Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Compacting Factor test

Compacting Factor test

The compacting factor test is a method used to assess the workability of concrete. It is especially suitable for concrete mixes containing aggregates up to one and a half inches in size. This test involves specialized equipment consisting of two truncated cone-shaped hoppers and a cylinder, all with polished interior surfaces to minimize friction.
The procedure begins by placing concrete into the upper hopper without any compaction. Once filled, the bottom door of this hopper is opened,...

Types of Errors: Detection and Minimization

Types of Errors: Detection and Minimization

Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...

Statistical Analysis: Overview

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...

Convergence of Fourier Series

Convergence of Fourier Series

The Fourier series is a powerful mathematical tool for representing periodic signals as an infinite sum of complex exponentials. In practice, this infinite series is truncated to a finite number of terms, yielding a partial sum. This truncation makes the approximation of the signal feasible but introduces certain challenges, particularly near discontinuities, known as the Gibbs phenomenon.
The Gibbs phenomenon refers to the persistent oscillations and overshoots that occur near discontinuities...

Extraction: Partition and Distribution Coefficients

Extraction: Partition and Distribution Coefficients

The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...

The Squeeze Theorem

The Squeeze Theorem

Certain mathematical functions exhibit unpredictable or highly variable behavior near specific input values, making direct evaluation of their limits challenging. This complexity may arise from rapid oscillations or irregular patterns that obscure the function’s trend. In such cases, the Squeeze Theorem offers a reliable method for determining limits.According to the Squeeze Theorem, if a function is confined between two other functions near a particular point, and both outer functions...

You might also read

Related Articles

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

Sort by

Same author

Compression detects changes in spiking neural data from cortical lesions.

Journal of neural engineering·2026

Same author

Information-computation trade-offs in nonlinear transforms.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026

Same author

Compression Detects Changes in Spiking Neural Data from Cortical Lesions.

bioRxiv : the preprint server for biology·2026

Same author

A Framework for Compressive On-Chip Action Potential Recording.

IEEE transactions on bio-medical engineering·2025

Same author

Pan-conserved segment tags identify ultra-conserved sequences across assemblies in the human pangenome.

Cell reports methods·2023

Same author

Magnetic DNA random access memory with nanopore readouts and exponentially-scaled combinatorial addressing.

Scientific reports·2023

Same journal

Faster Maximal Exact Matches with Lazy LCP Evaluation.

Proceedings. Data Compression Conference·2024

Same journal

Recursive Prefix-Free Parsing for Building Big BWTs.

Proceedings. Data Compression Conference·2024

Same journal

Computing matching statistics on Wheeler DFAs.

Proceedings. Data Compression Conference·2024

Same journal

Augmented Thresholds for MONI.

Proceedings. Data Compression Conference·2024

Same journal

CSTs for Terabyte-Sized Data.

Proceedings. Data Compression Conference·2024

Same journal

PHONI: Streamed Matching Statistics with Multi-Genome References.

Proceedings. Data Compression Conference·2021

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 20, 2026

A Uniaxial Compression Experiment with CO2-Bearing Coal Using a Visualized and Constant-Volume Gas-Solid Coupling Test System

A Uniaxial Compression Experiment with CO2-Bearing Coal Using a Visualized and Constant-Volume Gas-Solid Coupling Test System

Published on: June 12, 2019

Compression for Similarity Identification: Computing the Error Exponent.

Amir Ingber¹, Tsachy Weissman²

¹Yahoo! Labs, 701 First Ave., Sunnyvale, CA 94089.

Proceedings. Data Compression Conference

|October 20, 2017

Summary

This summary is machine-generated.

This study introduces a computable method for data compression focused on similarity identification. We establish a cardinality bound for the identification exponent, resolving a long-standing problem in information theory.

More Related Videos

Author Spotlight: Advancing Understanding of Age-Related Lens Stiffness Changes

Author Spotlight: Advancing Understanding of Age-Related Lens Stiffness Changes

Published on: April 5, 2024

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Related Experiment Videos

Last Updated: Feb 20, 2026

A Uniaxial Compression Experiment with CO2-Bearing Coal Using a Visualized and Constant-Volume Gas-Solid Coupling Test System

A Uniaxial Compression Experiment with CO2-Bearing Coal Using a Visualized and Constant-Volume Gas-Solid Coupling Test System

Published on: June 12, 2019

Author Spotlight: Advancing Understanding of Age-Related Lens Stiffness Changes

Author Spotlight: Advancing Understanding of Age-Related Lens Stiffness Changes

Published on: April 5, 2024

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Area of Science:

Information Theory
Data Compression
Coding Theory

Background:

The problem of compressing data sequences for similarity identification was introduced by Ahlswede et al. (1997).
Existing methods lack computability due to unbounded auxiliary random variables in the identification exponent formula.

Purpose of the Study:

To develop a computable expression for the identification exponent in data compression for similarity identification.
To address the open problem left by Ahlswede et al. regarding the computability of the identification exponent.

Main Methods:

Derivation of a cardinality bound for the auxiliary random variable in the identification exponent.
Utilizing a novel proof technique involving the decomposition of the Lagrangian by coordinate.
Applying a Carathéodory-style argument to complete the proof.

Main Results:

A computable expression for the identification exponent is established.
The cardinality bound effectively resolves the computability issue for the identification exponent.
The new proof technique offers a novel approach to optimization problems in information theory.

Conclusions:

The study successfully makes the identification exponent computable, advancing the field of data compression for similarity identification.
The findings provide a practical tool for analyzing compression schemes where similarity is the primary goal.
This work opens new avenues for research in information theory and related computational problems.