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

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

The distribution of discourse relations within and across turns in spontaneous conversation

Time pressure and topic negotiation may impose constraints on how people leverage discourse relations (DRs) in spontaneous conversational contexts. In this work, we adapt a system of DRs for written language to spontaneous dialogue using crowdsourced annotations from novice annotators. We then test whether discourse relations are used differently across several types of multi-utterance contexts. We compare the patterns of DR annotation within and across speakers and within and across turns. Ultimately, we find that different discourse contexts produce distinct distributions of discourse relations, with single-turn annotations creating the most uncertainty for annotators. Additionally, we find that the discourse relation annotations are of sufficient quality to predict from embeddings of discourse units.Comment: Proceedings of Computational Approaches to Discourse 2023, collocated with the 2023 meeting of the Association for Computational Linguistics, Toronto, Canad