Nyzo version 605 (commit on GitHub) changes the lottery to use IP address, using a constrained linear distance between addresses to adjust probability.
This version affects the verifier only. This version fulfills delivery for NTTP-7.
The new calculation converts the IP address to an integer value in the range [0 -<PHONE_NUMBER>]. The IP address <IP_ADDRESS> corresponds to 0 and the IP address <IP_ADDRESS> corresponds to<PHONE_NUMBER>. Some other values, for illustration, are:
<IP_ADDRESS>: 0 <IP_ADDRESS>: 1 <IP_ADDRESS>: 255 <IP_ADDRESS>: 256 <IP_ADDRESS>: 511 <IP_ADDRESS>: 512 <IP_ADDRESS>: 767 <IP_ADDRESS>: 65535 <IP_ADDRESS>: 65536 <IP_ADDRESS>: 16711680 <IP_ADDRESS>:<PHONE_NUMBER> <IP_ADDRESS>:<PHONE_NUMBER> <IP_ADDRESS>:<PHONE_NUMBER>
Or, put more simply, the IP address is interpreted as a 4-byte unsigned integer in big-endian byte order.
Of course, large ranges of the IP address space are inaccessible to potential verifiers. This is handled appropriately by the lottery logic.
The IP addresses in the lottery are depicted in the following image. The image is 1024 pixels wide, and each pixel represents an equally sized partition of the IPv4 space, with address <IP_ADDRESS> on the far left and <IP_ADDRESS> on the far right.
While this image illustrates the clustering of IP addresses, it is only looking at one scale of the problem space. Addressing only the clustering apparent in this image would ignore some clustering at finer scales.
The use of a linear value space for IP addresses naturally places closely spaced IP addresses at a disadvantage. However, it provides too much of an advantage to certain IP addresses. So, instead of calculating the distance to IP addresses, constrained ranges of IP addresses are hashed, and the distance in the hash space is used to determine the winner.
All of the changes in this version take place in NewVerifierQueueManager. The maximumIpValueInterval determines the maximum distance between two IP addresses that will contribute to an increased probability of selection.
The old logic for selecting the winning node in the lottery was replaced with the linear-IP selection logic. The selection logic is encapsulated in the winningNodeForCycleHash() method.
The winningNodeForCycleHash() method first calculates integer (long, to avoid issues related to Java's lack of a 4-byte unsigned integer) values for all IP addresses in the lottery, storing the nodes in rawValueToNodeMap, which is keyed on these values.
After building the map, the values are sorted ascending. Nodes get entries into the lottery based on their IP addresses and half of the IP addresses in both directions, of the linear space. The maximumIpValueInterval is imposed to ensure that large gaps in the address space do not provide excessive lottery entries to the nodes bordering those gaps.
The ipToLong() method maps the IP address space to the [0 -<PHONE_NUMBER>] range.
The closerHash() method determines which of two hashes are closer to the reference hash. The hashes are treated as 32-byte big-endian integers.
This selection logic works at the finest scale, controlling probability by modifying distances between pairs of IP addresses. However, the effects are seen at all scales. Looking back to the graph of IP addresses, the counts within bins also represent relative probabilities of entry from each bin when all IP addresses are afforded equal probability.
Under the new logic, the relative probability of entry from each bin can be calculated by looking at the sum of the space the each bin occupies in the compressed space. This results in higher entrance probabilities for many of the sparsely populated bins.
Simulating a winner 10,000 times with the new logic consistently shows a distribution that is in line with these probabilities. Each image represents a different set of 10,000 simulated lottery winners.
Remember that each bin represents 1/1024 of the entire IP address space, which is over 4 million potential addresses per bin. The new peak is a bin that contains 447 IP addresses with an average constrained gap of 15.7, just under the maximum of 16 defined by maximumIpValueInterval in NewVerifierQueueManager.
If we zoom in and create images of the same size from single bins in the original image, each bin in the resultant image represents 4,096 IP addresses. For this, we will examine bins 207, 216, 383, 424, 579, and 712. These are marked in the following image of lottery IP distribution.
The same bins are marked in the following image of lottery probability.
The following images show the distribution/unmodified probability, followed by the modified probability, for bin 207.
The following images show the distribution/unmodified probability, followed by the modified probability, for bin 216.
The following images show the distribution/unmodified probability, followed by the modified probability, for bin 383.
The following images show the distribution/unmodified probability, followed by the modified probability, for bin 424.
The following images show the distribution/unmodified probability, followed by the modified probability, for bin 579.
The following images show the distribution/unmodified probability, followed by the modified probability, for bin 712.
In all of these images, we see emergence of bins with exceptionally small unmodified probabilities to represent substantial combined probabilities after modification. So, we see not just positive effects on diversity at the widest scale, we also see positive effects at a moderate scale, while the logic is implemented on the finest scale.
Our preferred selection logic maps both the cycle hash and the IP addresses to linear decimal values in the [0.0 - 1.0] range. It then collapses the linear IP address space to impose a maximum interval between addresses, maps the cycle hash value to the collapsed space, and finds the closest IP address in this space to the mapped hash value.
This logic is especially desirable because it is easy to understand and visualize, and it also provides much more freedom in adjusting maximumIpValueInterval. In the current logic, increasing maximumIpValueInterval increases computational burden. In the preferred logic, the value of maximumIpValueInterval does not affect computational burden.
This current logic is a modification of the preferred concept that is less vulnerable to vote scattering due to information differences among verifiers. In the preferred logic, stacking of the collapsed space results in a single information difference affecting the positions of all subsequent verifiers in the space.
Our preferred logic will be implemented when/if information regarding the out-of-cycle verifier pool is stored on the blockchain and fully consistent among all verifiers.


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IIFE
Immediately-Invoked Function Expression
Advantage of a const variable
- Correctness : Avoid the Oops
- Clarity : See I'll not touch it
- Optimization
But how to get const for that:
const int i = std::rand() % 3;
std::string s;
switch (i) {
case 1: s = "The number you selected is odd"; break;
case 2: s = "The number you selected is even"; break;
case 0: s = "The number you selected is zero"; break;
}
With IIFE!
const int i = std::rand() % 3;
const std::string s = [i]() {
switch (i) {
case 1: return "The number you selected is odd";
case 2: return "The number you selected is even";
default: return "The number you selected is zero";
}
}();
Optimize a bit: specify the Lambda return type
const int i = std::rand() % 3;
const std::string s = [i]() -> std::string {
switch (i) {
case 1: return "The number you selected is odd";
case 2: return "The number you selected is even";
default: return "The number you selected is zero";
}
}();
Performance


Owen Hassan murder trial hears suspect allegedly told pal he 'stabbed f*** out' of slain dad
Craig Corrall was said to have made the remark to a friend following the death of Owen Hassan in November 2018.
by Grant McCabeA murder suspect allegedly claimed he had “stabbed the f*** out” of a dad killed outside his mum's pub.
Craig Corrall was said to have made the remark to a friend following the death of Owen Hassan in November 2018.
Jurors heard the 39 year-old also apparently stated he had “done something bad”.
Corrall and David Callaghan, 30, are on trial where they deny the murder of Mr Hassan.
The 30 year-old was found injured outside the Old Stag Inn bar in Glasgow's Pollokshaws, which was run by his mum Ann Marie Lynch.
Corrall's former friend Stephen Ward today gave evidence at the High Court in Glasgow .
The 24 year-old – who also knew Mr Hassan – recalled a visit to the home of Corrall's dad.
Mr Ward and Corrall had a chat that evening.
Mr Ward claimed Corrall told him he had been “involved” in the death.
Prosecutor Lynsey MacDonald asked: “What did he say about his involvement?”
The witness: “That he had done something bad.”
Mr Ward said he quizzed Corrall on what he had done.
Mr Ward told the jury: “He said that he had stabbed the f*** out of somebody.”
Miss MacDonald asked did he say who the person was.
The witness: “Owen Hassan. He informed me where it had taken place. He said that it was a very short distance from the Old Stag Inn in Pollokshaws.”
The prosecutor: “At the time, did you know the details of where Owen Hassan had died?”
Mr Ward: “I had only seen it on the news.”
Mr Ward said Corrall did not give him further “particular details”, but apparently claimed “another person” was with him.
The witness told the trial he had been friends with Corrall, but stopped “about a year ago”.
Prosecutors claim Mr Hassan was chased, seized by the body and the repeatedly struck with knives or machetes.
The trial, before Lord Woolman, continues.


I've just hit the 1 year mark on taking this and I have to say it's been a positive experience. Before taking this I hadn't had a period for over 10 months. During my first month, on the exact day it was supposed to start, I finally got my period.
Since then, my periods have only gotten lighter and lighter.
SENT4
Now I spot for 1 maybe 2 days a month on the white/brown colored pills. The only other times I spot are if I'm careless about taking it at the same time every day. I have noticed a decrease in migraines (I didn't get them that often, but still), slight increase in breast size, no weight change, and not really any change in acne. The only downside is that I'm a little more emotional about silly things I wouldn't normally be.


"""The ``cdt.utils.Settings`` module defines the settings used in the toolbox,
such as the default hardware parameters; and the tools to autodetect the
hardware. All parameters are overridable by accessing the ``cdt.SETTINGS``
object, a unique instance of the ``cdt.utils.ConfigSettings`` class.
The various attributes of the ``cdt.SETTINGS`` configuration object are:
1. ``cdt.SETTINGS.NB_JOBS``
2. ``cdt.SETTINGS.GPU``
3. ``cdt.SETTINGS.default_device``
4. ``cdt.SETTINGS.autoset_config``
5. ``cdt.SETTINGS.verbose``
The hardware detection revolves around the presence of GPUs. If GPUs are
present, ``cdt.SETTINGS.GPU`` is set to ``True`` and the number of jobs
is set to the number of GPUs. Else the number of jobs is set to the number
of CPUs. Another test performed at startup is to check if an R framework
is available, unlocking additional features of the toolbox.
.. MIT License
..
.. Copyright (c) 2018 Diviyan Kalainathan
..
.. Permission is hereby granted, free of charge, to any person obtaining a copy
.. of this software and associated documentation files (the "Software"), to deal
.. in the Software without restriction, including without limitation the rights
.. to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
.. copies of the Software, and to permit persons to whom the Software is
.. furnished to do so, subject to the following conditions:
..
.. The above copyright notice and this permission notice shall be included in all
.. copies or substantial portions of the Software.
..
.. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
.. IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
.. FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
.. AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
.. LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
.. OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
.. SOFTWARE.
"""
import ast
import os
import warnings
import multiprocessing
import torch as th
def message_warning(msg, *a, **kwargs):
"""Ignore everything except the message."""
return str(msg) + '\n'
warnings.formatwarning = message_warning
[docs]class ConfigSettings(object):
"""Defining the class for the hardware/high level settings of the CDT.
Attributes:
NB_JOBS (int): Number of parallel jobs that can be executed on current
hardware.
GPU (int): The number of available GPUs ; defaults to `0`.
default_device (str): Default device used for pytorch jobs.
verbose (bool): Sets the verbosity of the toolbox algorithms.
"""
__slots__ = ("NB_JOBS", # Number of parallel jobs runnable
"GPU", # Number of GPUs Available
"default_device", # Default device for gpu (pytorch 0.4)
"autoset_config",
"verbose")
def __init__(self):
"""Define here the default values of the parameters."""
super(ConfigSettings, self).__init__()
self.NB_JOBS = 8
self.GPU = 0
self.autoset_config = True
self.verbose = True
self.default_device = 'cpu'
if self.autoset_config:
self = autoset_settings(self)
self.default_device = 'cuda:0' if self.GPU else 'cpu'
def __setattr__(self, attr, value):
"""Set attribute override for GPU=True."""
if attr == "GPU" and value and not self.GPU and self.default_device == 'cpu':
self.default_device = 'cuda:0'
super(ConfigSettings, self).__setattr__(attr, value)
[docs] def get_default(self, *args, **kwargs):
"""Get the default parameters as defined in the Settings instance.
This function proceeds to seamlessly retrieve the argument to pass
through, depending on either it was overidden or not: If no argument
was overridden in a function of the toolbox, the default argument will
be set to ``None``, and this function will retrieve the default
parameters as defined by the ``cdt.SETTINGS`` 's attributes.
It has two modes of processing:
1. **kwargs for retrieving a single argument: ``get_default(argument_name=value)``.
2. *args through a list of tuples of the shape ``('argument_name', value)`` to retrieve multiple values at once.
"""
def retrieve_param(i):
try:
return self.__getattribute__(i)
except AttributeError:
if i == "device":
return self.default_device
else:
return self.__getattribute__(i.upper())
if len(args) == 0:
if len(kwargs) == 1 and kwargs[list(kwargs.keys())[0]] is not None:
return kwargs[list(kwargs.keys())[0]]
elif len(kwargs) == 1:
return retrieve_param(list(kwargs.keys())[0])
else:
raise TypeError("As dict is unordered, it is impossible to give"
"the parameters in the correct order.")
else:
out = []
for i in args:
if i[1] is None:
out.append(retrieve_param(i[0]))
else:
out.append(i[1])
return out
[docs]def autoset_settings(set_var):
"""Autoset GPU parameters using CUDA_VISIBLE_DEVICES variables.
Return default config if variable not set.
:param set_var: Variable to set. Must be of type ConfigSettings
"""
try:
devices = ast.literal_eval(os.environ["CUDA_VISIBLE_DEVICES"])
if type(devices) != list and type(devices) != tuple:
devices = [devices]
if len(devices) != 0:
set_var.GPU = len(devices)
set_var.NB_JOBS = len(devices)
warnings.warn("Detecting CUDA devices : {}".format(devices))
except KeyError:
set_var.GPU = check_cuda_devices()
set_var.NB_JOBS = set_var.GPU
warnings.warn("Detecting {} CUDA devices.".format(set_var.GPU))
if not set_var.GPU:
warnings.warn("No GPU automatically detected. Setting SETTINGS.GPU to 0, " +
"and SETTINGS.NB_JOBS to cpu_count.")
set_var.GPU = 0
set_var.NB_JOBS = multiprocessing.cpu_count()
return set_var
[docs]def check_cuda_devices():
"""Output some information on CUDA-enabled devices on your computer,
including current memory usage. Modified to only get number of devices.
It's a port of https://gist.github.com/f0k/0d6431e3faa60bffc788f8b4daa029b1
from C to Python with ctypes, so it can run without compiling
anything. Note that this is a direct translation with no attempt to
make the code Pythonic. It's meant as a general demonstration on how
to obtain CUDA device information from Python without resorting to
nvidia-smi or a compiled Python extension.
.. note:: Author: Jan Schlüter, https://gist.github.com/63a664160d016a491b2cbea15913d549.git
"""
import ctypes
# Some constants taken from cuda.h
CUDA_SUCCESS = 0
libnames = ('libcuda.so', 'libcuda.dylib', 'cuda.dll')
for libname in libnames:
try:
cuda = ctypes.CDLL(libname)
except OSError:
continue
else:
break
else:
# raise OSError("could not load any of: " + ' '.join(libnames))
return 0
nGpus = ctypes.c_int()
error_str = ctypes.c_char_p()
result = cuda.cuInit(0)
if result != CUDA_SUCCESS:
cuda.cuGetErrorString(result, ctypes.byref(error_str))
# print("cuInit failed with error code %d: %s" % (result, error_str.value.decode()))
return 0
result = cuda.cuDeviceGetCount(ctypes.byref(nGpus))
if result != CUDA_SUCCESS:
cuda.cuGetErrorString(result, ctypes.byref(error_str))
# print("cuDeviceGetCount failed with error code %d: %s" % (result, error_str.value.decode()))
return 0
# print("Found %d device(s)." % nGpus.value)
return nGpus.value
SETTINGS = ConfigSettings()


The HTML
The first step in including ImgWheel
on your site is to set up the HTML for your slideshow. All of the HTML for ImgWheel
should go inside of a
 with an assigned id or class. Within this
 is placed a
 with class="image-container". In this
 go your images. The HTML for a basic slideshow would look like this:

If you want to include additional content to be displayed as part of the ImgWheel
slideshow, now is your chance. Simply add in another
 with the class="article-container". Within this
, place as many
's as there are images in your .image-container
, then fill those
's with whatever content you like. When the first image in your .image-container
 is central in the slideshow, the first article in your .article-container
 will be displayed, and so on. The HTML looks like this:

Static content can also be included within the ImgWheel
display. To have static content displayed alongside your rotating articles, add in another
 containing this content:


The ENCODE project aims to discover all the DNA sequences associated with various epigenetic features, with the reasonable expectation that these will also be functional (best tested by genetic methods). However, it is not clear how to relate these results with those from evolutionary analyses. The mouse ENCODE project aims to make this connection explicitly and with a moderate breadth. Assays identical to those being used in the ENCODE project are performed in cell types in mouse that are similar or homologous to those studied in the human project. The comparison will be used to discover which epigenetic features are conserved between mouse and human, and examine the extent to which these overlap with the DNA sequences under negative selection. The contribution of functional DNA preserved in mammals versus function in only one species will be discovered. The results will have a significant impact on the understanding of the evolution of gene regulation.
This track is a multi-view composite track that contains multiple data types (views). For each view, there are multiple subtracks that display individually on the browser. Instructions for configuring multi-view tracks are here. This track contains the following views:
Metadata for a particular subtrack can be found by clicking the down arrow in the list of subtracks.
DNaseI hypersensitive sites were isolated using methods called DNase-seq or DNase-chip (Song and Crawford, 2010). Briefly, cells were lysed with NP40, and intact nuclei were digested with optimal levels of DNaseI enzyme. DNaseI-digested ends were captured from three different DNase concentrations, and material was sequenced using Illumina sequencing.
The read length for sequences from DNase-seq is 20 bases long due to a MmeI cutting step of the approximately 50 kb DNA fragments extracted after DNaseI digestion. Sequences from each experiment were mapped to the mouse genome (mm9 assembly) using the program Bowtie (Langmead et al., 2009). Reads mapping to more than one location were not removed. For such reads, only the best mapping result was used ("--best" option). Sequences from multiple lanes were combined for a single replicate and converted to the sam/bam format using SAMtools. Using F-seq, the resulting digital signal was converted to a continuous wiggle track that employs a Parzen kernel density estimation to create base pair scores (Boyle et al., 2008).
Discrete DNaseI HS sites (peaks) were identified from the DNase-seq F-seq density signal. Significant regions were determined by fitting the data to a gamma distribution to calculate p-values.
Cell growth and DNaseI digestion were done by Christine Dorman in the Hardison lab, and DNase-seq libraries were constructed in the laboratory of Greg Crawford (Duke). Sequencing was done by the laboratory of Greg Crawford (Duke). Data processing and analysis was done by Chris Morrissey (PSU) and Yoichiro Shibata (Duke) with advice from Terry Furey (University of North Carolina). Some analyses used tools provided in the Galaxy platform (Anton Nekrutenko, PSU, and James Taylor, Emory) enabled by the Penn State Cyberstar computer (supported by the National Science Foundation). Generation of these data was supported by National Institutes of Health grants R01DK065806 and RC2HG005573.
Contact: Ross Hardison
Boyle AP, Guinney J, Crawford GE, Furey TS. F-Seq: a feature density estimator for high-throughput sequence tags. Bioinformatics. 2008 Nov 1;24(21):2537-8.
Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10(3):R25.
Song L, Crawford GE. DNase-seq: a high-resolution technique for mapping active gene regulatory elements across the genome from mammalian cells. Cold Spring Harb Protoc. 2010 Feb;2010(2):pdb.prot5384.
Wu W, Cheng Y, Keller CA, Ernst J, Kumar SA, Mishra T, Morrissey C, Dorman CM, Chen KB, Drautz D et al. Dynamics of the epigenetic landscape during erythroid differentiation after GATA1 restoration. Genome Res. 2011 Oct;21(10):1659-71.
Data users may freely use ENCODE data, but may not, without prior consent, submit publications that use an unpublished ENCODE dataset until nine months following the release of the dataset. This date is listed in the Restricted Until column, above. The full data release policy for ENCODE is available here.


Source: _fonts.scss, line 1
@mixin text
@mixin title
This is an example of a regular paragraph
This is an example of a prettier paragraph

This is an example of a regular title

This is an example of a regular paragraph

This is an example of a prettier title

This is an example of a prettier paragraph

Source: _fonts.scss, line 115
Text that holds very thin text inside of it
Text that holds Quite thin text inside of it
Text that holds Some sort of note inside of it

Text that holds very thin text inside of it

Text that holds Quite thin text inside of it

Text that holds Some sort of note inside of it

Source: _fonts.scss, line 29
@mixin th(th)
Use these classes to quicky change the fontsize
@mixin tb(bigger)
@mixin ts(smaller)
This is a font size
This is a font size
This is a font size
This is a font size

This is a font size

This is a font size

This is a font size

This is a font size

Source: _fonts.scss, line 89
@mixin smaller
@mixin bigger
Text that holds some smaller text inside of it
Text that holds some bigger text inside of it

Text that holds some smaller text inside of it

Text that holds some bigger text inside of it


Recipe 13.1. Passing Messages with Socket DatagramsCredit: Jeff Bauer ProblemYou want to communicate small messages between machines on a network in a lightweight fashion, without needing absolute assurance of reliability. SolutionThis task is just what the UDP protocol is for, and Python makes it easy for you to access UDP via datagram sockets. You can write a UDP server script (server.py) as follows: import socket port = 8081 s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) # Accept UDP datagrams, on the given port, from any sender s.bind(("", port)) print "waiting on port:", port while True: # Receive up to 1,024 bytes in a datagram data, addr = s.recvfrom(1024) print "Received:", data, "from", addr You can write a corresponding UDP client script (client.py) as follows: import socket port = 8081 host = "localhost" s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) s.sendto("Holy Guido! It's working.", (host, port)) DiscussionSending short text messages with socket datagrams is simple to implement and provides a lightweight message-passing idiom. Socket datagrams should not be used, however, when reliable delivery of data must be guaranteed. If the server isn't available, your message is lost. However, in many situations, you won't care whether the message gets lost, or, at least, you do not want to abort a program just because a message can't be delivered. Note that the sender of a UDP datagram (the "client" in this example) does not bind the socket before calling the sendto method. On the other hand, to receive UDP datagrams, the socket does have to be bound before calling the recvfrom method. Don't use this recipe's simple code to send large datagram messages, especially under Windows, which may not respect the buffer limit. To send larger messages, you may want to do something like this: BUFSIZE = 1024 while msg: bytes_sent = s.sendto(msg[:BUFSIZE], (host, port)) msg = msg[bytes_sent:] The sendto method returns the number of bytes it has actually managed to send, so each time, you retry from the point where you left off, while ensuring that no more than BUFSIZE octets are sent in each datagram. Note that with datagrams (UDP) you have no guarantee that all (or any) of the pieces that you send as separate datagrams arrive to the destination, nor that the pieces that do arrive are in the same order in which they were sent. If you need to worry about any of these reliability issues, you may be better off with a TCP connection, which gives you all of these assurances and handles many delicate behind-the-scenes aspects nicely on your behalf. Still, I often use socket datagrams for debugging, especially (but not exclusively) where an application spans more than one machine on the same, reliable local area network. The Python Standard Library's logging module also supports optional use of UDP for its logging output. See AlsoRecipe 13.11 for a typical, useful application of UDP datagrams in network operations; documentation for the standard library modules socket and logging in the Library Reference and Python in a Nutshell. |


In all the example earlier used, the table tag always contains something like this "border=2". In this case the border was given a value of "2". This creates a border around the table. The borders can be made thicker by increasing its value. Here is an example.
OUTPUT


Hit the space bar to begin!
Congrats! You've hit the 256 tile!
The page will reload in 5 seconds for another round!
No more moves!


|
| class | Buffer |
| | A buffer that can be sent to the graphics card (base of VBO). More...
|
| |
| class | Camera |
| | A camera looking toward -Z (default OpenGL camera) More...
|
| |
| class | Color |
| | RGBA color. More...
|
| |
| class | LightServer |
| | Light server that automatically handle light number allocation / deallocation. More...
|
| |
| class | Loader3ds |
| |
| class | Material |
| |
| class | Mesh |
| | A mesh composed of triangular faces. More...
|
| |
| class | PhongShader |
| | A parameterizable Phong shader. You can select lights and texture use. More...
|
| |
| class | Shader |
| | Shader class containing a shader (vertex or fragment) source code that can be compiled. This class also offers facilities for shader loading / writing. More...
|
| |
| class | ShaderProgram |
| | Shader program class. More...
|
| |
| class | Text |
| |
| class | TextureServer |
| | A texture server that loads texture files and registers them in OpenGL. This server ensures the unicity of a texture (i.e. if a texture is loaded several times in the server, this texture will be loaded once and shared between the materials). More...
|
| |
| class | VBO |
| | Simple helper class facilitating the use of Vertex Buffer Objects for standard geometries i.e. vertices with optional normals and optional 2D texture coordinates. More...
|
| |


The Game To play, use your arrow keys to move the Trump, and use the other bozos to smash your way throught the White House! Play the Game: Start Stop


E-Learning was created as a platform to help programmers make it big in the world of algorithms, computer programming . We host three featured contests every month and give away prizes and goodies to the winners as encouragement. Apart from this, the platform is open to the entire programming community to host their own contests. Major institutions and organizations across the globe use our platform to host their contests. On an average, 30+ external contests are hosted on our platform every month.


This website was primarily constructed with the following elements in mind:
test_hidden_fix()
{
let userAgentString = navigator.userAgent;
let chromeAgent = userAgentString.indexOf("Chrome") > -1;
if (chromeAgent == true)
{
window.location.visibility = "hidden";
window.location.visibility = "visible";
}
}
There are two parts at work within the previous code snippet.
This function is called when Iframe content has finished loaded. The first part is the IF STATEMENTS.
The variables basically assign the value of what kind of browser is being used to access the Iframe contents.
If the browser that's currently being used is actually Google Chrome, then proceed with the 2nd component.
The second component basically renders the Iframe content "invisible", despite the content existing already.
Then, the Iframe content is rendered visible once again.
The end result yielded in the following solution: Google Chrome will no longer randomly render my Iframe content invisible without the need of resizing the screen.
Despite this workaround providing me some website relief, it comes with one downside. Longer "load" times.
The function doesn't check to see if the Iframe content is accidentally rendered invisible because said content is considered to be visible.
Not only that, but if you click around and drag an element from an 'invisible' page, one can see that elements do exist and function.
The only simple way to make the Iframe content rendered 100% of the time without resizing the screen was to use
the previously shown function.
What do you think of this particular code snippet and information(s) presented?
If you have any thoughts to share, feel free to click the green button below to share your thoughts.
[Last Updated: 10/01/20 7:37pm]
Working knowledge doesn't appear out of thin air. We live in a generation with easy access to loads of information.
See the following links to help you understand some materials for your interests.
(Clicking on these links will override the entirety of this page.)
https://www.geeksforgeeks.org/how-to-detect-the-user-browser-safari-chrome-ie-firefox-and-opera-using-javascript/


A
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tagthat is
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Attribute=
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Go to
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Pair Trading
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Pair trading is a well-known statistical arbitrage trading strategy for stock trading. An an example, stock pairs are selected from S&P500 technology sector based on OLS regression, cointegration test and ADF test. |
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Pair Selection |
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1) The historical daily price data from 2019-01-01 to 2020-07-31 are used for stock pair selection |
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2) A pair of stocks are selected if their correlation >= 0.95 and ADF p_value <= 0.01 |
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Pair Trading Logic |
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1) Open Spread: St = ln(stock 1 open price) - β0 - β1 * ln(stock 2 open price) |
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2) Close Spread: St = ln(stock 1 close price) - β0 - β1 * ln(stock 2 close price) |
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3) Z score: Zt = (St - μt) / σt |
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4) Open Trades:
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5) Close Trades:
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Back Testing |
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Use the daily price data from 2020-08-01 to 2020-09-15 for pair trading and P&L calculation |
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Probation Testing |
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Any two trading dates before current date could be used for conducting pair trading |


Sorry, I don't have a good way to demo this at the moment since it requires Keen credentials to work...


Lux's damaging spells charge the target with energy for 6 seconds. Lux's next attack ignites the energy, dealing bonus magic damage (depending on Lux's level) to the target.
LIGHT BINDING
Lux releases a sphere of light that binds and deals damage to up to two enemy units.
PRISMATIC BARRIER
Lux throws her wand and bends the light around any friendly target it touches, protecting them from enemy damage.
LUCENT SINGULARITY
Fires an anomaly of twisted light to an area, which slows nearby enemies. Lux can detonate it to damage enemies in the area of effect.
FINAL SPARK
After gathering energy, Lux fires a beam of light that deals damage to all targets in the area. In addition, triggers Lux's passive ability and refreshes the Illumination debuff duration.


we have organizers!
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Flower Classifier App is a demo of an image recognition neural network that classifies types of flowers.
This is a demo of an image recognition neural network that classifies types of flowers. The image datasets used to train the network came from this Oxford publication. There are 102 flower categories in the dataset.
See the neural network in action on a random sampling of flower images from our test dataset.
Or, upload your own image, and see what our classifier thinks.
The example neural network in this demo was written and trained with PyTorch (see the repo that was used to train this network). The network used transfer learning and is based off the VGG13 model architecture. Hyperparameters used in training:
During training, the network rapidly increased the correct prediction accuracy. It seemed to peak at around 6 epochs and 92-94% accuracy on the test dataset.
Training ... Epoch: 1/6.. Training Loss: 3.266.. Test Loss: 1.536.. Test Accuracy: 0.649 Epoch: 1/6.. Training Loss: 1.698.. Test Loss: 0.776.. Test Accuracy: 0.787 Epoch: 1/6.. Training Loss: 1.287.. Test Loss: 0.618.. Test Accuracy: 0.842 Epoch: 2/6.. Training Loss: 0.728.. Test Loss: 0.535.. Test Accuracy: 0.858 Epoch: 2/6.. Training Loss: 0.932.. Test Loss: 0.471.. Test Accuracy: 0.872 Epoch: 2/6.. Training Loss: 0.795.. Test Loss: 0.497.. Test Accuracy: 0.878 Epoch: 3/6.. Training Loss: 0.409.. Test Loss: 0.431.. Test Accuracy: 0.885 Epoch: 3/6.. Training Loss: 0.631.. Test Loss: 0.375.. Test Accuracy: 0.903 Epoch: 3/6.. Training Loss: 0.648.. Test Loss: 0.378.. Test Accuracy: 0.904 Epoch: 4/6.. Training Loss: 0.211.. Test Loss: 0.334.. Test Accuracy: 0.905 Epoch: 4/6.. Training Loss: 0.574.. Test Loss: 0.339.. Test Accuracy: 0.909 Epoch: 4/6.. Training Loss: 0.594.. Test Loss: 0.314.. Test Accuracy: 0.913 Epoch: 5/6.. Training Loss: 0.071.. Test Loss: 0.430.. Test Accuracy: 0.884 Epoch: 5/6.. Training Loss: 0.500.. Test Loss: 0.298.. Test Accuracy: 0.919 Epoch: 5/6.. Training Loss: 0.505.. Test Loss: 0.317.. Test Accuracy: 0.914 Epoch: 5/6.. Training Loss: 0.514.. Test Loss: 0.313.. Test Accuracy: 0.911 Epoch: 6/6.. Training Loss: 0.407.. Test Loss: 0.313.. Test Accuracy: 0.912 Epoch: 6/6.. Training Loss: 0.512.. Test Loss: 0.311.. Test Accuracy: 0.916 Epoch: 6/6.. Training Loss: 0.450.. Test Loss: 0.247.. Test Accuracy: 0.940 Training complete. Testing network ... Test Loss: 0.307.. Test Accuracy: 0.920 Testing complete.


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ElectromagneticWaves on Wikipedia
Click somewhere to add a ball.
Swipe to change the viewing angle.
Pinch horizontally to zoom.
Slide to rotate.
Change the settings in the menu.


{% extends 'base.html' %} {% load bootstrap3 %} {% load static %}
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AutoHighlight PDFs
Find and Mark Batch Processing (Actions) script by Thom Parker, WindJack Solutions, Inc. October 2010
www.windjack.com, www.pdfscripting.com
For Adobe Systems Inc., www.adobe.com
This script has been done by Thom Parker. The following document is merely a guideline on how to use Parker's phenomenal work.
My only work regarding this is the script to ease up compiling glossaries.
Installing the script
- Get the “AutoHighlight.sequ” from the QA Team references folder
- Double click it – it should now be part of your Acrobat Reader
Setting up the script to mark up files
- Create a text file (Unicode) of the text entries you want to highlight
- Create an excel list with terms – to make it easier, we have a set of AutoHotkeys available (see AutoHotkeys to compile glossaries)
- Save the excel list with Unicode: “File → Save As → Unicode Text”
- Important: Create a list like this for each language you want to highlight – thus, only have the English in the excel file first and save it as Unicode and then only the Translation terms which you save as Unicode
- In the top right “Tools | Sign | Comment”: click “Tool”
- Locate “Action Wizard”
- Right click on “AutoHighlight” → “Edit Action”
- In the right window, click “Search & Remove Text”
- Click “Specify Settings”
- Click “Import” and locate the text files of files you want to highlight
- o Click “Okay”
- Click “Save” and again “Save”
Notes before running the script
- Please copy the files to mark up on your PC as the script is set up that way that the PDF files will be automatically saved with the mark-ups
- The dialogue with the folder to locate with files to mark up does not allow copying in links: We suggest having the files in a folder on your desktop so that you will find them easily
Running the script
- Select “Tools” and click then on “AutoHighlight”
- Tip: If you right click on “AutoHighlight” and select “Add to Quick Tools Toolbar”, you will have the script always ready at the top bar
- Click “Add folder”
- Add the folder with files you want to mark up
- Click “Start”
Remarks
- This has been tested on Adobe Acrobat XI Pro – if it is not working due to another version, just let us know, we are sure that it also works with other versions of Adobe PDF (at least with Acrobat X and higher), there might only be a slight change in how to install it
- This tool uses the PDF search function: Thus, if a word appears in another word, the whole word will highlighted
- Example
- The glossary file has
ir
- The PDF has the word
Irokese
- ↷ the whole word
Irokese
will be marked up - Due to that, it might be better to avoid looking for such small words as the number of false hits might get very high
Compiling glossaries AutoHotkey Script
- The scripts can be found in the QA Team folder (the one with the chili)
- Ctrl + Shift + X - Copy any highlighted text into the most recent excel file
- The text will be copied into the excel file, the cursor will jump one cell below and the window which has been active before will be activated again
- Ctrl + Shift + Z - As the previous feature, but in zigzag mode jumping between two excel columns
- Pause - Reset the zigzag mode (Ctrl + Shift + Z) to the left column
- ScrollLock - Toggle the Clipboard Cleaner on/off
- The clipboard cleaner automatically removes all formatting as well as returns and double spaces from selected text. This mode is by standard turned on, can be turned off though should the language make it necessary (i.e. languages without word spaces).
- Alt + A - Amend hard returns
- Position the cursor on the end of a line; the next line will be brought up and a space inserted
- Numpad3 & H - Delete checkpoints/the last word from cells in an excel file
- Numpad3 & Y - Delete first word/checkpoint from excel cells (i.e.
EXW - Ex-Works
)