If a standard table is in front of the unit, only the top 5.5 feet are visible.
This unit takes about 15 minutes to put up and 8 minutes to take down. The included instructions are fairly simple to follow. Or just watch this 8.5 minute video of Lisa Majkowski assembling the unit.


text:
Cali Love Music & Arts Festival returns to 10 Barrel Brewing Co. rooftop
Cali Love Music & Arts Festival will be livestreamed from the rooftop of 10 Barrel Brewing Co. in East Village
(Courtesy of 10 Barrel Brewing Co.)
Phantogram, Mating Ritual and Balto to perform during livestreamed show in East Village on Oct. 24
By Sara ButlerStaff Writer
Festival season didn't go as planned in 2020, but the pandemic isn't stopping Cali Love Music & Arts Festival from coming back for a second year.
The festival, which held its inaugural event last year, is returning this month to a virtual stage. Presented by 10 Barrel Brewing Co. and Pitchfork, the livestreamed show will be held on the rooftop of 10 Barrel's East Village brewpub on Oct. 24.
Due to COVID-19 guidelines, the second festival will be a pared-down version of 2019's eight-hour edition that offered nine acts spread out on three stages, along with local artists and vendors.
This year, three music acts will play in a three-hour window. Alternative music duo Phantogram headlines the festival, which kicks off at 5 p.m. (Fun fact: Phantogram's Sarah Barthel dated San Diego native and Olympic gold medalist snowboarder Shaun White for five years; however, the couple split up in 2019.)
The inaugural Cali Love Music & Arts Festival, held at 10 Barrel Brewing Co. in 2019, featured nine music acts on three stages (Courtesy of 10 Barrel Brewing Co.)
Two opening acts — indie-pop duo Mating Ritual and alternative rock band Balto — will also perform, with commentator Selema Masekela serving as the evening's host.
The show is free to (virtually) attend. Festival-goers can watch the livestream on Pitchfork's website and YouTube channel. For San Diegans missing that in-person festival feel, Cali Love Take Home Kits are available to purchase.
Each festival package consists of a four-pack of 10 Barrel Brewing Co.'s Cali Love IPA, along with a pint glass, t-shirt, face mask, sticker sheet and koozie. A Phantogram themed print, designed by San Diego-based artist Nicholas Danger, will also be included.
Josh Carter, left, and Sarah Barthel of Phantogram
(Timothy Saccenti)
The limited-edition kits are $30; a portion of proceeds will be donated to the American Foundation for Suicide Prevention. They are currently available to buy online shop.10barrel.com/cali-love-festival.html and must be picked up at the East Village brewery between 11 a.m. and 10 p.m. from Oct. 17 to Oct. 24. (Kits can be purchased in person at 10 Barrel Brewing starting Oct. 17.)
For details about the event or to buy a festival kit, visit 10barrel.com/event/cali-love-festival-livestream-with-phantogram.
MusicEventsThings To DoCraft Beer
Sara has always been two things: a writer and a San Diegan.
From clubs and theaters to drive-in parking lot gigs: San Diego's best concerts (pre- and post-pandemic)
Switchfoot, Charles McPherson and the Mainly Mozart Festival Orchestra did memorable drive-in concerts, while Lyle Lovett, Michael Kiwanuka and Yolo were among top indoor performers
Eight songs I played on endless repeat to deal with 2020
Luckily, the worst year gifted us some pretty good music

redpajama_set_name: RedPajamaCommonCrawl


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A great concept, and it delivers!
Coming back in 2019…stay tuned for details!
We are in the process of planning the next ENT Update Europe – stay tuned for details!
At the event, you will receive a complete update of all relevant developments in otorhinolaryngology within 2.5 days. The most relevant clinical papers of the past year will be reviewed and put into perspective, and today's burning issues will be addressed by leading experts in their fields.
Our scientific committee is working on putting together an attractive and relevant scientific program. Both clinicians and practitioners will profit immensely from the lectures, and will improve the quality of their daily work. Interact with colleagues and speakers, get update across the ENT board, and take home a complete print compendium.


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Ho ripreso il mio PL 2017 ed un'altra settimana è nel mio album con tutti i suoi racconti e ne sono felice! L'approccio di quest'anno, lo sapete, è stato molto minimal, uso tantissimo cards digitali che modifico e stampo, abbellimenti qua e là, molti stickers, alfabeto e soprattutto frasi che raccontino la foto e poi scrivo cascate di parole perché amo a dismisura raccontare storie che immortalo attraverso le foto e che resteranno per sempre li! Non riesco ad immaginare di trascorrere il tempo senza fare fotografie! Impossibile! Non rimarrebbe nulla del tempo passato se non i ricordi, no, non riuscirei mancherebbero dei prezzi troppo importanti!
I have created something new for my PL 2017 and another week is in my album with all its stories and I'm happy! This year's approach, you know, was very minimal, I use a lot of digital cards that I usually modify and create, embellishments here and there, many stickers, alphabet and especially phrases that tell something more about the picture and then I write waterfalls because I love it is too much, I very like to tell stories that will be immortalized through the photos forever! I can not imagine spending time without taking photographs! Impossible! There would be nothing left in the past unless the memories, no, I could not have too much pricing!
Oh great, stop writing I want to show you this amazing new collection from Paislee Press at the THE Lilypad shop, I was really in love with it!
Qui ho aggiunto diversi stickers in paroline e piccole frasi ed alcuni fiorellini, brads, bottoni ed un cuoricino in legno, tutto preso dai documenter kit di Studio Calico. Ho infine usato la macchina da cucire, una delle mie fisse, non chiedetemi perché ma i dettagli cuciti mi fanno impazzire!
Here I have added several stickers in small words and small phrases, and some flowers, brads, buttons and a wooden heart, all taken from Studio Calico's documenter kits. I finally used the sewing machine, one of my obsessions, do not ask me why but the sewn details make me crazy!
Alla seconda pagina ho giusto aggiunto piccolissimi dettagli, ho voluto usare il Page protector con le 9 taschine e mi piaceva l'idea di usare una foto 10x15 cm tagliata in due. Le foto sono quasi tutte con bordino bianco, il template di Paislee Press è perfetto! Ma quanto mi piace il Project Life? Il fatto che mi permetta di raccontare tutti i giorni della nostra vita? Quelli si e quelli no, eppure anche quando magari arriva un periodo che penso sia nero vado a riguardare il mio cellulare, la mia macchina fotografica e c'è comunque qualcosa di bello sempre!
On the second page I just added tiny details, I wanted to use the Page protector with the 9 pockets and I liked the idea of using a 4x6 in cut photo in two. The photos are almost all white with a white border, Paislee Press's template is perfect! But how much do I like Project Life? The fact that it lets tell all the days of our lives? Those are ok and not, and even when maybe there is a black period I go through my photos, my camera and there is still something nice ever!
Che ve ne pare? Spero di riuscire a fare un bel video sul mio Project Life spread process in modo da farvi vedere passo dopo passo come mischio elementi digitali e tradizionali dello scrapbooking. Questo mondo io lo adoro alla follia! E' proprio il mio mondo!
Beh la strada per terminare e recuperare il racconto di molte settimane è lunga ma è bello proprio il fatto che io possa scrappare ancora tantissime storie.
What do you think? I hope I can make a nice video on my Project Life spread process so you can see step by step how scrapbooking digital and traditional items are together. I love this world of madness! It's just my world!
Well the way to end and recover the story of many weeks is long but it's nice that I can still scrap lots of stories.


text:
I'll admit I've been very torn over Mike Leigh's Happy-Go-Lucky ever since I saw it nearly two months ago. It's hard to deny that there is something light and refreshing about Leigh's deft direction and positive outlook, but there's also something ingratiating and a little off-putting.
Some (i.e. Jeffrey Wells), have attacked the film for its "emotional fascism," but I think that is an oversimplification, albeit not too far off the mark. In a way it describes why the film bothered me, but as Mike Leigh pointed out, misses the point.
The film centers around Poppy (Sally Hawkins), a bubbly, perpetually sunny kindergarten teacher who goes through life with a indefatigably positive outlook. Her happiness is so strong and so infectious that it effects everyone around her - but not always for the better. As she bounces through life, she encounters people whose outlooks swing in the complete opposite direction, including her perpetually miserable, sternly religious, and virulently racist driving instructor, Scott (Eddie Marsan), whose deep seeded anger is brought to the boiling point by his dealings with Poppy, who has finally met the one person who mere happiness cannot cure.
To be honest, for most of the running time I was annoyed by Poppy. I found her childish, irresponsible, and reckless, the kind of person who would drive me crazy in real life. She doesn't seem to take anything seriously, and treats every serious statement with a cute, sing-songy retort. She almost doesn't seem to be living in the real world, and Hawkins seems to play her as the one note smile machine she comes across as.
But then Leigh does something unexpected and welcomed...he gives Poppy a human side, a sense that there is more to her than her sunny exterior, a sadness beneath the surface, and at long last we see Hawkins' performance for what it is - a remarkable hat trick. Her performance seems one dimensional because Poppy appears one dimensional at first glance, and that how many (Scott most of all) perceive her. But Poppy's positivity stems from a true desire to brighten the days of those around her, to help in any way she can. She is in some ways, as Leigh rightly points out, an extension of the central character in Leigh's last film, Vera Drake. The tones are vastly disparate, but both characters share an almost obsessive need to help anyone in any way possible, and in both instances, they find that their "help" can come back to haunt them.
Unlike Vera Drake, however, the tone of Happy-Go-Lucky is ultimately a positive one. Leigh's confident direction and the mostly improvised performances of the cast (a Leigh trademark) offer a breezy and charming experience. It may seem maddening at first, and indeed I found myself hating Poppy on more than one occasion, but it eventually settles into a groove and once you get used to it, it becomes much more tolerable. Leigh is neither blanketly endorsing or condemning his protagonists actions, nor is he making a statement as simple as some, myself included, may have initially thought. Happiness for happiness sake is fruitless and ultimately as empty as Scott's self-made miserablism.
There is a lot more going on here than initially meets the eye, even more than I initially gave it credit for. It is not Leigh's best work - it seems almost inconsequential when compared with his masterful Vera Drake - but you have to respect what he has achieved here. Happy-Go-Lucky has an almost wistful charm to it, a kind of bated optimism in a cold and cynical world. It is a film that, while most certainly flawed, can't help but bring a smile to the face, and maybe, just maybe, a little something more.
GRADE - *** (out of four)
HAPPY-GO-LUCKY; Directed by Mike Leigh; Stars Sally Hawkins, Alexis Zegerman, Andrea Riseborough, Samuel Roukin, Sinead Matthews, Eddie Marsan; Rated R for language; Opens tomorrow, October 10, in select cities.
Well, I am very glad that we are actually on the same wavelength in terms of this film, even if I like it more that doesn't really matter, right now, I'm happy :)
I also was rather annoyed by Poppy, but all in all I liked this a little bit more than you did, and found it a refreshing change of pace for Mr. Leigh, whose VERA DRAKE as you rightly imply was an intense experience.
Ironically, I saw the film on a U.K. DVD that was sent to me (and arrived) yesterday, so I watched it last night! LOL.
You really did a great job with this review, it's terrifically comprehensive and an excellent piece of writing.


text:
'use strict';

/**
 * @module chlg-release
 */

var fs     = require('fs');
var os     = require('os');
var path   = require('path');

var async  = require('async');
var semver = require('semver');

var StreamInsert = require('stream-insert');

var chlgShow  = require('./chlg-show');
var dates     = require('./common/dates');
var defaults  = require('./common/defaults');
var formatter = require('./format/raw');

var search = /## \[Unreleased\]/;

var validators = {
  file: function (file, callback) {
    return callback(null, file);
  },
  date: function (input, callback) {
    var date = (typeof input === 'string') ? dates.parse(input) : new Date();

    if (typeof input === 'string' && date === null) {
      return callback(new Error('Date format must be YYYY-MM-DD'));
    }

    if (date !== null && isNaN(date.getTime())) {
      return callback(new Error('Invalid date: ' + input));
    }

    return callback(null, date);
  }
};

/**
 * Gets the next release for the specified change log.
 * If a release number is passed, checks if the number is superior to the last release in the file.
 * If an increment is passed, returns the release number incremented from the last release in the file.
 * It also checks if the release date is superior to the last release date in the file.
 *
 * @param  {string}   release  - Release number or increment (major, minor or patch)
 * @param  {Date}     date     - Release date
 * @param  {string}   file     - Change log file name/path
 * @param  {Function} callback - Callback invoked with an optional error and and the next release number
 */
function getNextRelease(release, date, file, callback) {
  var increment = (['major', 'minor', 'patch'].indexOf(release) !== -1);

  if (!increment && semver.valid(release) === null) {
    return callback(new Error(''' + release + '' is not valid semver version'));
  }

  chlgShow({file: file, releases: ['all']}, function (error, logs) {
    if (error) {
      return callback(error);
    }

    var releases = Object.keys(logs);

    if (releases.length > 1) {
      var latest = releases[1];

      if (logs[latest].date.getTime() >= date.getTime()) {
        return callback(new Error('The release date is prior to the last (' + dates.format(logs[latest].date) + ')'));
      }

      if (increment) {
        return callback(null, semver.inc(latest, release));
      }

      if (!semver.gt(release, latest)) {
        return callback(new Error('Last release (' + latest + ') is greater than ' + release));
      }
    } else if (increment) {
      return callback(null, semver.inc('0.0.0', release));
    }

    return callback(null, release);
  });
}

/**
 * @typedef  {object} Options
 * @property {string} [file=CHANGELOG.md] - The change log file name/path.
 * @property {string} [date=now]          - Force a specific release date, default to the current date.
 */

/**
 * Add a new release to the change log containing all current messages.
 *
 * @param  {string}   release   - Release number (semver version) or increment (major, minor or patch)
 * @param  {Options}  [options] - Options
 * @param  {Function} callback  - Callback invoked with an optional error when the release has been added in change log
 */
module.exports = function chlgRelease(release, options, callback) {
  if (typeof options === 'function') {
    callback = options;
    options = {};
  }

  async.parallel({
    file: async.apply(validators.file, options.file || defaults.file),
    date: async.apply(validators.date, options.date)
  }, function (error, results) {
    if (error) {
      return callback(error);
    }

    getNextRelease(release, results.date, results.file, function (error, nextRelease) {
      if (error) {
        return callback(error);
      }

      var tmp    = path.join(os.tmpdir(), 'chlg-' + Date.now() + '.md');
      var insert = '\n' + formatter.release(nextRelease, results.date);

      var stream = fs.createReadStream(results.file, {encoding: 'utf8'})
        .on('error', callback)
        .pipe(new StreamInsert(insert, search))
        .on('error', callback)
        .pipe(fs.createWriteStream(tmp))
        .on('error', callback);

      stream.on('finish', function () {
        fs.createReadStream(tmp, {encoding: 'utf8'}).pipe(fs.createWriteStream(results.file)).on('finish', function () {
          fs.unlink(tmp, callback);
        }).on('error', callback);
      });
    });
  });
};

redpajama_set_name: RedPajamaGithub


text:
Carlos Girón (né le  à Mexicali et mort le  à Mexico) est un plongeur mexicain, spécialiste du tremplin de 3 m.

Biographie

Carlos Girón est médaillé d'argent aux Jeux olympiques d'été de 1980.

Notes et références

Liens externes

Plongeur mexicain
Médaillé d'argent olympique mexicain
Plongeur aux Jeux olympiques d'été de 1980
Naissance en novembre 1954
Naissance à Mexicali
Décès à 65 ans
Décès à Mexico
Mort d'une pneumonie
Décès en janvier 2020

redpajama_set_name: RedPajamaWikipedia


text:
Genos
Not to be confused with Geno's Steaks.
For the communes in France, see Génos (disambiguation).
In ancient Greece, a genos (Greek: γένος, "race, stock, kin",[1] plural γένη genē) was a social group claiming common descent, referred to by a single name (see also Sanskrit "Gana"). Most gene were composed of noble families—Herodotus uses the term to denote noble families—and much of early Greek politics seems to have involved struggles between gene. Gene are best attested in Athens, where writers from Herodotus to Aristotle dealt with them.[citation needed]
Early modern historians postulated that gene had been the basic organizational group of the Dorian and Ionian tribes that settled Greece during the Greek Dark Ages,[citation needed] but more recent scholarship has reached the conclusion that gene arose later as certain families staked a claim to noble lineage.[citation needed] In time, some, but not necessarily all, gene came to be associated with hereditary priestly functions.[citation needed]
Phratry
Phyle
^ γένος, Henry George Liddell, Robert Scott, A Greek-English Lexicon, on Perseus
Fine, John V.A. The Ancient Greeks: A Critical History. Harvard University Press, 1983. ISBN 0-674-03314-0
Hornblower, Simon, and Anthony Spawforth ed. The Oxford Classical Dictionary. Oxford University Press, 2003. ISBN 0-19-866172-X
Retrieved from "https://en.wikipedia.org/w/index.php?title=Genos&oldid=975385402"


Laois's returns are a bit of a curate's egg; they struggled to convert the possession they had into shots (60% shot rate was the lowest of the 8 quarter finalists – next lowest was Down on 75%) but when they did shoot their conversion rate was excellent (only Cork's 68% versus Kildare bettered them).
Not for the first time this year Dublin's shooting was below average. Their returns have been very inconsistent with both their Shot Rate & Success Rates varying from game to game. If they put it all together they will be hard to beat however the concern must be that we are in the middle of August and the complete game hasn't occurred yet.
To only have 8 shots from play in a QF is remarkable; Down managed 11 & Kildare managed 17 in their two big losses. Probably more remarkable is that in the 2nd half, when they were within sight of an All – Ireland semi final, they only had 6 shots on goal of which 1 was from play (Munnelly dropped it into Cluxton's hands at the start of the half). For all Laois's endeavour it was Dublin's wastefulness, and unnecessary fouling (why foul anywhere inside the 45m line against such a shot shy team?) that kept them in the game.
In the review of the Meath game I highlighted how the starting 5 forwards outside of Brogan have had average returns – that none of them were stepping forward to help him on a consistent basis. That's still the case. Flynn & Cluxton had their shooting boots on in this game but Flynn missed all three shots against Meath; Cluxton hit 3 from 6 deadballs in the 3 games prior to this.
Despite the fact that Brogan has been inconsistent, and the other forwards have not stepped up, Dublin are winning. They have scraped over the line in games where Brogan hasn't played. Winning ugly is the mark of a champion … but you have to feel that the better class of teams they will now meet will not be as lenient as their opponents have been to date.


text:
Heeeeeey!
COOKING WITH MS JUICY
Ms.Juicy
THE QUEEN OF ATLANTA, BOO!
Shirlene King Pearson, famously known as Ms. Juicy and the self-proclaimed Queen of Atlanta Boo, is "Sweet N Low" and packing plenty of power.
Born in Corsicana, Texas and barely standing 4 feet tall, Ms. Juicy is definitely not classified as the girl next door. With a wide range of ability, she is an exceptional commodity in the entertainment industry. The challenges of the world were not an obstacle, but more of an opportunity to achieve the goals she had set for herself. This was proem to make her an unstoppable force to be reckoned with. You can truly say that Ms. Juicy is a little lady with many talents.
Ms Juicy is currently a star on Lifetime's Little Women Atlanta for the 6th season.
Ms. Juicy launched her own successful clothing line which includes her trademark slogans and one-liners, her own bobblehead where you can hear her signature phrases and quick one-liners, as well as her own sunglasses line, Eminence Eyewear by Juicy.
Ms Juicy's latest acting chops have been seen on Sundance TV's Hap and Leonard Season 2 and TNT's Claws Season 2, 3, and she is currently filming Season 4.
Ms. Juicy is truly living her dreams!
Book ME >
KEEPING UP WITH THE JUICE
You are officially a #JBaby!
© 2020 The Real Ms. Juicy Baby


text:
from __future__ import unicode_literals

from copy import deepcopy

from django.contrib import admin
from django.utils.translation import ugettext_lazy as _

from mezzanine.blog.models import BlogPost, BlogCategory
from mezzanine.conf import settings
from mezzanine.core.admin import DisplayableAdmin, OwnableAdmin
from mezzanine.twitter.admin import TweetableAdminMixin

blogpost_fieldsets = deepcopy(DisplayableAdmin.fieldsets)
blogpost_fieldsets[0][1]["fields"].insert(1, "categories")
blogpost_fieldsets[0][1]["fields"].extend(["content", "allow_comments"])
blogpost_list_display = ["title", "user", "status", "admin_link"]
if settings.BLOG_USE_FEATURED_IMAGE:
    blogpost_fieldsets[0][1]["fields"].insert(-2, "featured_image")
    blogpost_list_display.insert(0, "admin_thumb")
blogpost_fieldsets = list(blogpost_fieldsets)
blogpost_fieldsets.insert(1, (_("Other posts"), {
    "classes": ("collapse-closed",),
    "fields": ("related_posts",)}))
blogpost_list_filter = deepcopy(DisplayableAdmin.list_filter) + ("categories",)

class BlogPostAdmin(TweetableAdminMixin, DisplayableAdmin, OwnableAdmin):
    """
    Admin class for blog posts.
    """

    fieldsets = blogpost_fieldsets
    list_display = blogpost_list_display
    list_filter = blogpost_list_filter
    filter_horizontal = ("categories", "related_posts",)

    def save_form(self, request, form, change):
        """
        Super class ordering is important here - user must get saved first.
        """
        OwnableAdmin.save_form(self, request, form, change)
        return DisplayableAdmin.save_form(self, request, form, change)

class BlogCategoryAdmin(admin.ModelAdmin):
    """
    Admin class for blog categories. Hides itself from the admin menu
    unless explicitly specified.
    """

    fieldsets = ((None, {"fields": ("title",)}),)

    def in_menu(self):
        """
        Hide from the admin menu unless explicitly set in ``ADMIN_MENU_ORDER``.
        """
        for (name, items) in settings.ADMIN_MENU_ORDER:
            if "blog.BlogCategory" in items:
                return True
        return False

admin.site.register(BlogPost, BlogPostAdmin)
admin.site.register(BlogCategory, BlogCategoryAdmin)


text:
Study the Past
Why We Have the Federal Education Law called the Elementary and Secondary Education Act (ESEA)
Now more than ever, people need to understand ESEA. Listen to one explanation I gave on the War Report. I stumbled a bit in getting the twin goals stated so I do highly recommend people read what JFK was thinking when it came to federal education law.
Just think back….
Racial and socioeconomic discrimination resulted in unequal distribution of quality schools. Local and state officials have never sufficiently addressed the issue on their own. In some cases, local and state officials actually caused the problem. The problem is unequal access to quality education. That is why the writing of ESEA happened.
The country saw the most significant narrowing of the "achievement" differences between white and "minority" students and the socioeconomic classes (the achievement gap) during the 70's and 80's.
To judge the original law by results now – 50 years later – is really unfair to the original law because so many changes have been made over time that No Child Left Behind (and now the Every Student Succeeds Act) no longer represents the goals, beliefs, values, and principles of the writers of the 1965 Elementary and Secondary Education Act (ESEA).
Then and Now: How We Came To This Crossroads
In 1965, the belief was that poverty-ridden communities were not offering the quality of education that other communities could afford. Fairness was to be established through the Elementary and Secondary Education Act (ESEA).
But by the 80's the outcome-based education reform theory was taking root in political circles. By the 90's, what became known as the standards movement was transformed to what we now call the standards, testing, and accountability movement. This outcome-based theory of "reform" swept through state legislatures and educational establishments (institutions, foundations & organizations) before becoming federalized through enactment of the No Child Left Behind (NCLB) Act – the 2001 "version" of the 1965 ESEA.
So now, the war on public education is more correctly seen as the standards, testing, accountability, and technology movement led by the education oligarchy.
Lawmakers are continuing on the path of outcome-based education. They don't seem to care that it has failed! They never considered the original community-based path.
Study the Past: Consider the Future
Consider this, some people would rather not have an alternative offered. I felt under attack during this interview. Listen to part 2 on ESEA on the War Report.
What is Past is Prologue
A difference of opinion or the way a lobbyist keeps his job? You decide.
Then, please listen to the last interview I did before the Every Child Achieves bill was turned into the Every Student Succeeds Act and passed into law.
The original 1965 ESEA (Elementary and Secondary Education Act) seems to be a forgotten and misunderstood piece of our past. Rediscover. And examine the 1966 review of the law by the National advisory Council on the Education of Disadvantaged Children.
Thank you for caring enough to learn more about what most consider a dry subject!
14 thoughts on "Federal Education Law"
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Independence Day: Why the Republic Requires Rebellion
"Money Follows the Student" is Voucher Funding
A Clear Plan: The Revolution in School Policy
How Laws are Created: Congress' Role in Destroying the Republic
The Graphic Truth About Our Education System
1965 Elementary and Secondary Education Act
A Nation at Risk
effective schools
ESEA
ESEA Reauthorization
Lamar Alexander
Marc Tucker
National Governors Association (NGA)
outcome-based education reform
President Kennedy
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test-based accountability
The Elementary and Secondary Education Act
The Every Child Achieves Act
William (Bill) Bennett
American Education Wars
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text:
Q: Trying to pass java member function as a var
If I run the following code in the REPL
(let [f '.startsWith] (f "abab" "a"))

it is evaluated to "a" instead of 'true'. Could someone please explain me this surprising result?
Actually, the real code, I want to make work is the following.
(defn set-up-bean! [bean functions-and-parameters]
  (doseq [[f p] functions-and-parameters]
    (f bean p))
  (.init bean))

What I want to achieve is, to make the following two function calls do the same thing.
(set-up-bean! bean [['.setMember "a"]])

and
(do
  (.setMember bean "a")
  (.init bean))

A: One conventional approach is to use an anonymous function
(let [f (fn [a b] (.startsWith ^String a ^String b))] (f "abab" "a"))

...as this lets you type-hint parameters as-needed. You might also consider memfn:
(let [f (memfn startsWith String)] (f "abab" "a"))

In any event -- dot notation is syntactical sugar for interop, rather than providing real callable functions.

redpajama_set_name: RedPajamaStackExchange


Erik, who was born and raised in Baltimore, ran personal bests in high school of 4:25 in the mile and 8:50 in the 3,000 meters, and in cross country he broke his school's course record that had stood for 30 years. At Duquesne University, Erik earned All Atlantic Ten Conference honors in 2010 and 2011 in cross country, and was ranked among the top 10 runners to have ever graduated from the program. Erik's goals with GRC are to set personal bests in all distances from the 5,000 to the marathon. Erik works as an instructional coordinator, a teacher, and a coach.


text:
import { Injectable } from '@angular/core';
import { Action, Selector, State, StateContext } from '@ngxs/store';
import { Help } from '../model/help.model';
import * as actionHelp from './help.action';

export class HelpStateModel {
    public last: Help;
}
@State<HelpStateModel>({
    name: 'help',
    defaults: {
        last: null,
    }
})
@Injectable()
export class HelpState {
    constructor() { }

    @Selector()
    static last(state: HelpStateModel) {
        return state.last;
    }

    @Action(actionHelp.AddHelp)
    add(ctx: StateContext<HelpStateModel>, action: actionHelp.AddHelp) {
        const state = ctx.getState();
        ctx.setState({
            ...state,
            last: action.payload
        });
    }
}


The exclusive magnetic mini leaflet is an ideal advertising magnet to be used as mini product catalogue, booklet with formulas or important graphics, company information, manuals etc. The mini leaflet is small and practical and the magnetic front and backside ensures that the pages are kept in place. Quality product for visible exposure of important information.
Ideal for the wallet, notice board etc.
Specialists in orders from 100.000 – 500.000 pcs.
Exclusive magnetic mini leaflet for the private company and the public institution. 20 pages to inform, inspire and motivate customers and employees. The magnetic mini leaflet is an excellent product to provide information to existing employees or to advise new customers of products or brands.
The size makes it possible to place the product in the wallet or on the notice board by means of a small piece of magnetic tape.
Watch video for the advertising magnet here.
The magnetic mini leaflet is printet in the best possible quality to achieve excellent and clear colors. A sample of the product will always be sent to the customer for approval before finalizing the production. This to ensure the high quality and to harmonize expectations. EPS files or AI files shall be used for printing (arranged by the customer).
The magnetic mini leaflet is produced in dimension 83 x 54 mm wih 20 pages as a standard. If 24 pages are needed, please consult us for prices. This advertising magnet is quantity-based and minimum order is 3000 pcs. We specialize in producing large quantities up to 500.000 pcs.
See our range of advertising magnets here.


The Dark Heart Mini Maxi Anemone has an inky purple body leading out to a green and fuchsia rim. Some specimens also develop a blue and green striated base below their tentacles. These anemones are roughly 1.5"+ and can grow to 4" to 5" max.


text:
Q: Permissions Denied in Terminal So I am a little new to working in the terminal and trying to set up yo and a localhost environment. No matter what I do it keeps throwing a permissions denied error. I have installed node and npm already and when I run the yo install
npm install -g yo bower grunt-cli gulp i get this error message. My buddy and I have tried so many different things to fix this but we cant get anywhere. I can access and run using sudo but I need to actually have the permissions to get this working. Help please?
I am sorry if that is not detailed enough, I am new to working in the terminal and may not know entirely what I am doing. If all else fails I was going to reset my desktop to factory settings and start over with setting my users and groups.
Screenshot of errors Here

A: sudo chown -R `whoami` /usr/local

or
npm install npm -g

...will do the trick.
You're not the first one to experience those problems and they are (finally) actively addressed in npm@3.X.X


Going to Pond Inlet? Find the best month to visit Pond Inlet based on the weather.
Expect -33°C daytime maximum temperatures in the shade with on average 2 hours of sunshine per day in Pond Inlet in February. Check more long-term weather averages for Pond Inlet in February before you book your next holiday to Nunavut in 2020/2021.
The February weather guide for Pond Inlet, Nunavut shows long term weather averages processed from data supplied by CRU (University of East Anglia), the Met Office & the Netherlands Meteorological Institute. Find out more about our data sources.
The average maximum daytime temperature in Pond Inlet in February is a very cold -33°C (-27°F). The average night-time temperature is usually a very cold -40°C (-40°F). There are usually 2 hours of bright sunshine each day, which represents 31% of the 6 hours of daylight.
Expect 21mm (0.8 inches) of rainfall across the whole of February with 5 days with some rain falling.
The average sea temperature in and around Pond Inlet in February is -2°C (28°F).
Browse the sunrise & sunset times for Pond Inlet in February 2020. Select a month to view Pond Inlet sunrise & sunset times for the next twelve months.
What is the weather usually like in Pond Inlet in February?
How hot is Pond Inlet in February?
How sunny is Pond Inlet in February?
How wet is Pond Inlet in February?
How warm is the sea around Pond Inlet in February?


Amalgam's video prop makers do love a challenge and design agency Stripeland's video directors and producers know where to turn for innovation and unusual props. So we like to think we were their first thought when asked to make this promotional video for the Red Cross's on-line campaign promoting "RappedUp", a first-aid app for babies and children.
We took a small carry cot and a large pram chassis, which were not designed to go together so emphasised the scale discrepancy, and created a hybrid vehicle. The reference material the client supplied was the ubiquitous low-rider car beloved of the hip-hop fraternity, so we also had to incorporate chrome spinners and a controllable bounce mechanism. Under-body blue LED down-lighters and gold flames along the sides completed the look. The prop was used during the filming of a new first aid campaign, teaching the essentials of first aid for babies and toddlers. As the pram had to have a child sat in it during filming it had to be very sturdy, and all the modifications we made were robust and child-safe. Although it weighed a huge amount once assembled, mainly due to the enormous wheels we fitted, it had to 'zoom' out of shot in a controlled manner so working axles were vital. Reports from the shoot day suggest that the baby who got to ride in it loved it so much that he drifted off to sleep.
This Video prop was a unique challenge for our prop makers and specialist makers – but so much of what we do is. We like to think we can rise to the occasion no matter what is required. Why not contact us here to see what we can do for you?


text:
\section{Introduction}
\label{sec:intro}

Many current and upcoming cosmic microwave background (CMB) experiments aim to measure the B-mode polarization of the CMB.
B-modes are predicted as a result of primordial gravitational waves generated during inflation, a theorized period of exponential expansion in the early universe.
Their detection would not only provide compelling evidence for inflation, but would also shed light into physics at energy scales orders of magnitude beyond those accessible to particle accelerators and would potentially provide the first evidence for quantum gravity \citep{Krauss2014}.

The primordial B-mode signal is extremely faint, with current upper limits on the tensor-to-scalar ratio of $r < 0.036$ \citep{Ade2021}.
Further complicating its detection, a number of astrophysical foregrounds impart a B-mode polarization signature which must be carefully modeled and subtracted to leave only the primordial B-mode signal.
In particular, the dominant foregrounds of synchrotron emission and thermal dust emission overwhelm the predicted B-mode signal across all frequencies, with synchrotron dominating at low frequencies
($\lesssim 70$ GHz) and dust at high frequencies ($\gtrsim 70$ GHz).

These foregrounds are polarized as a result of their interaction with the galactic magnetic field.
Synchrotron emission is produced by the motion of charged particles through the
galactic magnetic field,
and is polarized perpendicular to the magnetic field.
It is generally modeled as a power law in frequency, although more complex models can include a spectral curvature parameter or spectral breaking.
Dust grains absorb UV photons from the interstellar radiation field and re-emit thermal radiation in the far-infrared.
The grains tend to align themselves so that their short axis is parallel to the direction of the magnetic field, while they emit most efficiently along their long axis.
This results in emission with polarization perpendicular to the local magnetic field.
Variations in the orientation of the magnetic field along the line of sight can thus result in frequency decorrelation.

At millimeter or longer wavelengths, thermal dust emission is often modeled as a modified blackbody with a power law emissivity.
While this model gives a good approximation of dust emission at the frequencies commonly used for CMB observations, the true emission spectrum is expected to be more complicated.
The true dust emission will be affected by variations in both the dust grain properties and the intensity of the local interstellar radiation field.
With the unprecedented sensitivities of upcoming CMB polarization experiments, foregrounds must be modeled with greater accuracy to be able to accurately recover the faint primordial B-mode signal.

Several studies have shown that inaccuracies in modelling foregrounds can lead to a bias in the tensor-to-scalar ratio $r$ \citep{ArmitageCaplan2012, Fantaye2011, Kogut2016, Stompor2016, Hensley2018}.
\cite{ArmitageCaplan2012} consider a series of simulated input foreground spectra and various models, finding that a mismatch in the complexity of the true foregrounds and the models used can bias $r$ by as much as $\delta r \approx 0.03$.
An experiment such as PICO \citep{Hanany2019} has a sensitivity of $r \approx 1 \cdot 10^{-4}$, illustrating the need to allow sufficient complexity while minimizing the number of free parameters.

\cite{Remazeilles2016} consider the effect of using overly simplistic foreground models in the context of the proposed CMB satellite experiments \textit{LiteBIRD} \citep{Matsumura2014}, \textit{COrE} \citep{COrE-Collaboration2011}, \textit{COrE+}, \textit{PRISM} \citep{Andre2014}, \textit{EPIC} \citep{Bock2008}, and \textit{PIXIE} \citep{Kogut2011}.
Two possible dust models were considered for the input sky maps -- a single modified blackbody model, and a two-component modified blackbody model.
A mismatch between the foreground model and the simulations was introduced by assuming a single component for the dust model.
Even when excluding frequencies above 360 GHz to minimize the impact of inaccurate modeling, the tensor-to-scalar ratio was still strongly biased by $3\sigma$ or more for all considered experimental configurations.
In most cases, poor $\chi^2$ values signaled the unsuccessful separation of CMB B-modes and foregrounds, however experiments with lower sensitivity and fewer high-frequency channels may not be able to detect a poor fit from the $\chi^2$ value.

The focus of this paper is on thermal dust emission.
Our goal is to place bounds on the possible dust complexity and assess its expected impact on bias in the tensor-to-scalar ratio.
In section \ref{sec:expected_bias} we analyze \textit{Planck} thermal dust maps to estimate the variation in dust temperature and spectral index along the line of sight.
We then estimate the bias in the tensor-to-scalar ratio as a function of standard deviation assuming a Gaussian distribution in temperature or spectral index and using a single modified blackbody model.
In section \ref{sec:moment_method} we perform component separation simulations within a single pixel utilizing a dust parameterization known as the moment method \citep{Chluba2017, Rotti2020, Remazeilles2020, Vacher2022}, and assess its flexibility in modeling the effects of a continuous distribution of dust parameters and reducing bias.
Finally, we discuss what we could learn about dust properties from the moments returned by this method in section \ref{sec:maxent}.
We present our conclusions in \ref{sec:conclusions}.

\begin{figure}
    \centering
    \includegraphics[width = 0.7\columnwidth]{fig_disks_nside_002_mask.png}
    \caption{A \textit{Planck} dust temperature map overlaid with disks of equal radius based on a HEALPix grid with $N_{side} = 2$. The disk radius shown here is 12.5 degrees. The galactic plane is masked out (indicated by the gray region).}
	\label{fig:map_disks}
\end{figure}

\section{Expected bias due to dust variations}
\label{sec:expected_bias}

Each beam contains a superposition of multiple modified blackbody spectra, leading to observed dust temperature and spectral index variations across the sky.
However, even in the limit of a pencil beam variations along the line of sight are likely to result in spatial averaging.
As CMB experiments become increasingly sensitive, thermal dust emission models must be improved to capture dust complexity along the line of sight to avoid biasing the tensor-to-scalar ratio.

\subsection{Estimated line-of-sight variations in dust properties}
\label{sec:expected_var}

Observations show that the dust physical parameters vary when looking in different directions on the sky, so it is likely that similar variations occur along the line of sight.
To place bounds on the dust complexity along the line of sight, we analyze \textit{Planck} thermal dust maps produced using the Commander algorithm \citep{Eriksen2004} and use variations in the transverse direction as a proxy.
We quantify these variations by dividing the sky into disks and measuring the standard deviations in temperature and spectral index as a function of disk radius.
This is illustrated in figure \ref{fig:map_disks}, where we show a \textit{Planck} dust temperature map overlaid with sample disks of radius 12.5 degrees.
The grey region shows a sample mask, removing 20\% of the sky near the galactic plane.

Disk centers and radii are chosen by using HEALPix grids of various resolution orders and setting the disk radius to the minimum distance from any cell center to its boundary to avoid any overlap.
The disks shown in figure \ref{fig:map_disks} correspond to a HEALPix grid with $N_{side}=2$.
We then compute the standard deviation within each disk using all pixels from the full-resolution map ($N_{side}=256$) contained within that disk.
We note that no apparent dependence is observed between the galactic latitude and the standard deviation in either parameter.
The results for each disk are then averaged to estimate the expected variation in temperature and spectral index along the line of sight.

This was repeated using various \textit{Planck} sky masks.
A standard deviation was calculated for any disk for which more than half of its pixels were unmasked, and only those pixels were used in the calculation.
The standard deviations in temperature and spectral index as a function of disk radius are shown in figure \ref{fig:std_radius} for each considered sky mask.
The standard deviations appear to be nearly independent of the chosen masks.
In both cases, the standard deviation increases as a function of radius and begins to flatten out significantly beyond a radius of about ten degrees.

Without direct measurements of the line-of-sight dust complexity, we use variations in the transverse direction as a proxy for those along the line of sight.
We want to choose a disk radius such that dust complexity in the transverse direction is similar to that along the line of sight.
Near the galactic poles, the simplest geometry (transverse distance equal to line of sight distance) would suggest a fairly large disk radius.
Since figure \ref{fig:std_radius} shows that the standard deviation flattens out after about ten degrees, for simplicity we adopt a disk radius of 30 degrees throughout the remainder of this paper.
For a disk radius of 30 degrees and considering the cleanest 80\% of the sky, the expected standard deviations in temperature and spectral index are approximately 1.6 K and 0.045, respectively.

\begin{figure}
    \centering
    \includegraphics[width = 0.49\columnwidth]{fig_std_vs_disk_radius_temp.pdf}
    \includegraphics[width = 0.49\columnwidth]{fig_std_vs_disk_radius_beta.pdf}
    \caption{The mean standard deviation in temperature (left) and spectral index (right) as a function of disk radius for several sky masks. The GAL090 mask removes all but the cleanest 90\% of the sky, GAL080 the cleanest 80\%, etc. The choice of mask does not significantly affect the results.}
	\label{fig:std_radius}
\end{figure}

\subsection{Impact of dust variations on bias in the tensor-to-scalar ratio}

Several studies have demonstrated that a mismatch in the complexity of the true dust emission spectrum and the model used can result in a significant bias on the tensor-to-scalar ratio $r$
\citep{ArmitageCaplan2012, Fantaye2011, Kogut2016, Stompor2016, Hensley2018}.
The goal of this section is to directly quantify the relationship between the complexity of the true dust relative to the model and the resulting bias in $r$.
To do this, we generate dust and synchrotron spectra and simulate component separation using an overly simplistic dust model.

Synchrotron emission is often modeled as a power law, with a spectrum given by
\begin{equation}
    I_s(\nu) = \epsilon_{s,0} \left( \frac{\nu}{\nu_0} \right)^{\beta_s}
    \label{eqn:synch_mbb}
\end{equation}
where $\epsilon_{s,0}$ is the emissivity at reference frequency $\nu_0$ and $\beta_s = -1.1$ is the synchrotron spectral index \citep{Planck-Collaboration2020a}.
Note that here we are using units of surface brightness.
For this test, the input synchrotron spectrum exactly matches the complexity of the model used.

Thermal dust emission is generally modeled as a single modified blackbody, with a spectrum given by
\begin{equation}
    I_d(\nu) = \epsilon_{d,0} \left( \frac{\nu}{\nu_0} \right)^{\beta_d} B_\nu(T_d)
    \label{eqn:dust_mbb}
\end{equation}
where $\epsilon_{d,0}$ is the emissivity at reference frequency $\nu_0$, $\beta_d = 1.53$ is the dust spectral index, and $B_\nu(T_d)$ is the Planck blackbody spectrum corresponding to the dust temperature $T_d = 19.6$ K \citep{Planck-Collaboration2020a}.
Realistically however, within any given beam there will be a superposition of dust spectra at various temperatures and spectral indices.
For this test, the simulated dust consists of a superposition of modified blackbody spectra with either a Gaussian distribution in temperature and a constant spectral index:
\begin{equation}
    I(\nu) = \int e^{-\frac{1}{2} \left(\frac{T - T_d}{\sigma_T}\right)^2} \left( \frac{\nu}{\nu_{\textrm{ref}}} \right)^{\beta_d} B_\nu(T) \,dT
    \label{eqn:gauss_T}
\end{equation}
or a Gaussian distribution in spectral index with a constant temperature:
\begin{equation}
    I(\nu) = \int e^{-\frac{1}{2} \left(\frac{\beta - \beta_d}{\sigma_\beta}\right)^2} \left( \frac{\nu}{\nu_{\textrm{ref}}} \right)^{\beta} B_\nu(T_d) \,d\beta
    \label{eqn:gauss_beta}
\end{equation}
where $T_d = 19.6$K, $\beta_d = 1.53$, and $\sigma_T$ and $\sigma_\beta$ are the standard deviations of temperature and spectral index, respectively.
We model dust as only a single modified blackbody and vary the width of the Gaussian distribution to observe the relationship between the standard deviation and the resulting bias in $r$.
Input spectra are generated in the PICO frequency bands, using noise scaled by $10^{-6}$ to perform an approximately noiseless simulation while still preserving the relative frequency weighting of the PICO channels.
No CMB component is included so that the best-fit CMB amplitude for each fit directly represents the bias.
Note that we are not fitting multipoles, but rather fitting an SED within an individual line of sight. A Markov Chain Monte Carlo (MCMC) method is used to perform the fit.

\begin{figure}
    \centering
    \includegraphics[width = 0.5\columnwidth]{fig_bias_vs_sigma_temp_PICO.pdf}
    \caption{The bias in the CMB component as a function of standard deviation in dust temperature when a single modified blackbody model is used to fit the dust component in the PICO frequency bands. The corresponding $r$ values are shown for $l \in$ [80, 100] and a vertical line shows the expected standard deviation in temperature found in section \ref{sec:expected_var}.}
	\label{fig:bias_sigma_temp}
\end{figure}

Bias due to foreground complexity depends not only on the intrinsic dust variations, but also on the frequency channels used in a given experiment.
Here we evaluate spectra in the PICO frequency bands, with 21 frequency channels ranging from 21 to 800 GHz.
First, we consider dust with a Gaussian distribution of temperatures and uniform spectral index along the line of sight.
The synchrotron spectrum is described by a power law with a spectral index $\beta_s = -1.1$ and is normalized to the \textit{Planck} polarized intensity at 30 GHz.
The dust has a spectral index $\beta_d = 1.53$ and a central temperature $T_{d,0} = 19.6$ K, and the standard deviation of the dust temperature distribution $\sigma_T$ is varied between $0$ and $4$ K.
Each simulated spectrum was fit with a model containing dust, synchrotron, and CMB components, where the dust is modeled as a single modified blackbody, the synchrotron emission is modeled as a power law so that the complexity of the synchrotron model exactly matches the true complexity, and the CMB component is fit as an amplitude in nK.

\begin{figure}
    \centering
    \includegraphics[width = 0.5\columnwidth]{fig_bias_vs_sigma_beta_PICO.pdf}
    \caption{The bias in the CMB component as a function of standard deviation in dust spectral index when a single modified blackbody model is used to fit the dust component in the PICO frequency bands. The corresponding $r$ values are shown for $l \in$ [80, 100] and a vertical line shows the expected standard deviation in spectral index found in section \ref{sec:expected_var}.}
	\label{fig:bias_sigma_beta}
\end{figure}

Figures \ref{fig:bias_sigma_temp} and \ref{fig:bias_sigma_beta} show the bias in the CMB amplitude as a function of standard deviation in temperature and spectral index, respectively.
Since we are only interested in determining the bias in CMB B-modes, we should only consider the B-mode component of foreground emission.
We normalize our simulated foregrounds using \textit{Planck} total power, which includes both E- and B-modes.
These components do not have equal weight; approximately one third of the total power from polarized foregrounds comes from B-modes \citep{Planck-Collaboration2016b}.
We take this into account by scaling the bias by $1/\sqrt{3}$, finding that a standard deviation in temperature of 1.6 K corresponds to a bias in $r$ of approximately $1.4 \cdot 10^{-4}$ and a standard deviation in spectral index of 0.045 corresponds to a bias in $r$ of approximately $2 \cdot 10^{-5}$.
While the expected bias due to spectral index variations is negligible, that due to temperature variations will become significant for future CMB experiments such as PICO, illustrating the need for improved dust models.

\section{Fitting foregrounds using the moment method}
\label{sec:moment_method}

Several toy models of dust emission have been developed which account for possible distributions of the dust temperature and spectral index, however the distribution of dust properties is not well-understood.
Component separation methods must be capable of capturing complex variations in the dust emission spectrum with minimal prior knowledge of the true distribution of dust properties within a given pixel.

\subsection{Dust Emission Models}

Thermal dust emission is often modeled as a modified blackbody.
However, the size, composition, temperature, and spectral index of dust grains all affect the resulting emission spectrum and are expected to vary along the line of sight.
A number of empirical models have been developed which consider variations in these properties.

Dust consists largely of carbonaceous and silicate components, however it is not known whether these components exist primarily as separate species or composite particles.
\cite{Finkbeiner1999} consider a model with two components representing distinct dust species, motivated in part by \cite{Pollack1994}, finding that this model produces a significantly better fit to observational data than a single modified blackbody model.
However, if dust exists as distinct carbonaceous and silicate species, the temperatures of these two populations would be expected to differ.
This would result in a fractional polarization which varies with frequency, while observations show a relatively flat fractional polarization spectrum \citep{Ashton2018}.
\cite{Draine2021} present an alternative dust model, \textit{astrodust}, in which dust is represented by a single population consisting of larger composite grains formed through coagulation along with nanoparticles resulting from grain fragmentation.
Because it is a single population of dust particles, this picture of dust in the interstellar medium (ISM) is more consistent with observations of the polarization fraction.
\cite{Guillet2018} propose a dust model compatible with \textit{Planck} observations consisting of polycyclic aromatic hydrocarbons (PAHs), amorphous carbon, and astrosilicates.

While a two-component model can reproduce some of the observed flattening of the dust emission spectrum, it assumes two distinct dust species with fixed spectral indices.
\cite{Paradis2011} present a two-level systems (TLS) model which successfully reproduces observed variations in the dust spectral index without needing to divide the dust into separate populations.
This model considers the internal structure of amorphous silicate grains, resulting in a spectral index that depends on both temperature and frequency.

The size of dust grains also affects their thermal emission.
Very small grains have heat capacities that are small compared to the energy imparted by a single UV photon, resulting in a spike in temperature followed by a cool-down process in which photons are emitted through vibrational transitions \citep{Meny2007}.
The temperature distribution of these very small grains is described by the transient heating model, which has a constant spectral index and a temperature distribution with a peak at low temperatures and a tail to high temperatures.

It is important to note that our goal is not to reproduce physical dust properties in detail, but rather to capture broad features due to dust complexity along the line of sight.
We start by considering a model with a Gaussian distribution of temperatures with either one or two peaks and a constant spectral index.
Figure \ref{fig:model_emissivities} shows the emissivity distributions for the one-Gaussian and two-Gaussian models.
The width of the one-Gaussian model is chosen to reflect the expected standard deviation in dust temperature found in section \ref{sec:expected_bias}.
The central temperature and spectral index for the one-Gaussian model are taken from a \textit{Planck} single-component fit, while the central temperatures and spectral indices for the Two-Gaussian model are taken from the \cite{Finkbeiner1999} two-component fit.
We then adopt the transient heating model as an example of a broad temperature distribution.
Figure \ref{fig:model_diffs} shows the fractional difference between each of these dust emission models after they have been normalized to the \textit{Planck} polarized intensity at 353 GHz.

\begin{figure}
    \centering
    \includegraphics[width = 0.6\columnwidth]{fig_model_emissivities_2.png}
    \caption{The temperature distributions and spectral indices used for the one-Gaussian (top) and two-Gaussian (bottom) models.}
	\label{fig:model_emissivities}
\end{figure}

\begin{figure}
    \centering
    \includegraphics[width = 0.6\columnwidth]{fig_sim_dust_diffs.pdf}
    \caption{The fractional difference between each of the toy models considered and a single modified blackbody spectrum. The spectra are normalized at 353 GHz.}
	\label{fig:model_diffs}
\end{figure}

It is important to choose a parameterization which allows sufficient complexity without making overly restrictive assumptions about the dust emission.
The moment method, introduced by \cite{Chluba2017}, expands the fundamental dust spectral energy distribution (SED) in terms of the dust physical parameters to obtain spectral basis functions characteristic of the effects of spatial averaging.

\subsection{Dust Parameterization}

Considering only variations along the line of sight, the observed emission spectrum is represented by a superposition of modified blackbody spectra with unique amplitude, temperature, and spectral index at each point along the line of sight:
\begin{equation}
    I_\nu = \int I_\nu(A(r), \beta(r), T(r)) dr.
\end{equation}
In the moment method, the amplitudes corresponding to each $(\beta, T)$ can be thought of as describing a probability density distribution in temperature and spectral index $\rho(\beta, T)$ \citep{Rotti2020}:
\begin{equation}
    I_\nu = \int \rho(\beta, T) I_\nu(A_0, \beta, T) d\beta dT
\end{equation}
where $A_0$ is a constant reference amplitude.
A multidimensional Taylor expansion of the fundamental spectral energy distribution (SED) is then performed around the average parameters $\bar{p}$, producing a set of spectral basis functions consisting of derivatives and cross-derivatives with respect to the dust physical parameters.
The model parameters to be optimized are then the central moments of the dust temperature and spectral index, providing direct insight into the nature of the dust.

The fundamental SED describing each emitting element along the line of sight is given by a modified blackbody spectrum:
\begin{equation}
    I_\nu(A, \beta, T) = A \left( \frac{\nu}{\nu_0} \right)^{\beta} B_\nu(T).
\end{equation}
All derivatives with respect to the dust physical parameters commute.
Following \cite{Chluba2017}, we expand with respect to the inverse of the dust temperature $\tau \equiv T^{-1}$ as computing the SED derivatives is more straightforward in this parameterization.
The spectral basis functions then have the form:
\begin{equation}
    I_{m,n}(\nu, \bar{p}) =
    \bar{A} \left( \frac{\nu}{\nu_0} \right)^{\bar{\beta}} \ln^m \left( \frac{\nu}{\nu_0} \right)
    \frac{\partial^n}{\partial^n \tau} B_\nu \left( \frac{1}{\bar{\tau}} \right)
\end{equation}
for $m$ derivatives with respect to $\beta$ and $n$ derivatives with respect to $\tau$.
Similarly, the central moments are given by:
\begin{equation}
    \omega_{m,n} = \left \langle (\beta - \bar{\beta})^m (\tau - \bar{\tau})^n \right \rangle
\end{equation}
Figure \ref{fig:mm_basis} shows the contribution of each order of the expansion to the spatially averaged spectrum.
As in \cite{Chluba2017}, the first-order term is kept at zero to fix the average parameters and thus is not shown.
For a Gaussian distribution of dust temperatures, the first two terms dominate the expansion, however dust with a tail to high temperatures requires several moments to be accurately represented.

\subsection{Implementation}

We now want to test the moment method on simulated spectra based on the toy models described above to determine whether fitting the dust component with this parameterization can reduce the expected bias in the tensor-to-scalar ratio and to assess what future CMB experiments could tell us about the distribution of dust properties.

One challenge in using the moment method is that the number of parameters can increase rapidly with increasing expansion order.
A recent analysis by \cite{Osumi2021} finds no significant variation in the dust spectral index, suggesting temperature variations may have a greater impact on spatial averaging of dust SEDs.
The results described in section 2.2 also suggest that the expected bias due to line-of-sight variations in spectral index is lower than that expected from temperature variations.
To reduce the number of free parameters, we therefore only expand the modified blackbody spectrum in terms of $\tau$.
In this case, incrementing the expansion order adds only one additional parameter.
The spectral basis functions are then:
$$ I_{0,n}(\nu, \bar{p}) =
\bar{A} \left( \frac{\nu}{\nu_0} \right)^{\bar{\beta}} \frac{\partial^n}{\partial^n (\tau)} B_\nu \left( \frac{1}{\bar{\tau}} \right) $$
and the model parameters are the statistical moments of $\tau$.

Simulated dust spectra are obtained by generating two-dimensional opacity distributions in temperature and spectral index according to the chosen model and calculating the spectrum produced by the superposition of each component in the PICO frequency bands.
The resulting spectrum is then normalized to the Planck polarized dust intensity at 353 GHz.
The true moments are calculated according to the equation below, where $A(\beta, \tau)$ represents the input dust opacity at each $(\beta, \tau)$.
$$ \omega_{0,n} =
\frac{1}{A_{tot}} \sum_{\beta, \tau} A(\beta, \tau) (\tau - \bar{\tau})^n $$
Derivatives with respect to $\tau$ are calculated numerically for each set of parameters.

\begin{figure}
    \centering
    \includegraphics[width = 0.49\columnwidth]{fig_basis_model3.pdf}
    \includegraphics[width = 0.49\columnwidth]{fig_basis_model5.pdf}
    \caption{Contribution of each expansion order for two different dust models. The Gaussian model (left) is dominated by the first two expansion orders, while the transient heating model (right) requires a higher-order expansion.}
	\label{fig:mm_basis}
\end{figure}

\subsection{Fitting Methods}

The \textsc{emcee}\footnote{\url{https://emcee.readthedocs.io/en/stable/}} \citep{Foreman-Mackey2013} python package was used to fit simulated spectra using an ensemble Markov Chain Monte Carlo (MCMC) approach.
The dust component was modeled using the moment method.
To improve the performance, a zeroth-order fit was performed initially and the expansion order was incremented until the target order was reached, using the previous fits and uncertainties to initialize the walkers at each step.

The convergence of the distribution was monitored by tracking the integrated autocorrelation time for each parameter.
The chains were set to evolve until either the chains converged (i.e. the number of iterations reached 100 times the maximum autocorrelation time) or a set maximum number of iterations ($N_{\textrm{max}} = 10^{6}$) was reached.
In the results presented below, the MCMC chains fully converged in all cases.

\subsection{Results}
\label{sec:results}

To assess the performance of this method, we generate noiseless simulated spectra containing only dust emission using the PICO frequency channels.
We then perform multiple tests fitting both dust and CMB components at each expansion order to determine the order required to avoid significant bias in the tensor-to-scalar ratio.
We perform noiseless tests and use the root-mean-square fractional residual as the goodness-of-fit.
For each expansion order, 20 simulated fits are performed.

Figure \ref{fig:bias_vs_order_PICO} shows the mean CMB bias and root-mean-square fractional residual as a function of expansion order using the PICO frequency bands for the One-Gaussian, Transient Heating, and Two-Gaussian models.
We choose the root-mean-square fractional residual as our goodness of fit as we are performing noiseless simulations.
Since the input models include no CMB component, the fitted CMB amplitude (top panel) represents the bias.
The root-mean-square fractional residual (bottom panel) indicates how sensitive channels need to be to detect a bias.
Note that in this case, a higher fractional residual indicates a better ability to detect bias.
We find that when using the moment method with this approach, the bias could be reduced to
$|A_{\textrm{CMB}}| < 1$ nK ($r < 1.0 \times 10^{-4}$) in all cases without requiring prior knowledge of the true dust temperature distribution.
It is not clear why the second-order fit is worse than the zeroth-order fit for the single Gaussian model, however it is important to note that because we are expanding with respect to inverse temperature rather than temperature, two moments are not necessarily sufficient to adequately describe the distribution.

\begin{figure}
    \centering
    \includegraphics[width = 0.32\textwidth]{fig_pars_cmb_vs_order_PICO_N3_S1e+08_SIM300_FIT101_B0_Tinv1.pdf}
    \includegraphics[width = 0.32\textwidth]{fig_pars_cmb_vs_order_PICO_N3_S1e+08_SIM500_FIT101_B0_Tinv1.pdf}
    \includegraphics[width = 0.32\textwidth]{fig_pars_cmb_vs_order_PICO_N3_S1e+08_SIM400_FIT101_B0_Tinv1.pdf}
    \caption{Best-fit CMB amplitude in nK and root-mean-square fractional residual using the One-Gaussian model (a), the Transient Heating model (b), and the Two-Gaussian model (c) for the simulated dust component.
    Horizontal lines show a corresponding $r$ value of $10^{-4}$.
    For the center panel, this line is above the upper $y$-limit.
    }
	\label{fig:bias_vs_order_PICO}
\end{figure}

\section{Recovering the dust temperature distribution}
\label{sec:maxent}

While our primary motivation in utilizing the moment method is in minimizing bias in the tensor-to-scalar ratio by allowing for more complexity and flexibility in the dust model, this technique could potentially tell us more about the underlying distribution of dust physical parameters as well.
For second- or higher-order expansions, an approximate distribution of dust temperatures can be recovered from the truncated list of moments returned by the moment method using the principle of maximum entropy.

The classical moment problem \citep{Shohat1943} asks whether, given a sequence of moments, there is a unique probability function having those moments.
If one only has a finite number of moments, this problem is under-determined.
The idea of using a maximum entropy approach to solve an under-determined inverse problem is first introduced in \cite{Shannon1948}, and is first applied to the classical moment problem by \cite{Mead1984}.
The maximum entropy approach to the moment problem has found many applications across scientific disciplines \citep{Abramov2010}.

\begin{figure}
    \centering
    \includegraphics[width = 0.32\columnwidth]{fig_pymaxent_inv_one_gauss.pdf}
    \includegraphics[width = 0.32\columnwidth]{fig_pymaxent_inv_transient_heating.pdf}
    \includegraphics[width = 0.32\columnwidth]{fig_pymaxent_inv_two_gauss.pdf}
    \caption{Recovered distributions using the maximum entropy method based on the true moments for three sample distributions based on our models.
    A distribution with two peaks would require higher-order information to adequately capture.
    }
	\label{fig:pymaxent}
\end{figure}

The principle of maximum entropy states that of all possible distributions satisfying a finite set of moment constraints, that with the largest Shannon entropy is most representative of our knowledge about the system.
\textsc{PyMaxEnt} \citep{Saad2019} is a Python package which takes a finite list of moment constraints and optimizes a parameter distribution to maximize the Shannon entropy.
This could be used to obtain an approximate distribution of dust temperatures along individual lines of sight.
Figure \ref{fig:pymaxent} shows distributions recovered from the theoretical moment values using \textsc{PyMaxEnt} as a function of the number of known moments for three sample distributions based on the considered dust models.
Note that the x-axis represents the inverse temperature, as we are expanding in terms of this parameter.

If the variation in dust properties along the line of sight is well-represented by a Gaussian distribution, two moments in the dust expansion are sufficient to recover the distribution of dust emissivity as a function of temperature along individual lines of sight.
More complex distributions require additional moments.
A single extended distribution requires expansion to fourth order of higher, while a distribution with two well-separated peaks is poorly fit at all orders.
We note, however, that the recovery process here makes no assumptions regarding the underlying dust complexity.
\cite{John2007} describe alternative approaches to recovering a distribution from a list of moments which, given additional assumptions about the distribution, can provide better recovered distributions with the available moments.

Figure \ref{fig:pymaxent} uses the moments calculated directly from the full spectral energy distributions of the different dust models.
In practice, the frequency spectra are sampled at discrete frequencies.
Figure \ref{fig:pymaxent_PICO_Transient_Heating_avg} shows the recovered distributions from the truncated list of moments obtained by the noiseless transient heating model simulation using only the PICO frequency bands.
The lowest (second-order) expansion is sufficient to show an extended distribution in temperature, with modest improvement seen at higher order.

\begin{figure}
    \centering
    \includegraphics[width = 0.6\columnwidth]{fig_dist_dust_pymaxent_avg1_PICO_N3_S1e+08_SIM500_FIT101_B0_Tinv1.pdf}
    \caption{Recovered distribution of dust temperatures for the transient heating model with PICO}
	\label{fig:pymaxent_PICO_Transient_Heating_avg}
\end{figure}

\section{Conclusions}
\label{sec:conclusions}

Our goal is to estimate dust complexity along the line of sight and its impact on bias in the tensor-to-scalar ratio for measurements of the B-mode polarization of the CMB.
Here, we use the scatter in dust temperature and spectral index observed for \textit{Planck} data in the plane of the sky as a proxy for similar variation along a single line of sight.
Note that every realistic beam will include at least some variations across the map as well as along the line of sight. These effects are qualitatively the same, but any realistic beam will likely have at least somewhat larger variation than a true pencil beam.
We find that an estimated line-of-sight standard deviation of 1.6K in temperature or 0.045 in spectral index will source non-trivial bias in the tensor-to-scalar ratio if fitted using a single modified blackbody model.
Bias of order $\delta r = 1.4 \times 10^{-4}$ is negligible for current upper limits to $r$ but could become significant at sensitivity levels anticipated for futuristic missions such as PICO.
Variations in temperature are more important to capture than variations in spectral index, as the observed degree of temperature variation leads to a larger bias if not properly accounted for.

Replacing a simple modified blackbody model with an expansion in moments of the temperature distribution significantly reduces the bias without requiring \textit{a priori} knowledge of the dust physical parameters.
An expansion to second order accurately reproduces a Gaussian distribution of dust temperatures along an individual line of sight, while higher orders are required for more complicated distributions.
A futuristic experiment with information in enough frequency bands such as PICO could provide information on the dust temperature distribution along a given sightline using the statistical moments obtained from the moment method.
This could allow us to distinguish between competing dust models, shedding light on dust emission mechanisms.
That being said, as the order of the expansion is increased it is expected that there will be some trade-off between better modeling of the dust distribution and larger uncertainties in $r$.

\bibliographystyle{aasjournal}

redpajama_set_name: RedPajamaArXiv


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text:
Vérteskethely  este un sat în districtul Kisbér, județul Komárom-Esztergom, Ungaria, având o populație de  de locuitori (2011).

Demografie

Conform recensământului din 2011, satul Vérteskethely avea  de locuitori. Din punct de vedere etnic, majoritatea locuitorilor (%) erau maghiari, cu o minoritate de romi (%). Nu există o religie majoritară, locuitorii fiind romano-catolici (%), reformați (%), persoane fără religie (%) și luterani (%). Pentru % din locuitori nu este cunoscută apartenența confesională.

Note

Sate din județul Komárom-Esztergom


text:
Q: Efficiently using Numpy to assign function values to array I am interested in finding the fastest way of carrying a simple operation in Python3.6 using Numpy. I wish to create a function and from a given array to an array of function values. Here is a simplified code that does that using map:
import numpy as np
def func(x):
    return x**2
xRange = np.arange(0,1,0.01)
arr_func = np.array(list(map(func, xRange)))

However, as I am running it with a complicated function and using large arrays, runtime speed is very important for me. Is there a known faster way?
EDIT My question is not the same as this one, because I am asking about assigning from a function, as opposed to a generator.

A: Check the related How do I build a numpy array from a generator?, where the most compelling option seems to be preallocating the numpy array and setting values, instead of creating a throwaway intermediate list.
arr_func = np.empty(len(xRange))
for i in range(len(xRange)):
  arr_func[i] = func(xRange[i])

A: With a complex function that can't be rewritten with compiled numpy functions, we can't make big improvements in speed.
Define a function with math methods that require scalars, for example:
def func(x):
    return math.sin(x)**2 + math.cos(x)**2

In [868]: x = np.linspace(0,np.pi,10000)

For reference do a straight forward list comprehension:
In [869]: np.array([func(i) for i in x])
Out[869]: array([ 1.,  1.,  1., ...,  1.,  1.,  1.])

In [870]: timeit np.array([func(i) for i in x])
13.4 ms ± 211 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

Your list map is slightly faster:
In [871]: timeit np.array(list(map(func, x)))
12.6 ms ± 12.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

For 1d array like this, np.array can be replaced with np.fromiter.  It works with a generator as well, including the Py3 map.
In [875]: timeit np.fromiter(map(func, x),float)
13.1 ms ± 176 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

So that could get around the possible time penalty of creating a whole list first.  But in this case it doesn't help.
Another iterator is np.frompyfunc.  It is used by np.vectorize, but usually is faster with less overhead.  It returns a dtype object array:
In [876]: f = np.frompyfunc(func, 1, 1)
In [877]: f(x)
Out[877]: array([1.0, 1.0, 1.0, ..., 1.0, 1.0, 1.0], dtype=object)
In [878]: timeit f(x)
11.1 ms ± 298 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [879]: timeit f(x).astype(float)
11.2 ms ± 85.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

A slight speed improvement.  I noticed more of an improvement with 1000 item x.  This is even better if your problem requires several arrays that may be broadcasted against each other.
Assigning to a preallocated out array may save memory, and is often recommended as a alternative to the list append iteration.  But here it doesn't not give a speed improvement:
In [882]: %%timeit
     ...: out = np.empty_like(x)
     ...: for i,j in enumerate(x): out[i]=func(j)
16.1 ms ± 308 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

(the use of enumerate is slightly faster than range iteration).


I just held a baby doggie in my hands right here in the office, and I feel a bit consecrated. He smelled like baby powder, I swear. Dogs make me so happy, it's nutty.
I often forget that I work at Warner Bros. In my mind, it's often just The Man. Yesterday, though, I had such a beautiful moment. I was walking to lunch (I work on the Lot) through the town square that is currently the home of the Gilmore Girls but used to be the Music Man set (oh Robert Preston!)and all sorts of silly and cool places. A private tour was going through a side street and I heard someone say, "This is the last remaining set from Casablanca. This is Paris." I felt like I got punched in the gut. What a wonderful place to be, really.
Put your money where your mouth is, Red States.
Heading back to the dust bowl today. I'm sure it will be New Mexcillent. To all of those friends who are going to be out there, please call me on my cell and we will try to get together. I hope you have my cell number because I'm not posting it here, fools. Email me if you need it.
The Sun, or the Son, has returned. May all of your lights shine brightly in these longest of nights.


Staff Selection Commission is going to recruit fresher candidates in India for Lower Division Clerk / Junior Secretariat Assistant, Postal Assistant Posts. So, those candidates who are willing to work with this organization they May apply for this post. Last Date of Apply 18-December-2017. Total numbers of vacancies are 3259 Posts. Only those candidates are eligible who have passed 12th Standard or equivalent examination from a recognized Board or University. Indian citizen having age in between 18 to 27 years can apply for this Staff Selection Commission Recruitment 2017.
Educational Criteria: Only those candidates are eligible who have passed 12th Standard or equivalent examination from a recognized Board or University.


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text:
Chillingly Realistic: Hitler Bunker Gets Tussauds Treatment
Chillingly Realistic Hitler Bunker Gets Tussauds Treatment
Madame Tussauds opened its new waxwork museum in Berlin to reporters on Thursday and, predictably, the attention was mainly focused on a startlingly realistic figure of Adolf Hitler in his bunker. His dark portrayal as a broken man may defuse criticism that including him was tasteless.
Von David Crossland
There he sits staring at his desk with a look of abject desperation on his face, his black hair dishevelled, a film of sweat on his grey skin, enclosed by the oppressive concrete walls of his bunker.
Whether Madame Tussauds likes it or not, the wax figure of Adolf Hitler will be the star attraction of its new museum opening in Berlin on Saturday. It is a startlingly realistic portrayal of Hitler as a broken man in his final days.
One almost expects him to bang his fist on the table, walk around his desk and explode into one final fit of rage in front of the giant map of Europe on the wall.
Critics had condemned the inclusion of Hitler as tasteless and liable to attract neo-Nazis, but Madame Tussauds said it had conducted market research that indicated a majority of Germans believed the dictator should feature in the display of 75 figures, which include Albert Einstein, Beethoven, Karl Marx and of course Chancellor Angela Merkel.
Hitler's Last Days
Madame Tussauds said it had deliberately refrained from portraying the Nazi leader at the height of his power.
"We wanted to show him as he was in his final days," said Meike Schulze, Germany manager for the Midway division of United Kingdom-based group Merlin Entertainments. "We conducted a survey and found that most people said he belongs to history and should be shown, so we were guided by that."
Madame Tussauds put a sign on Hitler's desk telling visitors not to take photos. It says that while the museum encourages guests to touch and interact with its figures, "we would ask that to avoid insult to other guests and out of respect for the millions who died during World War II, you refrain from taking photographs or attempting to 'pose' alongside Hitler's figure."
Unlike the other exhibits, Hitler will be kept cordoned off and will be guarded permanently by a member of staff, and the area will be monitored by video surveillance cameras, said Schulze.
Some 200 journalists, photographers and camera teams jostled and elbowed each other to catch a glimpse of the Führer at Thursday's press launch.
"We were aware the figure would attract a lot of interest but we hope people will also pay attention to our other great exhibits," said Schulze. "It would be a shame if the focus were just on this one figure."
Inappropriate and Superfluous?
The Berlin museum is the eighth Madame Tussauds, and the only one in Germany. The London museum also has a Hitler figure but that one is shown standing up and did not serve as a model for the Berlin waxwork.
"We used 2,000 images of Hitler from all perspectives to create the figure," said Schulze. The realism is aided by the gloomy lighting against the stark grey bunker backdrop.
Berlin politicians have called the inclusion of Hitler inappropriate and superfluous -- especially given that the museum is located just a few blocks away from the site of the real Hitler bunker -- and Berlin's mayor Klaus Wowereit urged the organizers to deal with the exhibit in a sensitive way.
Johannes Tuchel, a researcher who works for a Berlin memorial to the opponents of the Nazi regime recently told Reuters: "A waxworks museum is meant to entertain and to amuse. It's not appropriate to have a Hitler figure there."
But the Council of Jews in Germany has taken a more relaxed approach. Stephan Kramer, general secretary of the council, said Hitler should not become a tourist attraction in Berlin. "But if such an exhibition helps to normalize the view of Hitler and to demystify him, one should try it," he said.
Hitler is the fourth figure visitors encounter as they enter the museum, after the "Iron Chancellor" Bismarck, Karl Marx and Sophie Scholl, a member of the non-violent Nazi resistant movement who was executed in 1943 aged 21 for distributing anti-war leaflets.
Visitors emerge from the gloom of Hitler's bunker to see Winston Churchill and a host of other leaders including Ronald Reagan and Mikhail Gorbachev. As with most waxworks, some figures are better than others.
Merkel's likeness with her wax double is arguable, but former German national goalkeeper Oliver Kahn, depicted with his mouth wide open in his trademark primeval scream, is spot on, as is Marlene Dietrich.
Madame Tussauds in Berlin: Not All Are Pleased about Wax Hitler


text:
Dragon Ball Debate Surfaces Over Goku's Ugliest Transformation
By Kofi Outlaw - January 7, 2020 06:24 pm EST
The Dragon Ball franchise has managed to sustain its brand power and awareness via one main avenue: the strength of its iconic imagery. Whether its the original Dragon Ball series, Dragon Ball Z or the modern Dragon Ball Super era, artwork and images of Goku, Vegeta (and so many other characters) in various stages of transformation or power-up has been the biggest lure for new fans to discover the series. Recently debate has popped up on social media, with Dragon Ball fans turning to debate about which of Goku and Vegeta's various transformations is actually the least impressive, visually speaking.
Well, the debate over Dragon Ball's ugliest transformations has boiled down to two main contenders: Super Saiyan 3 and Super Saiyan 4:
bruh super saiyan three is the ugliest god damn mother fucking abomination i have ever seen in my god damn life 🤢🤢 pic.twitter.com/81E4dvXJ03
— 👑 𝕤𝕙𝕒𝕟 🖤 (@senpaishan) January 5, 2020
Funny way of spelling Super Saiyan 4 pic.twitter.com/3JWYUaRj6S
— The New CVR123 (@Chrisredlos) January 5, 2020
If we're all being honest here, both Super Saiyan 3 and Super Saiyan 4 came out of one of the least impressive eras of Dragon Ball's run. Super Saiyan 3 was born in the latter portion of Dragon Ball Z (The Majin Buu Saga), while Super Saiyan 4 was born during the infamous run of the Dragon Ball GT series. Both transformation debuted in the mid-to-late '90s (in Japan), a period of time when Dragon Ball was exhausting good, meaningful ideas, and falling into the gimmickry of Super Saiyan numbering. It's no surprise then, that both forms aren't getting much love from fans, compared to others.
However, things have come around now. A lot of Dragon Ball Super fans are actually clamoring for Super Saiyan 4 to get an official canonized debut in Dragon Ball Super - and thanks to the Dragon Ball Super: Broly movie, the door has been opened for it to become a reality. So if we're talking about whether SSJ3 or SSJ4 is the lamest, it seems like the answer has become much more clear with time.
The Japanese-language and English dub releases of Dragon Ball Super are now complete and available to stream with FunimationNOW and Crunchyroll. Viz Media is releasing new chapters of the manga at a monthly rate that can be read entirely for free through the Shonen Jump digital library, and Dragon Ball Super's big movie, Dragon Ball Super: Broly, is now available on Blu-ray and DVD. Fans in Japan are also able to enjoy fresh non-canon adventures from the franchise with new episodes of Super Dragon Ball Heroes' promotional anime series.
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