Visitor pattern

A visitor pattern is a software design pattern and separates the algorithm from the object structure. Because of this separation new operations can be added to existing object structures without modifying the structures. It is one way to follow the open/closed principle in object-oriented programming and software engineering. In essence, the visitor allows adding new virtual functions to a family of classes, without modifying the classes. Instead, a visitor class is created that implements all of the appropriate specializations of the virtual function. The visitor takes the instance reference as input, and implements the goal through double dispatch. Programming languages with sum types and pattern matching obviate many of the benefits of the visitor pattern, as the visitor class is able to both easily branch on the type of the object and generate a compiler error if a new object type is defined which the visitor does not yet handle. Overview The Visitor design patte

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Acute stress alters individual risk taking in a time-dependent manner and leads to anti-social risk

Samuel Bendahan, Lorenz Goette, Fiona Hollis, Leyla Loued-Khenissi, Maria del Carmen Sandi Perez, John Christian Thoresen

Decision-making processes can be modulated by stress, and the time elapsed from stress induction seems to be a crucial factor in determining the direction of the effects. Although current approaches consider the ﬁrst post-stress hour a uniform period, the dynamic pattern of activation of the physiological stress systems (i.e., the sympathetic nervous system and hypothalamic-pitui- tary-adrenal axis) suggests that its neurobehavioural impact might be heterogeneous. Here, we evaluate economic risk prefer- ences on the gain domain (i.e., risk aversion) at three time points following exposure to psychosocial stress (immediately after, and 20 and 45 min from onset). Using lottery games, we examine decisions at both the individual and social levels. We ﬁnd that risk aversion shows a time-dependent change across the ﬁrst post-stress hour, evolving from less risk aversion shortly after stress to more risk averse behaviour at the last testing time. When risk implied an antisocial outcome to a third party, stressed individuals showed less regard for this person in their decisions. Participants’ cortisol levels explained their behaviour in the risk, but not the antisocial, game. Our ﬁndings reveal differential stress effects in self- and other-regarding decision-making and high- light the multidimensional nature of the immediate aftermath of stress for cognition.

2017

Colored Local Type Inference (colored version) (black and white version)

Martin Odersky, Matthias Zenger

We present a type system for a language based on F-sub, which allows certain type annotations to be elided in actual programs. Local type inference determines types by a combination of type propagation and local constraint solving, rather than by global constraint solving. We refine the previously existing local type inference system of Pierce and Turner [ Local Type Inference, POPL 98] by allowing partial type information to be propagated. This is expressed by coloring types to indicate propagation directions. Propagating partial type information allows us to omit type annotations for the visitor pattern, the analogue of pattern matching in languages without sum types.

2001

The Places of Our Lives: Visiting Patterns and Automatic Labeling from Longitudinal Smartphone Data

Daniel Gatica-Perez

The location tracking functionality of modern mobile devices provides unprecedented opportunity to the understanding of individual mobility in daily life. Instead of studying raw geographic coordinates, we are interested in understanding human mobility patterns based on sequences of place visits which encode, at a coarse resolution, most daily activities. This paper presents a study on place characterization in people's everyday life based on data recorded continuously by smartphones. First, we study human mobility from sequences of place visits, including visiting patterns on different place categories. Second, we address the problem of automatic place labeling from smartphone data without using any geo-location information. Our study on a large-scale data collected from 114 smartphone users over 18 months confirms many intuitions, and also reveals findings regarding both regularly and novelty trends in visiting patterns. Considering the problem of place labeling with 10 place categories, we show that frequently visited places can be recognized reliably (over 80%) while it is much more challenging to recognize infrequent places.

2014

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