Dyslexia in the Workplace: A Guide for Unions

[Excerpt]We have written this guide principally for trade union members and their representatives. In it we aim to help foster a fuller understanding of dyslexia and its effects on employees[.

Measuring Catastrophic Forgetting in Neural Networks

Deep neural networks are used in many state-of-the-art systems for machine perception. Once a network is trained to do a specific task, e.g., bird classification, it cannot easily be trained to do new tasks, e.g., incrementally learning to recognize additional bird species or learning an entirely different task such as flower recognition. When new tasks are added, typical deep neural networks are prone to catastrophically forgetting previous tasks. Networks that are capable of assimilating new information incrementally, much like how humans form new memories over time, will be more efficient than re-training the model from scratch each time a new task needs to be learned. There have been multiple attempts to develop schemes that mitigate catastrophic forgetting, but these methods have not been directly compared, the tests used to evaluate them vary considerably, and these methods have only been evaluated on small-scale problems (e.g., MNIST). In this paper, we introduce new metrics and benchmarks for directly comparing five different mechanisms designed to mitigate catastrophic forgetting in neural networks: regularization, ensembling, rehearsal, dual-memory, and sparse-coding. Our experiments on real-world images and sounds show that the mechanism(s) that are critical for optimal performance vary based on the incremental training paradigm and type of data being used, but they all demonstrate that the catastrophic forgetting problem has yet to be solved.Comment: To appear in AAAI 201

Mechanisms of memory retrieval in slow-wave sleep : memory retrieval in slow-wave sleep

Study Objectives: Memories are strengthened during sleep. The benefits of sleep for memory can be enhanced by re-exposing the sleeping brain to auditory cues; a technique known as targeted memory reactivation (TMR). Prior studies have not assessed the nature of the retrieval mechanisms underpinning TMR: the matching process between auditory stimuli encountered during sleep and previously encoded memories. We carried out two experiments to address this issue. Methods: In Experiment 1, participants associated words with verbal and non-verbal auditory stimuli before an overnight interval in which subsets of these stimuli were replayed in slow-wave sleep. We repeated this paradigm in Experiment 2 with the single difference that the gender of the verbal auditory stimuli was switched between learning and sleep. Results: In Experiment 1, forgetting of cued (vs. non-cued) associations was reduced by TMR with verbal and non-verbal cues to similar extents. In Experiment 2, TMR with identical non-verbal cues reduced forgetting of cued (vs. non-cued) associations, replicating Experiment 1. However, TMR with non-identical verbal cues reduced forgetting of both cued and non-cued associations. Conclusions: These experiments suggest that the memory effects of TMR are influenced by the acoustic overlap between stimuli delivered at training and sleep. Our findings hint at the existence of two processing routes for memory retrieval during sleep. Whereas TMR with acoustically identical cues may reactivate individual associations via simple episodic matching, TMR with non-identical verbal cues may utilise linguistic decoding mechanisms, resulting in widespread reactivation across a broad category of memories

The construction and evaluation of an achievement test of musical knowledge for grades VII-XII.

Thesis (M.M.E.)--Boston Universit