Evidence Based Complementary Intervention for Insomnia

Increasing scientific evidence point to a non-pharmacological complementary treatment for insomnia: white noise. Its presentation has been shown to induce sleep in human neonates and adults, probably by reducing the signal-to-noise ratio of ambient sound. White noise may be a simple, safe, cost-effective alternative to hypnotic medication in many psychiatric disorders, especially acute stress disorder and PTSD

Trust Strategies in Voiced-Agent Multiple-Component Home Automation

Trust is a critical factor in successful and productive human-automation interactions. When automation malfunctions, trust is negatively affected. The development of increasingly complex multiple-component systems, or those with a several autonomous elements, introduces even more ways for a system to err. One example is in smart home control systems where different subsystems may be controlled by different autonomous routines or rules. Multiple studies suggest that one error-prone component can lower user trust in the remaining components (the “pull down” effect). Other research suggests that certain types of information, when presented to the user, can reduce the strength of the pull-down effect by promoting heterogeneity of agents. The current study investigated the effectiveness of increasing the number of voiced agents within a system as a strategy for decreasing the strength of the pull down effect. Participants interacted with either a single- or four-agent system. A simulated smart home task required participants to adjust the lighting for several rooms of a house. Participants first completed a block with all reliable room lightings, and then a block with all but one reliable room lighting. Inconsistent with the current literature, the results did not reveal any pull down effect. In both agent conditions the presence of the unreliable room lighting did not decrease trust in the reliable room lightings. In the single-agent condition trust in the reliable room lightings increased between both reliability blocks. However, this trend was not seen with the four-agent condition. Future studies should investigate the effects of anthropomorphism, automation domain, and task characteristics on trust

Comparative study of analog and digital hearing aids

The purpose of the present study was to determine if objective and/or subjective differences between analog and digital hearing aids exist when blinding is utilized in the protocol and circuitry is controlled. Ten normal hearing and seven hearing impaired subjects were monaurally fitted with analog and digital hearing aids. Probe microphone measures were obtained at the plane of the tympanic membrane at two output levels (40 dB SPL and 70 dB SPL). Listener performance in quiet was evaluated via word recognition testing, listener performance in noise was evaluated via the Hearing in Noise Test, and listener preference was evaluated via a questionnaire. Results indicated similar performance for all objective and subjective tasks for both hearing aids with the exception of better performance in quiet at the 40 dB SPL presentation level with the analog hearing aid for the hearing impaired group. These results indicate that listeners performed as well or significantly better with the analog hearing aid than with the digital hearing aid. Furthermore, future investigation is recommended to evaluate the effectiveness of some features available on digital hearing aids that are not available on analog hearing aids, such as expansion and noise reduction

Adaptor Grammars for Unsupervised Paradigm Clustering

This work describes the Edinburgh submission to the SIGMORPHON 2021 Shared Task 2 on unsupervised morphological paradigm clustering. Given raw text input, the task was to assign each token to a cluster with other tokens from the same paradigm. We use Adaptor Grammar segmentations combined with frequency-based heuristics to predict paradigm clusters. Our system achieved the highest average F1 score across 9 test languages, placing first out of 15 submissions

Taming the Sigmoid Bottleneck: Provably Argmaxable Sparse Multi-Label Classification

Sigmoid output layers are widely used in multi-label classification (MLC) tasks, in which multiple labels can be assigned to any input. In many practical MLC tasks, the number of possible labels is in the thousands, often exceeding the number of input features and resulting in a low-rank output layer. In multi-class classification, it is known that such a lowrank output layer is a bottleneck that can result in unargmaxable classes: classes which cannot be predicted for any input. In this paper, we show that for MLC tasks, the analogous sigmoid bottleneck results in exponentially many unargmaxable label combinations. We explain how to detect these unargmaxable outputs and demonstrate their presence in three widely used MLC datasets. We then show that they can be prevented in practice by introducing a Discrete Fourier Transform (DFT) output layer, which guarantees that all sparse label combinations with up to k active labels are argmaxable. Our DFT layer trains faster and is more parameter efficient, matching the F1@k score of a sigmoid layer while using up to 50% fewer trainable parameters. Our code is publicly available at https://github.com/andreasgrv/sigmoid-bottleneck

Inflecting when there’s no majority: Limitations of encoder-decoder neural networks as cognitive models for German plurals

Can artificial neural networks learn to represent inflectional morphology and generalize to new words as human speakers do? Kirov and Cotterell (2018) argue that the answer is yes: modern Encoder-Decoder (ED) architectures learn human-like behavior when inflecting English verbs, such as extending the regular past tense form -(e)d to novel words. However, their work does not address the criticism raised by Marcus et al. (1995): that neural models may learn to extend not the regular, but the most frequent class -- and thus fail on tasks like German number inflection, where infrequent suffixes like -s can still be productively generalized. To investigate this question, we first collect a new dataset from German speakers (production and ratings of plural forms for novel nouns) that is designed to avoid sources of information unavailable to the ED model. The speaker data show high variability, and two suffixes evince 'regular' behavior, appearing more often with phonologically atypical inputs. Encoder-decoder models do generalize the most frequently produced plural class, but do not show human-like variability or 'regular' extension of these other plural markers. We conclude that modern neural models may still struggle with minority-class generalization.Comment: To appear at ACL 202