A life story in three parts: the use of triptychs to make sense of personal digital data

Many social media platforms support the curation of personal digital data, and, more recently, the use of that data for review and reflection. We explored the process of reflection by asking users to create a meaningful ‘triptych’ of photographs drawn from their Facebook accounts. In a first study, we asked participants to manually trawl their own accounts and select three relevant images, which we then framed and used as an interview probe. In a second study, we designed an automated triptych generation system and assessed participants’ experiences of using this system. We conducted qualitative analyses of participant interviews from both studies. Consistent with other ‘slow technology’ work, we found the act of creating a physical artefact from social media data gave that data new meaning, albeit with notable differences between manual versus automatically generated triptychs. We conclude by discussing possible improvements to the design of the automated triptych system.     

A classification approach to efficient global optimization in presence of non-computable domains

Gaussian-Process based optimization methods have become very popular in recent years for the global optimization of complex systems with high computational costs. These methods rely on the sequential construction of a statistical surrogate model, using a training set of computed objective function values, which is refined according to a prescribed infilling strategy. However, this sequential optimization procedure can stop prematurely if the objective function cannot be computed at a proposed point. Such a situation can occur when the search space encompasses design points corresponding to an unphysical configuration, an ill-posed problem, or a non-computable problem due to the limitation of numerical solvers. To avoid such a premature stop in the optimization procedure, we propose to use a classification model to learn non-computable areas and to adapt the infilling strategy accordingly. Specifically, the proposed method splits the training set into two subsets composed of computable and non-computable points. A surrogate model for the objective function is built using the training set of computable points, only, whereas a probabilistic classification model is built using the union of the computable and non-computable training sets. The classifier is then incorporated in the surrogate-based optimization procedure to avoid proposing new points in the non-computable domain while improving the classification uncertainty if needed. The method has the advantage to automatically adapt both the surrogate of the objective function and the classifier during the iterative optimization process. Therefore, non-computable areas do not need to be a priori known. The proposed method is applied to several analytical problems presenting different types of difficulty, and to the optimization of a fully nonlinear fluid-structure interaction system. The latter problem concerns the drag minimization of a flexible hydrofoil with cavitation constraints. The efficiency of the proposed method compared favorably to a reference evolutionary algorithm, except for situations where the feasible domain is a small portion of the design space.     

脑电的非线性检测与双谱分析

本文应用高阶谱技术构成以微机为核心的脑电非线性检测及双谱分析系统。采用非高斯参数模型对脑电信号进行建模,利用模型参数检测脑电信号的非线性特性,分析脑电的参数化双谱估计,分别讨论双谱技术的离散实现和系统硬件设置。     

An inductive database system based on virtual mining views

Inductive databases integrate database querying with database mining. In this article, we present an inductive database system that does not rely on a new data mining query language, but on plain SQL. We propose an intuitive and elegant framework based on virtual mining views, which are relational tables that virtually contain the complete output of data mining algorithms executed over a given data table. We show that several types of patterns and models that are implicitly present in the data, such as itemsets, association rules, and decision trees, can be represented and queried with SQL using a unifying framework. As a proof of concept, we illustrate a complete data mining scenario with SQL queries over the mining views, which is executed in our system.     

XEM: An explainable-by-design ensemble method for multivariate time series classification

Data Mining and Knowledge Discovery - We present XEM, an eXplainable-by-design Ensemble method for Multivariate time series classification. XEM relies on a new hybrid ensemble method that combines...     

Effect of hydrodynamics during sol–gel synthesis of TiO2 nanoparticles: From morphology to photocatalytic properties

In this study, the role of mixing hydrodynamics during the sol–gel synthesis of titania nanoparticles and the consequences on their photocatalytic properties were investigated. For the first time three different T-mixer geometries were tested. Alcoholic solutions of titanium tetra-isopropoxide and water were mixed in three different T-mixers with turbulence promoters and thus different mixing characteristics. The changes of nanoparticle sizes during the induction time of the sol–gel process were followed by dynamic light scattering and velocity and turbulence fields were simulated by Computational Fluid Dynamics (CFD) for the three T-mixer geometries. The results indicated that macro-mixing is crucial during the first step as it determines the nucleation rate and then the primary particle size. The micro-mixing has an influence on particle properties, especially on particle stability. Titanium dioxide nanoparticles synthesized by the sol–gel process were deposited on alumina supports. A homogeneous film of about 200 nm was deposited in all cases. Degradation of Acid Orange 7 (AO7) was used to evaluate the photocatalytic activity of TiO₂ coatings. No difference was observed between the photoactivity of synthesized TiO₂. Total mineralization of the dye occurred after 24 h irradiation.