Online Updating of Statistical Inference in the Big Data Setting

We present statistical methods for big data arising from online analytical processing, where large amounts of data arrive in streams and require fast analysis without storage/access to the historical data. In particular, we develop iterative estimating algorithms and statistical inferences for linear models and estimating equations that update as new data arrive. These algorithms are computationally efficient, minimally storage-intensive, and allow for possible rank deficiencies in the subset design matrices due to rare-event covariates. Within the linear model setting, the proposed online-updating framework leads to predictive residual tests that can be used to assess the goodness-of-fit of the hypothesized model. We also propose a new online-updating estimator under the estimating equation setting. Theoretical properties of the goodness-of-fit tests and proposed estimators are examined in detail. In simulation studies and real data applications, our estimator compares favorably with competing approaches under the estimating equation setting.Comment: Submitted to Technometric

1,3-Bis{[5-(pyridin-2-yl)-1,3,4-oxadiazol-2-yl]sulfan­yl}propan-2-one

In the distorted W-shaped mol­ecule of the title compound, C17H12N6O3S2, a twofold axis passes through the carbonyl group. The mol­ecules stack in the crystal through π–π inter­actions [centroid—centroid distance = 3.883 Å] and weak C—H⋯N hydrogen-bonding inter­actions, forming a three-dimensional architecture

A Generalized Wine Quality Prediction Framework by Evolutionary Algorithms

Wine is an exciting and complex product with distinctive qualities that makes it different from other manufactured products. Therefore, the testing approach to determine the quality of wine is complex and diverse. Several elements influence wine quality, but the views of experts can cause the most considerable influence on how people view the quality of wine. The views of experts on quality is very subjective, and may not match the taste of consumer. In addition, the experts may not always be available for the wine testing. To overcome this issue, many approaches based on machine learning techniques that get the attention of the wine industry have been proposed to solve it. However, they focused only on using a particular classifier with a specific set of wine dataset. In this paper, we thus firstly propose the generalized wine quality prediction framework to provide a mechanism for finding a useful hybrid model for wine quality prediction. Secondly, based on the framework, the generalized wine quality prediction algorithm using the genetic algorithms is proposed. It first encodes the classifiers as well as their hyperparameters into a chromosome. The fitness of a chromosome is then evaluated by the average accuracy of the employed classifiers. The genetic operations are performed to generate new offspring. The evolution process is continuing until reaching the stop criteria. As a result, the proposed approach can automatically find an appropriate hybrid set of classifiers and their hyperparameters for optimizing the prediction result and independent on the dataset. At last, experiments on the wine datasets were made to show the merits and effectiveness of the proposed approach

Neutron Reflectometry for Studying Proteins/Peptides in Biomimetic Membranes

The development of biomimetic surfaces for protein and peptide adsorptions is continuously expanding. Their biological functions can be influenced by the properties of the underlying artificial environment but the detailed mechanism is still not clear. In the past 30 years, neutron reflectometry has been widely applied to characterise the molecular structure of proteins or multi-protein complexes and their interactions with fluid artificial membrane that mimics the cellular environment. The specific interactions, bindings or structural changes between proteins and membranes play a crucial role in cellular responses and have promising potential in diagnostics and other biosensor applications. This chapter presents the progression of surface design for protein adsorption/interactions on membranes in detail, ranging from a simple phospholipid monolayer setup to more complicated artificial lipid bilayer systems. Furthermore, a new development of designed surfaces for studying the integral membrane protein system is also discussed in this chapter. A brief overview of various membrane mimetic surfaces is first outlined, followed by presenting specific examples of protein-membrane interactions studied by neutron reflectometry. The author demonstrates how to use neutron reflectometry as an advanced technique to provide step-by-step structural details for biomolecular applications in a well-controlled manner

Noise suppression of on-chip mechanical resonators by chaotic coherent feedback

We propose a method to decouple the nanomechanical resonator in optomechanical systems from the environmental noise by introducing a chaotic coherent feedback loop. We find that the chaotic controller in the feedback loop can modulate the dynamics of the controlled optomechanical system and induce a broadband response of the mechanical mode. This broadband response of the mechanical mode will cut off the coupling between the mechanical mode and the environment and thus suppress the environmental noise of the mechanical modes. As an application, we use the protected optomechanical system to act as a quantum memory. It's shown that the noise-decoupled optomechanical quantum memory is efficient for storing information transferred from coherent or squeezed light

Social housing residents\u27 community participation under the impact of lease period restrictions

Community participation is the foundation of a community’s healthy environment and sustainable development. Social housing can provide people without their own homes and underprivileged groups with more secure conditions to live and work and thereby realize housing justice and reduce social vulnerability. In terms of community management, residents’ engagement in community affairs can dramatically reduce the subsequent burden of environmental maintenance and community management, which encourage residents in the community to actively pass on the habit of maintenance and to collectively create resilient and sustainable communities. However, lease term restrictions in Taiwan’s social housing policy stipulates that ordinary tenants can only rent the house for 6 years at a maximum and tenants with special conditions for 12. This study attempts to understand whether lease term restrictions affect residents’ willingness to participate in community affairs. In addition, we also try to find out how to motivate residents to participate in community construction under the existence of lease term restrictions. The scope of this study focuses on citizens who qualified to rent social housing in the Greater Taipei area (including Taipei City and New Taipei). We designed a questionnaire for our target audience, tested its reliability and validity and picked random-selected samples to finish the questionnaire. Analyzing from the perspective of Egoism, we find out that the result of this research shows that residents do not commonly avoid participation in community affairs. Although lease term restrictions do have some effects on residents\u27 willingness to participate, they are still willing to participate since issues of safety and environmental quality have a direct impact on their lives. However, the residents’ chief consideration is how time spent in participation affects one’s time. Also, though substantial returning benefit is not the main consideration when deciding whether to participate, it does effectively boost residents’ willingness. Furthermore, community member relations is found to have a positive correlation with their willingness to participate