L2 LEARNERS’ ADAPTATION TO AN L2 STRUCTURE THAT IS DIFFERENT FROM L1

The present study investigates how L2 learners adapt their production preferences following immediate and cumulative experience with a syntactic structure when an L2 structure differs from an L1 structure in terms of verb subcategorization frame and argument structure. Korean learners of English described causative events in English in a picture-matching game. The meaning of a causative sentence in English (e.g., Jen had her computer fixed) is expressed with an active transitive sentence in Korean (e.g., Jen-NOM computer-ACC fixed). The results demonstrated that both immediate and cumulative experience with a causative structure increased the likelihood of producing grammatical causative descriptions (e.g., Jen had her computer fixed), while decreasing the production of ungrammatical active transitive descriptions (e.g., Jen fixed her computer). The findings provide novel evidence that an implicit learning mechanism is involved in L2 learners’ processing and learning of an L2 structure that is different from L1.

Elastic O-RAN Slicing for Industrial Monitoring and Control: A Distributed Matching Game and Deep Reinforcement Learning Approach

In this work, we design an elastic open radio access network (O-RAN) slicing for the industrial Internet of things (IIoT). Unlike IoT, IIoT poses additional challenges such as severe communication environment, network-slice resource demand variations, and on-time information update from the IIoT devices during industrial production. First, we formulate the O-RAN slicing problem for on-time industrial monitoring and control where the objective is to minimize the cost of fresh information updates (i.e., age of information (AoI)) from the IIoT devices (i.e., sensors) while maintaining the energy consumption of those devices with the energy constraint as well as O-RAN slice isolation constraints. Second, we propose the intelligent ORAN framework based on game theory and machine learning to mitigate the problem’s complexity. We propose a two-sided distributed matching game in the O-RAN control layer that captures the IIoT channel characteristics and the IIoT service priorities to create IIoT device and small cell base station (SBS) preference lists. We then employ an actor-critic model with a deep deterministic policy gradient (DDPG) in the O-RAN service management layer to solve the resource allocation problem for optimizing the network slice configuration policy under time varying slicing demand. While the matching game helps the actor-critic model, the DDPG enforces the long-term policy-based guidance for resource allocation that reflects the trends of all IIoT devices and SBSs satisfactions with the assignment. Finally, the simulation results show that the proposed solution enhances the performance gain for the IIoT services by serving an average of 50% and 43.64% more IIoT devices than the baseline approaches. <br>

Elastic O-RAN Slicing for Industrial Monitoring and Control: A Distributed Matching Game and Deep Reinforcement Learning Approach

In this work, we design an elastic open radio access network (O-RAN) slicing for the industrial Internet of things (IIoT). Unlike IoT, IIoT poses additional challenges such as severe communication environment, network-slice resource demand variations, and on-time information update from the IIoT devices during industrial production. First, we formulate the O-RAN slicing problem for on-time industrial monitoring and control where the objective is to minimize the cost of fresh information updates (i.e., age of information (AoI)) from the IIoT devices (i.e., sensors) while maintaining the energy consumption of those devices with the energy constraint as well as O-RAN slice isolation constraints. Second, we propose the intelligent ORAN framework based on game theory and machine learning to mitigate the problem’s complexity. We propose a two-sided distributed matching game in the O-RAN control layer that captures the IIoT channel characteristics and the IIoT service priorities to create IIoT device and small cell base station (SBS) preference lists. We then employ an actor-critic model with a deep deterministic policy gradient (DDPG) in the O-RAN service management layer to solve the resource allocation problem for optimizing the network slice configuration policy under time varying slicing demand. While the matching game helps the actor-critic model, the DDPG enforces the long-term policy-based guidance for resource allocation that reflects the trends of all IIoT devices and SBSs satisfactions with the assignment. Finally, the simulation results show that the proposed solution enhances the performance gain for the IIoT services by serving an average of 50% and 43.64% more IIoT devices than the baseline approaches. <br>

Do You Know What I Know? Children Use Informants’ Beliefs About Their Abilities to Calibrate Choices During Pedagogy

Models of pedagogy highlight the reciprocal reasoning underlying learner-teacher interactions, including that learners’ inferences should be shaped by what they believe a teacher knows about them. Yet, little is known about how this influences learning, despite the fact that even young children make rapid inferences about teaching from sparse data. In the current work, six- to eight-year-olds’ performance on a picture-matching game was either overestimated, underestimated, or accurately represented by a confederate (the “Teacher”), who then presented three new matching games of varying assessed difficulty (too easy, too hard, just right). A simple model of this problem predicts that while children should follow the recommendation of an accurate Teacher, learners should choose easier games when the Teacher overestimated their abilities, and harder games when she underestimated them. Results from our experiment support these predictions, providing insight into children’s ability to consider teachers’ knowledge when learning from pedagogy.

The Matching Game: Experimenting with Librarian Mediated Patron-Driven Acquisition

pAre you thinking of piloting Patron-Driven Acquisition in your library? In this session, we share our experiences with PDA. We will lead a discussion on the implementation decisions for selection and workflow and the differences and similarities between patron-selected vs. librarian-selected titles; and usage of electronic vs. print titles./p