GG-odometers and their almost 1-1 extensions

In this paper we recall the concepts of $G$-odometer and $G$-subodometer for $G$-actions, where $G$ is a discrete finitely generated group, which generalize the notion of odometer in the case G=\ZZ. We characterize the $G$-regularly recurrent systems as the minimal almost 1-1 extensions of subodometers, from which we deduce that the family of the $G$-Toeplitz subshifts coincides with the family of the minimal symbolic almost 1-1 extensions of subodometers.Comment: 18 page

Enhancing the movement of natural persons in the ASEAN region: Opportunities and constraints

The overall objective of the movement of natural persons (MNP) in the ASEAN region is to contribute to expanding trade in services and to deepening economic integration. However, the regional movement of human resources has proceeded beyond the expansion of trade and has persisted in response to labor market imbalances.Movement of Natural Persons (MNP),ASEAN Framework Agreements on Services (AFAS)

Stability and optimality in parametric convex programming models

Equivalent conditions for structural stability are given for convex programming models in terms of three point-to-set mappings. These mappings are then used to characterize locally optimal parameters. For Lagrange models and, in particular, LFS models the characterizations are given relative to general (possibly unstable) perturbations

Presidential Administration and FDA Guidance: A New Hope

Assessments of a President’s first 100 days in office typically focus on legislative priorities and executive orders. Less attention is paid to early victories achieved via guidance and other informal acts of “presidential administration.” The COVID-19 pandemic has opened a window for the Biden Administration to effectuate critical public health policies through guidance issued by the Food and Drug Administration. This brief essay highlights the power—and pitfalls—of effectuating public health policy this way, and discusses the lasting power of guidance for any new administration

A data-driven approach to improve online consumer subscriptions by combining data visualization and machine learning methods

Effective online consumer research helps companies on defining a successful strategy to increase user loyalty and shape brand engagement. Digital innovation introduced a dramatic change in businesses, particularly in the online news industry. Content consumers have a wide offer across different channels which increases the digital challenge for online news media companies to retain their readers and convert them into online subscribers. Furthermore, digital news publishers often strive to balance revenue sources in online business models. Thus, this study fills a gap in the literature on media consumer research by proposing a data-driven approach that combines two machine learning models to allow managers dynamically improve their marketing and editorial strategies. Firstly, the authors present an online user profiling to identify consumer segments based on the interplay between several engagement’ variables substantiated in the literature research. Second, as few studies have explored the factors influencing users’ intention to pay for such services, the XGBoost machine learning algorithm identifies the predictors of consumer's willingness to pay. Third, a dashboard presents the key performance indicators across the audience funnel. Thus, practical implications and business suggestions are presented in a two-fold strategy to maximize revenue from digital subscriptions and advertising. Findings provide new insights into an engagement approach and the relation to acquire a digital subscription in online content platforms. We believe that the provided recommendations are potentially useful to help marketing and editorial teams to manage their customer engagement process across the funnel in a more efficient way.info:eu-repo/semantics/publishedVersio

Using customer lifetime value and neural networks to improve the prediction of bank deposit subscription in telemarketing campaigns

Customer lifetime value (LTV) enables using client characteristics, such as recency, frequency and monetary value, to describe the value of a client through time in terms of profitability. We present the concept of LTV applied to telemarketing for improving the return-on-investment, using a recent (from 2008 to 2013) and real case study of bank campaigns to sell long-term deposits. The goal was to benefit from past contacts history to extract additional knowledge. A total of twelve LTV input variables were tested, under a forward selection method and using a realistic rolling windows scheme, highlighting the validity of five new LTV features. The results achieved by our LTV data-driven approach using neural networks allowed an improvement up to 4 pp in the Lift cumulative curve for targeting the deposit subscribers when compared with a baseline model (with no history data). Explanatory knowledge was also extracted from the proposed model, revealing two highly relevant LTV features, the last result of the previous campaign to sell the same product and the frequency of past client successes. The obtained results are particularly valuable for contact center companies, which can improve predictive performance without even having to ask for more information to the companies they serve.info:eu-repo/semantics/acceptedVersio

Incorporating Annotator Uncertainty into Representations of Discourse Relations

Annotation of discourse relations is a known difficult task, especially for non-expert annotators. In this paper, we investigate novice annotators' uncertainty on the annotation of discourse relations on spoken conversational data. We find that dialogue context (single turn, pair of turns within speaker, and pair of turns across speakers) is a significant predictor of confidence scores. We compute distributed representations of discourse relations from co-occurrence statistics that incorporate information about confidence scores and dialogue context. We perform a hierarchical clustering analysis using these representations and show that weighting discourse relation representations with information about confidence and dialogue context coherently models our annotators' uncertainty about discourse relation labels