Reflections of a lifetime reader : an address delivered by Carl R. Augusto to the Conference of Librarians Serving Blind and Physically Handicapped Individuals, Baltimore, Maryland, May 4, 1992.

Shipping list no.: 93-0073-P.

St. John’s wort and Kava in treating major depressive disorder with comorbid anxiety : a randomised double-blind placebo-controlled pilot trial

Objective - We report the first randomised controlled trial (RCT) using a combination of St. John’s wort (SJW) and Kava for the treatment of major depressive disorder (MDD) with comorbid anxiety. Methods - Twenty-eight adults with MDD and co-occurring anxiety were recruited for a double-blind RCT. After a placebo run-in of 2 weeks, the trial had a crossover design testing SJW and Kava against placebo over two controlled phases, each of 4 weeks. The primary analyses used intention-to-treat and completer analyses. Results - On both intention-to-treat ( p¼0.047) and completer analyses ( p¼0.003), SJW and Kava gave a significantly greater reduction in self-reported depression on the Beck Depression Inventory (BDI-II) over placebo in the first controlled phase. However, in the crossover phase, a replication of those effects in the delayed medication group did not occur. Nor were there significant effects on anxiety or quality of life. Conclusion - There was some evidence of antidepressant effects using SJW and Kava in a small sample with comorbid anxiety. Possible explanations for the absence of anxiolysis may include a potential interaction with SJW, the presence of depression, or an inadequate dose of Kava.

Clustering of ad-hoc microphone arrays for robust blind beamforming

This paper proposes a clustered approach for blind beamfoming from ad-hoc microphone arrays. In such arrangements, microphone placement is arbitrary and the speaker may be close to one, all or a subset of microphones at a given time. Practical issues with such a configuration mean that some microphones might be better discarded due to poor input signal to noise ratio (SNR) or undesirable spatial aliasing effects from large inter-element spacings when beamforming. Large inter-microphone spacings may also lead to inaccuracies in delay estimation during blind beamforming. In such situations, using a cluster of microphones (ie, a sub-array), closely located both to each other and to the desired speech source, may provide more robust enhancement than the full array. This paper proposes a method for blind clustering of microphones based on the magnitude square coherence function, and evaluates the method on a database recorded using various ad-hoc microphone arrangements.

Ties that blind? How strong ties affect small business owner-managers’ perceived trustworthiness of their advisors

This research investigates how a strong personal relationship (strong tie) between a small business owner-manager and his professional or informal advisor affects the relationship between the advisor's recent performance and the owner-manager's perceptions of the advisor's trustworthiness in terms of ability, benevolence and integrity. A negative moderating effect could point to a 'tie that blinds': the owner-manager may be less critical in evaluating the advisor's perceived trustworthiness in light of their recent performance, because of the existing personal relationship. A conceptual model is constructed and examined with survey data comprising 153 young Finnish businesses. The results show that strong ties increase the owner-manager's perception of the advisor's integrity, disregarding their recent performance. For professional advisors, strong ties reduce the impact of recent performance in the owner-manager's evaluation of their ability. For informal advisors, a strong tie makes it more likely that their benevolence will be evaluated highly in light of their recent performance. While the results show that 'ties can blind' under certain circumstances, the limitations of the study raise the need for further research to specify these contextual factors and examine the causal link between the choice of advisor and business performance.

Clustered blind beamforming from ad-hoc microphone arrays

Microphone arrays have been used in various applications to capture conversations, such as in meetings and teleconferences. In many cases, the microphone and likely source locations are known \emph{a priori}, and calculating beamforming filters is therefore straightforward. In ad-hoc situations, however, when the microphones have not been systematically positioned, this information is not available and beamforming must be achieved blindly. In achieving this, a commonly neglected issue is whether it is optimal to use all of the available microphones, or only an advantageous subset of these. This paper commences by reviewing different approaches to blind beamforming, characterising them by the way they estimate the signal propagation vector and the spatial coherence of noise in the absence of prior knowledge of microphone and speaker locations. Following this, a novel clustered approach to blind beamforming is motivated and developed. Without using any prior geometrical information, microphones are first grouped into localised clusters, which are then ranked according to their relative distance from a speaker. Beamforming is then performed using either the closest microphone cluster, or a weighted combination of clusters. The clustered algorithms are compared to the full set of microphones in experiments on a database recorded on different ad-hoc array geometries. These experiments evaluate the methods in terms of signal enhancement as well as performance on a large vocabulary speech recognition task.

Microphone array beamforming approach to blind speech separation

In this paper, we present a microphone array beamforming approach to blind speech separation. Unlike previous beamforming approaches, our system does not require a-priori knowledge of the microphone placement and speaker location, making the system directly comparable other blind source separation methods which require no prior knowledge of recording conditions. Microphone location is automatically estimated using an assumed noise field model, and speaker locations are estimated using cross correlation based methods. The system is evaluated on the data provided for the PASCAL Speech Separation Challenge 2 (SSC2), achieving a word error rate of 58% on the evaluation set.