The Care and Cleaning of Furniture and Furnishings

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BowScribe: Supporting the violinist's performance model

Musicians often learn about their vision of a piece through practicing it and listening to recordings. However, this does not always free the player to develop his or her own interpretation of the piece, especially when technique is lacking. We have developed software, the BowScribe markup language, that supports a violinist in creating a ``performance model'' of a piece currently beyond his or her playing skills, by allowing the player fine control over tempo, volume, and articulation, including playing of chords, at a level of expressiveness and flexibility that is significantly beyond the MIDI playback modes of popular music notation software. BowScribe has been used by the first author (who was trained as a prfessional violinist) to create a model of the entire Bach Chaconne (edited by Glamian), a long and demanding piece of music for solo violin that has many phrases that span groups of chords as well as melodic passages. The markup language specified chords to be rolled in two classic ways, as well as a wide variety of other strokes, including greater volume for individual notes in long slurs and small but essential variations in tempo

Active data structures on GPGPUs

Active data structures support operations that may affect a large number of elements of an aggregate data structure. They are well suited for extremely fine grain parallel systems, including circuit parallelism. General purpose GPUs were designed to support regular graphics algorithms, but their intermediate level of granularity makes them potentially viable also for active data structures. We consider the characteristics of active data structures and discuss the feasibility of implementing them on GPGPUs. We describe the GPU implementations of two such data structures (ESF arrays and index intervals), assess their performance, and discuss the potential of active data structures as an unconventional programming model that can exploit the capabilities of emerging fine grain architectures such as GPUs

Optimisation and parallelism in synchronous digital circuit simulators

Digital circuit simulation often requires a large amount of computation, resulting in long run times. We consider several techniques for optimising a brute force synchronous circuit simulator: an algorithm using an event queue that avoids recalculating quiescent parts of the circuit, a marking algorithm that is similar to the event queue but that avoids a central data structure, and a lazy algorithm that avoids calculating signals whose values are not needed. Two target architectures for the simulator are used: a sequential CPU, and a parallel GPGPU. The interactions between the different optimisations are discussed, and the performance is measured while the algorithms are simulating a simple but realistic scalable circuit

Emotion words and categories: evidence from lexical decision

We examined the categorical nature of emotion word recognition. Positive, negative, and neutral words were presented in lexical decision tasks. Word frequency was additionally manipulated. In Experiment 1, "positive" and "negative" categories of words were implicitly indicated by the blocked design employed. A significant emotion–frequency interaction was obtained, replicating past research. While positive words consistently elicited faster responses than neutral words, only low frequency negative words demonstrated a similar advantage. In Experiments 2a and 2b, explicit categories ("positive," "negative," and "household" items) were specified to participants. Positive words again elicited faster responses than did neutral words. Responses to negative words, however, were no different than those to neutral words, regardless of their frequency. The overall pattern of effects indicates that positive words are always facilitated, frequency plays a greater role in the recognition of negative words, and a "negative" category represents a somewhat disparate set of emotions. These results support the notion that emotion word processing may be moderated by distinct systems

The Care and Cleaning of Furniture and Furnishings.

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Testing the limits of contextual constraint: interactions with word frequency and parafoveal preview during fluent reading

Contextual constraint is a key factor affecting a word's fixation duration and its likelihood of being fixated during reading. Previous research has generally demonstrated additive effects of predictability and frequency in fixation times. Studies examining the role of parafoveal preview have shown that greater preview benefit is obtained from more predictable and higher frequency words versus less predictable and lower frequency words. In two experiments, we investigated effects of target word predictability, frequency, and parafoveal preview. A 3 (Predictability: low, medium, high) × 2 (Frequency: low, high) design was used with Preview (valid, invalid) manipulated between experiments. With valid previews, we found main effects of Predictability and Frequency in both fixation time and probability measures, including an interaction in early fixation measures. With invalid preview, we again found main effects of Predictability and Frequency in fixation times, but no evidence of an interaction. Fixation probability showed a weak Predictability effect and Predictability-Frequency interaction. Predictability interacted with Preview in early fixation time and probability measures. Our findings suggest that high levels of contextual constraint exert an early influence during lexical processing in reading. Results are discussed in terms of models of language processing and eye movement control

Assessing the impact of autonomous motivation and psychological need satisfaction in explaining adherence to an exercise referral scheme

Given the mixed findings concerning self-determination theory in explaining adherence to exercise referral schemes (ERS), the present study attempted to examine whether autonomous motivation and psychological need satisfaction could predict ERS adherence. Participants referred to an 8-week ERS completed self-report measures grounded in self-determination theory and basic needs theory at baseline (N = 124), mid-scheme (N = 58), and at the end of the scheme (N = 40). Logistic regressions were used to analyse the data. Autonomous motivation measured at mid-scheme explained between 12 and 16% of the variance in ERS adherence. Autonomy, relatedness and competence measured at mid-scheme explained between 18 and 26% of the variance in ERS adherence. This model also explained between 18 and 25% when measured at the end of the scheme. The study found limited evidence for the role of autonomous motivation in explaining ERS adherence. Stronger support was found for the satisfaction of the three needs for autonomy, relatedness and competence in predicting ERS adherence. Future research should tap into the satisfaction of all three needs collectively to help foster ERS adherence