric employed in transistors. Well-developed unipolar switching behaviors in amorphous LaLuO3 make it suited for not only logic but memory applications using the conventional semiconductor or the emerging nano/CMOS architectures. The conduction transition between high- and low- resistance states is attributed to the change in the separation between oxygen vacancy sites in the light of the correlated barrier hopping theory. The mean migration distances of vacancies responsible for the resistive switching are demonstrated in nanoscale, which could account for the ultrafast programming speed of 6 ns. The origin of the distributions in switching parameters in oxides can be well understood according to the switching principle. Furthermore, an approach has also been developed to make the operation voltages predictable for the practical applications of resistive memories.Comment: 18 pages, 6 figure

Vehicle trajectory clustering based on dynamic representation learning of internet of vehicles

With the widely used Internet of Things, 5G, and smart city technologies, we are able to acquire a variety of vehicle trajectory data. These trajectory data are of great significance which can be used to extract relevant information in order to, for instance, calculate the optimal path from one position to another, detect abnormal behavior, monitor the traffic flow in a city, and predict the next position of an object. One of the key technology is to cluster vehicle trajectory. However, existing methods mainly rely on manually designed metrics which may lead to biased results. Meanwhile, the large scale of vehicle trajectory data has become a challenge because calculating these manually designed metrics will cost more time and space. To address these challenges, we propose to employ network representation learning to achieve accurate vehicle trajectory clustering. Specifically, we first construct the k-nearest neighbor-based internet of vehicles in a dynamic manner. Then we learn the low-dimensional representations of vehicles by performing dynamic network representation learning on the constructed network. Finally, using the learned vehicle vectors, vehicle trajectories are clustered with machine learning methods. Experimental results on the real-word dataset show that our method achieves the best performance compared against baseline methods. © 2000-2011 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Feng Xia” is provided in this record*

Not Every Couple Is a Pair: A Supervised Approach for Lifetime Collaborator Identification

While scientific collaboration can be critical for a scholar, some collaborator(s) can be more significant than others, a.k.a. lifetime collaborator(s). This work-in-progress aims to investigate whether it is possible to predict/identify lifetime collaborators given a junior scholar\u27s early profile. For this purpose, we propose a supervised approach by leveraging scholars\u27 local and network properties. Extensive experiments on DBLP digital library demonstrate that lifetime collaborators can be accurately predicted. The proposed model outperforms baseline models with various predictors. Our study may shed light on the exploration of scientific collaborations from the perspective of life-long collaboration

Effects of morphological traits on living body weight of wild Cyclina sinensis in different geographical populations

Eleven coastal geographical populations of wild Cyclina sinensis in China were collected in February 2020, and the effects of four morphological traits (shell length; shell height; shell width; external ligament length) on one weight trait (living body weight) were studied by correlation analysis, path analysis, determination coefficient analysis, and regression analyses. The statistical results showed that the coefficient of body weight variation was generally greater than morphological traits(P＜0.05). The correlation analysis results showed that the coefficient of correlation between morphological traits (except for external ligament length) and body weight are significantly positive (P＜0.05) in all populations. Based on the results of path analysis and determination coefficient analysis, shell length has the greatest direct effect on body weight in the Yancheng population; shell height has the greatest direct effect on body weight in Dandong, Fuzhou and Tangshan populations; shell width has the greatest direct effect on body weight in Zhanjiang, Wenzhou, Dongtai, Ningbo, Tianjin, Dongying, and Wanning populations. Multiple regression equations were obtained with body weight as the dependent variable, shell length, height, and width and external ligament length as independent variables. The results of systematic clustering showed that there are no apparent geographical differentiation characteristics among eleven geographical populations in morphology. This study provided a scientific basis for selective and genetic breeding and can guide the development and utilization of wild C. sinensis seed resources

Scholar2vec : vector representation of scholars for lifetime collaborator prediction

While scientific collaboration is critical for a scholar, some collaborators can be more significant than others, e.g., lifetime collaborators. It has been shown that lifetime collaborators are more influential on a scholar's academic performance. However, little research has been done on investigating predicting such special relationships in academic networks. To this end, we propose Scholar2vec, a novel neural network embedding for representing scholar profiles. First, our approach creates scholars' research interest vector from textual information, such as demographics, research, and influence. After bridging research interests with a collaboration network, vector representations of scholars can be gained with graph learning. Meanwhile, since scholars are occupied with various attributes, we propose to incorporate four types of scholar attributes for learning scholar vectors. Finally, the early-stage similarity sequence based on Scholar2vec is used to predict lifetime collaborators with machine learning methods. Extensive experiments on two real-world datasets show that Scholar2vec outperforms state-of-the-art methods in lifetime collaborator prediction. Our work presents a new way to measure the similarity between two scholars by vector representation, which tackles the knowledge between network embedding and academic relationship mining. © 2021 Association for Computing Machinery

Venue topic model-enhanced joint graph modelling for citation recommendation in scholarly big data

Natural language processing technologies, such as topic models, have been proven to be effective for scholarly recommendation tasks with the ability to deal with content information. Recently, venue recommendation is becoming an increasingly important research task due to the unprecedented number of publication venues. However, traditional methods focus on either the author's local network or author-venue similarity, where the multiple relationships between scholars and venues are overlooked, especially the venue-venue interaction. To solve this problem, we propose an author topic model-enhanced joint graph modeling approach that consists of venue topic modeling, venue-specific topic influence modeling, and scholar preference modeling. We first model the venue topic with Latent Dirichlet Allocation. Then, we model the venue-specific topic influence in an asymmetric and low-dimensional way by considering the topic similarity between venues, the top-influence of venues, and the top-susceptibility of venues. The top-influence characterizes venues' capacity of exerting topic influence on other venues. The top-susceptibility captures venues' propensity of being topically influenced by other venues. Extensive experiments on two real-world datasets show that our proposed joint graph modeling approach outperforms the state-of-The-Art methods. © 2020 ACM

Comparative investigations of the crystal structure and photoluminescence property of eulytite-type Ba3Eu(PO4)3 and Sr3Eu(PO4)3

In this study, the Ba3Eu(PO4)3 and Sr3Eu(PO4)3 compounds were synthesized and the crystal structures were determined for the first time by Rietveld refinement using powder X-ray diffraction (XRD) patterns. Ba3Eu-(PO4)3 crystallizes in cubic space group I4¯3d, with cell parameters of a = 10.47996(9) Å, V = 1151.01(3) Å3 and Z = 4; Ba2+ and Eu3+ occupy the same site with partial occupancies of 3/4 and 1/4, respectively. Besides, in this structure, there exists two distorted kinds of the PO4 polyhedra orientation. Sr3Eu(PO4)3 is isostructural to Ba3Eu(PO4)3 and has much smaller cell parameters of a = 10.1203(2) Å, V = 1036.52(5) Å3. The bandgaps of Ba3Eu(PO4)3 and Sr3Eu(PO4)3 are determined to be 4.091 eV and 3.987 eV, respectively, based on the UV–Vis diffuse reflectance spectra. The photoluminescence measurements reveal that, upon 396 nm n-UV light excitation, Ba3Eu(PO4)3 and Sr3Eu(PO4)3 exhibit orange-red emission with two main peaks at 596 nm and prevailing 613 nm, corresponding to the 5D0 → 7F1 and 5D0 → 7F2 transitions of Eu3+, respectively. The dynamic disordering in the crystal structures contributes to the broadening of the luminescence spectra. The electronic structure of the hosphates was calculated by the first-principles method. The analysis elucidats that the band structures are mainly governed by the orbits of phosphorus, oxygen and europium, and the sharp peaks of the europium f-orbit occur at the top of the valence bands