Feasibility of an Online Acute Stressor in Preschool Children of Mothers with Depression

Maternal depression is a risk factor for future mental health problems in offspring, with stress-system function as a candidate vulnerability factor. Here we present initial validation of an online matching-task paradigm in young children exposed to maternal depression (N=40), a first in stressor-paradigm research for this age group. Investigations of stress-system reactivity that can be conducted online are an innovative assessment approach, accelerated by the COVID-19 pandemic. Results indicate high feasibility, with ~80% success across measures, similar-to or better-than in-person success rates in young children. Overall, the online matching task elicited significant HR but not cortisol reactivity. Individual differences in child mental health symptoms were a moderator of reactivity to the stressor such that children with lower, but not higher, behavioural problems exhibited the expected pattern of cortisol reactivity to the online matching task. Results are aligned with allostatic load models, which suggest down-regulation of stress-system reactivity as a result of experiencing adversity and mental health vulnerability. Consistent with in-person research, this suggests an early phenotype for the emergence of behaviour problems may be linked to altered stress-system reactivity. Results hold potential clinical implications for intervention development and the future of online stress-system research.

The Secret to Finding a Match: A Field Experiment on Choice Capacity Design in an Online Dating Platform

Online matching platforms require new approaches to market design because firms can now control many aspects of the search and interaction process through various IT-enabled features. Although choice capacity—the number of candidates a user can view and select—is a key design feature of online matching platforms, its effect on engagement and matching outcomes remains unclear. We examine the effect of different choice capacities on market performance by conducting a randomized field experiment in collaboration with an online dating platform. Specifically, we design four treatment groups with different choice capacities in which users can only interact with other users in the same group and randomly assign the users to the treatment groups. We find that providing more choice capacity to male and female users has different effects on choice behaviors and matching outcomes. Although increasing the choice capacity of male users yields the highest engagement, increasing the choice capacity of female users is the most effective method to increase matching outcomes. We empirically demonstrate four mechanisms underlying the effectiveness of different choice capacity designs and generalize our findings by discussing how choice capacity can be designed to increase engagement and matching outcomes.

Allocation Problems in Ride-sharing Platforms

Bipartite-matching markets pair agents on one side of a market with agents, items, or contracts on the opposing side. Prior work addresses online bipartite-matching markets, where agents arrive over time and are dynamically matched to a known set of disposable resources. In this article, we propose a new model, Online Matching with (offline) Reusable Resources under Known Adversarial Distributions ( OM-RR-KAD ) , in which resources on the offline side are reusable instead of disposable; that is, once matched, resources become available again at some point in the future. We show that our model is tractable by presenting an LP-based non-adaptive algorithm that achieves an online competitive ratio of ½-ϵ for any given constant ϵ > 0. We also show that no adaptive algorithm can achieve a ratio of ½ + o (1) based on the same benchmark LP. Through a data-driven analysis on a massive openly available dataset, we show our model is robust enough to capture the application of taxi dispatching services and ride-sharing systems. We also present heuristics that perform well in practice.

Indoor Localization Based on Optimized KNN

In recent years, with the continuous development of the economic situation, the price of low-end smart phones continues to reduce, the coverage of wireless local area network (WLAN) continues to improve, and individual users pay more and more attention to the real-time information around them, so indoor positioning technology has become a research hotspot. Among them, the indoor positioning based on the location fingerprint method quickly becomes the “Navigator” of indoor positioning direction by virtue of the simplicity of layout, the cost reduction of hardware facilities and the accuracy of positioning effect. However, the traditional indoor positioning methods usually rely on WiFi signal and KNN algorithm. When the KNN algorithm is implemented, there will be a lot of calculation and heavy workload to establish the location fingerprint database offline, and the efficiency and accuracy of online matching positioning points are low. This paper proposes an OKNN algorithm based on the improved KNN algorithm. By improving the efficiency of matching algorithm, the algorithm indirectly improves the positioning accuracy and optimizes the indoor positioning effect.