Jove
Visualize
Contact Us

Related Concept Videos

Time-Series Graph00:54

Time-Series Graph

5.0K
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...
5.0K
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

672
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
672
Generation Time01:22

Generation Time

1.5K
Bacterial generation time, the period required for a bacterial population to double during its exponential growth phase, serves as a critical measure of microbial growth dynamics under optimal conditions. This parameter varies significantly across bacterial species and can be influenced by factors such as temperature, pH, and the availability of nutrients. For example, Escherichia coli can achieve a generation time of approximately 20 minutes, while Mycobacterium tuberculosis exhibits a much...
1.5K
Mixing Time01:19

Mixing Time

478
The concept of mixing time is significant in producing a uniform concrete mix with the required strength. The mixing period starts once all components are in the mixer. Initially, the mixer is charged with 10% of the water, followed by the consistent addition of solids and then 80% of the water. The remaining water is added later, within the first quarter of the mixing period. The minimum mixing time varies according to the mixer's capacity; for example, mixers with up to 1 cubic yard...
478
Mean free path and Mean free time01:22

Mean free path and Mean free time

5.1K
Consider the gas molecules in a cylinder. They move in a random motion as they collide with each other and change speed and direction. The average of all the path lengths between collisions is known as the "mean free path."
5.1K
Binary Fission01:26

Binary Fission

2.7K
Binary fission is the primary mode of asexual reproduction in prokaryotes, such as bacteria. It results in the production of two genetically identical daughter cells. This highly efficient process ensures the rapid propagation of bacterial populations under favorable conditions and involves coordinated cellular and molecular events.DNA Replication and SeparationThe process begins with the replication of the bacterial chromosome. The circular DNA molecule unwinds at a specific origin of...
2.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

YOLO-I3D: Optimizing Inflated 3D Models for Real-Time Human Activity Recognition.

Journal of imaging·2024
Same author

Probing the killing potency of tumor-infiltrating lymphocytes on microarrayed colorectal cancer tumoroids.

NPJ precision oncology·2024
Same author

Semi-automated approaches for interrogating spatial heterogeneity of tissue samples.

Scientific reports·2024
Same author

Fully automated sequential immunofluorescence (seqIF) for hyperplex spatial proteomics.

Scientific reports·2023
Same author

Node-Pore Coded Coincidence Correction: Coulter Counters, Code Design, and Sparse Deconvolution.

IEEE sensors journal·2018
Same author

BARKER-CODED NODE-PORE RESISTIVE PULSE SENSING WITH BUILT-IN COINCIDENCE CORRECTION.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)·2018
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems·2026
Same journal

Self-Supervised Continuous Dynamic Graph Representation Learning via Hawkes Processes.

IEEE transactions on neural networks and learning systems·2026
Same journal

cPU: Consistent Risk Estimator for Positive-Unlabeled Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing.

IEEE transactions on neural networks and learning systems·2026
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jan 27, 2026

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
14:28

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver

Published on: June 27, 2025

1.0K

A New Timing Error Cost Function for Binary Time Series Prediction.

Francois Rivest, Richard Kohar

    IEEE Transactions on Neural Networks and Learning Systems
    |March 26, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces the squared timing error (STE) to improve artificial intelligence forecasting for binary time series. STE outperforms traditional methods like sum of squared errors (SSE) in predicting event timing.

    More Related Videos

    Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring
    13:35

    Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

    Published on: June 13, 2025

    1.4K
    Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
    07:59

    Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

    Published on: June 9, 2023

    1.9K

    Related Experiment Videos

    Last Updated: Jan 27, 2026

    Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
    14:28

    Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver

    Published on: June 27, 2025

    1.0K
    Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring
    13:35

    Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

    Published on: June 13, 2025

    1.4K
    Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
    07:59

    Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

    Published on: June 9, 2023

    1.9K

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Time Series Analysis

    Background:

    • Accurate prediction is crucial for artificial intelligence.
    • Current machine learning struggles with long-term temporal dependencies, unlike animal learning.
    • Existing methods like sum of squared errors (SSE) are suboptimal for precise event timing prediction in binary time series.

    Purpose of the Study:

    • To address limitations in predicting binary time series with temporal precision.
    • To evaluate existing cost functions like SSE and dynamic time warping (DTW).
    • To propose and validate a novel cost function, squared timing error (STE), for improved temporal forecasting.

    Main Methods:

    • Evaluated SSE and DTW cost functions on synthetic and real-world binary time series.
    • Introduced the squared timing error (STE) by applying DTW to event space and SSE to timing error.
    • Developed a gradient descent algorithm for STE on a recurrent neural network.

    Main Results:

    • STE provides richer information on timing errors (phase shift, warping, missing events).
    • STE is differentiable and efficiently computable online.
    • STE-based gradient descent algorithm generally outperformed SSE- and logit-based algorithms on real-world data.

    Conclusions:

    • The squared timing error (STE) offers a more effective approach for time series forecasting with temporal precision.
    • STE enhances prediction accuracy and provides better insights into timing discrepancies.
    • The proposed STE algorithm shows significant promise for advancing AI forecasting capabilities.