Percolating through networks of random thresholds: Finite temperature electron tunneling in metal nanocrystal arrays

We investigate how temperature affects transport through large networks of nonlinear conductances with distributed thresholds. In monolayers of weakly-coupled gold nanocrystals, quenched charge disorder produces a range of local thresholds for the onset of electron tunneling. Our measurements delineate two regimes separated by a cross-over temperature $T^*$. Up to $T^*$ the nonlinear zero-temperature shape of the current-voltage curves survives, but with a threshold voltage for conduction that decreases linearly with temperature. Above $T^*$ the threshold vanishes and the low-bias conductance increases rapidly with temperature. We develop a model that accounts for these findings and predicts $T^*$.Comment: 5 pages including 3 figures; replaced 3/30/04: minor changes; final versio

Loans, logins and lasting the course: Academic library use and student retention

Activities and services that improve student engagement and retention in the higher education sector are important not only to individual student’s success but also to university planning and funding. This paper reports on a quantitative study that was carried out to explore whether use of the library by new university students is associated with continued enrolment. Students’ socioeconomic background and age were also examined in relation to library use. Limited to commencing students in March 2010 at Curtin University, the study drew on demographic data from the University’s enrolment system and instances of library use from the Library’s management system. Results of the statistical analyses indicate that library use is associated with retention, and importantly, library use in the early weeks of a student’s first semester is associated with retention. ‘Mature aged’ (21 years and over) students displayed different library use patterns than their younger colleagues and there was some variation in library use between students from different socioeconomic backgrounds. Findings from this study suggest that academic libraries can contribute to the retention of students and that carefully targeted programs and services may improve library use by some groups of students

Tracking human skill learning with a hierarchical Bayesian sequence model

Perceptuo-motor sequences that underlie our everyday skills from walking to language have higher-order dependencies such that the statistics of one sequence element depend on a variably deep window of past elements. We used a non-parametric, hierarchical, forgetful, Bayesian sequence model to characterize the multi-day evolution of human participants’ implicit representation of a serial reaction time task sequence with higher-order dependencies. The model updates trial-by-trial, and seamlessly combines predictive information from shorter and longer windows onto past events, weighting the windows proportionally to their predictive power. We fitted the model to participants’ response times (RTs), assuming that faster responses reflected more certain predictions of the upcoming elements. Already in the first session, the model fit showed that participants had begun to rely on two previous elements (i.e., trigrams) for prediction, thereby successfully adapting to the higher-order task structure. However, at this early stage, local histories influenced their responses, correctly captured by forgetting in the model. With training, forgetting of trigrams was reduced, so that RTs were more robust to local statistical fluctuations – evidence of skilled performance. However, error responses still reflected forgetting-induced volatility of the internal model. By the last training session, a subset of participants shifted their prior further to consider a context even deeper than just two previous elements. Our model was able to predict the degree to which individuals enriched their internal model to represent dependencies of increasing orders

Tracking the Unknown: Modeling Long-Term Implicit Skill Acquisition as Non-Parametric Bayesian Sequence Learning

Long perceptuo-motor sequences underlie skills from walking to language learning, and are often learned gradually and unconsciously in the face of noise. We used a non-parametric Bayesian n-gram model (Teh, 2006) to characterize the multi-day evolution of human subjects’ implicit representation of a serial reaction time task sequence with second-order contingencies. The reaction time for an element in the sequence depended on zero, one and more preceding elements at the same time, predicting frequency, repetition and higher-order learning effects. Our trial-level dynamic model captured these coexistent facilitation effects by seamlessly combining information from shorter and longer windows onto past events. We show how shifting their priors over window lengths allowed subjects to grow and refine their internal sequence representations week by week