The construction of viewpoint aspect: the imperfective revisited

This paper argues for a constructionist approach to viewpoint Aspect by exploring the idea that it does not exert any altering force on the situation-aspect properties of predicates. The proposal is developed by analyzing the syntax and semantics of the imperfective, which has been attributed a coercer role in the literature as a de-telicizer and de-stativizer in the progressive, and as a de-eventivizer in the so-called ability (or attitudinal) and habitual readings. This paper proposes a unified semantics for the imperfective, preserving the properties of eventualities throughout the derivation. The paper argues that the semantics of viewpoint aspect is encoded in a series of functional heads containing interval-ordering predicates and quantifiers. This richer structure allows us to account for a greater amount of phenomena, such as the perfective nature of the individual instantiations of the event within a habitual construction or the nonculminating reading of perfective accomplishments in Spanish. This paper hypothesizes that nonculminating accomplishments have an underlying structure corresponding to the perfective progressive. As a consequence, the progressive becomes disentangled from imperfectivity and is given a novel analysis. The proposed syntax is argued to have a corresponding explicit morphology in languages such as Spanish and a nondifferentiating one in languages such as English; however, the syntax-semantics underlying both of these languages is argued to be the same

Current cannabis use and smoking cessation among treatment seeking combustible smokers

INTRODUCTION: Combustible tobacco smoking and cannabis use frequently occur together, and the use of both substances is associated with overall greater severity of tobacco and cannabis related problems. Observational work has found that cannabis use is associated with tobacco cessation failure, but research directly testing the longitudinal associations of cannabis use on tobacco cessation during smoking cessation treatment is lacking. The current study examined the impact of current cannabis use on combustible tobacco cessation outcomes. METHODS: 207 daily combustible tobacco smokers (M = 38.24 years, SD = 14.84, 48.1 % male) were enrolled in a randomized controlled smoking cessation trial. Survival analyses and multi-level modeling were used to assess lapse and relapse behavior through 12-week follow up. The current study is a secondary data analysis. RESULTS: Results of the current study suggest that cannabis use is associated with faster time to lapse (OR = 0.644, se = .188, p =  .019), but not relapse (OR = -0.218, se = .403, p =  .525), compared to combustible tobacco-only smokers. Additionally, cannabis use was associated with lower likelihood of achieving any 7-day point prevalence abstinence during the 12 week follow up (b = 0.93, se = 0 0.24, p =  0.0001). CONCLUSIONS: The current study provides novel evidence that cannabis use may be related to combustible tobacco use in terms of faster time to lapse and lower likelihood of any 7-day point prevalence abstinence following smoking cessation treatment. Developing integrated cannabis-tobacco cessation treatments is an important next step in research focused on tobacco-cannabis use

On role allocation in RoboCup

Abstract. A common problem in RoboCup is role allocation: given a team of players and a set of roles, how should be roles be allocated to players? Drawing on our previous work in multi-robot task allocation, we formalize the problem of role allocation as an iterated form of optimal assignment, which is a well-studied problem from operations research. From this perspective, we analyze the allocation mechanisms of a number of RoboCup teams, showing that most of them are greedy, and that many are in fact equivalent, as instances of the canonical Greedy algorithm. We explain how optimal, yet tractable, assignment algorithms could be used instead, but leave as an open question the actual benefit in terms of team performance of using such algorithms.