Let us begin with an illustration.
The reader will remember the sensation that was created by the speech delivered by M. A. Stakhovich, Marshal of the Nobility of Orel Gubernia, at a missionary congress, in the course of which he urged that freedom of conscience be recognised by law. The conservative press, led by Moskovskiye Vedomosti, is conducting a furious campaign against Mr. Stakhovich. It cannot find names vile enough with which to call him and almost goes so far as to accuse the entire Orel nobility of high treason for having re-elected Mr. Stakhovich as Marshal. Now, this re-election is indeed very significant and to a certain degree it bears the character of a demonstration of the nobility against police tyranny and outrage.
The sacred truth! This is precisely the case. It is because religious "delusions" are so widespread among the masses that the Stakhoviches and the Oblomovs," and all our capitalists who live by the labour of the masses, and even Moskovskiye Vedomosti itself, "sleep peacefully". And the more education spreads among the people, the more will religious prejudices give way to socialist consciousness, the nearer will be the day of victory for the proletariat —the victory that will emancipate all oppressed classes from the slavery they endure in modern society.
But having blurted out the truth on one point, Moskovskiye Vedomosti disposed, far too easily, of another interesting point. It is obviously mistaken in believing that the Stakhoviches "do not realise" the significance of religion, and that they demand liberal forms out of sheer "thoughtlessness". Such an interpretation of a hostile political trend is too childishly näive. The fact that in this instance Mr. Stakhovich came forward as advocate of the entire liberal trend was proved best of all by Moskovskiye Vedomosti itself; otherwise, what need was there for waging such a campaign against a single speech? What need was there for speaking, not about Stakhovich, but about the Stakhoviches, about the "intellectual crowd"?
All these socialists forget that the interests of the autocracy coincide only with certain interests of the proper tied classes, and only under certain circumstances; frequently it happens that its interests do not coincide with the interests of these classes, as a whole, but only with those of certain of their strata. The interests of other bourgeois strata and the more widely understood interests of the entire bourgeoisie, of the development of capitalism as a whole, necessarily give rise to a liberal opposition to the autocracy. For instance, the autocracy guarantees the bourgeoisie opportunities to employ the crudest forms of exploitation, but, on the other hand, places a thousand obstacles in the way of the extensive development of the productive forces and the spread of education; in this way it arouses against itself, not only the petty bourgeoisie, but at times even the big bourgeoisie. The autocracy guarantees (?) the bourgeoisie protection against socialism, but since the people are deprived of rights, this protection is necessarily transformed into a system of police outrages that rouse the indignation of the entire people. What the result of these antagonistic tendencies is, what relative strength of conservative and liberal views, or trends, among the bourgeoisie obtains at the present moment, cannot be learned from a couple of general theses, for this depends on all the special features of the social and political situation at a given moment. To determine this, one must study the situation in detail and carefully watch all the conflicts with the government, no matter by what social stratum they are initiated. It is precisely the "class point of view" that makes it impermissible for a Social-Democrat to remain indifferent to the discontent and the protests of the "Stakhoviches".
The reasoning and activity of the above-mentioned socialists show that they are indifferent to liberalism and thus reveal their incomprehension of the basic theses of the Communist Manifesto, the "Gospel" of international Social-Democracy. Let us recall, for instance, the words that the bourgeoisie itself provides material for the political education of the proletariat by its struggle for power, by the conflicts of various strata and groups within it, etc. Only in politically free countries has the proletariat easy access to this material (and then only to part of it). In enslaved Russia, however, we Social-Democrats must work hard to obtain this "material" for the working class, i.e., we must ourselves undertake the task of conducting general political agitation, of carrying on a public exposure campaign against the autocracy. This task is particularly imperative in periods of political ferment. We must bear in mind that in one year of intensified political life the proletariat can obtain more revolutionary training than in several years of political calm. For this reason the tendency of the above-mentioned socialists consciously or unconsciously to restrict the scope and content of political' agitation is particularly harmful.
Let us recall also the words that the Communists support every revolutionary movement against the existing system. Those words are often interpreted too narrowly, and are not taken to imply support for the liberal opposition. It must not be forgotten, however, that there are periods when every conflict with the government arising out of progressive social interests, however small, may under certain conditions (of which our support is one) flare up into a general conflagration. Suffice it to recall the great social movement which developed in Russia out of the struggle between the students and the government over academic demands, or the conflict that arose in France between all the progressive elements and the militarists over a trial in which the verdict had been rendered on the basis of forged evidence. Hence, it is our bounden duty to explain to the proletariat every liberal and democratic protest, to widen and support it, with the active participation of the workers, be it a conflict between the Zemstvo and the Ministry of the Interior, between the nobility and the police régime of the Orthodox Church, between statisticians and the bureaucrats, between peasants and the "Zemstvo" officials, between religious sects and the rural police, etc., etc. Those who contemptuously turn up their noses at the slight importance of some of these conflicts, or at the "hopelessness" of the attempts to fan them into a general conflagration, do not realise that all-sided political agitation is a focus in which the vital interests of political education of the proletariat coincide with the vital interests of social development as a whole, of the entire people, that is, of all its democratic elements. It is our direct duty to concern ourselves with every liberal question, to determine our Social-Democratic attitude towards it, to help the proletariat to take an active part in its solution and to accomplish the solution in its own, proletarian way. Those who refrain from concerning themselves in this way (whatever their intentions) in actuality leave the liberals in command, place in their hands the political education of the workers, and concede the hegemony in the political struggle to elements which, in the final analysis, are leaders of bourgeois democracy.
The class character of the Social-Democratic movement must not be expressed in the restriction of our tasks to the direct and immediate needs of the "labour movement pure and simple". It must be expressed in our leadership of every aspect and every manifestation of the great struggle for liberation that is being waged by the proletariat, the only truly revolutionary class in modern society. Social-Democracy must constantly and unswervingly spread the influence of the labour movement to all spheres of the social and political life of contemporary society. It must lead, not only the economic, but also the political, struggle of the proletariat. It must never for a moment lose sight of our ultimate goal, but always carry on propaganda for the proletarian ideology —the theory of scientific socialism, viz., Marxism—guard it against distortion, and develop it further. We must untiringly combat any and every bourgeois ideology, regardless of the fashionable and striking garb in which it may drape itself. The socialists we have mentioned above depart from the "class" point of view also because, and to the extent that, they remain indifferent to the task of combating the "criticism of Marxism". Only the blind fail to see that this "criticism" has taken root more rapidly in Russia than in any other country, and has been more enthusiastically taken up by Russian liberal propaganda than by any other, precisely for the reason that it is one of the elements of the bourgeois (now consciously bourgeois) democracy now information in Russia.
It is particularly in regard to the political struggle that the "class point of view" demands that the proletariat give an impetus to every democratic movement. The political demands of working-class democracy do not differ in principle from those of bourgeois democracy, they differ only in degree. In the struggle for economic emancipation, for the socialist revolution, the proletariat stands on a basis different in principle and it stands alone (the small producer will come to its aid only to the extent that he enters, or is preparing to enter, its ranks). In the struggle for political liberation, however, we have many allies, towards whom we must not remain indifferent. But while our allies in the bourgeois-democratic camp, in struggling for liberal reforms, will always glance back and seek to adjust matters so that they will be able, as before, "to eat well, sleep peace fully, and live merrily" at other people's expense, the proletariat will march forward to the end, without looking back. While the confreres of R. N. S. (author of the preface to Witte's Memorandum) haggle with the government over the rights of the authoritative Zemstvo, or over a constitution, we will struggle for the democratic republic. We will not forget, however, that if we want to push someone forward, we must continuously keep our hands on that someone's shoulders. The party of the proletariat must learn to catch every liberal just at the moment when he is prepared to move forward an inch, and make him move forward a yard. If he is obdurate, we will go forward without him and over him.
 Oblomov—the central character in the novel of that name by I. Goncharov. Oblomov was the personification of routine, stagnation, and inertia.
 The collection Proletarskaya Borba (Proletarian Struggle), No. 1, was published by the Ural Social-Democratic Group in 1899. The authors, who espoused "Economist" views, denied the necessity of establishing an independent working-class political party and believed that a political revolution could be accomplished by means of a general strike, without the preliminary organisation and preparation of the masses and without an armed uprising.
 "The Social-Democratic Workers' Library"—a series of pamphlets published illegally in Vilno and St. Petersburg in 1900-01.
 See The Manifesto of the Communist Party, Marx and Engels, Selected Works, Vol. I, Moscow, 1959, pp. 21-65.
 The reference is to the general strike of students organised in the winter of 1901-02. Some 30,000 students took part in the strike.
 Lenin refers to the case of Dreyfus, a French General Staff officer, a Jew, who, in 1894, was court-martialled and sentenced to life imprisonment on an obviously trumped-up charge of espionage and high treason. That provocative trial was organised by French reactionary circles. The general movement for the defence of Dreyfus that developed in France exposed the corruption of the court and sharpened the struggle between republicans and royalists. In 1899 Dreyfus was pardoned and released. It was not until 1906, after a fresh examination of the case, that Dreyfus was rehabilitated.


O God! My heart is loving You at all times.
My love today I feel is even more real.
today I have just understood that it was for my own sake.
I really feel extremely remorseful in my heart.
and removing the mixture in my love.
Your love is the breath of my life.
and even more have the pain of refining.
my corrupt disposition is really hateful.
Today it's You who come to save me.
You have occupied my heart.
and Your glory has appeared on earth.
and I have so many feelings of love to pour out.
and in my heart have been kept Your kind words.


We all love Bear Grylls and most men would love to go on the adventures that Bear embarks on, so we have created The 10 commandments of Bear Grylls for you to follow.
If you can follow these 10 commandments during you everyday life then you'll become a little more like the man we all admire. These have been designed for normal live in the city and at the office!
If you see a bus you need to catch run for it. Jump on in even if it's still moving.
Every meal you eat has to include meat.
Eat something that you have to catch or pick from the ground.
Slide down banisters, never take lifts and alway stride more than one step at a time if taking the stairs.
Take short cuts, don't take the long way round even if it means getting dirty or wet.
Hop at least 7 fences a day, be that garden or railings.
Always have spare dry clothes with you.
Make a raft or a shelter each day.
Good luck with the The 10 commandments of Bear Grylls!


text:
Хајнрих Ранцау (11. март 1526—31. децембар 1598) је био дански државник, писац и астроном.

Биографија
Рођен је у Стеинбургу. Хајнрихов отац, Јохан, био је успешан немачки генерал и државник. Од 1556. године Ранцау је краљев намесник у Шлезвигу и Холштајну. Издао је два дела са војном тематиком. Написао је "Историју Дитмаршенског рата" која је 1570. године објављена у Базелу. У њој описује покоравање слободних сељака Дитмаршена од стране данских феудалаца (1559. године). Дело представља вредну и доста објективну хронику. Хајнрих се бавио и астрономијом, заједно са Тихом Брахеом.

Види још
 Тихо Брахе

Извори
 Војна енциклопедија, том 7 (731)

Рођени 1526.
Умрли 1598.
Дански астрономи

redpajama_set_name: RedPajamaWikipedia


Providing Affordable and Accessible Educational Material to Students Locally and Abroad.
"One man's junk is another man's treasure." In 2013, that became more than just a saying for our founder, Chris Janssen.
As a student at Western University, Chris noticed that many of his peers had stacks of old textbooks sitting in their dorm rooms. They loved donating this old material to help other students.
While lecturing in East Africa, Chris found a very different scenario. Smart and ambitious students were sharing photocopied, out-of-date material.
With these experiences and an entrepreneurial spirit, Chris and co-founder Tom launched Textbooks for Change in 2014 to improve the educational landscape for post-secondary students around the world.
What began as the vision of one social entrepreneur has since inspired students, professors, and community members from across the globe to grow Textbooks for Change into a successful for-profit social enterprise and top-rated B Corp.
Learn more about how we use textbooks to create incredible impact.
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If you feel like you're the only one working harder and still struggling to fill your open roles, think again. A recent Bersin study found that the average time to fill has increased by 8%, reflecting an increased push to hire more while keeping the size and budgets of recruiting teams relatively flat. With unemployment rates dropping in the U.S. and more opportunities opening up for skilled talent—it's taking more time and resources for organizations to quickly acquire the talent they need.
For recruiters who operate in constant hiring mode, the struggle is all too real. To help, we've pulled 3 particularly savvy tips that can help save precious time and boost efficiency in the face of that never-ending pile of open roles.
For more tips, check out The Tactical Guide to High-Volume Hiring.
Tip #1: Eliminate any confusion between you and your hiring manager.
Just a few minor upfront miscommunications between you and your hiring manager could cost you hours of unnecessary work down the road. Whether it's a misunderstanding around candidate requirements, role expectations, or even desired soft skills—you risk losing time sourcing and engaging the wrong candidates, delaying the process, and seriously dragging out your time-to-fill.
Any candidate you consider for this role must be able to achieve these performance goals without a doubt. Knowing this helps you identify and deliver the right candidates, faster.
Now that you and your hiring manager have defined the 2-3 necessary goals for success in the role, ask your hiring manager what tasks must be completed to achieve said goals. These are more tactical in nature, and they are a good idea to include in your job description and focus on in your phone screen.
As you and your hiring manager work through the first two areas, pay attention to any skills, personality characteristics, strengths and more that might help a candidate be successful. Then, have a conversation with your hiring manager to confirm these skills and qualities are desired and helpful to success in the role. Convey these traits and soft skills in your job description, look for them within cover letters, and screen for them during your initial phone calls.
When you know exactly what you're looking for, you're better equipped to quickly spot the candidates who can get the job done.
Tip #2: Source candidates most likely to be interested in your opportunities.
Gone are the days of either active or passive talent. Nearly 90% of talent on LinkedIn reports being open to a new opportunity if the right one presents itself. This is great news for recruiters, but it also means you could waste time engaging more candidates rather than the right ones. Don't fall into that trap.
The key to sourcing faster is to engage candidates who have already indicated some relationship with your organization i.e. your Company Page followers, past applicants, those who have interacted with your LinkedIn status updates (by liking, sharing, or commenting), and even those connected to your employees.
Your Company Page followers are 81% more likely to respond to your InMails.
People who share a former employer with you are 27% more likely to respond to your InMail.
Talent who share a LinkedIn group with you are 21% more likely to respond to your InMail.
If you are a LinkedIn customer, you can prioritize qualified talent to engage directly from within LinkedIn Recruiter. After running a search for the skills and experiences you're looking for, use Recruiter's "spotlights" to prioritize your search results by those who have a pre-existing relationship with your organization in some way.
You're guaranteed to save time sorting through candidates and dramatically increase your response rates.
Tip #3: Create personalized templates to reach more candidates, faster.
Sending that first message or InMail to a prospective candidate(s) is a fine balance between efficiency and personalization. On one hand, you can send out twice as many InMails about an opportunity, role requirements and your company culture if you use a standard template. But on the other hand, you know that personalized messages have a major impact on the likelihood of eliciting a response.
To strike a balance, craft InMail templates that allow for a healthy level of personalization (especially in your intro). You can repurpose ~80% of your template for a given role, but include ample opportunities for you to personalize the note. Take10 minutes to read through a candidate's LinkedIn profile and tailor your message to their background—it will not only reflect well on you as a recruiter, but will also yield a higher response rate.
But don't go overboard. Our InMail data suggests shorter InMails are actually more effective, and the sweet spot is right around 500 characters. Any longer and your message won't be read, but too much shorter and you'll get lost in the noise.
The best recruiters work smarter, not harder, to find the talent their organization needs to thrive. So let's review: By aligning upfront with your hiring manager on who exactly you're looking for, using signals like Company Page Followers to prioritize candidates most likely to be interested in your opportunities, and by starting your outreach with a base template—you're setting yourself up to save precious time and energy.
For more quick tactics guaranteed to help you recruit faster, check out our newest ebook, The Tactical Guide to High-Volume Hiring. The ebook also includes tried-and-true tips from talent acquisition leaders at Starbucks, Novo Nordisk, LinkedIn, and Likeable Media who've helped their teams manage high-volume hiring.


text:
Panthers blow fourth-quarter lead, officially eliminated with loss to Tampa Bay
In a must-win game for Carolina, last week's injury loss of cornerback Jaycee Horn proved to be too much to overcome.
Carolina Panthers head coach Steve Wilks watches during the first half of an NFL football game between the Carolina Panthers and the Tampa Bay Buccaneers on Sunday, Jan. 1, 2023, in Tampa, Fla. (AP Photo/Chris O'Meara)(Chris O'Meara | AP)
By WBTV Web Staff
Published: Jan. 1, 2023 at 4:26 PM EST
TAMPA BAY, FLA. (WBTV) - The Carolina Panthers were officially eliminated from playoff contention Sunday after losing to the Tampa Bay Buccaneers in a must-win game.
Despite holding a 21-10 early in the fourth quarter, the Panthers (6-10) allowed 21 points in the fourth quarter and Tampa Bay (8-8) won the NFC South with a 30-24 victory.
In a must-win game for Carolina, last week's injury loss of cornerback Jaycee Horn proved to be too much to overcome as Tampa Bay wide receiver Mike Evans hauled in 207 receiving yards and three touchdowns. Tom Brady threw for 432 yards against starting cornerbacks C.J. Henderson and Keith Taylor, the fourth most ever by a QB against the Panthers.
pic.twitter.com/V4F2XH9mHc
— Carolina Panthers (@Panthers) January 1, 2023
Carolina quarterback Sam Darnold threw for 278 yards and three touchdowns but had an interception and lost a key fumble late in the fourth quarter.
The win is heartbreaking for the Panthers, who haven't made the playoffs since 2017. After a 1-4 start to the season that led to the firing of head coach Matt Rhule, interim head coach Steve Wilks helped put Carolina back in playoff contention thanks to a weak division.
The Panthers still had a chance to win with 1:04 remaining in the game after kicker Eddy Pineiro hit a 49-yard field goal to make it 30-24 but the Buccaneers held on thanks to a saved punt that kept the Panthers out of better field position.
Carolina will travel to face the New Orleans Saints in the final game of the year next Sunday.
Also Read: NFL Players Association files grievance against Panthers, NFL for 'hardness' of field
Copyright 2022 WBTV. All rights reserved.

redpajama_set_name: RedPajamaCommonCrawl


"Average" didn't seem to please Tesla.
Update: Consumer Reports has issued a response to Tesla's criticisms: "Tesla appears unhappy that CR expects the new-to-market Tesla Model 3 to be of average reliability, which is generally a positive projection for any first model year of a car." And, "Tesla seems to misunderstand or is conflating some of what we fundamentally do." You can find CR's full response here.
DETROIT — New technology to stream music into dashboards or boost fuel efficiency is making cars less reliable, although electric cars such as the Tesla Model 3 and the Chevrolet Bolt should fare better than many conventional models, Consumer Reports magazine said on Thursday.
The Consumer Reports survey of 640,000 vehicles owned by consumers showed that all-new vehicles or models with newly updated technology are more likely than older models to have a "wonky engine, a jerky transmission, or high-tech features that fail outright."
Electric cars do away with many of the mechanical systems that prompt consumer complaints about conventional cars, the magazine said. Tesla Inc's Model 3, despite recent production problems, should have "average" reliability because it relies on technology already used on the older Tesla Model S, Jake Fisher, the magazine's head of automotive testing, said on Thursday at a meeting of the Detroit Automotive Press Association.
The Bolt is the most reliable car in the Chevrolet brand, he said.
The magazine's annual survey of new vehicle reliability predicts which cars will give owners fewer or more problems than their competitors, based on data provided by the owners of those 640,000 vehicles. Its scorecard is influential among consumers and industry executives.
But "average" didn't seem to please Tesla, which in a statement on Thursday criticized Consumer Reports. because it previously declared the Model S "to be the best car ever and then revoked the rating after being questioned by Tesla skeptics."
"Time and time again, our own data shows that Consumer Reports' automotive reporting is consistently inaccurate and misleading to consumers," Tesla said. It accuses Consumer Reports of conducting tests and surveys that "lack basic scientific integrity." In CR's response on Friday, the consumer organization responded that it conducts a long-established battery of 50 standardized tests — and purchases every car it reviews to avoid any implication of favor.
As for the Model 3, Tesla said, "It's important to note that Consumer Reports has not yet driven a Model 3, let alone do they know anything substantial about how the Model 3 was designed and engineered."
First, CR in 2015 did in fact briefly bestow its highest-ever rating to the Tesla Model S, giving it 103 points on its 100-point scale. That after the Model S had received an "average" rating the year before — in part because faulty door handles locked testers out of the car. But just months after that top overall rating, when survey results from 1,400 Tesla owners came in, the magazine rated the car's reliability as "worse than average." By 2017, Tesla had pulled its overall brand rating for reliability up to No. 8. In this new survey, Tesla comes in at No. 21.
As for the Model 3: With brand-new models that don't yet have a reliability track record, the magazine has a means of making an educated prediction. CR, in predicting the Model 3, is using the same approach it applies to all other new models, and is transparent about its methodology: "We will make a prediction for a brand new or redesigned model, or a model with insufficient data, based on the manufacturer's track record, history of the previous generation, or similar models that shared the same components. Of course, this is only a prediction, and these scores are not a guarantee of the reliability of any individual car."
And of course, Consumer Reports hasn't driven the Model 3 through no fault of its own but because Tesla had delivered only 220 of them as of a few weeks ago. CR in its Friday response said, "You can rest assured that we will thoroughly test and evaluate the Model 3 with the same care and scrutiny we apply to all the cars we test just as soon as we can get one — we're waiting patiently along with other consumers."
Elsewhere in the ratings, for the fifth straight year Japanese automaker Toyota placed first in the magazine's brand ranking with the most reliable vehicles on average. General Motors' Cadillac brand was last among 27 brands ranked.
With many new cars, customers complain about problems with continuously variable transmissions and eight- and nine-speed gear boxes designed to boost fuel mileage, Fisher said.
Hard to use infotainment systems also continue to annoy customers, Fisher said. But over-the-air updates are helping automakers alleviate problems more quickly, he said.
Reporting by Joe White. Material from Autoblog's archives was used in this report.


text:
\section{Introduction}

Modern physically-based production renderers rely primarily on variants of the unidirectional path tracing algorithm~\cite{ToGProduction}, in which paths are traced from the camera and scattered in the scene until they reach an emitter. Path tracing scenes with high geometric and radiometric complexity can result in high-variance (i.e.,\ noisy) images unless they take a prohibitively large number of samples.
To alleviate this, many Monte Carlo variance reduction-based solutions have been proposed, such as variants of importance sampling~\cite{Veach95Multiple,Owen98Safe,Talbot2005,bitterli20spatiotemporal} and path guiding~\cite{Vorba2019PGP}.
However, even with advanced sampling techniques in place, hundreds of samples per pixel are typically required to produce a converged image, which is prohibitive for real-time applications.

Even though recent improvements in GPU ray tracing and effective denoising have greatly improved path tracing performance for complex scenes, real-time path tracing budgets remain limited to only few indirect scattering events (often just one) and scenes with only modestly complex illumination. As such, interactive graphics methods necessarily resort to approximate global illumination methods \emph{in addition} to importance sampling. One such recent approach combines world-space irradiance probes with visibility-aware interpolation to rapidly approximate multi-bounce indirect illumination (DDGI)~\cite{Majercik2019Irradiance}.
Crucially, the normal-dependent irradiance can also be interpreted as cosine-prefiltered radiance~\cite{Majercik2021ScalingGI} in the normal direction, allowing its use in both rough glossy as well as Lambertian diffuse transport.
As with most approximate solutions, these \textit{dynamic probes} introduce bias -- in the form of the transport that is computed -- in exchange for a noise-free and fast result.

In contrast to interactive indirect illumination sampling, the sample efficiency and approachable scene complexity for ray-traced direct illumination has been greatly improved by recent a spatio-temporal resampling scheme (ReSTIR)~\cite{bitterli20spatiotemporal}.
In the graphics pipeline, sampling is followed by full-screen post-processing, including denoising~\cite{Bitterli2016Denoising,Boughida2017Denoising,Schied2017Denoising,Vogels2018Denoising,Bako2017Denoising,Chaitanya2017Denoising,Mara2017Denoise,Xu2019Denoising,Huo2021DenoisingSurvey}.
So, the goal of a modern real-time sampling algorithm is not a fully converged image, but rather one with sufficiently low noise that after post-processing it is acceptably close to a converged image for the application. We address the efficient sampling problem in this work, assuming a downstream denoiser. We note that today's commodity denoisers both desirably conceal sampling noise and also undesirably exhibit various residual temporal errors that are beyond the scope of this work. Hence, we show the impact of denoising in Fig.~\ref{fig:teaser} for a static image to validate the unconverged targets, and then show the pre-denoising output of our method in all other results.

We present a new real-time global illumination method that combines the advantages of spatio-temporal resampling (improved sample efficiency for stochastic estimators) and dynamic diffuse probe volumes (smooth, interpolation-friendly transport proxies). Our method is efficient, treats direct and multi-bounce indirect effects, and is robust to radiometrically- and geometrically-complex settings.
Na\"\i{}vely combining DDGI with ReSTIR (see Fig.~\ref{fig:main-result1spp}, ``Primary DDGI'' column) will neither eliminate the probe grid artifacts nor address the discrepancy in their respective artifact/noise characteristics: DDGI's noise-free and biased indirect illumination, and ReSTIR's noisy and unbiased direct illumination.

We alleviate this discrepancy by postponing DDGI queries by one bounce (from the eye) -- where sample contributions are evaluated during ReSTIR
(see Fig.~\ref{fig:main-result1spp}, ``DDGI in ReSTIR'' column) -- similarly to \textit{final gathering} with photon mapping~\cite{Jensen1996PhotonMap}. This hides a large portion of DDGI's bias and allows us to combine direct and indirect samples during resampling, selecting each sample proportional to its \textit{global} transport contribution.
Where the original ReSTIR approach would normally resample direct illumination, it now treats both direct and indirect samples, each with similar noise characteristics due to the final gathering-like postponed DDGI sample, resulting in a sample efficient global shading estimate that is amenable to commodity denoising methods (see Fig.~\ref{fig:teaser}).

Our method takes a principled approach to balancing the performance-quality trade-off inherent to any combination of DDGI and ReSTIR, as evidenced in our analysis and benchmark against
purely path traced and purely probe-based
variants (Sec.~\ref{Sec:Discussion}). Concisely, our contributions are as follows:
\begin{itemize}
    \item a categorization of contributions from each constituent technique,
    \item an in-depth analysis of sampling-based methods applied to these contributions,
    \item an interactive global illumination algorithm, motivated by our analysis, that combines resampling with a global transport approximation.
\end{itemize}

\section{Background and Related Work}

\paragraph*{(Ir)radiance Caching.}
Most modern real-time global illumination caches have their roots in Ward's seminal irradiance cache~\cite{Ward88IrradianceCache} and other early approaches to precomputed light transport~\cite{Arvo86backwardray,heckbert1990adaptive}.
While initial work focused on adapting precomputed light transport to the harsh constraints of real-time rendering in \emph{static} settings~\cite{Abrash:1997:quake,Oat:2005:irradiance,GeomericsEnlighten}, many recent techniques offer at least partial dynamics, such as dynamic lighting through precomputed radiance transfer~\cite{Sloan:2002:precomputed}.
Orthogonally, radiance caching techniques overcome the Lambertian assumption by including the directional domain~\cite{Krivanek:2005:caching}.
Although today's selection of high-quality \emph{partially dynamic} techniques is vast~\cite{Gilabert:2012:deferred,Silvennoinen:2017,Seyb:2020,Vardis:2014:radiance,Scherzer:2012,Rehfeld:2014,Schwarzhaupt:2012:hessian}, our goal is \emph{fully dynamic} global illumination with as few constraints as possible.

To this end, we build upon the DDGI volume~\cite{Majercik2019Irradiance}, which consists of a 3D grid of directionally resolved irradiance probes that is updated in real-time through hardware ray-tracing.
Crucially, DDGI contains visibility information to prevent light leaking and it can approximate sufficiently rough \emph{glossy} transport by re-purposing its directional dependence~\cite{Majercik2021ScalingGI}.
These features make it -- with mild modifications (Sec.~\ref{sec:algorithm}) -- a reasonably versatile approximation of global illumination for most phenomena other than specular reflections.
By querying DDGI \emph{at secondary path vertices}, we employ a similar strategy to final gathering to conceal DDGI's limitations (e.g.,\ coarse spatial discretization) behind the blurring effect of the primary scattering interaction at the cost of sampling noise. We then address the sampling noise by applying spatio-temporal reservoir sampling~\cite{bitterli20spatiotemporal}.

M\"uller et al.~\cite{mueller2021realtime} use a real-time trained neural radiance cache to approximate fully-dynamic global illumination.
They evaluate their cache at later path vertices to hide the artifacts of their neural network, but unlike this paper, they do not employ specialized importance sampling.
Crucially, their method is compatible with ours in that the caches can be interchanged in future investigations.

\paragraph*{Importance Sampling.}
In offline rendering, importance sampling is the dominant technique for reducing sampling noise: the closer the sampling distribution matches the distribution of light, the less noise there is~\cite{VeachPhD}.
With the advent of hardware ray tracing, it is now important to adapt established techniques to the real-time setting~\cite{AreWeDoneWithRayTracing}.

Like with caching techniques, spatio-temporal reuse is key for high-quality importance sampling, as evidenced by bidirectional techniques~\cite{Lafortune93,Veach:1994:BPT,Veach97,Keller97-IRad,Georgiev:2012:LTS}, resampling and mutation strategies~\cite{Talbot2005,Veach97,Kelemen:2002,10.1145/2766997,Hachisuka:2014:MMLT}, as well as learned distributions via neural networks~\cite{mueller2019nis,Zheng:2019,MLandIEQ} or path guiding~\cite{Jensen1995,LW1995A5TTRTVOMCRT,Vorba:2014:OnlineLearningPMMinLTS,mueller2017practical,Dahm16}.
Unfortunately, most reuse strategies come with a significant performance penalty, meaning that while they handle difficult illumination well, they are outperformed by na\"ive unidirectional path tracing under simple illumination.
This disqualifies them from the real-time setting that we strive for.
Path guiding has generally little overhead, which makes it a good choice for production rendering~\cite{Vorba2019PGP}, however adapting its underlying data structures to animated content in real-time is non-trivial ongoing work~\cite{2020_tsr_nee_pg_rtpt}.

Our importance sampling method of choice is ReSTIR~\cite{bitterli20spatiotemporal}: a recent combination of importance \emph{resampling}~\cite{Talbot2005} and classic weighted reservoir sampling~\cite{vitter85,EFRAIMIDIS2006181,chao82} that permits the reuse of a massive number of samples in constant time.
For estimating \emph{direct} lighting, ReSTIR generates many candidate samples on the light sources in the scene, resamples those candidates across space and time proportional to their predicted contribution, and then traces a shadow ray to determine the visibility of the selected sample(s).
This scheme not only has negligible overhead compared to simple path tracing, but also gracefully handles dynamic content.

In contrast, we apply ReSTIR to \emph{global} illumination by targeting it at the sum of direct illumination from light sources and indirect illumination stored in DDGI probes~\cite{Majercik2019Irradiance} of irradiance (for diffuse reflection) and angularly-filtered radiance~\cite{Majercik2021ScalingGI} (for rough glossy reflection).
This converts the global illumination problem into a purely direct illumination problem for the high variance case of low-frequency angular scattering, by treating DDGI as a light source.
In contrast to multiple importance sampling~\cite{Veach95Multiple}, this means that we draw a single sample approximately proportional to the total transported light rather than multiple samples that are heuristically combined.
Note that the result has two symbiotic levels of reuse: (i) screen-space resampling by ReSTIR for both direct and indirect light as well as (ii) world space DDGI probes which contain spatio-temporal aggregates of global illumination.

Concurrent work of Ouyang et al.~\cite{ReSTIRGI} proposes an alternative mechanism for spatio-temporal resampling of global illumination: they target ReSTIR at single-sample Monte Carlo estimates -- rather than a cache -- which can be likened to importance sampling of virtual point lights.
Compared with our approach, theirs is capable of less biased (optionally unbiased) rendering, but lacks world-space spatio-temporal reuse and early path termination, which in our case is handled by the DDGI volume.

As we show in our results, combining the underlying ideas of ReSTIR, DDGI, and path tracing into a new sampling strategy produces a less-biased and less noisy result than na\"\i{}vely compositing the results of separately-computed ReSTIR direct, DDGI diffuse/rough-glossy indirect, and path traced near-specular contributions.

\section{Algorithm}\label{sec:algorithm}

In order to render images with global illumination in
real-time, we strive to efficiently simulate radiance transport.
The rendering equation \cite{Kaj:86}
\begin{equation} \label{Eqn:REQ}
  L = L_e + T_f \; L
\end{equation}
describes the \emph{outgoing} radiance $L : \R^3 \times \mathbb{S}^2 \rightarrow \R^3$
as the sum of the radiance $L_e$ emitted by the light sources
and the transported radiance $T_f L$, where $T_f$ is a higher order operator that maps between two functions (similar to notation
defined by Veach~\cite{VeachPhD}).
The symbol $f$ represents
the bidirectional scattering distribution function (BSDF),
describing how the surfaces in the scene transport radiance.
For our derivation, it is useful to partition radiance transport
\[
  T_f = T_{\bsdfDiff} + T_{\bsdfGlossy}
\]
into diffuse $T_{\bsdfDiff}$ and glossy $T_{\bsdfGlossy}$ transport, the latter of which including specular transport as the special case of small surface roughness.

With this operator notation at hand,
the dynamic diffuse global illumination (DDGI)
algorithm \cite{Majercik2019Irradiance} is
\begin{equation}
  L
  \approx L_e
  + T_{\bsdfDiff} \; L_e
  + L_{\text{DDGI}}
  + T_{\bsdfGlossy} \; L \;, \label{Eqn:DDGI}
\end{equation}
in which $\Lddgi \approx \sum_{i=2}^\infty (T_{\bsdfDiff})^i \; L_e$ is an approximation of the diffuse transported radiance without the directly visible light sources ($i$ starts at 2).
The other terms are estimated by sampling.
Inserting the DDGI approximation into equation~(\ref{Eqn:DDGI}) and assuming equality results almost in the rendering equation~(\ref{Eqn:REQ}), missing only higher order glossy transport.

Majercik et al.~\cite{Majercik2021ScalingGI} extended DDGI to include this missing contribution for the special case of specular reflection, which we will build on in \autoref{Sec:Glossy}.
The ``Primary DDGI'' column in Fig.~\ref{fig:occlusionComparison} and \ref{fig:main-result1spp}
shows results of this extended DDGI algorithm. As compared to path tracing
\cite{Kaj:86}, the images are smooth and noise only results
from sampling the direct diffuse illumination $T_{\bsdfDiff} \; L_e$ and the recursion for glossy transport $T_{\bsdfGlossy} \; L$.
Yet, the irradiance probe approximation of $L_{\text{DDGI}}$
shows visible artifacts that we resolve by postponing its evaluation to secondary path vertices.
We develop our new algorithm in the following sections.

\begin{figure}
    \small
    \includegraphics[width=\linewidth]{img/Algorithm-2}%
    \setlength{\unitlength}{0.01\linewidth}%
    \begin{picture}(0,0)
        \put(-62.5, -2) {$\pos$}
        \put(-55.5, 11.5) {$\posy$}
        \put(-13, -2) {$\pos$}
        \put(1, 30.5) {$\posz$}
    \end{picture}
    \caption{We compute direct and indirect illumination in a unified way by targeting the ReSTIR algorithm at the sum of emitted light $L_e$ and the DDGI approximation $\Lddgi$.
    Left: two points are sampled. One (red) is sampled on the emissive surfaces and another (green, $\posy$) by BSDF sampling at the primary vertex $\pos$.
    Right: with all samples across pixels and past frames converted to the area measure, a single sample $\posz$ is selected with probability proportional to ${L_e+\Lddgi}$
    and a shadow ray (red) is traced.
    The radiance leaving $\pos$ towards the camera is estimated with low variance by combining the reflected light from $\posy$ and $\posz$ through the method of Kollig and Keller~\cite{Kollig2006Singularity}.
    (Near-)specular transport at $\posy$ and $\posz$ is estimated through recursion (blue) and the remaining transport by $\Lddgi$.
}\label{fig:diagrams}
\end{figure}

\subsection{Sampling all Surfaces with ReSTIR} \label{Sec:Sampling}

As the DDGI approximation can be queried at any point
of the scene surface, we consider the whole scene surface as a
light source rather than only the actual light sources.
Our new sampling algorithm performs the following steps:
\begin{enumerate}
    \item Generate candidate samples on {\em all} surfaces of the scene.
    To this end, we uniformly sample a small number of positions on emissive surfaces and generate additional samples by tracing secondary rays in directions sampled from the BSDF.
    \item Select one candidate by first sampling from a weighted reservoir proportional to the sample contribution, then
    resample spatially among neighboring pixels, and resample temporally among previous frames, proportional to the sum ${L_e + \Lddgi}$.
    \item Trace one shadow ray to the selected sample point.
    \item If visible, shade from that sample point, using the emissive contribution $L_e$ and adding the contribution
    from the DDGI volume.
\end{enumerate}
As with original ReSTIR, our unified sampling scheme traces only a single ray to the selected sample (see Figure~\ref{fig:diagrams}).
However, as the original DDGI volume only contains the diffuse
\emph{indirectly reflected} light without direct reflections as shown in equation~(\ref{Eqn:DDGI}),
we need to add these, as well as glossy contributions, which we describe next.

\subsection{Augmenting the DDGI Approximation} \label{Sec:Augmentation}

As mentioned in Sec.~\ref{Sec:Sampling},
integrating $L_{\text{DDGI}}$ from secondary vertices over the hemisphere at the primary vertex
would result in too dark images, because
the DDGI approximation lacks the diffuse direct illumination
and the radiance from non-diffuse transport.
In fact, adding these missing terms to the DDGI approximation recovers an approximation of the transport operator
\begin{equation} \label{Eqn:DGI}
  L_{\text{DDGI}} + T_{\bsdfDiff} \; L_e + T_{\bsdfGlossy} \; L
  \approx T_{\bsdfDiff} \; L + T_{\bsdfGlossy} \; L
  = T_f \; L .
\end{equation}

\begin{figure}
    \small
    \centering
    \hspace*{1cm}\begin{tabularx}{\columnwidth}{XX}%
        Primary DDGI & Secondary DDGI
    \end{tabularx}
    \input{fig_naive_bias}
    \caption{Using the DDGI approximation $\Lddgi$ at the primary vertex (left) shows light leaking artifacts beneath the couch and does not correctly reproduce indirect contact shadows on the table, vases, and plate. Using our augmented $\LddgiP$ at the \emph{secondary} vertex (middle) exhibits no leaks, recovers some of the indirect contact shadows and is closer to the path traced reference (right).
    }\label{fig:occlusionComparison}
\end{figure}

Adding the missing terms
by recursive path tracing would increase the noise and involve
further shading and tracing cost.

Instead, we augment the DDGI approximation in two steps, which are most easily explained by examining the underlying irradiance approximation that DDGI makes.
Given a 3D position and 2D normal vector, the DDGI probe volume stores irradiance from indirect diffuse reflections, which is approximated by repeatedly evaluating the following double-bounce transport~\cite{Majercik2019Irradiance}
\begin{align}
    \Eddgi(\pos, \normal) &\approx \int (\Lddgi + T_{\bsdfDiff} \; L_e)(h(\pos, \diri), -\diri) \langle \normal, \diri \rangle \,\Diff{\diri} \,, \nonumber \\
    \Lddgi(\pos, \diri) &:= \frac{\rho(\pos)}{\pi} \; \Eddgi(\pos, \normal) \,,
\end{align}
where $h$ is the ray tracing operation and $\rho$ is the diffuse albedo.
Repeated Monte Carlo estimation of this equation at the centers $\pos$ of all DDGI probes converges to the aforementioned approximation $$\Lddgi \approx \sum_{i=2}^\infty (T_{\bsdfDiff})^i \; L_e \,.$$
To include direct reflections and non-diffuse transport, we replace the double-bounce transport with simpler single-bounce recursion
\begin{align}
    \EddgiP(\pos, \normal) \approx \int (\LddgiP + L_e)(h(\pos, \diri), -\diri) \langle \normal, \diri \rangle \,\Diff{\diri} \,,
    \label{eq:probeUpdate}
\end{align}
where $L_e$ no longer undergoes a diffuse interaction and $\LddgiP$ will be adapted to include glossy transport in the following section.
The approximation thus converges to the full transport operator
$$\LddgiP \approx T_{\bsdf} \; L \,.$$

Including direct illumination (i.e.\ untransported $L_e$) in the probe update equation~(\ref{eq:probeUpdate}) produces results that are too bright whenever probe centers are closer to light sources than the surfaces that they shade.
This causes surfaces to receive more (secondary) direct illumination than they should -- a common problem in probe-based caches.
To alleviate this, we approximate the true distance to the surface by querying the average visibility already computed by the probes in the \emph{backwards} direction~\cite{Majercik2019Irradiance} (dashed red lines), clipping that value to the probe boundary (pink), and adding it to the length of the corresponding probe update rays (solid red arrows). Because the update rays are traced per probe, this costs just a texture read without any probe weight computation.
\begin{wrapfigure}{r}{0.4\linewidth}
    \small
    \vspace{-1mm}\hspace*{-3mm}\includegraphics[width=1.1\linewidth]{img/probe-direct.pdf}%
    \setlength{\unitlength}{0.011\linewidth}%
    \begin{picture}(0,0)
    \end{picture}\\[-2mm]
\end{wrapfigure}
We then use the inverse square of the \emph{total} distance, i.e.,\ solid + dashed, to attenuate $L_e$.
In contrast to direct illumination computed at probe centers, this heuristic slightly underestimates direct illumination.
We therefore expose a user-tunable slider to scale the additional attenuation per scene to correct for brightness differences that would otherwise be a significant source of bias.

\begin{figure}
    \centering
    \vspace{12mm}\input{fig_glossy_ddgi}
    \caption{For shading, we recursively trace (near-)specular interactions until their footprint grows sufficiently large to blur away small-scale artifacts of the DDGI approximation (middle).
    This produces less biased results than always querying the DDGI approximation at the secondary vertex (left).}
    \label{fig:glossyDDGI}
    \vspace{-4mm}
\end{figure}

\subsection{Including Glossy Illumination} \label{Sec:Glossy}

Recall that the reflected light from a diffuse material with albedo $\rho$ can be cheaply approximated by looking up $\frac{\rho(\pos)}{\pi} \;\EddgiP(\pos, \normal)$.

Majercik et al.~\cite{Majercik2021ScalingGI} make the observation that the irradiance approximation can \emph{additionally} be used
to very coarsely approximate specular transport by substituting the normal $\normal$ with the direction of specular reflection $\dirr$.
The irradiance $\Eddgi(\pos, \dirr)$ can then be \emph{re-interpreted} as a prefiltered (by the cosine term) coarse approximation of incident radiance up to a normalization factor of $2\pi$ which must be added.
In the illustration, the green lobes visualize cosine-weighted prefiltering of incident radiance and the red arrows indicate the query direction of $\Eddgi$.

\begin{figure}[h]
    \small
    \centering
    \vspace{-1mm}\hspace*{0mm}\includegraphics[width=0.8\linewidth]{illustrations/ddgi-leftright.png}%
    \setlength{\unitlength}{0.008\linewidth}%
    \begin{picture}(0,0)
        \put(-73, 4) {$\pos$}
        \put(-70, 30) {$\normal$}
        \put(-28, 4) {$\pos$}
        \put(-25, 30) {$\normal$}
        \put(-68, 10) {$\diri$}
        \put(-8, 10) {$\diri$}
        \put(-2, 20) {$\dirr$}
        \put(-90, 20) {$\diro$}
        \put(-44, 20) {$\diro$}
        \put(-82, -5) { Diffuse }
        \put(-37, -5) { Specular }
    \end{picture}\\[2mm]
\end{figure}

While Majercik et al.\ use this strategy for just specular transport, we generalize it to arbitrary glossy BSDFs -- in our case microfacet models -- by Monte Carlo estimation.
More specifically, we approximate reflected radiance by our augmented DDGI model
\begin{align}
    \LddgiP(\pos, \diro) \label{Eqn:lddgiplus}
    &= \underbrace{\frac{\rho(\pos)}{\pi} \; \EddgiP(\pos, \normal)}_{\text{Diffuse}}\\
    &+ \underbrace{\frac{1}{2\pi} \int \EddgiP(\pos, \diri) \bsdfGlossy(\pos, \diro, \diri) \langle \normal, \diri \rangle \,\Diff{\diri} }_{\text{Glossy}} \nonumber
\end{align}
as the decomposition into diffuse and glossy transport, where we estimate the glossy integral with a single Monte Carlo sample drawn proportionally to the glossy BSDF component $\bsdfGlossy$.

\paragraph*{Specular recursion for shading.}
We use our radiance approximaton $\LddgiP$ in three parts of the algorithm: (i) in the update rule of $\EddgiP$~(\ref{eq:probeUpdate}), (ii) to resample proportional to ${(L_e + \LddgiP)}$, and (iii) for shading.
The latter use case -- shading -- requires special care to avoid exposing too much of the inherent bias of $\LddgiP$ through (near-)specular interactions.

We thus trace shading paths recursively until their scattering footprint is sufficiently spread out to blur DDGI's visual artifacts.
To this end, we adopt the path termination strategy of Müller et al.~\cite{mueller2021realtime,mueller2020neural}, which is based on the isotropic path footprint approximation of Bekaert et al.~\cite{bekaert2003}.

More specifically, we approximate the footprint of a path with vertices $\pos_1\cdots\pos_n$ as
\begin{align}
    a(\pos_1\cdots\pos_n) &= {\left( \sum_{i=1}^{n-1} \sqrt{\frac{\|\pos_{i} - \pos_{i+1}\|^2}{p_i(\dir_{\mathrm{i},i} \,|\,\pos_{i}, \dir_{\mathrm{o},i}) \, \langle \normal_{i+1}, -\dir_{\mathrm{i},i} \rangle}} \right)}^2 \,,
\end{align}
where $p_i$ is the BSDF sampling density at the $i$-th vertex.
At the primary vertex, $p_1$ is proportional to the entire BSDF $\bsdf$, whereas ${p_i; \, i>1}$ is proportional to only the glossy portion of the BSDF $\bsdfGlossy$, because we simply terminate the recursion early and query $\frac{\rho(\pos_n)}{\pi}\;\EddgiP(\pos_n, \normal_n)$ when the diffuse portion is sampled.
When the glossy portion is sampled, we terminate the recursion by estimating the glossy integral of Eq.~(\ref{Eqn:lddgiplus}) as soon as $a(\pos_1\cdots\pos_n) > a_0 \cdot c$, where
\begin{align}
    \AreaHeuristic_0 := \frac{\|\pos_0 - \pos_1\|^2}{4\pi \; \langle \normal_1, -\dir_{\mathrm{i},i} \rangle}
\end{align}
is an approximation (up to constant factors) of the pixel footprint projected into the scene, $\pos_0$ is the camera position, and $c$ is a user-chosen threshold.
See Bekaert et al.~\cite{bekaert2003} and Müller et al.~\cite{mueller2021realtime,mueller2020neural} for details.

We empirically set the threshold ${c = 0.2}$ to obtain a satisfactory noise versus bias trade-off.
Fig.~\ref{fig:glossyDDGI} confirms that this helps reduce artifacts on highly glossy surfaces without affecting rough glossy and diffuse surfaces.

\subsection{Correcting Self-shadow Bias} \label{Sec:SelfShadowBias}

\begin{figure}
\centering
\vspace{-1mm}
\input{fig_ss_bias_1}
\includegraphics[width=1.015\linewidth]{img/self-shadow-bias.pdf}
\setlength{\unitlength}{0.01\linewidth}%
    \begin{picture}(0,0)
        \put(43, 23) {$d$}
        \put(93.5, 21.5) {$d$}
    \end{picture}
    \begin{subfigure}{0.49\linewidth}
        \caption{Default DDGI self-shadow bias }
    \end{subfigure}
    \begin{subfigure}{0.49\linewidth}
        \caption{New view path bias}
    \end{subfigure}
    \vspace{-3mm}
    \caption{DDGI combats self-shadowing by pushing the evaluation point away from surfaces (violet arrow) by a fixed distance $d$ that is proportional to the probe size. Left: DDGI uses a linear combination of the view direction and the surface normal.
    This may push the evaluation point through geometry, leading to light leaking.
    Right: we instead move the evaluation point a fixed distance back along the path, which is guaranteed to not penetrate surfaces.}\label{fig:ssbiasdiagram}
    \vspace{-1mm}
\end{figure}

The original DDGI algorithm applies an offset to the positions at which $\Lddgi$ is queried to avoid computing visibility weights exactly at a surface where their variance is high. This offset is called the ``self-shadow'' bias, and is computed as a weighted combination of the view and normal vector at the queried point.

While the self-shadow bias was sufficient for the original primary-vertex DDGI algorithm, using it unmodified in our indirect sampling strategy showed light leaking artifacts (Figure~\ref{fig:ssbiasdiagram}). These artifacts arise from very short distances between the view point and the query point, as might arise from sharp corners. To address these artifacts, we bias the query point along the \emph{view path} as opposed to the surface normal and view direction. Results using our view path bias against the original self-shadow bias are shown in Figure~\ref{fig:ssbiasdiagram}.

\subsection{Taming the Weak Singularity in the Geometry Term}%
\label{Sec:Singularity}

In order to perform spatio-temporal resampling among different shading locations, the geometry term is introduced as part of the change of the integration measure.
Unfortunately, the weak singularity in the geometry term may lead to unbounded variance in the resampling procedure in geometric creases; see Fig.~\ref{fig:geometryBounding} (middle).

In typical path tracing applications, uniform multiple importance sampling~\cite{Veach95Multiple} of light sources would bound the variance when light sources (almost) touch the surfaces that they shade.
But this is an impractical solution in our case, where the entire scene is considered a light source.
It may be feasible to \emph{non-uniformly} sample all relevant scene surfaces according to a data structure that is constructed online, but we instead take a simpler approach that does not require multiple importance sampling: we partition the integrand to bound the variance~\cite{Kollig2006Singularity}.

To avoid the weak singularity caused by samples very close to the primary vertex, we clamp the computed geometry term to a maximum value of $G_\text{max} = 1$ and use the notation
$\lceil T_{\bsdf} \rceil$ for the
transport with this bounded geometry term.
The residual transport is denoted by $\lfloor T_{\bsdf} \rfloor$,
such that
\[
  T_{\bsdf} = \lceil T_{\bsdf} \rceil + \lfloor T_{\bsdf} \rfloor .
\]
$\lfloor T_{\bsdf} \rfloor$ is evaluated by
an auxiliary BSDF sample in the sampling step described in Sec.~\ref{Sec:Sampling}.
The improved variance is shown in Fig.~\ref{fig:geometryBounding}.

\begin{figure}
    \centering
    \input{fig_geometry_bounding}
    \caption{Even though spatio-temporal resampling of DDGI (middle) improves the noise of path tracing (left), outliers (``fireflies'') cause distracting artifacts in geometric concavities.
    These fireflies are due to the weak singularity in the geometry term of the rendering equation. Using the method of Kollig and Keller~\cite{Kollig2006Singularity}, we bound the geometry term during resampling and then trace an auxiliary ray to estimate the residual transport (right). This eliminates fireflies and preserves a significant noise reduction. The images were rendered at 128 samples per pixel.
}
    \label{fig:geometryBounding}
\end{figure}

\begin{figure}
    \centering
    \input{fig_caching}
    \caption{Caching the DDGI query for resampling is more efficient: while the increase in noise level and MAPE is negligible in query caching, the reduction in frame rendering time is substantial.}
    \label{fig:caching}
    \vspace{-4mm}
\end{figure}

In summary, our new algorithm to approximate the rendering equation
amounts to summing separate estimators of each term of
\begin{equation}
  L
  \approx L_e
   + \underbrace{\lceil T_{\bsdf} \rceil \; (L_e + \LddgiP)}_{\text{bounded geometry term}}
  + \underbrace{\lfloor T_{\bsdf} \rfloor \; (L_e + \LddgiP)}_{\text{residual transport}} , \label{Eqn:Algorithm}
\end{equation}
where
\begin{itemize}
    \item $L_e$ is evaluated at the primary vertex,
    \item $\lfloor T_{\bsdf} \rfloor$ by importance sampling the BSDF, and
    \item $\lceil T_{\bsdf} \rceil$ by spatio-temporal reservoir resampling.
\end{itemize}

\subsection{Improved Performance by Caching DDGI Queries}\label{sec:caching}

During the spatio-temporal resampling step, our algorithm requires numerous DDGI queries:
one for each candidate's contribution to the shaded pixel, which, in our implementation, amounts to 1 temporal and 3 spatial candidates per pixel.
\begin{wrapfigure}{r}{0.5\linewidth}
    \small
    \vspace{-1mm}\hspace*{-5mm}\includegraphics[width=1.1\linewidth]{img/ddgi-resampling.pdf}%
    \setlength{\unitlength}{0.011\linewidth}%
    \begin{picture}(0,0)
        \put(-63, 2) {$\pos_2$}
        \put(-48, 2) {$\pos_1$}
        \put(-33, 2) {$\pos_3$}
        \put(-10, 42) {$\posy_2$}
        \put(-20, 42) {$\posy_1$}
        \put(-40, 42) {$\posy_3$}
    \end{picture}\\[-6mm]
\end{wrapfigure}
In the illustration, we depict the simpler case of resampling among just 2 spatial candidates $\posy_2$ and $\posy_3$ for shading at the center vertex $\pos_1$.
For each candidate, we must query its contribution $\LddgiP(\posy_i, \posy_i\!\rightarrow\!\pos_1)$ to the center vertex, where ${\posy\!\rightarrow\!\pos}$ is the direction vector pointing from $\posy$ towards $\pos$ (dashed arrows).

These ${\text{\#vertices} \times \text{\#candidates}}$ queries can be avoided by approximating them with the $\text{\#vertices}$ values $\LddgiP(\posy_i, \posy_i\!\rightarrow\!\pos_i)$, which have to be evaluated for shading each vertex $\pos_i$ in any case.
This saves a factor of $\text{\#candidates}$ queries.
Importantly, using approximate values in the resampling step does not introduce additional bias in the rendered image -- merely additional noise.

Nonetheless, we analyze the error of this approximation by considering its three sources:
\begin{enumerate}
    \item the scene and the DDGI volume may change each frame,
    \item the shading location $\pos$ is different, and
    \item the view-path bias (Sec.~\ref{Sec:SelfShadowBias}) depends on the path prefix
\end{enumerate}

each of which become small if we apply the assumptions of our method: local pixel neighborhoods, near-diffuse scattering, and short frame times.
The approximation error is thus reasonably small in practice (see Fig.~\ref{fig:caching}) and we use this optimization in all our results.

\section{Results}

\begin{figure*}
    \input{fig_main_result_1}
    \caption{Comparison of rendering algorithms at 1 sample per pixel with respect to noise and bias.
    All algorithms are based on an unbiased path tracer that uses ReSTIR~\cite{bitterli20spatiotemporal} to estimate direct illumination at the primary vertex.
    As compared to this baseline, dynamic diffuse global illumination (Primary DDGI)
    renders smooth indirect illumination. Bias artifacts inherent with the DDGI
    approximation can be ameliorated by pushing the query of DDGI one bounce further into the light path (Secondary DDGI).
    Facilitating the ReSTIR sampling mechanism to jointly importance sample these DDGI queries with direct illumination, the induced noise is reduced (+DDGI in ReSTIR), and is further amenable to commodity denoising methods (+Denoising).
    We also show a failure case (\LivingRoom{}) where the outer walls consist of flat planes that receive strong sunlight from the outside.
    In this difficult scene, our method is unable to hide DDGI light leaking artifacts (red inset) and BSDF importance sampling (Secondary DDGI) performs slightly better than ReSTIR sampling.}
    \label{fig:main-result1spp}
\end{figure*}

We implemented DDGI resampling in Direct3D 12 using the Falcor rendering framework~\cite{Benty20}.
All results were generated on a high-end desktop machine with an Intel i7-6800K CPU and an NVIDIA RTX 3090 GPU.

Fig.~\ref{fig:main-result1spp} summarizes our results, comparing visuals, render time, and mean absolute percentage error (MAPE) of 5 different rendering techniques at 1 sample per pixel.
MAPE is defined as $\frac{1}{N} \sum_{i=1}^N |v_i - \hat{v}_i| / (\hat{v}_i + \epsilon)$, where $\hat{v}_i$ is the value of the $i$-th pixel in the reference image, $v_i$ is the value of the $i$-th rendered pixel, and $\epsilon = 0.01$ prevents near-black pixels from dominating the metric.

All rendering techniques are based on an unbiased path tracer that uses ReSTIR~\cite{bitterli20spatiotemporal} to estimate direct illumination at the primary vertex.
\begin{enumerate}
    \item \textbf{PT + ReSTIR:} The aforementioned baseline path tracer.
    \item \textbf{Primary DDGI:} The original DDGI algorithm~\cite{Majercik2019Irradiance,Majercik2021ScalingGI}, which approximates \emph{indirect} illumination at the \emph{primary} vertex of the path.
    \item \textbf{Secondary DDGI:} Our augmented DDGI algorithm approximates \emph{global} illumination at the \emph{secondary} vertex of the path (Sec.~\ref{Sec:Augmentation}--\ref{Sec:SelfShadowBias}). Near-specular transport is traced recursively.
    \item \textbf{+ DDGI in ReSTIR:} The same as before, but the DDGI approximation is included in the ReSTIR algorithm (Sec.~\ref{Sec:Sampling}). The diffuse weak singularity is separately sampled (Sec.~\ref{Sec:Singularity}).
    \item \textbf{+ Denoising:} The same as before, but with OptiX denoising.
\end{enumerate}
As expected, the DDGI algorithm~\cite{Majercik2019Irradiance,Majercik2021ScalingGI} has the least noise with the greatest performance (``Primary DDGI'' column).
However, it also exhibits significant visual artifacts such as flat shading (e.g.,\ in the \GreekVilla{}), a lack of ambient occlusion (e.g.,\ in the \PinkRoom{}), and light leaking (e.g.,\ in the \LivingRoom{}).
At the other extreme, an unbiased path tracer produces highly accurate results but requires a long time for its noise to converge away in globally illuminated scenes, even when using ReSTIR~\cite{bitterli20spatiotemporal} for direct lighting (``PT + ReSTIR'' column).

\begin{table}
    \caption{\label{tab:equal_quality}
        Time to converge to equal MAPE
    }%
    \vspace{-2mm}
    \input{tab_equal_quality.tex}
\end{table}

Our proposed \emph{indirect} use of DDGI probes -- provided that the probes contain the energy of the full transport (Sec.~\ref{Sec:Augmentation} and \ref{Sec:Glossy}) -- reduces noise while introducing only little bias, because the inaccuracies of the DDGI probes are hidden behind the first scattering event (``Secondary DDGI'' column).
Additionally, the performance is improved, because paths are terminated early into DDGI probes.
Incorporating the indirect lighting injected by the DDGI probes in the resampling step further reduces noise (``DDGI in ReSTIR'' column).
Overall, the noise is now low enough that adding the OptiX denoiser results in more visually pleasing images than the original DDGI algorithm, albeit at an on average 1.5-2$\times$ greater cost.

We also show a failure case (\LivingRoom{}) where the outer walls consist of flat planes that receive strong sunlight from the outside.
In this difficult scene, secondary DDGI queries are unable to hide DDGI's light leaking artifacts (red inset) and BSDF importance sampling (``Secondary DDGI'' column) performs slightly better than ReSTIR sampling.
This shows that further work is needed in making DDGI more resilient to thin geometry as well as in understanding the circumstances in which ReSTIR can be outperformed by its candidate generation strategies.

\paragraph*{Quantitative evaluation.}
To gauge the convergence improvements offered by using DDGI at the secondary path vertex (with or without resampling), we list the time it takes for all noisy methods to reach equal error in Tab.~\ref{tab:equal_quality}.
In all scenes, except for the \LivingRoom{}, DDGI resampling converges the fastest -- in some scenes by a significant margin -- even when taking into account the slightly increased cost of rendering.

To better understand this cost, we break it down by component in Tab.~\ref{tab:timings}.
As expected, terminating paths into DDGI reduces the ray tracing cost because paths are shorter, even when taking into account the additional cost of querying DDGI during the shading step, as well as the cost of updating the DDGI volume every frame.
However, perhaps unexpectedly, resampling only becomes marginally more expensive by incorporating DDGI in it (by $1.7$ ms on average) thanks to the caching of previous DDGI queries (Sec.~\ref{sec:caching}).
The remaining overhead of DDGI resampling arises in tracing \& shading of (near-)specular path suffixes, which must now be performed twice per pixel: once for the BSDF sample that estimates $\lfloor T_{\bsdf} \rfloor$ and once for the resampled vertex that estimates $\lceil T_{\bsdf} \rceil$ (see Sec.~\ref{Sec:Singularity}).

\begin{table}
    \caption{\label{tab:timings}
     Rendering cost by component
    }%
    \vspace{-2mm}
    \input{tab_timings.tex}
\end{table}

\section{Discussion and Future Work} \label{Sec:Discussion}

The following discussion focuses on future avenues for potential performance and noise improvements.

\paragraph*{Subsampling.}
To reduce the remaining resampling cost, it might be feasible to subsample the generation of new candidates: for example, only every $n$-th pixel could generate novel samples from the BSDF and from light sources.
All other pixels would merely resample among spatio-temporal neighbors.
Since this strategy is rather orthogonal to our work -- it would benefit all ReSTIR-type algorithms -- we did not include it in our technique.
However, we feel that it warrants a detailed analysis in the future.

\paragraph*{Alternative Caching Strategies.}
While we focused on the specific use of DDGI, we believe other caching strategies, such as the recent neural radiance caching~\cite{mueller2021realtime}, can similarly benefit from importance sampling through ReSTIR.\@
Another example is the concurrent work of Ouyang et al.~\cite{ReSTIRGI} who apply ReSTIR to a cache that is effectively a collection of spatio-directional point light sources.
Their approach represents a different point on the Pareto front that trades off noise, bias, and performance -- one that has lower bias (and can even be unbiased) at the cost of tracing longer paths, leading to extra noise.

\subsection{Alternative Variance Reduction Strategies}

In real-time rendering, sampling with low variance is key due to the limited budget of rays to be traced.
In this context, we also analyzed the efficiency of utilizing DDGI as a control variate, as well as that of using it for path guiding instead of resampling.
Both approaches were overall less efficient than the presented DDGI resampling, but we will nonetheless describe them in the hope that future algorithms may build on top of them.

\begin{figure}
    \input{fig_control_variate}
    \caption{Analysis of DDGI Resampling (left split) augmented by DDGI used as a control variate at the primary vertex (right split).
    The transient artifacts visible at low sampling rates are due to the underlying probe volume as can be seen by the visualization of DDGI.
    At 1 spp, these are too salient to ignore, although with higher sample counts both DDGI Resampling and DDGI Resampling with control variate converge to the same result.}
    \label{fig:CVcomparison}
\end{figure}

\paragraph*{DDGI as Control Variate.}
For scenes with simple occlusion, the original DDGI data structure provides a low-bias, noise-free result. This can sometimes be desired over our new resampling, which will introduce noise even in cases where the resulting lower bias is not visually noticeable. To combat this,
we explored extending our solution by using primary hitpoint irradiance $\EddgiP$ as a control variate of diffuse transport.
That is, we assumed that ${\frac{\rho}{\pi}\,\EddgiP}$ at primary vertices yields the correct indirect illumination and we resampled proportional to the remaining non-diffuse transport \emph{plus} (neglecting constants for brevity) the absolute difference between ${(\LddgiP + L_e)}$ at the secondary vertex and the spatio-directionally nearest DDGI update ray among those that were traced while rendering the last frame.
This amounts to using the DDGI update rays as a spatio-directionally piecewise constant control variate and their average, $\EddgiP$, as its exact integral.

When shading, we then applied DDGI to the primary vertex (as described in \cite{Majercik2019Irradiance}) and added {\em the difference} (not the absolute value) between ${L_e + \LddgiP}$ at the secondary vertex and the nearest DDGI update ray.
Adding this difference in the shading step may result in negative radiance values, which frequently manifest as negative fireflies that cause black pixels.
We omitted spatio-temporal probe interpolation for
the DDGI control variate, as otherwise the value of $\LddgiP$ at the primary vertex
would not be an exact integral of the control variate.

Fig.~\ref{fig:CVcomparison} shows our results.
Though this algorithm converges to the same (biased) solution as plain DDGI resampling, at one sample per pixel, the
control variate exposes light leaks similar to the na\"\i{}ve
ReSTIR + DDGI combination. At the same time, the noise level does not improve upon plain DDGI resampling, which produces less prominent
light leaks.

While using DDGI as a control variate may be beneficial
in some settings,
in general, it appears not sufficiently efficient and remains an avenue for future research.

\paragraph*{DDGI for Path Guiding.}
Based on the DDGI data structure, guiding rays towards where
the radiance comes from is straightforward. Given a position
in space, we can query the closest DDGI probe and build the
cumulative distribution function (CDF) from the results of
its update rays to sample a guiding direction.
While the results of the probe rays are stored, building
the CDF and sampling from it was too slow
to increase the overall efficiency of our algorithm.

In addition, using $\LddgiP$ to normalize the PDF and sample the guiding direction
in an unbiased way requires the same modifications to be made to the DDGI volume that were
made to the control variate. These make the volume less stable and thus less effective for path guiding.
An alternative would be porting efficient path guiding data structures~\cite{Vorba2019PGP}
to the GPU.

\paragraph*{Combining Primary and Secondary DDGI.}
Even though we focus on hiding the bias of DDGI behind scattering interactions, there are situations in which DDGI's bias is very low in the first place and does not warrant the extra noise caused by secondary queries; see the orange inset of the \RedBall scene in Fig.~\ref{fig:main-result1spp}.
It likely pays off to design an automatic mechanism for detecting such situations and to select among primary and secondary DDGI queries accordingly.

\paragraph*{Path Space Filtering.}
Lastly, alternatively to screen-space denoising, we believe that additional spatio-temporal reuse through path space filtering could be beneficial.
While ReSTIR amounts to spatio-temporal filtering of sampling probabilities and DDGI amounts to spatio-temporal filtering of radiance at \emph{secondary} vertices, path space filtering would provide additional spatio-temporal filtering of radiance at \emph{primary} vertices.
Recent hash-based implementations~\cite{binder2021massively} can run with little overhead on modern GPUs.

\subsection{Limitations}
The limitations of our approach are largely inherited from its components: ReSTIR and DDGI.\@
ReSTIR is a screen-space technique whose reuse capability depends highly on the availability of motion vectors, slow camera motion, and transparency.
These downsides can be mitigated by transitioning to a world-space representation~\cite{regir}, but another limitation remains: through resampling, samples become spatio-temporally correlated, which reduces the total amount of information available to modern denoisers.
Effective denoising in the presence of ReSTIR samples is still ongoing work.

The limitations induced by DDGI are two-fold.
First, being grid-based, DDGI does not scale easily to expansive scenes where the camera may only focus on small regions.
And second, DDGI is incapable of resolving fine spatio-directional detail such as intricate caustics, shadows, ambient occlusion or near-specular reflections.
These limitations are hidden to a degree when DDGI is queried after a primary scattering interaction that is sufficiently diffuse or after undergoing sufficiently many secondary interactions, but better quality can be achieved by using a more accurate cache~\cite{mueller2021realtime}.

\section{Conclusion}

We have combined DDGI~\cite{Majercik2021ScalingGI} and ReSTIR~\cite{bitterli20spatiotemporal}, which, at first glance, might seem orthogonal.
DDGI renders indirect illumination and ReSTIR direct illumination.
However, the appearance is deceiving and a small modification to the DDGI algorithm -- querying the probe volume at secondary path vertices rather than primary ones -- unifies the two approaches into an algorithm that performs much better than its constituent parts.
By querying DDGI at secondary vertices, it acts as a scene-spanning light source, effectively turning the global illumination problem into a much simpler direct illumination problem, which ReSTIR can holistically attack.
This observation is a general one that has inspired rendering algorithms since the 90s, making us excited about future work in this direction.

Quantitatively, the combined algorithm is about 60\% more expensive than either of the two prior works, but makes up for its cost in terms of low noise and bias.
It achieves equal error over an order of magnitude more quickly.
Together with denoising, real-time rendering of global illumination in dynamic scenes is feasible.

\section*{Acknowledgements}
We would like to thank David Luebke and Aaron Lefohn for supporting this work. We also thank Benedikt Bitterli and Chris Wyman for providing the initial codebase in Falcor~\cite{Benty20}, Chris Wyman and Peter Shirley for many early ideas and fruitful discussion, and Nikolaus Binder and Christoph Schied for proofreading the article and many valuable comments. The Pink Room scene was modified from ``The Modern Living Room" by Wig42 and the Living Room scene was modified from ``The White Room" by Jay-Artist. The Greek Villa was modified from ``3D Greek Villa" by holy\_diver.

\printbibliography

\end{document}

redpajama_set_name: RedPajamaArXiv


text:
Q: Compare Table Row Count to Previous Table Row Count of Same Table inside a While Loop Trying to understand the basic logic to have a table read a count of SQL table rows - store that value.
The next time the table is read - only do something ( in this case write an event to a MS Log File) if the row count has increased.
Basically - looking for a new row that's been added to the account to trigger an event.
The while($true) is to make this PowerShell script run indefinitely in the background - so this is using a sleep to check table status intermittently
    while($true)
{
### there's a bunch of code to make the connection and variables set before this:

$DataAdapter = new-object System.Data.SqlClient.SqlDataAdapter $SqlCommand
$dataset = new-object System.Data.Dataset
[void]$DataAdapter.Fill($dataset)
$DataSet.Tables[0].'Table Name'
$sqlConnection.Close()
$sqlConnection.Dispose()

### set the number of rows in table since last count

$dataset.Tables[0].Rows.Count | set-content set.txt

$LastRowCount = Get-Content set.txt #this is where I'm stuck on how to apply this last count
                                    #do I need another loop inside?

if($dataset.Tables[0].Rows.Count -gt 0 -and $dataset.Tables[0].Rows.Count -gt $LastRowCount) {

     write-Eventlog –LogName HUGs –Source Exciter –EventID 101 -Message "testing 123"
 }
 else{
      Write-Host "NO CHANGE IN RECORD COUNT FROM LAST RUN"

     }
#TAKE A FIVE MINUTE BREAK
Start-Sleep -Seconds 30

 }

A: You need 2 variable but Im not familiar with powershell so is just some pseudo code
$LastRowCount = Get-Content; // Set the variable before the loop

while($true)
{
  $CurrentRowCount = Get-Content; // Get the update count

 If $LastRowCount <> $CurrentRowCount then
 {
    write-Eventlog –LogName HUGs –Source Exciter –EventID 101 -Message "testing 123"
    $LastRowCount = $CurrentRowCount // Update the variable for the next loop

 } else {
    Write-Host "NO CHANGE IN RECORD COUNT FROM LAST RUN"
 }

redpajama_set_name: RedPajamaStackExchange


A keystone to CLF's work to secure a clean energy future for the region is completing the transition to a coal-free New England. It is a time of historic progress: cleaner, cheaper alternatives are driving coal out of the market, and old coal plants are closing their doors. But Massachusetts remains a critical battleground for CLF's work, with two costly old coal-fired power plants continuing to jeopardize public health and stoke climate change.
That's why we're delighted to tell you about an event hosted by Cambridge City Councilor Marjorie Decker entitled "The True Cost of Energy: Coal." Councilor Decker has invited the public to a panel discussing the true costs of coal and a free screening of the critically acclaimed documentary The Last Mountain in Cambridge, MA, on Wednesday, May 9. With stunning footage of the practice of mountaintop removal mining, the film bears dramatic witness to the social, public health, and environmental damage wrought by coal and power companies, and chronicles the grassroots fight against coal in Appalachia and around the country. The New York Times called The Last Mountain a "persuasive indictment" of coal; I think you'll agree.
The Last Mountain producer Eric Grunebaum will be on hand for a panel discussion to discuss the film and the future of coal-fired power in Massachusetts and New England. I will be available before and after the event to answer any questions you may have about CLF's work to secure a coal-free Massachusetts.
When: Wednesday, May 9, 2012. 5-8:30 pm.
Where: Cambridge Public Library, 449 Broadway, Cambridge, MA 02138 (map).
Bring your friends and family, and email me at scleveland@clf.org with any questions. I hope to see you there!


Services - Alco Industries, Inc.
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We repair what we sell and guarantee the work.
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text:
6th Annual Circle Trail Tour a 14-mile trek around Wichita Falls
For the Times Record News Richard Carter
What: 6th Annual Circle Trail Tour
When: 10 a.m. to 2 p.m. Saturday
Where: Lucy Park, 100 Sunset Dr. to Hamilton Park, 2901 Hamilton Blvd. to Lake Wichita Park, 5205 Fairway Blvd.
It's time again for the 6th annual Wichita Falls Circle Trail Tour from 10 a.m. to 2 p.m. Saturday put on by the City of Wichita Falls Wellness Committee.
Participants will walk on the scenic Circle Trail from Lucy Park to Lake Wichita Park, while checking in at 14 rest stops for drinks and/or snacks and getting their passports stamped. Everyone who has all 14 stamps will be eligible for a grand prize, and there may be more than one.
Particpants who complete the 14 miles of trail first, second or third in their transportation class: cyclists, runners and walkers, will receive a medal. Everyone should have a good time, said Laci Edwards, Angela Bosma and Holly Morgan, members of the City's wellness committee.
Traditionally, the tour opens at Lucy Park, where the Circle Trail intersects Sunset Drive in front of the log cabin. The Trail then leads to Williams Park, Hamilton Park and then ends at Lake Wichita Park, which is where the finish line will be with music, they said.
Participants may begin at any of those parks, they said. Everyone is welcome to be a part of the tour, walkers, runners, bikers, and people pushing strollers. A sag wagon will run to each of those four parks to take people back to where they began.
The idea behind the tour is to promote the Circle Trail, by walking, running, jogging or cycling the scenic trail that goes through a gorgeous North-South route through Wichita Falls. Once the Trail is eventually completed, it will circle 26 or so miles around the city.
Fourteen businesses/non-profits will do rest stops each mile, including Sam's Club, 4H, Texoma Women's Business Council and the Wichita Falls Health Department.
"At Stop 4, Williams Park," Edwards said, "Animal Services will be there with adoptable dogs. Kona Ice will also be there. We realize for a lot of our families when they get to Williams Park, some of their memberswill start getting tired. We wanted to have a 'Hey, you made it. Good job' thing."
Participants are advised to stay hydrated, wear comfortable shoes and loose clothing. "Know your body, know yourself. if you feel like you need to stop, stop. We encourage everyone who can to go as far as they can," said Morgan.
One of the things they are adding this year is that each person who goes through Lucy Park will get a decorated bag to carry things, at least as long as the bags are available. The bag has a safety reflective strip on it.
"We know there are a lot of things going on in the community on the 27th and that's phenomenal," Edwards said. "We think the Circle Trail Tour a great opportunity to get out with your family and enjoy nature and our gorgeous Trail and still have plenty of time to do other things on Saturday."


The mission of the Center for Global Initiatives is to expand global opportunities at the University of North Carolina at Chapel Hill through collaborative programs and projects and competitive awards and fellowships that connect people across borders and disciplines.
The center is especially interested in opening access to global opportunities to students, faculty and staff traditionally underrepresented in this sphere. The goal is to raise, for example, the number of students from diverse academic backgrounds studying abroad, students with disabilities conducting global internships, males taking more foreign languages, first generation college students earning Fulbright awards, students with significant financial need performing global public service and Native Americans applying to join the foreign service.
Founded in 1993, the Center for Global Initiatives has received more than $30 million in grants from agencies and private donors including Carnegie, Ford, Freeman, Gates, MacArthur, Mellon, NSF, Z. Smith Reynolds, Rockefeller, Rotary International, United Nations University, USAID, U.S. Department of Education, U.S. Department of State and the World Bank.


text:
Birds of America: Stories
By Lorrie Moore
Read by Natasha Soudek
Lorrie Moore Blackstone Publishing 9780679445975
10.18 Hours • 06/25/2019 • 1
The celebrated collection of twelve stories from one of the finest authors at work today From the opening story, "Willing"—about a second-rate movie actress in her thirties who has moved back to Chicago, where she makes a seedy motel room her home and becomes involved with a mechanic who has not the least idea of who she is as a human being—Birds of America unfolds a startlingly brilliant series of portraits of the unhinged, the lost, the unsettled of our America. In what may be her most stunning book yet, Lorrie Moore explores the personal and the universal, the idiosyncratic and the mundane, with all the wit, brio, and verve that have made her one of the best storytellers of our time.
A BuzzFeed Books Pick of Best Short Story Collections
The celebrated collection of twelve stories from one of the finest authors at work today
From the opening story, "Willing"—about a second-rate movie actress in her thirties who has moved back to Chicago, where she makes a seedy motel room her home and becomes involved with a mechanic who has not the least idea of who she is as a human being—Birds of America unfolds a startlingly brilliant series of portraits of the unhinged, the lost, the unsettled of our America.
In what may be her most stunning book yet, Lorrie Moore explores the personal and the universal, the idiosyncratic and the mundane, with all the wit, brio, and verve that have made her one of the best storytellers of our time.
"At once sad, funny, lyrical, and prickly, Birds of America attests to the deepening emotional chiaroscuro of [Moore's] wise and beguiling work." —New York Times
"[Moore's] dialogue snaps with fun." —San Francisco Chronicle
"Natasha Soudek's narration often drips with the irony and wry wit for which Moore is known…She captures Moore's droll, biting humor with her sardonic tone and unhurried pace." —AudioFile
"A fine collection…The reader will be forever susceptible to seeing absurdity everywhere." — Chicago Tribune
"Lorrie Moore soars with Birds of America…A marvelous, fiercely funny book." —Newsweek
"A marvelous collection…[Moore's] stories are tough, lean, funny, and metaphysical…Birds of America has about it a wild beauty that simply makes one feel more connected to life." —Boston Globe
"Lorrie Moore…is a veritable master of the short story. Her third collection, Birds of America, is arguably her best, but you wouldn't go wrong if you were to pick up her full collected stories." —BuzzFeed
Author Bio: Lorrie Moore
Lorrie Moore is the author of the story collections Birds of America, Like Life, and Self-Help and the novels Who Will Run the Frog Hospital? and Anagrams. Her work has won honors from the Lannan Foundation and the American Academy of Arts and Letters, as well as the Irish Times International Prize for Fiction, the Rea Award for the Short Story, and the PEN/Malamud Award. She is the Gertrude Conaway Vanderbilt Professor of English at Vanderbilt University.
Category: Fiction/Short Stories
Publisher: Blackstone Publishing Publisher: Blackstone Publishing
Runtime: 10.18


Nov.12th is the last day for all "Poinsettia" orders and the end of our first 8th grade fundraiser. This would be a great time for an official fundraiser/ promotion planning meeting. As you all know, time seems to fly by so fast and before a blink of an eye, the end is here! What Kora and I need from all of you to make this year extra special are parent volunteers and help to create a plan before the Holiday Season/New Year.
· Parent Fundraising: This group will help assist Kora with ideas, planning, gathering supplies, advertisements etc. for individual fundraisers, and volunteering parent hours as needed.
· Promotion Planning: This group will help assist Skye with ideas, planning ceremony and reception afterward, video and photos, decorations, food, etc.


Dr. Duane Hennion MD is a male health care provider with Family Medicine listed as his primary medical specialization. His credentials are: MD.
Dr. Duane Hennion MD's office is located at 1 Jarrett White Rd (tripler Army Medical Center) Tripler Amc, HI 96859-5001. Their profile includes their phone number, directions, ratings, comparisons, and specialties.
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The following profiles are additional Family Medicine providers in Tripler Amc and / or near Dr. Duane Hennion MD. Profiles may include costs for specific services and procedures, common referrals, ratings, and reviews.
Dr. Oh, Robert M.D., M.P.H.


March 6 marks the anniversary of the fall of the Alamo back in 1836. For more than 13 days, 186 brave and determined patriots withstood Santa Anna's seasoned army of over 4,000 troops. To a man, the defenders of that mission fort knew they would never leave those ramparts alive. They had several opportunities to leave and live. Yet, they chose to fight and die. How foolish they must look to this generation of spoiled Americans.
It is difficult to recall that stouthearted men such as Davy Crockett (a nationally known frontiersman and former congressman), Will Travis (only 23 years old with a little baby at home), and Jim Bowie (a wealthy landowner with properties on both sides of the Rio Grande) really existed. These were real men with real dreams and real desires. Real blood flowed through their veins. They loved their families and enjoyed life as much as any of us do. There was something different about them, however. They possessed a commitment to liberty that transcended personal safety and comfort.
Liberty is an easy word to say, but it is a hard word to live up to. Freedom has little to do with financial gain or personal pleasure. Accompanying Freedom is her constant and unattractive companion, Responsibility. Neither is she an only child. Patriotism and Morality are her sisters. They are inseparable: destroy one and all will die.
As you read those words, remember that Travis and the others did not have the A.C.L.U., P.E.T.A., the National Education Association, and the Southern Poverty Law Center telling them how intolerant and narrow-minded their notions of honor and patriotism were. A hostile media did not constantly castigate them as a bunch of wild-eyed extremists. As schoolchildren, they were not taught that their forefathers were nothing more than racist jerks. Neither did they have pastors constantly filling their hearts and minds with this imbecilic "Obey-the-government-no-matter-what" misinterpretation of Romans chapter 13.
The brave men at the Alamo labored under the belief that America (and Texas) really was "the land of the free and the home of the brave." They believed God was on their side and the freedom of future generations depended on their courage and resolve. They further believed their posterity would remember their sacrifice as an act of love and devotion. It all looks pale now.
Please try to remember the heroes of the Alamo as you watch our gutless political and religious leaders surrender to globalism, corporatism, socialism, and political correctness. Try to recall the time in this country when ordinary men and women had the courage of their convictions and were willing to sacrifice their lives for freedom and independence.
One thing is certain: those courageous champions at the Alamo did not die for a political party or for some "lesser of two evils" mantra. They fought and died for a principle, and that principle was liberty and independence. So did the men at Lexington and Concord. That is our heritage.
Today, however, our national leaders are in the process of turning the United States over to the very forces that the Alamo defenders (and America's Founding Fathers) gave their lives resisting. On second thought, do they look foolish, or do we?