mteb/BrightProPsychologyRetrieval huggingface dataset:
Content_comparison_using_image_distance_measures
"Content-based image retrieval") .  [   edit
 ]

The most common method for comparing two images in content-based image
retrieval (typically an example image and an image from the database) is using
an image distance measure. An image distance measure compares the similarity
of two images in various dimensions such as color, texture, shape, and others.
For example, a distance of 0 signifies an exact match with the query, with
respect to the dimensions that were considered. As one may intuitively gather,
a value greater than 0 indicates various degrees of similarities between the
images. Search results then can be sorted based on their distance to the
queried image.  [11]  Many measures of image distance (Similarity Models) have
been developed.  [12]
###  VARIABLES
Data by Location
###   Ocean Heat Content Rises
In WODselect:
##  Data Sources
###  Article Tags
###  Data Specifics
* **WOD** (World Ocean Database) 2009 (CTD, XBT, MBT at observed levels):
_ftp link_ : [ ftp of geographic sorted data from NODC NOAA server
](ftp://ftp.nodc.noaa.gov/pub/WOD09/GEOGRAPHIC/)
_website_ :  NOAA NODC WOD
pg_3.gif
June 14, 2017
Does the dataset include yearly salt content data?
Cross-entropy_loss_function_and_logistic_regression "Cross-entropy")

 Clustering  |

*  Silhouette  "Silhouette \(clustering\)")
*  Calinski-Harabasz index 
*  Davies-Bouldin 
*  Dunn index 
*  Hopkins statistic 
*  Jaccard index 
*  Rand index 
* Similarity measure
*  SMC 
*  SimHash 

 Ranking  "Ranking \(information
retrieval\)") |

*  MRR 
*  DCG 
*  NDCG 
*  AP 

 Computer Vision  |
[  ](/news/long-term-view-gulf-mexico
Broken links corrected.
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Data by Year
Use of different similarity measure formulas  

Different types of similarity measures exist for various types of objects,
depending on the objects being compared. For each type of object there are
various similarity measurement formulas.  [2]

**Similarity between two data points**

 Image
shows the path of calculation when using the Euclidean distance formula

There are many various options available when it comes to finding similarity
between two data points, some of which are a combination of other similarity
methods. Some of the methods for similarity measures between two data points
include Euclidean distance, Manhattan distance, Minkowski distance, and
Chebyshev distance. The Euclidean distance formula is used to find the
distance between two points on a plane, which is visualized in the image
below. Manhattan distance is commonly used in  GPS 
applications, as it can be used to find the shortest route between two
addresses. When you generalize the Euclidean distance formula and Manhattan
distance formula you are left with the Minkowski distance formula, which can
be used in a wide variety of applications.

*  Euclidean distance 
*  Manhattan distance 
*  Minkowski distance
###   World Ocean Atlas 2023
Similarity_metric "Hierarchical clustering")

Clustering or  Cluster analysis 
is a data mining technique that is used to discover patterns in data by
grouping similar objects together. It involves partitioning a set of data
points into groups or clusters based on their similarities. One of the
fundamental aspects of clustering is how to measure similarity between data
points.

Similarity measures play a crucial role in many clustering techniques, as they
are used to determine how closely related two data points are and whether they
should be grouped together in the same cluster. A similarity measure can take
many different forms depending on the type of data being clustered and the
specific problem being solved.

One of the most commonly used similarity measures is the  Euclidean distance
 , which is used in many
clustering techniques including  K-means clustering
 and  Hierarchical clustering
 . The Euclidean
distance is a measure of the straight-line distance between two points in a
high-dimensional space. It is calculated as the square root of the sum of the
squared differences between the corresponding coordinates of the two points.
For example, if we have two data points  (  x  1  ,  y  1  )  {\displaystyle
(x1,y1)}  
and  (  x  2  ,  y  2  )  {\displaystyle (x2,y2)}  
, the Euclidean distance between them is  d  =  √  [  (  x  2  −  x  1  )  2
\+  (  y  2  −  y  1  )  2  ]  {\displaystyle d=\surd
A Long-term View of the Gulf of Mexico
Boyer, T.P., J.I. Antonov, O.K. Baranova, C. Coleman, H.E. Garcia, A. Grodsky,
D.R. Johnson, R.A. Locarnini, A.V. Mishonov, T.D. O'Brien, C.R. Paver, J.R.
Reagan, D. Seidov, I.V. Smolyar, and M.M. Zweng, 2013: World Ocean Database
2013., S. Levitus, Ed., A. Mishonov, Technical Ed.; NOAA Atlas NESDIS 72, 209
pp.,  doi:10.7289/V5NZ85MT  .
mceclip0.png
##  Ocean Profiles
##  About Quality Control
March 31, 2021
###   A Long-term View of the Gulf of Mexico
Boyer, T.P., O.K. Baranova, C. Coleman, H.E. Garcia, A. Grodsky, R.A.
Locarnini, A.V. Mishonov, C.R. Paver, J.R. Reagan, D. Seidov, I.V. Smolyar, K.
Weathers, M.M. Zweng,(2018): World Ocean Database 2018. A.V. Mishonov,
Technical Ed., NOAA Atlas NESDIS 87. 
https://www.ncei.noaa.gov/sites/default/files/2020-04/wod_intro_0.pdf
World Ocean Database
[ Read More ](/news/long-term-view-gulf-mexico
###  Access
Ocean areas for each set of variable min/max ranges
 range_basin_list.msk
*  Cosine similarity 
*  Euclidean distance 
*  Pearson correlation coefficient 

*  Confusion matrix 

Retrieved from " 
https://en.wikipedia.org/w/index.php?title=Similarity_measure&oldid=1210618540

"

 Categories  :

*  Clustering criteria 
*  Statistical classification 
*  Statistical distance 
*  Similarity measures 

Hidden categories:

*  CS1 maint: others 
*  Articles with short description 
*  Short description is different from Wikidata 
*  Articles with excerpts
Code Table Library
[  ](/news/marie-tharp
We have pentadal salinity anomalies back to 1955. See  Global Heat and Salt Content  , but yearly data only spans the Argo time period (i.e., 2005 onward) and is available on the salinity anomaly page.
What are the units for 0-700 m heat content?
Use in recommender systems  

Similarity measures are used to develop  recommender systems
 . It observes a user's
perception and liking of multiple items. On recommender systems, the method is
using a distance calculation such as ** Euclidean Distance  ** or ** Cosine
Similarity  ** to generate a ** similarity matrix  ** with values representing
the similarity of any pair of targets. Then, by analyzing and comparing the
values in the matrix, it is possible to match two targets to a user's
preference or link users based on their marks. In this system, it is relevant
to observe the value itself and the absolute distance between two values.  [7]
Gathering this data can indicate a mark's likeliness to a user as well as how
mutually closely two marks are either rejected or accepted. It is possible
then to recommend to a user targets with high similarity to the user's likes.

Recommender systems are observed in multiple online entertainment platforms,
in social media and streaming websites. The logic for the construction of this
systems is based on similarity measures.  [8]
1.    [ Home ](../mld/home.php)
2.    [ Data Description ](../mld/Data_Description.php)
3.    [ Data Access ](../mld/Data.php)
1. [ • L1 Profile Data (links only) ](../mld/Profile_Data.php)
2. [ • L2 Pointwise Data ](../mld/data_request.php)
3. [ • L3 Gridded Data ](../mld/gridded_data.php)
1. [ \- MLD ](../mld/Surface_Mixed_Layer_Depth.php)
2. [ \- TTD ](../mld/Surface_Warm_Layer_Depth.php)
3. [ \- BLT ](../mld/Subsurface_Barrier_Layer_Thickness.php)
4. [ \- BLP ](../mld/Subsurface_Barrier_Layer_Porosity.php)
4.    [ References ](../mld/References.php)
5.    [ Links ](../mld/Links.php)
6.    [ Acknowledgments ](../mld/Acknowledgments.php)
7.    [ Contact ](../mld/Contact.php)
##  Corrections
##  WOD Programs
1.  Home 
2.  Products 
3. World Ocean Database
We do include information on the method used for nutrient (and other) data
when it is available. Variable description for the second header 6 may be
found on  WOD codes
 page, or
accessed directly through the  methods list
 .
For additional help, please contact  OCL.help@noaa.gov
 .
The WOD contains records dating to the second of three voyages of notable
British Naval explorer Capt. James Cook. Launched in 1772, the expedition led
by Cook on the HMS Resolution circumnavigated the globe at an extreme southern
latitude and became one of the first ships to cross the Antarctic Circle in
January 1773. Naturalists and botanists on his voyages made observations and
discoveries. Log books of weather and other scientific information from Cook’s
voyages are kept in  The National Archives
 at Kew, England.
*  Expendable Bathythermograph (XBT) Corrections 
*  XBT References Table 
*  Mechanical Bathythermograph (MBT) Corrections
The World Ocean Database and World Ocean Atlas are available for public use without restriction. Please let us know about WOD and WOA based publications by sending citations to  ncei.info@noaa.gov. 
How do you extract header information from historical temperature profiles
without processing the data?
##  Ocean Data Instruments
There isn’t a definitive set of methods for measuring or calculating nutrient
data. From page 45 of the  WOD Introduction
 :
Aquatic Sciences
For additional documentation and access methods, visit the  dataset landing
page  .
Use in computer vision  

This section is an excerpt from [ Content-based image retrieval § Content
comparison using image distance measures ](/wiki/Content-
based_image_retrieval
Download Observations
L1 Profile Data
See also  

*  Affinity propagation 
*  Latent space  – Embedding of data within a manifold based on a similarity function
*  Similarity learning  – Supervised learning of a similarity function
*  Self-similarity matrix 
*  Semantic similarity  – Natural language processing
*  Similarity (network science)  "Similarity \(network science\)") – in network analysis, when two nodes (or other more elaborate structures) fall in the same equivalence class  Pages displaying wikidata descriptions as a fallback
*  Similarity (philosophy)  "Similarity \(philosophy\)") – Relation of resemblance between objects
*  Statistical distance  – Distance between two statistical objects
*  String metric  – metric that measures the distance between two strings of text  Pages displaying wikidata descriptions as a fallback
*  Similarity search  – Searching for similar items in a data set
*  tf–idf  – Estimate of the importance of a word in a document
*  Recurrence plot  , a visualization tool of recurrences in dynamical (and other) systems
Which methods are used to measure nutrient data?
###  Current Version: WOD18
comparing two probability distributions that represent different categories.
For example, can be used to classify images based on their features. Hellinger
distance is a powerful tool for  text mining  and classifying documents. Generally, it is used on probability
distributions that represent the frequency of different words, allowing you to
find similar documents.

*  Mahalanobis distance 
*  Bhattacharyya distance 
*  Hellinger distance 

**Similarity between two sets**

A set is a collection of items with no order or repetition. They are generally
used to represent relationships or associations between objects or even
people. The Jaccard index formula measures the similarity between two sets
based on the number of items that are present in both sets relative to the
total number of items. The Jaccard index is commonly used in  recommendation
systems  and  social
media analysis  . For
example, could recommend a new application to download based on your previous
downloads. The Sørensen–Dice coefficient also compares the number of items in
both sets to the total number of items present but the weight for the number
of shared items is larger. The Sørensen–Dice coefficient is commonly used in 
biology  applications, measuring the similarity
between two sets of genes or species.

*  Jaccard index 
*  Sørensen–Dice coefficient
Boyer, T.P., J.I. Antonov, H.E. Garcia, D.R. Johnson, R.A. Locarnini, A.V.
Mishonov, M T. Pitcher, O.K. Baranova, I.V. Smolyar, 2006. World Ocean
Database 2005. S. Levitus, Ed., NOAA Atlas NESDIS 60, 190 pp., 
https://repository.library.noaa.gov/view/noaa/1131
####  **Observation File Columns**
###  Global Ocean Heat and Salt Content
WOD User Manual
WOD Updates
*  PSNR 
*  SSIM 
*  IoU 

 NLP  |

*  Perplexity 
*  BLEU 

Deep Learning Related Metrics  |

*  Inception score 
*  FID 

 Recommender system  |

*  Coverage 
*  Intra-list Similarity ")

Similarity  |
**Similarity between two sequences**

When comparing temporal sequences (time series), some similarity measures must
additionally account for similarity of two sequences that are not fully
aligned.

*  Dynamic time warping
*  MAE 
*  sMAPE 
*  MAPE 
*  MASE 
*  MSPE 
*  RMS 
*  RMSE/RMSD 
*  R  2  
*  MDA 
*  MAD 

 Classification  |

*  F-score 
*  P4 
*  Accuracy 
*  Precision 
*  Recall 
*  Kappa 
*  MCC 
* [ AUC ](/wiki/Receiver_operating_characteristic
wod_nc.f
 Sample
FORTRAN program for reading WOD ragged array netCDF files  readFOR.txt
 Readme
file describes the wodFOR programs  wodFOR.f
 Sample
FORTRAN program for reading the data  sampFOR.txt
 Sample of
output from wodFOR.f  readASC.txt
 Describes
the use of wodASC.f  wodASC.f
 Outputs a
user selected variable in either tabular or comma separated columns 
wodASC.exe

Executable for wodASC.f program  sampASC.txt
 Sample
output data from wodASC.f  wodSUR.f
 Writes
surface-only data out in a comma-separated-value (CSV) format  wodSUR.exe
 Microsoft
compatible executable for wodSUR.f  sampSUR.txt
 Sample of
output from wodSUR.exe  instructions from WOD to csv

Instructions to convert WOD format to ArcMap readable 'csv' format 
csvfromwod.c (β - version)
 C
program for conversion data from WOD format to ArcMap readable 'csv' format [
csvfromwod.exe
##  Frequently Asked Questions
Range basins list
 sd_multiplier.msk
*  Chebyshev distance 

**Similarity between strings**

For comparing strings, there are various measures of  string similarity
 that can be used. Some of these methods
include edit distance, Levenshtein distance, Hamming distance, and Jaro
distance. The best-fit formula is dependent on the requirements of the
application. For example, edit distance is frequently used for  natural
language processing  applications and features, such as spell-checking. Jaro distance
is commonly used in record linkage to compare first and last names to other
sources.

*  Edit distance 
*  Levenshtein distance 
*  Lee distance 
*  Hamming distance 
*  Jaro distance 

**Similarity between two probability distributions**

When comparing  probability distributions  the Mahalanobis distance formula, Bhattacharyya
distance formulas, and the Hellinger distance formula are all very powerful
and useful. The Mahalanobis distance formula is commonly used in  statistical
analysis  . It measures
the distance between two probability distributions that have different means
and variances. This makes it useful for finding outliers across the datasets.
The Bhattacharyya distance formula is generally used in image processing,
*  @NOAANCEI 
*  @NOAAData 
*  @NOAANCEI 
*  News Feed
Salinity/Density
In this article, we'll talk about:
##  Major Releases
*  Pages displaying wikidata descriptions as a fallback via Module:Annotated link 

* This page was last edited on 27 February 2024, at 14:07  (UTC)  .
* Text is available under the  Creative Commons Attribution-ShareAlike License 4.0   ; additional terms may apply. By using this site, you agree to the  Terms of Use  and  Privacy Policy  . Wikipedia® is a registered trademark of the  Wikimedia Foundation, Inc.  , a non-profit organization.

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*  About Wikipedia 
*  Disclaimers 
*  Contact Wikipedia 
*  Code of Conduct 
*  Developers 
* [ Statistics ](https://stats.wikimedia.org/
Click Save and you are done!
Water Temperature
###  WOD User Manual
5-degree standard deviation multiplier
number = "5",
*  Home 
*  “Publications” 
*  LinkedIn 
*  Facebook 
*  About
The code is available in  github  .
There shouldn’t be any significant quirks involved.. just be aware that the
data-set is large and after pre-processing the data you easily run out of
memory if you have an older computer – or you experiment with e.g. increasing
the number of history samples.
Use in the field  

 Over 100 MEG systems are known to
operate worldwide, with Japan possessing the greatest number of MEG systems
per capita and the United States possessing the greatest overall number of MEG
systems. A very small number of systems worldwide are designed for infant
and/or fetal recordings.

In research, MEG's primary use is the measurement of time courses of activity.
MEG can resolve events with a precision of 10 milliseconds or faster, while 
functional magnetic resonance imaging
 (fMRI), which depends on changes in blood flow, can at best resolve
events with a precision of several hundred milliseconds. MEG also accurately
pinpoints sources in primary auditory, somatosensory, and motor areas. For
creating functional maps of human cortex during more complex cognitive tasks,
MEG is most often combined with fMRI, as the methods complement each other.
Neuronal (MEG) and  hemodynamic  fMRI
data do not necessarily agree, in spite of the tight relationship between
local field potentials (LFP) and blood oxygenation level-dependent (BOLD)
signals. MEG and BOLD signals may originate from the same source (though the
BOLD signals are filtered through the hemodynamic response).

MEG is also being used to better localize responses in the brain. The openness
of the MEG setup allows external auditory and visual stimuli to be easily
introduced. Some movement by the subject is also possible as long as it does
not jar the subject's head. The responses in the brain before, during, and
after the introduction of such stimuli/movement can then be mapped with
greater spatial resolution than was previously possible with EEG.  [19]
Psychologists are also taking advantage of MEG neuroimaging to better
understand relationships between brain function and behavior. For example, a
number of studies have been done comparing the MEG responses of patients with
psychological troubles to control patients. There has been great success
isolating unique responses in patients with schizophrenia, such as auditory
gating deficits to human voices.  [20]  MEG is also being used to correlate
standard psychological responses, such as the emotional dependence of language
comprehension.  [21]

Recent studies have reported successful classification of patients with 
multiple sclerosis  , 
Alzheimer's disease  , 
schizophrenia  ,  Sjögren's syndrome
 ,  chronic alcoholism
 ,  facial pain
 and  thalamocortical dysrhythmias
 . MEG can
be used to distinguish these patients from healthy control subjects,
suggesting a future role of MEG in diagnostics.  [22]  [23]

A large part of the difficulty and cost of using MEG is the need for manual
analysis of the data. Progress has been made in analysis by computer,
comparing a patient's scans with those drawn from a large database of normal
scans, with the potential to reduce cost greatly.  [24]
month = nov,
Share this:
*   * 
* 
* 
*
Also, I gave a try for few new tools this time:
journal = "Neuron",
doi = "10.1016/0896-6273(95)90095-0",
##  Access to Document
Magnetically shielded room (MSR)  ") ]

A magnetically shielded room (MSR) model consists of three nested main layers.
Each of these layers is made of a pure aluminium layer plus a high-
permeability  ferromagnetic  layer,
similar in composition to molybdenum  permalloy  . The ferromagnetic layer is supplied as 1 mm sheets, while the
innermost layer is composed of four sheets in close contact, and the outer two
layers are composed of three sheets each. Magnetic continuity is maintained by
overlay strips. Insulating washers are used in the screw assemblies to ensure
that each main layer is electrically isolated. This helps eliminate  radio
frequency  radiation, which would
degrade SQUID performance. Electrical continuity of the aluminium is also
maintained by aluminium overlay strips to ensure  AC
  eddy current
 shielding, which is important at
frequencies greater than 1 Hz. The junctions of the inner layer are often
electroplated with silver or gold to improve conductivity of the aluminium
layers.  [11]
ER -
Monash University data protection policy
TY - JOUR
Magnetic shielding  

Since the magnetic signals emitted by the brain are on the order of a few
femtoteslas, shielding from external magnetic signals, including the  Earth's
magnetic field  , is
necessary. Appropriate  magnetic shielding  can be obtained by constructing rooms made of 
aluminium  and  mu-metal  for reducing high-frequency and low-frequency  noise
 , respectively.

 Entrance to MSR, showing the separate
shielding layers
*  __ Help & FAQ
#  Recent Comments
##  Shortly about the Data-set
*  Home 
*  Profiles  
*  Research units  
*  Equipment  
*  Projects  
*  Outputs  
*  Prizes  
*  Activities  
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The data-set is same as in my earlier post: I’m using the data from  ICANN
2011 MEG mind reading challenge
 . The data is split
into 1s patches and each patch has 200 samples from 204 channels. In addition
to the raw MEG signals, the data-set includes 5 differently filtered versions
of the signals (from 2Hz up to 35Hz).
SP - 1065
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&  Elsevier Fingerprint Engine™
Magnetoencephalography

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[ Edit links
](https://www.wikidata.org/wiki/Special:EntityPage/Q384188
AU - Stuart, Greg
**Amplification of EPSPs by axosomatic sodium channels in neocortical
pyramidal neurons.**
 /  Stuart, Greg
 ; Sakmann, Bert.
In:  Neuron  , Vol. 15, No. 5, 11.1995, p. 1065-1076.
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From Wikipedia, the free encyclopedia

**Shunting inhibition** , also known as **divisive inhibition** , is a form of
 postsynaptic potential inhibition  that can be represented mathematically as
reducing the  excitatory potential  by division, rather than linear
subtraction.  [1]  The term "shunting" is used because of the  synaptic
  conductance   short-circuit  currents that are generated at adjacent  excitatory synapses
 . If a shunting inhibitory
synapse is activated, the  input resistance  is reduced locally. The amplitude of subsequent  excitatory
postsynaptic potential  (EPSP) is reduced by this, in accordance with  Ohm's
Law  .  [2]  This simple scenario arises if
the inhibitory synaptic reversal potential is identical to or even more
negative than the resting potential.  [3]
IS - 5
U2 - 10.1016/0896-6273(95)90095-0
*  10.1016/0896-6273(95)90095-0  90095-0)