The lived experiences of experienced Vipassana Mahasi meditators: an interpretative phenomenological analysis.

Research into the effects and mechanisms of mindfulness training draws predominantly on quantitative research. There is a lack of understanding about the subjective experiences of experienced mindfulness meditators, which may provide additional insights into the effects, processes and context of mindfulness training. This qualitative study explored the lived experiences of a novel group of experienced mindfulness meditators who practise Vipassana Mahasi (VM) meditation. The study aimed to understand how experienced VM practitioners make sense of the effects of practice and what processes they ascribe to it. Participants attended semistructured interviews, and their responses were analysed using interpretative phenomenological analysis. Results yielded overarching themes including (a) improvements in hedonic and eudaimonic well-being; (b) insights into self, others and perception of reality; (c) attaining equanimity; and (d) physical and interpersonal difficulties. Participants perceived VM as a ‘cleansing’ process whereby maladaptive responses were eliminated through mindfulness, other supportive mental qualities, decentering and nonattachment. The findings revealed a complex and dynamic set of interdependent outcomes and processes, which are reinforced by Buddhist teachings and ethical practices. This study highlights the need for additional interdisciplinary research into topics such as insight generation and supportive mental qualities cultivated during VM, novel states of well-being informed by Buddhist constructs and interpersonal difficulties related to long-term practice. Findings also suggest that incorporating Buddhist teachings and ethics into mindfulness-based interventions may enhance practitioner understanding and implementation of meditation techniques.N/

Improving understandability of feature contributions in model-agnostic explainable AI tools

Model-agnostic explainable AI tools explain their predictions by means of ’local’ feature contributions. We empirically investigate two potential improvements over current approaches. The first one is to always present feature contributions in terms of the contribution to the outcome that is perceived as positive by the user (“positive framing”). The second one is to add “semantic labeling”, that explains the directionality of each feature contribution (“this feature leads to +5% eligibility”), reducing additional cognitive processing steps. In a user study, participants evaluated the understandability of explanations for different framing and labeling conditions for loan applications and music recommendations. We found that positive framing improves understandability even when the prediction is negative. Additionally, adding semantic labels eliminates any framing effects on understandability, with positive labels outperforming negative labels. We implemented our suggestions in a package ArgueView[11]

How playlist evaluation compares to track evaluations in music recommender systems

Most recommendation evaluations in music domain are focused on algorithmic performance: how a recommendation algorithm could predict a user's liking of an individual track. However, individual track rating might not fully reflect the user's liking of the whole recommendation list. Previous work has shown that subjective measures such as perceived diversity and familiarity of the recommendations, as well as the peak-end effect can influence the user's overall (holistic) evaluation of the list. In this study, we investigate how individual track evaluation compares to holistic playlist evaluation in music recommender systems, especially how playlist attractiveness is related to individual track rating and other subjective measures (perceived diversity) or objective measures (objective familiarity, peak-end effect and occurrence of good recommendations in the list). We explore this relation using a within-subjects online user experiment, in which recommendations for each condition are generated by different algorithms. We found that individual track ratings can not fully predict playlist evaluations, as other factors such as perceived diversity and recommendation approaches can influence playlist attractiveness to a larger extent. In addition, inclusion of the highest and last track rating (peak-end) is equally good in predicting playlist attractiveness as the inclusion of all track evaluations. Our results imply that it is important to consider which evaluation metric to use when evaluating recommendation approaches

Natural Clostridium botulinum Type C Toxicosis in a Group of Cats

Clinical signs of botulism were observed in a group of eight cats, four of which died, after being fed pelican carrion. Clostridium botulinum type C was isolated from one cat. The microorganism and its toxin were found in the pelican. This is apparently the first report of natural botulism in cats

The Cosmic Ray Hodoscope for Testing Thin Gap Chambers at the Technion.

The cosmic ray hodoscope built at the Technion for the test of ATLAS Thin Gap Chambers (TGCs) is described. The mechanical structure, readout electronics, data acquisition and operating settings are presented. Typical TGC test results are presented and discussed