On the Roles of Complexity and Symmetry in Cued Tapping of Well-formed Complex Rhythms

Production of relatively few rhythms with non-isochronous beats has been studied. So we assess reproduction of most well-formed looped rhythms comprising K=2-11 cues (a uniform piano tone, indicating where participants should tap) and N=3-13 isochronous pulses (a uniform cymbal). Each rhythm had two different cue interonset intervals. We expected that many of the rhythms would be difficult to tap, because of ambiguous non-isochronous beats and syncopations, and that complexity and asymmetry would predict performance. 111 participants tapped 91 rhythms each heard over 129 pulses, starting as soon as they could. Whereas tap-cue concordance in prior studies was generally >> 90%, here only 52.2% of cues received a temporally congruent tap, and only 63% of taps coincided with a cue. Only −2 ms mean tap asynchrony was observed (whereas for non-musicians this value is usually c. −50 ms). Performances improved as rhythms progressed and were repeated, but precision varied substantially between participants and rhythms. Performances were autoregressive and mixed effects cross-sectional time series analyses retaining the integrity of all the individual time series revealed that performance worsened as complexity features K, N, and cue inter-onset interval entropy increased. Performance worsened with increasing R, the Long: short (L: s) cue interval ratio of each rhythm (indexing both complexity and asymmetry). Rhythm evenness and balance, and whether N was divisible by 2 or 3, were not useful predictors. Tap velocities positively predicted cue fulfilment. Our data indicate that study of a greater diversity of rhythms can broaden our impression of rhythm cognition.

Efficiency of time series homogenization: method comparison with 12 monthly temperature test datasets

The aim of time series homogenization is to remove non-climatic effects, such as changes in station location, instrumentation, observation practices, etc., from observed data. Statistical homogenization usually reduces the non-climatic effects, but does not remove them completely. In the Spanish MULTITEST project, the efficiencies of automatic homogenization methods were tested on large benchmark datasets of a wide range of statistical properties. In this study, test results for 9 versions, based on 5 homogenization methods (ACMANT, Climatol, MASH, PHA and RHtests) are presented and evaluated. The tests were executed with 12 synthetic/surrogate monthly temperature test datasets containing 100 to 500 networks with 5 to 40 time series in each. Residual centred root mean square errors and residual trend biases were calculated both for individual station series and for network mean series.The results show that a larger fraction of the non-climatic biases can be removed from station series than from network-mean series. The largest error reduction is found for the long-term linear trends of individual time series in datasets with a high signal-to-noise ratio (SNR), there the mean residual error is only 14 – 36% of the raw data error. When the SNR is low, most of the results still indicate error reductions, although with smaller ratios than for large SNR. Generally, ACMANT gave the most accurate homogenization results. In the accuracy of individual time series ACMANT is closely followed by Climatol, while for the accurate calculation of mean climatic trends over large geographical regions both PHA and ACMANT are recommended.

Multi-source transfer learning of time series in cyclical manufacturing

This paper describes a new transfer learning method for modeling sensor time series following multiple different distributions, e.g. originating from multiple different tool settings. The method aims at removing distribution specific information before the modeling of the individual time series takes place. This is done by mapping the data to a new space such that the representations of different distributions are aligned. Domain knowledge is incorporated by means of corresponding parameters, e.g. physical dimensions of tool settings. Results on a real-world problem of industrial manufacturing show that our method is able to significantly improve the performance of regression models on time series following previously unseen distributions. Graphic abstract

Paleo calendar-effect adjustments in time-slice and transient climate-model simulations (PaleoCalAdjust v1.0): impact and strategies for data analysis

The “paleo calendar effect” is a common expression for the impact that changes in the length of months or seasons over time, related to changes in the eccentricity of Earth's orbit and precession, have on the analysis or summarization of climate-model output. This effect can have significant implications for paleoclimate analyses. In particular, using a “fixed-length” definition of months (i.e., defined by a fixed number of days), as opposed to a “fixed-angular” definition (i.e., defined by a fixed number of degrees of the Earth's orbit), leads to comparisons of data from different positions along the Earth's orbit when comparing paleo with modern simulations. This effect can impart characteristic spatial patterns or signals in comparisons of time-slice simulations that otherwise might be interpreted in terms of specific paleoclimatic mechanisms, and we provide examples for 6, 97, 116, and 127 ka. The calendar effect is exacerbated in transient climate simulations in which, in addition to spatial or map-pattern effects, it can influence the apparent timing of extrema in individual time series and the characterization of phase relationships among series. We outline an approach for adjusting paleo simulations that have been summarized using a modern fixed-length definition of months and that can also be used for summarizing and comparing data archived as daily data. We describe the implementation of this approach in a set of Fortran 90 programs and modules (PaleoCalAdjust v1.0).

Idiographic Dynamics of Positive Emotion in GAD: Modeling Emotion Regulation at the Person Level

GAD is associated with worry and emotion regulation difficulties. The contrast-avoidance model suggests that individuals with GAD use worry to regulate emotion: by worrying, they maintain a constant state of negative affect (NA), avoiding a feared sudden shift into NA. We tested an extension of this model to positive affect (PA). During a week-long EMA period, 96 undergraduates with a GAD analog provided four daily measurements of worry, dampening (i.e. PA suppression), and PA. We hypothesized a time-lagged mediation relationship in which higher worry predicts later dampening, and dampening predicts subsequently lower PA. A lag-2 structural equation model was fit to the group-aggregated data and to each individual time-series to test this hypothesis. Although worry and PA were negatively correlated in 87 participants, our model was not supported at the nomothetic level. However, idiographically, our model was well-fit for about a third (38.5%) of participants. We then used automatic search as an idiographic exploratory procedure to detect other time-lagged relationships between these constructs. While 46 individuals exhibited some cross-lagged relationships, no clear pattern emerged across participants. Findings suggest heterogeneity in the function of worry as a regulatory strategy, and the importance of temporal scale for detection of time-lagged effects.

Paleo calendar-effect adjustments in time-slice and transient climate-model simulations (PaleoCalAdjust v1.0): impact and strategies for data analysis

The “paleo calendar effect” is a common expression for the impact that the changes in the length of months or seasons over time, related to changes in the eccentricity of Earth's orbit and precession, have on the analysis or summarization of climate-model output. This effect can have significant implications for paleoclimate analyses. In particular, using a “fixed-length” definition of months (i.e. defined by a fixed number of days), as opposed to a “fixed-angular” definition (i.e. defined by a fixed number of degrees of the Earth's orbit), leads to comparisons of data from different positions along the Earth's orbit when comparing paleo with modern simulations. This effect can impart characteristic spatial patterns or signals in comparisons of time-slice simulations that otherwise might be interpreted in terms of specific paleoclimatic mechanisms, and we provide examples for 6, 97, 116, and 127 ka. The calendar effect is exacerbated in transient climate simulations, where, in addition to spatial or map-pattern effects, it can influence the apparent timing of extrema in individual time series and the characterization of phase relationships among series. We outline an approach for adjusting paleo simulations that have been summarized using a modern fixed-length definition of months and that can also be used for summarizing and comparing data archived as daily data. We describe the implementation of this approach in a set of Fortran 90 programs and modules (PaleoCalAdjust v1.0).

FOREIGN DIRECT INVESTMENT, TRADE OPENNESS AND GDP IN CHINA, INDIA AND MEXICO

This paper examines the short and long-run relationships between Foreign Direct Investment (FDI), Trade Openness and GDP in China, India and Mexico from 1980 to 2011. Based on the properties of individual time series data, the paper estimates the VAR or VECM of the three variables to determine short and long-run causal relationships. The results confirm the existence of long-run causal relationships between the three variables for China and Mexico. The results also point to sharp differences in short-run causal relationships in the three countries and several plausible explanations consistent with the findings are offered.