Mining range associations for classification and characterization

In this paper, we propose a method that is able to derive rules involving range associations from numerical attributes, and to use such rules to build comprehensible classification and characterization (data summary) models. Our approach follows the classification association rule mining paradigm, where rules are generated in a way similar to association rule mining, but search is guided by rule consequents. This allows many credible rules, not just some dominant rules, to be mined from the data to build models. In so doing, we propose several sub-range analysis and rule formation heuristics to deal with numerical attributes. Our experiments show that our method is able to derive range-based rules that offer both accurate classification and comprehensible characterization for numerical data

Exploiting contextual information in attacking set-generalized transactions

Transactions are records that contain a set of items about individuals. For example, items browsed by a customer when shopping online form a transaction. Today, many activities are carried out on the Internet, resulting in a large amount of transaction data being collected. Such data are often shared and analyzed to improve business and services, but they also contain private information about individuals that must be protected. Techniques have been proposed to sanitize transaction data before their release, and set-based generalization is one such method. In this article, we study how well set-based generalization can protect transactions. We propose methods to attack set-generalized transactions by exploiting contextual information that is available within the released data. Our results show that set-based generalization may not provide adequate protection for transactions, and up to 70% of the items added into the transactions during generalization to obfuscate original data can be detected by our methods with a precision over 80%

Macrophage depletion disrupts immune balance and energy homeostasis.

Increased macrophage infiltration in tissues including white adipose tissue and skeletal muscle has been recognized as a pro-inflammatory factor that impairs insulin sensitivity in obesity. However, the relationship between tissue macrophages and energy metabolism under non-obese physiological conditions is not clear. To study a homeostatic role of macrophages in energy homeostasis, we depleted tissue macrophages in adult mice through conditional expression of diphtheria toxin (DT) receptor and DT-induced apoptosis. Macrophage depletion robustly reduced body fat mass due to reduced energy intake. These phenotypes were reversed after macrophage recovery. As a potential mechanism, severe hypothalamic and systemic inflammation was induced by neutrophil (NE) infiltration in the absence of macrophages. In addition, macrophage depletion dramatically increased circulating granulocyte colony-stimulating factor (G-CSF) which is indispensable for NE production and tissue infiltration. Our in vitro study further revealed that macrophages directly suppress G-CSF gene expression. Therefore, our study indicates that macrophages may play a critical role in integrating immune balance and energy homeostasis under physiological conditions

Modelling Confidence for Quality of Service Assessment in Cloud Computing

The ability to assess the quality of a service (QoS) is important to the emerging cloud computing paradigm. When many cloud service providers exist offering many functionally identical services, the prospective users of these services will wish to use one that offers the best quality. Many techniques and tools have been proposed to assess QoS, and the ability to deal with uncertainty surrounding the QoS verdicts given by any such techniques or tools is essential. In this paper, we present a probabilistic model to quantify confidence in QoS assessment. More specifically, we take the number of QoS data items used in assessment and the variation of data in the dataset into account in our measure of assessment reliability. Our experiments show that our confidence model can help consumers to select services based on their requirements effectively

The platelet-derived growth factor receptor alpha promoter-directed expression of cre recombinase in mouse placenta.

BackgroundNumerous pathologies of pregnancy originate from placental dysfunction. It is essential to understand the functions of key genes in the placenta in order to discern the etiology of placental pathologies. A paucity of animal models that allow conditional and inducible expression of a target gene in the placenta is a major limitation for studying placental development and function.ResultsTo study the platelet-derived growth factor receptor alpha (PDGFRα)-directed and tamoxifen-induced Cre recombinase expression in the placenta, PDGFRα-CreER mice were crossed with mT/mG dual-fluorescent reporter mice. The expression of endogenous membrane-localized enhanced green fluorescent protein (mEGFP) and/or dTomato in the placenta was examined to identify PDGFRα promoter-directed Cre expression. Pregnant PDGFRα-CreER;mT/mG mice were treated with tamoxifen at various gestational ages. Upon tamoxifen treatment, reporter protein mEGFP was observed in the junctional zone (JZ) and chorionic plate (CP). Furthermore, a single dose of tamoxifen was sufficient to induce the recombination.ConclusionsPDGFRα-CreER expression is restricted to the JZ and CP of mouse placentas. PDGFRα-CreER mice provide a useful tool to conditionally knock out or overexpress a target gene in these regions of the mouse placenta

Will cultural diversity block the process of urbanization? — Empirical Study from the perspective of dialect

Based on the data samples of 276 cities at prefecture level and above in China from 2000 to 2012, using dialect diversity as a proxy to measure cultural diversity, using random effect model, system generalized moment estimation, two-stage least square method and other methods, this paper conducted an empirical investigation on the impact of cultural diversity on China's urbanization for the first time. It is found that dialect diversity has a significant negative impact on urbanization rate; considering the possibility of missing variables, the influence of dialect diversity on urbanization rate is still significantly negative; after using the historical immigration as the instrumental variable of dialect diversity, this negative influence still exists, but the degree of influence has decreased. Therefore, the cultural variables represented by dialects are an important factor affecting the process of urbanization

Anchoring the value of cryptocurrency

A decade long thrive of cryptocurrency has shown its potential as a source of alternative-finance and the security and the robustness of the underpinning blockchain technology. However, most cryptocurrencies fail to show inimitability and their meanings in the real world. As a result, they usually start off as favourites but quickly become the outcasts of the digital asset market. The blockchain society attempts to anchor the value of cryptocurrency with real values by employing smart contracts and link it with computation resources and the digital-productivity that have value and demands in the real world. But their attempts have some undesirable effects due to a limited number of practical applications. This limitation is caused by the dilemma between high performance and decentralisation (universal joinability). The emerging of blockchain sharding models, however, has offered a possible solution to address this dilemma. In this paper, we explore a financial model for blockchain sharding that will build an active link between the value of cryptocurrency and computation resources as well as the market and labour behaviours. Our model can adjust the price of resources and the compensation for maintaining a system based on those behaviours. We anchor the value of cryptocurrency by the amount of computation resources participated in and give the cryptocurrency a meaning as the exchange between computation resources globally. Finally, we present a working example which, through financial regularities, regulates the behaviour of anonymous participants, also incents/discourages participation dynamically

Adiponectin Deficiency Impairs Maternal Metabolic Adaptation to Pregnancy in Mice.

Hypoadiponectinemia has been widely observed in patients with gestational diabetes mellitus (GDM). To investigate the causal role of hypoadiponectinemia in GDM, adiponectin gene knockout (Adipoq-/- ) and wild-type (WT) mice were crossed to produce pregnant mouse models with or without adiponectin deficiency. Adenoviral vector-mediated in vivo transduction was used to reconstitute adiponectin during late pregnancy. Results showed that Adipoq-/- dams developed glucose intolerance and hyperlipidemia in late pregnancy. Increased fetal body weight was detected in Adipoq-/- dams. Adiponectin reconstitution abolished these metabolic defects in Adipoq-/- dams. Hepatic glucose and triglyceride production rates of Adipoq-/- dams were significantly higher than those of WT dams. Robustly enhanced lipolysis was found in gonadal fat of Adipoq-/- dams. Interestingly, similar levels of insulin-induced glucose disposal and insulin signaling in metabolically active tissues in Adipoq-/- and WT dams indicated that maternal adiponectin deficiency does not reduce insulin sensitivity. However, remarkably decreased serum insulin concentrations were observed in Adipoq-/- dams. Furthermore, β-cell mass, but not glucose-stimulated insulin release, in Adipoq-/- dams was significantly reduced compared with WT dams. Together, these results demonstrate that adiponectin plays an important role in controlling maternal metabolic adaptation to pregnancy

Strategic signals in the app economy: an empirical study of Google Play Store

The dominance of Android and iOS have created a duopoly in the smartphone market. In this context, both Android and iOS need to select high quality apps from its massive number of app developers to recommend to app consumers. Generally speaking, when the market is small, the platform like Android and iOS can allocate resources to manual test the app quality for selection; but as the market size increased, the required resource for selection is also increased and would challenge the platform’s capability, thus the platform faces the selection difficulty. Unlike iOS, Android adopts an open strategy to compete against iOS for innovative developers. This strategy has led to inadvertent consequences including: "fragmentation" issues, weakened governance power, entry of non-competent developers and, notably, vetting of the quality of apps and developers. An app market is a two-sided platform based market. Research in platforms (Gawer and Cusumano, 2013) have primarily focused on what the technology platform owners should do. They assume that developers will equally respond to their platform strategy, for example, leaving the market when the platform owner sets higher barriers or enters when the platforms are more friendly. A few recent platform literatures (Boudreau, 2012; Eisenmann et al., 2011) start to look at developers’ unequal behaviour, such as developers with different competence would have different switching behaviours between different markets. However, in a duopoly setting, developers have limited alternatives but continue to work with both markets. The duopoly setting is interesting as the communication of app quality is critical for competition, and this thesis aims to examine the competent developers’ strategic behaviours from the lens of signal theory. I argue that competent developers will engage in costly actions to compete against non- competent counterparts in such a duopoly context. For example, competent developers will spend time to make their apps to be unique and innovative in the Android app market, or enhance the technology used in their apps to protect app consumers’ privacy, and other more behaviours discussed in the thesis. It is inspired to understand these unique behaviour by competent developers as strategic signals to communicate their innovative behaviour in the app economy. Information asymmetry (Stiglitz, 2002) is believed to cause the difficulty for platform selection based on the developer’s innovativeness in such a crowded space. Based on the signalling theory (Spence, 1973), the thesis develops a signalling selection model to understand the motivation and implication of these strategic signals. iOS is not open and is, therefore, difficult to collect research data. To study strategic signals, I collected a panel data set composed of 93% of all apps and their developers in the Google Play Store which is the official Android app market. The thesis firstly filtered out all developers who are featured as "Top Developer" by Google in the Google Play Store. They are treated as innovative developers. The thesis then filtered out all non-featured developers who are relevant to these innovative developers and treated them as non-innovative developers. The thesis qualitatively and quantitatively analyses innovative developers’ strategic behaviour in the Google Play Store. It is found that these strategic signals are unlikely to be generated by non-innovative developers because they have a higher cost. The cost could be due to, for example, the R&D on technology advancement, the challenge of creative business model design, etc. It provides the platform owner with an opportunity to observe these signals only uniquely by innovative developers, and feedback with featuring awards. These awards would bring huge customer access and generate large long-term benefits on business performance. The benefits motivate the innovative developers to continue on these strategic signals generation, which increases the availability of innovative apps. The signals can also be observed by other developers and motivate non-featured developers to learn from featured innovative developers to increase their featuring opportunity, which is studied as the peer effect in the thesis. This peer effect in learning enables the opportunity for the platform owner to develop and maintain the app market into an ecosystem. Rather than managing all developers, as emphasised by existing platform strategies, this thesis argues the platform owner can focus on selecting a small number of innovative developers to influence the large developers community with the signalling selection model. The contribution of this thesis is to shift the focus of platform strategy from platform centric to developer centric. I argue that innovative developers would behave differently from others. Their strategic behaviour serves as a signal for the platform owner to vet the quality of innovativeness. The thesis studied a duopoly context of app economy where switching is limited to two dominant platforms. It develops a signalling selection model to solve the selection difficulty of innovativeness in such a unique context. According to my knowledge, it is the first time the signalling theory has been applied to the app economy. The selection of innovativeness is friendly to newly entranced developers which is less focused by existing platform centric strategies. The research on app economy is still rare but the importance of the app economy is significantly increased in daily life. The large-scale data set, methodology and results from this research should be valuable for future research in the app economy