scholarly journals Time Stand Still: Effects of Temporal Window Selection on Eye Tracking Analysis

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Jonathan E. Peelle ◽  
Kristin J. Van Engen
Keyword(s):  
Eye Tracking ◽  
Degrees Of Freedom ◽  
Time Series Data ◽  
Time Window ◽  
Time Windows ◽  
Basis Set ◽  
Series Data ◽  
Parameter Estimates ◽  
Start Time ◽  
Window Selection

The number of possible approaches to conducting and analyzing a research study—often referred to as researcher degrees of freedom—has been increasingly under scrutiny as a challenge to the reproducibility of experimental results. Here we focus on the specific instance of time window selection for time series data. As an example, we use data from a visual world eye tracking paradigm in which participants heard a word and were instructed to click on one of four pictures corresponding to the target (e.g., “Click on the hat”). We examined statistical models for a range of start times following the beginning of the carrier phrase, and for each start time a range of window lengths, resulting in 8281 unique time windows. For each time window we ran the same logistic linear mixed effects model, including effects of time, age, noise, and word frequency on an orthogonalized polynomial basis set. Comparing results across these time ranges shows substantial changes in both parameter estimates and p values, even within intuitively “reasonable” boundaries. In some cases varying the window selection in the range of 100–200 ms caused parameter estimates to change from positive to negative. Rather than rush to provide specific recommendations for time window selection (which differs across studies), we advocate for transparency regarding time window selection and awareness of the effects this choice may have on results. Preregistration and multiverse model exploration are two complementary strategies to help mitigate bias introduced by any particular time window choice.

scholarly journals Time stand still: Effects of temporal window selection on eye tracking analysis

2020 ◽  
Author(s):  
Jonathan E. Peelle ◽  
Kristin J. Van Engen
Keyword(s):  
Eye Tracking ◽  
Degrees Of Freedom ◽  
Time Series Data ◽  
Time Window ◽  
Time Windows ◽  
Basis Set ◽  
Series Data ◽  
Parameter Estimates ◽  
Start Time ◽  
Window Selection

The number of possible approaches to conducting and analyzing a research study—often referred to as researcher degrees of freedom—has been increasingly under scrutiny as a challenge to the reproducibility of experimental results. Here we focus on the specific instance of time window selection for time series data. As an example we use data from a visual world eye tracking paradigm in which participants heard a word and were instructed to click on one of four pictures corresponding to the target (e.g., “Click on the hat”). We examined statistical models for a range of start times following the beginning of the carrier phrase, and for each start time a range of window lengths, resulting in 8281 unique time windows. For each time window we ran the same logistic linear mixed effects model, including effects of time, age, noise, and word frequency on an orthogonalized polynomial basis set. Comparing results across these time ranges shows substantial changes in both parameter estimates and p values, even within intuitively “reasonable” boundaries. In some cases varying the window selection in the range of 100–200 ms caused parameter estimates to change from positive to negative. Rather than rush to provide specific recommendations for time window selection (which differs across studies), we advocate for transparency regarding time window selection and awareness of the effects this choice may have on results. Preregistration and multiverse model exploration are two complementary strategies to help mitigate bias introduced by any particular time window choice.

scholarly journals An Interpretable Early Dynamic Sequential Predictor for Sepsis-Induced Coagulopathy Progression in the Real-World Using Machine Learning

2021 ◽  
Vol 8 ◽  
Author(s):  
Ruixia Cui ◽  
Wenbo Hua ◽  
Kai Qu ◽  
Heran Yang ◽  
Yingmu Tong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Time Series Data ◽  
Time Window ◽  
Medical Center ◽  
Characteristic Curve ◽  
Series Data ◽  
Gradient Boosting ◽  
Early Management ◽  
Extreme Gradient Boosting

Sepsis-associated coagulation dysfunction greatly increases the mortality of sepsis. Irregular clinical time-series data remains a major challenge for AI medical applications. To early detect and manage sepsis-induced coagulopathy (SIC) and sepsis-associated disseminated intravascular coagulation (DIC), we developed an interpretable real-time sequential warning model toward real-world irregular data. Eight machine learning models including novel algorithms were devised to detect SIC and sepsis-associated DIC 8n (1 ≤ n ≤ 6) hours prior to its onset. Models were developed on Xi'an Jiaotong University Medical College (XJTUMC) and verified on Beth Israel Deaconess Medical Center (BIDMC). A total of 12,154 SIC and 7,878 International Society on Thrombosis and Haemostasis (ISTH) overt-DIC labels were annotated according to the SIC and ISTH overt-DIC scoring systems in train set. The area under the receiver operating characteristic curve (AUROC) were used as model evaluation metrics. The eXtreme Gradient Boosting (XGBoost) model can predict SIC and sepsis-associated DIC events up to 48 h earlier with an AUROC of 0.929 and 0.910, respectively, and even reached 0.973 and 0.955 at 8 h earlier, achieving the highest performance to date. The novel ODE-RNN model achieved continuous prediction at arbitrary time points, and with an AUROC of 0.962 and 0.936 for SIC and DIC predicted 8 h earlier, respectively. In conclusion, our model can predict the sepsis-associated SIC and DIC onset up to 48 h in advance, which helps maximize the time window for early management by physicians.

Short-Term Prediction of Ship Motion Based on EMD-SVM

2014 ◽  
Vol 571-572 ◽  
pp. 252-257
Author(s):  
Sun Bo Liu ◽  
Ping An Shi ◽  
Lei Wu
Keyword(s):  
Degrees Of Freedom ◽  
Time Series Data ◽  
Ship Motion ◽  
Series Data ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Model Decomposition ◽  
Aircraft Landing ◽  
Non Linear ◽  
Short Term Prediction

Ship sailing at sea is affected by many factors, such as winds, waves and so on, which makes six degrees of freedom motions and thus influences the shipboard arms control, aircraft landing and other operations. In view of the non-linear and non-stationary features of ship motion in waves, a new method based on EMD (Empirical Model Decomposition) and SVM (Support Vector Machine) is presented to predict the ship motion. The EMD is used to decompose the ship motion time series data into several IMFs (intrinsic mode functions) and a residual trend term, which decreases the difficulty of prediction. As the IMF is relatively stationary, but also non-linear, these features are fit to be processed by using SVM. Then the decompositions are used as inputs into SVM to forecast ship motion. The simulation and comparison analysis show that the EMD-SVM prediction model can effectively forecast the ship motion in waves.

Bias in the Estimation of Functional Relationships from Time Series Data

1985 ◽  
Vol 42 (1) ◽  
pp. 147-149 ◽  
Author(s):  
Carl J. Walters
Keyword(s):  
Time Series ◽  
Measurement Errors ◽  
Time Series Data ◽  
Series Data ◽  
Parameter Estimates ◽  
Natural Experiments ◽  
Independent Variables ◽  
Functional Relationships ◽  
Stock Recruitment ◽  
The Relationship

Functional relationships, such as stock–recruitment curves, are generally estimated from time series data where natural "random" factors have generated both deviations from the relationship and also informative variation in the independent variables. Even in the absence of measurement errors, such natural experiments can lead to severely biased parameter estimates. For stock–recruitment models, the bias is misleading for management: the stock will appear too productive when it is low, and too unproductive when it is large. The likely magnitude of such biases can and should be determined for any particular case by Monte Carlo simulations.

Two-frequency excitation of single-mode Faraday waves

2015 ◽  
Vol 764 ◽  
pp. 538-571 ◽  
Author(s):  
W. Batson ◽  
F. Zoueshtiagh ◽  
R. Narayanan
Keyword(s):  
Degrees Of Freedom ◽  
Time Series Data ◽  
Single Frequency ◽  
Series Data ◽  
Nonlinear Phenomena ◽  
Nonlinear Behaviour ◽  
Faraday Waves ◽  
Low Frequencies ◽  
Axisymmetric Mode ◽  
Frequency Excitation

AbstractThe purpose of this work is to investigate, for the first time, excitation of Faraday waves in small containers using two commensurate frequencies. This spatial restriction, which is encountered at low frequencies, leads to a wave composed primarily of one spatial eigenmode of the container. When two frequencies are used, the mode resonates primarily with one frequency, while the role of the second is to alter the instability threshold and the resulting nonlinear dynamics. As the parameter space expands greatly as a result of the introduction of three new degrees of freedom, viz. the frequency, amplitude and phase of the new component, the linear theory is first used as a guide to highlight basic two-frequency phenomena. These predictions and nonlinear phenomena are then studied experimentally with the system of Batson, Zoueshtiagh & Narayanan (J. Fluid Mech., vol. 729, 2013, pp. 496–523), who studied single-frequency excitation of different modes in a cylindrical cell. The two-frequency experiments of this work focus on excitation of the fundamental axisymmetric mode, and are quantitatively compared to the model via a posteriori Fourier decomposition of the parametric input. In doing so, experimental dependence of the instability on the new degrees of freedom is demonstrated, in accordance with the model predictions. This is done for a variety of frequency ratios, and overall agreement between the observed and predicted onset conditions is identical to that already reported for the single-frequency experiment. For each frequency ratio, the nonlinear behaviour is experimentally characterized by bifurcation and time series data, which is shown to differ significantly from comparable single-frequency excitations. Finally, we present and discuss a wave in which both temporal frequencies are used to simultaneously excite different spatial modes.

Start Time Control of Micro Gas Turbine CHP System With Heat Storage

2011 ◽  
Author(s):  
Osamu Kurata ◽  
Norihiko Iki ◽  
Takayuki Matsunuma ◽  
Tetsuhiko Maeda ◽  
Satoshi Hirano ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Room Temperature ◽  
High Efficiency ◽  
Heat Storage ◽  
Time Series Data ◽  
Global Climate ◽  
Series Data ◽  
Start Time ◽  
Micro Gas Turbine ◽  
Chp System

Combined heat and power (CHP) systems are widely used considering the prevention of global climate change and the reduction of energy costs. In distributed CHP systems, both high efficiency of elements and good coordination of the systems are considered as the points to solve. We had been researched and demonstrated the micro gas turbine CHP system with heat storage at Sapporo City University from April 2006 to March 2010. At first, the start times of microturbine (MGT) and heat storage system (HST) was set up by schedule timers. In 2008 the schedule timers were substituted to a new programmable logic controller (PLC) and the start times of MGT and HST were calculated as the function of temperature outside and room temperature. Setting the start time of MGT at maximum 5 hours before 8:00 and interlocking relays of HST on MGT, the start times were calculated from temperature outside and room temperature at 21:00 the day before. Control of start time using PLC was demonstrated from Feb. 21, 2009 to June 1 and from Nov. 16 to Jan. 7, 2010. It is shown the time series data of temperature and analysis of the CHP with the original boiler heating system.

On the degrees of freedom in MCMC-based Wishart models for time series data

2015 ◽  
Vol 98 ◽  
pp. 59-64 ◽  
Author(s):  
Yuewen Xiao ◽  
Yu-Cheng Ku ◽  
Peter Bloomfield ◽  
Sujit K. Ghosh
Keyword(s):  
Time Series ◽  
Degrees Of Freedom ◽  
Time Series Data ◽  
Series Data

scholarly journals Analytical Derivation of Nonlinear Spectral Effects and 1/f Scaling Artifact in Signal Processing of Real-World Data

2017 ◽  
Vol 29 (7) ◽  
pp. 2004-2020 ◽  
Author(s):  
Claudia Lainscsek ◽  
Lyle E. Muller ◽  
Aaron L. Sampson ◽  
Terrence J. Sejnowski
Keyword(s):  
Signal Processing ◽  
Real World ◽  
Time Series Data ◽  
Time Window ◽  
Low Frequency ◽  
Series Data ◽  
Infinite Length ◽  
Scaling Exponent ◽  
Sampled Data ◽  
Frequency Components

In estimating the frequency spectrum of real-world time series data, we must violate the assumption of infinite-length, orthogonal components in the Fourier basis. While it is widely known that care must be taken with discretely sampled data to avoid aliasing of high frequencies, less attention is given to the influence of low frequencies with period below the sampling time window. Here, we derive an analytic expression for the side-lobe attenuation of signal components in the frequency domain representation. This expression allows us to detail the influence of individual frequency components throughout the spectrum. The first consequence is that the presence of low-frequency components introduces a 1/f[Formula: see text] component across the power spectrum, with a scaling exponent of [Formula: see text]. This scaling artifact could be composed of diffuse low-frequency components, which can render it difficult to detect a priori. Further, treatment of the signal with standard digital signal processing techniques cannot easily remove this scaling component. While several theoretical models have been introduced to explain the ubiquitous 1/f[Formula: see text] scaling component in neuroscientific data, we conjecture here that some experimental observations could be the result of such data analysis procedures.

scholarly journals The Impact of Audit Committee Characteristics on the Creative Accounting Practices Reduction in Jordanian Commercial Banks

2019 ◽  
Vol 13 (6) ◽  
pp. 113
Author(s):  
Khalil Suleiman Abu Saleem
Keyword(s):  
Commercial Banks ◽  
Audit Committee ◽  
Degrees Of Freedom ◽  
Time Series Data ◽  
Stock Exchange ◽  
Hypothesis Test ◽  
Annual Reports ◽  
Series Data ◽  
Creative Accounting ◽  
The Impact

This study aimed at determining the impact of the characteristics of the Audit Committee (The effect of Activity of the Audit Committee, the size of the Audit Committee, and Independence of the Audit Committee) in reducing creative accounting practicesin Jordanian commercial banks.The study population is composed of all Jordanian banks listed on the Amman Stock Exchange (16), during the period from 2011 to 2017. The study sample is represented by all Jordanian commercial banks. The current study is based on panel data since the data combine one-time and cross-section data for a period of time. The data was composed of a set of indicators for 13 Jordanian commercial banks for the period from 2011 to 2017, and data have been collected from the banks' annual reports. The adoption of the study on the analysis of time-series data comes from the increase in degrees of freedom. The results of the hypothesis test indicate that there is a significant effect of Audit Committee characteristics on the reduction of creative accounting practices in Jordanian banks at a level of significance of 0.05 except for variable (size of the Audit Committee).

scholarly journals Does Financial Liberalization Lead to Poverty Alleviation? New Evidence from Nigeria

2020 ◽  
Vol 8 (3) ◽  
pp. p22
Author(s):  
Onwuka Ifeanyi Onuka ◽  
Nwadiubu Anthony Odinakachukwu
Keyword(s):  
Private Sector ◽  
Poverty Alleviation ◽  
Agricultural Sector ◽  
Financial Liberalization ◽  
Time Series Data ◽  
Manufacturing Sector ◽  
Financial System ◽  
Series Data ◽  
Parameter Estimates ◽  
Bounds Testing

The study examined anew the empirical question of whether financial liberalization induces poverty alleviation. There is a theoretical expectation that liberalizing the financial market will lead to greater savings mobilization, greater access to credit facilities and poverty alleviation. Using a time-series data spanning 38 years (1980-2018), the study analyzed the effect of financial liberalization on credit availability to the private sector, the manufacturing sector especially the small & medium enterprises and the agricultural sector in Nigeria. The Bounds testing approach to co-integration employed within the framework of Autoregressive Distributed Lag model (ARDL) was used to generate the coefficients. The coefficient of financial liberalization-though positive in all the parameter estimates, it is not significant. This lead us to the conclusion that despite the advantages of financial liberalization, its benefits is yet to bring about significant positive increases or changes in the volume of credit to the private sector and in poverty alleviation. Inferring upon this, we deduced that the continued liberalization of the financial system though indicating a positive long run impact on financial widening (or financial deepening as the case may be), its manifestation on quantum of credit to the private sector and on poverty alleviation is yet to be realized in Nigeria. The study recommended, amongst others, that government should re-think and re-tool the process in ways that will generate stability in the financial system and unleash the potentials of the process to generate greater savings and ultimately greater investment in the real sectors of the economy.

Sign in / Sign up

Export Citation Format

Share Document