Relational Deep Reinforcement Learning
Author(s): Zambaldi, ViniciusRaposo, DavidSantoro, AdamBapst, VictorLi, YujiaBabuschkin, IgorTuyls, KarlReichert, DavidLillicrap, TimothyLockhart, EdwardShanahan, MurrayLangston, VictoriaPascanu, RazvanBotvinick, MatthewVinyals, OriolBattaglia, Peter Abstract: We...
Experiences of Donating Personal Data to Mental Health Research: An Explorative Anthropological Study
Author: Joanna Sleigh Abstract: Technological developments, such as the advent of social networking sites, apps, and tracking 'cookies', enable the generation and collection of unprecedented quantities of rich personal and behavioural data, opening up a vast new...
Individuals on alert: digital epidemiology and the individualization of surveillance
Author(s): Samerski, Silja Abstract: This article examines how digital epidemiology and eHealth coalesce into a powerful health surveillance system that fundamentally changes present notions of body and health. In the age of Big Data and Quantified Self, the...
Neural Approaches to Conversational AI
Author Gao, Jianfeng Galley, Michel Li, Lihong Abstract The present paper surveys neural approaches to conversational AI that have been developed in the last few years. We group conversational systems into three categories: (1) question answering agents, (2)...
Stress experiences in neighborhood and social environments (SENSE): a pilot study to integrate the quantified self with citizen science to improve the built environment and health
Author(s): Chrisinger, Benjamin W.King, Abby C. Abstract: Background Identifying elements of one’s environment—observable and unobservable—that contribute to chronic stress including the perception of comfort and discomfort associated with different settings, presents...
Examining Self-Tracking by People with Migraine: Goals, Needs, and Opportunities in a Chronic Health Condition
Author(s): Jessica Schroeder Chia-Fang ChungDaniel A. Epstein Ravi Karkar Adele Parsons Natalia Murinova James Fogarty Sean A. Munson Abstract: Self-tracked health data can help people and their health providers understand and manage chronic conditions. This paper...
Backdrop: Stochastic Backpropagation
Author(s): Golkar, SiavashCranmer, Kyle Abstract: We introduce backdrop, a flexible and simple-to-implement method, intuitively described as dropout acting only along the backpropagation pipeline. Backdrop is implemented via one or more masking layers which are...
Tracked and Fit: FitBits, Brain Games, and the Quantified Aging Body
Author(s): Stephen KatzBarbara L.Marshall Abstract: This paper explores the technical turn to new ways of quantifying and standardizing measurements of age as these intersect with discourses of anti-aging and speculative futures of ‘smart’ quantified aging bodies....
The temporal flows of self-tracking: Checking in, moving on, staying hooked
Author(s): Lomborg, StineThylstrup, Nanna BondeSchwartz, Julie Abstract: This article conceptualizes the experience of self-tracking as flow, a central technique, utilized by digital media companies to hook their users. We argue the notion of flow is valuable for...
Seeking connectivity to everyday health and wellness experiences: Specificities and consequences of connective gaps in self-tracking data
Author(s): Yli-Kauhaluoma, Sari Pantzar, Mika Abstract: Objective Self-tracking technologies have created high hopes, even hype, for aiding people to govern their own health risks and promote optimal wellness. High expectations do not, however, necessarily materialize...