Elementary mathematics

Spatial-numerical association of response codes

The spatial-numerical association of response codes (SNARC) is an example of the spatial organisation of magnitude information. Put simply, when presented with smaller numbers (0 to 4), people tend to respond faster if those stimuli are associated with the left extrapersonal hemiside of their perceived surroundings; when presented with larger numbers (6 to 9), people respond faster if those stimuli are instead associated with the right extrapersonal hemiside of their perceived surroundings. The SNARC effect is this automatic association that occurs between the location of the response hand and the semantic magnitude of a modality-independent number. Even for tasks in which magnitude is irrelevant, like parity judgement or phoneme detection, larger numbers are faster responded to with the right response key while smaller numbers are faster responded to with the left. This also occurs when the hands are crossed, with the right hand activating the left response key and vice versa. The explanation given by Dehaene and colleagues is that the magnitude of a number on an oriented mental number line is automatically activated. The mental number line is assumed to be oriented from left to right in populations with a left-to-right writing system (e.g. English), and oriented from right to left in populations with a right-to-left writing system (e.g. Iranian) (Wikipedia).

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Frequency Response Descriptions for LTI Systems

http://AllSignalProcessing.com for free e-book on frequency relationships and more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. An introduction to the description of the input output characteristics of linear time-invariant systems b

From playlist Introduction and Background

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Frequency Response Magnitude and Poles and Zeros

http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Graphical interpretation of the magnitude response of a system described by a linear constant-coefficient difference equation in terms of the locati

From playlist The z-Transform

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10b Data Analytics: Spatial Continuity

Lecture on the impact of spatial continuity to motivate characterization and modeling of spatial continuity.

From playlist Data Analytics and Geostatistics

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The tool that engineers use to design buildings in earthquake zones | The response spectrum

Earthquakes are one of the most destructive forces of nature. They could induce substantial movement in the ground, which results in the development of excessive forces in structural components, resulting in their failure. The intent of the analysis is to somehow predict the **maximum resp

From playlist Summer of Math Exposition Youtube Videos

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Frequency Response

Transfer Functions, Resonance, and Frequency Response. My Patreon page is at: https://www.patreon.com/EugeneK

From playlist Physics

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Recommender Systems -Memory Based Collaborative Filtering - Session 4

Memory based collaborative filtering Matrix representation of user-item interactions User vector similarity measures Computational cost and Approximate nearest neighbours Evaluation for collaborative filtering

From playlist Recommenders Systems (Hands-on)

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Estimate the Correlation Coefficient Given a Scatter Plot

This video explains how to estimate the correlation coefficient given a scatter plot.

From playlist Performing Linear Regression and Correlation

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10c Data Analytics: Variogram Introduction

Lecture on the variogram as a measure to quantify spatial continuity.

From playlist Data Analytics and Geostatistics

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How to find the magnitude and unit vector from a given vector

http://www.freemathvideos.com In this video series you will learn multiple math operations. I teach in front of a live classroom showing my students how to solve math problems step by step. My math tutorials should be used to review previous lessons, complete your homework, or study for

From playlist Vectors

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Climate Models and Climate Sensitivity

Dan Lunt, University of Bristol, UK, delivers a talk entitled, "Climate Models and Climate Sensitivity", at the YCEI conference, "Uncertainty in Climate Change: A Conversation with Climate Scientists and Economists".

From playlist Uncertainty in Climate Change: A Conversation with Climate Scientists and Economists

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Remembering and the Brain: Can Brain Scans Detect Memories?

(October 23, 2009) Stanford Professor of psychology and neuroscience, Anthony Wagner PhD, discusses how the brain supports memory for everyday events, and will evaluate whether "mind reading" with brain imaging can detect when a person remembers the past and how this might be used as evide

From playlist Reunion Homecoming

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Numerator dynamics

Numerator dynamics can be predicted by understanding how to expand the transfer function and reason about derivatives

From playlist Laplace

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Numerical relativity: Mathematical formulation by Harald Pfeiffer

PROGRAM: GRAVITATIONAL WAVE ASTROPHYSICS (ONLINE) ORGANIZERS : Parameswaran Ajith, K. G. Arun, Sukanta Bose, Bala R. Iyer, Resmi Lekshmi and B Sathyaprakash DATE: 18 May 2020 to 22 May 2020 VENUE: Online Due to the ongoing COVID-19 pandemic, the original program has been cancelled. Howe

From playlist Gravitational Wave Astrophysics (Online) 2020

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Lattice Supersymmetric Field Theories (Lecture 3) by David Schaich

PROGRAM NONPERTURBATIVE AND NUMERICAL APPROACHES TO QUANTUM GRAVITY, STRING THEORY AND HOLOGRAPHY (HYBRID) ORGANIZERS: David Berenstein (University of California, Santa Barbara, USA), Simon Catterall (Syracuse University, USA), Masanori Hanada (University of Surrey, UK), Anosh Joseph (II

From playlist NUMSTRING 2022

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EMinar 3,4: Michael Pyrcz - Machine Learning-based Geoscience

The subsurface resource industry has a long history of working with large, complicated geoscience and engineering datasets. The subsurface industry been working with ‘big data’ for decades! There is a growing toolbox of legacy and new emerging data-driven methods available that may offer i

From playlist Random Talks

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18 Machine Learning: Conclusion

Final lecture with the take-aways from the Subsurface Machine Learning course to help you succeed with machine learning for spatial, subsurface applications.

From playlist Machine Learning

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GED for spatial filtering and dimensionality reduction

Generalized eigendecomposition is a powerful method of spatial filtering in order to extract components from the data. You'll learn the theory, motivations, and see a few examples. Also discussed is the dangers of overfitting noise and few ways to avoid it. The video uses files you can do

From playlist OLD ANTS #9) Matrix analysis

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36. Final Lecture

MIT 10.34 Numerical Methods Applied to Chemical Engineering, Fall 2015 View the complete course: http://ocw.mit.edu/10-34F15 Instructor: James Swan This session dedicated to a review of all different numerical methods students learned from this course. License: Creative Commons BY-NC-SA

From playlist MIT 10.34 Numerical Methods Applied to Chemical Engineering, Fall 2015

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Anthony Patera: Parametrized model order reduction for component-to-system synthesis

Abstract: Parametrized PDE (Partial Differential Equation) Apps are PDE solvers which satisfy stringent per-query performance requirements: less-than or approximate 5-second problem specification time; less-than or approximate 5-second problem solution time, field and outputs; less-than or

From playlist Numerical Analysis and Scientific Computing

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Ex: Find the Magnitude of a Vector in 3D

This video explains how to determine the magnitude of a vector in 3D. Site: http://mathispower4u.com

From playlist Vectors in Space (3D)

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