Flexible Interaction Model for Complex Interactions of Multiple Anesthetics

2006 ◽  
Vol 105 (2) ◽  
pp. 286-296 ◽  
Author(s):  
Matthew Fidler ◽  
Steven E. Kern
Keyword(s):  
Drug Interactions ◽  
Interaction Model ◽  
Data Sets ◽  
Additive Interaction ◽  
New Model ◽  
Complex Interactions ◽  
Anesthetic Drugs ◽  
Proposed Model ◽  
Quantitative Manner ◽  
Using Data

Background Minto et al. (Anesthesiology 2000) described a mathematical approach based on response surface methods for characterizing drug-drug interactions between several intravenous anesthetic drugs. To extend this effort, the authors developed a flexible interaction model based on the general Hill dose-response relation that includes a set of parameters that can be statistically assessed for interaction significance. Methods This new model was developed to identify pharmacologically meaningful interaction-related parameters and address mathematical limitations in previous models. The flexible interaction model and the model of Minto et al. were compared in their assessment of additivity using simulated sample data sets. The flexible interaction model was also compared with the Minto model in describing drug interactions using data from several other clinical studies of propofol, opioids, and benzodiazepines from Short et al. (Anesthesiology 2002) and Kern et al. (Anesthesiology 2004). Results The flexible interaction model was able to accurately classify an additive interaction based on the classic definition proposed by Loewe, with at most an 8% difference between the two surfaces. Also, the proposed model fit the clinical interaction data as well or slightly better than that of Minto et al. Conclusions The new model can accurately classify additive and synergistic drug interactions. It also can classify antagonistic interactions with biologically rational surfaces. This has been a problem for other interaction models in the past. The statistically assessable interaction parameters provide a quantitative manner to assess the interaction significance.

scholarly journals Forecasting Realized Volatility Using a Nonnegative Semiparametric Model

2019 ◽  
Vol 12 (3) ◽  
pp. 139 ◽  
Author(s):  
Anders Eriksson ◽  
Daniel P. A. Preve ◽  
Jun Yu
Keyword(s):  
Predictive Accuracy ◽  
Estimation Method ◽  
Semiparametric Model ◽  
Error Component ◽  
New Model ◽  
Finite Samples ◽  
Proposed Model ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Using Data

This paper introduces a parsimonious and yet flexible semiparametric model to forecast financial volatility. The new model extends a related linear nonnegative autoregressive model previously used in the volatility literature by way of a power transformation. It is semiparametric in the sense that the distributional and functional form of its error component is partially unspecified. The statistical properties of the model are discussed and a novel estimation method is proposed. Simulation studies validate the new method and suggest that it works reasonably well in finite samples. The out-of-sample forecasting performance of the proposed model is evaluated against a number of standard models, using data on S&P 500 monthly realized volatilities. Some commonly used loss functions are employed to evaluate the predictive accuracy of the alternative models. It is found that the new model generally generates highly competitive forecasts.

A Regional Zenith Tropospheric Correction Model Based on the Guangxi CORS Network

2013 ◽  
Vol 353-356 ◽  
pp. 3438-3443
Author(s):  
Li Long Liu ◽  
Liang Ke Huang ◽  
Teng Xu Zhang ◽  
Miao Zhou ◽  
Chao Long Yao
Keyword(s):  
Interpolation Method ◽  
Meteorological Data ◽  
Correction Model ◽  
New Model ◽  
Model Based ◽  
Proposed Model ◽  
New Models ◽  
Using Data ◽  
The Relationship ◽  
Tropospheric Delays

In this paper, the relationship between zenith tropospheric delays and the altitude of stations is analyzed using the EGNOS tropospheric correction model. The new model (EHT model) is proposed for estimating zenith tropospheric delays from regional CORS data without meteorological data. The proposed model is compared with the direct interpolation method and the remove-restore method using data from Guangxi CORS. The results show that the new models significantly improve the calculated precision.

scholarly journals Macroscopic Traffic-Flow Modelling Based on Gap-Filling Behavior of Heterogeneous Traffic

2021 ◽  
Vol 11 (9) ◽  
pp. 4278
Author(s):  
Muhammad Umair Khan ◽  
Salman Saeed ◽  
Moncef L. Nehdi ◽  
Rashid Rehan
Keyword(s):  
Traffic Flow ◽  
Heterogeneous Traffic ◽  
Gap Filling ◽  
New Model ◽  
Flow Models ◽  
Flow Modelling ◽  
Complex Interactions ◽  
Proposed Model ◽  
Traffic Flow Models ◽  
Traffic Flow Modelling

Traffic-flow modelling has been of prime interest to traffic engineers and planners since the mid-20th century. Most traffic-flow models were developed for the purpose of characterizing homogeneous traffic flow. Some of these models are extended to characterize the complex interactions involved in heterogeneous traffic flow. Existing heterogeneous traffic-flow models do not characterize the driver behavior leading to gap filling in heterogeneous traffic conditions. This study aimed at explaining the gap-filling behavior in heterogeneous traffic flow by using the effusion model of gas particles. The driver’s behavior leading to gap filling in heterogeneous traffic was characterized through developing analogies between the traffic flow and the Maxwell–Boltzmann equation for effusion of gases. This model was subsequently incorporated into the Payne–Whitham (PW) model by replacing the constant anticipation term. The proposed model was numerically approximated by using Roe’s scheme, and numerical simulation of the proposed model was then carried out by using MATLAB. The results of the proposed and PW models were therefore compared. It is concluded that the new model proposed in this study not only produces better results compared to the PW model, but also better captures the expected reality. The main difference between the behavior of the two models is that the effect of bottleneck in the density of traffic is propagated in the form of a shockwave travelling backwards in time in the new model, while the PW model does not exhibit this effect.

scholarly journals Marshll–Olkin Extended Inverse Pareto Distribution and its Application

2017 ◽  
Vol 6 (6) ◽  
pp. 71
Author(s):  
M- Gharib ◽  
B-I- Mohammed ◽  
W-E-R- Aghel
Keyword(s):  
Maximum Likelihood ◽  
Order Statistics ◽  
Simulation Study ◽  
Pareto Distribution ◽  
Real Data ◽  
Failure Criteria ◽  
Data Sets ◽  
New Model ◽  
Proposed Model ◽  
Family Of Distributions

This paper introduces a new extension of the Inverse Pareto distribution along with in the framework of Marshal-Olkin (1997) family of distributions. This model is capable of modeling various shapes of aging and failure criteria. The statistical properties of the new model are discussed and the maximum likelihood and maximum product spacing’s methods are used to estimate the parameters involved. Explicit expressions are derived for the moments and the order statistics are examined for the new proposed model. Finally, the usefulness of the new model for modeling reliability data is illustrated using two real data sets with simulation study.

scholarly journals A discrete-time-evolution model to forecast progress of Covid-19 outbreak

2020 ◽  
Author(s):  
Evaldo M. F. Curado ◽  
Marco R. Curado
Keyword(s):  
South Korea ◽  
Time Evolution ◽  
Discrete Time ◽  
Data Sets ◽  
Infected Person ◽  
New Model ◽  
Infected People ◽  
Proposed Model ◽  
Infection Models ◽  
Many Sources

AbstractBased on well-known infection models, we constructed a new model to forecast the propagation of the Covid-19 pandemic which yields a discrete-time evolution with one day interval. The proposed model can be easily implemented with daily updated data sets of the pandemic publicly available by many sources. It has only two adjustable parameters and is able to predict the evolution of the total number of infected people in a country for the next 14 days, if parameters do not change during this time. The model incorporates the main aspects of the disease such as the the fact that there are asymptomatic and symptomatic phases (both capable of propagating the virus), and that these phases take almost two weeks before the infected person status evolves to the next (asymptomatic becomes symptomatic or symptomatic becomes either recovered or dead). One advantage of the model is that it gives directly the number of total infected people in each day (in thousands, tens of thousands or hundred of thousands). The model was tested with data from Brazil, UK and South Korea, it predicts quite well the evolution of the disease and therefore may be a useful tool to estimate the propagation of the disease.

Assessment of Regional Explosion Discriminants Using Data Sets of Unparalleled Spatial Sampling

2012 ◽  
Author(s):  
Kate C. Miller ◽  
Lindsay L. Worthington ◽  
Steven Harder ◽  
Scott Phillips ◽  
Hans Hartse ◽  
...  
Keyword(s):  
Spatial Sampling ◽  
Data Sets ◽  
Using Data

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

scholarly journals UAV Image Multi-Labeling with Data-Efficient Transformers

2021 ◽  
Vol 11 (9) ◽  
pp. 3974
Author(s):  
Laila Bashmal ◽  
Yakoub Bazi ◽  
Mohamad Mahmoud Al Rahhal ◽  
Haikel Alhichri ◽  
Naif Al Ajlan
Keyword(s):  
Data Augmentation ◽  
Feature Representation ◽  
Aerial Image ◽  
Remote Sensing Images ◽  
Training Set ◽  
Proposed Model ◽  
Class Labels ◽  
Using Data ◽  
Uav Image

In this paper, we present an approach for the multi-label classification of remote sensing images based on data-efficient transformers. During the training phase, we generated a second view for each image from the training set using data augmentation. Then, both the image and its augmented version were reshaped into a sequence of flattened patches and then fed to the transformer encoder. The latter extracts a compact feature representation from each image with the help of a self-attention mechanism, which can handle the global dependencies between different regions of the high-resolution aerial image. On the top of the encoder, we mounted two classifiers, a token and a distiller classifier. During training, we minimized a global loss consisting of two terms, each corresponding to one of the two classifiers. In the test phase, we considered the average of the two classifiers as the final class labels. Experiments on two datasets acquired over the cities of Trento and Civezzano with a ground resolution of two-centimeter demonstrated the effectiveness of the proposed model.

RoRePo: Detecting the role information and relative position information for contexts in multi-turn dialogue generation

2021 ◽  
Vol 40 (5) ◽  
pp. 10003-10015
Author(s):  
Zibang Gan ◽  
Biqing Zeng ◽  
Lianglun Cheng ◽  
Shuai Liu ◽  
Heng Yang ◽  
...  
Keyword(s):  
Relative Position ◽  
Latent Variables ◽  
Role Ambiguity ◽  
Position Information ◽  
New Model ◽  
Proposed Model ◽  
Relevant Context ◽  
Position Representation ◽  
Current Speaker ◽  
Relative Position Information

In multi-turn dialogue generation, dialogue contexts have been shown to have an important influence on the reasoning of the next round of dialogue. A multi-turn dialogue between two people should be able to give a reasonable response according to the relevant context. However, the widely used hierarchical recurrent encoder-decoder model and the latest model that detecting the relevant contexts with self-attention are facing the same problem. Their given response doesn’t match the identity of the current speaker, which we call it role ambiguity. In this paper, we propose a new model, named RoRePo, to tackle this problem by detecting the role information and relative position information. Firstly, as a part of the decoder input, we add a role embedding to identity different speakers. Secondly, we incorporate self-attention mechanism with relative position representation to dialogue context understanding. Besides, the design of our model architecture considers the influence of latent variables in generating more diverse responses. Experimental results of our evaluations on the DailyDialog and DSTC7_AVSD datasets show that our proposed model advances in multi-turn dialogue generation.

Sign in / Sign up

Export Citation Format

Share Document