-
Signaling Post-Snowden Era, New iPhone Locks Out N.S.A.
www.nytimes.com
Intelligence agencies say the move, in which the new iPhone creates a unique code that scrambles information, is the first of new technologies ...
-
The Internet Braces for the Crazy Shellshock Worm | WIRED
www.wired.com
A nasty bug in many of the world’s Linux and Unix operating systems could allow malicious hackers to create a computer worm that wreaks havoc on ...
-
MFSA 2014-73: RSA Signature Forgery in NSS
www.mozilla.org
You are here: Security Center > Mozilla Foundation Security Advisories > MFSA 2014-73
-
Handy tips for developing Windows Phone apps … and a Quiz, too | DVLUP
blog.dvlup.com
If you’re new to developing apps for Windows Phone – and we expect there are a few of you out there! – we’ve just released a new SlideShare deck that ...
-
Hyperlapse Lets You Selfielapse: This Week in Social Media |
www.socialmediaexaminer.com
Social media news and new developments making social media marketing easier. (Selfielapse, Facebook links, Facebook app, AmazonWishList, Promoted ...
-
Mobile users favor productivity over security -- as they should
www.infoworld.com
If you force employees to choose between productivity and security, you're the problem
-
Tesco Hudl 2 release date, price and specs UK: New tablet set for 3 ...
www.pcadvisor.co.uk
Tesco will launch the Hudl 2 tablet in the next few weeks so here are all the release date, price and specifications details for the budget Android ...
-
El espía estaba en el bolsillo: ¿Vigilan los iPhone los movimientos de sus ...
actualidad.rt.com
Los iPhone tienen una función escondida que anota todos los desplazamientos de su propietario, premitiendo al móvil determinar dónde vive.
-
‘Cazador cazado’: Seis orcas matan y devoran a un tiburón
actualidad.rt.com
En este sorprendente video filmado por el biólogo marino Edwar Herreño puede verse a varias orcas dando caza a un tiburón tigre en la Isla del Coco. ...
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iOS 8 vs Windows Phone 8.1: My Experience So Far
www.gottabemobile.com
This fall many users who have the opportunity to choose between purchasing a device that runs Windows Phone 8.1 or iOS 8. Here's what you know.
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Video: Hallan en Suecia la momia de un obispo del siglo XVII muy bien ...
actualidad.rt.com
Expertos suecos han exhumado una de las momias del siglo XVII mejor conservadas en Europa. Ahora los científicos intentan explicar el secreto de tan ...
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Windows 9 Technical Preview: the complete summary of leaked features (part ...
pureinfotech.com
Windows 9 Technical Preview leaks show us many features coming to the operating system, here the complete summary of everything you need to know of ...
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Microsoft to give "Windows 9" a new name » Pureinfotech
pureinfotech.com
Microsoft plans to announce a new name for Windows 9 on September 30, it could be just "Windows" or totally different name, analysts say.
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7 Android apps that I've recommended and still love
www.citeworld.com
Apps come and apps go, but these are the Android apps I still rely upon all the time.
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Google tightens its grip on Android manufacturers
www.itworld.com
Google wants all Android phones to include more of its services. Plus: Sony Xperia Z3 unboxed underwater, and reviews of the Samsung Galaxy Alpha ...
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GNOME 3.14 Released - Slashdot
tech.slashdot.org
An anonymous reader writes "GNOME 3.14 was released today and it includes some interesting changes such as re-worked default theme, multi-touch ...
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OpenVMS.org - OpenVMS Community Portal, News and Info
www.openvms.org
Sue says " It is the privilege of VMS Software to be the host of the DECUServe environment. This system will be down for the next several weeks. This ...
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Irish Girls Win Google Science Fair With Astonishing Crop Yield ...
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An anonymous reader writes Irish teenagers Ciara Judge, Émer Hickey and Sophie Healy-Thow, all 16, have won the Google Science Fair 2014. Their ...
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Security tips: bash "Shellshock" CVE-2014-6271 and CVE-2014-7169; OpenVMS ...
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0.5; 27-Sept-2014 23:00 UTC — another bash-related CVE (6277), with associated links. 0.4; 27-Sept-2014 12:30 UTC — doubled the number of ...
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Why Ada Lovelace was 100 years too early for the computer
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Lenovo N20 review - CNET
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Lenovo's N20p is long-lasting touchscreen Chromebook with a design on the nicer side of budget, but its semi-hybrid hinge feels like a half-measure.
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Major retailer Phones 4U has gone into administration leaving 5,500 jobs at risk and iPhone 6 pre-orders in the balance. Ordered an iPhone 6 from ...
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The new Nexus smartphone, the Nexus X or Nexus 6 (codenamed Motorola Shamu), is thought to be the next flagship Android smartphone from Google. So ...
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Now available: Charting Diversity – Working together towards diversity in ...
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1 Comment BY Julia Kloppenburg ON September 9th, 2014
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rigb.org
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streams, hdr = tv.read(fname)
streamlines = [i[0] for i in streams]
Perform QuickBundles clustering using the MDF metric and a 10mm distance
Load fornix streamlines.
fornix = load_tractogram(fname, "same",
bbox_valid_check=False).streamlines
streamlines = Streamlines(fornix)
Perform QuickBundles clustering using the MDF metric and a 10mm distance
threshold. Keep in mind that since the MDF metric requires streamlines to have


---
title: "Frambozen"
layout: "post"
permalink: "/2013/01/frambozen.html"
uuid: "4679802285102240976"
guid: "tag:blogger.com,1999:blog-4897882164686544357.post-4679802285102240976"
date: "2013-01-18 23:00:00"
updated: "2013-01-18 23:00:00"
description:
blogger:
siteid: "4897882164686544357"
postid: "4679802285102240976"
comments: "0"
categories: [raspberry brown ale, pancakes, fruit beer, frambozen, new belgium, brown ale, colorado]
author: bradorego
---
Lots of posts to come in the upcoming days/weeks! The past two weeks have seen a lot of beer and a lot of pancakes, so hopefully that'll make up for the lapse since our Buffalo edition. Get excited!
Today's edition comes to us from New Belgium Brewing in the form of their holiday special, Frambozen. Frambozen is a Raspberry Brown Ale and is quite delightful. It sports just the right amount of raspberry while letting the rich brown ale come through. As a Brown Ale, it's not quite as smooth as the popular wheat+fruit combinations, but I applaud both the effort and the execution in making something different.
|
|
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How festive of a label!
|
But enough about the beer, on to the pancakes!
Prep/Cooking:
The batter came out a color very similar to the beer itself. Kinda dark brownish, kinda reddish. This really isn't a shock, but it made for an interesting batter color (of course, not the most interesting we've seen on this blog). The batter's aroma was delicious - not quite as beery as the beer itself, but just as raspberry-y. Get excited!
|
|
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Rassssssppbeerrrrrryyyyy-yyyyy
|
Results:
This one might go down as one of the biggest disappointments I've come across so far. I've had the beer more than once and it's always delightful. The batter smelled great, and there were no complications in cooking. However, the resultant pancakes were pretty bad. The characteristic raspberry flavor is completely gone, and even the caramel tones of the brown ale were subdued. I basically felt like I was eating normal pancakes.
The Ternary Scale:
0 - No way, Jose. Don't waste your time or the beer on these. They're much better off in beer form. Syrup couldn't even save them.
Ever had a Frambozen? What're your thoughts on it? Any ideas why such a delicious beer turned into such flavorless pancakes? Drop a line below!
{% include twitter.html tweetAt=" from @newbelgium" %}


Research expert for neuromorphic computing, AI accelerators and power-efficient computing for AI, machine learning and robotics in the team AI, Robotics, Cognitive Systems within the Department Research, New Technologies, Innovations.
Member of the BMW Group Ph.D.-program ProMotion within the Department Research, New Technologies, Innovations.
Participation in several public and private projects concerning different fields of robotic research within the Robot and Assistive Systems Department.
Worked on a project with the German Weather Service at the Institute of Applied Mathematics.
Analysis and comparison of precautionary insurance products.
Supervised the lectures Linear Algebra 1 and 2 as tutor and created computational algebra lecture notes at the Institute for Mathematics and Computer Sciences.


We build things sometimes. Sometimes they work.
We code things. We coded this.
We do outreach and other stuff.
Somehow I think?Somehow I think?Somehow I think?Somehow I think?Somehow I think?Somehow I think?Somehow I think?Somehow I think?Somehow I think?Somehow I think?Somehow I think?Somehow I think?Somehow I think?Somehow I think?Somehow I think?Somehow I think?Somehow I think?Somehow I think?Somehow I think?Somehow I think?Somehow I think?Somehow I think?Somehow I think?Somehow I think?Somehow I think?Somehow I think?


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Latest News
Fish Oil May Have Positive Effects on Mood, Alcohol Craving, New Study Shows
May 27, 2011 10:21 AM
Omega 3 fatty acids may be beneficial for more than just the heart. Researchers at the Indiana University School of Medicine have found at a molecular level a potential therapeutic benefit from these dietary supplements for treating alcohol abuse and psychiatric disorders.
MORE


NEWSIES A film review by David Fristrom Copyright 1992 David Fristrom
Yesterday I saw a preview of the eagerly awaited (by me, anyway) new Disney movie NEWSIES (opening April 10th). NEWSIES is an original, live-action musical, Disney's first in years, with music by Alan Menken (THE LITTLE MERMAID, BEAUTY AND THE BEAST). It is based on an actual strike by newsboys in New York at the turn of the century, although the characters and incidents in the movie are fictitious.
First the good news: there is a fair amount to like in NEWSIES. To begin with, an original movie musical is always a good idea, and I'm glad to see that Disney seems to be putting some marketing muscle behind it. The cast is quite engaging, especially Christian Bale (the lead in EMPIRE OF THE SUN, "boy" in HENRY V) as the ring-leader of the strike, but all of the boys do a good job. The basic story line is strong -- a little obvious at times (do I have to tell you whether the strike succeeds?), but a good structure and decent writing. And I'm a sucker for period costumes and back lot sets.
Alas, there is also plenty that is wrong with NEWSIES. Filming dance is an art that appears to be lost, which is strange considering how simple it is. As Fred Astaire showed in his best movies, the way you film dance is with long takes showing the full body. Long takes, because it is the dancer who is dancing, not the camera; full body shots because dancing is done with the body, not the feet. Of course, while this approach is simple for the camera person and editor it is much harder for the choreographer, who must actually choreograph a full dance, with a definite structure building to a climax.
That's not the way it is done in NEWSIES. Instead, when dancing is called for the screen is suddenly filled with athletic dancers performing really amazing gymnastic feats or a bunch of people moving in unison, with no take lasting more than a few seconds. The dances have no structure; they begin with a burst of energy, continue at the same level for a few minutes, and then stop. No emotion is expressed other than a pseudo-excitement.
Besides dance, the other crucial element of a musical are the songs, and here NEWSIES fares somewhat better. Menken is, of course, best known for his work with the late Howard Ashman, and I'm sure he and his lyricists will soon be sick of comparisons. The unknown (whose name I am afraid I don't remember) who has provided the lyrics for NEWSIES is no Ashman, but he does a reasonably good job and has produced some clever lines. But while the songs are basically pretty good (although the lyrics are hard to make out during some of the chorus numbers), they don't really fit well with the story. Choosing where to put the songs, and then writing the lead-ins, is one of the hardest parts of creating a musical -- especially nowadays, when audiences no longer accept the old convention of stopping the story for a marginally related song. It has become a cliche that songs should advance the story, at least emotionally if not in plot terms -- and the fact it is a cliche doesn't stop it from being true.
This integration of song and story is what is missing in NEWSIES. Like the dances, most of the songs are just bursts of undirected energy, which could largely be moved around interchangeably. The one exception is a more introspective song by Bale's character in which he dreams of going to Santa Fe, and that songs comes dangerously close to unintentional silliness. Still, after seeing the movie only once I have a couple of the songs bouncing around in my head, so maybe they pass the dreaded hummability test.
While basically sound, the plot does have a few oddities. Inevitably, there is a love interest for the main character, but, perhaps in deference to the intended audience of children, the love sub-plot is so down played that it might as well not be there. There is really only one scene devoted to it, and that comes off odd because it sets things up for a love song which never arrives. Also, Ann-Margret appears in a pointless role as a vaudevillian who implausibly owns her own theater. As she has no real plot function, you would think she was there as an excuse to get in some musical numbers, but all we get are fragments of a couple of songs.
If I had seen a preview of a Broadway show instead of a movie, I would be cautiously optimistic. The book needs a bit of tightening, the musical numbers need better lead ins, they need a new choreographer, and a love song or two have to be written. A lot of work, but just the kind of thing that you have previews for, and there is good material here and a great cast. But since it is a movie it is too late to make any changes, and we are left with an interesting failure. Fans of musicals will probably want to see it since it is the only thing going, and for all I know children will love it (and their parents at least won't be too bored). But I don't think I can recommend it to the average movie-goer.
David Fristrom Interleaf, Inc<EMAIL_ADDRESS>.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


The following snippet demonstrates how to request data from the US Regulatory Alerts - Financial Sector dataset:
from QuantConnect.DataSource import * self.dataset_symbol = self.AddData(RegalyticsRegulatoryArticles, "REG").Symbol
using QuantConnect.DataSource; _datasetSymbol = AddData<RegalyticsRegulatoryArticles>("REG").Symbol;


@@TPL_NOTE
| {item_row_number_label} | {item_description_label} | {item_quantity_label} | {item_price_label} | {item_discount_label} | {item_tax_label} | {item_line_total_label} |
|---|---|---|---|---|---|---|
| {item_row_number} | {item_description} | {item_quantity} | {item_price} | {item_discount} | {item_tax} | {item_line_total} |
| {amount_subtotal_label} | {amount_subtotal} |
|---|---|
| {tax_name} | {tax_value} |
| {amount_total_label} | {amount_total} |
| {amount_paid_label} | {amount_paid} |
| {amount_due_label} | {amount_due} |
|
Please make all cheques payable to DCR Strategies Inc.
Thank you for your business! |


Courses I have taken part in.
Welcome to my practice site. Currently I only added the courses from my exchange in Vienna.
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dynamics package¶
Submodules¶
dynamics.bath_corr_functions module¶
-
bcf_convert_sdl_to_exp
(lambda_sdl, gamma_sdl, omega_sdl, temp)¶ This function converts a shifted drude-lorentz spectral density parameters to the exponential equivalent.
- NOTE: THIS WILL NEED TO BE REPLACED WITH A MORE ROBUST FITTING
ROUTINE SIMILAR TO WHAT EISFELD HAS DONE PREVIOUSLY.
- Parameters
- 1. lamnda_sdlfloat
the reorganization energy [cm^-1]
- 2. gamma_sdlfloat
the reorganization time scale [cm^-1]
- 3. omega_sdlfloat
the vibrational frequency [cm^-1]
- 4. tempfloat
the temperature [K]
- Returns
- 1. g_expcomplex
the exponential prefactor [cm^-2]
- 2. w_expcomplex
the exponent [cm^-1]
-
bcf_exp
(t_axis, g, w)¶ This is the form of the correlation function alpha(t) = displastyleg exp(-w*t)
- Parameters
- 1. t_axisarray
array of time points
- 2. gcomplex floating point number
the exponential prefactor [cm^-2]
- 3. wcomplex floating point number
the exponent [cm^-1]
- Returns
- 1. bcfarray
the exponential bath correlation function sampled on t_axis
dynamics.eom_functions module¶
-
calc_delta_zmem
(z_mem, list_avg_L2, list_g, list_w, list_index_L2_by_mode, list_absindex_mode)¶ This updates the memory term. The form of the equation depends on expanding the memory integral assuming an exponential expansion.
NOTE: THIS ASSUMES THE NOISE HAS EXPONENTIAL FORM.
- NOTE: Again this function mixes relative and absolute
indexing which makes it much more confusing. z_mem : absolute list_avg_L2 : relative list_g : absolute list_w : absolute list_absindex_L2_by_mode : relative list_absindex_mode : mapping from relative–>absolute
- Parameters
- 1. z_memlist
a list of memory terms in absolute indices
- 2. list_avg_L2list
relative list of the expectation values of the L operators
- 3. list_glist
list of pre exponential factors for bath correlation functions [absolute]
- 3. list_wlist
list of exponents for bath correlation functions (w = γ+iΩ) [absolute]
- 4. list_absindex_L2_by_modelist
list of length equal to the number of ‘modes’ in the current hierarchy basis and each entry is an index for the absolute list_L2.
- 5. list_absindex_modelist
list of the absolute indices of the modes in current basis
- Returns
- 1. delta_z_memlist
a list of updated memory terms
-
calc_delta_zmem_absorption
(z_mem, list_avg_L2, list_g, list_w, list_index_L2_by_mode, list_absindex_mode)¶ This updates the memory term. The form of the equation depends on expanding the memory integral assuming an exponential expansion.
NOTE: THIS ASSUMES THE NOISE HAS EXPONENTIAL FORM.
- NOTE: Again this function mixes relative and absolute
indexing which makes it much more confusing. z_mem : absolute list_avg_L2 : relative list_g : absolute list_w : absolute list_absindex_L2_by_mode : relative list_absindex_mode : mapping from relative–>absolute
- Parameters
- 1. z_memlist
a list of memory terms in absolute indices
- 2. list_avg_L2list
relative list of the expectation values of the L operators
- 3. list_glist
list of pre exponential factors for bath correlation functions [absolute]
- 3. list_wlist
list of exponents for bath correlation functions (w = γ+iΩ) [absolute]
- 4. list_absindex_L2_by_modelist
list of length equal to the number of ‘modes’ in the current hierarchy basis and each entry is an index for the absolute list_L2.
- 5. list_absindex_modelist
list of the absolute indices of the modes in current basis
- Returns
- 1. delta_z_memlist
a list of updated memory terms
-
calc_norm_corr
(phi, z_hat, list_avg_L2, list_L2, nstate, list_index_phi_L2_mode, list_g, list_w)¶ This function computes the correction factor for propagating the normalized wavefunction.
- Parameters
- 1. phinp.array
the full hierarchy
- 2. z_hatlist
the memory term with both with random noise
- 3. list_avg_L2list
relative list of the expectation values of the L operators
- 4. list_L2list
list of L operators
- 5. nstateint
the current dimension (size) of the system
- 6. list_index_L2_by_modelist
list of length equal to the number of modes in the current hierarchy basis and each entry is an index for the relative list_L2.
- 7. list_glist
list of pre exponential factors for bath correlation functions [absolute]
- 8. list_wlist
list of exponents for bath correlation functions (w = γ+iΩ) [absolute]
- Returns
- 1. deltafloat
the norm correction factor
-
compress_zmem
(z_mem, list_index_L2_by_mode, list_absindex_mode)¶ This function compresses all of the memory terms into their respective slots for each L_operator.
- Parameters
- 1. z_memlist
a list of all the memory terms in absolute basis
- 2. list_index_L2_by_modelist
list of length equal to the number of modes in the current hierarchy basis
- 3. list_absindex_modelist
list of absolute mode indices in relative mode order
- Returns
- 1. dz_hatlist
a list of compressed memory terms indexed by list_index_L2_by_mode
-
operator_expectation
(oper, vec)¶ This is a function that calculates the expectation value of a sparse operator
- Parameters
- 1. opernp.array
dense or sparse operator
- 2. vecnp.array
a nx1 matrix
- Returns
- 1. expectation_valuefloat
expectation value of the operator, <vec|Oper|vec> /(<vec|vec>)
dynamics.eom_hops_ksuper module¶
-
calculate_ksuper
(system, hierarchy)¶ This calculates all the pieces we need to time-evolve the system EXCEPT for the normalization term.
- Parameters
- 1. systemHopsSystem Class
an instance of HopsSystem
- 2. hierarchyHopsHierarchy Class
an instance of HopsHierarchy
- Returns
- 1. K0sparse matrix
updated time invariant self interaction super operator
- 2. Z0sparse matrix
updated time varying self interaction super operator
- 3. Kp1sparse matrix
updated time invariant K+1 interaction super operator
- 4. Zp1sparse matrix
updated time varying K+1 interaction super operator
- 5. Km1sparse matrix
updated time invariant K-1 interaction super operator
-
update_ksuper
(K0, Z0, Kp1, Zp1, Km1, stable_aux, add_aux, stable_state, list_old_ilop, system, hierarchy, n_old, perm_index)¶ This function updates the EOM matrices to incorporate changes to the hierarchy. At the moment, this function is the key step to handling Dynamic Filtering of the hierarchy.
- Parameters
- 1. K0sparse matrix
time invariant self interaction super operator
- 2. Z0sparse matrix
time varying self interaction super operator
- 3. Kp1sparse matrix
time invariant K+1 interaction super operator
- 4. Zp1sparse matrix
time varying K+1 interaction super operator
- 5. Km1sparse matrix
time invariant K-1 interaction super operator
- 6. stable_auxlist
list of stable auxiliaries
- 7. add_auxlist
list of new auxiliaries
- 8. stable_statelist
list of stable states
- 9. list_old_iloplist
list of the absolute indices of L operators in the last basis
- 10. systemHopsSystem class
an instance of HopsSystem
- 11. hierarchyHopsHierarchy class
an instance of HopsHierarchy
- 12. n_oldint
size of the full hierarchy in the previous basis
- 13. perm_indexlist
a list of column list and row list that define the permutation matrix
- Returns
- 1. K0sparse matrix
updated time invariant self interaction super operator
- 2. Z0sparse matrix
updated time varying self interaction super operator
- 3. Kp1sparse matrix
updated time invariant K+1 interaction super operator
- 4. Zp1sparse matrix
updated time varying K+1 interaction super operator
- 5. Km1sparse matrix
updated time invariant K-1 interaction super operator
dynamics.hierarchy_functions module¶
-
check_long_edge
(aux_vec, list_boolean, kmax, kdepth)¶ - Parameters
- 1. auxtuple
an auxiliary to check
- 2. list_booleanlist
the modes to filter True-filter, False-No filter
- 3. kmaxint
the maximum depth of the hierarchy
- 4. kdepthint
the depth beyond which only edge members of hierarchy are kept for filtered modes
- Returns
- 1. check_auxbool
True-keep aux, False-remove aux
-
check_markovian
(aux, list_boolean)¶ This function checks whether a auxiliary is a an allowed markovian mode or a non-markovian mode PARAMETERS ———- 1. aux : AuxiliaryVector object 2. list_boolean : list
a list of boolean values True: markovian mode, False: non-markovian mode
- Returns
- 1. allowedboolean
True: non-markovian/ allowed markovian, False: disallowed markovian
-
filter_aux_longedge
(list_aux, list_boolean_by_mode, kmax, kdepth)¶ Beyond kdepth, only keep the edge terms upto kmax.
- Parameters
- 1. list_auxlist
list of auxiliaries
- 2. list_boolean_by_modelist
a list of booleans: True-Filter, False-No Filter
- 3. kmaxint
the maximum depth of the hierarchy
- 4. kdepthint
the depth beyond which only edge members of hierarchy are kept for filtered modes
- Returns
- 1. list_new_auxlist
a filtered list of auxiliaries
-
filter_aux_triangular
(list_aux, list_boolean_by_mode, kmax, kdepth)¶ If the mode has a non-zero value, then it is kept only if the value is less than kmax and the total auxillary is less than kdepth. This essentially truncates some modes at a lower order than the other modes.
- Parameters
- 1. list_auxlist
a list of auxiliary tuples
- 2. list_boolean_by_modelist
a list of booleans: True-Filter, False-No Filter
- 3. kmaxint
the largest value a filtered mode can have in an auxiliary
- 4. kdepthint
the largest depth of an auxiliary at which a filtered mode can have a non-zero value
- Returns
- 1. list_new_auxlist
a filtered list of auxiliaries
-
filter_markovian
(list_aux, list_boolean)¶ This function filter’s a list of auxiliaries based on whether it is a non markovian auxiliary or an allowed markovian auxiliary Parameters ———- 1. list_aux : list
list of unfiltered auxiliaries
- list_booleanlist
a list of boolean values True: markovian mode, False: non-markovian mode
- Returns
- list_auxlist
filtered auxiliary list
dynamics.hops_aux module¶
-
class
AuxiliaryVector
(aux_array, nmodes)¶ Bases:
collections.abc.Mapping
This is a class that encodes a sparse representation of auxiliary vectors with some extra helper functions to simplify some common actions, such as: determining the absolute index, adding a unit vector, and calculating the sum.
The class is not mutable - which is to say, once an auxiliary vector is defined, it cannot be changed.
- Attributes
- absolute_index
- array_aux_vec
- dict_aux_m1
- dict_aux_p1
- dict_aux_vec
- hash
- index
- tuple_aux_vec
Methods
add_aux_connect
(self, index_mode, aux_other, …)The function that updates the HopsAux object to contain a pointer to the other HopsAux objects it is connected to.
difference_by_mode
(self, other)Compares the current HopsAux object to another HopsAux object.
dot
(self, vec)This is a function that performs a sparse dot product between the auxiliary index vector and another vector.
e_step
(self, mode, step)This function returns a new Auxiliary Vector with the desired step in the given mode
get
(self, key[, default])get_values
(self, index_slice)This function gets the dense auxiliary vector values from a sub-indexed list
get_values_nonzero
(self, index_slice)This function gets the sparse auxiliary vector values from a sub-indexed list
hash_from_e_step
(self, mode, step)This function returns the hash of a new Auxiliary Vector with the desired step in the given mode
index_analytic
(self)This function provides an absolute index value for an auxiliary vector using an analytic function of the indices.
items
(self)keys
(self)This function returns an array of mode indices for the auxiliary vectors
remove_aux_connect
(self, index_mode, type)The function that removes the connection between the HopsAux object and another connected with type (+1/-1) along index mode.
remove_pointers
(self)The function that removes all pointers targeting the current HopsAux object from the set of HopsAux objects it has connections to.
sum
(self, \*\*unused_kwargs)This function returns the sum of the auxiliary vector values
toarray
(self)This function converts a dict to an array
todense
(self)This function will take a sparse vector and make it dense
tuple_from_e_step
(self, mode, step)Returns the sparse tuple representation of the auxiliary that is the given step length along the given absolute mode index away from the current auxiliary.
values
(self)This function returns an array of the auxiliary vector values
-
property
absolute_index
¶
-
add_aux_connect
(self, index_mode, aux_other, type)¶ The function that updates the HopsAux object to contain a pointer to the other HopsAux objects it is connected to.
- Parameters
- 1. index_modeint
the mode along which the two HopsAux objects are connected
- 2. aux_otherHopsAux
the HopsAux object it is connected to
- 3. typeint
+1 or -1 depending on if the other aux has a larger or smaller sum
- Returns
- None
-
array_aux_vec
¶
-
property
dict_aux_m1
¶
-
property
dict_aux_p1
¶
-
dict_aux_vec
¶
-
difference_by_mode
(self, other)¶ Compares the current HopsAux object to another HopsAux object. If they differ by only 1 step, then it returns the mode along which they differ. Parameters ———- 1. other: HopsAux object
The HopsAux object to which the current object is compared.
- diff_modeint or False
The mode index along which they differ or False if they differ by more than 1 step.
-
dot
(self, vec)¶ This is a function that performs a sparse dot product between the auxiliary index vector and another vector.
- Parameters
- 1. vecnp.array
a vector
- Returns
- 1. productfloat
the dot product value
-
e_step
(self, mode, step)¶ This function returns a new Auxiliary Vector with the desired step in the given mode
- Parameters
- 1. modeint
The absolute mode index
- 2. stepint
The change in the aux value for the given mode
- Returns
- 1. aux_vectuple
the new sparse auxiliary vector
-
get_values
(self, index_slice)¶ This function gets the dense auxiliary vector values from a sub-indexed list
- Parameters
- 1. index_slicelist
a list of indices
- Returns
- 1. valuesarray
an array of values at the given indices
-
get_values_nonzero
(self, index_slice)¶ This function gets the sparse auxiliary vector values from a sub-indexed list
- NOTE: the values are returned in key order not the order
they are present in index_slice
- Parameters
- 1. index_slicelist
a list of indices
- Returns
- 1. valuesarray
a sparse array of the non-zero auxiliary vector values
-
hash
¶
-
hash_from_e_step
(self, mode, step)¶ This function returns the hash of a new Auxiliary Vector with the desired step in the given mode
- Parameters
- 1. modeint
The absolute mode index
- 2. stepint
The change in the aux value for the given mode
- Returns
- hash :
the hash of the tuple sparse auxiliary vector created from e_step
-
index
¶
-
index_analytic
(self)¶ This function provides an absolute index value for an auxiliary vector using an analytic function of the indices. The basic idea is that the indices are always ordered by increasing hierarchy ‘level’ (i.e. the sum of the indices). Within a level they are ordered by first comparing the first values, then the second values, etc.
This gives the indexing a particularly simple form with a level: L = sum(i_0,…,i_n) (i_0, … i_N) = sum_n<N sum_L>ln>i_n ((N-n-1 , L-sum(aux[:n])-ln) where ((L,K)) denotes a L multichoose K.
The derivation of the following equations is given on p. 68 of Quantum Notebook #1. The sums have been removed by making use of the binomial sum property and the binomial symmetry property. The result is a equation that only sums over a number of elements equal to the number of non-zero terms in aux.
- Parameters
- 1. tup_auxtuple
index for the auxiliary represented by aux
- 2. n_hmodesint
number of modes that appear in the hierarchy
- Returns
- 1. indexint
the absolute index for an auxiliary
-
keys
(self)¶ This function returns an array of mode indices for the auxiliary vectors
- Parameters
- None
- Returns
- 1. keysarray
an array of mode indices with nonzero auxiliary index
-
remove_aux_connect
(self, index_mode, type)¶ The function that removes the connection between the HopsAux object and another connected with type (+1/-1) along index mode.
- Parameters
- 1. index_modeint
the mode along which the two HopsAux objects are connected
- 2. typeint
+1 or -1 depending on if the other aux has a larger or smaller sum
- Returns
- ——-
- 1. None
-
remove_pointers
(self)¶ The function that removes all pointers targeting the current HopsAux object from the set of HopsAux objects it has connections to.
- Parameters
- 1. None
- Returns
- None
-
sum
(self, **unused_kwargs)¶ This function returns the sum of the auxiliary vector values
- Parameters
- None
- Returns
- 1. sumfloat
the sum of the nonzero values of the auxiliary vectors
-
toarray
(self)¶ This function converts a dict to an array
- Parameters
- None
- Returns
- 1. arrayarray
a dict in an array form
-
todense
(self)¶ This function will take a sparse vector and make it dense
- Parameters
- None
- Returns
- 1. outputarray
the dense vector
-
tuple_aux_vec
¶
-
tuple_from_e_step
(self, mode, step)¶ Returns the sparse tuple representation of the auxiliary that is the given step length along the given absolute mode index away from the current auxiliary.
- Parameters
- 1. modeint
The absolute mode index
- 2. stepint
The change in the aux value for the given mode
- Returns
- 1. tuple_auxtuple
The sparse representation of the auxiliary (sorted mode order)
-
values
(self)¶ This function returns an array of the auxiliary vector values
- Parameters
- None
- Returns
- 1. valuesarray
an array of nonzero auxiliary index values
dynamics.hops_basis module¶
-
class
HopsBasis
(system, hierarchy, eom)¶ Bases:
object
Every HOPS calculation is defined by the HopsSystem, HopsHierarchy, and HopsEOM classes (and their associated parameters). These form the basis set for the calculation. HopsBasis is the class that contains all of these sub-classes and mediates the way the HopsTrajectory will interact with them.
- Attributes
- adaptive
- adaptive_h
- adaptive_s
- delta_h
- delta_s
- n_hier
- n_hmodes
- n_state
Methods
define_basis
(self, Φ, delta_t, z_step)This is the function that determines what basis is needed for a given full hierarchy (Φ) in order to construct an approximate derivative with error below the specified threshold.
error_boundary_state
(self, Φ, …)This function determines the set of states which were not in the previous basis S_0, but which need to be kept because there is sufficient flux going to them.
error_stable_state
(self, Φ, delta_t, z_step, …)This function finds the total error associated with removing n’ from the state basis.
hier_stable_error
(self, Φ, delta_t, z_step)This function finds the total error associated with removing k’ in H_0 from H_1.
initialize
(self, psi_0)This function initializes the hierarchy and equations of motion classes so that everything is prepared for integration.
update_basis
(self, Φ, state_update, aux_update)This function updates the derivative function and full hierarchy vector (Φ) for the new basis (hierarchy and/or system).
-
property
adaptive
¶
-
property
adaptive_h
¶
-
property
adaptive_s
¶
-
define_basis
(self, Φ, delta_t, z_step)¶ This is the function that determines what basis is needed for a given full hierarchy (Φ) in order to construct an approximate derivative with error below the specified threshold.
- Parameters
- 1. Φnp.array
the current full hierarchy
- 2. delta_tfloat
the timestep for the calculation
- 3. z_steplist
the list of noise terms (compressed) for the next timestep
- Returns
- state_update :
- list_state_newlist
list of states in the new basis (S_1)
- list_state_stablelist
list of stable states in the new basis (S_S)
- list_state_boundlist
list of boundary states in the new basis (S_B)
- hierarchy_update :
- list_aux_newlist
list of auxiliaries in new basis (H_1)
- list_stable_auxlist
list of stable auxiliaries in the new basis (H_S)
- list_aux_boundlist
list of boundary auxiliaries in the new basis (H_B)
-
property
delta_h
¶
-
property
delta_s
¶
-
error_boundary_state
(self, Φ, list_index_stable, list_index_aux_stable)¶ This function determines the set of states which were not in the previous basis S_0, but which need to be kept because there is sufficient flux going to them. We call these the `boundary’ states S_B. The error associated with not including n in the new basis is given by equation 61 in the LaTeX document. This can be simplified to an action of the Hamiltonian which is the only operator that can change the state index, giving equation 62 in the LaTeX document
\[\sum_{\vec{k} \in \mathbb{H}_{stab}} \vert \sum_{n \in \mathbb{S}_{stab}} H[n', n] \tilde{\Phi}[n, \vec{k}; \tilde{z}_t]\vert^2\]- Parameters
- 1. Φnp.array
the current full hierarchy vector
- 2. list_index_stablelist
a list of the stable states
- 3. list_index_aux_stablelist
a list relative indices for the stable auxiliaries
- Returns
- 1. list_index_nonzerolist
a list of the nonzero state boundary indices
- 2. list_error_nonzerolist
a list of the nonzero state boundary error values
-
error_stable_state
(self, Φ, delta_t, z_step, list_index_aux_stable)¶ This function finds the total error associated with removing n’ from the state basis. This includes the error associated with deleting a state from the basis, the error associated with losing all flux into a state, and the error associated with losing all flux out of a state. This is equation 59 in the LaTeX document
\[\Delta_{S}^2(n') = \sum_{\vec{k} \in \mathbb{H}_0 \cap \mathbb{H}_1} \vert \tilde{\Phi}[n', \vec{k}]/\Delta t \vert^2 + \sum_{\vec{k}'} \vert \frac{d\Phi_{stab}}{dt}[n', \vec{k}'] \vert^2\]\[+ \sum_{\vec{k}' \in \mathbb{H}_{B}} \vert \sum_{\vec{k} \in \mathbb{H}_{stab}} \hat{\mathcal{K}}(n', \vec{k}' \leftarrow n', \vec{k})\tilde{\Phi}[n', \vec{k}; \tilde{z}_t] \vert^2\]\[+ \sum_{\vec{k} \in \mathbb{H}_{stab}, n \in \{n'\}^\textrm{C}} \vert H[n, n'] \tilde{\Phi}[n', \vec{k}; \tilde{z}_t]\vert^2.\]- Parameters
- 1. Φnp.array
the current full hierarchy vector
- 2. delta_tfloat
the timestep for the calculation
- 3. z_steplist
the list of noise terms (compressed) for the next timestep
- 4. list_index_aux_stablelist
a list relative indices for the stable auxiliaries (H_S)
- Returns
- 1. errornp.array
list of error associated with removing each state in S_0
-
hier_stable_error
(self, Φ, delta_t, z_step)¶ This function finds the total error associated with removing k’ in H_0 from H_1. This includes the error associated with deleting a auxiliary from the basis, the error associated with losing all flux into a k’ auxiliary, and the error associated with losing all flux out of a k’ auxiliary. This is equation 50 in the LaTeX document
\[\Delta_{stable}^2(\vec{k}') = \sum_{n' \in \mathbb{S}_0} \vert \tilde{\Phi}[n', \vec{k}' ]/\Delta t \vert^2 \ + \sum_{n' \in \mathbb{S}_0} \left\vert \frac{d \tilde{\Phi}}{dt}[,n',\vec{k}'; z_{step}] \right\vert^2\]\[+ \sum_{n' \in \mathbb{S}_0^\textrm{C}} \left \vert \sum_{n \in \mathbb{S}_0} H[n', n] \tilde{\Phi}[n,\vec{k}'] \right\vert^2\]\[+ \sum_{\vec{k} \in \mathbb{H}_T\setminus \{\vec{k}'\}, n'\in \mathbb{S}_0} \vert \hat{\mathcal{K}}(n',\vec{k} \leftarrow n',\vec{k}') \tilde{\Phi}[n',\vec{k}'; \tilde{z}_t]\vert^2\]- Parameters
- 1. Φnp.array
the current full hierarchy vector
- 2. delta_tfloat
the timestep for the calculation
- 3. z_steplist
the list of noise terms (compressed) for the next timestep
- Returns
- 1. errornp.array
list of error associated with removing each auxiliary in H_0
- 2. E2_flux_upnp.array
- the error induced by neglecting flux from H_0 (or H_S)
to auxiliaries with lower summed index in H0^C.
- 3. E2_flux_downnp.array
- the error induced by neglecting flux from H_0 (or H_S)
to auxiliaries with higher summed index in H_0^C.
-
initialize
(self, psi_0)¶ This function initializes the hierarchy and equations of motion classes so that everything is prepared for integration. It returns the dsystem_dt function to be used in the integrator.
- Parameters
- 1. psi_0np.array
the initial wave function
- Returns
- 1. dsystem_dtfunction
this is the core function for calculating the time-evolution of the wave function
-
property
n_hier
¶
-
property
n_hmodes
¶
-
property
n_state
¶
-
update_basis
(self, Φ, state_update, aux_update)¶ This function updates the derivative function and full hierarchy vector (Φ) for the new basis (hierarchy and/or system).
- Parameters
- 1. Φnp.array
the current full hierarchy
- 2. state_updatelist
list of list containing list_state_new, list_stable_state, and list_add_state
- 3. aux_updatelist
list of list containing list_aux_new, list_stable_aux, and list_add_aux
- Returns
- 1. Φ_newnp.array
the updated full hierarchy
- 2. dsystem_dtfunction
the updated derivative function
dynamics.hops_eom module¶
-
class
HopsEOM
(eom_params)¶ Bases:
mesohops.util.dynamic_dict.Dict_wDefaults
HopsEOM is the class that defines the equation of motion for time evolving the hops trajectory. Its primary responsibility is to define the derivative of the system state. It also contains the parameters that determine what kind of adaptive-hops strategies are used.
- Attributes
- param
Methods
update_param
dynamics.hops_hierarchy module¶
-
class
HopsHierarchy
(hierarchy_param, system_param)¶ Bases:
mesohops.util.dynamic_dict.Dict_wDefaults
HopsHierarchy defines the representation of the hierarchy in the HOPS calculation. It contains the user parameters, helper functions, and the list of all nodes contained in the hierarchy.
- Attributes
- auxiliary_list
- param
- size
Methods
add_connections
(self, list_aux_add)The method responsible for adding the connections between HopsAux objects composing an auxiliary list.
apply_filter
(self, aux_list, filter_name, params)This is a function that implements a variety of different hierarchy filtering methods.
define_triangular_hierarchy
(n_hmodes, maxhier)This function creates a triangular hierarchy
filter_aux_list
(self, aux_list)A function that applies all of the filters defined for the hierarchy in “STATIC_FILTERS”
initialize
(self, flag_adaptive)This function will initialize the hierarchy.
update_param
-
add_connections
(self, list_aux_add)¶ The method responsible for adding the connections between HopsAux objects composing an auxiliary list.
- Parameters
- 1. list_aux_addlist
a list of HopsAux objects being added to the auxiliary list
- Returns
- None
-
apply_filter
(self, aux_list, filter_name, params)¶ This is a function that implements a variety of different hierarchy filtering methods. In all cases, this filtering begins with the current list of auxiliaries and then prunes down from that list according to a set of rules.
- Parameters
- 1. list_auxlist
the list of auxiliaries that needs to be filtered
- 2. filter_namestr
name of filter
- 3. paramslist
the list of parameters for the filter
- Returns
- 1. list_auxlist
a list of filtered auxiliaries
- ALLOWED LIST OF FILTERS:
- Triangular: bolean_by_mode, [kmax, kdepth]
If the mode has a non-zero value, then it is kept only if the value is less than kmax and the total auxillary is less than kdepth. This essentially truncates some modes at a lower order than the other modes.
- LongEdge: bolean_by_mode, [kmax, kdepth]
Beyond kdepth, only keep the edge terms upto kmax.
-
property
auxiliary_list
¶
-
static
define_triangular_hierarchy
(n_hmodes, maxhier)¶ This function creates a triangular hierarchy
- Parameters
- 1. n_hmodesint
number of modes that appear in the hierarchy
- 2. maxhierint
the max single value of the hierarchy
- Returns
- 1. list_auxlist
list of auxiliaries in the new triangular hierarchy
-
filter_aux_list
(self, aux_list)¶ A function that applies all of the filters defined for the hierarchy in “STATIC_FILTERS”
- Parameters
- 1. aux_listlist
the list of auxiliaries that is to be filtered
- Returns
- 1. list_auxlist
the filtered list of auxiliaries
-
initialize
(self, flag_adaptive)¶ This function will initialize the hierarchy.
- Parameters
- 1. flag_adaptiveboolean
boolean describing whether the calculation is adaptive or not
- Returns
- None
-
property
size
¶
-
listobj_or_none
(hash, list_obj, list_hash)¶ A convenience function for determining which object in a list to return based on a list of the hash values for the objects.
- Parameters
- 1. hashint
The hash value for the desired object
- 2. list_objlist
A list of objects
- 3. list_hashlist
The hash values for the objects in list_obj
- Returns
- 1. obj? or None
The object from list_obj with matching hash or None (if there was no such object)
dynamics.hops_noise module¶
-
class
HopsNoise
(noise_param, noise_corr)¶ Bases:
mesohops.util.dynamic_dict.Dict_wDefaults
This is the BaseClass for defining a hops noise trajectory. All noise classes will inherit from here. Anything that defines the input-output structure of a noise trajectory should be controlled from this class - rather than any of the model-specific child classes.
- Attributes
- param
Methods
get_noise
(self, t_axis)Gets the noise
unlock
update_param
-
get_noise
(self, t_axis)¶ Gets the noise
- Parameters
- 1. t_axislist
a list of time points
- Returns
- 1. noiselist
a list of list of noise values sampled at the given time points
-
property
param
¶
-
unlock
(self)¶
-
update_param
(self, param_usr)¶
dynamics.hops_storage module¶
-
class
FullHierarchyStorage
¶ Bases:
object
This is a class intended to simplify managing plotting and analyzing a full hierarchy trajectory.
Methods
append
get_aux
-
append
(self, time, list_aux, list_states, phi)¶
-
get_aux
(self, aux)¶
-
-
class
HopsStorage
(adaptive, storage_dic)¶ Bases:
object
This is an object that manages storing information for a HOPS trajectory.
- Attributes
- adaptive
- n_dim
Methods
store_step
(self, phi_new, aux_new, …)This is the function that inserts data into the HopsStorage class at each
-
property
adaptive
¶
-
property
n_dim
¶
-
store_step
(self, phi_new, aux_new, state_new, t_new, zmem_new)¶ This is the function that inserts data into the HopsStorage class at each time point of the simulation.
- Parameters
- 1. phi_newnp.array
the updated full hierarchy
- aux_newlist
a list of list defining the auxiliaries in the hierarchy basis [aux_crit, aux_bound]
- state_newlist
the list of current states in the system basis
- t_newfloat
the new time point (t+tau)
- 5. z_mem_newlist
a list of memory values
- Returns
- None
dynamics.hops_system module¶
-
class
HopsSystem
(system_param)¶ Bases:
object
HopsSystem is a class that stores the basic information about the system and system-bath coupling.
- Attributes
- g
- hamiltonian
- list_L2_coo
- list_absindex_L2
- list_absindex_mode
- list_index_L2_by_hmode
- list_state_indices_by_hmode
- list_state_indices_by_index_L2
- n_hmodes
- n_l2
- size
- state_list
- w
Methods
initialize
(self, flag_adaptive, psi_0)This function creates a state list depending on whether the calculation is adaptive or not
reduce_sparse_matrix
(coo_mat, state_list)This function takes in a sparse matrix and list which represents the absolute state to a new relative state represented in a sparse matrix
-
property
g
¶
-
property
hamiltonian
¶
-
initialize
(self, flag_adaptive, psi_0)¶ This function creates a state list depending on whether the calculation is adaptive or not
- Parameters
- 1. flag_adaptiveboolean
a boolean that defines the adaptivity True: Adaptive, False: Static
- 2. psi_0np.array
the initial user inputted wave function
- Returns
- None
-
property
list_L2_coo
¶
-
property
list_absindex_L2
¶
-
property
list_absindex_mode
¶
-
property
list_index_L2_by_hmode
¶
-
property
list_state_indices_by_hmode
¶
-
property
list_state_indices_by_index_L2
¶
-
property
n_hmodes
¶
-
property
n_l2
¶
-
static
reduce_sparse_matrix
(coo_mat, state_list)¶ This function takes in a sparse matrix and list which represents the absolute state to a new relative state represented in a sparse matrix
NOTE: This function assumes that all states associated with the non-zero elements of the sparse matrix are present in the state list!
- Parameters
- 1. coo_matnp.array
a sparse matrix
- 2. state_listlist
a list of relative index
- Returns
- 1. sparsenp.array
sparse matrix in relative basis
-
property
size
¶
-
property
state_list
¶
-
property
w
¶
dynamics.hops_trajectory module¶
-
class
HopsTrajectory
(system_param, eom_param={}, noise_param={}, hierarchy_param={}, storage_param={}, integration_param={'INCHWORM': False, 'INCHWORM_MIN': 5, 'INTEGRATOR': 'RUNGE_KUTTA'})¶ Bases:
object
HopsTrajectory is the class that a user should interface with to run a single trajectory calculation.
- Attributes
- auxiliary_list
- inchworm
- n_hier
- n_state
- phi
- psi
- state_list
- t
- z_mem
Methods
inchworm_integrate
(self, state_update, …)This function performs inchworm integration.
initialize
(self, psi_0)This function initializes the trajectory module by ensuring that each sub-component is prepared to begin propagating a trajectory.
make_adaptive
(self[, delta_h, delta_s])This is a convenience function for transforming a not-yet-initialized HOPS trajectory from a standard hops to an adaptive HOPS approach.
normalize
(self, phi)This is the function that re-normalizes the wave function at each step to correct for loss of norm due to finite numerical accuracy
propagate
(self, t_advance, tau)This is the function that perform integration along fixed time-points.
-
property
auxiliary_list
¶
-
property
inchworm
¶
-
inchworm_integrate
(self, state_update, aux_update, tau)¶ This function performs inchworm integration.
- Parameters
- 1. state_update
- list_state_newlist
list of new states
- state_stablelist
list of stable states in the current basis
- list_add_statelist
list of new states that were not in previous state list
- 2. aux_update
- aux_newlist
list of new auxiliaries
- stable_auxlist
list of stable auxiliaries in the current basis
- add_auxlist
list of new auxiliaries that were not in the previous aux list
- 3. taufloat
the time step
- 4. step_numberint
number of inchworm steps in this cycle
- Returns
- state_update
- list_state_newlist
list of new states
- state_stablelist
list of stable states in the current basis
- list_add_statelist
list of new states that were not in previous state list
- aux_update
- aux_newlist
list of new auxiliaries
- stable_auxlist
list of stable auxiliaries in the current basis
- add_auxlist
list of new auxiliaries that were not in the previous aux list
- 3. phinp.array
the full state of the hierarchy normalized if appropriate
-
initialize
(self, psi_0)¶ This function initializes the trajectory module by ensuring that each sub-component is prepared to begin propagating a trajectory.
- Parameters
- 1. psi_0np.array
Wave function at initial time
- Returns
- None
-
make_adaptive
(self, delta_h=0.0001, delta_s=0.0001)¶ This is a convenience function for transforming a not-yet-initialized HOPS trajectory from a standard hops to an adaptive HOPS approach.
- Parameters
- 1. delta_hfloat < 1
The value of the adaptive grid for the hierarchy of auxiliary nodes
- 2. delta_sfloat < 1
The value of the adaptive grid in the system basis
- Returns
- None
-
property
n_hier
¶
-
property
n_state
¶
-
normalize
(self, phi)¶ This is the function that re-normalizes the wave function at each step to correct for loss of norm due to finite numerical accuracy
- Parameters
- 1. phinp.array
the current full state of the hierarchy
- Returns
- 1. phinp.array
the full state of the hierarchy normalized if appropriate
-
property
phi
¶
-
propagate
(self, t_advance, tau)¶ This is the function that perform integration along fixed time-points. The kind of integration that is performed is controlled by ‘step’ which was setup in the initialization.
- Parameters
- 1. t_advancefloat
How far out in time the calculation will run
- 2. taufloat
the time step
- Returns
- None
-
property
psi
¶
-
property
state_list
¶
-
property
t
¶
-
property
z_mem
¶
dynamics.integrator_rk module¶
-
runge_kutta_step
(dsystem_dt, phi, z_mem, z_rnd, z_rnd2, tau)¶ This function performs a single Runge-Kutta step from the current time to a time tau forward.
- Parameters
- 1. dsystem_dtfunction
a function that calculates the system derivatives
- 2. phiarray
the full hierarchy vector
- 3. z_memarray
the memory terms for the bath
- 4. z_rndarray
the random numbers for the bath (at three time points)
- 5. taufloat
the timestep of the calculation
- Returns
- 1. phiarray
the updated hierarchy vector
- 2. z_memarray
the updated memory term
-
runge_kutta_variables
(phi, z_mem, t, noise, noise2, tau, storage)¶ This is a function that accepts a storage and noise objects and returns the pre-requisite variables for a runge-kutta integration step in a list that can be unraveled to correctly feed into runge_kutta_step.
- Parameters
- 1. storageHopsStorage object
an instantiation of HopsStorage associated with the trajectory
- 2. noiseHopsNoise object
an instantiation of HopsNoise associated with the trajectory
- 3. noise2HopsNoise object
an instantiation of HopsNoise associated with the trajectory
- 4. taufloat
the time step
- Returns
- 1. variablesdict
a dictionary of variables needed for Runge Kutta
dynamics.noise_fft module¶
-
class
FFTFilterNoise
(noise_param, noise_corr)¶ Bases:
mesohops.dynamics.hops_noise.HopsNoise
This is a class that describes the noise function for a calculation.
- Attributes
- param
Methods
get_noise
(self, t_axis)Gets the noise
prepare_noise
(self)This function is defined for each specific noise model (children classes of HopsNoise class) and provides the specific rules for calculating a noise trajectory using
unlock
update_param
-
property
param
¶
-
prepare_noise
(self)¶ This function is defined for each specific noise model (children classes of HopsNoise class) and provides the specific rules for calculating a noise trajectory using
- Parameters
- None
- Returns
- None
-
update_param
(self, param_usr)¶
dynamics.noise_trajectories module¶
-
class
NoiseTrajectory
¶ Bases:
abc.ABC
This is the abstract base class for Noise objects.
A noise object has two guaranteed functions: - get_noise(t_axis) - get_taxis()
Methods
get_noise
get_taxis
-
abstract
get_noise
(self, t_axis)¶
-
abstract
get_taxis
(self)¶
-
abstract
-
class
NumericNoiseTrajectory
(noise, t_axis, spline_interpolation=False)¶ Bases:
dynamics.noise_trajectories.NoiseTrajectory
This is the class for noise that is explicitly calculated and does not support interpolation.
Methods
get_noise
(self, taxis_req)This function simply returns the noise values for the selected times.
get_taxis
-
get_noise
(self, taxis_req)¶ This function simply returns the noise values for the selected times.
- NOTE: INTERPOLATION SHOULD BE IMPLEMENTED BY DEFAULT. USE FCSPLINE
FROM RICHARD TO DO IT!
- Parameters
- 1. taxis_reqlist
a list of requested time points
- Returns
- 1. noiselist
a list of list of noise at the requested time points
-
get_taxis
(self)¶
-
dynamics.noise_zero module¶
-
class
ZeroNoise
(noise_param, noise_corr)¶ Bases:
mesohops.dynamics.hops_noise.HopsNoise
This is a class that describes the noise function for a calculation.
- Attributes
- param
Methods
get_noise
(self, t_axis)This is a function that simply returns a zero array of the correct length.
prepare_noise
(self)This function is defined for each specific noise model (children classes of HopsNoise class) and provides the specific rules for calculating a noise trajectory using
unlock
update_param
-
get_noise
(self, t_axis)¶ This is a function that simply returns a zero array of the correct length.
- Parameters
- 1. taxislist
list of time points
- Returns
- 1. list_zeroslist
a list of zeros
-
property
param
¶
-
prepare_noise
(self)¶ This function is defined for each specific noise model (children classes of HopsNoise class) and provides the specific rules for calculating a noise trajectory using
- Parameters
- None
- Returns
- None
-
update_param
(self, param_usr)¶
dynamics.prepare_functions module¶
-
prepare_noise
(noise_param, system_param, flag=1)¶ This is a function that will return the proper noise class given the user inputs.
- Parameters
- 1. noise_paramdict
dictionary of noise parameter
- 2. system_paramdict
dictionary of system parameters
- 3. flagint
1-NOISE1 parameters, 2-NOISE2 parameters
- Returns
- 1. noiseHopsNoise object
an instantiation of HopsNoise based on user input


Dylan enjoys going to the beach and surfing with friends
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HurlyBurly
Director: Anthony Drazan.
Starring: Sean Penn, Kevin Spacey, Robin Wright, Chazz Palminteri, Garry Shandling, Anna Paquin, Meg Ryan.
What a contrast! In the space of two days, I saw Shakespeare In Love and HurlyBurly. I'm not sure you could get two more different movies. While the former was all over the top, telegraphing everything, shoving its philosophy of life down our throats, HurlyBurly was the complete opposite, wound so tight and with so much buried in its script, one could spend weeks thinking about it.
This is one interesting movie. It's very much in a similar vein to one of my favourite movies of '98, In the Company of Men, although not quite so nasty. There's lots of talking and some scary insights into the world of men.
HurlyBurly doens't contain much plot: we just follow Eddie (Sean Penn) around as he tries to make sense of his life and the people around him. His big question is: does it all pertain to him? And if it does, how is he supposed to feel about it all? "It" means everything: television, friends, events that happen. I guess it could be summed up more crudely as: what's it all about?
This is not a movie for answers. Donna (Anna Paquin) gives some but they're not really satisfying: they just lead to more questions. That's what makes this movie stay on your mind. These are the sorts of questions that occupy our time. Even when we push them to the background, however we resolve these questions influences everything we do.
The performances are all superb in HurlyBurly. I haven't seen Sean Penn in much before (in fact, I can't remember seeing him in anything), but he is brilliant in this. Eddie isn't a likeable character, but Sean Penn makes him understandable. I don't want to be his friend but I'd like to help him. Chazz Palminteri is also great as Phil, making him an awful person. I'd never want to know Phil but still I understand why Eddie wants to be his friend. Everyone in this film nails their performances, making this world real.
It's a nasty world; it's a sexist world. Unlike Shakespeare In Love which is unfailingly politically correct, HurlyBurly is a man's world, seen unapologetically through the eyes of men. I find this fascinating and on two counts, depressing. First, it's depressing that such a world exists. A large part of HurlyBurly's success is that it convinces me that such people are real and that this sort of environment exists every day in Hollywood; that there are a large number of men out there who believe women to be completely peripheral to their world, toys, playthings. Sure, everyone is background to everyone else but in this world, women are a long way in the background. Second, these are the movies that are being made: stories about men's world. Where are the stories about women's world? Not the soap operas and the "chick flicks" that are meant to satisfy women, but the real stories that touch on the essence of life like HurlyBurly does for men.
HurlyBurly is based on a stage play and that is evident from the moment the film starts. It's very talkative, intelligent and static. While this can make the film drag a bit, and people sometimes sound like they're quoting textbooks, mostly this is a good thing. Not all films have to be filled with special effects and sweeping panoramas. Not all films have to underestimate the intelligence of their audience. All it means is that HurlyBurly is probably just as effective on video as it is on the large screen. It also means that you have to pay attention.
Rating: D
© Nikki Lesley 1998<EMAIL_ADDRESS>http://www.cs.usyd.edu.au/~nikki/m_r/Intro.html ratings system: HD: High Distinction D: Distinction CR: Credit P: Pass CP: Conceded Pass F: Fail
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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Related links: index of all rec.arts.movies.reviews reviews


{% blocktranslate trimmed %}
Reviews are a valuable tool for speakers to improve their content and presentation.
Even a short review can prove valuable to a speaker! Please
take the time and communicate your feedback in a constructive way.
{% endblocktranslate %}
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{% extends "../transfers.html" %}
{% load get_env %}
{% block title %}Map | Transfers | Cannlytics {% endblock title %}
{% block console_css %}
{% endblock %}
{% block unit %}
Find a lab for transfers
Locate a lab to test your cannabis, browse analyses, and
engage with like-minded scientists around the world.


<hasInstance>
The <hasInstance>
element specifies that the contained subjects have an
INSTANCE-OF relationship with the container subject. In an INSTANCE-OF hierarchy, the child
subject is a specific entity or object and the parent subject is a type, kind, or class of
entity or object.
Category: Subject scheme elements


{% extends 'base.html' %} {% load i18n %} {% load bootstrap3 %} {% block content %}
As the first step, select the organisation that has personal data about you. Type in the name of the organisation below.
{% comment %} {% endcomment %}The website focuses on organisations in the United Kingdom. We have {{ organizations_count }} entities in our database. The basis of our database is the ICO's Register of Data Controllers.
Please note, if you want to make a Freedom of Information request, you should use a service such as WhatDoTheyKnow.


Really best extension for mobile devices view and development. I've tried few and non of them was great as this one - simple and intuitive ui, open separate actually sized window. It makes my life as web dev painless. Thank you dev!
George S.Like that it has a drop-down to select device.
Larry JuddExcellent Tools. Love you google team
Shahbaz Ahmed BhattiAll the love from the Kosmos...Maribel Garcia. c. th2e tihs: wazz worth the wait :)
Crew Gplus


--- layout: post title: So You Want to Hire an Intern? date: '2011-11-01T10:00:00-05:00' tags: - intern - gowalla - hire - mentor permalink: /post/12196285463/so-you-want-to-hire-an-intern ---
This is a re-post of an article I wrote for Gowalla’s Engineering Blog. You can view the original article here.
This past summer, Gowalla brought on its second intern, Michael May, a Computer Science undergrad from the University of Texas. In turn, Michael brought us a great amount of enthusiasm and ability.
I had the pleasure of mentoring Michael over his internship. It led me to reflect on my days of interning when I was still at Georgia Tech. Having seen both sides of the intern/employer relationship, I’d like to clear up some common misconceptions about interns, and offer suggestions on how to keep the relationship fruitful and symbiotic.
I hear this one all the time from developers — Instead of hiring one employee, they’ll hire three interns: instant three-fold increase in output. Sadly, The Mythical Man-Monthapplies at least as much for interns as for employees.
Interns are new employees. Like any new employee, they’ll need a certain amount of ramping-up time before they can start contributing. Others from the team will have to invest time to get them up to speed. And unlike a full-time employee, you lose all that training time when they leave to go back to school. They may be “cheap” on the pocketbook, but their cost goes far deeper than that. The good news: it forces your organization to get good at bringing people up to speed, and to put the right processes and tools in place to do so — an asset for a quickly-growing company.
In my first internship, I didn’t have to make coffee, but I did have to make thousands of manual z-folds for brochures we sent out. I also had to wash the bosses’ grill, and even disassemble and fix a toilet. This might have been great for the company I was working for, but it wasn’t exactly the work experience I would have liked to put on my post-college résumé.
Interns offer the chance to hire someone fresh out of college who was trained on the cheap and already knows your organization inside and out. If you insist on using your interns for nothing but cheap, mindless labor, you won’t find them knocking down your door after graduation.  But even if they’re willing to come back to work for you, how have those chores prepared them to be valuable contributors?
Give your interns the tools and resources they need to do their jobs — not just a desk and an internet connection, but also direction and feedback. The more you treat them like real employees, the more you’ll get back. If your employees aren’t learning from their mistakes, and celebrating their successes, how can the company know if it’s headed in the right direction? Be forward with expectations, and be vocal when those expectations aren’t met, or when they’ve been surpassed. This isn’t easy, but it has to be done.
When I worked for a major American appliance manufacturer, I played Flash games for a whole week because no one could find me anything “I could do.”
I’ve seen interns do incredible things. Once he was comfortable with the code and the team, Michael started knocking out features and closing quite a few GitHub issues. Just like full-time employees, interns want to do work that impresses and amazes. Just like full-time employees, interns work best when they can see how the work they’re doing affects the big picture. Pair them up with different members of your team and come up with a number of different projects your intern can work on. Don’t underestimate their abilities.
Interns take a lot of time and energy, but they can be incredibly rewarding to you and your company. If you want to hire one, make sure they have a dedicated mentor, someone who can and should regularly ask: “Do you have enough to do?”, “Is there anything you need?”, “What are you working on?” — and simply, “Are you happy?”. The mentor should have informal meetings with the intern on a regular basis — at least once a week, but perhaps as often as once a day.
You will also need several people interested in spec-ing out tasks for your junior employee — not just for the initial assignment, but to answer the occasional question during the task. And you’ll need employees to do pair programming with the intern until they’re familiar enough with your codebase to take on tasks by themselves. When your intern is busy, your intern is happy. And so is your company.


Mushrooms (P. cubensis)
My 1st encounter with psychedelia was not drug induced, it was lucid dream (i classify it in psychedelism because of its non-normal environnement). But, i did not knew how to define psychedelia until i took shrooms. I've done other drugs before, like weed, ether, alcool, xtc (MDMA isn't a psychedelic for me). I've done mushrooms a few times before this, but never feel any effects (as my other friends was feeling them).
Liège.Belgium.End of June. I'm 18. This was the end of my exam sessions and i planned on scoring some shroomz with some of my mates. So, after the exam, we went to Maastricht, Netherlands (which is near Liège, Belgium).
Because we bought several amounts of shrooms and were friendly, the nice merchant give us some additional mexican shrooms. We also bought some weed (White Widow as i remember, but i'm not sure).
When we came back in Liège, we changed our minds and decided to separate us (boys with boys and girls with girls). That evening i was sleeping at T's house, and his parents were out.  They kicked quickly and i started to feel VERY light, and it began to grow in intensity, then i get really scared because i though i wasn't in reality and everything i lived in my life has been an illusion, so i really freaked out. T tried to calm me down but i didn't want to believe him because he wasn't real. After a while, i entered in a good trip and all really became fantastic. I think the quick mental-state modification was the real cause of that panic attack.
I saw everything like if i was wearing some alien 3D glasses with a wide angle. Everything was living. I remember staring at the flame of a candle and it multiplied backward, and as i stared more and more precisely at the end of the 'copies', i realize that these copies, from a certain angle, was forming a kind of 'night street scene'...pretty weird...
I became also aware that the notion of time was obsolete.
And, when i closed my eyes, i could see some dancing patterns, alterning or morphing with strange images. Music was essential, because it defined the color of the trip. I thought music was 'tasting' like sugar, but later after the trip i remember we ate shrooms with some sugar (due to the taste).
This was a very intense trip and a big one for a real 1st trip.
I also had weird conversations with T, for example, i looked at him and saw that something was wrong in his face and i said him something like 'i know what's happening to you, don't worry, all it's fine'. Or we talked about brain structure. I really liked the experience and i've done a couple of other ones, now; every experience is different and mind expanding/glowing. I had other bad trips but i think they are very useful in the understanding of ourselves. I've also tried LSD and i strangely never had any bad trips with it.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Weather Modeling¶
-
delphin_6_automation.delphin_setup.damage_models.
algae
(relative_humidity_list: List[float], temperature_list: List[float], material={})[source]¶ Implement UNIVPM Algae Model Currently a dummy function!
:param relative_humidity_list :param temperature_list :param material: dictionary with relevant properties and their values
Return growth: list with eval. values
-
delphin_6_automation.delphin_setup.damage_models.
frost_curves
(temperature: List[float])[source]¶ Outputs Frost curves
Source: John Grünewalds Frost Model
-
delphin_6_automation.delphin_setup.damage_models.
frost_risk
(relative_humidity: numpy.core.multiarray.array, temperature: numpy.core.multiarray.array) → numpy.core.multiarray.array[source]¶ Evaluates frost risk
Source: John Grünewalds Frost Model
Returns: a list Containing 0’s and 1’s. Where a 0 indicates no frost risk and an 1 indicate frost risk
-
delphin_6_automation.delphin_setup.damage_models.
mould_index
(relative_humidity: List[float], temperature: List[float], draw_back: int, sensitivity_class: int, surface_quality: int) → List[float][source]¶ Computes a time series of the mould index
Source: T. Ojanen, H. Viitanen Mold growth modeling of building structures using sensitivity classes of materials 2010
-
delphin_6_automation.delphin_setup.damage_models.
mould_pj
(relative_humidity: List[float], temperature: List[float], aed_group='b') → Tuple[Iterable[T_co], Iterable[T_co]][source]¶ Outputs RH difference between measured and critical RH for each time step and two limits. Positive values imply how much RH exceed the critical RH. A low and upper critical RH are applied.
Literature:
-
delphin_6_automation.delphin_setup.damage_models.
u_value
(heat_loss: Union[numpy.ndarray, list], exterior_temperature: Union[numpy.ndarray, list], interior_temperature: Union[numpy.ndarray, list], area=0.68) → float[source]¶ Calculates the mean U-value given the outdoor and indoor temperature and the heat loss
-
delphin_6_automation.delphin_setup.damage_models.
wood_rot
(relative_humidity_list: List[float], temperature_list: List[float]) → Tuple[list, list][source]¶ Calculates the mass loss of wood due to rot
Source: Viitanen, H., Toratti, T., Makkonen, L. et al. Towards modelling of decay risk of wooden materials Eur. J. Wood Prod. 2010


--- title: "v1.26.0 - 2022-06-22" linkTitle: "v1.26.0 - 2022-06-22" weight: -106 ---
# of Merged PRs reviewed
# of words written in merged PRs
# of Review Comments in merged PRs
# of Pull Requests Merged
Lines of code (delta)
Average PR size (added+changed)
# of comments
# of words (excludes authored)
# of issues closed (excludes authored)


Across: 1. Unafraid, emit luminosity in a new direction, somewhat sensible in several senses (10) 6. Work produced soup (4) 9. Subtle cooperation seen in criminal's thuggery (10) 10. 2 of 9 (4) 12. 3 cunningly has the numbers (12) 15. Fashionable little fellow, we hear, always right with the times (9) 17. Clean your head - it's filthy! (5) 18. "'Morning all!" returned the beast (5) 19. Distract teen upset at trainwreck (9) 20.  Keen 22 returns (4) 25. Sore coming from work? This needs to improve! (10) 26. Takes in or takes off without the King (4) 27. Cleans up with income of gambling (10) Down: 1.  Pound and catch (4) 3. 12 in disarray took off (12) 4. Did 20, for instance, partly to hamper reductions (5) 5. Ruling, and also corrupt in the role (9) 7. One in capitol building - vote is a meter of his success (10) 8. Meeting Saturday is flying without starter plate (10) 11. Detailed accounts of adventurous mountaineers (12) 13. Make better me? Rationale needs to be devised without any heart (10) 14. In retro flats, I get Art students who might 10 (10) 16. A 9 might 10 this of some 5 7 with an extra run not in the extras (9) 21. Creature with some confusion about egg (5) 22. Star 24 returns (4) 23. Some 2 is left cut off (4)


Barrister Barnett Scott Scott & Associates London-England<EMAIL_ADDRESS>I am sorry for contacting you through this medium without a previous notice, I had to use the E-mail because it is an easier and more confidential way of making contact with people around the world. My name is Barrister Barnett Scott,a forty five (45) year old Attorney in practice here in London-England. I had a client by name Mr.Victor a contractor based in Sri- lanka/Indonesia now deceased. My client lost his life alongside his wife and only child in aceh sri-lanka. Details of this incidence can be checked on the following website;http://news.bbc.co.uk/2/hi/south_asia/4143487.stm prior to the death of my late client, He secured a contract of $12,500,000.00 million from the sri-lanka/Indonesia government, following which he received a 30%mobilization fee of US$3,750,000.00 Million and successfully executed the contract, but the balance of US$8,750,000.00 Million contract payment was in the Process of being transferred into my late client's account which he submitted to the government when he lost his life in the incidence. Few days after his sudden Death,The government of Indonesia effected the transfer of the balance of his Contract Funds into the account that my late client has on his file with the government. As his personal Attorney before his death,I have been officially notified and instructed by the bank here in U.K where the money is currently deposited that I should provide and forward the particulars of Mr.Victor's next of kin so that the Funds in his account can be remitted into his Next of Kin's account in accordance with British Laws. However, as the personal attorney and close confidant of late Mr.Victor, I want you to know that my late client died interstate, i.e, He died without leaving a Will.Hence,all my efforts to locate any of his relative whom I can present to the bank has proved abortive. Therefore I am seeking for your consent to present you as the deceased relative/next of kin and subsequently the beneficiary of the fund so that the proceed of this account valued at US$12,500,000.00 Million can be paid to you for subsequent disbursement between you and I. If you can cooperate with me and receive this funds as next of kin to Mr.Victor, confirm your interest by contacting me through my E-mail:-<EMAIL_ADDRESS>I can assure you that the deal is 100% risk free because i am in possesion of the deceased personal file which contains all information which I shall use to prove your relationship with the deceased therefore the money will be paid into the account of whomever I present as Mr.Victor's Next of kin with proofs that I will present to the bank when needed. When I receive a positive response and after proper discussion with you, i will furnish you all relevant information that will facilitate the release of the Funds to you. An Application will have to be filed to the Bank for processing of the release of the funds into your nominated bank account. I wait to hear from you. Barrister Barnett Scott. Alternative E-mail: barnett_scott@web2mail.com


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Nim is game in which two players take turns removing objects from distinct rows. On each turn, a player must remove at least one object, and may remove any number of objects provided they all come from the same row. The goal of the game is to take the last object.


Frequently Asked Questions
General
How is ATT&CK used for evaluations?
The evaluations use adversary emulation, which is a way of testing "in the style of" a specific adversary. This allows us to select a relevant subset of ATT&CK techniques to test. To generate our emulation plans, we use public threat intel reporting, map it to ATT&CK, and then determine a way to replicate the behaviors.
How do these evaluations relate to ATT&CK?
ATT&CK evaluations are built on the publicly-available information captured by ATT&CK, but they are separate from the ongoing work to maintain the ATT&CK knowledge base. The team who maintains ATT&CK will continue to accept contributions from anyone in the community. The ATT&CK knowledge base will remain free and open to everyone, and vendor participation in the evaluations has no influence on that process.
What can I do to help?
- Feedback. We are always looking for feedback on what works and what doesn’t in our results and methodology. Learning how you use the results and what you want to get out of them helps us shape our work to help you, and your peers. Provide feedback.
- Intel. We frame our evaluations in the context of the known threat to ensure our results are relevant and useful. These emulations are driven by available intel. If you share your insights, you can improve our plans. We welcome ideas for adversaries to emulate, as well as intel to help inform potential usage in our evaluation rounds
Participation
Where can I find information on when rounds were executed, who participated, etc.?
Each evaluation type (e.g., Enterprise or ICS) and round (e.g., APT3 or TRITON) has available information specific to those evaluations on this site. An overview page is available for each round to provide basic high level information, including the participants. For example, APT29's overview page is available here.
How does participation relate across rounds?
Each round's participation is independent.
Who can participate?
Participation is focused on capability type (e.g., detection of ATT&CK behaviors) rather than market segment (e.g., EDR, EPP, etc.). If a technology addresses the capability, there may be additional technical requirements. For example, Enterprise evaluations have the following requirements:
- Technology must address post-compromise behaviors as described by ATT&CK
- Technology must deploy into the Microsoft Azure environment
- Sensor/data, beyond those provided by default in Azure, must be provided by the vendor
- For detection evaluations, capabilities that could prevent the successful execution of our emulation (e.g., protections, preventions, responses) must be disabled. The sensors that drive these actions can still be used as data sources to identify behavior
How can I participate?
Visit the Get Evaluated section for more details on how to participate.
What do vendors get out of this?
Vendors get a third-party evaluation of their capabilities ability to address adversary behaviors, as described by ATT&CK. These evaluations are not ATT&CK certifications, nor are they a guarantee that you are protected against the adversary we are emulating. Adversary behavior changes over time. The evaluations provide vendors with insight and confidence into how their capabilities map to ATT&CK techniques. Equally important, because we are publicly releasing the results, we enable their customers, and potential customers, to understand how to utilize their tools to defend ATT&CK-categorized behaviors.
Are vendors paying to perform evaluations?
Yes. There was significant demand for unbiased ATT&CK evaluations and we needed to create a mechanism to open up evaluations to the security vendor market. Participating companies understand that all results will be publicly released, which is true to our mission of providing objective insight.
What information was available to participants?
All vendors receive the same information prior to evaluations. The majority of this information also gets posted on this site to ensure transparency. Vendors receive the scope of the evaluation (e.g., techniques that could be used), a named threat for whom the behaviors are being replicated, environment information, and a general idea of what data will be collected. Details on how the techniques will be implemented are not provided.
In the APT3 evaluation, what is the difference between Initial Cohort and Rolling Admissions?
Rolling admissions participants had access to additional information during the evaluation process due to the launch of the website, which contained the initial cohort’s results and the methodology.
Cybereason’s and FireEye’s feedback period occurred after the launch of the ATT&CK Evaluations website. F-Secure, McAfee, and Palo Alto Networks entire evaluation process took place after the ATT&CK Evaluations website was launched.
What can I do if I can’t participate?
Let us know your needs, and the current limitations of our methodology. This will help us shape our evaluation road map.
Can a vendor opt out of releasing their results?
No, all vendors signing up for the evaluation agree to have their results publicly released upon conclusion of their test.
Is there a private testing option?
Publicly released evaluations are the only vendor-paid evaluation provided at this time.
Process
How does the evaluation process work?
The ATT&CK evaluations are based on a four-phased approach:
- Setup: The vendor installs their tool in a MITRE Engenuity provided cyber range.
- Evaluation: During a joint evaluation session, MITRE Engenuity adversary emulators ("red team") execute an emulation in the style of an adversary group, technique-by-technique. The vendor being tested will provide the personnel who review tool output ("blue team"). MITRE Engenuity provides the personnel to oversee the evaluation and facilitate communication between red and blue, as well as capture results ("white team").
- Feedback: Vendors are provided an opportunity to offer feedback on the preliminary results, but the feedback does not obligate MITRE Engenuity to make any modification to the results.
- Release: MITRE Engenuity publicly releases the evaluation methodology and results of the tool evaluations.
Do you use the ATT&CK matrix stoplight chart?
The stoplight chart (which uses red, yellow, and green to indicate level of confidence for detection of techniques) has been used since ATT&CK's creation because it is a simple yet powerful way to understand ATT&CK coverage. While a stoplight chart may be useful to show coverage and gaps, we do not use this visualization because it is not granular enough to convey our results.
How are you addressing false positives?
While we understand the importance of minimizing false positives, they are often tied to environment noise which is specific to an organization. To enable you to evaluate the results applicability in your contexts, we address false positives indirectly in the following ways:
- Vendors are required to define how they configured their capabilities. With that provided configuration and the evaluation's results as a baseline, users can then customize detections to reduce false positives in their unique environment.
- We articulate how the tool can perform detection. By releasing how to detect, as well as our methodology, organizations can implement their own tests to determine how the tools operate in their specific environment.
Does this mean that all detections are created equal?
There is no one way to look at detection data. Some key concepts to consider include:
- Availability — Is the detection capability gathering the necessary data?
- Efficacy — Can the gathered data be processed into meaningful information?
- Actionability — Is the provided information sufficient to act on?


Ukraine successfully completes development of Neptun anti-ship missile complex (Video)
The complex was developed by Ukraine's Luch Design Bureau.
The development of the R-360 anti-ship missile of the Ukrainian-made RK-360MTs "Neptun" ("Neptune") complex is considered to be completed after its successful flight testing on November 28.
"For the first time, it flew over 250 km along a designated route and altitudes. That is, we can say that today the development of the system with the required characteristics has been completed," General Designer of the Luch Design Bureau Oleh Korostelyov said, according to the Ukrainian edition Defense Express.
The next stages of the testing will be needed to examine the combat characteristics of the missile. The R-360 missile weighs 870 kg; the weight of its warhead is 150 kg; its launch range is up to 280 km and speed is about 900 km/h. It is able to get at a height of from 3 m to 10 m above the surface.
The complex can simultaneously launch up to 24 missiles, i.e. a full salvo of 6 launchers, with an interval of launches in a salvo being from 3 to 5 seconds. The complex can be located at a distance of up to 25 km from the coastline, and its deployment lasts up to 15 minutes.


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WHAT IS SCOLIOSIS?
Scoliosis is a condition that affects the neuromuscular system,
causing the spine to bend in a C-shaped curve or S-shaped curve. About 80 percent of casesare adolescent idiopathic scoliosis
(AIS), which means of unknown cause.
AIS affects 2-3 percent of children. It's most
common in girls age 10 and older, yet boys get AIS, as well. Your child may not have obvious signs or pain if
scoliosis is detected in its early stages.
HOW HAS SCOLIOSIS BEEN TREATED?
Observation, bracing and surgery are the
three most common treatments for scoliosis. Often, doctors will recommend monitoring curves that measure less than 25 degrees, scoliosis braces when curves reach 25 degrees,
and surgery if curves progress to 40 degrees or greater. It is not the intention of ScoliSMART™
doctors to condemn the efforts of sincere and caring medical professionals who have dedicated their
lives to helping people with scoliosis, but we cannot
support any of these historic treatments because they all have poor outcomes.
Observing a mild curve simply allows time for the curve to worsen. The wait and watch period of six months or a year is
often stressful on you and your child because you want to be proactive. We do, too.
We opt to start non-invasive scoliosis. Auto Response Training exercises as soon as possible.
Bracing holds your child's spine straight
during the 18-23 hours a day it's worn. This rigid treatment, however, causes muscles to atrophy and joints to be still and sore. Scoliosis braces often cause pain and breathing difficulty in children who
were pain-free and breathing perfectly fine before
using one. Additionally, bracing doesn't stop scoliosis progression. Often,
a patient's scoliosis curve quickly
gets drastically worse when the brace treatment ends.
Spinal fusion surgery is the treatment we disagree with most. It's invasive, has severe complications, and causes disability in 40 percent of people within two decades, according to medical studies. It's an especially poor choice for teens whose spines are not yet developed.
RECOVERY
Pain Management — Just after scoliosis surgery, most patients are given PCA
(patient-controlled analgesia). PCA is a pump that delivers
morphine or other narcotic at the press of a button by the patient.
This controls pain very well for the first two to three days after surgery. Thereafter, PCA is stopped
and oral pain medication is administered. At hospital discharge, a prescription for pain medication is provided.
Adults may require medication at diminishing doses for weeks or months. Children are usually off medication within two weeks.
Drains — A drain prevents fluid accumulation at the incision site and is routine.
Most patients have a drain in their back or side
for two to three days after surgery. Drains in the side of the chest are termed 'chest tubes'
and prevent air and fluid from accumulating around the lungs. Drains are removed when fluid drainage is small.
Drain removal is not painful. A urinary catheter (Foley) helps to keep the patient
comfortable. It may be removed two or three days
after surgery.
Walking — Physical therapists and nurses help
the patient out of bed on the first or second day after surgery. Walking is guided by the physical therapist and is increased daily as tolerated.
Some patients may need a walker or cane early during recovery to help with balance and prevent a fall.
keep their balance. Most children do not need walking aids at home.


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