vault backup: 2023-05-22 17:32:00

Affected files:
.obsidian/community-plugins.json
.obsidian/graph.json
.obsidian/plugins/table-editor-obsidian/data.json
.obsidian/plugins/table-editor-obsidian/main.js
.obsidian/plugins/table-editor-obsidian/manifest.json
.obsidian/plugins/table-editor-obsidian/styles.css
.obsidian/workspace.json
Charities.md
Health/BWS.md
History/Britain.md
Lab/DNS.md
Lab/Deleted Packages.md
Lab/Ebook Laundering.md
Lab/Home.md
Lab/Mac.md
Lab/Photo Migration.md
Languages/Arabic.md
Money/Assets/Derivative.md
Money/Assets/Financial Instruments.md
Money/Assets/Security.md
Money/Econ.md
Money/Equity.md
Money/Giving.md
Money/Markets/Commodity.md
Money/Markets/Markets.md
Money/Markets/Types.md
STEM/AI/Literature.md
STEM/AI/Properties.md
STEM/CS/ABI.md
STEM/CS/Code Types.md
STEM/CS/Compilers.md
STEM/CS/Language Binding.md
STEM/CS/Languages/dotNet.md
STEM/CS/Quantum.md
STEM/CS/Resources.md
STEM/CS/Turing Machines.md
STEM/Maths/Algebra.md
STEM/Semiconductors/Equations.md
STEM/Signal Proc/Convolution.md
STEM/Signal Proc/Fourier Transform.md
STEM/Speech/Literature.md
STEM/img/ai-io.png
STEM/img/ai-nested-subjects.png
STEM/img/cli-infrastructure.png
Tattoo/Plans.md
Tattoo/img/chest.png

AI/Literature.md

@@ -1,3 +1,4 @@
+#lit
 [https://web.stanford.edu/~jurafsky/slp3/A.pdf](https://web.stanford.edu/~jurafsky/slp3/A.pdf)
 [Towards Data Science: 3 Things You Need To Know Before You Train-Test Split](https://towardsdatascience.com/3-things-you-need-to-know-before-you-train-test-split-869dfabb7e50)
 [https://machinelearningmastery.com/train-final-machine-learning-model/](https://machinelearningmastery.com/train-final-machine-learning-model/)

AI/Properties.md

@@ -0,0 +1,59 @@
+# Three Key Components
+
+1.  Representation
+	-   Declarative & Procedural knowledge
+	-   Typically human-readable symbols
+2.  Reasoning
+	-   Ability to solve problems
+	-   Express and solve range of problems and types
+	-   Make explicit and implicit information known to it
+	-   Control mechanism to decide which operations to use if and when, when a solution has been found
+3.  Learning
+
+An AI system must be able to
+
+1.  Store knowledge
+2.  Apply knowledge to solve problems
+3.  Acquire new knowledge through experience
+
+![[ai-nested-subjects.png]]
+
+# Expert Systems
+-   Usually easier to obtain compiled experience from experts than duplicate experience that made them experts for network
+
+# Information Processing
+## Inductive
+-   General patterns and rules determined from data and experience
+-   Similarity-based learning
+
+## Deductive
+-   General rules are used to determine specific facts
+-   Proof of a theorem
+
+Explanation-based learning uses both
+
+# Classical AI vs Neural Nets
+## Level of Explanation
+-   Classical has emphasis on building symbolic representations
+	-   Models cognition as sequential processing of symbolic representations
+-   Neural nets emphasis on parallel distributed processing models
+	-   Models assume information processing takes place through interactions of large numbers of neurons
+
+## Processing style
+-   Classical processing is sequential
+	-   Von Neumann Machine
+-   Neural nets use parallelism everywhere
+	-   Source of flexibility
+	-   Robust
+
+## Representational Structure
+-   Classical emphasises language of thought
+	-   Symbolic representation has quasi-linguistic structure
+	-   New symbols created from compositionality
+-   Neural nets have problem describing nature and structure of representation
+
+Symbolic AI is the formal manipulation of a language of algorithms and data representations in a top-down fashion
+
+Neural nets bottom-up
+
+![[ai-io.png]]

CS/ABI.md

@@ -1,8 +1,8 @@
-- How data structures & computational routines are accessed in machine code
+- How data structures & computational routines are accessed in machine code ([[Code Types]])
 	- Machine code therefore hardware-dependent
 - API defines this structure in source code
 - Adherence usually responsibility of
-	- Compiler
+	- [[Compilers]]
 	- OS
 	- Library author
 
@@ -13,7 +13,7 @@
 	- Stack organisation
 	- Memory access types
 - Size, layouts and alignments of basic data types
-- ___Calling convention___
+- [[Calling Conventions]]
 	- How function arguments are passed
 		- Stack or register
 		- Which registers for which function param

CS/Code Types.md

@@ -27,9 +27,9 @@ Portable Code
 -   Compact numeric codes, constants and references
 	-   Encode compiler output following analysis and validation
 -   Can be further compiled
-	-   JIT
+	-   [[Compilers#JIT]]
 -   Typically passed to VM
-	-   Java, Python
+	-   Java, [[Python]]
 
 ## Object Code
 -   Product of compiler

CS/Compilers.md

@@ -12,7 +12,8 @@ Just-in-Time
 -   Dynamic compilation
 -   Adaptive optimization
 	-   Dynamic recompilation
-	-   Microarchitecture-specific speedups
+	-   Microarchitecture-specific speedups 
+		- [[ISA]]
 
 ## AOT
 Ahead-of-Time

CS/Language Binding.md

@@ -4,22 +4,25 @@
 ## Runtime Environments
 
 ### Object Models
--   COM
-	-   Component Object Model
-	-   MS only cross-language model
--   CLI
-	-   .NET Common Language Infrastructure
--   Freedesktop.org D-Bus
-	-   Open cross-platform-language model
+- COM 
+	- [[C++]]
+	- Component Object Model
+	- MS only cross-language model
+- CLI 
+	- [[dotNet]]
+	- .NET Common Language Infrastructure
+- Freedesktop.org D-Bus
+	- Open cross-platform-language model
 
 ### Virtual Machines
--   CLR
-	-   .NET Common Language Runtime
--   Mono
-	-   CLI languages
-	-   Cross-platform
--   Adobe Flash Player
-	-   Tamarin
--   JVM
--   LLVM
--   Silverlight
+- CLR 
+	- [[dotNet]]
+	- .NET Common Language Runtime
+- Mono
+	- CLI languages
+	- Cross-platform
+- Adobe Flash Player
+	- Tamarin
+- JVM
+- LLVM
+- Silverlight

CS/Languages/dotNet.md

@@ -10,6 +10,7 @@
 -   JIT managed code into machine instructions
 -   Execution engine
 	-   VM
+	- [[Language Binding#Virtual Machines]]
 -   Services
 	-   Memory management
 	-   Type safety
@@ -27,4 +28,6 @@
 # Assemblies
 -   Compiled CLI code
 -   Portable executable (PE)
-	-   DLL, EXE
+	-   DLL, EXE
+
+![[cli-infrastructure.png]]

CS/Quantum.md

@@ -1 +1,2 @@
+#lit 
 [5 books](https://fivebooks.com/best-books/quantum-computing-chris-bernhardt/)

CS/Resources.md

@@ -1 +1,2 @@
+#lit 
 [Wigle - wifi enumerating](http://wigle.net)

CS/Turing Machines.md

@@ -0,0 +1,16 @@
+# David Hilbert
+-   Wondered if there was a universal algorithmic process to decide whether any mathematical proposition was true
+-   Then suggested that there were no unsolvable problems
+
+# Incompleteness Theorem
+## Kurt Godel
+
+You might be able to prove every conceivable statement about numbers within a system by going outside the system in order to come up with new rules and axioms, but by doing so you'll only create a larger system with its own unprovable statements
+
+# Turing Machine
+-   Model of computation
+	-   Resolves whether or not mathematics contained problems were incomputable
+	-   No algorithmic solution
+
+### Church-Turing Thesis
+Any algorithm capable of being devised can be run on a Turing machine

Maths/Algebra.md

@@ -0,0 +1,18 @@
+# Field
+
+-   Set on which addition and multiplication defined
+	-   Behave same as on rational and real numbers
+	-   Subtraction, division implied
+-   Examples
+	-   Rational numbers
+	-   Real numbers
+	-   Complex numbers
+-   Any field can be used as scalars for a vector space
+-   A commutative ring where 0 =/= 1 and all nonzero elements are invertible
+
+## Vector Space
+-   Set of vectors
+	-   Can be added together and multiplied by scalar
+		-   Can be scaled by complex numbers
+			-   Part of definitions
+		-   Must satisfy vector axioms

Semiconductors/Equations.md

@@ -11,7 +11,7 @@ $$J=\sigma E$$
 
 $$V_{bi} = \frac{kT}{q}ln(\frac{N_D N_A}{n_i^2})$$
 - $V_{bi}$ = Built-in Potential
-
+[[Doping]]
 $$J=nev$$
 - $n$ = Charge Density
 - $e$ = Charge

Signal Proc/Convolution.md

@@ -0,0 +1,26 @@
+Integral operator
+-   Satisfies mathematical properties of integral operator
+-   Product of two after one has been reversed and shifted
+
+$$x(t)=x_1(t)\circledast x_2(t)=\int_{-\infty}^\infty x_1(t-\tau)\cdot x_2(\tau)d\tau$$
+
+# Properties
+1. $x_1(t)\circledast x_2(t)=x_2(t)\circledast x_1(t)$
+	1. Commutativity
+2. $(x_1(t)\circledast x_2(t))\circledast x_3(t)=x_1(t)\circledast (x_2(t)\circledast x_3(t))$
+	1. Associativity
+3.  $x_1(t)\circledast [x_2(t)+x_3(t)]=x_1(t)\circledast x_2(t)+ x_1(t)\circledast x_3(t)$
+	1. Distributivity
+4. $Ax_1(t)\circledast Bx_2(t)=AB[x_1(t)\circledast x_2(t)]$
+	1. Associativity with Scalar
+5. Symmetrical graph about origin
+
+# Applications
+
+1. Communications systems
+	- Shift signal in frequency domain (Frequency modulation)
+2. System analysis
+	- Find system output given input and transfer function
+
+# Polynomial Multiplication
+-   Convolving coefficients of two poly gives coefficients of product

Signal Proc/Fourier Transform.md

@@ -0,0 +1,66 @@
+$$X(\omega)=\int_{-\infty}^{\infty}x(t)e^{-j\omega t}dt$$
+$$x(t)=\frac{1}{2\pi}\int_{2\pi}X(\omega)e^{j\omega t}d\omega$$
+## Discrete-Time
+$$X(\omega)=\sum_{-\infty}^{\infty}x[n]e^{-j\omega n}$$
+$$x[n]=\frac{1}{2\pi}\int_{2\pi}X(\omega)e^{j\omega n}d\omega$$
+
+## Discrete Fourier Transform
+Digital Signal
+$$X[k]=\sum_{n=0}^{N-1}x[n]e^{-j\omega_{k}n}$$
+$$x[n]=\frac{1}{N}\sum_{k=0}^{N-1}X[k]e^{j\omega_{k}n}, n=0,1,\ldots,N-1$$
+
+## Power Spectral Density
+PSD
+$$P[k]=|X[k]|^2$$
+
+## Spectrogram
+-   PSD vertically
+-   Frequency power over time horizontally
+-   ___Time and frequency resolution inversely proportional___
+-   Resolution
+	-   Frequency
+		-   $fs/N$
+	-   Time
+		-   $N/fs$
+-   STFT has fixed resolution depending on window size
+	-   Wider window
+		-   Better frequency res
+		-   Worse time resolution
+			-   Can't tell where stuff changes with big window
+	-   Can't use too wide
+		-   Frequency can change during window
+-   20-30ms window of speech usually treated as quasi-stationary
+-   Overlapping window
+	-   Hop size of 5ms
+-   Appending windows can cause discontinuities
+	-   Use window function to smooth
+		-   Hann
+
+## Fast-Fourier
+FFT
+-   Faster version of DFT
+	-   Three parts
+		-   Shuffling
+			-   Bit reversal
+			-   Shuffle N-dimensional input into N one-dimensional signals
+		-   N one-point DFTs
+		-   Merge
+			-   N one-point DFTs into one N-point DFT
+			-   Butterfly merging equations
+
+## Short-Time Fourier Transform
+STFT
+
+-   Short-term
+-   N-point windowed DFT
+	-   Probably use FFT
+$$x[k,m]=\sum_{n=0}^{N-1}x[m\delta+n]w(n)e^{-j\omega_kn}$$
+-   $\omega$
+	-   Discrete angular frequency
+-   $m$
+	-   Time-frame index
+-   $\delta$
+	-   Hop size
+-   $w(n)$
+	-   Window function
+	-   Hann

Speech/Literature.md

@@ -0,0 +1,15 @@
+#lit 
+Daniel Jurafsky
+James H. Martin
+
+[Speech and Language Processing - 3rd Ed. Draft](https://web.stanford.edu/~jurafsky/slp3/ed3book.pdf)[Hidden Markov Models](https://web.stanford.edu/~jurafsky/slp3/A.pdf)
+
+# Coursework
+-   [Stack Overflow, Spectrogram Matlab Explanation](https://stackoverflow.com/questions/29321696/what-is-a-spectrogram-and-how-do-i-set-its-parameters)
+-   [Matlab - LPC Analysis and Synthesis of Speech](https://uk.mathworks.com/help/dsp/ug/lpc-analysis-and-synthesis-of-speech.html)
+-   [Matlab - Formant Estimation with LPC Coefficients](https://uk.mathworks.com/help/signal/ug/formant-estimation-with-lpc-coefficients.html)
+-   [Matlab - Linear Prediction and Autoregressive Modeling](https://uk.mathworks.com/help/signal/ug/linear-prediction-and-autoregressive-modeling.html)
+-   [Quefrency Paper](https://www.researchgate.net/publication/3321562_From_Frequency_to_Quefrency_A_History_of_the_Cepstrum)
+-   [Aalto Uni - Pre-emphasis](https://wiki.aalto.fi/display/ITSP/Pre-emphasis)
+-   [Preemphasis paper](https://mini.dcs.shef.ac.uk/wp-content/papercite-data/pdf/loweimi_nolisp13.pdf)
+-   [Quora - Preemphasis](https://www.quora.com/Why-is-pre-emphasis-i-e-passing-the-speech-signal-through-a-first-order-high-pass-filter-required-in-speech-processing-and-how-does-it-work)