Ask questions about the core tech here, but not questions about prices or personal questions or GUI and please don't DM JL777.
https://dexstats.info/richlistlookup.php?address=RLVzC4tr9cNKvuw2z4m8KuMfZURwCehx55
https://medium.com/@jameslee777
100 messages
Before 05-Dec-19 12:00 AM
hastrup25-Jul-19 09:23 AM
peddycb25-Jul-19 06:55 PM
@jl777c do you have an opinion too Stakenet (XSN) ???
jl777c25-Jul-19 06:56 PM
never heard of it
they seem to have a lot of buzzwords, any working tech ?
Acura25-Jul-19 06:57 PM
They want to do Atomic swaps by Lightning.
peddycb25-Jul-19 06:58 PM
Lightning Atomic swaps
actionslacks25-Jul-19 06:58 PM
i had some back and forth w/them Q3 2019 is the hopium
peddycb25-Jul-19 06:58 PM
would like to hear your opinion
jl777c25-Jul-19 06:58 PM
i tried that, but it isnt very efficient
i implemented subatomic swaps, where you exchange a little bit at a time via a payment channel
but you end up doing so many tx, it is slow and LN isnt exactly trustless for small payments
building on top of LN, it seems a very risky plan
people can barely get LN working for its intended usecase of micropayments
how they will make it work seamlessly for swaps, seems a mystery
also what about coins without LN?
who holds the keys during the swap?
or maybe they use LN just for the broadcast network, which is quite an inefficient usage
tl:dr wait until they get something into beta release before believing they can get it to actually work
peddycb25-Jul-19 07:02 PM
I just watch the project and want to learn what is good and evil
KomodoWorld25-Jul-19 07:02 PM
I was reading about Stakenet today, hard to understand what it's based on. My understanding so far is that XSN masternodes act as full wallet for different coins, users have a light wallet. Masternodes are connected via LN, they do the order matching thing and trade between them. Users withdraw real coins when done. Seems an highly efficient proxy-token dex. I could be wrong though, details are scarce
Acura25-Jul-19 07:03 PM
I had a chat with one guy from xsn.
Asked him how i need xsn ?
For which reason ?
This he answered to me:
Primary:
Payment of all fees (onchain tx, offchain tx, lightning swap fee).
Burn verification of dApps.
Payment of DB and CPU renting.
But you don't have to own xsn to do this. For example, if you want to pay a merchant with LTC who only accepts BTC LN payments, you can do this with LTC thanks to LN swaps and the swap fees are calculated in the backend with XSN and deducted over a separate LN swap without you noticing.
We migrate all LN features into our MN layer (channel funding, facotry channels, watchtowers, swap resolver, ...). Consequently, the MN operators are also those who benefit from most of the fees. On the other hand they have to run potent hardware to host the fullnodes with multiple blockchains or dapps.
Translated with www.DeepL.com/Translator
MN = Masternode
jl777c25-Jul-19 07:04 PM
so it is a custodial process where all MN become money transmittors
good luck with that
Acura25-Jul-19 07:04 PM
jl777c25-Jul-19 07:06 PM
"Masternodes are connected via LN, they do the order matching thing and trade between them"
seems they just use LN as a network. strange as it makes more sense to use the p2p network as a network
so they have LN there for the buzzword value, not technical
but maybe they need LN for all these fees going back and forth
sounds very complex
we have proven atomic swap tech that will scale to millions via decentralized nSPV system
peddycb25-Jul-19 07:11 PM
I'm going to rest tomorrow, that's just too much for my head
actionslacks25-Jul-19 07:27 PM
the argument about regulation was the the mn would be anonymous so "the govt couldn't get them"
jl777c25-Jul-19 07:27 PM
well then, go all in!
but actually, regulations or not, a custodial process is obsolete
2
Oszy26-Jul-19 03:06 PM
what are the b addresses for and can anyone have a KMD address starting with b or is that a DEX thing?
CHMEX26-Jul-19 03:08 PM
b addresses are multi sig addresses
And addresses with redeem script
Oszy26-Jul-19 03:10 PM
ah okay
thanks
PHBA206126-Jul-19 03:10 PM
or in a generic way, addresses that start with b are "Pay to script hash" addresses
1
1
radix4228-Jul-19 03:31 PM
so $VEIL is interested in getting added to AtomicDEX, but they are just adding ElectrumX support and are wondering if they need to get their coin upstreamed into the main ElectrumX repo or if they can operate ElectrumX servers for it if that takes a long time to happen
cc: @jl777c
jl777c28-Jul-19 03:37 PM
i think that is best, usually it doesnt take very long
radix4229-Jul-19 02:17 PM
great, thanks
mrtzmp330-Jul-19 11:19 AM
When usdc pair ?
Will it come?
gcharang30-Jul-19 11:28 AM
@mrtzmp3 are you asking about AtomicDEX?
tomsmith2630-Jul-19 03:25 PM
I was invited to become a tester for android device but it said app not available ? How can I get it for testing atomicDEx
yes. I did and this is a notice I received on my email
AtomicDEX Mobile Beta
Congratulations!
You have been invited to the AtomicDEX beta. To join the beta and download the app go to the following link:
https://play.google.com/apps/testing/com.komodoplatform.atomicdex
If the link does not work it means you had subscribed with an email that was not attached to your google play account. In this case contact us through Discord.
About The Test
During the next 24 hours, we will be conducting extensive tests with our AtomicDEX wallet application.
Please note that our tests are primarily focused on testing atomic swaps and network connectivity issues. As a secondary focus, we are also collecting feedback about the overall wallet functionality and user experience.
Let’s get started!
hastrup30-Jul-19 07:01 PM
@tomsmith26 use this channel(edited)
#atomicdex-beta
mrtzmp331-Jul-19 12:59 PM
@gcharang was asking also on bigger exchange binance i.e. but atomicd will have it I suppose(edited)
gcharang31-Jul-19 01:00 PM
@mrtzmp3 atomicDEX already has it
mrtzmp331-Jul-19 01:02 PM
Nice mate :)
Neko01-Aug-19 04:30 PM
thinking about sending my CV, I don't need the money but I would like to help KMD in a way, are there many offers so far
Hi, we are running a blockchain project and we wish to adapt Dpow technology into our chain. But we are not very familiar with the code. Who should we contact if we wish to find a 3rd support to help with the technique integration?
CHMEX07-Aug-19 05:09 AM
@dukeleto ^
1
grewalsatinder07-Aug-19 09:07 AM
@bztianjiao there are few devs to contact.
@dukeleto is most experienced implementing dPoW to 3rd party coin projects.
There's also @blackjok3r who can help with such dev work.
And I think @radix42 too
bztianjiao07-Aug-19 09:30 AM
Thanks bro
grewalsatinder07-Aug-19 09:40 AM
np
Bitboy07-Aug-19 06:25 PM
"Looking for confirmation on the 98% figure. And is that 98% of KMD swept was returned or 98% of KMD reclaim forms were processed?" @jl777c asking for a friend
jl777c07-Aug-19 06:26 PM
i dont have exact figures, but it can be tallied from the blockchain. I have put in about 750,000 KMD and all but 30k have been refunded.
approx numbers are 7M swept, 7.75 million refunded, total stolen is 7.8? mil but not 100% knowable
total claims left are about 30,000 but from 261 addresses, so an average of 115KMD
@jl777c i read something recently about some devs adding a vault, whereby the BTC is sent to a special address. Then once a tx is created to use that utxo, the owner has x amount of time to VETO a tx basically. Dont know how it is supposed to work but would something like that work with KMD?
Bitcoin Core developer Bryan Bishop has proposed a feature to delay transfers from cold storage so users can stop thieves from draining their wallets.
requires constant monitoring, special GUI
seems not a big project
hyperDEX08-Aug-19 08:39 AM
Do you know how they can stop someone with your private keys from spending it with a higher fee?
jl777c08-Aug-19 08:41 AM
i think it is a bit like atomic swap, some clever scripting to allow vaulting
KomodoWorld08-Aug-19 08:42 AM
months ago I talked with @mrlynch about developing a CC for a bounty from me, based on the original Vault paper by prof. Sirer, but it's still far from completed
hyperDEX08-Aug-19 08:43 AM
Will be interesting to learn how this is done
jl777c08-Aug-19 08:45 AM
Under Bishop’s proposal, if an “unlock period” were initiated, an owner could choose to react or not react.
so it is like atomic swap timeout, with yourself
hyperDEX08-Aug-19 08:47 AM
But if someone has your private key, how does the blockchain know who is the owner?
jl777c08-Aug-19 08:47 AM
you send funds to a p2sh address that requires an unlock utxo that is matured
so there is another special utxo that needs to be spent first, the spending of this starts the unlock period
hyperDEX08-Aug-19 08:47 AM
So maybe they are thinking about it the wrong way, the default should be not to send and if you are sending a valid tx then you send that through
jl777c08-Aug-19 08:48 AM
i think ALL spends need to go through this, so if a one day delay, then all access to these funds will take a day
in the event you see the unlocking txid activated, you have one day to spend (with a different privkey?) the original funds, which are still in the conditional p2sh
something like that
if you can find the design document that he posted wherever, i can look at it and see how hard it is to do
hyperDEX08-Aug-19 08:49 AM
Great will have a hunt around
jl777c08-Aug-19 08:50 AM
probably the bitcoin mailing list
mrlynch08-Aug-19 08:50 AM
there are 2 keys involved from my understanding
hyperDEX08-Aug-19 08:50 AM
But now thinking about what you wrote, i wonder if it is much easier just to get say 3 yubikeys and setup a multisig address somehow


Boost for Dundalk as vaccine producers announce 200 jobs
by By Digital Desk staffThere was a major jobs boost in Co Louth this morning as a Chinese company who make vaccines announced a big investment in the area.
There will be 200 jobs created at WuXi Vaccines in Dundalk as the company announced a €200m construction of new production facility.
The facility will be built on the WuXi Biologics Campus in the town.
Wuxi are "a leading global open-access biologics technology platform offering end-to-end solutions to empower organizations to discover, develop and manufacture biologics from concept to commercial manufacturing," according to the company website.
The Hong Kong listed company has a presence in China, Ireland, Singapore and the US.
The new investment is being supported by IDA Ireland.
READ MORE
TSB apologises after payment glitch leaves UK customers without wages


Flat 40% OFF on premium brands
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Choose among the best in the world.
Our exquisite collection of shirts.


---
permalink: /
layout: index
title: Homepage
show_call_to_action: false
show_chownow: true
---
Harlowe's French Dip
Saloon & Sandwich in Oldtown Pasadena
Centered in historic Old Pasadena, CA, the handsome wood Art Deco interior of Harlowe’s French Dip surrounds and transports you back in time. Harlowe's is more than French Dips: it's an ensemble of signature sandwiches and upscale pub fare. By using only the freshest product we aim to reinvent classic favorites into modern staples. Special events, rare beers, and daily happy hour? That's Harlowe's.


A common task for web applications is to search some data in the database with user input. In a simple case, this could be filtering a list of objects by a category. A more complex use case might require searching with weighting, categorization, highlighting, multiple languages, and so on. This document explains some of the possible use cases and the tools you can use.
We’ll refer to the same models used in Making queries.
Text-based fields have a selection of simple matching operations. For example, you may wish to allow lookup up an author like so:
>>> Author.objects.filter(name__contains='Terry')
[<Author: Terry Gilliam>, <Author: Terry Jones>]
This is a very fragile solution as it requires the user to know an exact
substring of the author’s name. A better approach could be a case-insensitive
match (icontains
), but this is only marginally better.
If you’re using PostgreSQL, Django provides a selection of database specific tools to allow you to leverage more complex querying options. Other databases have different selections of tools, possibly via plugins or user-defined functions. Django doesn’t include any support for them at this time. We’ll use some examples from PostgreSQL to demonstrate the kind of functionality databases may have.
Searching in other databases
All of the searching tools provided by django.contrib.postgres
are
constructed entirely on public APIs such as custom lookups and database functions. Depending on your database, you should
be able to construct queries to allow similar APIs. If there are specific
things which cannot be achieved this way, please open a ticket.
In the above example, we determined that a case insensitive lookup would be
more useful. When dealing with non-English names, a further improvement is to
use unaccented comparison
:
>>> Author.objects.filter(name__unaccent__icontains='Helen')
[<Author: Helen Mirren>, <Author: Helena Bonham Carter>, <Author: Hélène Joy>]
This shows another issue, where we are matching against a different spelling of
the name. In this case we have an asymmetry though - a search for Helen
will pick up Helena
or Hélène
, but not the reverse. Another option
would be to use a trigram_similar
comparison, which compares
sequences of letters.
For example:
>>> Author.objects.filter(name__unaccent__lower__trigram_similar='Hélène')
[<Author: Helen Mirren>, <Author: Hélène Joy>]
Now we have a different problem - the longer name of “Helena Bonham Carter” doesn’t show up as it is much longer. Trigram searches consider all combinations of three letters, and compares how many appear in both search and source strings. For the longer name, there are more combinations which appear in the source string so it is no longer considered a close match.
The correct choice of comparison functions here depends on your particular data set, for example the language(s) used and the type of text being searched. All of the examples we’ve seen are on short strings where the user is likely to enter something close (by varying definitions) to the source data.
Simple database operations are too simple an approach when you start considering large blocks of text. Whereas the examples above can be thought of as operations on a string of characters, full text search looks at the actual words. Depending on the system used, it’s likely to use some of the following ideas:
There are many alternatives for using searching software, some of the most prominent are Elastic and Solr. These are full document-based search solutions. To use them with data from Django models, you’ll need a layer which translates your data into a textual document, including back-references to the database ids. When a search using the engine returns a certain document, you can then look it up in the database. There are a variety of third-party libraries which are designed to help with this process.
PostgreSQL has its own full text search implementation built-in. While not as powerful as some other search engines, it has the advantage of being inside your database and so can easily be combined with other relational queries such as categorization.
The django.contrib.postgres
module provides some helpers to make these
queries. For example, a simple query might be to select all the blog entries
which mention “cheese”:
>>> Entry.objects.filter(body_text__search='cheese')
[<Entry: Cheese on Toast recipes>, <Entry: Pizza recipes>]
You can also filter on a combination of fields and on related models:
>>> Entry.objects.annotate(
... search=SearchVector('blog__tagline', 'body_text'),
... ).filter(search='cheese')
[
<Entry: Cheese on Toast recipes>,
<Entry: Pizza Recipes>,
<Entry: Dairy farming in Argentina>,
]
See the contrib.postgres
Full text search document for
complete details.
Oct 01, 2019


This page collects Universal Reverse ClickJacking (also sometimes called Same Origin Method Execution or SOME) vulnerabilities.
A Universal Reverse ClickJacking vulnerability arises when a JSONP
callback parameter (i.e. a user-controlled parameter reflected in a
JS context) is controllable by an attacker by passing something like
element.click
if the parameter ends up in the JSONP callback
directly or something like %26callback%3Delement.click%23
(the
final # discards any other character that follows) to a routine that builds
the JSONP call in an insecure way (for instance, using string concatenation
and putting the user-provided input in another parameter but still allowing
parameter pollution).
This grants an attacker the ability to execute javascript code in the context of the page, thus performing actions with side effects, such as clicking buttons and submitting forms, on the same page or on different pages in the same origin, using frames or popups.
If the JSONP endpoint is restrictive in terms of allowed charset for the callback this vulnerability does not become a full XSS, since a useful XSS vector would contain forbidden characters.
<script
src="/jsonp?callback=%q">
).
<script
src="/jsonp?callback=callbackFunc&other=%q">
).
In these tests the vulnerable JS snippet is placed in a page with no actionable DOM object: in this case, exploitation can be achieved by interacting with other pages in the same domain.
X-Frame-Options
header to
DENY
, or, in some cases, also SAMEORIGIN
, but
this page allows framing.
X-Frame-Options
header to
DENY
, or, in some cases, also SAMEORIGIN
, but
this page allows framing.
X-Frame-Options
header is set, but popup
blockers in browsers might prevent this attack vector from working. This
page does not allow framing.
X-Frame-Options
header is set, but popup
blockers in browsers might prevent this attack vector from working. This
page does not allow framing.


--- layout: default years: [2012, 2013, 2014, 2015, 2016] ---
{{ page.speakerBio }}
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Hello! This is the page where the CG chairperson of CG {{class_name[:-2]}}/{{class_name[-2:]}} can update
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3.1. Generalized Linear Models¶
The following are a set of methods intended for regression in which the target value is expected to be a linear combination of the input variables. In mathematical notion, if is the predicted value.
Across the module, we designate the vector as coef_ and as intercept_.
To perform classification with generalized linear models, see Logisitic regression.
3.1.1. Ordinary Least Squares¶
LinearRegression fits a linear model with coefficients to minimize the residual sum of squares between the observed responses in the dataset, and the responses predicted by the linear approximation.
LinearRegression will take in its fit method arrays X, y and will store the coefficients of the linear model in its coef_ member:
>>> from sklearn import linear_model
>>> clf = linear_model.LinearRegression()
>>> clf.fit ([[0, 0], [1, 1], [2, 2]], [0, 1, 2])
LinearRegression(fit_intercept=True, normalize=False, overwrite_X=False)
>>> clf.coef_
array([ 0.5, 0.5])
However, coefficient estimates for Ordinary Least Squares rely on the independence of the model terms. When terms are correlated and the columns of the design matrix have an approximate linear dependence, the matrix becomes close to singular and as a result, the least-squares estimate becomes highly sensitive to random errors in the observed response, producing a large variance. This situation of multicollinearity can arise, for example, when data are collected without an experimental design.
Examples:
<IP_ADDRESS>. Ordinary Least Squares Complexity¶
This method computes the least squares solution using a singular value decomposition of X. If X is a matrix of size (n, p ) this method has a cost of , assuming that .
3.1.2. Ridge Regression¶
Ridge regression addresses some of the problems of Ordinary Least Squares by imposing a penalty on the size of coefficients. The ridge coefficients minimize a penalized residual sum of squares,
Here, is a complexity parameter that controls the amount of shrinkage: the larger the value of , the greater the amount of shrinkage and thus the coefficients become more robust to collinearity.
As with other linear models, Ridge will take in its fit method arrays X, y and will store the coefficients of the linear model in its coef_ member:
>>> from sklearn import linear_model
>>> clf = linear_model.Ridge (alpha = .5)
>>> clf.fit ([[0, 0], [0, 0], [1, 1]], [0, .1, 1])
Ridge(alpha=0.5, fit_intercept=True, normalize=False, overwrite_X=False,
tol=0.001)
>>> clf.coef_
array([ 0.34545455, 0.34545455])
>>> clf.intercept_
0.13636...
<IP_ADDRESS>. Ridge Complexity¶
This method has the same order of complexity than an Ordinary Least Squares.
<IP_ADDRESS>. Setting alpha: generalized Cross-Validation¶
RidgeCV implements ridge regression with built-in cross-validation of the alpha parameter. The object works in the same way as GridSearchCV except that it defaults to Generalized Cross-Validation (GCV), an efficient form of leave-one-out cross-validation:
>>> from sklearn import linear_model
>>> clf = linear_model.RidgeCV(alphas=[0.1, 1.0, 10.0])
>>> clf.fit([[0, 0], [0, 0], [1, 1]], [0, .1, 1])
RidgeCV(alphas=[0.1, 1.0, 10.0], cv=None, fit_intercept=True, loss_func=None,
normalize=False, score_func=None)
>>> clf.best_alpha
0.1
References
- “Notes on Regularized Least Squares”, Rifkin & Lippert (technical report, course slides).
3.1.3. Lasso¶
The Lasso is a linear model that estimates sparse coefficients. It is useful in some contexts due to its tendency to prefer solutions with fewer parameter values, effectively reducing the number of variables upon which the given solution is dependent. For this reason, the Lasso and its variants are fundamental to the field of compressed sensing.
Mathematically, it consists of a linear model trained with L1 prior as regularizer. The objective function to minimize is:
The lasso estimate thus solves the minimization of the least-squares penalty with added, where is a constant and is the L1-norm of the parameter vector.
The implementation in the class Lasso uses coordinate descent as the algorithm to fit the coefficients. See Least Angle Regression for another implementation:
>>> clf = linear_model.Lasso(alpha = 0.1)
>>> clf.fit([[0, 0], [1, 1]], [0, 1])
Lasso(alpha=0.1, fit_intercept=True, max_iter=1000, normalize=False,
overwrite_X=False, precompute='auto', tol=0.0001)
>>> clf.predict([[1, 1]])
array([ 0.8])
Also useful for lower-level tasks is the function lasso_path that computes the coefficients along the full path of possible values.
Examples:
<IP_ADDRESS>. Setting alpha¶
The alpha parameter control the degree of sparsity of the coefficients estimated.
<IP_ADDRESS>.1. Using cross-validation¶
The scikit exposes objects that set the Lasso alpha parameter by cross-validation: LassoCV and LassoLarsCV. LassoLarsCV is based on the Least Angle Regression algorithm explained below.
For high-dimensional datasets with many collinear regressors, LassoCV is most often preferrable. How, LassoLarsCV has the advantage of exploring more relevant values of alpha parameter, and if the number of samples is very small compared to the number of observations, it is often faster than LassoCV.
<IP_ADDRESS>.2. Information-criteria based model selection¶
Alternatively, the estimator LassoLarsIC proposes to use the Akaike information criterion (AIC) and the Bayes Information criterion (BIC). It is a computationally cheaper alternative to find the optimal value of alpha as the regularization path is computed only once instead of k+1 times when using k-fold cross-validation. However, such criteria needs a proper estimation of the degrees of freedom of the solution, are derived for large samples (asymptotic results) and assume the model is correct, i.e. that the data are actually generated by this model. They also tend to break when the problem is badly conditioned (more features than samples).
3.1.4. Elastic Net¶
ElasticNet is a linear model trained with L1 and L2 prior as regularizer.
The objective function to minimize is in this case
3.1.5. Least Angle Regression¶
Least-angle regression (LARS) is a regression algorithm for high-dimensional data, developed by Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani.
The advantages of LARS are:
- It is numerically efficient in contexts where p >> n (i.e., when the number of dimensions is significantly greater than the number of points)
- It is computationally just as fast as forward selection and has the same order of complexity as an ordinary least squares.
- It produces a full piecewise linear solution path, which is useful in cross-validation or similar attempts to tune the model.
- If two variables are almost equally correlated with the response, then their coefficients should increase at approximately the same rate. The algorithm thus behaves as intuition would expect, and also is more stable.
- It is easily modified to produce solutions for other estimators, like the Lasso.
The disadvantages of the LARS method include:
- Because LARS is based upon an iterative refitting of the residuals, it would appear to be especially sensitive to the effects of noise. This problem is discussed in detail by Weisberg in the discussion section of the Efron et al. (2004) Annals of Statistics article.
The LARS model can be used using estimator Lars, or its low-level implementation lars_path.
3.1.6. LARS Lasso¶
LassoLars is a lasso model implemented using the LARS algorithm, and unlike the implementation based on coordinate_descent, this yields the exact solution, which is piecewise linear as a function of the norm of its coefficients.
>>> from sklearn import linear_model
>>> clf = linear_model.LassoLars(alpha=.1)
>>> clf.fit ([[0, 0], [1, 1]], [0, 1])
LassoLars(alpha=0.1, eps=..., fit_intercept=True,
max_iter=500, normalize=True, overwrite_X=False, precompute='auto',
verbose=False)
>>> clf.coef_
array([ 0.717157..., 0. ])
Examples:
The Lars algorithm provides the full path of the coefficients along the regularization parameter almost for free, thus a common operation consist of retrieving the path with function lars_path
<IP_ADDRESS>. Mathematical formulation¶
The algorithm is similar to forward stepwise regression, but instead of including variables at each step, the estimated parameters are increased in a direction equiangular to each one’s correlations with the residual.
Instead of giving a vector result, the LARS solution consists of a curve denoting the solution for each value of the L1 norm of the parameter vector. The full coeffients path is stored in the array coef_path_, which has size (n_features, max_features+1). The first column is always zero.
References:
- Original Algorithm is detailed in the paper Least Angle Regression by Hastie et al.
3.1.7. Orthogonal Matching Pursuit (OMP)¶
OrthogonalMatchingPursuit and orthogonal_mp implements the OMP algorithm for approximating the fit of a linear model with constraints imposed on the number of non-zero coefficients (ie. the L 0 pseudo-norm).
Being a forward feature selection method like Least Angle Regression, orthogonal matching pursuit can approximate the optimum solution vector with a fixed number of non-zero elements:
Alternatively, orthogonal matching pursuit can target a specific error instead of a specific number of non-zero coefficients. This can be expressed as:
OMP is based on a greedy algorithm that includes at each step the atom most highly correlated with the current residual. It is similar to the simpler matching pursuit (MP) method, but better in that at each iteration, the residual is recomputed using an orthogonal projection on the space of the previously chosen dictionary elements.
Examples:
References:
3.1.8. Bayesian Regression¶
Bayesian regression techniques can be used to include regularization parameters in the estimation procedure: the regularization parameter is not set in a hard sens but tuned to the data at hand.
This can be done by introducing some prior knowledge over the parameters. For example, penalization by weighted norm is equivalent to setting Gaussian priors on the weights.
The advantages of Bayesian Regression are:
- It adapts to the data at hand.
- It can be used to include regularization parameters in the estimation procedure.
The disadvantages of Bayesian Regression include:
- Inference of the model can be time consuming.
<IP_ADDRESS>. Bayesian Ridge Regression¶
BayesianRidge tries to avoid the overfit issue of Ordinary Least Squares, by adding the following prior on :
The resulting model is called Bayesian Ridge Regression, it is similar to the classical Ridge. is an hyper-parameter and the prior over performs a shrinkage or regularization, by constraining the values of the weights to be small. Indeed, with a large value of , the Gaussian is narrowed around 0 which does not allow large values of , and with low value of , the Gaussian is very flattened which allows values of . Here, we use a non-informative prior for . The parameters are estimated by maximizing the marginal log likelihood. There is also a Gamma prior for and :
- By default , i.e.
- very slightly informative priors.
Bayesian Ridge Regression is used for regression:
>>> from sklearn import linear_model
>>> X = [[0., 0.], [1., 1.], [2., 2.], [3., 3.]]
>>> Y = [0., 1., 2., 3.]
>>> clf = linear_model.BayesianRidge()
>>> clf.fit (X, Y)
BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, compute_score=False,
fit_intercept=True, lambda_1=1e-06, lambda_2=1e-06, n_iter=300,
normalize=False, overwrite_X=False, tol=0.001, verbose=False)
After being fitted, the model can then be used to predict new values:
>>> clf.predict ([[1, 0.]])
array([ 0.50000013])
The weights of the model can be access:
>>> clf.coef_
array([ 0.49999993, 0.49999993])
Due to the Bayesian framework, the weights found are slightly different to the ones found by Ordinary Least Squares. However, Bayesian Ridge Regression is more robust to ill-posed problem.
Examples:
References
- More details can be found in the article Bayesian Interpolation by MacKay, David J. C.
3.1.9. Automatic Relevance Determination - ARD¶
ARDRegression adds a more sophisticated prior , where we assume that each weight is drawn in a Gaussian distribution, centered on zero and with a precision :
with . There is also a Gamma prior for and :
- By default , i.e.
- very slightly informative priors.
<IP_ADDRESS>. Mathematical formulation¶
A prior is introduced as a distribution over the parameters. This distribution is set before processing the data. The parameters of a prior distribution are called hyper-parameters. This description is based on the Bayes theorem :
- With :
- the likelihood : it expresses how probable it is to observe given .
- the marginal probability of the data : it can be considered as a normalizing constant, and is computed by integrating with respect to .
- the prior over the parameters : it expresses the knowledge that we can have about before processing the data.
- the conditional probability (or posterior probability) : it expresses the uncertainty in after observing the data.
All the following regressions are based on the following Gaussian assumption:
where is the precision of the noise.
References
- Original Algorithm is detailed in the book Bayesian learning for neural networks by Radford M. Neal
3.1.10. Logisitic regression¶
If the task at hand is to do choose which class a sample belongs to given a finite (hopefuly small) set of choices, the learning problem is a classification, rather than regression. Linear models can be used for such a decision, but it is best to use what is called a logistic regression, that doesn’t try to minimize the sum of square residuals, as in regression, but rather a “hit or miss” cost.
The LogisticRegression class can be used to do L1 or L2 penalized logistic regression. L1 penalization yields sparse predicting weights. For L1 penalization sklearn.svm.l1_min_c allows to calculate the lower bound for C in order to get a non “null” (all feature weights to zero) model.
Examples:
- example_logistic_l1_l2_coef.py
- Path with L1- Logistic Regression
3.1.11. Stochastic Gradient Descent - SGD¶
Stochastic gradient descent is a simple yet very efficient approach to fit linear models. It is particulary useful when the number of samples (and the number of features) is very large.
The classes SGDClassifier and SGDRegressor provide functionality to fit linear models for classification and regression using different (convex) loss functions and different penalties.
References


{% extends "cms/admin_home.html" %} {% load crispy_forms_tags sekizai_tags i18n danceschool_tags %} {% block content %}
{% blocktrans %}You have selected the following staff members for whom to update compensation information. Current compensation rules for these individuals are also listed for reference.{% endblocktrans %}
{% blocktrans %} Please select the categories for which you wish to reset compensation information. Custom compensation rules may either be deleted entirely, or the category defaults may be copied to the selected staff members, if category defaults exist. To enable/disable generation of staff expense items using category defaults only, check/uncheck the "Use staff category default wages to generate expenses" box in Financial global settings. {% endblocktrans %}
{% blocktrans %} Note: Deleting existing compensation rules will result in the deletion of any expenses generated under those rules, and is not recommended unless you know what you are doing. {% endblocktrans %}
{% addtoblock "js" %} {{ form.media }} {% endaddtoblock %} {% endblock %}


Dans Hobbies
My Homepage
Gaming
Info
What I like
Consoles:
Xbox 360
Pc
Games:
Call Of Duty
Fifa
Garrys Mod
Sports
football
rugby
basketball


Israelis, unlike Canadians and Americans, don’t take s—- from anybody. When the security agency in Israel (the ISA) started to tighten security and we had to wait in line for — not for hours — but 30 or 40 minutes, all hell broke loose here. We said, ‘We’re not going to do this. You’re going to find a way that will take care of security without touching the efficiency of the airport.
The ‘Israelification’ of airports: High security, little bother (via isnottv)


Jim Goodwin urges St Mirren to make Paisley a fortress and build on home frugality
Buddies boss knows side are entering a crucial period.
by Fraser Wilson, https://www.dailyrecord.co.uk/authors/fraser-wilson/Jim Goodwin isn’t losing sleep at the thought of old Buddie Jack Ross raiding St Mirren to bolster his Hibs side.
Not when the Irishman knows his best young talents are on the radar of other clubs anyway.
Scotland U21 star Kyle Magennis has been linked with his former boss who was this week unveiled as the new manager at Easter Road. Goalkeeper Vaclav Hladky is another who is attracting interest from elsewhere.
But Goodwin, whose side welcome Ross and Hibs to Paisley next Tuesday, said: “I don’t think Hibs at the moment have got to do a whole lot of recruitment.
“There is a good group of players there but we have players Jack is familiar with.
“We have some very good, talented players, who are catching the eye of a number of clubs.
“I’m not going to say they might not be looking but a lot of teams are.
“Jack will do what he has to do in January. We have players he speaks well of and enjoyed working with.”
Goodwin admits tomorrow’s home clash with Ross County is St Mirren’s biggest of the season as they look to claw their way off bottom spot.
Despite their lowly status the Buddies have conceded just once in five home league games going into the match - and that was a stunning Borna Barisic free kick against Rangers.
The problem has been at the other end although, ironically, just when they do notch a double against Hearts last time out the defence decide to cave in and ship five.
Goodwin said: “The Hearts game seems like a lifetime ago now and we are hoping to redeem ourselves.
“It was one of those days that everything Hearts got on target went in and that’s not like us. They had six attempts on target and scored five.
“We have lost one goal at home in the league and that was a fantastic free-kick against Rangers. We are confident at home and enjoy playing there.
“The fans have created a fantastic environment and are so positive.
“It is important anyone coming to Paisley knows they won’t have an easy afternoon. We have to make sure we get in their faces, are hard to beat and be horrible to play against.
“I want my team to be entertaining going forward but out of possession I want us to be difficult to play.
“It is probably the most important game of our season so far.
“They have a cushion on us but if we win it closes to three points with a game in hand.
“There are six or seven teams who are in a league of our own, battling it out to be in the top six. I think Hibs will improve, they have a fantastic squad and Jack Ross is a great appointment.
“Hearts are too good to be languishing at the bottom.
“We know where we are, that’s not having the best squad or budget but we make the best of what we have. If we can continue with the organisation before the Hearts game, the guys up front are showing glimpses of clicking which is good.”


OGC standardization process
The work of the OGC is done by email, teleconferences, and at in-person meetings. The OGC organizes four meetings per year, each lasting five days, and hosted by member organizations that sponsor the event (companies, universities, research centres, etc). The host continent alternates between Europe and North America, with a growing presence in Asia since 2011. These meetings are usually attended by between 50 and 100 participants from among the hundreds of members of the OGC. Some participants are present at almost all the meetings, forming the pillars of the organization. The meetings of the OGC offer opportunities for exchange among members from diverse backgrounds.
The creation of a OGC standard begins with a gathering of organizations or individuals with a common interest in an issue. A working group is proposed as a Domain Working Group (DWG) or as a Standard Working Group (SWG). DWGs are open to all members of the OGC, while SWGs require that their participants enter into an agreement not to hinder the distribution of the standard through intellectual property claims.
Standard Working Group (SWG) procedures
In order to be accepted, a standardization project must be supported by a minimum number of members belonging to distinct organizations. These founding members draft a charter defining the objectives of the SWG, which must be approved by the Technical Committee of the OGC. Each founding member is endowed with the right to vote, with a limit of one voting member per organization. Each new member that wishes to join the SWG after its creation is granted the role of observer, and receives on request the right to vote after several months of observation.
A SWG may contain several dozen members, but the volunteers performing the bulk of the work are usually fewer. Their proposals are submitted to the entire membership of the group, who may accept them by unanimous consent. Any objections must be debated, and an alternative proposed. SWGs usually try to debate an issue until a consensus emerges rather than move ahead despite negative votes, even if those opposed are in a minority. The decisions of the group are then integrated into the specifications by a member who assumes the role of editor.
As far as possible, the working group must structure the specifications as a core around which various extensions might be built. A series of tests must accompany the standard, allowing implementations to be classified by the level of test passed. There must be at least one reference implementation that passes all the tests in order to demonstrate that the standard is usable.
When the standard is considered ready, the SWG votes on a motion proposing its submission to a vote by the higher authorities of the OGC. This process takes several months. There is a faster process for approving de facto standards, but it is applied sparingly.
The Architecture Board (OAB) and the Technical Committee (TC)
All proposals for standards are first examined by the OGC Architecture Board (OAB). This board ensures that the standard conforms to the requirements of the OGC in form, modularization, and in terms of integration with other standards. If the OAB approves it, the standard is next submitted to a vote by the members of the Technical Committee (TC). This committee consists of the principal members of the OGC, and only they are capable of granting final approval. If approved, the standard is made publicly available for comments during a period of several months. At the end of this period, the SWG must examine and respond to each comment. The eventual modifications of the standard are submitted to the OAB, then the standard is published in its final form. This distribution is announced in a press release by the OGC.
Certain members of the OGC and the TC also act as liaisons with the International Organization for Standardization (ISO). Cooperation between the two organizations goes two ways: the OGC adopts the ISO standards as a foundation on which to develop new standards, and certain OGC standards become ISO standards.
Procedure for the submission of proposals for modification
All users, whether or not they are members of the Open Geospatial Consortium, may propose modifications to OGC standards. A list of current proposals for changes, along with a form for submitting new proposals, is available online. Each proposal is reviewed by the SWG.
Some working groups use other parallel systems for submissions, for example GitHub merge requests, hosted outside of the structures of the OGC.


[§342D-36] Treatment of single operational upset. For the purpose of this part, a single operational upset which leads to simultaneous violations of more than one water pollutant parameter shall be treated as a single violation. [L 1989, c 212, pt of §2]


Match Play. Red leads 2-0 to 5. How should Red play 55?
pips Red=132 White=215
(CorrectAnswer= 0/0, ErrorScore= 0)
White has a well-timed back game and Red needs to correct his own timing. Ideally, he wants to get the five checkers on his 6-pt into the field of battle but that is not possible with this 55.
His best play is 20/5, 8/3, hoping to be hit so he can recirculate his checkers. Any other play is at least a blunder.
His best play is 20/5, 8/3, hoping to be hit so he can recirculate his checkers. Any other play is at least a blunder.
1. Cubeful 2-ply 20/5* 8/3 Eq.: +0.267 0.535 0.296 0.047 - 0.465 0.077 0.003 2. Cubeful 2-ply 20/5* 13/8 Eq.: +0.177 (-0.090) 0.510 0.287 0.043 - 0.490 0.080 0.003 3. Cubeful 0-ply 20/15 13/3 8/3 Eq.: +0.007 (-0.261) 0.463 0.260 0.044 - 0.537 0.085 0.002 4. Cubeful 0-ply 20/10 8/3(2) Eq.: +0.002 (-0.265) 0.470 0.245 0.039 - 0.530 0.092 0.003 5. Cubeful 0-ply 13/3(2) Eq.: +0.000 (-0.267) 0.460 0.265 0.041 - 0.540 0.086 0.002


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|
JavaTM 2 Platform Standard Ed. 5.0 |
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See:
Description
| Interface Summary | |
|---|---|
| AttributeList | Deprecated. This interface has been replaced by the SAX2
Attributes
interface, which includes Namespace support. |
| Attributes | Interface for a list of XML attributes. |
| ContentHandler | Receive notification of the logical content of a document. |
| DocumentHandler | Deprecated. This interface has been replaced by the SAX2
ContentHandler
interface, which includes Namespace support. |
| DTDHandler | Receive notification of basic DTD-related events. |
| EntityResolver | Basic interface for resolving entities. |
| ErrorHandler | Basic interface for SAX error handlers. |
| Locator | Interface for associating a SAX event with a document location. |
| Parser | Deprecated. This interface has been replaced by the SAX2
XMLReader
interface, which includes Namespace support. |
| XMLFilter | Interface for an XML filter. |
| XMLReader | Interface for reading an XML document using callbacks. |
| Class Summary | |
|---|---|
| HandlerBase | Deprecated. This class works with the deprecated
DocumentHandler
interface. |
| InputSource | A single input source for an XML entity. |
| Exception Summary | |
|---|---|
| SAXException | Encapsulate a general SAX error or warning. |
| SAXNotRecognizedException | Exception class for an unrecognized identifier. |
| SAXNotSupportedException | Exception class for an unsupported operation. |
| SAXParseException | Encapsulate an XML parse error or warning. |
This package provides the core SAX APIs. Some SAX1 APIs are deprecated to encourage integration of namespace-awareness into designs of new applications and into maintenance of existing infrastructure.
See http://www.saxproject.org for more information about SAX.
One of the essential characteristics of SAX2 is that it added
feature flags which can be used to examine and perhaps modify
parser modes, in particular modes such as validation.
Since features are identified by (absolute) URIs, anyone
can define such features.
Currently defined standard feature URIs have the prefix
http://xml.org/sax/features/
before an identifier such as
validation
. Turn features on or off using
setFeature. Those standard identifiers are:
| Feature ID | Access | Default | Description |
|---|---|---|---|
| external-general-entities | read/write | unspecified | Reports whether this parser processes external general entities; always true if validating. |
| external-parameter-entities | read/write | unspecified | Reports whether this parser processes external parameter entities; always true if validating. |
| is-standalone | (parsing) read-only, (not parsing) none | not applicable | May be examined only during a parse, after the startDocument() callback has been completed; read-only. The value is true if the document specified standalone="yes" in its XML declaration, and otherwise is false. |
| lexical-handler/parameter-entities | read/write | unspecified | A value of "true" indicates that the LexicalHandler will report the beginning and end of parameter entities. |
| namespaces | read/write | true | A value of "true" indicates namespace URIs and unprefixed local names for element and attribute names will be available. |
| namespace-prefixes | read/write | false | A value of "true" indicates that XML qualified names (with prefixes) and attributes (including xmlns* attributes) will be available. |
| resolve-dtd-uris | read/write | true | A value of "true" indicates that system IDs in declarations will
be absolutized (relative to their base URIs) before reporting.
(That is the default behavior for all SAX2 XML parsers.)
A value of "false" indicates those IDs will not be absolutized;
parsers will provide the base URI from
Locator.getSystemId().
This applies to system IDs passed in
|
| string-interning | read/write | unspecified | Has a value of "true" if all XML names (for elements, prefixes, attributes, entities, notations, and local names), as well as Namespace URIs, will have been interned using java.lang.String.intern. This supports fast testing of equality/inequality against string constants, rather than forcing slower calls to String.equals(). |
| unicode-normalization-checking | read/write | false | Controls whether the parser reports Unicode normalization errors as described in section 2.13 and Appendix B of the XML 1.1 Recommendation. If true, Unicode normalization errors are reported using the ErrorHandler.error() callback. Such errors are not fatal in themselves (though, obviously, other Unicode-related encoding errors may be). |
| use-attributes2 | read-only | not applicable | Returns "true" if the Attributes objects passed by this parser in ContentHandler.startElement() implement the org.xml.sax.ext.Attributes2 interface. That interface exposes additional DTD-related information, such as whether the attribute was specified in the source text rather than defaulted. |
| use-locator2 | read-only | not applicable | Returns "true" if the Locator objects passed by this parser in ContentHandler.setDocumentLocator() implement the org.xml.sax.ext.Locator2 interface. That interface exposes additional entity information, such as the character encoding and XML version used. |
| use-entity-resolver2 | read/write | true | Returns "true" if, when setEntityResolver is given an object implementing the org.xml.sax.ext.EntityResolver2 interface, those new methods will be used. Returns "false" to indicate that those methods will not be used. |
| validation | read/write | unspecified | Controls whether the parser is reporting all validity errors; if true, all external entities will be read. |
| xmlns-uris | read/write | false | Controls whether, when the namespace-prefixes feature is set, the parser treats namespace declaration attributes as being in the http://www.w3.org/2000/xmlns/ namespace. By default, SAX2 conforms to the original "Namespaces in XML" Recommendation, which explicitly states that such attributes are not in any namespace. Setting this optional flag to "true" makes the SAX2 events conform to a later backwards-incompatible revision of that recommendation, placing those attributes in a namespace. |
| xml-1.1 | read-only | not applicable | Returns "true" if the parser supports both XML 1.1 and XML 1.0. Returns "false" if the parser supports only XML 1.0. |
Support for the default values of the namespaces and namespace-prefixes properties is required. Support for any other feature flags is entirely optional.
For default values not specified by SAX2, each XMLReader implementation specifies its default, or may choose not to expose the feature flag. Unless otherwise specified here, implementations may support changing current values of these standard feature flags, but not while parsing.
For parser interface characteristics that are described
as objects, a separate namespace is defined. The
objects in this namespace are again identified by URI, and
the standard property URIs have the prefix
http://xml.org/sax/properties/
before an identifier such as
lexical-handler
or
dom-node
. Manage those properties using
setProperty(). Those identifiers are:
| Property ID | Description |
|---|---|
| declaration-handler | Used to see most DTD declarations except those treated as lexical ("document element name is ...") or which are mandatory for all SAX parsers (DTDHandler). The Object must implement org.xml.sax.ext.DeclHandler. |
| document-xml-version | May be examined only during a parse, after the startDocument() callback has been completed; read-only. This property is a literal string describing the actual XML version of the document, such as "1.0" or "1.1". |
| dom-node | For "DOM Walker" style parsers, which ignore their parser.parse() parameters, this is used to specify the DOM (sub)tree being walked by the parser. The Object must implement the org.w3c.dom.Node interface. |
| lexical-handler | Used to see some syntax events that are essential in some applications: comments, CDATA delimiters, selected general entity inclusions, and the start and end of the DTD (and declaration of document element name). The Object must implement org.xml.sax.ext.LexicalHandler. |
| xml-string | Readable only during a parser callback, this exposes a TBS chunk of characters responsible for the current event. |
All of these standard properties are optional; XMLReader implementations need not support them.
|
JavaTM 2 Platform Standard Ed. 5.0 |
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Copyright © 2004, 2010 Oracle and/or its affiliates. All rights reserved. Use is subject to license terms. Also see the documentation redistribution policy.


O.F.
ook
Det.
Alain
H.
Liogier,
1970.
Tree,
12
m
high,
flowers
red.
In
woods
on
slope,
above
the
Isa
river
dam.
-^lt.
350
m.
On
limestone
Villa
Altagracia.
Bro.
Alain
H.
Liogier
29
March
1969
Supported
by
funds
from
the
National
Science
Foundation.
1619017
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


{% extends 'base.html' %} {% block content %}
Your account has been blocked because your messages were reported as abusive three times.
Please get in touch with<EMAIL_ADDRESS>for further steps to reactivate your account. We are sorry for any trouble caused by this quality assurance step.


AWS::ServiceDiscovery::Service HealthCheckCustomConfig
A complex type that contains information about an optional custom health check. A custom health check, which requires that you use a third-party health checker to evaluate the health of your resources, is useful in the following circumstances:
-
You can't use a health check that's defined by
HealthCheckConfig
because the resource isn't available over the internet. For example, you can use a custom health check when the instance is in an Amazon VPC. (To check the health of resources in a VPC, the health checker must also be in the VPC.) -
You want to use a third-party health checker regardless of where your resources are located.
If you specify a health check configuration, you can specify either
HealthCheckCustomConfig
or HealthCheckConfig
but not both.
To change the status of a custom health check, submit an
UpdateInstanceCustomHealthStatus
request. AWS Cloud Map doesn't monitor the status of the
resource, it just keeps a record of the status specified in the most recent
UpdateInstanceCustomHealthStatus
request.
Here's how custom health checks work:
-
You create a service.
-
You register an instance.
-
You configure a third-party health checker to monitor the resource that's associated with the new instance.
Note AWS Cloud Map doesn't check the health of the resource directly.
-
The third-party health-checker determines that the resource is unhealthy and notifies your application.
-
Your application submits an
UpdateInstanceCustomHealthStatus
request. -
AWS Cloud Map waits for 30 seconds.
-
If another
UpdateInstanceCustomHealthStatus
request doesn't arrive during that time to change the status back to healthy, AWS Cloud Map stops routing traffic to the resource.
Syntax
To declare this entity in your AWS CloudFormation template, use the following syntax:
JSON
{ "FailureThreshold" :
Double
}
YAML
FailureThreshold:
Double
Properties
FailureThreshold
-
Important This parameter is no longer supported and is always set to 1. AWS Cloud Map waits for approximately 30 seconds after receiving an
UpdateInstanceCustomHealthStatus
request before changing the status of the service instance.The number of 30-second intervals that you want AWS Cloud Map to wait after receiving an
UpdateInstanceCustomHealthStatus
request before it changes the health status of a service instance.Sending a second or subsequent
UpdateInstanceCustomHealthStatus
request with the same value before 30 seconds has passed doesn't accelerate the change. AWS Cloud Map still waits30
seconds after the first request to make the change.Required: No
Type: Double
Minimum:
1
Maximum:
10
Update requires: No interruption
See also
-
Return values in the topic AWS::ServiceDiscovery::Service
-
HealthCheckCustomConfig in the AWS Cloud Map API Reference


Fn::Join
The intrinsic function Fn::Join
appends a set of values into a single value, separated
by the specified delimiter. If a delimiter is the empty string, the set of values
are concatenated with no
delimiter.
Declaration
JSON
{ "Fn::Join" : [ "
delimiter
", [comma-delimited list of values
] ] }
YAML
Syntax for the full function name:
Fn::Join: [
delimiter
, [comma-delimited list of values
] ]
Syntax for the short form:
!Join [
delimiter
, [comma-delimited list of values
] ]
Parameters
- delimiter
-
The value you want to occur between fragments. The delimiter will occur between fragments only. It will not terminate the final value.
- ListOfValues
-
The list of values you want combined.
Return Value
The combined string.
Examples
Join a Simple String Array
The following example returns: "a:b:c"
.
JSON
"Fn::Join" : [ ":", [ "a", "b", "c" ] ]
YAML
!Join [ ":", [ a, b, c ] ]
Join Using the Ref Function with Parameters
The following example uses Fn::Join
to construct a string value. It uses the Ref
function with
the Partition
parameter and the AWS::AccountId
pseudo parameter.
JSON
{ "Fn::Join": [ "", [ "arn:", { "Ref": "Partition" }, ":s3:::elasticbeanstalk-*-", { "Ref": "AWS::AccountId" } ] ] }
YAML
!Join - '' - - 'arn:' - !Ref Partition - ':s3:::elasticbeanstalk-*-' - !Ref 'AWS::AccountId'
Note
Also see the Fn::Sub function for similar functionality.
Supported Functions
For the Fn::Join
delimiter, you cannot use any functions. You must specify
a string value.
For the Fn::Join
list of values, you can use the following functions:
-
Fn::Base64
-
Fn::FindInMap
-
Fn::GetAtt
-
Fn::GetAZs
-
Fn::If
-
Fn::ImportValue
-
Fn::Join
-
Fn::Split
-
Fn::Select
-
Fn::Sub
-
Ref


public class OtpOutputStream
extends java.io.ByteArrayOutputStream
Note that this class is not synchronized, if you need synchronization you must provide it yourself.
| Modifier and Type | Field and Description |
|---|---|
static int |
defaultIncrement
The default increment used when growing the stream (increment at least
this much).
|
static int |
defaultInitialSize
The default initial size of the stream.
|
| Constructor and Description |
|---|
OtpOutputStream()
Create a stream with the default initial size (2048 bytes).
|
OtpOutputStream(int size)
Create a stream with the specified initial size.
|
OtpOutputStream(OtpErlangObject o)
Create a stream containing the encoded version of the given Erlang term.
|
| Modifier and Type | Method and Description |
|---|---|
void |
ensureCapacity(int minCapacity)
Increases the capacity of this OtpOutputStream instance, if
necessary, to ensure that it can hold at least the number of elements
specified by the minimum capacity argument.
|
int |
getPos()
Get the current position in the stream.
|
int |
length()
Get the current capacity of the stream.
|
void |
poke4BE(int offset,
long n)
Write the low four bytes of a value to the stream in bif endian order, at
the specified position.
|
void |
trimToSize()
Trims the capacity of this OtpOutputStream instance to be the
buffer's current size.
|
void |
write_any(OtpErlangObject o)
Write an arbitrary Erlang term to the stream.
|
void |
write_atom(java.lang.String atom)
Write a string to the stream as an Erlang atom.
|
void |
write_big_integer(java.math.BigInteger v) |
void |
write_binary(byte[] bin)
Write an array of bytes to the stream as an Erlang binary.
|
void |
write_bitstr(byte[] bin,
int pad_bits)
Write an array of bytes to the stream as an Erlang bitstr.
|
void |
write_boolean(boolean b)
Write a boolean value to the stream as the Erlang atom 'true' or 'false'.
|
void |
write_byte(byte b)
Write a single byte to the stream as an Erlang integer.
|
void |
write_char(char c)
Write a character to the stream as an Erlang integer.
|
void |
write_compressed(OtpErlangObject o)
Write an arbitrary Erlang term to the stream in compressed format.
|
void |
write_compressed(OtpErlangObject o,
int level)
Write an arbitrary Erlang term to the stream in compressed format.
|
void |
write_double(double d)
Write a double value to the stream.
|
void |
write_external_fun(java.lang.String module,
java.lang.String function,
int arity) |
void |
write_float(float f)
Write a float value to the stream.
|
void |
write_fun(OtpErlangPid pid,
java.lang.String module,
long old_index,
int arity,
byte[] md5,
long index,
long uniq,
OtpErlangObject[] freeVars) |
void |
write_int(int i)
Write an integer to the stream.
|
void |
write_list_head(int arity)
Write an Erlang list header to the stream.
|
void |
write_long(long l)
Write a long to the stream.
|
void |
write_map_head(int arity) |
void |
write_nil()
Write an empty Erlang list to the stream.
|
void |
write_pid(OtpErlangPid pid)
Write an Erlang PID to the stream.
|
void |
write_pid(java.lang.String node,
int id,
int serial,
int creation)
Write an Erlang PID to the stream.
|
void |
write_port(OtpErlangPort port)
Write an Erlang port to the stream.
|
void |
write_port(java.lang.String node,
int id,
int creation)
Write an Erlang port to the stream.
|
void |
write_ref(OtpErlangRef ref)
Write an Erlang ref to the stream.
|
void |
write_ref(java.lang.String node,
int[] ids,
int creation)
Write an Erlang ref to the stream.
|
void |
write_ref(java.lang.String node,
int id,
int creation)
Write an old style Erlang ref to the stream.
|
void |
write_short(short s)
Write a short to the stream.
|
void |
write_string(java.lang.String s)
Write a string to the stream.
|
void |
write_tuple_head(int arity)
Write an Erlang tuple header to the stream.
|
void |
write_uint(int ui)
Write a positive integer to the stream.
|
void |
write_ulong(long ul)
Write a positive long to the stream.
|
void |
write_ushort(short us)
Write a positive short to the stream.
|
void |
write(byte b)
Write one byte to the stream.
|
void |
write(byte[] abuf) |
void |
write(byte[] b,
int off,
int len) |
void |
write(int b) |
void |
write1(long n)
Write the low byte of a value to the stream.
|
void |
write2BE(long n)
Write the low two bytes of a value to the stream in big endian order.
|
void |
write2LE(long n)
Write the low two bytes of a value to the stream in little endian order.
|
void |
write4BE(long n)
Write the low four bytes of a value to the stream in big endian order.
|
void |
write4LE(long n)
Write the low four bytes of a value to the stream in little endian order.
|
void |
write8BE(long n)
Write the low eight (all) bytes of a value to the stream in big endian
order.
|
void |
write8LE(long n)
Write the low eight bytes of a value to the stream in little endian
order.
|
void |
writeLE(long n,
int b)
Write any number of bytes in little endian format.
|
void |
writeN(byte[] bytes)
Write an array of bytes to the stream.
|
void |
writeTo(java.io.OutputStream out) |
void |
writeToAndFlush(java.io.OutputStream out) |
public static final int defaultIncrement
public static final int defaultInitialSize
public OtpOutputStream()
public OtpOutputStream(int size)
public OtpOutputStream(OtpErlangObject o)
public void ensureCapacity(int minCapacity)
minCapacity
- the desired minimum capacitypublic int getPos()
public int length()
public void poke4BE(int offset, long n)
size()
, when is is necessary to insert data into the stream before it is
known what the actual value should be. For example:
int pos = s.size(); s.write4BE(0); // make space for length data, // but final value is not yet known [ ...more write statements...] // later... when we know the length value s.poke4BE(pos, length);
offset
- the position in the stream.n
- the value to use.public void trimToSize()
public void write_any(OtpErlangObject o)
o
- the Erlang term to write.public void write_atom(java.lang.String atom)
atom
- the string to write.public void write_big_integer(java.math.BigInteger v)
public void write_binary(byte[] bin)
bin
- the array of bytes to write.public void write_bitstr(byte[] bin, int pad_bits)
bin
- the array of bytes to write.pad_bits
- the number of zero pad bits at the low end of the last bytepublic void write_boolean(boolean b)
b
- the boolean value to write.public void write_byte(byte b)
b
- the byte to use.public void write_char(char c)
c
- the character to use.public void write_compressed(OtpErlangObject o)
o
- the Erlang term to write.public void write_compressed(OtpErlangObject o, int level)
o
- the Erlang term to write.level
- the compression level (0..9)public void write_double(double d)
d
- the double to use.public void write_external_fun(java.lang.String module, java.lang.String function, int arity)
public void write_float(float f)
f
- the float to use.public void write_fun(OtpErlangPid pid, java.lang.String module, long old_index, int arity, byte[] md5, long index, long uniq, OtpErlangObject[] freeVars)
public void write_int(int i)
i
- the integer to use.public void write_list_head(int arity)
arity
- the number of elements in the list.public void write_long(long l)
l
- the long to use.public void write_map_head(int arity)
public void write_nil()
public void write_pid(OtpErlangPid pid)
pid
- the pidpublic void write_pid(java.lang.String node, int id, int serial, int creation)
node
- the nodename.id
- an arbitrary number. Only the low order 15 bits will be used.serial
- another arbitrary number. Only the low order 13 bits will be
used.creation
- yet another arbitrary number. Only the low order 2 bits will
be used.public void write_port(OtpErlangPort port)
port
- the port.public void write_port(java.lang.String node, int id, int creation)
node
- the nodename.id
- an arbitrary number. Only the low order 28 bits will be used.creation
- another arbitrary number. Only the low order 2 bits will
be used.public void write_ref(OtpErlangRef ref)
ref
- the referencepublic void write_ref(java.lang.String node, int[] ids, int creation)
node
- the nodename.ids
- an array of arbitrary numbers. Only the low order 18 bits of
the first number will be used. At most three numbers
will be read from the array.creation
- another arbitrary number. Only the low order 2 bits will be used.public void write_ref(java.lang.String node, int id, int creation)
node
- the nodename.id
- an arbitrary number. Only the low order 18 bits will be used.creation
- another arbitrary number.public void write_short(short s)
s
- the short to use.public void write_string(java.lang.String s)
s
- the string to write.public void write_tuple_head(int arity)
arity
- the number of elements in the tuple.public void write_uint(int ui)
ui
- the integer to use.public void write_ulong(long ul)
ul
- the long to use.public void write_ushort(short us)
us
- the short to use.public void write(byte b)
b
- the byte to write.public void write(byte[] abuf)
write
in class java.io.OutputStream
public void write(byte[] b, int off, int len)
write
in class java.io.ByteArrayOutputStream
public void write(int b)
write
in class java.io.ByteArrayOutputStream
public void write1(long n)
n
- the value to use.public void write2BE(long n)
n
- the value to use.public void write2LE(long n)
n
- the value to use.public void write4BE(long n)
n
- the value to use.public void write4LE(long n)
n
- the value to use.public void write8BE(long n)
n
- the value to use.public void write8LE(long n)
n
- the value to use.public void writeLE(long n, int b)
n
- the value to use.b
- the number of bytes to write from the little end.public void writeN(byte[] bytes)
bytes
- the array of bytes to write.public void writeTo(java.io.OutputStream out) throws java.io.IOException
writeTo
in class java.io.ByteArrayOutputStream
java.io.IOException
public void writeToAndFlush(java.io.OutputStream out) throws java.io.IOException
java.io.IOException


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Blacksmith Pinter:
I see you have not found the stolen item yet. Please hurry and find it. My life depends upon your success. You know who the thieves are, right? We strongly suspect the
Vuku Orc Fighters
. Hurry and catch them!


Not Accepted
406
The function {func} does not exist!
Control Panel
Posibile function calls
'insert_room' inserts a room (5 parameters)
'update_room' updates room preferences
(6 parameters)
[! room is suposed to exist first !]
'delete_room' deletes a room and unpairs sensors paired to this room
(1 parameter)
'assign_sensor' assigns a sensor to a specific room
(2 parameters)
[-1 for outside 'NULL' for unassigned]
'get_room_preferences' return room preferences
(1 parameter)
'get_rooms' returns all the rooms with sensor data
(0 parameters)
'get_unassigned_devices' returns all devices that are unassigned
(0 parameters)
'get_outside_sensors' returns all sensors that are assigned to outside use
(0 parameters)


| Endpoint | Description |
|---|---|
| /health | Health status |
| /stats | Statistics |
| /metrics | Prometheus metrics |
| /callback | Callback - use with remote_event_callback plugin |
| /graph | Python pickled - use with remote plugin |
| /graph.txt | Plain Text |
| /graph.json | JSON |
| /graph.graphml | GraphML |
| /graph.gexf | GEXF |
| /graph.net | Pajek |


Riley: "This is too scary I should just go back home."
As she runs back home a zombie emerges from the alleyway leading away from the lab.
 Her last words were.....
Riley: “Such a bad decision...”

*Fade to black.
Restart!You reload your gun and stare down at your enemies. You point the gun in front of the first
zombie point blank at the head successfully shooting him piercing the skull and blood gushing
out you have now found your calling.
Shortly after the zombie bites you leaving you paralysed and unable to get up....
*Fade to black.
Restart!Riley: “Sorry but we're skipping town and walking to the country.”
Riley (In Her Head): “Yes that's the best excuse to give him."
???: “Oh well thanks anyway my name is Greg by the way.”
Riley: “Riley, well it was nice to see you.”
Greg: “Until we meet again I guess.”
Coming Soon.
Riley: “........”
Restart!


Drink Name is required.
Drink Style is required.
Drink ABV is required.
Drink color is required.
Drink glass type is required.
Drink Origin is required.


Visit Places
Accomodation
Accessible accomodation
Bed & Breakfasts & guest houses
Caravan & camping
Cottages & cabins
Hostels
Hotels
Weird & wonderful
Cities & Regions
Cities
Regions
Map
Nature
Forests & woodlands
Islands
Landscapes
Mountains
Northern lights
Outdoor activities
Rivers, lakes & seas
Wildlife
See & Do
Attractions
Castles & monuments
Events
Food
Tours & itineraries
Travel
Getting to & around:
Air
Bus & coach
Car & vehicle hire
Ferry
Sailing
Scenic driving routes
Train
Travel passes & offers:
Safe travel guide
Travel offers
TRAVEL
To the end of the world!
Getting to & around
Travelling around the globe
Travel passes & offers
Make the most of every trip


Welcome to the CODING QUIZ!
Rules: Answer each multiple choice
question before the timer runs out!
Start
You have 75 seconds to answer 5 questions!
When you feel ready, press start!
Highscores


The paper studies the effect of catastrophic forgetting on representations in neural networks.
Techniques:
Representational Similarity Measures
Layer Freezing
Layer Reset
Datasets
Split CIFAR-10
CIFAR-10 dataset is split into m (=2) tasks, where each task is a n way classification task.
The underlying network has a shared trunk with m heads, one head per task.
Split CIFAR-100 Distribution Shift
Network Architecture
Are all representations (throughout the network) equally responsible for forgetting?
Higher layer (layers closer to the output) are the primary source of catastrophic forgetting.
Central Kernel Alignment (CKA) technique is used to compare the similarity between the layer representations, before and after training on the second task.
Higher layer representations change significantly when training over two tasks while the lower layer representations remain stable.
When finetuning on the second task, freezing the lower layers has only a minor effect on the accuracy of the second task.
In layer reset experiments, after training on the second task, the weights of some of the layers are reset to their values after training on the first task.
Do common approaches for countering catastrophic forgetting work by stabilizing the higher layers?
Yes - both EWC and replay-based approaches counter catastrophic forgetting work by stabilizing the higher layers.
This is demonstrated by showing that as the quadratic penalty for EWC (or fraction of data from replay buffer) increases (to reduce catastrophic forgetting), the representations for higher layers change less during the second task.
When training over a sequence of tasks, are similar tasks more likely to be forgotten than different tasks?
Setup I
Training over a sequence of two binary classification tasks.
Task 1: Two related classes (say ship
and truck
)
Task 2: Two related classes, which may or may not be related to the classes for Task 1. For example, the classes could be
cat
and horse
(not related to classes of the first task)
plane
and car
(related to the classes of the first task)
Training over semantically similar tasks (here plane
and car
) leads to less forgetting.
Setup II
Training over a sequence of two classification tasks.
Task 1: Four classes where the classes can be grouped into two groups (say deer
, dog
, ship
and truck
)
Task 2: Two related classes, which may be related to group 1 or group 2. For example, the classes could be two animals or two objects.
After training on the second task, classes (from Task 1), which are in the different group as classes from Task 2, are forgotten less.
Conclusion
Task representational similarity is a function of both underlying data and optimization procedure.
Forgetting is most severe for task representations of intermediate similarity.
Representational similarity is necessary but not a sufficient condition for forgetting.
How does catastrophic forgetting change as the task similarity changes?
If the model learns different representations for dissimilar tasks, increasing dissimilarity can help to avoid forgetting.
When training the two-task, two-class (per task) CIFAR-10 setup with an “others” class (classes not already used in the setup), the forgetting is reduced.


Selected Academic Projects
Boston Property Assessment (2018)
Boston property assessment dataset from Boston.gov classifies properties in greaterboston area into it’s present overall condition (Poor to Excellent).
4 classification algorithms (Naives Bayes, RandomForest, IBk and Decision Table) were modeled using 5 different selection attributes using Weka.
Performance measures such as TP Rate, FP Rates, ROC Area etc were used to determine the overall performance of each classifier model.
Project page : View here
MBTA Data Visualization and Real time app (2017)
1. The project analyses the MBTA real-time data of 1 week data between Dec 1- Dec 8.
2. Advanced Data visualization of Travel times and Head way times are implemented with R and Plotly in-order to determine the problems that exist in the Boston subway lines. The Intend of doing this was to develop a weekly reporting app that can be used to determine weekly estimates of the issues that needs to be looked upon by the MBTA.
3. A real-time MBTA app is developed using R and shiny. This app also shows real-time clustering of the trains based on parameters such as subway line length, number of active trains in a direction, and the distance to the next nearest train etc.
4. The shiny app provides a simple general population facing app where anyone can keep track of the train locations with the simplest of UI.
5. The shiny app from the MBTA facing side, predicting/visualizing train clusters and giving a birds eye dashboard in each train so that the driver would be able to adjust the train speed in-order to avoid clustering. This should at-least reduce the frequency of trains going in express mode and make the waiting time for trains at stations much more uniform. Usually, the cluster keeps building up until someone from the control room calls up the driver of the specific train (as the driver is unaware that a train is following till he is contacted from the control room) to move express to certain stop.
Project page : View here
On Switch-based Controller Hand-offs in Software Defined Wireless Mesh Networks (2016)
We use Expected Transmission Time (ETT) as the metric for controller hand-off inOpenFlow WMNs. ETT reflects various physical-layer characteristics, such as link traffic and end-to-end bandwidth. The experimental results showed that ETT is a better metric compared to RTT and ETX in a dynamic network with variable load across the links. ETT-based hand-off is able to respond to the excessive load in the link and make suitable hand-off decisons, whereas RTT and ETX fails in accomplishing the same with lower hand-off delay and packet dropouts.
Paper : View here
Software Defined MICRONet (2016)
Software Defined MICRONet architecture provides intelligent communication among physical boat clusters in the sea. This will solve the technology challenges faced by the fishermen community in India today, specifically by providing software defined Intelligent and adaptable communication and connectivity while they are out at sea. A scaled down model of Software Defined MICRONet environment was emulated in a testbed.
Final Report : View here
Repository : Software Defined MICRONet (2016)
Navigation in a Virtual Environment using IMU MPU-6050 (2015)
Head Mounted Display(HMD) is one of the revolutionary Virtual Reality(VR) inventions of all times. But how do you move around in a Virtual Environment?. For a true VR experience you need to move around freely and naturally. Imagine a game where the user can freely roam around their backyard or walk on a frictionless surface and navigate in a virtual environment rather than sitting idle in a chair. Developing a low-cost system for such a VR experience which can be implemented onto a HMD, is always a challenge. In this project we have done a hardware implementation to navigate in a virtual environment using a low-cost Inertial Measurement Unit(IMU).
Repository : Navigation in a Virtual Environment using IMU MPU-6050 (2015)
32 bit RISC Microprocessor in VHDL language and implemented on Altera FPGA (2014)
Developed a 32 bit RISC Microprocessor in VHDL language and implemented on Altera FPGA. The Test bench module is executed in the model-sim software and the LCD module is implemented on the FPGA to display the Register value, Memory value and the Program counter.
Repository : 32 bit RISC Microprocessor in VHDL language and implemented on Altera FPGA (2014)
2D convolution using Xlinix of a 128*128 window and by using a 3*3 convolution matrix (2014)
For each input pixel window, the values in that window are multiplied by the convolution mask. Next, those results are added together and divided by the number of pixels in the window. This value is the output for the origin pixel of the output image for that position. The input pixel window is always the same size as the convolution mask. The output pixel is rounded to the nearest integer. The results for this algorithm carried over an entire input image will result in an output image with reduced salt-and-pepper noise. This flexibility allows for many powerful uses.


Please sent all STAMP related questions and comments to me at colinr23 AT gmail DOT com. Please try to include as much information as possible. If you would like the Visual Studio Project, let me know.
They kind of data that started me on this research!


Application Name: Android App
Version: 1.0
Release Date: 05/04/2014
Release Note: Application release notes.


| Mercury | |
|---|---|
| Mercury |
Mercury is the closest planet to the sun. As such, it circles the sun faster than all the other planets, which is why Romans named it after their swift-footed messenger god. The Sumerians also knew of Mercury since at least 5,000 years ago. It was often associated with Nabu, the god of writing. Mercury was also given separate names for its appearance as both a morning star and as an evening star. |
| Venus | |
| Earth | |
| Mars | |
| Jupiter | |
| Saturn | |
| Uranus | |
| Neptune | |
| Pluto |


Trump did not respond to the final question of his first news conference since the convention when asked if he could affirmatively state that no one connected to him or his campaign had contact with Russia during the campaign.
Ann Compton of ABC News asked: “Mr. President-elect, can you stand here today, once and for all, and say that no one connected to you or your campaign had any contact with Russia leading up to or during the presidential campaign?” Jim Acosta indicated that Trump answered "no" as he was leaving.
Source: New York Times