OpenLLM-France/wikimedia huggingface dataset:
# Business Intelligence/Data Mart Data Model

* Purpose:
* Input: List of KPI's, Dimensions and Budgets
* Activities: Create an ERD for the frame's data mart
* Outputs: ERD and Relational data model
* Documentation: ERD and Relational data model diagrams
Data Model Defined
A data model is a collection of concepts used to describe the structure of a database. This means the data types, relationships and constraints that should hold for the data. Data models are categories as (Elmasri and Navathe 2005):
* Conceptual: provide concepts that are related to the way users understand the data
* Physical: provide concepts that describe how the data is stored in the computer
* Representational/Implementation: Concepts understood by end users but that are not too far removed from the way data is organized within the computer
This chapter focuses on creating a conceptual data model. Conceptual data models use concepts such as entities, attributes and relationships. Chen proposed the entity-relationship data model in 1976.
Entity: Real-world object or concept Relationship: Property of interest that further describes an entity Attribute: An associated among two or more entities
Representation data models include the widely used relationship data model.
# Business Intelligence/Distill KPI budgets scorecard

* Purpose: To develop KPI's, dimensions and budgets for the reports and dashboard
* Input: Scorecard
* Activities: Identify and list the KPI's, dimensions and budgets
* Outputs: List of dimensions, facts and budgets
* Documentation: Dimensions, facts and budgets

## Introduction

This step begins the transitions from focusing on strategy to focusing on the measures of effective strategy implementation. The assumption going forward is that the causal link map was verified amd the strategy is sound. The next step is to measure leading indicators (KPI's) that indicate success. Again, assuming that X causes Y then we want to determine if the operational activities are achieving Y. This requires extracting the measures objectives, (KPI's), budgets and initiatives from scorecards.
This chapter extracts these items for the reports and dashboard. All these items come from the scorecard. These items become the list of KPI's (measures), budgets and dimensions for the data mart. It is here where we start the transition between focusing on strategy diagrams and building the tools for monitoring operation effectiveness.

## Scorecard

Assume that the input for this stage is the following scorecard:

## List of dimensions, facts and budgets

This is how we extract the dimensions, facts and budgets from the business unit's strategy map. Each business unit leader receives a scorecard, or a slice, from the enterprise strategy map outlining his/her objectives. The leader then determines how to achieve the objectives by creating objectives for his/her subordinates.
# Artificial Neural Networks/Boltzmann Machines

Boltzmann learning compares the input data distribution P with the output data distribution of the machine, Q [24]. The distance between these distributions is given by the Kullback-Leibler distance:
    $w_{ij}[n+1]=w_{ij}[n]-{\frac {\partial G}{\partial w_{ij}}}$
Where:
    ${\frac {\partial G}{\partial w_{ij}}}=-{\frac {1}{T}}[p_{ij}-q_{ij}]$
Here, pij is the probability that elements i and j will both be on when the system is in its training phase (positive phase), and qij is the probability that both elements i and j will be on during the production phase (negative phase). The probability that element j will be on, pi, is given by:
    $p_{i}={\frac {1}{1+e^{\frac {-\Delta E_{i}}{T}}}}$
T is a scalar constant known as the temperature of the system. Boltzmann learning is very powerful, but the complexity of the algorithm increases exponentially as more neurons are added to the network. To reduce this effect, a restricted Boltzmann machine (RBM) can be used. The hidden nodes in an RBM are not interconnected as they are in regular Boltzmann networks. Once trained on a particular feature set, these RBM can be combined together into larger, more diverse machines.
Because Boltzmann machine weight updates only require looking at the expected distributions of surrounding neurons, it is a plausible model for how actual biological neural networks learn.
# Artificial Neural Networks/ART Models

In adaptive resonance theory (ART) networks, an overabundance of neurons leads some neurons to be committed (active) and others to be uncommitted (inactive). The weight vector, also known as the prototype, is said to resonate with the input vector if the two are sufficiently similar. Weights are only updated if they are resonating in the current iteration. ART networks commit an uncommitted neuron when a new input pattern is detected that does not resonate with any of the existing committed neurons. ART networks are fully-connected networks, in that all possible connections are made between all nodes.
# Artificial Neural Networks/Biological Neural Networks
## Biological Neural Nets

In the case of a biological neural net, neurons are living cells with axons and dendrites that form interconnections through electro-chemical synapses. Signals are transmitted through the cell body (soma), from the dendrite to the axon as an electrical impulse. In the pre-synaptic membrane of the axon, the electrical signal is converted into a chemical signal in the form of various neurotransmitters. These neurotransmitters, along with other chemicals present in the synapse form the message that is received by the post-synaptic membrane of the dendrite of the next cell, which in turn is converted to an electrical signal.
This page is going to provide a brief overview of biological neural networks, but the reader will have to find a better source for a more in-depth coverage of the subject.

### Synapses

The figure above shows a model of the synapse showing the chemical messages of the synapse moving from the axon to the dendrite. Synapses are not simply a transmission medium for chemical signals, however. A synapse is capable of modifying itself based on the signal traffic that it receives. In this way, a synapse is able to “learn” from its past activity. This learning happens through the strengthening or weakening of the connection. External factors can also affect the chemical properties of the synapse, including body chemistry and medication.

## Neurons

Cells have multiple dendrites, each receives a weighted input. Inputs are weighted by the strength of the synapse that the signal travels through. The total input to the cell is the sum of all such synaptic weighted inputs. Neurons utilize a threshold mechanism, so that signals below a certain threshold are ignored, but signals above the threshold cause the neuron to fire. Neurons follow an “all or nothing” firing scheme, and are similar in this respect to a digital component. Once a neuron has fired, a certain refraction period must pass before it can fire again.

## Biological Networks

Biological neural systems are heterogeneous, in that there are many different types of cells with different characteristics. Biological systems are also characterized by macroscopic order, but nearly random interconnection on the microscopic layer. The random interconnection at the cellular level is rendered into a computational tool by the learning process of the synapse, and the formation of new synapses between nearby neurons.
# Artificial Neural Networks/Boltzmann Learning

Boltzmann learning is statistical in nature, and is derived from the field of thermodynamics. It is similar to error-correction learning and is used during supervised training. In this algorithm, the state of each individual neuron, in addition to the system output, are taken into account. In this respect, the Boltzmann learning rule is significantly slower than the error-correction learning rule. Neural networks that use Boltzmann learning are called Boltzmann machines.
Boltzmann learning is similar to an error-correction learning rule, in that an error signal is used to train the system in each iteration. However, instead of a direct difference between the result value and the desired value, we take the difference between the probability distributions of the system.
# Artificial Neural Networks

Artificial neural networks are a computational tool, based on the properties of biological neural systems. Neural networks excel in a number of problem areas where conventional von Neumann computer systems have traditionally been slow and inefficient. This book is going to discuss the creation and use of artificial neural networks.

## Table of Contents

* Introduction

### Overview

* Neural Network Basics
* Biological Neural Networks
* History
* MATLAB Neural Networking Toolbox
* Activation Functions

### ANN Models

* Feed-Forward Networks
* Radial Basis Function Networks
* Recurrent Networks
* Echo State Networks
* Hopfield Networks
* Self-Organizing Maps
* Competitive Models
* ART Models
* Boltzmann Machines
* Committee of Machines
* Autoencoders
* Convolutional Neural Networks

### Teaching and Learning

* Learning Paradigms
* Error-Correction Learning
* Hebbian Learning
* Competitive Learning
* Boltzmann Learning
* ART Learning
* Self-Organizing Maps

### Applications

* Pattern Recognition
* Clustering
* Feature Detection
* Series Prediction
* Data Compression
* Curve Fitting
* Optimization
* Control

### Future Work

* Criticisms and Problems
* Artificial Intelligence

## Resources

* Resources
* Licensing
# Artificial Neural Networks/ART Learning
## ART Learning

Adaptive Resonance Theory (ART) learning algorithms compare the weight vector, known as the prototype, to the current input vector to produce a distance, r. The distance is compared to a specified scalar, the vigilance parameter p. All output nodes start off in the uncommitted state. When a new input sequence is detected that does not resonate with any committed nodes, an uncommitted node is committed, and it’s prototype vector is set to the current input vector.
# Annotations to James Joyce's Ulysses/Proteus/049
## Annotations

nebeneinander (German) side by side. See 037.13.
tripudium (Latin) In ancient Rome, the tripudium was a solemn ceremonial dance. The word denotes a triple stroke, in which the Earth is stamped three times with the foot. Joyce does not italicize the word, treating it as an English borrowing.
Tiens, quel petit pied! (French) My, what a small foot!
diebus ac noctibus iniurias patiens ingemiscit. (Latin) days and nights it [Creation] sighs patiently over injustices. A quotation from the Commentary on the Epistle of Blessed Paul to the Romans, long attributed to Saint Ambrose but now ascribed to a contemporary of his styled Ambrosiaster. The comment is to Romans 8:22, For we know that the whole creation groaneth and travaileth in pain together until now,
Paul's συστεναζει (sustenazei) means both sighs together and groans together. Saint Jerome's Latin translation ingemiscit also carries both meanings, sighs and groans. When read in context, however, Paul's point is that all of creation has been groaning, as though in the pangs of childbirth, right up to the present time.
Thornton suggests that Joyce probably came across the comment "in some choir manual or sodality manual." Marsh's Library, however, has a copy of the complete works of St Ambrose (Basle, 1506), which includes the commentary on Romans.

## References

* ↑ Gifford (1988) 63.
* ↑ Tripudium.
* ↑ Gifford (1988) 64.
* ↑ Gifford, Don; Seidman, Robert J. (1988). Ulysses Annotated. University of California Press. p. 64. Thornton, Weldon (1968). Allusions in Ulysses. University of North Carolina Press. p. 65.
* ↑ Bible Gateway In Epistolam Beati Pauli Ad Romanos
* ↑ Strong's Greek.
* ↑ Omnia opera; Ambrosius episcopi Mediolanensis, F. Conradus Leontorius (editor), Basle (1506). In Epistolam Ad Romanos.
# Annotations to James Joyce's Ulysses/Scylla and Charybdis/180
## Annotations

limbo patrum In Catholic theology, Limbo is the name for a place or state occupied by the souls of those who have not been admitted to Heaven but who have not merited the eternal damnation of Hell. The name comes from the Latin expression in limbo, meaning on the border, as Limbo was imagined to form the borderland of Hell.
In theological usage the name is applied to (a) the temporary place or state of the souls of the just who, although purified from sin, were excluded from the beatific vision until Christ's triumphant ascension into Heaven (the limbus patrum or border of the fathers) or (b) to the permanent place or state of those unbaptized children and others who, dying without grievous personal sin, are excluded from the beatific vision on account of original sin alone (the limbus infantium or limbus puerorum, border of infants or border of children).
The Fathers, Patres, are specifically the Patriarchs of the Old Testament, the righteous men and women who were not admitted to Heaven until Christ's Harrowing of Hell. Stephen's use of the term, therefore, is theologically incorrect, as the soul of Hamlet's father would have had no business being in limbo patrum.
In Elizabethan England, however, Limbo Patrum was a slang term for prison. The phrase occurs in Shakespeare and Fletcher's play Henry VIII 5:4:67-68: I have some of 'em in Limbo Patrum, and there they are like to dance these three days;. In the same scene Paris is held up as an exemplar of corruption.

## References

* ↑ Catholic Encyclopedia.
* ↑ Gifford (1988) 203. Thornton (1968) 161.
* ↑ Henry VIII 5:4:67-68.
# Annotations to James Joyce's Ulysses/Scylla and Charybdis
## Contents

The following list contains links to each of the thirty-four pages of the ninth episode Scylla and Charybdis. The pagination is that of the original edition of Ulysses, published in Paris on 2 February 1922.
Episode 9: Scylla and Charybdis
* Page 176
* Page 177
* Page 178
* Page 179
* Page 180
* Page 181
* Page 182
* Page 183
* Page 184
* Page 185
* Page 186
* Page 187
* Page 188
* Page 189
* Page 190
* Page 191
* Page 192
* Page 193
* Page 194
* Page 195
* Page 196
* Page 197
* Page 198
* Page 199
* Page 200
* Page 201
* Page 202
* Page 203
* Page 204
* Page 205
* Page 206
* Page 207
* Page 208
* Page 209
# Annotations to James Joyce's Ulysses/Scylla and Charybdis/178
## Annotations

Pfuiteufel ! (German) Pfui is the German for fie! and Teufel means Devil. Together, Pfui Teufel! means Disgusting! Yuck! Ugh! Ew! I have not found a German instance in which the two words are yoked together as here.

## References

* ↑ Gifford (1988) 198.
* ↑ Pfui Teufel.
# Annotations to James Joyce's Ulysses/Proteus/048
## Annotations

piuttosto (Italian) rather.
Et vidit Deus. Et erant valde bona. (Latin) And God saw [every thing that he had made], and, behold, it was very good. Adapted from Genesis 1:31.

## References

* ↑ Gifford (1988) 63.
* ↑ Bible Gateway Gifford, Don; Seidman, Robert J. (1988). Ulysses Annotated. University of California Press. p. 63. Thornton, Weldon (1968). Allusions in Ulysses. University of North Carolina Press. p. 63.
# Annotations to James Joyce's Ulysses/Proteus/050
## Annotations

Prix de Paris (French) The Grand Prix de Paris (Great Prize of Paris) was the most important event in French horse racing. The mention of Old Father Ocean has reminded Stephen, perhaps, of the steeds of Mananaan See 032.17.
Stephen is probably also punning on the story of the Judgement of Paris, in which the Prince of Troy awarded the Apple of Discord to Aphrodite, precipitating the Trojan War. Paris's prize for choosing Aphrodite was Helen of Troy.
beware of imitations In a palinode attributed to the Greek poet Stesichorus, the Trojan War was fought over a phantom Helen, while the real Helen was living in Egypt under the protection of King Proteus. The historian Herodotus recounts a similar tradition in his Histories. The Greek playwright Euripides also draws upon this tradition for his drama Helen, in which Helen is reunited with her husband Menelaus when he is marooned in Egypt on his way home from Troy. This latter event, of course, is the Homeric background Joyce drew upon when he wrote the Proteus episode of Ulysses.
Lucifer, dico, qui nescit occasum (Latin) The Morning Star, I say, who knows no setting.
Stephen's adapts this quotation from the Easter Vigil, the Catholic service for Holy saturday. The Exultet or Easter Proclamation, sung by the deacon before the Paschal Candle, concludes with the words:
Exultet :
| May the early Morning Star find his flames [still burning]: He, I say, the Morning Star, who knows no setting. Christ your Son, Who, returned from the dead, has shed his peaceful light on the human race, And who lives and reigns for ever and ever. Amen | Flammas eius lúcifer matutínus invéniat: ille, inquam, Lúcifer, qui nescit occásum. Christus Fílius tuus, qui, regréssus ab ínferis, humáno géneri serénus illúxit, et vivit et regnat in sæcula sæculórum. Amen. |
Lucifer means the bringer of light and was applied to the Morning Star by the ancient Romans. In the Exultet it refers to the risen Christ. But Lucifer is also a name for Satan, so Stephen's phrase can also be translated: Lucifer, I say, who knows no fall.
See Isaiah 14:12: How art thou fallen from heaven, O Lucifer, son of the morning! how art thou cut down to the ground, which didst weaken the nations!
See Luke 10:18: I beheld Satan as lightning fall from Heaven.
Già (Italian) Indeed. According to Gifford, Stephen is using the word to express impatience; he feels that he has spent enough time wandering across Sandymount Strand and is urging himself to get a move on.

## References

* ↑ Gifford (1988) 65. Thornton (1968) 66.
* ↑ Dio Chrysostom, Discourses 11:40 ff.
* ↑ Herodotus, Histories 2:112.
* ↑ Euripides, Helen.
* ↑ Gifford, Don; Seidman, Robert J. (1988). Ulysses Annotated. University of California Press. p. 65. Thornton, Weldon (1968). Allusions in Ulysses. University of North Carolina Press. pp. 66–67.
* ↑ Strong's Hebrew.
* ↑ Bible Gateway
* ↑ Bible Gateway
* ↑ Gifford (1988) 66.
# Annotations to James Joyce's Ulysses/Scylla and Charybdis/177
## Annotations

Orchestral Satan, weeping many a rood ... fatto trombetta (Italian) Stephen here conflates two lines from Milton's Paradise Lost and one line from Dante's Inferno. The lines from Milton refer first to Satan's huge bulk as he lies prone in the burning lake of Hell and then to the tears he lets drop as he is about to address his fallen angels:
Paradise Lost 1:196 ... 1:620 :
* Lay floating many a rood...
* Tears such as angels weep burst forth....
The line from Dante is the closing line of the twenty-first canto of the Inferno. It refers not to Satan but to another infernal demon Barbariccia, one of the Malabranche. Barbariccia is the leader of a troop of demons who escort Dante and Virgil through the fifth of the Malebolge that comprise the Eighth Circle of Hell. His fellow demons salute him by sticking out their tongues and he responds by farting:
Inferno 21:139 :
| ed elli avea del cul fatto trombetta. | and he made a trumpet of his arse. |
Stephen has modernized Dante's spelling of elli (he)
and one more to hail him : ave, rabbi (Latin) hail, master. The words, spoken by Judas just before he kisses and betrays Christ, are taken from the Latin Vulgate translation of Matthew 26:49. Cranly is presumably being identified here by Joyce as Judas. Joyce and J. F. Byrne (the model for Cranly) became estranged in 1902, shortly before Joyce met Oliver St. John Gogarty (the model for Buck Mulligan), though it was Byrne who felt that he had been betrayed by Joyce.

## References

* ↑ Gifford (1988) 194. Thornton (1968) 153.
* ↑ John Milton, Paradise Lost.
* ↑ Dante, Inferno.
* ↑ Gifford (1988) 195.
* ↑ Matthew 26.
* ↑ Ellmann (1983) 116-117.
# Blender 3D: Noob to Pro/Part 1 - Preparing the Scene

| previous module: "Create an animated GIF wallpaper (Blender/GIMP)" | • Table of Contents • Glossary | next module: "Creating Weapons based on 2D Images" |

## Start a new scene in Blender

* Delete the default cube.

## Prepare the individual particles

Go to layer 2 -
Add a UV Sphere with 8 segments and 6 rings(Go to the toolshelf and change the values)
Add a new material with any bright color. Change the emit value to 2 or higher -

### Duplication of the particles

Duplicate the sphere and move it nearby ( Shift + D ). Duplicate the material by clicking on the 'plus' button indicated in the picture below. Change the color to another bright color
Repeat this process until you are satisfied with the no. of spheres with different colours. Now create a group ( Ctrl + G ) Name it anything you like in the toolshelf ( T toggles the toolshelf).
Now you're ready to start creating the animation and particles!

## Defining the motion of the particles

Go back to layer 1.

### Creating the path for the particles to follow

If you haven't learnt about Bezier curves already, go ahead and do so now.
Add a bezier curve, and tab into edit mode. Now you have to model the path by extruding and subdividing the curve. The shape of the curve defines the path of the particles. After you are done with the curve, you can proceed to create the emission object.

### Creating the 'emitter' of the particles

Add a UV Sphere with 32 segments and 16 rings. This is going to be the emitter, i.e. the object that gives out those coloured spheres.
This is going to have a completely transparent material since hiding it hides the whole thing.
So go to material properties, add a new material. Scroll down to the Transparency panel, check it and change the alpha value to zero, making it completely transparent. Also change the specular intensity to zero. Leave everything else as such.

### Making the sphere follow the path

In this part, you are going to make the sphere follow the curve you had modeled earlier. Select the cube, and add a Follow Path Constraint. Select the target, i.e. the curve and click on Animate Path. All these steps speak for themselves, there's no need for me to explain. You could check follow curve, but since this is a sphere this is not needed. All it does is make the object face the direction it is moving towards.
As for the speed of the object, you can change it by -
* Select the curve.
* Go to Object properties -
* Scroll down to Path Animation panel.
* Change Path Animation Frames value to the no. of frames you want the animation to last. I prefer it to be 150.
In the end, make a few changes until you are satisfied with the result. Now you are ready to proceed to part 2.
| previous module: "Create an animated GIF wallpaper (Blender/GIMP)" | • Table of Contents • Glossary | next module: "Creating Weapons based on 2D Images" |