text:
The narrative of independent regulators
August 2, 2013 August 2, 2013 ~ aburman
This post first appeared as an article on Bar and Bench on July 31, 2013. The original can be accessed here.
Those following important policy developments recently will notice numerous announcements proposing new "independent" regulators. Beginning with SEBI in the early 1990s, and TRAI in the late 90s, a number of independent regulators have been set up. These include the Central and State Electricity Regulatory Commissions (CERC and SERCs respectively), the Competition Commission of India (CCI), the Insurance and Development Regulatory Authority of India (IRDA), the Pension Fund Regulatory and Development Authority (PFRDA), the Airport Economic Regulatory Authority (AERA), the Petroleum and Natural Gas Regulatory Board (PNGRB) and the Tariff Authority for Major Ports (TAMP).
Recently, there have been proposals for a biotechnology regulator, a real estate regulator, acoal regulator, and even a roads regulator. In the financial sector, the report of the Financial Sector Legislative Reforms Commission (FSLRC) recommends an overhaul of the financial sector regulatory architecture by merging some existing regulators, and creating new ones. The most often talked about rationale for the creation of independent regulators is to ensure greater competition in a given sector, and to create a level playing field for different entities competing in that sector. This fairly regulated competitive market, it is argued, will be of greater benefit to consumers than the existing regime.
While this article does not seek to contest this above-mentioned premise of promoting competition, we need to examine whether the laws and processes creating these independent regulators actually create "independent" regulators who also remain accountable to Parliament. Since many of these regulators create appellate tribunals, or provide for appeals to High Courts and the Supreme Court, this article also discusses the implications of this regulatory sprawl on the existing judicial system. Lastly, the rationale for the creation of independent regulators needs to be debated carefully before more and more regulators are established. I argue that the long-term implications for the establishment of such a large number of sectoral regulators are something lawyers, policy experts and administrators need to examine carefully.
Independence and performance
Independence of regulators from the government is desirable to enable them to perform without political interference. This is especially important in India where the state is a major market participant in many economic sectors. However, not all regulators are equally independent, since the laws establishing them do not follow a uniform standard. Additionally, these laws rarely have the right mix of independence and accountability; a lot of these issues arguably stem from poor legislative design.
To be truly independent from the government, not only must the regulator be an independent statutory authority, it must also be financially and administratively independent from the government. The executive cannot be allowed to either interfere, or arm-twist the regulator to do its bidding. More importantly, since the onus of meeting the regulator's objectives lies with the regulator, the government cannot be allowed to have unbridled discretion in how the regulator hires and manages personnel, and uses its finances.
For example, in 1999, TRAI, after holding extensive consultations issued its first Tariff Order (TO), a landmark for infrastructure regulatory agencies in India as it attempted to set tariffs to reflect costs more closely. After an uproar in Parliament, and opposition from other quarters, the Department of Telecommunications sent TRAI a 3-line note directing it to put its order on new phone rates on hold. TRAI refused. In response,
"[T]he government issued two gazetted notifications … The first related to salaries, allowances and conditions of service of TRAI officials. Here, instead of setting rules for the chairman of TRAI, it made different rules depending on if he were a retired judge of the Supreme Court, a retired chief justice of a high court or if he were a serving judge. It also cut down on the allowances for foreign travel [of TRAI], ostensibly in the wake of a recent Comptroller and Auditor General report."
(Sourced from here)
Laws establishing regulators must therefore, be drafted to protect against such instances. At the same time, there must also be mechanisms to ensure regulators remain accountable to Parliament. The CAG for example, set up an institute in Jaipur to train officers in environmental audits. As a constitutional authority, the CAG is arguably responsible for auditing the government's expenditure. While environmental audit may be desirable in itself, it is debatable whether such audits come within the CAG's existing mandate.
The report of the FSLRC recommends physical, legal and administrative separation of the regulator from the government, implying that regulators must have independent infrastructure, personnel. With regard to financial independence, the FSRLC recommends independent sourcing of finances from sources such as fees. With regard to strengthening accountability, it recommends that regulators (a) be given clear, precise regulatory objectives, (b) explain their regulatory actions to the general public, and regulatory changes be made after prior consultation with the public, and (c) report to Parliament on how they fared on pursuing their regulatory objectives, and the outcomes achieved.
Appellate Mechanisms
Independent tribunals, or some other appellate mechanisms are usually created to entertain appeals or disputes from orders of regulators. SEBI has SAT, the CCI has COMPAT, TRAI has TDSAT, the CERC and SERCs have the Appellate Tribunal on Electricity (APTEL), IRDAs appellate forum is SAT, appeals from PNGRB go to APTEL, and so on. While tribunals perform an adjudicatory function, and are thus not as prone to interference from the executive as the regulators, their relationship with the executive also needs to be looked into.
One such example is the use of APTEL's use of its suo moto powers. In November 2011, APTEL passed an order exercising its suo moto powers, directing all SERCs to revise electricity tariffs regularly. Under the Electricity Act, 2003, all SERCs are mandated to revise electricity tariffs regularly on the basis of documentation provided to them by state electricity utilities. This was however not being done by most SERCs, and electricity utilities continued to suffer losses as tariffs remained low compared to the cost of producing and supplying electricity.
Significantly, APTEL exercised this suo moto power on the basis of a letter from the Ministry of Power complaining that most state distribution utilities had failed to file annual tariff revisions in time, and as a result, tariff revision has not taken place for a number of years in many states. It also stated that SERCs have not revised tariffs suo moto, and as a result, state distribution utilities are in poor health. While the final order was not an improper exercise of APTEL's power, this instance can be construed as one where the central government was attempting to regulate the functioning of SERCs (over which it otherwise has no jurisdiction) by writing to APTEL.
The increasing specialization in the administration of justice through the establishment of sector specific tribunals also has repercussions for the broader system of administration of justice. As most legislations establishing these tribunals provide for appeals to the Supreme Court, such laws usually insist that the chairpersons or members of the tribunal, or both be (a) retired Supreme Court judges, (b) serving Supreme Court judges, (c) High Court chief justices, or (d) judges who have served in High Courts for a particular length of time. This arguably creates a high level of expertise in the dispensation of justice in these tribunals.
However, there is no mechanism by which members serving in such tribunals can be re-inducted (if not past the age of superannuation) into the mainstream judiciary. This arguably creates a situation where technical expertise in specific sectors is not channelized back into the mainstream judiciary. The creation of such a two-way process may become imperative in the long run if the increasing tribunalization of justice persists. The present system of promotion and induction into the judiciary emphasizes experience in practicing law or administering justice in different courts and on varied subjects. If tribunals are becoming an important part of the judicial landscape, it is only logical that experience as members of such tribunals also be given weight while considering appointments to the higher judiciary. This would also create a virtuous cycle where good judges would consider serving on tribunals for a period of time before returning to their service in the higher judiciary.
Rationale for independent regulation
The last, but perhaps the most significant issue remains the rationale for creating independent regulators. While in some cases there is a need for independent regulation, in some other cases the need for an independent regulator is less easily justifiable. The recent road regulator being proposed is one such example. The need for a regulator has been felt within the sector due to the large number of stalled road projects throughout the country. It was felt that the regulator should deal with "tariff setting, toll policy and modifications, compliance of service levels (for commuters), address public concerns." The government being one of the parties to such contracts, cannot be the proper entity to mediate such disputes.
None of these functions i.e. ensuring competition, fair-play, and consumer protection however, are those that are usually entrusted to an independent regulator. In other words, the re-negotiation of contracts, regulations regarding toll collection, and other aspects of highway construction are not functions that need to be entrusted to a regulator in the usual course of events. These are not activities, the performance of which, enhances fair competition within the sector. The resolution if these issues may make the development of roads more efficient, but these functions are not those assigned to independent regulators. An official from the Planning Commission has stated that, "In case of roads, everything is decided in the contract itself, and be it the toll rates, other tariff. So, what do we need a regulator for?" Some commentators are justifying the need for such a regulator since the executive has failed to resolve these issues in an efficient and impartial manner.
We therefore need to carefully examine at what point the creation of an independent regulator is in effect an abdication of an essential state function. Many such state functions are questions of policy. They require political negotiation and decision-making. In essence they are policy issues, not regulatory ones. The state cannot abdicate such functions and hand them over to an unaccountable, unelected regulator because it has been unable to perform these functions effectively.
While there are no easy answers to these issues, they do need careful deliberation. There is therefore a necessity for ensuring that while attempts to promote competition within the economy are encouraged, the creation of new regulators and their linkages to the rest of the legal system are thought through.
Posted in Law, Supreme Court bar and benchdemocracydemocracy in indiagovernmentgovernment in Indiaindependent regulatorsindia as an evolving polityIndian democracyIndian governmentIndian politicsindian politypoliticspolitics in Indiapolity in indiapolity of indiapolityinindiaregulatory authoritiesregulatory governanceregulatory governance in indiaretired bureaucratsSEBIstatutory regulatorsTRAI
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Board-certified in Family Medicine, Dr. Hauser is on the physician team for Kelsey-Seybold's Executive Health Assessment program. She cares for patients in the Mavis & Mary Kelsey Suite at Kelsey-Seybold's Main Campus clinic. She earned her medical degree from The University of Texas Health Science Center at Houston in 1994. She completed her Family Practice internship at the University of Southern California and her residency at The University of Texas Health Science Center at Houston.
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text:
Why Walmart Is Finally Joining McKesson for Generic Purchasing
Well, pierce my ears and call me drafty. Yesterday, Walmart announced that it will jointly source generic drugs to McKesson, its long-time brand-name drug supplier. Read the press release.
Below, I outline why this long-expected deal makes sense for both companies. Walmart finally bowed to the inexorable economics of generic purchasing, while McKesson captured a much-needed partner that it had been aggressively chasing.
The deal also formalizes an intriguing alignment between the three largest wholesalers, the three largest pharmacy benefit managers (PBMs), and the four (soon to be three) largest retail chains. See what you think about my summary chart.
Here are four key points to understand about yesterday's Walmart-McKesson announcement.
1) Walmart lacked the scale of the new generic power buyers.
In section 3.3.5 of our 2016 Economic Report on Retail, Mail, and Specialty Pharmacies, I describe the changing generic sourcing relationships between wholesalers and large pharmacies. As generics come to dominate pharmacy dispensing, those pharmacies and wholesalers with below-average acquisition costs will be the best positioned to succeed.
Consequently, the largest wholesalers and drugstore chains have entered into new combinations that aggregate generic purchasing power:
As part of Amerisourcebergen's supply agreement with Walgreens Boots Alliance (WBA), ABC can source generic drugs and other products through Walgreens Boots Alliance Development (WBAD). Note that ABC can cancel WBA's equity warrants if WBAD terminates the Generic Pharmaceuticals Purchasing Services Agreement. For background, see Why Walgreens Boots Alliance is Triggering a Huge AmerisourceBergen Stock Buyback.
Red Oak Sourcing is the generic purchasing joint venture between CVS Health and Cardinal Health.
Unlike its wholesale peers, McKesson has channeled its customers' generic purchasing volume into the company's own OneStop generic program, giving additional leverage and scale to McKesson's negotiations with generic manufacturers. In 2015, McKesson established McKesson Global Procurement & Sourcing Limited, a London-based subsidiary focused on global manufacturer negotiations.
We estimate that in 2015, the four largest buyers accounted for 80% of total U.S. generic drug purchases from manufacturers. Note that these estimates exclude the impact of mergers and acquisitions in 2015 and the newest Walmart news.
The press release does not mention OneStop. Instead, it states that the companies "will collaborate on sourcing generic pharmaceuticals for their respective U.S. operations, adding scale and value for both companies." Translation: Walmart is not ceding all authority to McKesson.
2) Walmart is finally surrendering to the new realities of drug wholesaling.
Wholesalers' biggest customers have historically purchased generics directly from manufacturers rather than through the wholesale channel. However, the reduced acquisition costs from these retail-wholesale purchasing relationships have encouraged other pharmacies to shift generic purchasing to wholesalers. Examples include Publix (AmerisourceBergen), Albertsons (McKesson), and OptumRx (Cardinal Health).
These arrangements are increasing the wholesale market's size, by shifting generic volume to the wholesale channel. They also add heft to the generic power buyers shown in the chart above.
In 2015, Walmart was the fourth-largest pharmacy, with revenues (including Sam's Club) of about $19.9 billion. (See The Top 15 Pharmacies of 2015.)
Despite Walmart's size, it was rapidly becoming an also-ran in generic buying. We estimate that its pharmacy business grew slowly in 2015. The two factors that propelled its sales—an industry-leading generic drug discount program and aggressive participation in Medicare Part D preferred network arrangements—have already been matched by other pharmacies. Don't forget that Walmart has been having profitability problems in its pharmacy business, as I discussed in What's Behind Walmart's Pharmacy Profit Warning?
Before yesterday's announcement, Walmart was one of the last big retailers to purchase generic drugs directly from manufacturers. Its estimated $2.5 billion in generic purchasing volume will move from the "All Other Buyers" category in the chart to a new McKesson/Walmart partnership slice.
3) McKesson partially solves its M&A-driven scale problems.
By extending its distribution agreement with Walmart, McKesson locks down the purchasing volume of its third-biggest customer. This partially offsets McKesson's bad luck—it had found itself on the wrong end of multiple recent drug channel consolidations:
In 2014, Rite Aid shifted its purchasing to OneStop. This volume, however, will move to WBAD once WBA completes its Rite Aid acquisition. See point 6 in The Walgreens-Rite Aid Deal: Ten Things You Should Know.
Omnicare expanded its relationship with McKesson to include the supply of both brand-name and generic pharmaceuticals. (The companies' previous agreement included only brand-name drugs.) Alas, CVS Health acquired Omnicare, which now sources generics via Red Oak.
McKesson had supplied brand-name drugs to the mail and specialty pharmacies of UnitedHealth's OptumRx PBM, which was McKesson's fifth-largest customer. Following OptumRx's acquisition of Catamaran, Cardinal Health signed an agreement with OptumRx to supply generic and brand pharmaceuticals to OptumRx's pharmacies.
McKesson's profits from the Walmart deal will not be as great as its profits from retailers that buy via OneStop. Since the companies will "collaborate" on generic sourcing, I presume that Walmart will share in more of the economics than would a typical customer.
4) The deal finalizes an intriguing industry channel alignment.
I have previously mentioned the so-called Rule of Three and Four, which was coined by Bruce Henderson, founder of Boston Consulting Group. He posited that "a stable, competitive industry will never have more than three significant competitors."
We now have three large wholesalers, three large PBMs, and three large retail pharmacy chains. Check out the big customer relationships, most of which are locked up in multi-year arrangements between wholesalers and their customers.
Per our list of the largest pharmacies, the wholesale customers—five companies, post WBA-RAD—shown in the chart accounted for almost two-thirds of U.S. prescription dispensing revenues. Hmmm.
These alignments have significant implications for manufacturer channel strategies, because wholesalers are becoming virtually impossible to displace for traditional brand-name and generic products.
Specialty drugs are another story, which we'll consider in my next article.
Adam J. Fein, Ph.D. on Tuesday, May 17, 2016

redpajama_set_name: RedPajamaCommonCrawl


text:
Tory candidate will fight for schools – despite crippling cuts
December 11th, 2019 Martin Walker News 0 comments 883
The Conservative parliamentary candidate for Sedgefield in Thursday's general election says he will fight for local authority funding – despite seeing crippling cuts under a Tory government.
Alarming figures, revealed by the independent campaign group Stop School Cuts, show how Aycliffe schools will be even more worse off in 2020 under Tory rule.
Greenfield Community College is the worst example, with a staggering £473-per-pupil cut when compared to 2015 levels, according to the figures.
Labour candidate Phil Wilson is the only Sedgefield candidate to have signed a petition supporting Stop School Cuts' campaign.
And despite being asked to get behind the campaign, Conservative candidate Paul Howell – himself a school governor and a councillor on both Durham and Darlington councils – said he needed more time to consider.
Mr Howell – pictured above (left) with national party chairman James Cleverley – told Aycliffe Today: "I'm not aware of the campaign but I'd never sign up to something like that without being able to give it due consideration.
"I was elected as the Tory candidate very late on, so I haven't had much time to campaign. In a very short time I've received hundreds of emails from various pressure groups and campaigners asking me to offer my support to this campaign and that, but it would be unwise to jump into anything without giving it my proper thought.
"And my last few weeks have been full-on, campaigning all over the constituency from Wingate to Hurworth and everywhere in between."
Mr Howell, who's a school governor with Horndale primary school in Aycliffe and Ferryhill Station school, added: "It's one of the Conservative Party pledges to increase spending on education.
"I want to fight for more local authority funding and if I'm elected on Thursday that's what I'll do."
Labour candidate Phil Wilson said: "These figures from Stop School Cuts show just how much damage this government has already done to our local schools.
"I can see it when I've visited schools around the constituency – we're seeing increased class sizes, which means not as much quality time for students, and that can have a hugely-negative impact.
"Only under Labour will we see a stop to school cuts – and only under a Labour candidate in Sedgefield will we see an MP fighting for our local schools."
The figures, which Labour say are supported by the Institute for Fiscal Studies (IFS), are available at: www.schoolcuts.org.uk
Six people are bidding to become Newton Aycliffe's MP in Thursday's election. They are:
• David Bull (Brexit Party) from Suffolk
• John Furness (The Green Party) from Darlington
• Paul Howell (Conservative Party) from Darlington
• Michael Joyce (Independent) from Darlington
• Dawn Welsh (Lib Dems) from Gateshead
• Phil Wilson (Labour Party) from Heighington
Next article A close call as Phil Wilson faces fight of his life
Previous article Aycliffe Juniors round-up


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This year, Calling Caldecott has zoomed by! It's not just our imagination. Last year there were 22 weeks between Labor Day and ALA; this year, only 18. But it feels rushed every year because there are so many good books out there. Even posting three times a week — and sometimes two books to a post — will leave us with books left out.
After looking back over the comments and at which books made it onto your Top Five lists, Martha and I have come up with a ballot of 25 books. Don't think for a moment that it was easy. We worry that whatever eventually wins the real Caldecott might not be on our ballot, and we both had moments of sadness when we realized that a favorite wasn't likely to be a contender. This stage is just as difficult for the Real Committee. I remember those pre-vote moments when it felt as if I was saying goodbye to a best friend who was moving away. The fact that it is inevitable doesn't make it hurt any less.
We're assuming that you all have New Years events to attend, so this is our last post for the week. When our vote goes live at 9 a.m. Monday morning, this blog truly becomes a Mock Caldecott.
Since Robin is temporarily sidelined, I get to be the heavy who pleads with you all to vote, but please do not make this a popularity contest by sending the ballot link to all and sundry with instructions to vote for your favorites. We want all of our voters to be serious about books. If you haven't read every single title, that's okay. But do try to read as many as you can before voting. Please DO send this list to others who know this year's picture books but haven't necessarily been following the blog.
As you revisit your favorites, remember that you will vote exactly as the real Caldecott Committee does: you will vote for three books: your first, second, and third choices. When the ballot closes, we will weight them differently. The number of first place vote will be multiplied by 4, second place by 3, and third place by 2. So it's important to decide not just your top three, but what order you want them to be on the ballot.
At this point the Real Committee is busy rereading all their nominated books, making notes on what they appreciate and what concerns them. (Experience tells me that a lot of time will given to the concerns. Each member has to be ready to defend against others' concerns and to lay out their own concerns in a way that the others can hear. Minds will have to be changed!) The Real Committee starts face-to-face deliberations on Friday, January 8, so they are down to the wire, just as we are here.
And now we would love to hear pleas for your favorite books in the comment section below. Have a happy New Year and we'll see you Monday when you vote.
Wow, that is one pretty list of 25 books. For some reason, year in year out, I always find it harder to choose just 3 in a Mock Caldecott than in a Mock Newbery. I'm sure there is a reason for that, but I'm not sure what it is.
At any rate, I really can't make pleas for my favorites because I've got nothing but love for pretty much all of those titles up there.
Such a great list. 🙂 I'm still hoping for Float to win it all and Last Stop On Market Street and If You Plant a Seed as definite Honors. So many good ones. One I wish had made it to your final 25 is Hiawatha and the Peacemaker! Looking forward to voting next week. Happy New Year.
This is a great list. Thank you both for a great year! You both wrote some excellent reviews/studies of books on this list. It will take me while to finalize my vote. There are some tough choices ahead. Looking forward to Monday. Happy New Year.


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Together We Can Community Resource Center Inc. Delivers Groceries from Oresky &Associates, pllc to Over 200 Families in Corona
Food Distribution Event Kicks off Partnership between Law Firm and Nonprofit Group
On May 9, twenty volunteers from Together We Can Community Resource Center Inc. (TWC) departed from the Queens Museum at Flushing Meadow Corona Park to distribute groceries to more than 200 families in the western Queens community of Corona who have been impacted by the coronavirus.
In an effort to assist these families, TWC recently announced that Oresky & Associates, pllc joined in its battle to fight hunger and assist the Corona community through this difficult time by filling its food banks with groceries for those who are in need. After learning that the Center did not have funding for the month of May, Jacob Oresky, Founder and Managing Attorney, Oresky & Associates, pllc, volunteered to stock the food bank with groceries for these families. As an attorney, Mr. Oresky has represented many members of the Corona community for the past 25 years.
TWC is a 501(c)(3), volunteer-led nonprofit that services the residents of Jackson Heights, Elmhurst and Corona with the mission to empower New York residents by linking them to community resources and providing educational opportunities. The group assists with SNAP enrollment, transportation assistance, job placements, resources and referrals, youth empowerment, parent support and more.
For more information, visit www.togetherwecanrc.org. For more information on Oresky & Associates, pllc, visit www.oreskylaw.com.
* Photo is attached.
Volunteers from Together We Can Community Resource Center Inc. pose with the bags of groceries provided from the law firm Oresky & Associates, pllc before leaving from the Queens Museum and distributing them to more than 200 families in the western Queens community of Corona on May 9.
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text:
Q: Using grep/sed/awk to classify log file entries I need to process a very large log file with many lines in different formats.
My goal is to extract unique  line entries who have the same starting pattern, e.g. '^2011-02-21.*MyKeyword.*Error', effectively obtaining a list of samples for each line pattern, therefore identifying the patterns.
I only know a few patterns so far, and browsing through the file manually is definitely not the option.
Please note that besides the known patterns, there is a number of unknown ones too, and I'd like to automate extracting those as well.
What is the best way to do this? I do know regular expressions quite well, but haven't done much work with awk/sed which I imagine would be used at some point in this process.

A: If I understand correctly, you have a bunch of patterns, and you want to extract one match per pattern. The following awk script should do the trick. It prints the first occurrence of the given pattern, and records that the pattern has been seen so as not to print subsequent occurrences.
awk '
/^2011-02-21.*MyKeyword.*Error/ {
    if (!seen["^2011-02-21.*MyKeyword.*Error"]++) print;
    next;
}
1 {if (!seen[""]++) print}  # also print the first line that matches no pattern
'

Here's a variant that keeps one MyKeyword.*Error line per day.
awk '
/^[0-9]{4}-[0-9]{2}-[0-9]{2}.*MyKeyword.*Error/ {
    if (!seen[substr($0,10) "MyKeyword.*Error"]++) print;
    next;
}
'

A: It's not clear whether you are trying to group lines by unknown output patterns or unknown keywords in known patterns.
In the first case, if you have logs such as:
[2010-04-02 12:00:00] Error: BaseController Something went wrong
2010-04-02 12:01:00 Warning - Something happened
UserController (2010-04-02 12:02:00) failed with exit status: 1
[2010-04-02 12:03:00] Error: BaseController Something went wrong
[2010-04-02 12:04:00] Error: BaseController Something went wrong
2010-04-02 12:04:01 Warning - Something else happened
UserController (2010-04-02 12:05:00) failed with exit status: 2
UserController (2010-04-02 12:06:00) failed with exit status: 10

then you could use tr and sort and/or uniq to explore the patterns:
$ tr '[:alpha:]' x < file.log | tr '[:digit:]' d | sort -u

dddd-dd-dd dd:dd:dd xxxxxxx - xxxxxxxxx xxxxxxxx
dddd-dd-dd dd:dd:dd xxxxxxx - xxxxxxxxx xxxx xxxxxxxx
[dddd-dd-dd dd:dd:dd] xxxxx: xxxxxxxxxxxxxx xxxxxxxxx xxxx xxxxx
xxxxxxxxxxxxxx (dddd-dd-dd dd:dd:dd) xxxxxx xxxx xxxx xxxxxx: d
xxxxxxxxxxxxxx (dddd-dd-dd dd:dd:dd) xxxxxx xxxx xxxx xxxxxx: dd

or, if you wanted to count occurances:
$ tr '[:alpha:]' x < file.log | tr '[:digit:]' d | sort | uniq -c
      1
      1 dddd-dd-dd dd:dd:dd xxxxxxx - xxxxxxxxx xxxxxxxx
      1 dddd-dd-dd dd:dd:dd xxxxxxx - xxxxxxxxx xxxx xxxxxxxx
      3 [dddd-dd-dd dd:dd:dd] xxxxx: xxxxxxxxxxxxxx xxxxxxxxx xxxx xxxxx
      2 xxxxxxxxxxxxxx (dddd-dd-dd dd:dd:dd) xxxxxx xxxx xxxx xxxxxx: d
      1 xxxxxxxxxxxxxx (dddd-dd-dd dd:dd:dd) xxxxxx xxxx xxxx xxxxxx: dd

But if the pattern is always the same, such as MyKeyword is always in the same place (such as [2010-04-02 12:00:00] Error: BaseController Something went wrong) but you want to find out what strings may be in that position, you simply:
$ awk '{a[$3]++} END {for (i in a) {printf("%4d %s\n", a[i], i) } }' file.log

which should give you something like
   3 Error
   1 Info
   2 Warning

redpajama_set_name: RedPajamaStackExchange


The SWS-La Mirada proposes to upgrade a portion of the existing water storage facility that is located on a 4.16 acre site in the NE portion of the City. The existing facility contains two 2.8 million gallon above ground water reservoir tanks, a 7.5 million gallon in-ground reservoir tank, and one building for operation and maintenance. *See NOD for full details.


When we design our proposed additions on the existing buildings we ensure to integrate the proposed addition so it blends in with the existing building and makes it hard to see where the old building ends and the new proposal begins, ensuring that should you resell the property the entire design looks complete and not like a tack on.
With the capability of full 3D design we can show you different options and give you the full understanding of how the proposed add-on blends in and flows with the existing house. Ensuring once you build there are no surprises.


text:
\section{Introduction}
We begin with the statement of the problem studied in this article.
We are given two point sets $Y$ (servers) and $X$ (clients) in the plane,
a coverage function $\kappa: X \rightarrow \Natural$, and a constant $\alpha \geq 1$.
An assignment $r: Y \rightarrow \Real^+$ of {\em radii} to the points in $Y$
corresponds to ``building'' a disk of radius $r_y$ centered at each $y \in Y$.
For an integer $j \geq 0$, let us say that a point $x \in X$ is $j$-covered
under the assignment if $x$ is contained in at least $j$ of the disks, i.e.
\[| \{ y \in Y \ | \ ||y-x||_2 \leq r_y \}| \geq j\]
The goal is to find an assignment that $\kappa(x)$-covers each point $x \in X$
and minimizes $\sum_{y \in Y}r_y^{\alpha}$. We call this the {\em non-uniform
minimum-cost multi cover} problem (non-uniform MCMC problem).

We are interested in designing a polynomial time
algorithm that outputs a solution whose cost is at
most some factor $f \geq 1$ times the cost of an optimal solution. We call such
an algorithm an $f$-approximation, and it is implicit that the algorithm is actually polynomial-time.

The version of this problem where $\kappa(x) = k, \ \forall x \in X$, for some
given $k > 0$, has received particular attention. Here, all the clients have the same coverage requirement of $k$. We will refer to this as the {\em uniform MCMC} problem. In the context of the uniform MCMC, we will refer to a $j$-cover
as an assignment of radii to the servers under which each client is $j$-covered.

\subsection{Related Work}
In the rest of this section, we will focus on the uniform MCMC problem, and be specific when remarking on generalizations to the non-uniform problem.
The (uniform) MCMC problem was considered in two recent papers, motivated by fault-tolerant
sensor network design that optimizes energy consumption.  Abu-Affash et al.~\cite{ACKM} considered the case $\alpha = 2$, which corresponds to minimizing the sum of the areas of the server disks. They gave an $O(k)$ approximation for the problem using mainly geometric ideas. Bar-Yehuda
and Rawitz \cite{Yehuda2013Note} gave another algorithm that achieves the same approximation
factor of $O(k)$ for any $\alpha$, using an analysis based on the local ratio technique. The central question that we investigate in this article is whether an approximation guarantee that is independent of $k$ is possible.

There is a considerable amount of work on clustering and covering problems related
to the MCMC problem, and we refer the reader to the previous papers for a detailed
survey \cite{ACKM, Yehuda2013Note}. Here, we offer a view of some of that work from
the standpoint of techniques that may be applicable to the problem at hand. For the
case $k = 1$ of the problem, constant factor approximations can be obtained using
approaches based on linear programming, and in particular, the primal-dual method
\cite{Charikar2001Clustering,Freund2004Combinatorial}. The $O(k)$ approximation of
Bar-Yehuda and Rawitz \cite{Yehuda2013Note} for $k > 1$ can be situated in this line
of work.

There has been some recent work on the geometric set multi-covering problem
\cite{Chekuri2009Set,Bansal2012Weighted}. In particular, the recent work of Bansal
and Pruhs \cite{Bansal2012Weighted} addresses the following problem. We are a given
a set of points in the plane, a set of disks each with an arbitrary non-negative
weight, and an integer $k$. The goal is to pick a subset of the disks so that each
of the given points is covered at least $k$ times. The objective function we want
to minimize is the sum of the weights of the chosen disks. Bansal and Pruhs
\cite{Bansal2012Weighted} give an $O(1)$ approximation for the problem, building
on techniques developed for the case $k = 1$ \cite{Varadarajan2010Weighted, Chan2012Weighted}.

It would seem that the problem considered in this paper can be reduced to the problem
solved by Bansal and Pruhs: for each $y \in Y$ and $x \in X$, add a disk centered at
$y$ with radius $||x-y||_2^{\alpha}$, and let $X$ be the set of points that need to be covered.
The reason this reduction does not work is that we have to add an additional constraint
saying that we can use only one disk centered at each $y \in Y$. Notice that this
additional constraint is not an issue for the case $k = 1$, since here if the returned
solution uses two disks centered at the same $y \in Y$, we can simply discard the smaller one.

In the geometric set cover problems considered by \cite{Chekuri2009Set,
Varadarajan2010Weighted, Chan2012Weighted, Bansal2012Weighted}, the
input disks are ``immutable'', and the complexity of the problem stems from the
combinatorial geometry of the disks. For the MCMC application, it would be
more fruitful to consider geometric set multi-cover problems where the algorithm
is allowed to slightly enlarge the input disks. This version of covering with
$k = 1$ is considered by Har-Peled and Lee \cite{HarPeled2012Weighted}. For $k > 1$,
however, we still have the above-mentioned difficulty of reducing MCMC to a set multi-cover problem.

The case $k = 1$ of our MCMC problem actually admits a polynomial time
approximation scheme (PTAS) using dynamic programming on top of randomly
shifted quad-trees \cite{Erlebach2005PolynomialTime,Chan2003Polynomialtime}.
This was shown by the work of Bilo et al. \cite{Bilo2005Geometric}, following
the work of Lev-Tov and Peleg \cite{Tov2005Polynomial} for $\alpha = 1$.
The difficulty with extending these results for $k = 1$ to general $k$ is that
the ``density'' of the solution grows with $k$, and therefore the number of
sub-problems that the dynamic program needs to solve becomes exponential in $k$.
It is conceivable that further discretization tricks \cite{HarPeled2012Weighted}
can be employed to get around this difficulty, but we have not succeeded in this
effort. On the other hand, we are also not aware of any hardness result that rules
out a PTAS. The problem is known to be NP-hard even for $k = 1$
and any $\alpha > 1$ \cite{Bilo2005Geometric, Alt2006}.

\subsection{Our Results}

In this article, we obtain an $O(1)$ approximation for the uniform MCMC problem. That is, we demonstrate an approximation bound that is independent of $k$.

Our approach revolves around the notion of an {\em outer cover}. This is an
assignment of radii to the servers under which each client $x \in X$ is
covered by a disk of radius at least $||y^k(x)-x||_2$, where $y^k(x)$ is the $k$-th nearest neighbor of $x$ in $Y$. To motivate the notion, consider any
$k$-cover, and in particular, the optimal one. Consider the set of disks
obtained by picking, for each client $x \in X$, the largest disk covering $x$
in the $k$-cover. (Several clients can contribute the same disk.) This set
of disks is seen to be an outer cover.

We provide a mechanism for extending any $(k-1)$-cover to a
$k$-cover so that the increase in objective function cost is bounded by
a constant times the cost of an optimal outer cover. This naturally
leads to our algorithm in Section~\ref{sec:algo} -- recursively compute a $(k - 1)$-cover
and then extend it to a $k$-cover. To bound its approximation ratio, we
argue in Section \ref{sec:analysis} that the optimal solution can be partitioned into a $(k-1)$-cover
and another set of disks that is almost an outer cover. Finally, we need
a module for computing an approximately optimal outer cover. We show in
Section \ref{sec:outercover} that
an existing primal-dual algorithm for $1$-covering can be generalized
for this purpose.

The idea of an outer cover has its origins in the notion of {\em primary disks} used by Abu-Affash et al.~\cite{ACKM}. Our work develops the idea and its
significance much further, and this is partly what enables our $O(1)$ approximation bound.

Our algorithm and approximation guarantee of $O(1)$ works for the non-uniform MCMC problem as well. We therefore present our work in this slighly more general setting.

\section{Preliminaries}
\label{sec:terminology}
For convenience, we solve the variant of the non-uniform MCMC problem where we have
$\Linfty$ disks rather than $l_2$ disks.
Our input is two point sets $Y$ and $X$ in $\Real^2$, a coverage function $\kappa: X \rightarrow \Natural
\cup \{0\}$, and the constant $\alpha \geq 1$. (It will be useful to allow $\kappa(x)$ to be $0$ for some $x \in X$.) We also assume that
$\kappa(x) \leq |Y|$ for each $x \in X$, for otherwise there is no feasible solution.

We describe
an algorithm for assigning a {\em radius} $r_y \geq 0$ for each $y \in Y$,
with the guarantee that for each $x \in X$, there are at least $\kappa(x)$ points
$y \in Y$ such that the $\Linfty$ disk of radius $r_y$ centered at $y$ contains $x$. In other words the guarantee is that for each $x \in X$,
\[ | \{ y \in Y \ | \ ||x-y||_{\infty} \leq r_y \} | \geq \kappa(x)\]

Our objective is to minimize $\sum_{y \in Y} r_y^{\alpha}$. For this optimization problem, we will show
that our algorithm outputs an $O(1)$ approximation. Clearly, this also
gives an $O(1)$ approximation for the original problem, where distances
are measured in the $l_2$ norm. We will use
$||\cdot||$ to denote the $\Linfty$ norm.

For each $x \in X$, fix an ordering of the points in $Y$ that is non-decreasing
in terms of $\Linfty$ distance to $x$. For $1 \leq j \leq |Y|$, let $y^j(x)$
denote the $j$-th point in this ordering. In other words, $y^j(x)$ is the
$j$-th closest point in $Y$ to $x$. For brevity, we denote $y^{\kappa(x)}(x)$ by $y^{\kappa}(x)$.

Let $\disk(p,r)$ denote the $\Linfty$ disk of radius $r$ centered at $p$.
The {\em cost} of a set of disks is defined to the sum of the $\alpha$-th
powers of the radii of the disks. The cost of an assignment of radii to the
servers is defined to be the cost of the corresponding set of disks.

\section{OuterCover: Algorithm to generate a preliminary cover}
\label{sec:outercover}
Given $X' \subseteq X$, $Y$, $\kappa$ and $\alpha \geq 1$, an {\em outer cover}
is an assignment $\rho: Y \rightarrow \Real^+$
of radii to the servers such that
for each client $x \in X'$, there is a server $y \in Y$ such that
\begin{enumerate}
	\item The disk $\delta(y, \rho_y)$ contains $x$
	\item Disk radius $\rho_y \geq ||x - y^{\kappa}(x)||$
\end{enumerate}

Our goal in this section is to compute an outer cover that minimizes the cost $\sum_y \rho_y^{\alpha}$.
In the rest of this section, we describe and analyze a procedure
$\oc(X',Y,\kappa, \alpha)$ that returns an outer cover
$\rho: Y \rightarrow \Real^+$ whose cost is $O(1)$ times that of an optimal
outer cover. Since this result is used as a black box in our algorithm for
the non-uniform MCMC, the remainder of this section could be skipped on
a first reading.

The procedure $\oc(X',Y,\kappa, \alpha)$ is implemented via a modification of
the primal-dual algorithm of Charikar and Panigrahy~\cite{Charikar2001Clustering}.
Note that their algorithm can be viewed as solving the case where $\kappa(x)
= 1$ for each $x \in X'$. As we will see, their algorithm and analysis readily
generalize to the problem of computing an outer cover.

\subsection{Linear Programming Formulation}
We begin by formulating the problem of finding an optimal outer cover as
an integer program.
For each server $y_i \in Y$ and radius $r \geq 0$, let $z_i^{(r)}$ be an indicator variable that denotes whether
the disk $\disk(y_i,r)$ is chosen in the outer cover.\footnote{For a server
$y_i \in Y$, only the disks whose radius is from the
set $\{ ||y_i - x_j|| \ \mid \ x_j \in X' \}$ will play a role in much of our
algorithm. For describing the algorithm, however, it will be convenient to
allow any $r \geq 0$.}
For any server $y_i \in Y$ and client $x_j \in X'$, we define the
\textit{minimum eligible radius} $R_{\min}(y_i, x_j)$ to be:
\[
    R_{\min}(y_i, x_j) = \max (||y_i- x_j||, ||y^{\kappa}(x_j) - x_j||)
\]

A disk centered at $y_i$ serves $x_j$ in an outer cover exactly when its
radius is at least $R_{\min}(y_i, x_j)$. Finally, let $C_i(r) = \{ x_j \in X' \ \mid \ r \geq R_{\min}(y_i,x_j)\}$. The set $C_i(r)$ consists of those clients
that $\disk(y_i,r)$ can serve.

The problem of computing an optimal outer cover is that of minimizing
\begin{equation}
   \sum_{i,r} r^{\alpha} \cdot z_i^{(r)},
\end{equation}
subject to the constraints
\begin{eqnarray}
\label{eq:CovConstraint}
   \sum_{i,r: x_j \in C_i(r)} z_i^{(r)} & \geq & 1, \ \forall x_j \in X'\\
\label{eq:IntConstraint}
z_i^{(r)} & \in & \{0, 1\}, \ \forall i,r.
\end{eqnarray}

The first constraint, equation (\ref{eq:CovConstraint}), represents the condition
that for every client $x_j \in X'$, at least one disk that is capable
of serving it is chosen. The second constraint, equation (\ref{eq:IntConstraint}),
models the fact that the indicator
variables $z_i^{(r)}$ can only take boolean values $\{0, 1\}$. By relaxing the indicator
variables to be simply non-negative, i.e.
\begin{equation}
\label{eq:LPConstraint}
z_i^{(r)} \geq 0, \ \forall i,r,
\end{equation}
we get a linear program (LP), which we call the primal LP for the problem.

The dual of the above LP has a variable $\beta_j$ corresponding to every client $x_j \in X'$. The dual LP seeks to maximize

\begin{equation}
   \sum_{x_j \in X'} \beta_j,
\end{equation}

subject to the constraints
\begin{eqnarray}
\label{eq:dual_constraint}
\sum_{x_j \in C_i(r)} \beta_j & \leq & r^{\alpha}, \ \forall y_i, r\\
   \beta_j & \geq & 0, \ \forall x_j \in X'
\end{eqnarray}

\subsection{A Primal Dual Algorithm}
The primal dual algorithm is motivated by the above linear program. The
algorithm maintains a dual variable $\beta_j$ for each client $x_j$. This
variable will always be non-negative and satisfy the dual constraints
(\ref{eq:dual_constraint}). If at some point in the algorithm, the
dual constraint (\ref{eq:dual_constraint}) holds with equality for some
$y_i$ and $r$, the disk $\disk(y_i,r)$ is said to be {\em tight}. A client
$x_j$ is said to be tight if there is some tight disk $\disk(y_i,r)$ such
that $x_j \in C_i(r)$. (Note that $\beta_j$ is then part of the dual constraint
(\ref{eq:dual_constraint}) that holds with equality.)

Our algorithm, $\oc(X',Y,\kappa,\alpha)$, initializes each $\beta_j$ to $0$,
which clearly satisfies (\ref{eq:dual_constraint}). The goal of the while
loop in lines 1 and 2, which we refer to as the {\em covering phase} of the
algorithm, is to ensure that each client in $X'$ becomes tight, that is, covered by
some tight disk. It is easy to see that the covering phase achieves this. We
note in passing that since the $\beta_j$ are never decreased in the covering
phase, a client or disk that becomes tight at some point remains tight for
the rest of the phase.

 \begin{algorithm*}[hbt]
 \caption{$\oc(X',Y,\kappa,\alpha)$}
 \begin{algorithmic}[1]
 \WHILE {$\exists\ x_j \in X'$ that is not tight}
 \STATE Increase the non-tight variables $\beta_j$ arbitrarily till some constraint in (\ref{eq:dual_constraint}) becomes tight.
 \ENDWHILE
 \STATE Let $T$ be the set of tight disks.
 \STATE $F \leftarrow \varnothing$
 \WHILE {$T \neq \varnothing$}
        \STATE $\disk(y_i,r) \leftarrow$ The disk of largest radius in $T$
        \STATE $N \leftarrow$ Set of disks that intersect $\disk(y_i,r)$
        \STATE $F \leftarrow F \cup \{\disk(y_i,r)\}$
        \STATE $T \leftarrow T\ \backslash\ N$
 \ENDWHILE
 \STATE Assign $\rho: Y \rightarrow \Real^+$ as follows:
 \[
     \forall\ y_i \in Y, \rho(y_i) =
     \begin{cases}
          3r, & \mbox{if } \disk(y_i,r) \in F \\
          0,  & \mbox{if } F \mbox{ contains no disk centered at } y_i
     \end{cases}
 \]
 \end{algorithmic}
\end{algorithm*}

Steps 3--9 constitute the {\em coarsening phase} of the algorithm. This phase
starts with the set $T$ of tight disks computed by the covering phase. It computes a subset $F \subseteq T$ of pairwise disjoint disks by considering the disks
in $T$ in non-increasing order of radii, and adding a disk to $F$ if it does
not intersect any previously added disk.

Step 10 constitutes the {\em enlargement phase}. Each disk in $F$ is expanded
by a factor of $3$, and the resulting set of disks is returned by the algorithm.
Note that for $y_i \in Y$, $F$ contains at most one disk centered at $y_i$; thus
the assignment in Step 10 is well defined.

We argue that the disks returned by $\oc(X', Y, \kappa, \alpha)$ form an
outer cover. Consider any client $x_j \in X'$. Since $x_j$ is tight at the end
of the covering phase, there is a tight disk $\disk(y_i,r)$ such that $x_j
\in C_i(r)$. Thus $x_j$ is served in case  $\disk(y_i,r)$ was added to $F$
in the coarsening phase.
If $\disk(y_i,r)$ was not added to $F$, then it must have been intersected by some disk $\disk(y_{i'},r')$ that was added to $F$, such that $r' \geq r$. Clearly,
$x_j \in \disk(y_{i'},3r')$. Furthermore, $3r' \geq r \geq ||y^{\kappa}(x_j) - x_j||$. Thus, $x_j \in C_{i'}(3r')$, and $x_j$ is served by the output of $\oc(X', Y, \kappa, \alpha)$.

\subsection{Approximation Ratio}
Let the set of disks in an optimal outer cover be denoted by $OPT$.
We now show that the cost of the outer cover returned by
$\oc(X', Y, \kappa, \alpha)$ is at most $3^{\alpha} \cdot \text{cost}(OPT)$. We begin
by lower bounding $\text{cost}(OPT)$ in terms of the $\beta_j$. We
have

\begin{equation}
\label{eq:LPTight}
\text{cost}(OPT) \geq \sum_{\disk(y_i,r) \in OPT} \left( \sum_{x_j \in C_i(r)} \beta_j \right)
\geq \sum_{x_j \in X'} \beta_j.
\end{equation}

The first inequality follows because the $\beta_j$ satisfy (\ref{eq:dual_constraint}); the second is because each client in $X'$ is served by at least one disk
in  $OPT$, and the $\beta_j$ are non-negative.

Let $C$ denote the cost of the solution returned by $\oc(X', Y, \kappa, \alpha)$. We have
\begin{equation*}
C = 3^{\alpha} \cdot \text{cost}(F) = 3^{\alpha} \sum_{\disk(y_i,r) \in F} \left( \sum_{x_j \in
C_i(r)} \beta_j \right) \leq 3^{\alpha} \sum_{x_j \in X'} \beta_j \leq 3^{\alpha}
\cdot \text{cost}(OPT).
\end{equation*}
Here, the second equality is because each disk in $F$ is tight; since the disks
in $F$ are pairwise disjoint, each client $x_j \in X'$ is contained in at
most one disk in $F$, from which the next inequality follows; the final
inequality is due to Inequality~(\ref{eq:LPTight}).

Thus, we may conclude:

\begin{lemma}
\label{lem:oc}
The algorithm $\oc(X', Y, \kappa,\alpha)$ runs in polynomial time and
returns an outer cover whose cost is at most  $3^{\alpha}$ times that of
an optimal outer cover.
\end{lemma}

\section{Computing a covering for the non-uniform MCMC problem}
\label{sec:algo}
With our algorithm for computing an outer cover in place, we now address
the non-uniform MCMC problem. Recall that the input is a client set $X$,
a server set $Y$, a coverage function $\kappa: X \rightarrow \Natural
\cup \{0\}$, and the constant $\alpha$.

Given an assignment of radius $r_y$ to each $y \in Y$, we will say that
a point $x \in X$ is {\em $j$-covered} if at least $j$ disks cover it,
that is,
\[ | \{ y \in Y \ | \ ||x-y|| \leq r_y \} | \geq j.\]
We will sometimes say that $x$ is
$\kappa$-covered to mean that it is $\kappa(x)$-covered. Similarly, if we have a assignment of radii to each
$y \in Y$  such that for a set of points $P \subseteq X$, every point $x \in P$ is covered
by at least $\kappa(x)$ disks, we say that $P$ is $\kappa$-covered.

 \begin{algorithm*}[h]
 \caption{$\cover(X,Y,\kappa,\alpha)$}
 \label{alg:MC}
 \begin{algorithmic}[1]
   \IF {$\forall x \in X, \kappa(x) = 0$}
       \STATE Assign $r_y \leftarrow 0$ for each $y \in Y$, and return.
   \ENDIF
   \STATE Define $\kappa ' (x)$ as follows:
   \[
      \forall x \in X, \kappa '(x) =
         \begin {cases}
            0, & \mbox{if } \kappa(x) = 0 \\
            \kappa (x) - 1, & \mbox{if } \kappa(x) > 0
         \end {cases}
   \]
   \STATE Recursively call $\cover(X,Y,\kappa',\alpha)$.

   \STATE Let $X' \leftarrow \{x \in X \ | \ x \text{ is not } \kappa(x)\text{-covered } \}$
   \STATE Call the procedure $\oc(X',Y,\kappa,\alpha)$ to obtain an
          outer cover $\rho: Y \rightarrow \Real^+$.
   \STATE Let $Y' \leftarrow Y$.
   \STATE Let $\primary \leftarrow \varnothing$.
   \WHILE {$X' \neq \varnothing$}
   \STATE Choose $\overline{y} \in Y'$.
   \STATE $\primary \leftarrow \primary \cup \{\overline{y}\}$.
   \STATE Let $\xcluster_{\overline{y}} \leftarrow \varnothing$, $\ycluster_{\overline{y}} \leftarrow \varnothing$.
     \FORALL {$x' \in X'$}
     \IF {$x' \in \disk(\overline{y}, \rho_{\overline{y}})$ and $\rho_{\overline{y}} \geq ||x' - y ^{\kappa}(x')||$}
           \STATE $\xcluster_{\overline{y}} \leftarrow \xcluster_{\overline{y}} \cup \{x'\}$.
           \STATE $\ycluster_{\overline{y}} \leftarrow \ycluster_{\overline{y}} \cup \{y^1(x'), y^2(x'), \ldots, y^{\kappa}(x')\}.$
        \ENDIF
     \ENDFOR
     \STATE Let $\ycluster'_{\overline{y}} \subseteq \ycluster_{\overline{y}}$ be a set of at most
            four points such that
            \[ \bigcap_{y \in \ycluster'_{\overline{y}}} \disk(y, r_y) = \bigcap_{y \in \ycluster_{\overline{y}}} \disk(y, r_y). \]
     \STATE For each $y \in \ycluster'_{\overline{y}}$, increase $r_y$ by the smallest
            amount that ensures $\xcluster_{\overline{y}} \subseteq \disk(y,r_y)$.
     \STATE Remove $\overline{y}$ from $Y'$ and remove from $X'$ any points $x$ that are ${\kappa(x)}$-covered.
   \ENDWHILE
\end{algorithmic}
\end{algorithm*}

Our algorithm $\cover(X,Y,\kappa,\alpha)$ for non-uniform MCMC computes an assignment of radius $r_y$ to each server $y \in Y$ such that each client $x \in X$ is $\kappa(x)$-covered.
This algorithm is recursive, and in the base case we
have $\kappa(x) = 0$ for each $x \in X$. In the base case, the radius $r_y$ is assigned to
$0$ for each $y \in Y$. Otherwise, we define
\[ \kappa'(x) = \max \{0, \kappa(x) - 1\}, \mbox{ for each } x \in X,\]
and recursively call
$\cover(X,Y,\kappa',\alpha)$ to compute an assignment that $\kappa'(x)$-covers each $x \in X$.
We then compute $X' \subseteq X$, the set of points that are not
$\kappa(x)$-covered. We compute an outer cover $\rho: Y \rightarrow \Real^+$
for $X'$ using the procedure $\oc(X',Y,\kappa,\alpha)$ described in
Section~\ref{sec:outercover}. For any client $x \in X'$, the outer cover has
a disk $\disk(y, \rho_y)$ that serves it. That is, $x$ is contained in
$\disk(y, \rho_y)$ and $\rho_y \geq ||x - y^{\kappa}(x)||$.

The goal of the while-loop is to increase some of
the $r_y$ to ensure that each $x \in X'$, which is
currently $(\kappa(x) - 1)$-covered, is also $\kappa(x)$-covered.
To do this, we iterate via the while loop over each disk $\disk(\overline{y}, \rho_{\overline{y}})$ returned by $\oc(X',Y,\kappa,\alpha)$. We add all
points in $X'$ that are served in the outer cover by $\disk(\overline{y}, \rho_{\overline{y}})$ to a set $\xcluster_{\overline{y}}$. That is, $\xcluster_{\overline{y}}$ consists of all $x' \in X'$ that are contained in  $\disk(\overline{y}, \rho_{\overline{y}})$ and $\rho_{\overline{y}} \geq ||x' - y ^{\kappa}(x')||$.  The set $\ycluster_{\overline{y}}$ contains, for
each $x \in \xcluster_{\overline{y}}$, the ${\kappa(x)}$ nearest neighbors of $x$ in $Y$. For purposes of analysis, we add $\overline{y}$ to a set $\primary$ as well.

Next, we identify a set $\ycluster'_{\overline{y}} \subseteq \ycluster_{\overline{y}}$ of at most
$4$ points such that
 \[ \bigcap_{y \in \ycluster'_{\overline{y}}} \disk(y, r_y) = \bigcap_{y \in \ycluster_{\overline{y}}} \disk(y, r_y). \]
Why does such a $\ycluster'_{\overline{y}}$ exist? If, on the one hand, the intersection of
disks $\bigcap_{y \in \ycluster_{\overline{y}}} \disk(y, r_y)$ is empty, then Helly's Theorem tells us that there are three disks (or maybe even two) whose intersection is empty. On the other hand,
if the intersection $\bigcap_{y \in \ycluster_{\overline{y}}} \disk(y, r_y)$ is non-empty, then
it is a rectangle (as these are $\Linfty$ disks) and therefore equal to the intersection of four of the disks.

We enlarge the radius $r_y$ of each $y \in \ycluster'_{\overline{y}}$ by the minimum
amount needed to ensure that $\xcluster_{\overline{y}} \subseteq \disk(y,r_y).$ We
argue that after this each point in $\xcluster_{\overline{y}}$ is $\kappa$-covered. To
see why, consider any $x' \in \xcluster_{\overline{y}}$. Notice that $|\ycluster_{\overline{y}}| \geq {\kappa(x')}$, since
the $\kappa(x')$ nearest neighbors of $x'$ are included in $\ycluster_{\overline{y}}$. Thus before the enlargement,
$x'$ does not belong to $\bigcap_{y \in \ycluster_{\overline{y}}} \disk(y, r_y)$.
(Recall that no point in $\xcluster_{\overline{y}}$ was $\kappa$-covered.) Therefore,
$x'$ does not belong to $\bigcap_{y \in \ycluster'_{\overline{y}}} \disk(y, r_y)$.
It follows that there is at least one
$y \in \ycluster'_{\overline{y}}$ such that $\disk(y,r_y)$ did not contain $x'$ before
the enlargement. As a consequence of the enlargement, $\disk(y,r_y)$
does contain $x'$. Since $x'$ was $(\kappa(x') - 1)$-covered before the enlargement, it is now ${\kappa(x')}$-covered.

After increasing $r_y$ for $y \in \ycluster'_{\overline{y}}$ as stated, we discard
from $X'$ all points that are now $\kappa$-covered. The discarded set contains
$\xcluster_{\overline{y}}$ and possibly some other points in $X'$. We remove $\overline{y}$ from $Y'$. We go back and
iterate the while loop with the new $X'$ and $Y'$.

Since any point in $X'$ as computed in Line~5 is served by some disk in the
outer cover, it appears in $\xcluster_{\overline{y}}$ in some iteration of
the while loop (if it has not already been $\kappa$-covered serendipitously).
At the end of that iteration of the while loop, it gets $\kappa$-covered.
Thus, when $\cover(X,Y,\kappa,\alpha)$ terminates, each point $x \in X$ is
${\kappa(x)}$-covered.

\section{Approximation Ratio}
\label{sec:analysis}
In this section, we bound the ratio of the cost of the solution returned by
$\cover(X,Y,\kappa,\alpha)$ and the cost of the optimal solution. For this
purpose, the following lemma is central. It bounds the increase in cost
incurred by $\cover(X,Y,\kappa,\alpha)$ in going from a $\kappa'$-cover
to a $\kappa$-cover by the cost of the outer cover $\rho$ for $X'$.

\begin{lemma}
The increase in the objective function $\sum_{y \in Y}r_y^{\alpha}$ from the
time $\cover(X,Y,\kappa',\alpha)$ completes to the time $\cover(X,Y,\kappa,\alpha)$ completes
is $4 \cdot 3^{\alpha} \cdot \sum_{y \in Y} \rho_y^{\alpha}$.
\label{lem:increase}
\end{lemma}

\begin{proof}
    Let us fix an $\overline{y} \in \primary$, and focus on the iteration when $\overline{y}$ was
added to $\primary$. Notice that there is exactly one such iteration,
since $\overline{y}$ is removed from $Y'$ in the iteration it gets added to
$\primary$.

We will bound the increase in cost during this iteration. For this, we need
two claims.

\begin{claim}
    For any $x' \in \xcluster_{\overline{y}}$, we have
    \[ ||\overline{y} - x'|| \leq \rho_{\overline{y}} \]
\end{claim}

\begin{proof}
Recall that $x'$ is in $\xcluster_{\overline{y}}$ because $x' \in \disk(\overline{y}, \rho_{\overline{y}})$.
\end{proof}

\begin{claim}
    For any $y' \in \ycluster_{\overline{y}}$, we have
    \[||y' - \overline{y}|| \leq 2*\rho_{\overline{y}} \]
\end{claim}

\begin{proof}
Let $y'$ be added to $\ycluster_{\overline{y}}$ when $x' \in X'$ was added to $\xcluster_{\overline{y}}$.
Hence
\begin{align*}
   ||y' - x' || &\leq ||x' - y^{\kappa}(x')|| \\
                &\leq \rho_{\overline{y}},
\end{align*}
since $\disk(\overline{y}, \rho_{\overline{y}})$ serves $x'$ in the outer
cover (line 14 of Algorithm~\ref{alg:MC}).
Also, since $x' \in \disk(\overline{y}, \rho_{\overline{y}})$, $||x' - \overline{y}|| \leq \rho_{\overline{y}}$.
Therefore,
\begin{align*}
   ||y' - \overline{y}|| &\leq ||y' - x'|| + ||x' - \overline{y}|| \\
                         &\leq \rho_{\overline{y}} + \rho_{\overline{y}} \\
                         &= 2\rho_{\overline{y}}
\end{align*}
\end{proof}

Fix a $y \in \ycluster'_{\overline{y}}$. If $r_y$ was increased in this iteration,
it now equals $||y-x'||$ for some $x' \in \xcluster_{\overline{y}}$. By the above
two claims,
\begin{align*}
   ||y-x'|| &\leq ||y - \overline{y}|| + ||\overline{y} - x'||\\
            &\leq 3*\rho_{\overline{y}}
\end{align*}

Thus the increase in $r_y^{\alpha}$ is at most $3^{\alpha} (\rho_{\overline{y}})^{\alpha}$. Since $r_y$ is increased in this iteration only for $y \in \ycluster'_{\overline{y}}$, and $|\ycluster'_{\overline{y}}| \leq 4$, the increase in the objective
function $\sum_{y \in Y} r_y^{\alpha}$ (in the iteration
of the while loop under consideration) is
at most $4 \cdot 3^{\alpha} \cdot (\rho_{\overline{y}})^{\alpha}$.

We conclude that the increase in  $\sum_{y \in Y} r_y^{\alpha}$ over all the
iterations of the while loop is at most
\[ 4 \cdot 3^{\alpha} \cdot \sum_{\overline{y} \in \primary} (\rho_{\overline{y}})^{\alpha}
= 4 \cdot 3^{\alpha} \cdot \sum_{y \in Y} \rho_y^{\alpha}.\]
\end{proof}

We can now bound the approximation ratio of the algorithm.

\begin{lemma}
 Let $r':Y \rightarrow \Real^+$ be any assignment of radii to the points in $Y$
    under which each point $x \in X$ is ${\kappa(x)}$-covered. Then the cost of the output
of $\cover(X,Y,\kappa,\alpha)$ is at most $c * \sum_{y \in Y} {r'_y}^{\alpha}$, where $c = 4 \cdot 27^{\alpha}$.
\label{lem:constant}
\end{lemma}

\begin{proof}
Our proof is by induction on $\max_{x \in X} \kappa(x)$. For the base case, where $\kappa(x) = 0$ for each $x \in X$, the claim in
the theorem clearly holds.

Let $D = \{\disk(y, r'_y) \ | \ y \in Y\}$ be the set of disks corresponding
to the assignment $r'$. Our proof strategy is to show that there is a subset $D_{\kappa} \subseteq D$ such that

\begin{enumerate}
   \item The cost increase incurred by $\cover(X,Y,\kappa,\alpha)$ in going from
the $\kappa'$-cover to the $\kappa$-cover is at most $c$ times cost of the disks in $D_k$. (Recall that the \textit{cost} of a set of
disks is the sum of the $\alpha$-th powers of the radii of the disks.)
   \item The set of disks, $D \setminus D_{\kappa}$, $\kappa'(x)$-covers any point $x \in X$.
\end{enumerate}

By the induction hypothesis, the cost of the $\kappa'$-cover computed by
$\cover(X,Y,\kappa,\alpha')$ is at most $c$ times the cost of the disks in $D \setminus D_{\kappa}$. As
the increase in cost incurred by $\cover(X,Y,\kappa,\alpha)$ in turning the $\kappa'$-cover
to a $\kappa$-cover is at most $c$ times the cost of the disks in $D_{\kappa}$, the theorem follows.

We now describe how $D_k$ is computed, and then establish that it has the above
two properties. For each $x' \in X'$, let $\largest(x')$
be the largest disk from $D$ that contains $x'$. Since $x'$ is $\kappa(x')$-covered by $D$, we note that the radius of $\largest(x')$ is at least $||x' - y^{\kappa}(x')||$. Let
\[ D'_{\kappa} = \{ \largest(x') \  | \ x' \in X' \}.\]

Sort the disks in $D'_{\kappa}$ by decreasing (non-increasing) radii. Let $B
\leftarrow \varnothing$ initially. For each disk $d \in D'_{\kappa}$ in the sorted
order, performing the following operation: add $d$ to $B$ if $d$ does
not intersect any disk already in $B$.

Let $D_{\kappa}$ be the set $B$ at the end of this computation. Since no two
disks in $D_{\kappa}$ intersect, and $D$ $\kappa$-covers any point in $X$, it follows
that $D \setminus D_{\kappa}$ $\kappa '$-covers any point in $X$. This establishes
Property 2 of $D_{\kappa}$.

We now turn to Property 1. For this, consider $L_{\kappa}$, the set of disks obtained by increasing the
radius of each disk in $D_{\kappa}$ by a factor of 3. We argue that $L_{\kappa}$ is
an outer cover for $X'$. Fix any $x' \in X'$.
\begin{enumerate}
\item If $\largest(x') \in D_{\kappa}$, then the corresponding disk in
$L_{\kappa}$ contains $x'$ and has radius at least $||x' - y^{\kappa}(x')||$.
\item If $\largest(x') \not\in D_{\kappa}$, then there is an even larger disk in
$D_{\kappa}$ that intersects  $\largest(x')$. The corresponding disk in $ L_{\kappa}$ contains $x'$ and has radius at least $||x' - y^{\kappa}(x')||$.
\end{enumerate}

Since $L_{\kappa}$ is
an outer cover for $X'$, and the procedure
$\oc(X',Y,\kappa,\alpha)$ returns a $3^{\alpha}$ approximation
to the optimal outer cover, we infer that
\[ \sum_{y \in Y} \rho_y^{\alpha} \leq 3^{\alpha} \cdot \text{cost}(L_{\kappa})
\leq 9^{\alpha} \cdot \text{cost}(D_{\kappa}).\]

Thus the cost increase incurred by $\cover(X,Y,\kappa,\alpha)$ in going from
the $\kappa'$-cover to the $\kappa$-cover is, by Lemma~\ref{lem:increase}, at most
\[ 4 \cdot 3^{\alpha} \cdot \sum_{y \in Y} \rho_y^{\alpha} \leq
   4 \cdot 27^{\alpha} \cdot \text{cost}(D_{\kappa}) = c \cdot \text{cost}(D_{\kappa}).\]

This establishes Property 1, and completes the proof of the lemma.
\end{proof}

We conclude with a statement of the main result of this article. In this statement, cost refers to $l_2$ rather than $l_{\infty}$ disks. Since (a) an $l_2$ disk of radius $r$ is contained in the corresponding $l_{\infty}$ disk of radius
$r$, and (b) an $l_{\infty}$ disk of radius $r$ is contained in an $l_2$ disk
of radius $\sqrt{2}r$, the approximation guarantee is increased by
$(\sqrt{2})^{\alpha})$ when compared to Lemma~\ref{lem:constant}.

\begin{theorem}
\label{thm:main}
Given point sets $X$ and $Y$ in the plane, a coverage function $\kappa: X \rightarrow \{0,1,2,\ldots, |Y|\}$, and $\alpha \geq 1$, the algorithm $\cover(X, Y, \kappa, \alpha)$ runs in polynomial time and computes a $\kappa$-cover of $X$ with cost at most $4 \cdot (27 \sqrt{2})^{\alpha}$ times that of the optimal $\kappa$-cover.
\end{theorem}

\section{Concluding Remarks}
\label{sec:conclusions}
Our result generalizes to the setting where $X$ and $Y$ are points in
$\Real^d$, where $d$ is any constant. The approximation guarantee is now
$(2d) \cdot (27 \sqrt{d})^{\alpha}$. To explain, the intersection of a
finite family of $l_{\infty}$ balls equals the intersection of a sub-family
of at most $2d$ balls. That is why the $4$ in the approximation guarantee
of Theorem~\ref{thm:main} becomes $2d$. In the transition from $l_2$ to
$l_{\infty}$ balls in $\Real^d$, we lose a factor of $(\sqrt{d})^{\alpha}$.

This generalization naturally leads to the next question -- what can we say
when $X$ and $Y$ are points in an arbitrary metric space? Our approach
confronts a significant conceptual obstacle here, since one can easily
construct examples in which the cost of going from a $(k-1)$-cover to
a $k$-cover (for the uniform MCMC) cannot be bounded by a constant times the
cost of an optimal outer cover. Thus, new ideas seem to be needed for
obtaining an $O(1)$ approximation for this problem. The work of \cite{Yehuda2013Note} gives the best known guarantee
of $O(k)$. For the non-uniform version, their approximation guarantee is
$O(\max \{\kappa(x) \mid x \in X\})$.

redpajama_set_name: RedPajamaArXiv


H1Z1 and PlanetSide 2 developer Daybreak Game Company has suffered layoffs just days after it was reported that the company has links to Russian oligarch Viktor Vekselberg, whose assets the United States government froze earlier this week. Daybreak has denied the claims.
Vekselberg owns Renova Group, which is a subsidiary of Columbus Nova – a company that we've known as the owner of Daybreak. The studio denying any link to Columbus Nova is particularly surprising because former president John Smedley specifically mentioned the company in a press release in 2015. Additionally, screenshots taken from Daybreak's website show references to Columbus Nova as well.
It was current executive chairman Jason Epstein, former senior managing partner of Columbus Nova that acquired Daybreak, not Columbus Nova itself. That distinction was never corrected in the past, so we are correcting that now.
Fast-forward to Thursday, when Daybreak confirmed to Gamasutra that it has laid off some staff members as part of internal restructuring.
Daybreak confirms we are realigning our workforce to better position our company for the future. Because of this, we have had to make an extremely difficult decision to part ways with some of our employees across various disciplines at the studio. We are doing everything we can to take care of each affected individual by providing them appropriate transition assistance.
Speculation now suggests that the two developments are related. Whether that's true or not remains to be seen.
H1Z1 is set to enter open beta on PlayStation 4 on May 22.


Much like the real rats of New York City, it appears the "Trump Rat" that sprang up near Trump Tower on Monday will be making Manhattan its home for the long haul.
The 15-foot-tall vinyl rat made to look like the president, with its coiffed blond hair and signature hand gestures, remained outside Trump Tower near 59th Street Tuesday. Although it will come down later in the evening, fans of the Trump Rat will likely see it again (and soon) in Chelsea, according to John Post Lee, who commissioned the inflatable.
"We're here this afternoon and this evening … probably won't be coming back here, but I'm going to put it up in Chelsea," the Upper West Side resident said.
Lee, who co-owns the BravinLee art gallery in Chelsea, said there may be space outside of his business on West 26th Street, but he's also looking into some unused ground-floor space nearby.
The chicken might have the advantage: That inflatable is 23 feet tall.
The Trump Rat may eventually have a posse, however, as it looks to be just the first in a line of Trump administration characters produced by Lee and his backers on Kickstarter.
"I want to make a really small Jeff Sessions toy poodle," he said about the potential for a "Marvel Universe-style" cast of characters.
Monday's protest outside of Trump Tower ahead of the president's return to New York City was the first time Lee has displayed the Trump Rat.
Lee, 58, said he commissioned the piece to express his "disgust" with President Donald Trump. His inspiration? The giant inflatable rats that construction unions use when non-union workers are at a site.
Lee said after the November election, he was stunned at how many people would vote for a candidate without fully knowing what his policies would be.
Lee, like many who protested outside of Trump Tower on Monday, said he does not think Trump is fit to be president.
Though the gallery owner is no stranger to the use of public art to make a statement, this was the first time he and his wife, Karin Bravin, have made a political statement of this magnitude.
The inflatable rat was originally designed by artist Jeffrey Beebe and was made in the United States.
"Unlike Trump's suits, this was made in America," Lee said, referring to Trump's brand of clothing often being manufactured overseas.
Trump arrived in Manhattan Monday night following a day of protests outside of his namesake building. He is expected to remain in the city until Wednesday for a series of meetings.
More protests were planned for Tuesday outside of Trump Tower.


text:
Portrait of Leutnant Erwin Böhme by Professor Hans Kohlschein, the brother-in-law of Böhme's brother, Gerhard. The portrait was rendered in Dusseldorf in March 1917, while Böhme was recuperating from a wound suffered the previous month. In its original form the portrait obviously did not include the Pour le Merite which was later awarded to Böhme. A copy of the original version faces page 14 of 'Briefe Eines Deutschen Kampffliegers An Ein Junges Madchen.' After Böhme's death, the Pour le Merite was added to the portrait, as shown above, presumably by Professor Kohlshein.
The series of articles based on the personal letters of Erwin Böhme to his fiancee which have appeared in various issues of Over the Front were assembled and added to with additional information both textually and photographically by Dr.-Ingenieur Niedermeyer and translated by Douglas Fant. They are an exceptional portrayal of the life of a unique individual, an adventurer in every sense of the word. A mountain climber, skier, traveler, aviator, sportsman...hero.
The End of an Action-Filled Life: Leutnant Erwin Böhme's Final Letters by Douglas V. Fant and Dr.-Ing. Niedermeyer
Hertha's Album--A Pictorial Biography of the Alte Adler and Prussian Pilot Ltn. D. Res. Fritz John Jacobsen (FFA 1, Jasta 17, 9, 31 and 73) by Dieter H.M. Gröschel, M.D.
The "Unknown Writer" Behind the "Unknown Aviator:" The Story of War Birds by Lt. Col. Steven A. Ruffin, USAF
Becoming Operational Out of the First Oxford Detachment by Marvin L. Skelton
The Seminar | Research Grants | The Mike Carr Prize | An Index | Faxes and E-Mail
Airmen Died in the Great War, 1914-1918 | Handbook of German Military and Naval Aviation (War) 1914-1918 | The Royal Flying Corps in France | Richthofen | The Imperial Russian Air Service
Sir Keith Falter, Kt., FRCM | Charles Edward Whitehouse | Robert Alexander Anderson | Glenn E. Messer | Eduard Carl Brandt


text:
Leonhard Adelt (17 June 1881, Boizenburg – 21 February 1945) was a German translator and writer.

External links

1881 births
1945 deaths
People from Ludwigslust-Parchim
People from the Grand Duchy of Mecklenburg-Schwerin
Writers from Mecklenburg-Western Pomerania
German male non-fiction writers
20th-century German translators

redpajama_set_name: RedPajamaWikipedia


Each fall, thousands flock to their nearest apple orchard for autumn's bounty, and stories surface about adopted Hoosier, Johnny Appleseed, the "patron saint" of apples.
Appleseed was a barefoot wanderer in tattered clothing, armed with a sack of apples and a tin pot for a hat. That image alone could give anyone the notion he was a mythical character of American folklore, like John Henry or Paul Bunyan — even Bigfoot. Even Disney gave him the folk hero treatment.
Surprisingly, Johnny Appleseed was real. Born John Chapman near Leominster, Massachussetts, in 1774, he briefly settled in Pittsburgh where he planted an orchard. It was during this time that he acquired his nickname, Johnny Appleseed.
Benevolent vagabond or savvy businessman?
Chapman allegedly had no interest in money. He sold apples for pennies to get by or would barter for clothing. Some say Chapman would present travelers along the Ohio River with a packet of apple seeds from his orchard with the hope they would plant them on their way west.
Frontier law allowed settlers to lay claim to 100 acres of land through development of a homestead, but only by planting 50 apple trees on the land. Settlers needed a "crop," and the saplings provided the proof they were willing to remain on the land.
When Chapman himself traveled west, he harvested some of the apple, pear and peach saplings he planted and traveled down the Ohio River. Chapman stopped at open fields and glades and planted the saplings, laying claim to his own settlements, which he eventually sold to settlers.
What about that tin cap? In descriptions by local folks, he appeared to wear a pot on his head, but some indicate he fashioned a homemade visor to his hat which shielded him from the sun and gave the appearance of a pot. The image has worked well for the TinCaps baseball team in Fort Wayne, Appleseed's last home.
Chapman moved to Allen County in 1834 and purchased land along the Maumee River for a tree nursery. Fort Wayne has embraced his legacy, even making him a cultural icon with an annual festival.
Chapman died in Fort Wayne in 1845 at the age of 71. He owned more than 1,000 acres at the time of his death.
If you're reading this and crunching on a sweet orchard apple, Johnny Appleseed was not responsible for it. "Eating apples" that we are familiar with were brought to the colonies by the Europeans. Popular apples, such as Jonathan, Winesap or Honeycrisp, come from grafts.
Seed tree apples, such as the ones Johnny Appleseed planted, were acidic with high tannin content and good for little but making hard cider. They were called "spitters," because after one bite, you spit it out. A 176-year-old tree in Nova, Ohio, is thought to be the last known tree to be planted by Johnny Appleseed himself.
Hard cider was a favorite of William Henry Harrison, the former governor of the Indiana Territory. During his 1840 presidential campaign, he used the "log cabin and hard cider" platform to convince voters of his "everyman" status.
During pioneer days, water and milk were typically unsanitary. Cider was the most trusted beverage, despite the haphazard, primitive method in which it was made. Children drank it and it was on tap alongside whiskey and rum at the local taverns. Cider's popularity diminished in the 1840s, as German immigrants brought clean, almost hygienic beer-making techniques to America.
Since hard cider had about 8 percent alcohol, the Women's Christian Temperance Movement dealt it a devastating blow, probably literally. Carrie Nation's hatchet chopped open many a barrel of liquor — and undoubtedly chopped down some apple trees as well — as orchards were destroyed or burned to the ground by temperance zealots.
Prohibition was the nail on the coffin. "Prior to Prohibition, hard cider was widely produced and consumed, but production facilities were very small — and after the repeal of Prohibition, those small producers of hard cider did not restart," said Mark Seetin of the U.S. Apple Association. "Over the past decade, however, hard cider has seen a dramatic rebirth, with an industry estimate of 75 percent annual growth since 2011."
What to do with those surplus apples from the orchard?
While apple butter has its roots in Pennsylvania, the condiment holds a valued place in Indiana food history alongside the tenderloin sandwich and persimmon pudding. Fall in Indiana isn't complete without a spoonful on a fried biscuit — especially if you're in Brown County.
This 1909 recipe for apple butter came from an article in The Indianapolis Star.
Wash, drain and slice one peck of apples. Cook them in one gallon of sweet cider until soft, then press through a sieve or food mill. Return to the pot and boil until thick enough to heap in a spoon. Add four cups of sugar, one tablespoon cinnamon and one teaspoon cloves. Boil until thick. It will be brown. Boil until thick, then pour hot apple butter into hot glass jars. Process for 10 minutes in a boiling water bath.
Call IndyStar photo coordinator Dawn Mitchell at 317-444-6497. Follow her on Twitter: @dawn_mitchell61.


text:
Q: most performatic free database for file system tracking I'm tracking a linux filesystem (that could be any type) with pyinotify module for python (which is actually the linux kernel behind doing the job). Many directories/folders/files (as much as the user want to) are being tracked with my application and now i would like track the md5sum of each file and store them on a database (includes every moving, renaming, new files, etc).
I guess that a database should be the best option to store all the md5sum of each file... But what should be the best database for that? Certainly a very performatic one. I'm looking for a free one, because the application is gonna be GPL.

A: The first database I'd attempt would be SQLite3. SQLite3 is easy to use, very well tested, provides a large array of interface libraries and pre-written tools to work with databases, and it is very easy to "embed" into an application. (Far easier than getting MySQL or PostgreSQL installed on a system.)
SQLite3 also seems "easier" for people to work with than Berkeley DB, which is the main alternative to SQLite3.

A: Sounds like you want a key-value store rather than a full-blown database. You could take a look at LevelDB from Google. Given it doesn't have the features that a full-blown SQL db has, and was designed for efficiency, it's likely to be the most performatic solution. There's some performatance numbers on the linked page.

A: You could try Redis. It is most certainly fast.
But really, since you're tracking a filesystem, and disks are slow as snails in comparison to even a medium-fast database, performance shouldn't be your primary concern.


Laser printer won't pull the paper, it slipps. What to do?
Check your maunal for proper troubleshooting, if the manual troubleshooting is not whats wrong , try going to the webpage of the manufacturer, and if that doesnt work call the manufacturer and speak with the technical department and maybe they can further guide you. If you have an older printer it maybe be harder to get technical support for the issue but it cant hurt to try what i had indicated. You also may want to take it to a computer or office equipment repair facility so they can diagnose it appropriately and guide you if its worth repairing or replacing it overall..
Use rubbing alcohol with Q-tip or cloth towel on the rollers, they may be just dirty. If they aren't moving, look at XxSpootiesGirlxX answer.
My MPC200 Canon printer won,t print black only colour. Even the test page misses the black out and the nozzle test. Help.
Why is my printer not working? Its a Lexmark, When I try to print from my laptop it does not want to work. It was working from the desk top, but then it ran out of paper so I inserted more and it started doing a jacked up print job and now it wot print!
When I try to conect to LimeWire, it says I am firewalled. I went to my Windows security center and made exceptions to allow the program to run. That didn't work so, I turned the firewall off and that didn't work either. Any suggestions on what to do?
I have an S400 that won't print. It has a new pint head, the cartridges are canon and half full, it moves across teh paper in tune to the text but the paper is blank.


In a recent email newsletter, we announced that we were able to print Loaves & Fishes Issue 24. This is a major milestone, especially since our publishing schedule has been extra slow the last while.
The books arrived at our facility in Bedford, PA last week (September 15). Our printing company does excellent work, and it always feels wonderful to actually page through a new issue after many hours of looking at it on the computer screen.
Two days after the books came, volunteers from our church plus Jimmy (who works in the mail room) and his wife came to prepare the mailing. Several people worked on the single subscriptions while the rest packed the box subscriptions. Most of the people were new to the mailing process; but it went smoothly, and we finished it all in one long evening!
The singles and bulk subscriptions are leaving this week. The postage for all the single subscriptions and bulk boxes together was some over $9,500. We were able to pay the postage with a little money left.
Thank you for making Issue 24 possible. I am very excited about this issue reaching thousands of prisoners in many prisons and jails around the country. Pray for these books as they travel to their destinations and as prisoners read them, that the message in them would lead people to God and His word!
This report shows the items we distributed for free between the last two Loaves & Fishes issues from January 14 to September 19. This does not include tracts or the supporter newsletter.
Sign up for ministry updates and prayer requests.


I've been walking past this car for the last week and finally had a chance to capture it on "film". It seems in our neighborhood it's not a problem to park your car for days, weeks maybe even months at a time. No boots. No tickets. No towing to a lot with other abandoned cars. Granted most of the cars towed to the abandoned car lot in our old neighborhood had the wheels stolen off of it and a strong scent of bum piss. None the less, I love this new neighborhood.
Today is the one week mark of living in Portland. I will tell you it is rainy. It's also not that bad. It rains like San Francisco had fog, almost regularly but never enough to keep you from living your life. I have had a wonderful time in Portland thus far. Dinner with friends, new friends made and I'm no longer living out of boxes. It's a slower life here but there are just as many of the amenities here I loved about San Francisco; the mass transportation, DIY art culture and walkable neighborhoods without the things I loathed about San Francisco; Fell Street traffic and the high price paid to live in the ghetto. I'm already inspired to start two new projects and begin creating art all over again. Below are two shots from the week I left San Francisco and two from this last week in Portland.


text:
package com.cloudera.csd.tools.codahale;

import com.cloudera.csd.tools.JsonUtil;
import com.cloudera.csd.tools.codahale.CodahaleMetricTypes.HistogramMetricType;
import com.cloudera.csd.tools.codahale.CodahaleMetricTypes.MeterMetricType;
import com.cloudera.csd.tools.codahale.CodahaleMetricTypes.TimerMetricType;
import com.google.common.collect.Maps;

import java.io.File;

import org.apache.commons.io.FileUtils;

/**
 * This set of conventions is tightly coupled to the serialization format of
 * the yammer metrics-core 2.2.0 MetricsServlet. This tool is intended to
 * produce the conventions fixture.
 *
 * If passed an argument, this will write the fixture to a file. If passed no
 * arguments, it simply prints to the console.
 */
public class CodahaleMetricsServletMetricConventionsGenerator {

  public static CodahaleMetricConventions makeConventions() {
    CodahaleMetricConventions conventions = new CodahaleMetricConventions();

    conventions.gaugeContextSuffix = "::value";

    conventions.counterContextSuffix = "::count";

    conventions.meterContextSuffixes = Maps.newTreeMap();
    conventions.meterContextSuffixes.put(
        MeterMetricType.COUNT, "::count");
    conventions.meterContextSuffixes.put(
        MeterMetricType.MEAN_RATE_GAUGE, "::mean");
    conventions.meterContextSuffixes.put(
        MeterMetricType.ONE_MIN_RATE_GAUGE, "::m1");
    conventions.meterContextSuffixes.put(
        MeterMetricType.FIVE_MIN_RATE_GAUGE, "::m5");
    conventions.meterContextSuffixes.put(
        MeterMetricType.FIFTEEN_MIN_RATE_GAUGE, "::m15");

    conventions.timerContextSuffixes = Maps.newTreeMap();
    conventions.timerContextSuffixes.put(
        TimerMetricType.MIN, "::duration::min");
    conventions.timerContextSuffixes.put(
        TimerMetricType.MAX, "::duration::max");
    conventions.timerContextSuffixes.put(
        TimerMetricType.MEAN, "::duration::mean");
    conventions.timerContextSuffixes.put(
        TimerMetricType.STDDEV, "::duration::std_dev");
    conventions.timerContextSuffixes.put(
        TimerMetricType.MEDIAN, "::duration::median");
    conventions.timerContextSuffixes.put(
        TimerMetricType.PERCENTILE_75, "::duration::p75");
    conventions.timerContextSuffixes.put(
        TimerMetricType.PERCENTILE_99, "::duration::p99");
    conventions.timerContextSuffixes.put(
        TimerMetricType.PERCENTILE_999, "::duration::p999");
    conventions.timerContextSuffixes.put(
        TimerMetricType.COUNT, "::rate::count");
    conventions.timerContextSuffixes.put(
        TimerMetricType.ONE_MIN_RATE, "::rate::m1");
    conventions.timerContextSuffixes.put(
        TimerMetricType.FIVE_MIN_RATE, "::rate::m5");
    conventions.timerContextSuffixes.put(
        TimerMetricType.FIFTEEN_MIN_RATE, "::rate::m15");

    conventions.histogramContextSuffixes = Maps.newTreeMap();
    conventions.histogramContextSuffixes.put(
        HistogramMetricType.COUNT, "::count");
    conventions.histogramContextSuffixes.put(
        HistogramMetricType.MIN, "::min");
    conventions.histogramContextSuffixes.put(
        HistogramMetricType.MAX, "::max");
    conventions.histogramContextSuffixes.put(
        HistogramMetricType.MEAN, "::mean");
    conventions.histogramContextSuffixes.put(
        HistogramMetricType.STDDEV, "::std_dev");
    conventions.histogramContextSuffixes.put(
        HistogramMetricType.MEDIAN, "::median");
    conventions.histogramContextSuffixes.put(
        HistogramMetricType.PERCENTILE_75, "::p75");
    conventions.histogramContextSuffixes.put(
        HistogramMetricType.PERCENTILE_99, "::p99");
    conventions.histogramContextSuffixes.put(
        HistogramMetricType.PERCENTILE_999, "::p999");

    return conventions;
  }

  public static void main(String[] args) throws Exception {
    if (args.length > 1) {
      throw new RuntimeException("Unexpected arguments!");
    }
    String fixture = JsonUtil.valueAsString(makeConventions(), true);
    if (args.length == 0) {
      System.out.println(fixture);
    } else {
      FileUtils.write(new File(args[0]), fixture);
    }
  }
}

redpajama_set_name: RedPajamaGithub