In situ Observation of Sodium Dendrite Growth and Concurrent Mechanical Property Measurements Using an Environmental Transmission Electron Microscopy–Atomic Force Microscopy (ETEM-AFM) Platform

Akin to Li, Na deposits in a dendritic form to cause a short circuit in Na metal batteries. However, the growth mechanisms and related mechanical properties of Na dendrites remain largely unknown. Here we report real-time characterizations of Na dendrite growth with concurrent mechanical property measurements using an environmental transmission electron microscopy–atomic force microscopy (ETEM-AFM) platform. In situ electrochemical plating produces Na deposits stabilized with a thin Na2CO3 surface layer (referred to as Na dendrites). These Na dendrites have characteristic dimensions of a few hundred nanometers and exhibit different morphologies, including nanorods, polyhedral nanocrystals, and nanospheres. In situ mechanical measurements show that the compressive and tensile strengths of Na dendrites with a Na2CO3 surface layer vary from 36 to >203 MPa, which are much larger than those of bulk Na. In situ growth of Na dendrites under the combined overpotential and mechanical confinement can generate high stress in these Na deposits. These results provide new baseline data on the electrochemical and mechanical behavior of Na dendrites, which have implications for the development of Na metal batteries toward practical energy-storage applications

In Situ Measurements of the Mechanical Properties of Electrochemically Deposited Li₂CO₃ and Li₂O Nanorods

Solid-electrolyte interface (SEI) is “the most important but least understood (component) in rechargeable Li-ion batteries”. The ideal SEI requires high elastic strength and can resist the penetration of a Li dendrite mechanically, which is vital for inhibiting the dendrite growth in lithium batteries. Even though Li$_{2}$CO$_{3}$ and Li$_{2}$O are identified as the major components of SEI, their mechanical properties are not well understood. Herein, SEI-related materials such as Li$_{2}$CO$_{3}$ and Li$_{2}$O were electrochemically deposited using an environmental transmission electron microscopy (ETEM), and their mechanical properties were assessed by in situ atomic force microscopy (AFM) and inverse finite element simulations. Both Li$_{2}$CO$_{3}$ and Li$_{2}$O exhibit nanocrystalline structures and good plasticity. The ultimate strength of Li$_{2}$CO$_{3}$ ranges from 192 to 330 MPa, while that of Li$_{2}$O is less than 100 MPa. These results provide a new understanding of the SEI and its related dendritic problems in lithium batteries

Using Classification Methods to Label Tasks in Process Mining

We investigate a method designed to improve the accuracy of process mining in scenarios where the identification of task labels for log events is uncertain. Such situations are prevalent in business processes where events consist of communications between people, such as email messages. We examine how the accuracy of an independent task identifier, such as a classification or clustering engine, can be improved by examining the currently mined process model. First, a classification scheme based on identifying keywords in each message is presented to provide an initial labeling. We then demonstrate how these labels can be refined by considering the likelihood that the event represents a particular task as obtained via an analysis of the current representation of the process model. This process is then repeated a number of times until the model is sufficiently refined. Results show that both keyword classification and current process model analysis can be significantly effective on their own, and when combined have the potential to correct virtually all errors when noise is low (less than 20%), and can reduce the error rate by about 85% when noise is in the 30-40% range.Peer reviewed: YesNRC publication: Ye

STFLS: A Heuristic Method for Static and Transportation Facility Location Allocation in Large Spatial Datasets

Peer reviewed: YesNRC publication: Ye

A new perspective of privacy protection : Unique distinct l-SR diversity

More and more public data sets which contain information about individuals are published in recent years. The urgency to reduce the risk of the privacy disclosure from such data sets makes the approaches of privacy protection for data publishing be widely employed. There are two popular models for privacy protection: k-anonymity and l-diversity. kanonymity focuses on reducing the probability of identifying a particular person, which requires that each equivalence class (a set of records with same identifier attributes) contains at least k records. l-diversity concentrates on reducing the inference from released sensitive attributes. It requires that each equivalence class has at least l \u201cwell-represented\u201d sensitive attribute values. In this study, we view the privacy protection problem in a brand new perspective. We proposed a new model, Unique Distinct l- SR diversity based on the sensitivity of private information. Also, we presented two performance measures for how much sensitive information can be inferred from an equivalence class. l-SR diversity algorithm was implemented to achieve Unique Distinct l-SR diversity. We tested l-SR diversity on one benchmark data set and three synthetic data sets, and compared it with other l-diversity algorithms. The results show that our algorithm achieved better performance on minimizing inference of sensitive information and reached the comparable generalization data quality compared with other data publishing algorithms.Peer reviewed: YesNRC publication: Ye

Privacy measures for free text documents : bridging the gap between theory and practice

Privacy compliance for free text documents is a challenge facing many organizations. Named entity recognition techniques and machine learning methods can be used to detect private information, such as personally identifiable information (PII) and personal health information (PHI) in free text documents. However, these methods cannot measure the level of privacy embodied in the documents. In this paper, we propose a framework to measure the privacy content in free text documents. The measure consists of two factors: the probability that the text can be used to uniquely identify a person and the degree of sensitivity of the private entities associated with the person. We then instantiate the framework in the scenario of detection and protection of PHI in medical records, which is a challenge for many hospitals, clinics, and other medical institutions. We did experiments on a real dataset to show the effectiveness of the proposed measure.Peer reviewed: NoNRC publication: Ye

Expectation Propagation in GenSpace Graphs for Summarization

Summary mining aims to find interesting summaries for a data set and to use data mining techniques to improve the functionality of Online Analytical Processing (OLAP) systems. In this paper, we propose an interactive summary mining approach, called GenSpace summary mining, to find the interesting summaries based on user expectations. In the mining process, to record the user's evolving knowledge, the system needs to update and propagate new expectations. In this paper, we propose a linear method for consistently and efficiently propagating user expectations in a GenSpace graph. For a GenSpace graph where uninteresting nodes can be marked by the user before the mining process, we propose a greedy algorithm to determine the propagation paths in a GenSpace subgraph that reduces the time cost subject to a fixed amount of space.L'exploration de r\ue9sum\ue9s vise \ue0 trouver des r\ue9sum\ue9s int\ue9ressants dans un ensemble de donn\ue9es et \ue0 utiliser des techniques d'exploration de donn\ue9es pour am\ue9liorer la fonctionnalit\ue9 des syst\ue8mes de traitement analytique en ligne OLAP (Online Analytical Processing). Dans cet article, nous proposons une approche d'exploration interactive de r\ue9sum\ue9s, appel\ue9e exploration de r\ue9sum\ue9s GenSpace, qui permet de rep\ue9rer les r\ue9sum\ue9s int\ue9ressants en fonction des attentes de l'utilisateur. Dans le processus d'exploration, pour enregistrer les connaissances en \ue9volution de l'utilisateur, le syst\ue8me doit actualiser les attentes et propager les attentes nouvelles. Nous proposons une m\ue9thode lin\ue9aire pour propager syst\ue9matiquement et efficacement les attentes de l'utilisateur dans un graphe GenSpace. Afin d'obtenir un graphe GenSpace o\uf9 l'utilisateur peut marquer les noeuds inint\ue9ressants avant le processus d'exploration, nous proposons un algorithme glouton pour d\ue9terminer les chemins de propagation dans un sous-graphe GenSpace qui r\ue9duit le co\ufbt du temps en fonction d'une quantit\ue9 d'espace fixe.NRC publication: Ye

Adapting LDA Model to Discover Author-Topic Relations for Email Analysis

Analyzing the author and topic relations in email corpus is an important issue in both social network analysis and text mining. The Author-Topic model is a statistical method that identifies the author-topic relations. However, in its inference process, it ignores the information at the document level, i.e., the co-occurrence of words within documents are not taken into account in deriving topics. This may not be suitable for email analysis. We propose to adapt the Latent Dirichlet Allocation model for analyzing email corpus. This method takes into account both the author-document relations and the document-topic relations. We use the Author-Topic model as the baseline method and propose measures to compare our method against the Author-Topic model. We did empirical analysis based on experimental results on both simulated data sets and real Enron email data set to show that our method obtains better performance than the Author-Topic model.L'analyse des relations entre l'auteur et le sujet dans un corpus de courriels constitue un sujet important pour l'analyse sociale des r\ue9seaux et l'exploration de texte. Le mod\ue8le auteur-sujet est une m\ue9thode statistique qui identifie les relations auteur-sujet. Toutefois, son processus d'inf\ue9rence ne tient pas compte de l'information au niveau du document, c'est-\ue0-dire que les cooccurrences des mots au sein d'un document ne sont pas prises en compte pour la d\ue9rivation des sujets. Ceci peut ne pas \ueatre appropri\ue9 pour l'analyse des courriels. Nous nous proposons d'adapter le mod\ue8le d'allocation de Dirichlet latente (LDA) afin d'analyser un corpus de courriels. Cette m\ue9thode prend en compte les relations auteur-document et document-sujet. Nous utilisons la m\ue9thode auteur-sujet comme m\ue9thode de r\ue9f\ue9rence et nous proposons des mesures afin de comparer notre m\ue9thode avec cette m\ue9thode de r\ue9f\ue9rence. Nous avons effectu\ue9 une analyse empirique bas\ue9e sur les r\ue9sultats d'exp\ue9riences effectu\ue9es avec des jeux de donn\ue9es simul\ue9es et un jeu de donn\ue9es r\ue9elles de courriels d'Enron afin de d\ue9montrer que notre m\ue9thode offre un meilleur rendement que le mod\ue8le auteur-sujet.NRC publication: Ye