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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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Time-Series Graph00:54

Time-Series Graph

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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Related Experiment Video

Updated: Jun 29, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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Deep Learning for Dynamic Graphs: Models and Benchmarks.

Alessio Gravina, Davide Bacciu

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    |April 3, 2024
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    Summary
    This summary is machine-generated.

    Deep graph networks (DGNs) are advancing, but challenges remain for dynamic graphs. This study surveys DGNs for temporal and spatial learning and benchmarks current methods for predictive tasks.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Deep graph networks (DGNs) have matured, but face challenges in real-world dynamic systems.
    • Learning on graphs with evolving interconnected entities requires specialized approaches.

    Purpose of the Study:

    • To foster research in dynamic graphs by surveying current advancements.
    • To provide a comprehensive overview of state-of-the-art representation learning for dynamic graphs.
    • To establish a sound baseline for evaluating new dynamic graph learning methods.

    Main Methods:

    • Surveying recent advances in learning temporal and spatial information in dynamic graphs.
    • Conducting a fair performance comparison of popular dynamic graph learning approaches.
    • Utilizing rigorous model selection and assessment for node- and edge-level tasks.

    Main Results:

    • The study provides a comprehensive overview of the current state-of-the-art in dynamic graph representation learning.
    • A rigorous performance comparison of popular methods on node- and edge-level tasks is presented.
    • A sound baseline is established for future research in dynamic graph learning.

    Conclusions:

    • Addressing the need for DGNs in predictive tasks on evolving systems.
    • The survey and benchmark lay the groundwork for future research in dynamic graph representation learning.
    • This work facilitates the development of more effective DGNs for real-world dynamic systems.