Distributed coding of duration in rodent prefrontal cortex during time reproduction

As we interact with the external world, we judge magnitudes from sensory information. The estimation of magnitudes has been characterized in primates, yet it is largely unexplored in non-primate species. Here we use time interval reproduction to study rodent behavior and its neural correlates in the context of magnitude estimation. We show that gerbils display primate-like magnitude estimation characteristics in time reproduction. Most prominently their behavioral responses show a systematic overestimation of small stimuli and an underestimation of large stimuli, often referred to as regression effect. We investigated the underlying neural mechanisms by recording from medial prefrontal cortex and show that the majority of neurons respond either during the measurement or the reproduction of a time interval. Cells that are active during both phases display distinct response patterns. We categorize the neural responses into multiple types and demonstrate that only populations with mixed responses can encode the bias of the regression effect. These results help unveil the organizing neural principles of time reproduction and perhaps magnitude estimation in general.

New Magnitude Proxy for Earthquake Early Warning Based on Initial Time Series and Frequency

A new characteristic parameter Sdτ is proposed to improve the performance of magnitude estimation in earthquake early warning (EEW). Sdτ is the product of summation of absolute displacement multiplied by the maximum predominant period (τmaxP) for the first arriving seconds of a seismic wave. About 30,725 underground records at borehole stations for 3645 earthquakes with magnitude between 4.0 and 9.0 from the Japanese KiK-net were used to compare the magnitude proxy performance based on the proposed Sdτ with that based on either τmaxP or peak displacement Pd. The comparison results show that for a magnitude between 4.0 and 7.3, Sdτ has a better correlation with magnitude and higher estimated accuracy than either τmaxP or Pd. Hypocentral distance is not required when using Sdτ, but it can be used to further improve the accuracy of magnitude estimate. These results confirm that Sdτ can significantly improve the accuracy and timeliness of continuous magnitude estimation in an EEW system.

Earthquake Magnitude Estimation from High-Rate GNSS Data: A Case Study of the 2021 Mw 7.3 Maduo Earthquake

Peak ground displacement (PGD) and peak ground velocity (PGV) are critical parameters during earthquake early warning, as they can provide rapid magnitude estimation before rupture end. In this study, we used the high-rate Global Navigation Satellite System (GNSS) data from 55 continuous stations to estimate the magnitude of the 2021 Maduo earthquake in western China. We used the relative positioning method and variometric approach to acquire real-time GNSS displacement and velocity waveforms, respectively. The results showed the amplitude of displacement and velocity waveforms gradually decreased with increasing hypocentral distance. Our results showed that the fluctuation of PGD magnitudes over time is smaller than that of PGV magnitudes. Nonetheless, the earthquake magnitudes estimated from both methods were consistent with their counterparts (Mw 7.3) reported by the United States Geological Survey (USGS). The final magnitude estimated from the PGD and PGV methods were Mw 7.25 and Mw 7.31, respectively. In addition, our results highlighted how the number of high-rate GNSS stations could influence the stability and convergence time of magnitude estimation.

Estimating visual difference between reproduction gamuts: moving our pilot study from the lab to online delivery

Images reproduced for different output devices are known to be limited in the range of colours that can be reproduced. It is accepted that reproductions made with different print processes, and on different substrates, will not match, although the overall reproduction appearance can be optimized using an output rendering. However, the question remains: how different are they visually? This paper reports on a pilot study that tests whether visual difference can be reduced to a single dimensional scale using magnitude estimation. Subject to recent Covid restrictions, the experiment was moved from the lab to an online delivery. We compare the two methods of delivery: in-person under controlled viewing conditions, and online via a web-based interface where viewing conditions are unknown.

Accumulation of Corresponding Colours Under Extreme Chromatic Illuminations and modification of CAM16

The goal of this study was to investigate the chromatic adaptation under extreme chromatic lighting conditions using the magnitude estimation method. The locations of the lightings on CIE1976 u′v′ plane were close to the spectrum locus, so the colour purity was far beyond the previous studies, and the data could test the limitations of the existing models. Two psychophysical experiments were carried out, and 1,470 estimations of corresponding colours were accumulated. The results showed that CAT16 gave a good prediction performance for all the chromatic lightings except for blue lighting, and the degree of adaptation was relatively high, that is, D was close to 1. The prediction for blue lightings was modified, the results showed the performance of CAM16 could be improved by correcting the matrix instead of the D values.

Earthquake Magnitude Estimation using Precise Point Positioning

An accurate estimation of an earthquake magnitude plays an important role in targeting emergency services towards affected areas. Along with the traditional methods using seismometers, site displacements caused by an earthquake can be monitored by the Global Navigation Satellite Systems (GNSS). GNSS can be used either in real-time for early warning systems or in offline mode for precise monitoring of ground motion. The Precise Point Positioning (PPP) offers an optimal method for such purposes, because data from only one receiver are considered and thus not affected by other potentially not stable stations. Precise external products and empirical models have to be applied, and the initial convergence can be reduced or eliminated by the backward smoothing strategy or integer ambiguity resolution. The product for the magnitude estimation is a peak ground displacement (PGD). PGDs observed at many GNSS stations can be utilized for a robust estimate of an earthquake magnitude. We tested the accuracy of estimated magnitude scaling when using displacement waveforms collected from six selected earthquakes between the years 2016 and 2020 with magnitudes in a range of 7.5–8.2 Moment magnitude MW. We processed GNSS 1Hz and 5Hz data from 182 stations by the PPP method implemented in the G-Nut/Geb software. The precise satellites orbits and clocks corrections were provided by the Center for Orbit Determination in Europe (CODE). PGDs derived on individual GNSS sites formed the basis for ground motion parameters estimation. We processed the GNSS observations by the combination of the Kalman filter (FLT) and the backward smoother (SMT), which significantly enhanced the kinematic solution. The estimated magnitudes of all the included earthquakes were compared to the reference values released by the U. S. Geological Survey (USGS). The moment magnitude based on SMT was improved by 20% compared to the FLT-only solution. An average difference from the comparison was 0.07 MW and 0.09 MW for SMT and FLT solutions, respectively. The corresponding standard deviations were 0.18 MW and 0.22 MW for SMT and FLT solutions, which shows a good consistency of our and the reference estimates.

Magnitude Estimation for Earthquake Early Warning with Multiple Parameter Inputs and a Support Vector Machine

Accurately estimating the magnitude within the initial seconds after the P-wave arrival is of great significance in earthquake early warning (EEW). Over the past few decades, single-parameter approaches such as the τc and Pd methods have been applied to EEW magnitude estimation studies considering the first 3 s after the P-wave onset. However, these methods present considerable scatter and are affected by the signal-to-noise ratio (SNR) and epicentral distance. In this study, using Japanese K-NET strong-motion data, we propose a machine-learning method comprising multiple parameter inputs, namely, the support vector machine magnitude estimation (SVM-M) model, to determine earthquake magnitudes and resolve the aforementioned problems. Our results using a single seismological station record show that the standard deviation of the magnitude prediction errors of the SVM-M model is 0.297, which is less than those of the τc (1.637) and Pd (0.425) methods. The magnitudes estimated by the SVM-M model within 3 s after the P-wave arrival are not obviously affected by the SNR or epicentral distance, and not overestimated for MJMA≤5. In addition, in an offline EEW application, the magnitude estimation error of the SVM-M model gradually decreases with increasing time after the first station is triggered, and the underestimation of event magnitudes for 6.5≤MJMA gradually improves. These results demonstrate that the proposed SVM-M model can robustly estimate earthquake magnitudes and has potential for EEW.