Waterproofing Singapore can be a big issue and you would have to go with hell lot of changes in your house. But nowadays there is a trend in Singapore wherein companies offer Waterproofing services without even removing your existing tiles and finish. Sounds Surprising right? But it is true. In this article, we will discuss regarding the Waterproofing services provided in Singapore with regards to balcony, Toilet and Roofs. These services proved to be much useful and require not much of time, cost and labor.
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The most water seepage problem in a house is faced in the bathroom or Toilet area. The membrane in the concrete ground torn and faces break down due to the regular cleaning and showering. The water generally leaks from the tiles and sometimes the damages reaches to the adjoining wall as well. The first symptoms are the paint coming out from the walls along with the ceiling ready to drop due to wet cement. The Toilet waterproofing thus helps in sealing all the pores right from the ceiling to the ground. The most commonly used method is the flood infusion method and not removing of tiles as it is not very expensive. They simply put an overlay of waterproofed tiles and membranes and your Toilet waterproofing is done. Treatment:- Firstly Cleaning of the Toilet area to ward off any particles is done. Then the chemical solution is added to the Toilet area which is to be kept for few hours to let it get soaked in to the root. After the chemical solution soaking is done, the Toilet space is cleaned and then it's ready to use again.
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text:
Monitor turns black and windows disable my GPU driver AMD Software: Adrenalin Edition 23.1.2 for AMD Radeon™ RX 7900 Series Amernime Zone AMD Software: Adrenalin / Pro Driver - Release Discovery 22.12.2 WHQL NVIDIA GeForce 528.24 WHQL driver download & Discussion 7-Zip decompression speed test, the Intel Core i9-13900K is 60% faster than the i9-12900K. Forspoken implements Microsoft's DirectStorage API, faster load times; lowers FPS; raises FPS? 4th proprietary graphics driver is now available exclusively for AMD Radeon RX 7900 XTX and XT Intel Shares Fourth-Quarter and Full-Year 2022 Financial Results AMD Software: Adrenalin Edition 22.11.2 - Driver download and discussion Corsair 10GB/s MP700 PCIe Gen5 SSD got unveiled, but quickly gets hidden
Guru3D.com » News » 3dfx reveals range of new products: From PCI cards up-to smart TVs (Update)
3dfx reveals range of new products: From PCI cards up-to smart TVs (Update)
by Hilbert Hagedoorn on: 08/06/2021 08:51 AM | source: | 24 comment(s)
It was revealed last week that 3dfx Interactive, a former GPU designer, was interested in resuming its operations. At least the brand name has been resold towards a company. The remaining assets of the firm have been acquired by a newly formed US investor, Jansen Products, and new notifications have been made today.
A presentation has been released on the firm's fresh new Twitter account, which the company claims is authentic, demonstrating that Jansen Products took over the other business divisions in mid-July, according to the presentation. According to one presentation, the complete acquisition of the trademarks may take several more weeks, and the company intends to "produce cellphones, smart TVs, sound systems, and other products in addition to video cards."
A total of six devices in various categories, including a PCI plug-in board dubbed Voodoo 6, are already in the works. The Voodoo 5 was the final graphics card produced by the firm. A smartphone, a Bluetooth speaker, a home theater speaker setup, a tablet, and a smart television are all included on the list. These items are scheduled to be introduced at the 'CEC in 2022.' While this might be a reference to the IEEE Congress on Evolutionary Computation, which will be held in Glasgow, it is more likely that the Consumer Electronics Show (CES), which will be held in Las Vegas, is mentioned.
Huge goals, which leads us to believe that this is nothing more than a radiantly designed prank for the public's imagination. Examples include the goal of serving at least 900,000 clients with more than 300 additional workers, as well as plans for more than 600 projects, according to the company's website. Those are significant figures for a firm that has just recently risen from the ashes.
Update: as expected, the twitter account was just pulled. We can't wait to see an official ATI account soon ;)
Many people will not be surprised to learn that the comeback of 3dfx was a hoax, as it was in the past. Because the 'official' Twitter account no longer exists, it is impossible to locate the postings that were made in response to it. An alert Twitter user has been able to capture screenshots of the most recent messages that contain the hashtag @AtiParody (a Twitter account that has also been deleted in the meantime). Given the appearance of a poor joke, it is obvious that the presentation is bogus.
« Ryzen 5000G APUs Now Available · 3dfx reveals range of new products: From PCI cards up-to smart TVs (Update) · Gigabyte hit by ransomware attack by hacker collective RansomEXX »
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DarkQuark
#5935582 Posted on: 08/06/2021 09:42 AM
Like nature, the market abhors a vacuum. So if GPU's are going for over $2k that for sure will draw in some fresh competition given the supply issues. But one would think making anything with chips right now would be a rough deal to break in to.
Kaarme
If it's just a company that acquires an old, well-known trademark, it could be one of those cases of simply getting run-of-the-mill products from various factories and slapping their own brand on them. That's what has happened to an endless list of previously famous (domestically or internationally) European brands. In China/East Asia there's a number of video card manufacturers with little presence in the West, so I could see them, for example, getting cards from one of them and rebranding them.
Actually designing a brand new GPU is a monumental task. Even Apple hasn't considered it an ultimate top priority, all things considered, since they are still getting dedicated graphics cards from AMD, for the time being. 3dfx's patents are all about to expire (in the USA) since the company went under in 2002, which means there are no newer ones, so you could say anyone could do anything with them, but they wouldn't be of that much use in 2021, I reckon.
Mpampis
Didn't this turn out to be a joke or something?
SaberJ2X
using an image from a DA account
CEC instead of CES
voodoo6 PCI as the only actual accelerator... 10% of their initial output?
90% will be landfill-filling plastic ewaste people change every year basically...
as much as I want 3dfx logo on products... this will dissolve any good will the 3dfx logo had...
*wants a clunky 90s thinkpad looking brick with the old 3Dfx logo, and when opened it looks sleek and modern with an engraved modern 3dfx logo on the bottom of the OLED 3:2 in the bezel*
doesn´t even need to be good for gaming, I would get it for nostalgia kicks... and to replace my aging but extremely capable maxed out T440p
new old stock gpu dies on a modern PCI design, probably the same guy doing the black PCB recreation got paid good money for his ready to use designs... if I was a greedy company wanting to capitalize on the lack of GPUs and sell some retro hardware that would sell great...

redpajama_set_name: RedPajamaCommonCrawl


It's a fantasy novel set in a world of racial tensions, magic, and religion. At the intersection stands the Nocturne, an outcast who wields a power beyond any other man. I'll be talking a lot more about the book in the coming weeks, because it's definitely something I'm proud of. And I do plan on releasing it onto the Kindle Store in July, but we'll see how that pans out.


text:
Loesch speaking at the 2017 CPAC in Maryland.
Parroting squawkbox
And a dirty dozen more
Amity Shlaes
Jerome Corsi
Liz Wheeler
Steve Turley
Wafa Sultan
Dana Lynn Loesch (née Eaton; pronounced like "lash") (1978–) is a right-wing political commentator, activist, gun nut, and pundit who served as the rantswoman spokeswoman of the National Rifle Association. She had formerly been an editor for Breitbart and had also been the host of her own eponymous program, Dana, on TheBlaze. She also co-founded the St. Louis branch of the Tea Party in 2009.[1]
Loesch was, from 2017 to 2019, easily the NRA's most visible mouthpiece. Her propaganda-style rants in the NRA's television (NRATV) and social media ads plus her numerous appearances on cable television (she's made appearances on almost every network you can think of, from Fox News to CNN) showcase just how cult-like the NRA has sadly become.
1 Beginnings
2 Becoming a crank
3 Everyone's favorite gun girl
3.1 Propagandist-in-chief
4 Additional wingnuttery
Loesch actually used to be a Democrat, working on Bill Clinton's re-election campaign in 1996.[2] However, she became disillusioned with the Democratic Party following the Monica Lewinsky scandal and 9/11, and became a Republican.
Becoming a crank[edit]
See the main article on this topic: Crank
Loesch very quickly immersed herself in conservative politics in the 2000s, starting as a simple columnist for St. Louis Magazine. However, she would very quickly graduate to the world of conservative talk radio, getting her own nationally syndicated radio show (called The Dana Show: The Conservative Alternative) on Radio America in 2008.[3] She caught the eye of Andrew Breitbart in 2010 and was appointed editor-in-chief of his website, Big Journalism.
Loesch was hired on by CNN in February 2011 to serve as a political analyst for the 2012 presidential election.[4]
Loesch joined TheBlaze in January 2014 to host her own show, Dana. She remained with TheBlaze until November 2017.[5]
She initially supported Ted Cruz during the 2016 presidential election, but following Donald Trump's election victory, she has since become a vociferous defender of the Trump Administration.[6]
Everyone's favorite gun girl[edit]
Loesch became a high-ranking official and spokeswoman for the NRA in 2017. Officially, she is recognized as "Special Assistant to the Executive Vice President for Public Communication."[7]
Propagandist-in-chief[edit]
Loesch has perfected the unique brand of NRA fearmongering that her boss Wayne LaPierre pioneered in the late 1990s and early 2000s. One particular video that first came out in June 2017 has her saying the following:
They use their media to assassinate real news. They use their schools to teach children that their president is another Hitler. They use their movie stars and singers and comedy shows and award shows to repeat their narrative over and over again. And then they use their ex-president to endorse the resistance — all to make them march, make them protest, make them scream "Racism" and "Sexism" and "Xenophobia" and "Homophobia", to smash windows and burn cars, shut down interstates and airports, bully and terrorize the law-abiding, until the only option left is for the police to do their jobs and stop the madness. And when that happens, they'll use it as an excuse for their outrage. The only way we stop this, the only way we save our country and our freedom is to fight this violence of lies with the clenched fist of truth.
— Dana Loesch, NRATV
Well, that is damn terrifying, and borders on fascism. Multiple sources, including Black Lives Matter, former Obama speechwriter Jon Favreau (not the Iron Man director), and U.S. Senator Chris Murphy, condemned the video, claiming that it was promoting violence against liberals[8][9], and a petition was started to have it removed from Facebook. Even some fellow Republicans were offended.[10] Loesch has, of course, defended herself, claiming that she was condemning violence, not condoning it, and that she was not at all promoting violence against anyone.
She rendered her arguments null and void when she appeared in a second video:
Recently, the New York Times in its bid to remain relevant and influential, tried to disrupt the narrative on fake news by promoting itself as truth. Who are you fooling, New York Times? NRATV responded to that garbage immediately with a video of their own, and in response to that dose of truth, Eileen Murphy, a spokeswoman for the New York Times, said — and I quote — 'Our commitment to the truth isn't new. It dates back 166 years. And each and every story in the NRA's video, from Benghazi to crime in Chicago, was covered in deep and rich detail by Times reporters.' Oh, New York Times? Because on September 11, 2012, you ran with the fake news that the terror attack in Benghazi was caused by a 14-minute movie trailer on YouTube! You published that while men like Chris Paronto, John Teegan, and Mark Geist — contractors who were actually on the ground in Benghazi — set you straight that this was a terror attack. In fact, New York Times — you ever dutiful shill for the Left — you get brownie points for continuing Hillary Clinton's debunked blame game even after e-mails produced showed her admitted to her own daughter that this was not the result of a video, but rather a terror attack! Please, tell me all about how you covered this story in 'rich, deep detail'! Well, you're embarrassing your profession by continuing a narrative that your boss lady has given up already! One more example on your 'deep, rich detail', New York Times: you always blame the gun when publishing stories on the Chicago crime rate, and refuse to acknowledge the fall of the prosecution rate for felony crimes. You refuse to acknowledge when the Chicago Police Superintendent excoriated judges for making cops' jobs harder by releasing violent offenders over and over again. Story after story, to say nothing of your inability to delve into that 'deep, rich detail' when using the term "assault rifle" as your undefinable, go-to scary buzzword for all guns. We the people have had it! We've had it with your narratives, your propaganda, your fake news… we've had it with your constant protection of your Democrat overlords. Your refusal to acknowledge any truth that upsets the fragile construct that you believe is real life. And we've had it with your pretentious tone-deaf assertion that you are, in any way, truth or fact-based journalism! Consider this the shot across your proverbial bow: we're going to fisk[sic] the New York Times and find out just what 'deep, rich' means to this old gray hag, this dishonest, untrustworthy rag that has subsisted on the welfare of mediocrity for one, two, three more decades? We're going to laser-focus on your so-called honest pursuit of truth. In short - we're coming for you.
— Dana Loesch, NRA News
So, first, she totally doesn't advocate violence against liberals, and now, in a rant that is chock-full of half-truths and straight-up bullshit, she totally isn't making actual violent threats against free press.
However, karma finally hit the woman when the NRA shut down its "NRATV" arm in June 2019, with the organization dismissing her as spokeswoman. She's now mostly limited to condescendingly say "God bless!" to anyone who calls her out on Twitter.
Additional wingnuttery[edit]
See the main article on this topic: Wingnut
In the aftermath of the school shooting at Marjory Stoneman Douglas High School in Parkland, Florida in February 2018, while speaking at that year's CPAC, she accused the news media of "loving" school shootings for the sake of getting ratings.[11] Not long after this, she appeared on CNN in an interview with Alisyn Camerota, who harshly denounced Loesch's statements, with Loesch immediately turning around and attacking Camerota for her comments, claiming that she was attacking her and the NRA.[12]
She once claimed that Martin Luther King might still be alive if he'd had a gun with him. Seriously.[13]
She is absolutely convinced that gun control is racist, sexist and discriminatory.
She constantly rants about the supposed liberal bias in the mainstream media.
She had made negative comments about feminism in the past, once declaring that feminism was "dead" and that the goal of modern feminism was to "subjugate men."[14]
While still on TheBlaze, she accused the "godless Left" of defending Jihadism in the wake of the terror attack in San Bernardino, calling them "tragedy dry-humping whores."[15]
She once claimed that if religious liberty wasn't protected, it could be possible for gays to be "stoned in the streets."[16] On the topic of religious liberty, she also once claimed that marriage was a religious civil liberty that shouldn't be granted by government.[17]
She once called Elizabeth Warren a "racist hypocrite" for attacking Jeff Sessions… and also called her a "basic skinwalker" (which can be considered a Native American slur in this context.[note 1])[19]
She once claimed that the police were justified in their murder of Philando Castile because he "smelled like marijuana."[20]
↑ Loesch has claimed to be part-Cherokee.[18]
Wayne LaPierre
↑ https://www.riverfronttimes.com/newsblog/2011/12/06/dana-loesch-departs-st-louis-tea-party
↑ https://www.bustle.com/p/dana-loeschs-political-views-show-the-nra-spokesperson-wasnt-always-a-conservative-8326200
↑ https://en.wikipedia.org/wiki/Dana_Loesch#cite_note-14
↑ http://cnnpressroom.blogs.cnn.com/2011/02/10/cnn-looks-ahead-to-2012-election-season/
↑ https://www.washingtonexaminer.com/dana-loesch-ends-show-on-glenn-becks-the-blaze/article/2639862
↑ https://www.theatlantic.com/politics/archive/2017/12/conservatism-is-what-conservatives-think-say-and-do/548738/
↑ https://m.sfgate.com/realestate/article/NRA-Spokeswoman-Dana-Loesch-Buys-Gated-Estate-in-12720748.php?forceWeb=1#item-85307-tbla-44
↑ https://www.nytimes.com/2017/06/29/us/nra-ad-trump-protests.html
↑ http://www.businessinsider.com/national-rifle-association-ad-call-to-violence-2017-6
↑ https://www.washingtonpost.com/news/post-nation/wp/2017/06/29/the-nra-recruitment-video-that-is-even-upsetting-gun-owners/
↑ https://www.cnn.com/2018/02/22/politics/dana-loesch-cpac-media/index.html
↑ https://www.youtube.com/watch?v=1IDpQPW-l44
↑ https://bossip.com/1633039/really-nra-advocate-dana-loesch-on-mlk-hed-be-alive-today-if-he-had-a-gun/
↑ http://www.patheos.com/blogs/dispatches/2017/10/16/loesch-declares-feminism-dead/
↑ http://www.rightwingwatch.org/post/dana-loesch-melts-down-the-tragedy-dry-humping-whores-on-the-godless-left-have-blood-on-their-hands/
↑ http://www.rightwingwatch.org/post/loesch-failure-to-protect-religious-liberty-could-lead-to-gays-being-stoned-in-the-streets/
↑ https://www.politico.com/story/2013/03/gay-marriage-conservative-pundits-split-89382_Page2.html
↑ Flyover Nation by Dana Loesch (2016) Penguin Books. p. 176. ISBN 9780399563898.
↑ https://www.facebook.com/NRATV/videos/10154383497072898/
↑ https://www.thedailybeast.com/dear-dana-loesch-how-do-you-sleep-at-night
Retrieved from "https://rationalwiki.org/w/index.php?title=Dana_Loesch&oldid=2172630"
Political pundits
Antifeminism
Authoritarian wingnuttery
Conservative wingnuttery
Conspiracy theorists
Gun nuts
Homophobes
Internalized discrimination
Internet kooks
Leftists who turned right with age
Right-wing activists


text:
\section{Introduction}

The accurate estimation of redshifts from photometric data is a key
component to fulfilling the promise of next-generation cosmological surveys.
For instance, photometry to $R \sim$ 30 is expected for
billions of galaxies from the Large Synoptic Survey Telescope (LSST;
\citealt{Ivezic08}) alone; compare this to, e.g., the $\sim$ 10$^5$ spectra
collected to a depth $R \sim$ 24 by the DEEP2 Galaxy Redshift Survey
(\citealt{Davis03}, \citealt{Davis07}).  It is clear that
redshift-dependent analyses of galaxies that aim to undercover
signatures of, e.g., weak lensing or baryon acoustic oscillations
in imaging data will require the use of photometric redshifts.

Redshifts estimated via, e.g., $ugriz$ photometry will necessarily lack
the precision of those that are spectroscopically derived due to noise,
outliers, weak spectral features, and incomplete spectral energy
distribution (SED) templates.  For this reason, one major goal of
photometric redshift estimation is to generate accurate ensembles of
estimates, i.e., to have the mean redshift within a photometric redshift
bin be an accurate estimator of true redshift (see, e.g.,
\citealt{Albrecht06}, \citealt{Ma06}).
Such ensembles are typically generated via one of two
methods: either template fitting, wherein redshifted SED templates are
generally
compared to a given vector of magnitudes with the goal of minimizing the
$\chi^2$ statistic or maximizing the likelihood
(e.g., \citealt{FernandezSoto99}, \citealt{Benitez00},
\citealt{Feldmann06}),
or empirical methods, wherein one
uses photometry from a small collection of objects with
spectroscopically confirmed redshifts to train a model relating
photometric colours to redshifts (e.g., \citealt{Connolly95},
\citealt{Vanzella04}, \citealt{Collister04}, \citealt{Budavari05},
\citealt{Ball07}, \citealt{Ball08}, \citealt{Oyaizu08}).
Some combine the two approaches (e.g., \citealt{Ilbert06},
\citealt{Ilbert09}), while others propose folding in information
beyond photometric colours (e.g., \citealt{Collister04},
\citealt{Ball04}, \citealt{Wray08}, \citealt{Newman08}).

In this paper we propose a new empirical method for photometric
redshift estimation based on the diffusion map
(\citealt{Coifman:Lafon:06}, \citealt{LafonLee2006}),
which is an approach to {\it spectral connectivity analysis (SCA)}.
SCA is a suite of established non-linear eigen-techniques\footnote{
The name SCA is applied to these eigen-techniques
by \cite{LeeWasserman2009}, who study their statistical properties.}
that capture the underlying geometry of data by
propagating local neighborhood information through a Markov process.
SCA thus allows one to find a natural coordinate system for data
such as photometric colours whose original parametrization is not
amenable to available statistical techniques.
In \cite{Richards09a} and \cite{Richards09b}, we apply the diffusion map
to two different astronomical problems.  In \cite{Richards09a},
we develop a framework combining diffusion map and adaptive linear
regression and apply it to SDSS spectroscopic data, demonstrating how
it may be used to reduce the dimensionality of the data space and to
predict, e.g., redshifts in a computationally efficient
manner. We also demonstrate the superiority of the diffusion map to
principal components analysis, a related, much more commonly
used linear technique.  In \cite{Richards09b}, we utilize the diffusion
map and the K-means clustering algorithm to
determine optimal bases of simple stellar population spectra that we
use to estimate the star-formation histories of galaxies.

In {\S}\ref{sect:alg}, we review the basics of our diffusion map and
regression framework, and introduce a new component:
the application of the Nystr\"om
extension (see, e.g., \citealt{NumRep}), a
computationally efficient and accurate technique
for estimating diffusion coordinates for new objects
given those of the training set.
In {\S}\ref{sect:apply}, we apply our framework to
Sloan Digital Sky Survey data, specifically main sample galaxies
(MSGs) and luminous red galaxies (LRGs), and demonstrate that we
achieve accuracy on par with that of more computationally intensive
techniques.  We also apply our framework to data from the DEEP2
Galaxy Redshift Survey
that is matched to $ugriz$ photometry of the Canada-France-Hawaii
Telescope Legacy Survey (CFHTLS; \citealt{Gwyn08})
and demonstate that it provides
accurate estimation of redshifts to $z \approx 0.75$ given four colours alone.
We demonstrate that the bivariate distributions of
photometric and spectroscopic redshifts for SDSS and DEEP2 are affected
by attenuation bias, the tendency of measurement error in the predictor
to reduce the slope of linear models.  Last, in {\S}\ref{sect:summary},
we summarize our results and discuss how we can extend our framework to
the high redshift regime where spectroscopic coverage will be incomplete.

\section{Algorithm}

\label{sect:alg}

\subsection{Diffusion map}

\label{sect:diff}

In this section we review the basics of diffusion map construction,
an approach to SCA.
For more details, we refer the reader to \citet{Coifman:Lafon:06},
\citet{LafonLee2006}, and \citet{Richards09a}.  In
\citeauthor{Richards09a},
we compare and contrast the use of diffusion maps with a more
commonly utilized linear technique, principal components analysis,
and demonstrate the superiority of diffusion maps in predicting
spectroscopic redshifts of SDSS data from the galaxy spectra.

Here,``spectral connectivity analysis'' refers to a class of
methods which utilize a local distance measure to ``connect''
similar observations. The eigenmodes (i.e., ``spectral decomposition'')
of the rescaled matrix of similarities (see below for the definition
of this matrix) can reveal a natural coordinate system for data that
was absent in the original representation.  For instance, imagine
data in two dimensions that to the eye clearly exhibit spiral
structure (e.g., fig. 1 of \citealt{Richards09a}).
For such data, the Euclidean distance between
data points $\bmath{x}$ and $\bmath{y}$
would not be an optimal description of the
`true' distance between them along the spiral.
Diffusion map is a leading example of an approach to SCA.
In the diffusion
map framework, the `true'
distance is estimated via a fictive diffusion process over the data,
with one proceeding from $\bmath{x}$ to
$\bmath{y}$ via a random walk along the spiral.

We construct diffusion maps as follows.

We define a similarity measure $s(\bmath{x},\bmath{y})$ that quantitatively
relates two data points $\bmath{x}$ and $\bmath{y}$.
In this work, a data `point' is a vector of colours \{$c_1,\dots,c_p$\}
of length $p$ for a single galaxy,
and the similarity measure that we apply is the Euclidean distance
\begin{eqnarray}
s(\bmath{x},\bmath{y})~=~\sqrt{\sum_{i=1}^p \left( c_{\bmath{x},i} - c_{\bmath{y},i} \right)^2 } \nonumber \,.
\end{eqnarray}
A key feature of SCA is that
the choice of $s(\bmath{x},\bmath{y})$ is not crucial, as it is often simple to
determine whether or not two data points are `similar.'

We remove extreme outliers from our dataset, not
because of their effect on diffusion map construction
(a hallmark of the diffusion map is its robustness in the presence
of outliers), but rather because they can bias the coefficients of the
linear regression model (see {\S}\ref{sect:regress}) and because we find
that individual predictions made for these objects are highly inaccurate.
We compute the empirical distributions of Euclidean distances in colour space
from each object to its $n^{\rm th}$ nearest neighbor, where
$n \in [1,10]$.  These
distributions are well-described as exponential, with estimated
mean and standard deviation
${\hat \mu}_n = {\hat \sigma}_n = {\tilde x}_n/\log(2)$
for median value ${\tilde x}_n$.
We exclude all data whose $n^{\rm th}$ nearest neighbor is at a distance
$> {\hat \mu}_n + 5{\hat \sigma}_n = 6{\hat \sigma}_n$, for any value
of $n \in [1,10]$.  We find that
$\approx$ 80\% of extreme outliers are removed with the first
nearest-neighbor cut alone, with the fraction of those removed
falling as $n$ increases.

With outliers removed, we
construct a weighted graph where the nodes are the observed data points:
\begin{eqnarray}
w(\bmath{x},\bmath{y})~=~\exp\left( -\frac{s(\bmath{x},\bmath{y})^2}{\epsilon} \right) \,,
\label{eqn:weighted}
\end{eqnarray}
where $\epsilon$ is a tuning parameter that should be small enough that
$w(\bmath{x},\bmath{y}) \approx 0$ unless $\bmath{x}$ and $\bmath{y}$ are
similar, but large enough such that the graph is fully connected.
(We discuss how we estimate $\epsilon$ in {\S}\ref{sect:regress}.)
The probability of stepping
from $\bmath{x}$ to $\bmath{y}$ in
one step is
$p_1(\bmath{x},\bmath{y})~=~w(\bmath{x},\bmath{y})/\sum_z w(\bmath{x},\bmath{z})$.
We store the one-step probabilities between all $n$ data points in
an $n \times n$ matrix \textbfss{P}; then, by the theory of Markov chains, the
probability of stepping from $\bmath{x}$ to $\bmath{y}$ in $t$ steps is given by
the element $p_t(\bmath{x},\bmath{y})$ of the matrix \textbfss{P}$^t$.
The diffusion distance
between $\bmath{x}$ and $\bmath{y}$ at time $t$ is defined as
\begin{eqnarray}
D_t^2(\bmath{x},\bmath{y})~=~\sum_{j=1}^{\infty} \lambda_j^{2t}(\bpsi_j(\bmath{x})-\bpsi_j(\bmath{y}))^2 \nonumber \,,
\end{eqnarray}
where $\bpsi_j$ and
$\lambda_j$ represent eigenvectors and eigenvalues of
\textbfss{P}, respectively.  By retaining the $m$ eigenmodes corresponding
to the $m$ largest nontrivial eigenvalues and by introducing the diffusion map
\begin{equation}
\mathbf{\Psi}_t: \bmath{x} \mapsto [\lambda_1^t\bpsi_1(\bmath{x}), \lambda_2^t\bpsi_2(\bmath{x}),\cdots,\lambda_m^t\bpsi_m(\bmath{x})]
\label{eqn:diffusion_map}
\end{equation}
from $\mathbb{R}^p$ to $\mathbb{R}^m$, we have that
\begin{eqnarray}
D^2_t(\bmath{x},\bmath{y})~\simeq~\sum_{j=1}^m \lambda_j^{2t}(\bpsi_j(\bmath{x})-\bpsi_j(\bmath{y}))^2~=~||\mathbf{\Psi}_t(\bmath{x}) - \mathbf{\Psi}_t(\bmath{y})||^2 \,, \nonumber
\end{eqnarray}
i.e., the Euclidean distance in the $m$-dimensional embedding defined
by equation~\ref{eqn:diffusion_map} approximates diffusion distance.
(We discuss how we estimate $m$ in {\S}\ref{sect:regress},
and show that, in this work, the choice of $t$ is unimportant.)
We stress that the diffusion map reparametrizes the data into a
coordinate system that reflects the connectivity of the data,
and does not necessarily affect dimension reduction.
If the original parametrization in
$\mathbb{R}^p$ is sufficiently complex, then it may be the case that $m \gg p$.

\subsection{Regression}

\label{sect:regress}

As in \citet{Richards09a}, we perform linear regression
to predict the function
$z = r(\mathbf{\Psi}_t)$, where $z$ is true redshift and $\mathbf{\Psi}_t$ is
a vector of diffusion coordinates in $\mathbb{R}^m$, representing
a vector of photometric colours $\bmath{x}$ in $\mathbb{R}^p$:
\begin{eqnarray}
{\widehat r}(\mathbf{\Psi}_t)~&=&~\mathbf{\Psi}_t {\widehat \beta}~=~\sum_{j=1}^m {\widehat \beta}_j \mathbf{\Psi}_{t,j}(\bmath{x}) \nonumber \\
&=&~\sum_{j=1}^m {\widehat \beta}_j \lambda_j^t \bpsi_j(\bmath{x})~=~\sum_{j=1}^m {\widehat \beta}_j' \bpsi_j(\bmath{x}) \nonumber
\end{eqnarray}
We see that the choice of the parameter $t$ is unimportant,
as changing it simply leads to a rescaling in ${\widehat \beta}_j$,
with no change in ${\widehat \beta}_j'$.  We present relevant
regression formulae in Appendix \ref{sect:regform}.

We determine optimal values of the tuning parameters
($\epsilon$,$m$) by minimizing estimates of the prediction risk,
$R(\epsilon,m)~=~\mathbb{E}(L)$,
where $\mathbb{E}(L)$ is the expected value of a loss function $L$ over
all possible realizations of the data
(one example of $L$ is the so-called $L_2$ loss function, which
is simply the mean-squared error of the fit;
see, e.g., \citealt{Wasserman2006} for
a discussion of this and other topics introduced below).
$R$ quantifies the `bias-variance' tradeoff: too much smoothing ($m$ too low)
yields prediction estimators with low variance and high bias, while too
little smoothing ($m$ too high) yields estimators with high variance and low
bias.  Using the full data set to estimate $R$
underestimates the error and leads to a best-fit
model with high bias, thus we apply $10$-fold cross-validation (CV).
The data are partitioned into 10 blocks of (approximately) equal size.
We regress upon the data in nine of the blocks and use the best-fit
regression model to predict the responses ${\widehat z}_i$ for the data
in the tenth block.
(We note that for algorithmic consistency we use the Nystr\"om extension
to estimate the diffusion coordinates of the data in the tenth block;
see {\S}\ref{sect:nystrom}.)
The process is repeated 10 times, for different block combinations,
so that predictions are generated for each datum.
The individual predictions are combined into an overall risk estimate
\begin{eqnarray}
{\widehat R}_{CV}(\epsilon,m)~=~\sqrt{\frac{1}{n} \sum_i \delta_i^2}~=~\sqrt{\frac{1}{n} \sum_i \left(\frac{\vert {\widehat z}_i - Z_i \vert }{1 + Z_i}\right)^2} \,. \label{eqn:rcv}
\end{eqnarray}
where we apply the redshift-corrected rms dispersion
as our loss function.
$Z_i$ is the estimated spectroscopic redshift for object $i$.
(We capitalize to underscore the fact that the spectroscopic redshift
is a random variable not necessarily equal to the true redshift $z_i$.)
To ensure robustness, for each set of tuning parameters $(\epsilon,m)$,
we compute the mean $\bar{{\widehat R}}_{CV}(\epsilon,m)$ of 10 estimates of ${\widehat R}_{CV}(\epsilon,m)$,
and select those values of $(\epsilon,m)$ such that $\bar{{\widehat R}}_{CV}(\epsilon,m)$ is
minimized, i.e., $({\widehat \epsilon},{\widehat m}) = {\rm arg~min} \:\bar{{\widehat R}}_{CV}(\epsilon,m)$.

\subsection{Diffusion coordinate estimation via the Nystr\"om extension}

\label{sect:nystrom}

The computation of diffusion coordinates (equation~\ref{eqn:diffusion_map})
relies upon eigen-decomposition, which is computationally intractable for
datasets of $\ga$ 10$^4$ galaxies.  (However, see \citealt{Budavari09}, who
propose an incremental methodology for computing eigenvectors.)
Photometric datasets can, of
course, be much larger, and thus we require a computationally efficient
scheme for estimating eigenvectors for new galaxies given those
computed for a small set of galaxies used to train
the regression model.  A standard method in applied mathematics for
`extending' a set of eigenvectors is the Nystr\"om extension.

The implementation
is simple: determine the distance in colour space from each new galaxy
to its nearest neighbors in the training set, then take
a weighted average of those neighbors' eigenvectors.  Let \textbfss{X}
represent the $n \times k$ matrix containing the colour data of the
training set, where $n$ and $k$ are the number of
objects and colours, respectively.  Let \textbfss{X'} represent
a similar $n' \times k$ matrix containing colour data for
$n'$ objects in the validation set.  The first step
of the Nystr\"om extension is to compute the $n' \times n$ weight matrix
\textbfss{W}, with elements equivalent to those shown in
equation~\ref{eqn:weighted} above (except that there, $\bmath{x}$ and $\bmath{y}$ are
both members of the training set, while here, $\bmath{x}$ is a new point while
$\bmath{y}$ belongs to the training set).
We assume the same value ${\widehat \epsilon}$ as was selected during diffusion map
construction; since the training set is a random sample of galaxies from
our original set, we expect the validation set to be sampled from the
same underlying probability distribution.
We row-normalize \textbfss{W} by dividing by each element in
row $i'$ by $\rho_{i'} = \sum_i W_{i',i}$.

Let $\mathbf{\Psi}$ be the $n \times m$ matrix of eigenvectors with
corresponding vector of eigenvalues $\lambda$.  To estimate the eigenvectors
for the new galaxies, we compute the $n' \times m$ matrix
$\mathbf{\Psi}'$:
\begin{eqnarray}
\mathbf{\Psi}'~=~\mathbfss{W} \mathbf{\Psi} \mathbf{\Lambda} \,,
\label{eqn:nys}
\end{eqnarray}
where $\mathbf{\Lambda}$ is a $m \times m$
diagonal matrix with entries $1/\lambda_i$.  Then the redshift
predictions for the $n'$ objects are
$\bmath{{\widehat z}} = \mathbf{\Psi}' \bmath{\widehat \beta}$,
where $\bmath{\widehat \beta}$
are the linear regression coefficients generated for the original training set.

\section{Application to SDSS and DEEP2 datasets}

\label{sect:apply}

\subsection{SDSS spectroscopic data}

In this work,
we use the Princeton/MIT reductions of SDSS spectroscopic data\footnote{
See \tt http://spectro.princeton.edu}.
Features of these data include the so-called
`uber-calibration' of $ugriz$ magnitudes in six magnitude systems
(\citealt{Pad08}).  To facilitate a direct comparison of our results with
those of \citealt{Ball08}, we utilize colours, i.e.,
differences between the magnitudes measured in different bands determined
in each of four magnitude systems:
{\it psf}, {\it fiber}, {\it petrosian}, and {\it model}.  Thus the
colour data occupy a $p$ = 16 dimensional space.

The necessary data are contained in the files {\tt spAll-$<$rel$>$.fits},
where {\tt $<$rel$>$} = EDR and DR1$-$DR6.
We extract data from all publicly available plates for which
{\tt PROGNAME} = `main' and {\tt PLATEQUALITY} = `good,' keeping
1001 plates in all.  (We keep only one instance of each plate when
repeated observations are made, making the ad hoc choice to retain the
most recent observation.)
For each plate, we examine data for those fibers for
which {\tt CLASS} = `GALAXY,' {\tt Z} $>$ 0.01,
and {\tt ZWARNING} = 0.  For each of these fibers, we apply
extinction corrections \{$A$\} (from column {\tt EXTINCTION})
to the set of fluxes $\{F\}$
and the set of estimated standard errors $\{s_F\}$ (\citealt{Fink04}):
\begin{eqnarray}
F'~&=&~10^{0.4A}F \nonumber \\
s_{F'}~&=&~\frac{s_F}{\sqrt{10^{-0.8A}}} \nonumber \,.
\end{eqnarray}
If for any object, one or more elements of the
set $\{F'\} < 0$, we exclude the object from analysis.
The flux units are nanomaggies; the conversion from $F'$ to magnitude $m'$
is $m' = 22.5 - 2.5 \log_{10} F'$, while the conversion to colours is
$c_{i-j}' = 2.5 \log_{10}(F_j'/F_i')$.

The final number of galaxies in our sample is 417,224.

\subsubsection{Main sample galaxies}

From our data sample, we extract those 360,122 galaxies with Petrosian
$r$-band magnitude $< 17.77$ (or $F_{\rm Petro}^R >$ 77.983;
\citealt{Strauss02}).
This is our main sample galaxy or MSG sample.
We randomly select 10,000 galaxies from this sample
to train our regression model.  Application of the outlier-removal algorithm
described in {\S}\ref{sect:regress} leads to the removal of 251 galaxies from
this set.
The application of the algorithm outlined in {\S}{\S}2.1-2 yields tuning
parameter estimates (${\widehat \epsilon},{\widehat m}$) = (0.05,150), i.e., in order for a
linear model to be appropriate, the 16-dimensional colour data is reparametrized
into 150-dimensional space.

As each object's eigenvector estimates are independent of those for
other objects, we apply the Nystr\"om extension
to validation set objects one plate at a time,
then concatenate the resulting predictions.  We determine which members of
the validation set are 5$\sigma$ outliers relative to the members of the
training set, and compute the value of ${\widehat R}_{CV}$ with those objects
excluded.  (Not excluding these outliers, which lie too far from the
training set in colour space for their diffusion coordinates
to be estimated accurately,
results in ${\widehat R}_{CV}$ rising from $\approx$ 0.02 to 0.56.)
Out of 350,122 objects in the validation set, we exclude
9,133; the percentage of outliers is 2.61\%.  This is consistent
with the 2.51\% rate of outliers in the training set.

\begin{table}
\caption{Parameters of optimal regression}
\label{symbols}
\begin{tabular}{lccccc}
\hline
Dataset & $({\widehat \epsilon},{\widehat m})$ & ${\widehat R}_{CV}$ & $\eta$ (\%) & $n$ & $n_{\rm out}$ \\
\hline
MSG-T & (0.05,150) & 0.0206 & 0.010 & 9,749 & 251 \\
          &            & (0.0231) \\
MSG-V & & 0.0211 & 0.018 & 340,989 & 9,384 \\
 &            & (0.0240) \\
LRG-T & (0.012,200) & 0.0189  & 0.010 & 9,734 & 266 \\
          &            & (0.0258) \\
LRG-V & & 0.0195 & 0.034 & 20,082 & 884 \\
          &            & (0.0270) \\
DEEP2-T & (0.002,850) & 0.0507 & 1.67 & 5,223 & 304 \\
 &            & (0.1063) \\
DEEP2-T & (0.002,1050) & 0.0539 & 2.14 & 6,067 & 351 \\
       &            & (0.1123) \\
\hline
\end{tabular}

\medskip

In the column `Dataset,' T = training set and V = validation set.
$\eta$ is the rate of catastrophic failures (i.e., the rate at which
$\delta > 0.15$),
$n$ is the number of galaxies used in analysis after outlier removal, and
$n_{out}$ is the number of 5$\sigma$ outliers removed from sample.
The number (outside/inside) the parantheses in column ${\widehat R}_{CV}$
(includes/does not include) normalization by $(1+Z)$.
$u$-band data are excluded from LRG analyses.
For {\em DEEP2-T}, the first and second rows represent analyses of
objects for which {\tt ZQUALITY} = 4 and {\tt ZQUALITY} $\geq$ 3, respectively.
\label{tab:results}
\end{table}

We show our results in Table \ref{tab:results} and the top panel
of Fig.~\ref{fig:sdss}, in which we display
predictions for 10,000 randomly chosen objects of the validation set.
The accuracy of prediction via the Nystr\"om extension versus directly
fitting a linear regression model to the diffusion map coordinates
of the data is indicated in
Table \ref{tab:results}.  We find that ${\widehat R}_{CV}$ increases by
2.4\% from 0.0206 to 0.0211, with
catastrophic failure rate $\eta$ increasing but still small.
(Here, a catastrophic failure for object $i$ is defined as
$\delta_i >$ 0.15; see equation \ref{eqn:rcv} and, e.g., \citealt{Ilbert06}.)
The small degradation in accuracy is more than balanced by computational
speed; our naive implementation allowed
extension to 350,373 galaxies in $\sim$ 10 CPU hours on
a single GHz processor, a computation time that will be markedly
reduced in future implementations of the algorithm.
${\widehat R}_{CV}$ = 0.0211 (0.0240 without normalization by $1+Z$)
compares favorably with a myriad of other
analyses of MSG data (see, e.g.,
\citeauthor{Ball08}, who obtain $\sigma$ = 0.0207 without $1+Z$ normalization,
and references therein), and
the empirical bivariate distribution of $({\widehat z},Z)$ is
visually indistinguishable from those of, e.g., \citeauthor{Ball08}
and \cite{Collister04}.

\begin{figure}
\includegraphics[width=84mm]{pred_msg_lrg.ps}
\caption{Top: predictions for 10,000 randomly selected objects in
the MSG validation set, for $({\widehat \epsilon},{\widehat m})$ = (0.05,150).
Bottom: same as top, for the LRG validation set, with
$({\widehat \epsilon},{\widehat m})$ = (0.012,200).
In both cases, we remove 5$\sigma$ outliers from the sample
prior to plotting, thus the actual number of plotted points
is 9,740 (top) and 9,579 (bottom).
}
\label{fig:sdss}
\end{figure}

\begin{figure}
\includegraphics[width=84mm]{bias_figure.ps}
\caption{
Top Left: estimated bias ${\widehat z} - Z$ for MSG redshift estimates ${\widehat z}$,
computed in bins of width $\Delta Z$ = 0.01 in the range $Z \in [0.01,0.25]$.
Top Right: estimated standard deviation for MSG redshift estimates
(normalized by $1+Z$).
Middle Left and Right: same as top left and right,
except for LRG redshift estimates in bins of width $\Delta Z$ = 0.02
in the range $Z \in [0.20,0.44]$.
Bottom Left and Right: same as top left and right,
except for DEEP2 redshift estimates ({\tt ZQUALITY} = 4)
in bins of width $\Delta Z$ = 0.05
in the range $Z \in [0.0,1.5]$.
}
\label{fig:bias}
\end{figure}

We determine estimator bias by binning
the predictions ${\widehat z}$ as a function of $Z$, then
in each bin computing $\bar {\widehat z} - Z$, with
$\bar {\widehat z}$ being a 10\% trimmed mean.
See the top left panel of Fig.~\ref{fig:bias}.
It is readily apparent that there is a downward slope in the bias
(i.e., redshifts are overestimated at low $Z$, and underestimated at
high $Z$).  This is not caused by model bias (a bias that one would
mitigate by adding complexity to the model, e.g., changing from linear
to quadratic regression), but rather by {\it attenuation bias},
in which measurement error
(i.e., uncertainty in the predictor, in this case the diffusion
coordinates) reduces the slope of the regression line
(see Fig. \ref{fig:atten}; see also, e.g., \citealt{Carroll06}).
\begin{figure}
\includegraphics[width=84mm]{atten.ps}
\caption{
Simple demonstration of the effect of attenuation bias on linear
regression.  Left: example of linear regression fit to data with
no measurement error in the predictor and with response $Y \sim N(x,0.04)$,
where $x = \{1,2,3,4\}$, i.e.,
each value of $Y$ is sampled from a Gaussian distribution with mean
$x$ and variance 0.04.
The black dots indicate the observed data,
while the open circles show the true $(x,y)$ values.
Right: same as left, but with measurement error applied to the
predictor: $X \sim N(x,1)$.
The effect of this measurement error is to reduce the slope of the
regression line, on average.  The mean reduction in slope for this
toy example is 0.25 (from 1 to 0.75), as estimated via 10,000 simulations.
}
\label{fig:atten}
\end{figure}
To demonstrate that our data are affected by attenuation bias, we perform
a simple experiment.  First, we take the MSG
training set fluxes and resample
them according to the prescription given in Appendix \ref{sect:sample}.
This increases all measurement errors.  (To see this intuitively, imagine
sampling random variables $X \sim N(0,1)$, i.e.,
each value of $X$ is sampled from a Gaussian distribution with mean
0 and variance 1.
Then resample from the observed values $X$: $Y \sim N(X,1)$.  The
standard deviation of the resulting sample is now $\sqrt{2}$, i.e., the
error has been artificially increased by resampling.)
Then we resample fluxes for 1,000 randomly selected validation set
objects.  By doing each resampling (training set and validation set) 25
times, we build up a set of 625 predictions of ${\widehat z}$ for each of the 1,000
selected objects.  Following the same prescription as above, we estimate
the bias; the top panel of Fig. \ref{fig:comp_bias} shows how for the MSG
dataset, increasing the measurement error via resampling leads to
a steepening of the bias slope, i.e., the effect of attenuation bias
is magnified.

There exist methods for correcting the bias in linear regression coefficient
estimation caused by additive, heteroscedastic (i.e., non-constant)
measurement errors of known magnitude that are
based on the {\tt SIMEX}, or simulation-extrapolation, algorithm
(\citealt{Cook94}; see, e.g., \citealt{Carroll06} and references therein).
Indeed, one of the advantages to our approach is that the non-linearity
is in the reparametrization, not the fitted model. Hence, available methods
for correcting for measurement error could be utilized.
We are currently exploring the implementation of {\tt SIMEX}-based
methods in a computationally efficient manner, and we will
present our results in a future publication.

\begin{figure}
\includegraphics[width=84mm]{comp_bias.ps}
\caption{
Top: change in the estimated bias ${\widehat z} - Z$ induced by resampling
MSG training and validation set fluxes and refitting.  Because resampling
increases the measurement error (i.e., the error in the predictor, in this
case the diffusion coordinates), the slope of the regression line is
reduced, increasing overestimates of ${\widehat z}$ at low $Z$ and underestimates
of ${\widehat z}$ at high $Z$.  Bottom: same as top, for LRG datasets.
}
\label{fig:comp_bias}
\end{figure}

While attenuation bias is caused by measurement error, its magnitude is
affected by the distribution of the predictors, i.e., the design.
Expressions relating the design to the bias magnitude are highly problem
dependent.  In the
simplest, one-dimensional example of attenuation bias, the predictors are
assumed to be normally distributed--$X \sim N(\mu_x,\sigma_x^2)$--and the
effect on the slope $\beta_1$ is to reduce its value:
$\hat{\beta_1} \rightarrow \lambda \beta_1$, where $\lambda =
\sigma_x^2/(\sigma_x^2 + \sigma_u^2)$ and $\sigma_u$ is the measurement error.
The smaller the value of $\sigma_x^2$, the greater the effect upon the bias.
We mention this explicitly to underscore that analyzing samples for which
the predictors are, e.g., uniformly distributed may reduce the magnitude of
the bias magnitude but will not eliminate it since measurement error is still
present.  In Fig.~\ref{fig:bias_unif}, we show the estimated sample bias
as a function of $Z$ for a 10,000-galaxy sample constructed so as to be
uniform in $Z$ (though the
distribution of the predictors themselves--the diffusion coordinates--is not
necessarily uniform).  Comparing these results with the top panels of
Fig.~\ref{fig:bias}, we find that uniformity in $Z$ reduces the bias slightly
(while also slightly increasing sample standard
deviation).  This indicates that measurement error is the dominant cause
of the observed bias.

\begin{figure}
\includegraphics[width=84mm]{bias_unif.ps}
\caption{
Left: estimated bias ${\widehat z} - Z$ for MSG redshift estimates ${\widehat z}$,
computed in bins of width $\Delta Z$ = 0.01 in the range $Z \in [0.01,0.25]$,
for a 10,000-galaxy sample constructed so as to be uniform in $Z$.
Uniformity in $Z$ reduces the bias slightly (cf.~the top panel of
Fig.~\ref{fig:bias}).  This result indicates that measurement error is
the dominant cause of the bias.
Right: estimated standard deviation for MSG redshift estimates
(normalized by $1+Z$).
}
\label{fig:bias_unif}
\end{figure}

Nonparametric estimators such as k-nearest neighbor
(kNN) and local polynomial regression are also affected by measurement error
bias (whose mitigation is dubbed the ``deconvolution problem")
and design bias, and in addition by boundary bias (see, e.g.,
chapter 5 of \citealt{Wasserman2006} and
chapter 12 of \citealt{Carroll06} and references therein).
Thus the similarity
of our bivariate distribution to that of, e.g., \citeauthor{Ball08}  (See
their fig.~6.  In this figure, we note slightly larger deviations from the
$\hat{z} = Z$ locus at the endpoints than our bivariate distribution exhibits,
which may indicate boundary bias but also could be a result of the fact
that \citeauthor{Ball08} do not minimize risk and thus could be adopting
a solution with relatively higher bias and lower variance than our solution.)

In addition to estimator bias, we also examine the estimator variance, i.e.,
the width of the observed bivariate distribution
(given as a function of $Z$ in the right column of Fig.~\ref{fig:bias}).
Contributing to the variance
is (a) model uncertainty, i.e., the standard deviation of the estimates
${\widehat z}$ (given by the square root of the diagonal elements of the matrix
given in equation~\ref{eqn:vy}); (b) uncertainty in the flux
for each object;
and (c) intrinsic scatter, i.e., the
fact that the MSG sample does not necessarily contain a homogeneous set of
objects.  Model uncertainty contributes little to the observed scatter;
the mean, median, and standard deviation of
the model uncertainties are $\la$ 10$^{-5}$.  Flux uncertainty enters
via attenuation bias; as flux errors increase,
the linear regression slope flattens and acts to decrease the sample
variance within a redshift bin.  However, in our simple
attenuation-bias demonstration we observe only negligible changes
in the sample variance.
Thus we conclude that the observed sample variance
is primarily due to intrinsic scatter,
and can only be reduced by introducing more data (cf.~\citealt{Ilbert09},
who achieve ${\widehat R}_{CV} \la$ 0.01 by utilizing data from 30 bands in the
UV, optical, and IR regimes).

\subsubsection{Luminous red galaxies}

From our data sample, we extract those 30,700 galaxies for which
$Z > 0.2$ and {\tt PRIMTARGET} = 32 ({\tt TARGETGALAXYRED}; \citealt{Eisen01}).
This is our luminous red galaxy or LRG sample.  As with the MSG training
set, we randomly select 10,000 galaxies and then remove outliers.
Because the $u$ band data of high-redshift LRGs lacks constraining power
(as LRGs are faint in $u$ and thus the magnitudes are noisy),
we use only $griz$ fluxes in analyses
(so that $p$ = 12).  The training set contains 9,734 objects.
Application of the algorithm outlined in {\S}{\S}2.1-2.2
yields tuning parameter estimates $({\widehat \epsilon},{\widehat m})$ = (0.012,200).
The results of fitting are shown in Table \ref{tab:results} and
the bottom panel of Fig. \ref{fig:sdss}.  As in the case of the
MSG analysis, our value ${\widehat R}_{CV}$ = 0.0195 (0.0270 without $1+Z$ normalization)
compares favorably with, e.g., \citealt{Ball08}, who achieve
$\sigma$ = 0.0242 (without $1+Z$ normalization), and references therein.
We find that the outlier rate is consistent from training
set to validation set (increasing from 2.7\% to 3.1\%),
and that ${\widehat R}_{CV}$ increases by only 3.1\%
when we use the Nystr\"om extension as opposed to directly fitting the data.
(Note that if we include the $u$ band, the estimate of ${\widehat \epsilon}$ increases by
two orders of magnitude, indicating the scatter in colour space introduced
by non-constraining $u$-band data, although
${\widehat R}_{CV}$ itself only rises by $\approx$ 5\%.)
The LRG redshift predictions, like their MSG counterparts, are biased,
with a similar downward trend in the bias as a function of $Z$
(left middle panel, Fig.~\ref{fig:bias}).  We repeat
our simple resampling experiment with LRG data and find that the bias slope
increases upon resampling, demonstrating that attenuation bias also
affects LRG data analysis (as expected; see Fig.~\ref{fig:comp_bias}).

\subsection{DEEP2/CFHTLS data}

The DEEP2 Galaxy Redshift Survey (\citealt{Davis03}, \citealt{Davis07})
studied both galaxy properties and large-scale structure
primarily at redshifts $0.7 \la z \la 1.4$,
in four fields of total area $\sim$ 3 square degrees.
DEEP2 targets are selected to have $R_{AB} \leq$ 24.1 using CFHT BRI
photometric data (\citealt{Coil04}).
In three of the four DEEP2 fields, colour cuts are used to select
$z >$ 0.7 objects for observation; however, in this paper we utilize
the DEEP2 sample in the Extended Groth Strip, for which no colour cuts
have been applied.  DEEP2 collected spectra typically covering the
wavelength range 6,500$-$9,100 \AA~for $>$ 50,000 objects.
From the survey we select the 6,552 galaxies for which single-system
$ugriz$ photometry
exists from the CFHT Legacy Survey
(field D3)\footnote{
See {\tt http://www4.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/\\
community/CFHTLS-SG/docs/cfhtls.html} and \cite{Gwyn08}.
}
and for which
the DEEP2 {\tt ZQUALITY} flag is either 3 or 4
($>$ 95\% or 99.5\% confidence that the redshift is correct, respectively).
Thus the dimensionality of colour-space for these data is $p$ = 4.
We further remove data for which the redshift error, or any magnitude or
magnitude error, is not provided, leaving 6,418 galaxies; after outlier
removal, the final sample size is 6,067.  If we restrict ourselves to
data for which {\tt ZQUALITY} = 4, the sample size is 5,223.

Application of the algorithm outlined in {\S}{\S}2.1-2.2 yields
tuning parameter estimates $({\widehat \epsilon},{\widehat m})$ = (0.002,850) for
{\tt ZQUALITY} = 4 and (0.002,1050) for {\tt ZQUALITY} $\geq$ 3.
We display our results
in Table \ref{tab:results} and Fig. \ref{fig:deep2}; note that because
we do not apply the Nystr\"om extension here (but rather, fit to the
data directly after $({\widehat \epsilon},{\widehat m})$ are determined), the observed
scatter is smaller than we would observe with a
larger, Nystr\"om-extended dataset.
In both cases, we exclude 5.8\% of the objects from analysis as outliers.

\begin{figure}
\includegraphics[width=84mm]{pred_deep2.ps}
\caption{
Top: predictions for the 6,067 objects in the DEEP2 training
set for which {\tt ZQUALITY} $>$ 3.
For these data, $({\widehat \epsilon},{\widehat m})$ = (0.002,1050) and ${\widehat R}_{CV}$ = 0.0539.
Bottom: same as top, for the 5,223 objects for which {\tt ZQUALITY} = 4;
$({\widehat \epsilon},{\widehat m})$ = (0.002,850) and ${\widehat R}_{CV}$ = 0.0507.
}
\label{fig:deep2}
\end{figure}

In Fig. \ref{fig:deep2}, we observe that the quality of the
fits below $Z \approx$ 0.75 (${\widehat R}_{CV}$ = 0.038 for
{\tt ZQUALITY} = 4) is superior
to that at higher redshifts (${\widehat R}_{CV}$ = 0.064).  To understand why
this is so, we examine the DEEP2 colour data (Fig. \ref{fig:deep2colours}).
\begin{figure}
\includegraphics[width=84mm]{deep2colours.ps}
\caption{
Observed $ugriz$ colours for the 5,223 objects in the DEEP2 training set
for which {\tt ZQUALITY} = 4.
}
\label{fig:deep2colours}
\end{figure}
Pick an object at $Z \approx 0.75$, and compute the Euclidean distance in
colour-space to a random object at any other redshift $Z \in [0,1.5]$.
This distance is a nearly constant function of $\Delta Z$; thus for
values of $\epsilon$ similar to those chosen in the SDSS analyses, there
is only a slightly lesser probability of diffusing from $Z =$ 0.75 to, e.g.,
$Z =$ 0.2 as to, e.g., $Z =$ 0.74.  To achieve accurate predictions
at $Z \approx 0.75$, $\epsilon$ must be made smaller
(lessening the probability of large $\Delta Z$ jumps); this is what
our optimization yields.  A consequence of a smaller ${\widehat \epsilon}$ is that the
weighted graph of the DEEP2 objects is not fully connected
(see discussion around equation~\ref{eqn:weighted}).
One can discern connectedness by examining the vector of eigenvalues;
for ${\widehat \epsilon}$ = 0.002, the first $\approx$ 20 eigenvalues are all $>$ 0.95,
implying the presence of several disconnected clumps on the graph.
The most visually obvious
manifestation of disconnectedness in the DEEP2 analysis is the
presence of a marked knee in the predictions
at $Z \approx$ 0.75 for small values of $m$ (see Fig. \ref{fig:deep2ev});
the dominant
eigenvectors describe the low redshift data well, but not the high
redshift data.
\begin{figure}
\includegraphics[width=84mm]{deep2ev.ps}
\caption{
Predictions for the 5,223 objects in the DEEP2 training set for which
{\tt ZQUALITY} = 4, for ${\widehat \epsilon}$ = 0.002 and $m$ = 40 (top left),
100 (top right), 400 (bottom left), and 850 (${\widehat m}$; bottom right).
}
\label{fig:deep2ev}
\end{figure}
As $m$ increases, the knee straightens out; however, because
of the bias-variance tradeoff, $m$ can only increase so much before ${\widehat R}_{CV}$
begins to increase as well, due to increasing variance.  For
${\widehat m}$ = 850 ({\tt ZQUALITY} = 4) we have not yet achieved an
optimal description for the high-redshift data.  To demonstrate
that we can achieve a better description of these data,
we split the full dataset into low- and high-redshift sets
(at, e.g., $Z_{\rm cut}$ = 0.9) and
compute diffusion maps for each.  We find that we can achieve, e.g.,
${\widehat R}_{CV} \approx$ 0.035 for high-redshift data with as few as 40 eigenvectors,
while the predictions at low redshifts change only slightly.
While splitting the data yields better results for our DEEP2 sample,
we do not propose such
splitting as part of our general diffusion map framework, for multiple reasons:
(a) it adds a tuning parameter ($Z_{\rm cut}$), (b) it complicates the
Nystr\"om extension (to which data split do we assign a new object?), and
most importantly (c) a data split can be rendered moot with the inclusion of
new data in other bandpasses (e.g., the inclusion of near-IR data in the DEEP2
sample would mitigate the Euclidean-distance issue seen at $Z \approx 0.75$).

Concentrating on the regime $Z \la 0.75$, we find that our result
${\widehat R}_{CV} \approx 0.035$ with $\eta \approx$ 1.1\%
compares favorably with that of \cite{Ilbert06}, who train a template-based
photometric redshift code using 2,867 spectroscopic redshifts from the
VIMOS VLT Deep Survey (VVDS) in the CFHTLS D1 field and obtain
$\sigma = 0.032$ and $\eta =$ 4\% (see their {\S}6.3 and fig.~14).
Our smaller catastrophic failure rate is presumably largely due to our
removal of colour-space outliers prior to analysis.  We note that
\citeauthor{Ilbert06} perform
a similar analysis with CFHTLS $z$-band data removed,
with the result that a marked knee appears at $Z \approx$ 0.8 that is
similar to what we observe in analyzing our intrinsically bluer DEEP2 sample.
This supports the hypothesis that adding data from other bandpasses to
our DEEP2 sample will lead to a marked improvement in fit at redshifts
$Z \ga$ 1.

\section{Summary and future directions}

\label{sect:summary}

In this paper we apply an eigenmode-based framework utilizing the diffusion
map and linear regression to the problem of estimating redshifts
given SDSS and DEEP2/CFHTLS $ugriz$ photometry.  Because estimating
diffusion map coordinates via eigen-decomposition limits the size
of training sets to $\sim$ 10$^4$ objects, we implement the Nystr\"om
extension, which allows for computationally efficient estimation
of diffusion coordinates with a relatively small degradation of
accuracy.

For our SDSS MSG sample, we train our linear regression
model on 9,749 randomly selected objects
and via the Nystr\"om extension estimate
redshifts for another 340,989 galaxies.
Since the Nystr\"om extension is not robust to
extreme outliers, we use a nearest-neighbor algorithm to
eliminate 5$\sigma$ outliers in colour space; this eliminates
$\approx$ 2.5\% of the MSG sample.  The loss in accuracy resulting from
use of the Nystr\"om extension is $\approx$ 2.4\% (as compared with
directly fitting the data of the training set).
For our SDSS LRG sample, we train our regression model on 9,734 objects
and via the Nystr\"om extension estimate redshifts for another 20,082,
with an outlier rate $\approx$ 3\% and a degradation of accuracy
$\approx$ 3\%.  As the DEEP2/CFHTLS sample has only $\approx$ 6,000
objects (with an outlier rate of $\approx$ 5.8\%), we do not define
a validation set to check the accuracy of predictions generated via
the Nystr\"om extension.  However, we will apply our regression model
to a test set comprised of all galaxies in CFHTLS fields D1-D4 and make
that catalog publicly available.

The observed bivariate
distributions $({\widehat z},Z)$ for our SDSS datasets are
similar to those computed by, e.g.,
\cite{Collister04} using {\tt ANNz} (specifically, for the SDSS MSG dataset)
and by \cite{Ball08} using
a numerically intensive nearest-neighbor algorithm (for both
the SDSS MSG and LRG datasets), with dispersion
on par with those techniques (${\widehat R}_{CV} \sim 0.02$; see \citealt{Ball08}
and references therein).  These distributions indicate that redshifts
are generally overestimated at low $Z$ and underestimated at high $Z$.
We demonstrate that
this is a manifestation of attenuation bias, wherein measurement error
(uncertainty in the diffusion coordinates resulting from uncertainty
in the SDSS flux estimates) reduces the measured slope of the regression line.
In statistical parlance, the measured slope is not a consistent estimator
of the true slope.  In order to use photometric redshift estimates in
precision cosmology, it is vital that methods for producing consistent
estimates (i.e., mitigating the bias) be developed and implemented.
We are exploring using the
{\tt SIMEX}, or simulation-extrapolation, algorithm (e.g.,
\citealt{Carroll06}) to produce consistent estimates in a computationally
efficient manner, and we will present our results in a future publication.

For the DEEP2 data, the dominant feature in the observed bivariate
distribution, beyond attenuation bias, is a marked reduction in prediction
accuracy at redshifts $Z \ga$ 0.75.
We demonstrate that this is due to
a degeneracy in the colour-space manifold that would be mitigated
with the introduction of more data from other bandpasses.  We note that
we also can mitigate the effects of the degeneracy by splitting the
training set into low- and high-$Z$ samples, but we do not prefer this
approach because of the complexity it adds to the prediction algorithm
(through the addition of a tuning parameter $Z_{\rm cut}$ and the
necessity of providing a quantitative measure for robustly choosing between
the two predictions we would generate for each test object).
At lower redshifts,
we find that the observed bivariate distribution $({\widehat z},Z)$ compares
favorably with that derived by \cite{Ilbert06} (${\widehat R}_{CV} \approx$ 0.035 versus
$\sigma$ = 0.032).

Our current statistical framework yields a single photometric redshift
estimate for each object in the validation set, as opposed to
a probability distribution function (PDF) for each estimate
(cf.~\citealt{Ball08}).  This is a
valid approach for analyzing, at the very least, the galaxies of the SDSS
sample that we consider in this work, as \citeauthor{Ball08} demonstrate
that the PDFs in the low-redshift regime are approximately normal;
we expect our single estimates to match the PDF means.  However, we would have
to alter our framework if we were to analyze quasars, for which the
PDFs are often bimodal (e.g., fig.~5 of \citeauthor{Ball08}).  Bimodality
is an indication of (near-)degeneracy in the colour-space manifold; when its
colours are perturbed, a quasar's nearest neighbor sometimes belongs to
one range of redshifts, and sometimes to a completely different range.
Within our current framework,
such a degeneracy would not affect the computation of the diffusion map,
but the subsequent
application of linear regression would yield inaccurate redshift estimates
for those quasars in the vicinity of the degeneracy.  For quasar analysis,
we would explore a variety of options, which include (a) utilizing a
different form of regression, (b) incorporating
the response variables into the construction of the diffusion map
(\citealt{Costa05}), and/or
(c) incorporating gradient information into
diffusion map construction, such that nearby objects that lie along
the manifold have higher similarity measures.  Such schemes would mitigate
but not entirely lift the degeneracy and thus we would also have to
quantify the relative probabilities of dual estimates.

In this work, we demonstrate the efficacy of SCA, in particular
our diffusion map framework,
for analyzing datasets for which the spectroscopic redshifts are known.
The next step is to extend our framework such that it yields accurate
photometric redshift estimates for objects in datasets where the
spectroscopic coverage will be minimal, such as deep
sky surveys (e.g., LSST) or pointed surveys beyond $Z \approx 1$.
Even with long exposure times, the DEEP2 Galaxy Redshift Survey
is only able to
determine secure redshifts for $\sim$ 70\% of its objects,
with about half the missed targets being star-forming galaxies at
$Z > 1.4$ that have no features in DEEP2 spectral window;
cf.~\cite{Cooper06}.  Even when spectroscopic redshifts are available
for a significant subset of these objects, it is likely that they
will be gleaned from intrinsically luminous objects whose SEDs may not
closely match those for fainter objects.  Thus it becomes imperative
to fold additional information into analyses.  \cite{Collister04},
\cite{Ball04}, and \cite{Wray08} propose
using structural properties such as surface brightness and angular radius
to obtain more
accurate redshift estimates; however, this is of limited utility at
higher redshifts.
\cite{Newman08} proposes that photometric redshifts can be calibrated
using their correlations on the sky with objects of known redshift,
as a function of that known redshift.  A related idea would be to
take into account the redshifts of nearby objects on the sky in
estimating photometric redshifts (\citealt{Kovac09});
because of the clustering of galaxies, there is a significant probability
that two galaxies near each other on the sky are at very similar redshifts.

In a future work, we will fold additional quantities into our similarity
measure
and will determine
if photometric redshift can be estimated with sufficient accuracy so
as to fulfill their promise as a cosmological probe.

\section*{Acknowledgements}

We would like to thank both
the referee and Larry Wasserman for helpful comments.
This work was supported by NSF grant \#0707059.
Funding for the DEEP2 survey has been provided by NSF grants AST95-09298,
AST-0071048, AST-0071198, AST-0507428, and AST-0507483 as well as NASA LTSA
grant NNG04GC89G.  DEEP2 data presented herein were obtained at the W. M.
Keck Observatory, which is operated as a scientific partnership among the
California Institute of Technology, the University of California and the
National Aeronautics and Space Administration. The Observatory was made
possible by the generous financial support of the W. M. Keck Foundation.
The CFHTLS data were obtained with MegaPrime/MegaCam, a joint project of
CFHT and CEA/DAPNIA, at the Canada-France-Hawaii Telescope (CFHT) which
is operated by the National Research Council (NRC) of Canada, the Institut
National des Science de l'Univers of the Centre National de la Recherche
Scientifique (CNRS) of France, and the University of Hawaii. This work is
 based in part on data products produced at TERAPIX and the Canadian
Astronomy Data Centre as part of the Canada-France-Hawaii Telescope
Legacy Survey, a collaborative project of NRC and CNRS.

redpajama_set_name: RedPajamaArXiv


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text:
Christian Community Mourns the Death of Kobe Bryant
by Brooke Girley | 12:41 pm, January 27th, 2020
Yesterday the world was thrown into shock over the tragic news that NBA Legend Kobe Bryant had been killed in a helicopter accident along with his daughter Gianna and seven others.
The former Lakers star was on his way to his daughter's basketball game when the helicopter experienced difficulty caused by poor weather conditions. Eventually, the helicopter went down in a mountainous area in Calabasas, California killing everyone on board. The official cause of the crash is still under investigation.
As the news quickly spread, friends, fans and admirers alike flooded social media with expressions of disbelief at the loss and offered emotional tributes to one of the greatest basketball players of all time.
Within the Christian community, well-known leaders and preachers also wrote moving words for the father of four, underscoring the breadth and depth of his legacy.
"Such a tragedy. Praying for God's presence and comfort for Kobe's family," wrote Pastor Craig Groeschel of the multi-campus Life Church.
Such a tragedy. Praying for God's presence and comfort for Kobe's family. 🙏🏻❤ (photo via klkntv.com)
A post shared by Craig Groeschel (@craiggroeschel) on Jan 26, 2020 at 1:17pm PST
Minister and therapist Dr. Anita Phillips offered three pieces of advice to help cope with moments of grief and uncertainty.
"First, remember. Remember that our yet living energy and persistent faith still have power….Next Connect. Connect with your family, your friends, your online community….your people…[and] Finally feel. Feel whatever comes. Emotions aren't permanent but they are real. Don't expend precious energy fighting them off."
These are the moments that overwhelm and leave us with the worst kind of questions. Why them? Why this day? Why like this? There are no answers; certainly none that would ever satisfy our broken hearts. So, we are left grieving an unimaginable loss, jarred by an encounter with the impermanence of life, in a world that suddenly feels less safe. Now what ? _____ First, remember. Remember that our yet living energy and persistent faith still have power, so we harness that and send strong prayers to where the wound is deepest — the hearts of the families whose lives will never be the same. To Vanessa Bryant and their 3 daughters and to the loved ones of the other passengers. It matters . _____ Next, connect. Connect with your family, your friends, your online community….your people. Call, text, FaceTime. Send love and open yourself to deeply feel the love they send back to you. Let it wash over you and fill you. It matters . _____ Finally, feel. Feel whatever comes. Emotions aren't permanent but they are real. Don't expend precious energy fighting them off. Let them come when they come and pass when they pass. Painful feelings are an unavoidable part of life but I'm still grateful for life. Grateful for every moment of life. It matters . _____ #kobe
A post shared by Dr. Anita Phillips (@dranitaphillips) on Jan 26, 2020 at 2:41pm PST
Bishop T.D. Jakes of the Potter's House in Dallas called Bryant a national treasure and a symbol of hope.
"My heart is broken as @KobeBryant is a national treasure to our country and a symbol of hope and fatherhood to our community. We are all rattled and reminded of our own mortality, humanity and that of those we love. So sad for @VanessaBryant and the families of those whose candle light has disappeared on this side."
A post shared by T.D. Jakes (@bishopjakes) on Jan 26, 2020 at 2:44pm PST
Australian preacher and Bible teacher Christine Cain shared that she and her daughters were heartbroken by the news. "Like so many, I am devastated and heartbroken. My own girls pain and sorrow at this news cut deep. LA is mourning," she wrote before reposting Dr. Phillips' words.
Pastor Kim commented on how much she admired the bond between Bryant and his daughter. "So very heartbreaking! 😭😭 I loved their bond! I am Praying for this precious family," she wrote.
So very heartbreaking! 😭😭 I loved their bond! I am Praying for this precious family!
A post shared by REAL TALK KIM (@realtalkkim) on Jan 26, 2020 at 3:05pm PST
Pastor A.R. Bernard of the Christian Cultural Center in Brooklyn, spoke to Bryant's legacy as a family man, writing, "Husband, father, legend, icon! We will miss you Kobe and Gianna!! My thoughts and prayers… "
Husband, father, legend, icon! We will miss you Kobe and Gianna!! My thoughts and prayers… #kobe #KobeBryant #mambamentality
A post shared by A. R. Bernard (@therealarbernard) on Jan 26, 2020 at 4:44pm PST
Gospel music executive Kirk Franklin simply wrote "Jesus" in the caption to a picture of the basketball star.
A post shared by Kirk Franklin (@kirkfranklin) on Jan 26, 2020 at 12:08pm PST
(Photo by Harry How/Getty Images)


text:
City of Sugar Land
Sugar Land Airport
Visit Sugar Land
ecodev@sugarlandtx.gov
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Site Selection & Recruitment
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Incentives & Financing
Key Development Areas
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Demographics & Trends
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Live in Sugar Land
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Sugar Land Town Square is an award-winning, 32-acre, mixed-use public-private partnership.
Sugar Creek on the Lake is one of the many Class A office space properties in Sugar Land.
Founded as a sugar plantation in the mid 1800s and incorporated in 1959, Sugar Land is located in Fort Bend County, 20 miles southwest of downtown Houston.
Welcome to Sugar Land
Sugar Land means business. The City of Sugar Land is an award-winning community ideally located just southwest of Houston. Sugar Land has achieved its sustainable, strong local economy through an aggressive, focused economic development program that creates new wealth and jobs. As a regional employment center, with nearly 26 million square feet of commercial space, the City is home to high-profile corporations including Minute Maid, UnitedHealthcare, Texas Instruments, Schlumberger, Fluor Corporation, Noble Drilling Services, and Bechtel EO.
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Sugar Land Welcomes Greatwood and New Territory to City Family
On December 12, 2017 at 12:01 a.m., Greatwood and New Territory were annexed by the City of Sugar Land.
Greatwood and New Territory are premier communities with first-class infrastructure; beautiful parks; carefully planned, well maintained neighborhoods; and quality retail and commercial areas that are consistent with Sugar Land's commitment to excellence. These areas will not only enhance the quality and beauty of the city, but have grown the city to approximately 117,869 residents.
Based on the collaborative efforts between the city and leaders of both communities, the annexation was a seamless transition. The city is committed to providing full services on day one and welcomes Greatwood and New Territory residents to the Sugar Land family.
Over 63,000 SF of contiguous, Class A office space available at Sugar Creek 1. Outstanding access and visibility from the Southwest Freeway at the intersection of U.S. Highway 90A along with extensive landscaping of the project's mass of green space provide a serene and welcoming atmosphere for employees and visiting clients. The property enjoys tremendous accessibility to and from the rest of the Houston MSA as well as a close proximity to the numerous master-planned residential communities in the area, including First Colony, Telfair, Greatwood and New Territory.
City of Sugar Land Welcomes ABM, a Fortune 500 Company, to Sugar Land's Corporate Community
Sugar Land, TX - The Sugar Land Development Corporation recently approved an agreement with ABM as the Company relocates its corporate shared services operations to the City of Sugar Land.
ABM, a Fortune 500 company and a leading provider of facility solutions, has been serving Texas since 1945 and currently has more than 10,000 employees maintaining over 4,500 buildings throughout the state.
"ABM is excited to join the Sugar Land corporate community," said ABM Industries Incorporated Chief Financial Officer D. Anthony Scaglione. "The City presented itself as a compelling business environment and a prime residential location. We look forward to our partnership with Sugar Land."
ABM will bring approximately 390 jobs to the City of Sugar Land over the next two years as the Company occupies approximately 62,000 square feet within the Sugar Creek on the Lake office building at 14141 Southwest Freeway.
"Sugar Land is thrilled to welcome another Fortune 500 company to our corporate community," said City Manager Allen Bogard. "Sugar Land's pro-business attitude continues to attract businesses, bringing with them high quality jobs. We are proud ABM will call Sugar Land home."
An incentive package was created by the City's Office of Economic Development and approved by the Sugar Land Development Corporation to secure the Company in Sugar Land. The Office of Economic Development worked with the Greater Houston Partnership to relocate the ABM Shared Services divisional headquarters to Sugar Land following a regional search of potential sites.
"The Greater Houston Partnership greatly applauds ABM's selection of Sugar Land as the hub of its corporate shared services operations," said Greater Houston Partnership Chief Economic Development Officer Bob Pertierra. "ABM demonstrates a commitment to growing its corporate footprint in an area that offers a vast pool of talented and skilled workers, land for future growth, and superior quality of life assets."
Sugar Land, TX – Schlumberger has announced plans to consolidate the company's U.S. headquarters to the City of Sugar Land as part of its long-term resource management and consolidation strategy.
As part of this headquarters-consolidation project, the Fortune Global 500 company will reposition more than 500 jobs to its Sugar Land campus. The company will also be investing more than $200 million in capital improvements at its existing 200-acre Sugar Land campus along US Highway 90A to accommodate the move.
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Today's deal over at Amazon is another good one on a MicroSD card. Yesterday I posted on the SanDisk 128GB MicroSD card for $39. Today it gets one-upped by the Patriot 128GB card. Right now on Amazon you can pick up the Patriot 128GB Class 10 MicroSD card for $29.99. This card has a transfer rate of 70mb/sec so full HD video recording on this card will be no problem at all. The card comes with a SD card adapter as well, something that everyone can use a spare one or two.
for your device, you can pick up the same spec'd 64GB card for just $15.99.
As always, make sure you check your phone or camera to make sure it can handle the 128GB card. The price is good until supplies run out at Amazon.


Where possible, changes made in one dataset or recipe propagate to the datasets that consume it. Datasets that join, union, or lookup against your dataset are likely to be impacted if you delete columns or rows or otherwise change the data. In some cases, the recipes of these dependent datasets can break.
This section describes how to identify these dependency issues and includes general steps for fixing them.
When making edits to a recipe, you can verify if your changes potentially impact other recipes or reference datasets that rely on it. In the Transformer page, click the drop-down next to the current dataset's name to open the Recipe Navigator. Select the Flow View tab.
Tip: If your current dataset is connected to datasets to the right of it, those datasets are dependent on the current one. After you make changes to the current one, you should use the Recipe Navigator to open recipes and datasets that are connected to it and to the right of it in flow view.
In the above, the column Day does not exist in the current dataset, which is causing problems in the last two recipe steps. These types of errors may be generated when a column in the upstream dataset has been dropped or renamed .
If the recipe or dataset is from the same flow, you can use the Recipe Navigator in the Transformer Page. See Recipe Navigator.
If the recipe or dataset is in a different flow, use the Flows page to locate it (REF_CAL.txt in the above). See Flows Page.
In the Flow View tab, open the dataset referenced in the error message.
In the Recipe panel, locate the step where the column was removed.
Fix the issue. Details are below.
If you make changes to specific values in a dataset, recipe steps in downstream datasets can break if they rely on detecting specific values. Depending on the usage, the step may not actually be broken, but the generated results are incorrect.
If the company_name column is sourced from another dataset and the My Co. value is changed to My Company, the downstream dataset that includes this transform doesn't break in an easily noticeable way. The data is simply not removed from the dataset and any generated results.
Fix the issue in the source dataset. Verify that the change does not impact other datasets.
NOTE: If you fix the issue in the source dataset, you should verify if any other downstream datasets are impacted by this change.
Change the input dataset to use a dataset that is not broken.
Tip: If you must freeze the data in the dataset that you are using as an input, you can create a copy of the dataset as a snapshot. See Dataset Details Page.
To use the copy, repair or rebuild the integration using the copied version.
Fix the issue in the dataset that depends on it. In this case, you must redefine the transformation that brings in the data.


text:
Contemporary artist, Barbara Pickering, is best known for her non-traditional portraiture and figurative art. Her culturally diverse work depicts the human condition and speaks to the emotional self. The majority of her work is oil on canvas or linen. She also creates drawings using India Ink.
Barbara grew up in an artist's home. Her father, Canadian Artist, Jack L. Pickering was an important influence and they enjoyed painting together. Her great-great grandfather, Joseph L. Pickering was a Royal Academy Artist in England. Along with gallery shows in Canada and the Caribbean, Barbara's work is held in private collections worldwide. Her art holds international awards and media attention.
Artist's Statement:
Painting and drawing are my communication tools. My hope is that my art will stir something within the viewer and maybe entice them to see things in a new way. Faces and body language tell stories in artwork. Those stories may comfort, disturb or amuse but ultimately if they start conversations, I've served my purpose as an artist.
Email: barbara@bapickeringart.com
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Siemens Industry, Inc.'s SharePoint-based intranet serves more than 22,000 employees in the U.S. When engagement and readership waned, the company turned to Webtrends to track internal content and campaigns. Siemens used the data to create a more community-centered site, adding videos, contests and chances for readers to contribute. Readership grew 162 percent and employees started contributing content, making internal communications more effective overall.
Want a high-speed train? A gear box the size of a Volkswagen Beetle? How about some car headlights? Siemens makes them all. How does the company's internal online communications team keep all their thousands of employees on the same page? That page starts with a SharePoint intranet.
The team does not create intranet content itself. Instead, it trains more than 450 users to make their own with SharePoint-based collaboration sites.
The internal online communications team has had good content and management support: senior execs blog on the company intranet. The messages were fine — designed to inform and raise morale — and they drew a lot of comments. But readership and engagement had waned after the novelty wore off. The team needed data to show what was happening, and to test ways to fix it.
The team created a dashboard to show engagement, top downloads, visits and locations, and sent it to all business units and management regularly. It experimented with link and content types and placement, learning which combinations worked best.
The findings: Users love video. Text, not so much.
The internal online communications team, in conjunction with the Employee and Leadership Communications team, moved toward more open forums, letting people post videos and interact with other employees. The team kept the executive blog, but also let others post "day in the life" videos, contests and other content.
Siemens found that marketing tactics used on its external website, like contests, also worked on the intranet. Employees were eager to embark on an online scavenger hunt and search for clues, which included watching a C-level exec video carefully — noting the color of shirt worn as a sign of engagement.
The team also learned how content updates increase SharePoint use, prodding contributors to post new material more often — boosting usage and productivity.
Next up, the team wants to optimize internal search as part of a plan to match internal metrics with strategic business goals. "We want to streamline information and distribute those reports to the right people at the right time," LeCourt said.


text:
José Carbó is an Argentinian-Australian opera baritone. He has performed nationally and internationally for Opera Australia, the Sydney Symphony Orchestra and the Metropolitan Opera.

Carbó worked with Slava and Leonard Grigoryan to produce an album titled My Latin Heart. It was nominated for the 2012 ARIA Award for Best Classical Album. The trio then embarked on a tour also titled My Latin Heart featuring music from the album.

Along with classical guitarists Andrew Blanch and Ariel Nurhadi he formed the José Carbó Trio who first performed in 2015.

Discography

Albums

Awards and nominations
In 2004, Carbó won the Australian Singing Competition's Opera Award. He received a Helpmann Award for Best Male Performer in a Supporting Role in an Opera for Opera Australia's Die tote Stadt in 2013.

ARIA Music Awards
The ARIA Music Awards is an annual awards ceremony that recognises excellence, innovation, and achievement across all genres of Australian music. They commenced in 1987.

!
|-
| 2012
| My Latin Heart (with Slava and Leonard Grigoryan)
| Best Classical Album
|
|rowspan="2" |
|-

References

External links

Year of birth missing (living people)
Living people
Australian operatic baritones
Helpmann Award winners
Argentine expatriates in Australia

redpajama_set_name: RedPajamaWikipedia


EVENT Cinemas Tauranga Crossing is now open, bringing you big screens, amazing sound and star treatment – your movie experience will never be the same again. Elevating the traditional cinema experience, you now have the choice to upgrade your seat and choose the level of comfort that suits you.
EVENT Cinemas is open all Easter Weekend.
Our standard seats have been redesigned to provide additional comfort through contoured high backs, side tables and double the amount of legroom. Full recliner signature seats are designed for true comfort, with a fully motorised reclining system you can set to the perfect angle. The double daybed is the ultimate movie watching experience. Snuggle up and enjoy the movie in your very own daybed, positioned in the front of the cinema for the perfect unobstructed view.
The first purpose built, world class VMAX auditorium in New Zealand offers a big screen blockbuster movie experience, featuring stadium seating with a giant state of the art digital screen and Dolby Atmos sound. The latest breakthrough in sound technology will leave you on the edge of your seat. With more speakers than ever before, sound can be transported all around you in a three-dimensional space, making you part of the action. You've never heard anything like this before.
Sign up to the EVENT Cinemas movie club Cinebuzz. It's free to join and there are lots of perks. Members earn points for every movie experience, have the opportunity to enjoy a discount movie every week and advance screenings of the latest movies, and access special offers and promotions. Visit cinebuzz.co.nz to sign up today.


We're on Day 3 of our cross-country road trip. Last night, Julie and I arrived in Louisville at 10pm, at the affordable farm house her sister just recently moved into. We were both exhausted, so we went to sleep pretty quickly.
This morning, I was able to contact an old friend, Cathy, who lived with me and my mom when I was 14, and meet up with her at her work for her 30 minute break. It was nice to see her and get to catch up randomly. And, really, that's what this trip is all about for me. Taking things as they come. Practicing relaxing in stressful situations.
We left a bit late because we met up with a couple more of Julie's friends kind of at the last minute, but we should still get to Springfield (the stopping area of choice due to time constraints and our desire to camp). I kind of wanted to meet up with my cousin in Columbia, MO, but it's too far out of the way. This is unfortunately not a leisurely trip. Anyway, we just stopped at walmart (thanks, walmart iphone app!!) to pick up an extra sleeping bag, camping supplies and food. I drove all morning, so I'm super-exhausted. But I'll keep you updated. The scenery has been lots of corn, flat. Getting a bit more interesting now, but West Virginia was way prettier. That's all for now!


random youtube videos: how are they found?
I've always wondered what exactly causes people to find strange videos on youtube. A lot of the internet crazes like "star wars kid" and the "dramatic prairie dog" are word to moth referals. But how did it start? what made the first person want to click on that link and how long did it take?
Its just a fan trailer, and I've seen others that are popular. However, if it doesn't get the 1st wave of hype from when its a "new video" will it then be lost? I imagine the trailer will interest more people once the game's released and it has a more solid fanbase. But would people actually find it then? If it didn't have many hits before, it wouldn't come up very high on the search list.
though, I guess 4chan usually helps.


HIS Majesty Sultan Haji Hassanal Bolkiah Mu'izzaddin Waddaulah ibni Al-Marhum Sultan Haji Omar 'Ali Saifuddien Sa'adul Khairi Waddien, Sultan and Yang Di-Pertuan of Brunei Darussalam has emphasised the merits of cultivating patience in order to achieve success and prosperity according to the principles of Islam.
The monarch then outlined the example of the Prophet Ibrahim (pbuh), who was commanded to sacrifice his son, as a test of his obedience and submission to Allah the Almighty.
"As members of the present-day Ummah, we are fortunate to be tested by means of our religious obligations and adherence to the principles of Islamic law," said His Majesty.
"In religious worship, for instance, we are tested by being obliged to keep up daily prayer, fasting in the month of Ramadhan and so on. In terms of Islamic law, we are tested through our freedom to choose between what is lawful (Halal) and the forbidden (Haram), including our compliance with moral and judicial laws of Allah the Almighty.
His Majesty concluded the titah by encouraging Muslims to pray for Bruneian Haj pilgrims currently performing their Haj obligations in Makkah and Madinah.
"Let us pray that Allah the Almighty may grant us with enduring patience, particularly for our country's Haj pilgrims in the Holy Land," said His Majesty.
"As we all know, during this period of time they will encounter many hardships and ordeals, which are also experienced by pilgrims from the rest of the world.


The 1964 Afghanistan Constitution names both Dari (an Afghan dialect of Farsi) and Pashto as the official languages. Dari literally means language of the court (dabar or dar; not darra, language of the valley) and is the lingua franca and is used for education and bureaucracy, though the Constitution designates Pashto as the national language.
A special committee appointed in 1964 aimed to promote the growth and spread of Pashto. During the 1953-63 ministry of Sardar Mohammad Daoud Khan, a disastrous attempt to Pashto-ize all government inter-office memoranda collapsed in a welter of translation and re-translation as non-Pashto-speaking high-ranking officials had their assistants convert paperwork back-and-forth.
Vocabulary differences occur between Dari dialects, but all are mutually understandable. The Wakhi and Pamiri have more difficult understanding their respective archaic Dari (Avestan) dialects, however. Pashto is not mutually intelligible with Dari and has nine phonemes unknown to Dari. Dari and Pashto are as different as English and German, or French and Spanish.
Within Nuristan, valleys just a few hours from each other may have different terms for such important designations as father, mother, brother and sister. This remarkable resistance to acculturation serves to perpetuate a society's cultural ethos. In contrast, scattered Moghol communities have become Persianized-- several old men in Turkabad in 1961 "deplored the fact that younger men became more Persianized with each generation, and most Moghol speak Farsi, even at home" (Dupree 2002, p 74).
Afghan farsi. Most rural Afghans still refer to the language as Farsi, not Dari.
Farsi with many Turkic loan words.
Related but not identical to Tajiki spoken in Tajikistan. Afghans refer to Tajiki by the valley in which it is spoken, such as Panjsheri, Andarabi, etc.
Farsiwan farmers of western Afghanistan speak Iranian Farsi.
Heratis have an urban dialect all their own.
Kabulis speak the slurred Brooklynese of Dari dialects.
A softer dialect found in the Qandahar area, in the southwest of Afghanistan.
A harsher dialect spoken in Peshawar and the North West Frontier Province and most of the Tribal Agencies.
Spoken in southwestern Afghanistan. The Brahui, Dravidian-speakers living amonghte Baluch, speak Baluchi as a second language using the South Indian language almost exclusively in the home.
Within Nuristan. Include several disparate group. Often each village or valley attaches a specific name to its dialect, such as Ormuri, Pashai, Deghani, Wamai, Waigali, Kami, Kati (Bashgalior Kamdeshi), Prasun (Vermir), Ashuni.
Turkic dialects concentrated north of the Hindu Kush among the Uzbek, Turkoman and Kirghiz. Dialects vary from group to group, but most are mutually intelligible. The closer the Uzbek are to Persian speakers, the more Persian words get incorporated into their language. Turkic languages of Afghanistan use the Arabic script. However, former SSR's have had their languages transposed into the Cryllic alphabet.