A bias generating temporal distortions in serial perception

A precise estimation of event timing is essential for survival. Yet, temporal distortions are ubiquitous in our daily sensory experience. A specific type of temporal distortion is the time order error (TOE), which occurs when estimating the duration of events organized in a series. TOEs shrink or dilate objective event duration. Understanding the mechanics of subjective time distortions is fundamental since we perceive events in a series, not in isolation. In previous work, we showed that TOEs appear when discriminating small duration differences (20 or 60 ms) between two short events (Standard, S and Comparison, C), but only if the interval between events is shorter than 1 second. TOEs have been variously attributed to sensory desensitization, reduced temporal attention, poor sensory weighting of C relative to S, or idiosyncratic response bias. Surprisingly, the serial dynamics of relative event duration were never considered as a factor generating TOEs. In two experiments we tested them by swapping the order of presentation of S and C. Bayesian hierarchical modelling showed that TOEs emerge when the first event in a series is shorter than the second event, independently of event type (S or C), sensory precision or individual response bias. Participants disproportionately expanded first-position shorter events. Significantly fewer errors were made when the first event was objectively longer, confirming the inference of a strong bias in perceiving ordered event durations. Our finding identifies a hitherto unknown duration-dependent encoding inefficiency in human serial perception.

PSVI-20 Extent of genetic variation in feeding behavior and genetic associations with performance and feed efficiency in crossbred growing cattle

To the best of our knowledge, the genetic variability in feeding behavior, as well as relationships with performance and feed efficiency, has not been investigated in a cattle population of greater than 1,500 animals. Our objective was to quantify the genetic parameters of several feeding behavior traits, and their genetic associations with both performance and feed efficiency traits, in crossbred growing cattle. Feed intake and live-weight data were available on 6,088 bulls, steers and heifers; of these, 4,672 cattle had backfat and muscle ultrasound data, and 1,548 steers and heifers had feeding behavior data. Genetic (co)variance parameters were estimated using animal linear mixed models; fixed effects included test group, heterosis, recombination loss, dam parity, age in months at the end of test, and the two-way interaction between age in months at the end of test and sex. Heritability was estimated to be 0.51 (0.097), 0.61 (0.100), 0.44 (0.093), 0.48 (0.094), and 0.47 (0.095) for feed events per day, feeding time per day, feeding rate, feed event duration, and energy intake per feed event, respectively. Coefficients of genetic variation ranged from 0.11 (feeding time per day) to 0.22 (feed event duration). Genetically heavier cattle with a higher energy intake per day, and faster growth rate, had a faster feeding rate and a greater energy intake per feed event. Genetic correlations between feeding behavior and feed efficiency were generally not different from zero, however, there was a genetic correlation of 0.36 (0.11) between feeding time per day and residual energy intake. Significant heritable and exploitable genetic variation exists in several feeding behavior traits in crossbred growing cattle which are also correlated with several performance traits. As some feeding behavior traits may be relatively less resource intensive to measure, they could be useful as predictor traits in beef cattle genetic evaluations.

Brief communication: Rainfall thresholds based on Artificial neural networks can improve landslide early warning

In this communication we show how the use of artificial neural networks (ANNs) can improve the performance of the rainfall thresholds for landslide early warning. Results for Sicily (Italy), show how performance of a traditional rainfall event duration and depth power law threshold, yielding a true skill statistic (TSS) of 0.50, can be improved by ANNs (TSS = 0.59). Then we show how ANNs allow to easily add other variables, like peak rainfall intensity, with a further performance improvement (TSS = 0.64). This may stimulate more research on the use of this powerful tool for deriving landslide early warning thresholds.

Uncertainty analysis of a rainfall threshold estimate for stony debris flow based on the backward dynamical approach

A rainfall threshold is a function of some rainfall quantities that provides the conditions beyond which the probability of debris-flow occurrence is considered significant. Many uncertainties may affect the thresholds calibration and, consequently, its robustness. This study aims to assess the uncertainty in the estimate of a rainfall threshold for stony debris flow based on the backward dynamical approach, an innovative method to compute the rainfall duration and averaged intensity strictly related to a measured debris flow. The uncertainty analysis is computed by performing two Monte Carlo cascade simulations: (i) to assess the variability in the event characteristics estimate due to the uncertainty in the backward dynamical approach parameters and data and (ii) to quantify the impact of this variability on the threshold calibration. The application of this procedure to a case study highlights that the variability in the event characteristics can be both low and high. Instead, the threshold coefficients have a low dispersion showing good robustness of the threshold estimate. Moreover, the results suggest that some event features are correlated with the variability of the rainfall event duration and intensity. The proposed method is suitable to analyse the uncertainty of other threshold calibration approaches.

Statistical Analysis and Machine Learning Prediction of Fog-Caused Low-Visibility Events at A-8 Motor-Road in Spain

This work presents a full statistical analysis and accurate prediction of low-visibility events due to fog, at the A-8 motor-road in Mondoñedo (Galicia, Spain). The present analysis covers two years of study, considering visibility time series and exogenous variables collected in the zone affected the most by extreme low-visibility events. This paper has then a two-fold objective: first, we carry out a statistical analysis for estimating the fittest probability distributions to the fog event duration, using the Maximum Likelihood method and an alternative method known as the L-moments method. This statistical study allows association of the low-visibility depth with the event duration, showing a clear relationship, which can be modeled with distributions for extremes such as Generalized Extreme Value and Generalized Pareto distributions. Second, we apply a neural network approach, trained by means of the ELM (Extreme Learning Machine) algorithm, to predict the occurrence of low-visibility events due to fog, from atmospheric predictive variables. This study provides a full characterization of fog events at this motor-road, in which orographic fog is predominant, causing important traffic problems during all year. We also show how the ELM approach is able to obtain highly accurate low-visibility events predictions, with a Pearson correlation coefficient of 0.8, within a half-hour time horizon, enough to initialize some protocols aiming at reducing the impact of these extreme events in the traffic of the A-8 motor road.

Exploring Weather Data to Predict Activity Attendance in Event-based Social Network

Event-based social networks (EBSNs) connect online and offline lives. They allow online users with similar interests to get together in real life. Attendance prediction for activities in EBSNs has attracted a lot of attention and several factors have been studied. However, the prediction accuracy is not very good for some special activities, such as outdoor activities. Moreover, a very important factor, the weather, has not been well exploited. In this work, we strive to understand how the weather factor impacts activity attendance, and we explore it to improve attendance prediction from the organizer’s view. First, we classify activities into two categories: the outdoor and the indoor activities. We study the different ways that weather factors may impact these two kinds of activities. We also introduce a new factor of event duration. By integrating the above factors with user interest and user-event distance, we build a model of attendance prediction with the weather named GBT-W , based on the Gradient Boosting Tree. Furthermore, we develop a platform to help event organizers estimate the possible number of activity attendance with different settings (e.g., different weather, location) to effectively plan their events. We conduct extensive experiments, and the results show that our method has a better prediction performance on both the outdoor and the indoor activities, which validates the reasonability of considering weather and duration.

Characteristics of Valuable Microseismic Events in Heading Face of an Underground Coal Mine Using Microseismic System

Rock burst monitoring of heading face is a weak aspect of rock burst monitoring in China; acoustic emission (AE) monitoring is one of the few monitoring technologies used in heading face, but its target signals are small energy events which are easy to be disturbed. Researchers usually focus on the weak AE events but ignore the microseismic (MS) events (different from AE event and caused by a larger scale of coal fracture), while this kind of events can also reflect the pressure situation of heading face and have higher energy value which may become a better indicator for rock burst monitoring of heading face. So, the basic characteristics of MS events in heading face are studied based on a running vibration signal acquisition system, including the occurrence position, main frequency range, maximum amplitude (MA) range, event duration, and relationship with geological structure. This paper provides a development basis of the monitoring method for rock burst monitoring of heading face by using MS events.

Forced swim stress dynamically alters neural activity and bi-directionally modulates NMDA receptors in the prefrontal cortex

Repeated exposure to stress results in progressively divergent effects on cognitive behaviors that are dependent on the integrity of networks in the medial prefrontal cortex (mPFC). To investigate molecular mechanisms responsive to variable repetition of mild stress, we measured persistent neural activity, in vitro, from mPFC slices in mice that had been repetitively exposed to 10 minutes of forced swim stress for 3-10 days. 3-day short-term stress facilitated persistent neural activity by increasing event duration while 10 days suppressed event duration and amplitude. These dynamic changes were accompanied by a similar bi-directional modulation of the NMDA/AMPA receptor current ratio, an important synaptic mechanism for sustaining the persistency of neural activity. Specifically, short-term stress led to potentiated NMDA currents with slower decay kinetics, and extended stress produced smaller currents with faster decay. The inhibitory action of ifenprodil, a specific blocker of NR2B-containing NMDA receptors, was more effective in NMDA current suppression following light stress and less effective after longer stress compared to naive controls. Persistent activity and glutamate receptor balance in the neocortex have been linked to working memory and impulse control. Therefore, these results could provide insight for generating therapeutic strategies to prevent or reverse stress-induced cognitive deficits.