text:
Q: How do I work out the p-value of many click through rates I have a table which shows pages and click through rates, and I need to know whether a page's performance is down to chance or does perform well. Table looks like:
Page name | no_of_clicks | page_views | ctr
page1     |     3        |     6      |  0.5
page2     |     600      |    1800    |  0.33
page3     |     12       |    46      |  0.26
page4     |     50       |    200     |  0.25
...

CTR = no_of_clicks/page_views

Is it possible to add another column to show a p-value?
This is being done in python.

A: If page_views means the impression, so the CTR is a good measure for the visitors interest. The probability doesn't have meaning here.
There is another scenario; you know how many people are in your network if you are measuring the performance of adverts/campaigns; but the number of impressions/pages_views are different as you've shown in that table. This means that something may be wrong in showing the advert; otherwise, CTR shows how people are interacting with the adverts they've seen so the probability is meaningless to me.
ps: I am dealing with the same data in the company I work :)

A: It is difficult to know exactly what you mean by "[W}hether a page's performance is down to chance or does perform well."
If you believe that the proportion of views that result in clicks
is a valid indication of the performance of a page, then
you could try either prop.test or chisq.test in R to see
if the click-through rates are significantly different among
the pages:
It seems remarkable that Pages 1 and 3 have much lower counts
than the other two pages. One may wonder why that is true. Also,
whether it is reasonable to include these two pages in our
analysis of the data.
Test of proportions: This test does not show significant
differences among proportions at the 5% level because the
P-value $0.06 > 0.05 = 5\%.$ However, the small counts for
some of the pages leads to a warning message that the P-value
may not be correct.
x = c(3,  600, 12,  50)
n = c(6, 1800, 46, 200)
prop.test(x,n)

  4-sample test for equality of proportions
  without continuity correction

data:  x out of n
X-squared = 7.4009, df = 3, p-value = 0.06016
alternative hypothesis: two.sided
sample estimates:
   prop 1    prop 2    prop 3    prop 4
0.5000000 0.3333333 0.2608696 0.2500000

Warning message:
In prop.test(x, n) :
 Chi-squared approximation may be incorrect

Chi-squared test: A Pearson chi-squared test is on a
contingency table TBL of these counts is equivalent,
giving the same P-value.
TBL = rbind(x, n-x); TBL
  [,1] [,2] [,3] [,4]
x    3  600   12   50
     3 1200   34  150

chisq.test(TBL)

        Pearson's Chi-squared test

data:  TBL
X-squared = 7.4009, df = 3, p-value = 0.06016

Warning message:
In chisq.test(TBL) :
 Chi-squared approximation may be incorrect

From this test we can see the exact reason for the warning message:
both of the expected counts (in the first column for Page 1) used in the test are smaller than 5.
chisq.test(TBL)$exp
      [,1]      [,2]     [,3]      [,4]
  1.944444  583.3333 14.90741  64.81481
  4.055556 1216.6667 31.09259 135.18519

Warning message:
 In chisq.test(TBL) :
 Chi-squared approximation may be incorrect

However, as implemented in R, chisq.test can simulate
a P-value that is more accurate. In this instance, the
simulated P-value still (just barely) exceeds 5%.
There is nothing 'sacred' about the 5% level of significance.
It has often been used as one criterion for determining whether results are
worthy of publication in a journal. To help a company make borderline decision to investigate further whether and why
some pages seem to perform better than others, this result
may be useful.
chisq.test(TBL, sim=T)

        Pearson's Chi-squared test
        with simulated p-value
        (based on 2000 replicates)

data:  TBL
X-squared = 7.4009, df = NA, p-value = 0.05397

A questionable second look: We noted at the start that, by far, the
most-viewed pages are 2 and 4. We might consider that significance
at the 6% level (for all four pages) warrants looking just at those two pages. (Or we
might have focused only on those two pages from the start because of their relatively much large numbers of views.)
For what it may be worth, those two pages have significantly
different click-through rates. It may be worthwhile to
ponder why those two pages are most viewed and why Page 2
performs best.
prop.test(c(600, 50), c(1800,200))

        2-sample test for equality of proportions
        with continuity correction

data:  c(600, 50) out of c(1800, 200)
X-squared = 5.3245, df = 1, p-value = 0.02103
alternative hypothesis: two.sided
95 percent confidence interval:
 0.01671496 0.14995171
sample estimates:
   prop 1    prop 2
0.3333333 0.2500000

Notes: (1) Different statisticians might analyze these data in various ways.
@Mehdi doesn't see anything of interest here, and that may be the right point of view.
I wonder whether a relatively highly viewed page with a CTR of 1/3 might
be worthy of additional thought or investigation.
(2) There is, of course, no question that the four viewing counts
indicate unequal viewing of the four pages. (The null hypothesis is that given the four pages, all four
are viewed equally often; rejected with P=value nearly $0.)$
chisq.test(n)

        Chi-squared test for given probabilities

data:  n
X-squared = 4346, df = 3, p-value < 2.2e-16


text:
A term sheet is a bullet-point document outlining the material terms and conditions of a potential business agreement, establishing the basis for future negotiations between a seller and buyer. It is usually the first documented evidence of a possible acquisition. It may be either binding or non-binding.

After a term sheet has been "executed", it guides legal counsel in the preparation of a proposed "definitive agreement".  It then guides, but is not necessarily binding, as the signatories negotiate, usually with legal counsel, the final terms of their agreement.

Term sheets are very similar to "letters of intent" (LOI) in that they are both preliminary, mostly non-binding documents meant to record two or more parties' intentions to enter into a future agreement based on specified (but incomplete or preliminary) terms.  The difference between the two is slight and mostly a matter of style: an LOI is typically written in letter form and focuses on the parties' intentions; a term sheet skips most of the formalities and lists deal terms in bullet-point or similar format.  There is an implication that an LOI only refers to the final form.  A term sheet may be a proposal, not an agreed-to document.

Venture capital financing
Within the context of venture capital financing, a term sheet typically includes conditions for financing a startup company. The key offering terms in such a term sheet include (a) amount raised, (b) price per share, (c) pre-money valuation, (d) liquidation preference, (e) voting rights, (f) anti-dilution provisions, and (g) registration rights.

It is customary to begin the negotiation of a venture investment with the circulation of a term sheet, which is a summary of the terms the proposer (the issuer, the investor, or an intermediary) is prepared to accept. The term sheet is analogous to a letter of intent, a nonbinding outline of the principal points which the stock purchase agreement and related agreements will cover in detail.

The advantage of the abbreviated term sheet format is, first, that it expedites the process.  Experienced counsel immediately know generally what is meant when the term sheet specifies "one demand registration at the issuer's expense, unlimited piggybacks at the issuer's expense, weighted average antidilution"; it saves time not to have to spell out the long-form edition of those references. Second, since the term sheet does not propose to be an agreement of any sort, it is less likely that a court will find unexpected promissory content; a "letter of intent" can be a dangerous document unless it specifies very clearly, as it should, which portions are meant to be binding and which merely guide the discussion and drafting. Some portions of a term sheet can have binding effect, of course, if and to the extent an interlocutory memorialization is needed of some binding promises, that is, confidentiality of the disclosures made in the negotiation. The summary format of a term sheet, however, makes it less likely that any party will be misled into thinking that some form of enforceable agreement has been memorialized when it has not.

Some important terms to founders and venture capitalists-
 Return - Valuation/Dilution: One of the most important terms for founders is valuation; whether the evaluation is high or not tells the entrepreneurs whether or not the investor is credible. Also, option pools are part of the evaluation and usually founders have the power to negotiate option pool, they shouldn't get greedy and should present a strong case.   This term is important since option pool can lower the effective valuation.
 Control - BOD: A very common and usual arrangement is a board of three directors, one from investor and two from founders (for motivational purposes).
 Liquidation Preferences: This represents how much the founders will receive in an event of company sale etc. Thus it is very important as the ultimate goal is to make returns. Terms represented by CRV seem normal and acceptable.
 Founder Vesting: This plays a major role in motivating the founders
 Voting Rights: Voting rights are important to venture capital investors in relation to prospective actions taken by the founders, including the sale of the company, additional financing, election of board members and other matters that should be approved by a certain threshold of the Series A Preferred stockholders (the venture capital investor).
 Co-Sale Terms: If one of the common shareholders were to sell his/her shares, partially or completely, they should include the investors, or drag them along with the sale and sell part of theirs. This is very crucial to the investor.

References

External links
MaRS Term sheet template (angel or venture capital investors)

Legal documents

redpajama_set_name: RedPajamaWikipedia


You'll love this over-sized, sturdy caddy with lots of easy-to-access slip pockets and dividers to hold and organize all your supplies. Seven slip pockets on front, back, and one end provide easy access to tools and supplies.
Inside Out Mats feature a "no-bind" technique - these mats are made completely on the sewing machine.
Jelly Roll Rug Squared. Use 2 1/2" strips and cotton batting.
Super easy project perfect for 1 1/2" pre-cut strips.
When all eyes turn to the table, make a statement with a stylish table runner! Sherri Falls of This & That Pattern Company has designed a multitude of table runners for all seasons and reasons, so every gathering becomes a celebration.


Devastated train commuters in Southern California rode the rails again today, comforting friends after last week's accident that left 25 dead.
"Commuters returned to Southern California's rails today, the first workday after a deadly train collision in Chatsworth, as Los Angeles Mayor Antonio Villaraigosa and Metrolink officials tried to assure people that riding the train is still safer than navigating local freeways."
"Villaraigosa joined commuters this morning at the Chatsworth train station to try to reassure people about taking the train and that safety is Metrolink's first priority."
'I want to dispel any fears about taking the train," he said. "Safety has to be our No. 1 concern. Taking the train is still the safest option for commuters.'"
"Commuter Barbara Copelof, a supervisor for a credit union in Glendale, took the train from the Chatsworth station this morning, saying she wanted to overcome her fear after Friday's crash. She clutched a newspaper clipping about commuter Roger Spacey, a 60-year-old Simi Valley resident she knew from riding the train who was killed in the accident."
"'I needed to connect with my train people to make sure they were OK,' she said."


In the Post-Koken case of Schmerling v. LM General Ins. Co., No. 17-3659 (E.D. Pa. Nov. 8, 2018 DuBois, J.), Judge Jan E. Dubois of the Eastern District Federal Court of Pennsylvania the court addressed the carrier's motion regarding the manner in which this UIM case was to be presented to the jury.
The court ruled that the Plaintiff was prohibited from presenting evidence on the UIM benefits coverage as well as evidence on the settlement negotiations with the underlying tortfeasor defendant and the settlement agreement with the tortfeasor defendant.
After reviewing conflicting Pennsylvania Federal Court decisions on the issue, the Schmerling court entered these rulings under an application of general principles of relevance. The court noted that the evidence was irrelevant to the jury's determination of the Plaintiff's alleged damages, and that any probative value of such evidence was substantially outweighed by the danger of unfair prejudice to the Defendant.
In terms of the unfair prejudice of this information, the court noted that the evidence of the amount of the UIM limits, could supply the jury with an "anchor number" that does not reflect the Plaintiff's actual damages.
The court ruled with similar reasoning in excluding evidence of the Plaintiff's settlement negotiations and settlement agreement with the tortfeasor as irrelevant to the jury's proper assessment of damages and unfairly prejudicial to the defense.
The Schmerling Court's Order can be viewed HERE. The Opinion by the Court can be viewed at this LINK.
This recent Schmerling decision has already been cited in a similar ruling by President Judge Jason J. Legg of the Susquehanna County Court of Common Pleas on November 19, 2019 in the case of Phillips v. Nat'l Gen'l Assur. Co. which case was brought to my attention by Attorney Gerald Connor of the Scranton, PA office of Margolis Edelstein.


Introit: Glory be to God who has shown us the light Music © Gerry Fitzpatrick. St Mungo Music. Recorded by the St Mungo Singers.
Introit: Glory to the Father Music © Gerry Fitzpatrick. St Mungo Music. Recorded by the St Mungo Singers.
Introit: In the morning let me know your love Music © Gerry Fitzpatrick. St Mungo Music. Recorded by the St Mungo Singers.
Introit: Grace to you and peace A Processional Introit for choir and congregation and suitable for the installation or ordination of a bishop. Used as the Introit for the Mass of Pope Benedict 16th at Bellahouston in 2010.Music (c) Gerry Fitzpatrick. Recorded by the St Mungo Singers.
Introit: In the name of God Music © Gerry Fitzpatrick. St Mungo Music. Recorded by the St Mungo Singers.
Introit: They are Happy who dwell in your House: Music © Gerry Fitzpatrick. St Mungo Music.
Introit: Advent: I lift my soul to you Text ICEL. Music © Gerry Fitzpatrick. St Mungo Music. Recorded by the St Mungo Singers.
An Advent Introit: See the days are coming Words Stephen E Smyth; music Gerry Fitzpatrick; (c) Kevin Mayhew and published in Focus on Advent.
If you use this music and if you pay for a copyright licence, then please acknowledge the author, composer and publisher in your returns.
Notes taken from the General Instruction of the Roman Missal copyright © 2002 the International Committee on English in the Liturgy (ICEL) in the English translation. Copyright © 2005 Catholic Bishops' Conference of England and Wales, in the emendations and editorial arrangement.
46. The rites preceding the Liturgy of the Word, namely the Entrance, Greeting, Penitential Act, Kyrie, Gloria, and Collect, have the character of a beginning, introduction, and preparation. Their purpose is to ensure that the faithful, who come together as one, establish communion and dispose themselves to listen properly to God's word and to celebrate the Eucharist worthily.
In certain celebrations that are combined with Mass according to the norms of the liturgical books, the Introductory Rites are omitted or performed in a particular way.
The Entrance Song 47. After the people have gathered, the Entrance chant begins as the priest enters with the deacon and ministers. The purpose of this chant is to open the celebration, foster the unity of those who have been gathered, introduce their thoughts to the mystery of the liturgical season or festivity, and accompany the procession of the priest and ministers.
Note:Few parishes in the Archdiocese of Glasgow are showing any interest in singing the Latin Introits of the Graduale Simplex, but some might take a look at some material in 'By Flowing Waters' which has been described as an unofficial English version. Our culture has been affected by several hundred years of tuneful versions of psalms and hymns in English, at least 180 years of singing hymn at devotions, and 50 years of singing hymns at mass, which might explain why we seem to find melodic settings more accessible. There is no need for us to restrict ourselves to the clearly defined tunes that we are accustomed to see printed in 2/4, 3/4, 4/4, 6/8 etc , but on the other hand when you are working to build up participation then you tend to use what is memorable and practical.You are welcome to copy any of this music, but, if you deal with a copyright organisation such as CCL or Calamus, please include the title, the author, composer and publisher in your returns.


They either don't do the five interviews per week to fill the card.
They do the interviews and get a job before the card is filled.
Why do I push my unemployed job seekers to do informational interviews? Because they work! Plain and simple: When you start having meaningful, professional conversations with people in your field, you gain confidence, energy, and momentum. Talking to others gets you referred – and that gets you hired. You must make the effort to meet with as many people as you can to have discussions around work. It's the only way to show people your skills and abilities. Plus, it makes you memorable. Meaning, when they hear about a job, they'll remember to refer you to it. Don't underestimate the power of informational interviews. Honestly, the hardest part about them is setting them up. Yet, there are 1,000s of people out there online you can find and connect with as a way to ask for informational interviews. All it takes is a little effort. How much is a job worth to you? I think enough to want to set up five informational interviews each week. Try it and you'll be saying, "BINGO!" before you know it.
Why you need to stop looking for a job in order to find work.
How to remove the stigma of unemployment.
Where the real jobs are and how you can find them.


A cleaned-up, stylish antidote to the common cat scratcher.
Now available with a refillable insert ($25) -- this cat scratcher is one piece of furniture that you want your cat to scratch! We start by hand-rolling cardboard (100% recycled content) around a wooden center, then we seal the bottom and encase the pad in a hand-printed twill wrap. Once your cat has sufficiently shredded the insert (your couch will thank you), simply remove the outer wrap, recycle the the cardboard and refill with a kitty jones scratch pad insert.
To order an insert, please choose the "INSERT REFILL" option in the drop down menu.
We are currently able to ship within the contiguous US and Canada. If you live elsewhere, and are interested in the Scratch Pad, please contact us for a quote.


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Miley Cyrus and Liam Hemsworth tied the knot on Sunday, December 23, 2018, in what has become the best-kept Christmas secret in entertainment history. The couple kept their nuptials secret from paparazzi and retained full control over their wedding and how news of their ceremony went public. It was when both Miley Cyrus and hubby Liam Hemsworth shared photos in their wedding attire that people discovered the couple wed in an intimate ceremony. There was no denying that Miley Cyrus was wearing a wedding dress. It was later learned that the gorgeous, simple-yet-elegant gown was a Vivienne Westwood.
Here's everything fans want to know about Miley Cyrus' Vivienne Westwood wedding gown and the wedding decorations.
Miley Cyrus and Liam Hemsworth were married on their 33-acre, Tennessee farm that proved to be the perfect backdrop for their intimate wedding. Though Christmas would only be two days away, the home was decorated perfectly with a flowering, pink rose arch dotted with baby's breath and Miley carried a white anemone bouquet that perfectly complemented Liam Hemsworth's boutonnière that was affixed to his tuxedo's lapel.
Liam wore a black tuxedo with white sneakers, showing that they were keeping things low-key, comfortable and casual. The quiet ceremony was attended by the couple's closest friends and family members.
Though it's easy to see this was a reasonably priced affair, the greatest expense was the Vivienne Westwood wedding gown.
Miley shared a full-length color photo of the gown estimated to have cost $8,600. It is believed to be the Vivienne Westwood Long Cocotte Dress that was available on the Moda Operandi website but has since sold out. The dress exudes feminity and elegance with a graceful, yet overwhelming train that gently draped in soft cascades. It is described as having a sheath silhouette, draped bodice, and cowl neckline.
Here is the photo that Miley shared in her Instagram story.
Miley's and Liam's fans have scoured the Internet for photos and one picture that has gone viral shows Liam and Miley holding the knife as they prepared to cut their wedding cake.
Miley's father, Billy Ray Cyrus, shared another photo of the couple and their wedding cake from a different perspective. The photo shows Miley's siblings and the simple, yet elegant two-tiered wedding cake. You can see some of the cupcakes the couple ordered from Brentwood, Tennessee's Ivey Cake. The bakery is no stranger to celebrity celebrations. You will see Carrie Underwood standing by a cake on the website's front page.
Fans were surprised the couple didn't choose a more elaborate wedding cake, but it appears that Miley and Liam planned the perfect wedding that was tailored exactly for their specific wants and tastes.
Photos showing the wedding as well as Miley's wedding gown have gone viral.
Miley Cyrus and Liam Hemsworth have apparently tied the knot yesterday, in an intimate celebration!
Photos released online today show the couple getting ready to slice what seems to be the wedding cake.
Miley Cyrus's mother, Tish, also shared beautiful photos of Miley wearing her Vivienne Westwood gown. Those photos have also gone viral.
Miley and Liam chose to share black and white photos. Though many say the photos are beautiful, many fans say they appreciate the color photos that truly bring out the beauty of Miley's wedding gown to the surface.
What do you think of Miley Cyrus' wedding gown and decorations? Do you think her minimal, yet elegant wedding was a perfect choice for the newlyweds?
Kanye West Calls Out Drake After Realizing He Follows Kim Kardashian On Social Media - Reignites Their Feud!


Congrats Siva. Such a great news!!
this is my 4th time ep application, submitted on 24th November, still waiting for vetting process. I am very lucky that all last 3 application was selected for vicious checking, although there are no changes on my qualification. before this usually only takes less than two weeks, now I am getting nervous.
Is this your EP renewal or a fresh EP application.
What I have read in different forums, EP renewal is taking 2 weeks or so and new EP applications are taking somewhere 2+ months.
My EP application already completed 2 months and it is still showing "Pending".
this is my 3rd "fresh" ep application, I just moved to a different company. my goodness, they really dont want foreigner to work here.
Lets hope for the best buddy.
As I read on other forums is that be patient and things should move positively.
thanks buddy, yours already approved right? in total I've waited for almost 2 months already since my company decided to hire me, another 2 months for the ep, this is getting ridiculous.
Wow, congrats man. So you mean it took 22 days to get it approved.
Thanks buddy! Yeah, its approved about 22 days after submitted. That is my longest record waiting period yet. Its archi, local firm.
Good luck with yours, ask your hr to ask MOM, probably that will help.
Its already 70 days I am waiting. Mine is an indian MNC, dnt know how much it will take.
do you know if there are any singaporeans working there?
not about the ratio, thats for s-pass. Your company must be known for actively trying to hire singaporeans in the past, interviews, etc.
Last edited by shiv1408 on Thu, 15 Dec 2016 8:11 pm, edited 1 time in total.


FHA home loans are a great way to make your first real estate purchase. Are you looking at homes for sale and trying to decide which is the best mortgage loan for you?
If you are a first-time home buyer and feel a bit confused by what your real estate agent finds in MLS listing data while searching for "houses near me", remember that the FHA mortgage loan option is available to anyone who financially qualifies. If you are talking to a real estate agent, do not forget that FHA loans are not restricted to first-time home buyers or those who have never owned property before.
You can own other property and still buy a home to live in as your primary residence using an FHA loan whether you've purchased other property or not.
You have probably heard about other government loan programs; VA home loans, USDA mortgages for rural areas, etc.
Your FHA mortgage lender will negotiate interest rates with you based on your FICO score. Are you worried about your scores or about being a first-time home loan borrower? Work on your credit before applying for the loan for best results.
Want to build a home instead of buying real estate? Want to use floor plans you approve of? This is possible thanks to the FHA One-Time Close construction loan. These loans may feature more strict lender requirements than other mortgages but the end results can be worth the effort to meet these standards.
Your home loan options also include the ability to buy a fixer-upper using an FHA Rehabilitation Mortgage-not everyone wants a pristine piece of real estate to buy and move right into. The FHA Rehab loan is known in lending circles as an FHA 203(k) Rehab loan, which is also available as a refinance loan should you buy a home now and decide later you want to do some major renovations or smaller improvement projects.
203(k) Rehab Loans do not allow "luxury additions" to your home such as swimming pools, tennis courts, barbecues, etc. In some cases existing so-called luxury features of the home may be repaired or upgraded for safety, but not installed as a new feature.
If you want to add these features later after you have purchased the home, consider applying for FHA cash-out refinancing instead, as there are no limits to what you may do with those funds.
The seller of the home your agent found in the MLS listings cannot contribute money toward your down payment, but there is a trade-off.
The seller IS permitted to contribute up to six percent of the asking price of the house for sale in the form of seller-paid closing costs.
Even as a first-time home buyer you can apply to purchase a one-to-four unit property with an FHA mortgage. Don't forget that FHA loan requirements include occupancy since these loans are for owner-occupied residences only.


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text:
Smokestack Lightening
By Grist staff on Jan 29, 2005
ConocoPhillips will pay half a billion to clean up refineries
The largest refinery settlement in U.S. history was announced yesterday, as ConocoPhillips, the nation's largest oil refiner, agreed to spend more than $525 million to clean up nine refineries, a deal that will remove 47,000 tons of harmful pollutants from the air each year. This is the 13th such settlement since 1998. While coal-fired power plants produce far more total pollution — and are fighting much harder against EPA enforcement efforts — refineries can be more harmful to the health of communities where they are located, as their emissions come from low smokestacks and do not readily dissipate. Some critics question the strength of the settlements (California has opted out of this latest, saying it would be a step back from the state's current regs) and the EPA's will and ability to enforce them. Nonetheless, says John Walke of the Natural Resources Defense Council, the Bush administration's progress in cleaning up refineries "is pretty extraordinary."

redpajama_set_name: RedPajamaCommonCrawl


text:
Shiloh Industries Opens Nantong Facility to Manufacture Lightweight, High-Integrity Aluminum Cast Products
Valley City, Ohio, USA, May 7, 2018 – Shiloh Industries, a leading global supplier of lightweighting, noise and vibration solutions, recently opened their latest aluminum casting facility in Nantong, China. The state-of-the-art facility will launch producing new, innovative 9-speed, aluminum transmission carriers and reverse carriers through Shiloh's proprietary, high-integrity casting technology, and has additional roll-outs scheduled.
"We are excited to manufacture our innovative products in a facility strategically located to serve the growing market in China," said David Jaeger, senior vice president and managing director, CastLight®. "We have experienced significant global demand for our lightweighting solutions and continue to invest resources to meet this growing need."
The Nantong facility enhances Shiloh's current aluminum and magnesium casting capabilities in Asia, Europe and North America, allowing for superior service to global programs at a local level. The newly constructed, 150,000 square foot facility is located in the Jiangsu Nantong Science & Technology Industrial Zone.
With initial staffing planned at 60 employees, Shiloh plans to grow that number to nearly 300 in the coming years.
"Our growth at this location will include our proprietary ThinTech® technology, which optimizes the design and production of the lightest cast aluminum product. We will focus on body structure and specific products for the electric vehicle market," Weibo Weng, managing director, Asia explained. "In addition, we continue to enhance our research and development center here as well."
Shiloh designs and manufactures innovative lightweight products that enable OEMs to reduce on-vehicle weight without compromising strength, safety or performance. The company has taken a systematic approach to the market entering in 2015 with the opening of the Shiloh China Technical Center in Shanghai, followed by their first production facility in Suzhou. The Suzhou facility produces our proprietary ShilohCore® NVH dash panels supplied to leading OEMs. In 2017, Shiloh also began producing cast magnesium structural products in the region, which complements our production of aluminum structural products in Nantong. With three production facilities and our local technical center, Shiloh is well-prepared for continued growth in the region.
Mon, May 7, 2018


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"Ask God for my conversion and my sanctification,… so that I may accomplish at least a part of his designs for me and grant me His mercy for the rest." Excerpt from a letter of John Gabriel Perboyre to his brother, in charge of the Sacristy at St. Lazare, Motherhouse of the Congregation of the Mission. These are the words of a man called "a living saint." (P. Songe).
John Gabriel was born in Puech, a hamlet in the parish of Montgesty. He was the first of eight children of Pierre and Marie Perboyre. He was raised in a very Catholic family who lived on their farm. The priest of the parish loved to question John Gabriel on the points of the catechism saying, "Let's go, my little doctor!" In the family Jean Gabriel took as an example his uncle, James, a priest of the Mission, courageous at the time of the Revolution. From him John Gabriel learned how to live for Jesus Christ and serve him unto death.
In 1816 John Gabriel accompanied his younger brother to the Seminary in Motauban where he also stayed to continue his studies.
In December of 1818 John Gabriel entered the Congregation of the Missionand was ordained a priest in September 1824 in the Chapel of the Motherhouse of the Daughters of Charity in Paris. After his work as a theology professor at the Seminary in Saint Flour, he was called to the Motherhouse of the Congregation of the Missionin Parisas Director of the Internal Seminary.
The Epiphany, the star at the moment of his birth, continued to show him very far off horizons. In March 1835 he set out under God's protection and arrived on 29 August in Macao, port of access for the missions in China. Many were his apostolic activities in Ho-Nan, despite dangers and persecutions, until the moment of his betrayal and imprisonment in September 1839.
After months of moral and physical suffering and long and terrible torture, John Gabriel gave his last witness on 11 September 1840. He was hung from a cross and strangled by a rope.
Many witnesses say that a luminous cross appeared in the sky at the moment of the death of John Gabriel. The Epiphany had arrived at its summit, making present the words of the prophet Daniel (12:3): those with insight shall shine brightly like the splendor of the firmament, and those who lead the many to justice shall be like the stars forever.
His body was brought to Saint Lazare (Motherhouse,Paris) 20 years after his death.


text:
\section{Introduction}

Floods are one of the major natural disasters, exacerbated by climate change, affecting between 85 million to 250 million people annually and causing between $\$ 32$ to $\$36$ billion in economic damages~\cite{guha2015dat,emerton2016continental}. Some of these harms can be alleviated by providing early flood warnings, so that people can take proactive measures such as planned evacuation, move assets such as food and cattle and use sandbags for protection. One of the important user experience elements for an effective warning system is its overall accuracy, as false alerts lead to eroded trust in the system. Our work contributes towards improving the accuracy of flood warning systems such as~\cite{nevo2022flood}, by increasing the accuracy of the inundation module. The inundation model in ~\cite{nevo2022flood} learns a mapping between historical river water gauge levels and the corresponding flooded area which can be leveraged to predict future flooding extent based on forecast of the future river water gauge level. The accuracy of these forecasts is directly correlated with the accuracy of the underlying historical segmentation maps, hence we aim to improve the segmentation module through our contributions in this work.

In recent years, remote sensing technology has considerably improved and helped us in the timely detection of floods and monitoring their extent. It provides us with satellite data at different spatial resolutions and temporal frequencies. For example, MODIS ~\cite{justice1998moderate} provides low resolution data(250m) with high temporal frequency(~2days). There are medium spatial resolution satellites, from 10m to 30m range, such as Sentinel-2~\cite{drusch2012sentinel}, Sentinel-1~\cite{torres2012gmes} and Landsat~\cite{roy2014landsat} available, but they have a slightly lower temporal frequency (~6-15 days). High resolution data with resolution ranging from a few centimeters to 1m, can also be obtained on special demand using airborne radars. However, the process of obtaining airborne data is very expensive. Hence, Sentinel-1 and Sentinel-2 satellites are preferred to map the surface of water because they provide a good trade-off between both spatial and temporal resolutions along with open access to their data. Although, Sentinel-2 is better for water segmentation because it shows high water absorption capacity in short wave infrared spectral range (SWIR) and near infrared (NIR) spectrum, it cannot penetrate cloud cover. This limits its application for mapping historical floods as cloud cover is highly correlated with flooding events. On the other hand, radar pulses can readily penetrate clouds, making SAR data provided by Sentinel-1 satellite well suited for flood mapping~\cite{tarpanelli2022effectiveness,mason2021floodwater,vanama2020gee4flood}.

Thresholding algorithms~\cite{liang2020local,martinis2015backscatter,brown2016progress} are traditionally used to segment flooded regions from SAR images since water has a low back scatter intensity. Commonly used techniques like Otsu thresholding ~\cite{bao2021water} assume that the histogram of a SAR image has a bimodal distribution and they work well for many cases. However, its failure modes include generating false positives for mountain shadows and generating excessive background noise due to speckle in SAR imagery. This noise can be removed using Lee speckle filters ~\cite{lee1981refined} or mean filters, however this results in small streams being missed out. In recent years, Convolutional Neural Networks (CNN) have been used to segment flooded areas from satellite images. Unlike traditional pixel-wise methods, they can look at a larger context and incorporate spatial features from an image. A lot of work has focused on using opportunistically available cloud free Sentinel-2 images~\cite{mateo2021towards, akiva2021h2o}. Though these methods have good performance, their utility at inference time is limited because of the cloud cover issues mentioned previously. Another line of work fuses Sentinel-1 and Sentinel-2 images~\cite{konapala2021exploring, drakonakis2022ombrianet, tavus2020fusion, bai2021enhancement} to enhance surface water detection during flooded events. These methods not only a require a cloud free Sentinel-2 image, but also require that both images are acquired at about the same time to avoid alignment issues. There also has been some work done that uses multi temporal images~\cite{zhang2020use, sunkara2021memory, yadav2022attentive} containing a pre-flood and a post-flood event. These methods do change detection and exhibit better performance. In our work however, we focus on methods that only take a single Sentinel-1 timestamp image as input.

 Most of the prior work that uses a single Sentinel-1 image as input~\cite{ghosh2022automatic,katiyar2021near,helleis2022sentinel} for flood segmentation, makes use of Sen1Floods11~\cite{bonafilia2020sen1floods11} - a publicly available dataset with a small set of high quality hand labelled images and a larger set of weak labelled images. However, the limitation of using weak labelled data (despite using various regularization techniques), is that the model still learns the mistakes present in those labels. Previously, works such as ~\cite{wang2017multiclass, song2019selfie, huang2020self, zheng2021meta} have been explored to handle noisy label using loss adjustment. \textit{Wang et al.} ~\cite{wang2017multiclass} uses loss re-weighting to assign a lower weight to incorrect labels, ~\cite{song2019selfie, huang2020self} employ label refurbishing by using entropy to correct noisy labels or by doing a progressive refinement of the noisy labels using a combination of the current model output and the label. \textit{Zheng et al.} ~\cite{zheng2021meta} proposes a meta-learning based framework, where a label correction model is trained to correct the noisy labels and the main model is trained on these corrected labels. However, most of these methods have only been explored for classification task and are hard to optimize for segmentation tasks due to increased complexity. Our work also explores methods to do label correction, however we use a simpler idea of using historical temporal imagery to correct weak labels during training as described in Sec \ref{subsec:weak_label_improv}.

In contrast to the above work, we also explore leveraging unlabelled data through semi-supervised learning techniques as opposed to creating weak labels. Methods such as ~\cite{sohn2020fixmatch, wang2022semi, ahmed2022cleaning, paul2021flood} have explored semi supervised techniques to make use of both labelled and unlabelled data. The basic principle of ~\cite{sohn2020fixmatch} is that it uses pseudo labels predicted on the weak augmented image, to consistently train heavily augmented images. \textit{Ahmed et al.} ~\cite{ahmed2022cleaning} ensembles predictions from multiple augmentations to produce more noise resilient labels. However, most of these works focus on RGB images and their performance degrades in remote sensing images. This happens because most of the augmentations are hand crafted for RGB images only.
Motivated to use different modalities of satellite data of the same location as natural augmentations in semi-supervised methods and cross modal feature distillation~\cite{gupta2016cross}, we use a teacher student setup that extracts information from a more informative modality (Sentinel-2) to supervise paired Sentinel-1 SAR images with the help of a small hand labelled and a large unlabelled data set. Similar to~\cite{gupta2016cross}, we transfer supervision between different modalities. However, instead of supervising an intermediate feature layer like~\cite{gupta2016cross}, we transfer supervision at the output layer and apply this towards a new application (i.e. flood segmentation). Our main contribution in this work are:
\begin{itemize}
    \item We propose a cross modal distillation framework and apply it to transfer supervision between different modalities using paired unlabelled data.
    \item We propose a method to improve the quality of weak labels using past temporal data and use that to enhance the weak label baseline.
    \item We curate an additional large dataset (in addition to Sen1Floods11) from various flooding events containing paired Sentinel-1 and Sentinel-2 images and a weak label based on Sentinel-2 data.
\end{itemize}

\section{Data}

\subsection{Data features}
\label{subsection:input}

\textbf{Sentinel-1 image}: Sentinel-1~\cite{torres2012gmes} mission launched by European Space Agency (ESA) consists of 2 polar orbiting satellites to provide free SAR data. However, recently one of the satellite malfunctioned and currently remains out of service. ESA has plans to launch another satellite in 2023. This satellite is an example of active remote sensing satellite and uses radio waves operating at a centre frequency of 5.405 GHz. This allows it to see through cloud cover. It has a spatial resolution of 10m and has a return period of 6 days. We use the Sentinel-1 GRD product and utilize the bands that consist of dual polarized data: Vertical Transmit-Vertical Receive (VV) and Vertical Transmit-Horizontal Receive (VH). These bands representing the backscatter coefficient are converted to logarithmic(dB) scale. The backscatter coefficient is mainly influenced by the physical characteristics such as roughness and the geometry of the terrain and the dielectric constant of the surface. It is discriminative for detecting surface water as water reflects away all the emitted radiation from the satellite in a specular way.

\textbf{Sentinel-2 image}: Sentinel-2~\cite{drusch2012sentinel} mission was also launched by the European Space Agency (ESA) and has 2 polar orbiting satellites. It provides multispectral data at a resolution of 10m and has a return period of 5 days. We use the L1-C Top of Atmosphere (TOA) product. It is a passive remote sensing satellite operating in visible and infrared wavelength. Its images are affected by atmospheric conditions and often contain significant cloud cover. The multispectral data consists of 13 bands and in this work we use 4 bands: B2 (Blue), B3 (Green), B4 (Red) and B8 (NIR). These bands are used for various tasks like land cover/use monitoring, climate change and disaster monitoring.

\textbf{Weak label}: The weak labels are computed from the Sentinel-2 image. Though cloud free Sentinel-2 data is rarely available during a flooding event, we can still opportunistically sample timestamps from a long time range and get enough cloud-free views. Initially, the cloud mask is estimated using Sentinel-2 quality assurance band (QA60). The cloud mask is then dilated to mask out the nearby cloud shadows as the spectral signature of the cloud shadows is similar to that of water~\cite{li2013automatic}. Weak flood labels are then created by thresholding the Normalized Difference Water Index ($NDWI =(B3-B8)/(B3+B8)$) band. The pixels having value greater than 0 are marked as water and the rest are marked dry.

\textbf{Water occurrence map}: The water occurrence map~\cite{pekel2016high} shows how water is distributed temporally throughout the 1984-2020 period. It provides the probability of each pixel being classified as water averaged over the above time period. It has a 30m resolution data. The data was generated using optical data from Landsat 5, 7, and 8. This map can be used to capture both the intra and inter-annual changes and to differentiate seasonal/ephemeral flooding pixels from permanent water pixels. Each pixel was individually classified into water / non-water using an expert system and the results were collated into a monthly history for the entire time period.  Averaging the results of all monthly calculations gives the long-term overall surface water occurrence.

\subsection{Datasets}
\textbf{Sen1Floods11 dataset}: This is is a publicly available dataset~\cite{bonafilia2020sen1floods11}, containing 4831 tiles from 11 flooding events across 6 continents. It contains paired Sentinel-1 SAR and Sentinel-2 multi-spectral images. Each tile is $512{\times}512$ pixels at a resolution of 10m per pixel. Due to the high cost of labeling, only 446 tiles out of 4831 are hand labelled by remote sensing experts to provide high quality flood water labels. The authors provide an IID split of these hand labelled tiles, containing 252 training, 89 validation, and 90 test sample chips. The remaining 4,385 tiles have weak labels prepared by thresholding NDVI (Normalized Difference Vegetation Index) and MNDWI (Modified Normalized Difference Water Index) values. The weak labels are only used for training as they are not accurate enough to be used in validation or test. We also augment the dataset with the water occurrence map for every tile.

\textbf{Floods208 dataset}: We curated additional imagery by downloading closely acquired Sentinel-1 and Sentinel-2 images from Earth Engine \cite{gorelick2017google} during flood events provided to us by external partners.

The data is extracted using the following steps:
\begin{itemize}
    \item For each data point consisting of latitude, longitude and flooding event timestamp, get the Sentinel-1 image of the Area of Interest (AOI).
    \item Search for overlapping Sentinel-2 images within 12hrs of Sentinel-1 timestamp. Filter out images with $> 12\%$ cloud cover.
    \item Pick the Sentinel-2 image closest to Sentinel-1 timestamp from the filtered images. If none are available, we discard this data point.
\end{itemize}

The data points were extracted from 208 flooding events across Bangladesh, Brazil, Colombia, India and Peru. These regions are shown in Figure \ref{fig:s1} and were chosen according to the regions of interest for final deployment. We also extracted water occurrence map and a weak label for each image using the Normalized Difference Water Index (NDWI) band from the Sentinel-2 image. The extracted images had a resolution of 16m per pixel. Each image from a flooding event was then partitioned into multiple small tiles of size $320{\times}320$. The tiles at the edge of image were padded to fit the $320{\times}320$ tile size. Tiles having cloud percentage greater than $80\%$ were discarded. In total 23,260 valid tiles of size $320{\times}320$ were extracted from the whole process. Figure \ref{fig:s5} shows some selected data points from both the datasets.

\begin{figure*}[]
\centering
\includegraphics[width=13cm]{image_distribution.png}
\caption{Red points highlight the regions from where Sen1Floods11 flooding event data points were sampled and blue points indicate the same for Floods208 dataset.}
\label{fig:s1}
\end{figure*}

\begin{figure*}[]
\centering
\includegraphics[width=13cm]{data_features_all.png}
\caption{Selectively sampled data points from Sen1Floods11 weak labelled data (first 3 rows) and Floods208 Dataset (last 4 rows). In the weak label the mapping is green:dry, blue:water and white:clouded/invalid pixels. These examples highlight the poor quality of the weak labels.}
\label{fig:s5}
\end{figure*}

\begin{table}[]
\resizebox{\textwidth}{!}{

\begin{tabular}{llll}
\toprule
                      & Sen1Floods11 ( hand label)                                                                                & Sen1Floods11 ( weak label)                                                                                & Floods208 dataset ( weak label)            \\
\midrule
No. train tiles      & 252                                                                                & 4385                                                                                & 23260                                     \\
No. validation tiles & 89                                                                                & -                                                                                & -                                         \\
No. test tiles       & 90                                                                                & -                                                                                & -                                         \\
Tile Size & $320\times320$ & $320\times320$ & $320\times320$ \\
Resolution & 16m & 16m & 16m \\
Sentinel-1 bands      & VV, VH                                                                                & VV, VH                                                                                & VV, VH                                 \\
Sentinel-2 bands      & B2, B3, B4, B8                                                                                & -                                                                                & B2, B3, B4, B8                            \\
Sampling regions      & Bolivia, Colombia, Ghana, & Bolivia, Colombia, Ghana, & Bangladesh, Brazil, Peru,\\
 & India, Cambodia, Nigeria,  & India, Cambodia, Nigeria, & India, Columbia \\
 & Pakistan, Paraguay, USA, & Pakistan, Paraguay, USA, & \\
 & Sri-Lanka, Somalia & Sri-Lanka, Somalia & \\
\bottomrule
\end{tabular}}
\vspace{1em}
\caption{Summary of the key attributes of all the datasets used for training and evaluation.}
\label{tab:t1}
\end{table}

\subsection{Preprocessing}
The provided Sen1Floods11 dataset has Latitude / Longitude Projection and has a resolution of 10m per pixel. To match the projection and resolution of our Floods208 dataset, all the images are scaled to 16m per pixel input resolution and projected to the Universal Transverse Mercator (UTM) coordinate system. For Sentinel-1 image normalization, VV band was clipped to [-20, 0] and VH to [-30, 0] and then linearly scaled these values to the range [0,1]. For Sentinel-2 image, the 4 bands were clipped to [0, 3000] range and then linearly scaled them to [0, 1] range. Table \ref{tab:t1} summarises the final attributes of both the datasets.

\renewcommand{\thefootnote}{\fnsymbol{footnote}}
\section{Methods}
Our aim is to segment flooded pixels using Sentinel-1 SAR image as an input at inference time. Formally, let $X_{S1} \in R^{H\times W \times 2}$ be the SAR input space and let $Y \in R^{H \times W \times K}$ denote the pixel wise $K$ class one hot label in the output space ($K=2$ classes in our case: dry and floodded pixels). The paired Sentinel-2 images used in the training data are represented by $X_{S2} \in R^{H\times W \times 4}$.
The hand labelled training set is denoted by $D_l = \bigl\{X_{S1}^i, X_{S2}^i, Y^i\bigr\}_{i=1}^{N_l}$ and the larger weak labelled training set as $D_{wl} = \bigl\{X_{S1}^i, X_{S2}^i, \hat{Y}^i\bigr\}_{i=1}^{N_{wl}}$. Here $Y$ denotes a high quality label and $\hat{Y}$ denotes a noisy weak label. Our goal is to leverage both $D_l$ and $D_{wl}$ to train the segmentation network. The next section describes the supervised baseline, an approach to improve weak labels and the cross modal distillation framework.

\subsection{Supervised baseline}
\label{subsection:supervised}
We train two supervised models for comparison. The first model is trained only on hand labelled data $D_l$. The second model is trained only on the larger weak labelled dataset $D_{wl}$. The large size of the weak labeled data helps the network to generalize better (despite there model learning some of those label errors during training) ~\cite{rolnick2017deep}. We use Deeplab v3+~\cite{chen2018encoder} with an Xception 65 encoder~\cite{chollet2017xception} as the model architecture. Common regularization techniques like data augmentations (random crop with distortion, horizontal/vertical flips and colour jitter), dropout, weight decay and batch normalization are used to improve generalization. We also tried to fine tune the second model i.e the model trained on weak labelled data, using hand labelled data $D_l$. However, this additional training step did not improve the performance, so we decided not to include it in the baseline model.

\textbf{Edge weighted loss}

The network is trained to minimize the cross entropy loss. For every batch of images ( $B_{wl}$ ), the parameters $f$ of the network are updated by minimizing the cross entropy loss given by:

$$L = \frac{1}{B_{wl}} \sum l_{ce}(f(X_i), \hat{Y}_i, W_i)$$

Here $W_i$ represents the pixel wise weights for the $i^{th}$ image - $X_i$, applied to the cross entropy loss. We apply an edge based weighting that gives higher weights to the edges of the binary label. As suggested by~\cite{wu2016bridging}, from all the pixels in an image available for training, the pixels lying in the middle of an object can be easily discriminated by the model and are usually classified correctly. Hence, an edge weighted loss helps the network to focus on the harder to segment regions lying on the boundary during training. We compute two kinds of edges, inner and outer edges. Inner edges are obtained by subtracting the eroded labels from the original labels. Outer edges are obtained by subtracting the original labels from the dilated labels. All the other pixels are given a unit weight. The weights for inner and outer edges are decided by tuning these parameters during training. Figure \ref{fig:s4} shows these edges.

\begin{figure*}[]
\centering
\includegraphics[width=11cm]{edge_weights.png}
\caption{Randomly selected Sentinel-1 images from the training split with their corresponding label and edge map. In the label, blue pixels denote water, peach pixels denote dry region and black pixels denotes the invalid pixels. The edge map shows the inner and outer edges in white and grey color respectively. }
\label{fig:s4}
\end{figure*}

\subsection{Improving weak labels}
\label{subsec:weak_label_improv}

Despite using regularization techniques, we notice that the model learns the label mistakes present in the training data. Figure \ref{fig:s7} demonstrates this on the training set images. Often such mistakes consist of cases where a complete or a major part of the river is missing. While the network is resilient to learning small label mistakes (such as slightly overflooded pixels surrounding the true boundary or random noise similar to cut out augmentation), it does tend to overfit to cases when the label mistakes are large. This is because from an optimization perspective, pixels in an extremely noisy label will dominate the loss, forcing the network to overfit on them in order to reduce the loss.

\begin{figure*}[]
\centering
\includegraphics[width=12cm]{noisy_labels.png}
\caption{Selected examples from the training split demonstrating the effect of amount of label noise in memorizing the label mistakes. Left: The model learns these mistakes when the label is of very poor quality and is missing most part of the river.Right:  The model can overcome these mistakes when there is less noise. In the label and prediction, green pixels are dry region, blue pixels are wet region, black are invalid region and white are clouded regions.}
\label{fig:s7}
\end{figure*}

We aim to correct the labels having large parts of the river missing by using water occurrence map to get a rough estimate of such missing rivers. The label initially misses the river because they are formed by thresholding Sentinel-2 image and the label is sensitive to the value of the threshold. Since the water occurrence map~\cite{pekel2016high} is made by averaging long-term overall surface water occurrence, the permanent water pixels will have higher probability values and areas where water where sometimes occurs will have lower probability values. The weak label is improved by additionally marking the pixels having their water occurrence probability above a certain threshold, as wet in the label. This method will not be able to capture the seasonal/flooded or ephemeral water pixels, but these pixels mostly constitute a small majority of the label and as seen earlier, the model can learn to overcome such small mistakes in the labels. Figure \ref{fig:s8} shows some randomly selected tiles before and after the label improvement from the training set.

\begin{figure*}[]
\centering
\includegraphics[width=14cm]{water_correction.png}
\caption{Improving weak label from the training split using water occurrence map. In the weak label green pixels represent dry region, blue represents wet regions and black pixels mark the invalid pixels(out of bounds pixels)/clouded pixels in Sentinel-2 image.}
\label{fig:s8}
\end{figure*}

\subsection{Cross modal distillation}
\label{subsec:cross}
Though water occurrence map improved the weak labels in areas which are permanently water, it does not help with label mistakes in seasonal/flooded pixels. As a result, we can still get grossly incorrect weak labels in rare situations where flooded pixels constitute a majority portion of the water. To overcome this, we  explore a cross modal distillation framework that uses a network trained on a richer modality with a small set of hand labels, to generate more accurate labels on the training split. A teacher-student setup is used to transfer supervision between the two modalities. The teacher is trained on stacked Sentinel-1 and Sentinel-2 images using accurate labels from $D_l$, and is used to supervise a Sentinel-1 only student model on the unlabelled images from $D_{wl}$. The advantage of this method over binary weak labels (used in Section \ref{subsection:supervised}), is that the soft labels predicted by the teacher capture uncertainty better compared to the binary labels ~\cite{ba2014deep, hinton2015distilling}. The soft targets are also outputs of a network trained on hand labeled data and are more likely to be correct than weak labels. Compared to self distillation, cross modal distillation enables us to provide more accurate supervision by transferring information from a richer knowledge modality. Figure \ref{fig:s} summarizes the training setup used in our work. Both the teacher and the student have identical architecture backbones. Let $f_t$ and $f_s$ represent the teacher and student network function, respectively. The training is done in two stages as described below:

\begin{figure*}[]
\centering
\includegraphics[width=13cm]{architecture.png}
\caption{Overview of our cross modal distillation framework. In the training stage, a teacher model using Sentinel-1 and Sentinel-2 images is used to train a student using only the Sentinel-1 image. At inference time, only the student is used to make predictions.}
\label{fig:s}
\end{figure*}

\textbf{Stage 1: Training the teacher network}
Let $X_{S1+S2}^i$ denote the $i^{th}$ stacked Sentinel-1 and Sentinel-2 image. $X_{S1+S2}^i \in D_l$ is used as input to the teacher network. The teacher is trained in the same manner as the supervised baseline described in Section \ref{subsection:supervised}. The training set is small but contains data from geographic locations spanning 6 different continents. This helps the teacher generalize well to different geographies in the unlabelled data seen during the next stage of training.

\textbf{Stage 2: Training the student network}
The teacher weights from Stage 1 are kept frozen in this stage. We use paired Sentinel-1 and Sentinel-2 images from Sen1Floods11 data(i.e. both hand labelled and weak labelled) and Floods208 weak labelled data as the unlabelled data to train the student network. The data in each batch is sampled equally from both the data sources to ensure equal weighting for the datasets.
The stacked Sentinel-1 and Sentinel-2 image $X_{S1+S2}^i$ is passed through the teacher to obtain the probabilities $p_t = \sigma (f_t (X_{S1+S2}^i))$ and the augmented paired Sentinel-1 image $\tilde{X}_{S1}^i = Aug(X_{S1}^i)$ is passed through the student to get the student probabilities $p_s = \sigma (f_s (\tilde{X}_{S1}^i))$. Here $\sigma$ refers to the sigmoid function used to convert the model's output into a probability score. KL divergence loss ($L_{KD}$) is then minimized for $K=2$ classes to update the student weights:
$$ L_{KD} = -\sum_{i=1}^K p_t \log{p_s} $$

\section{Experiments and Results}
\subsection{Training details}

For all the models, we use Deeplab v3+ model~\cite{chen2018encoder} with Xception 65~\cite{chollet2017xception} as the backbone encoder. The skip connection from the encoder features to the decoder is applied at stride 4 and 2. We use a batch size of 64 with input image shape of $(321, 321, C)$ (here $C = 2$ for Sentinel-1 images and $C = 4$ for Sentinel-2 images). For optimization, Momentum optimizer is used with momentum set to 0.9. The learning rate is decayed with a polynomial schedule from initial value to zero with a power of 0.9. The models are trained for 30k steps. A learning rate and weight decay grid search hyperparameter tuning is done by choosing a learning rate from \{0.3, 0.1, 0.003, 0.001\} and weight decay from \{1e-3, 1e-4, 1e-5, 1e-6\}. For edge weighted cross entropy loss, the inner and outer edge weight is set to 10 and 5 respectively. A threshold of value 0.5 is used to create a binary mask of permanent water pixels from the water occurrence map. All the hyperparameter tuning and best model checkpoint selection is then done on the validation split. After the best checkpoint selection, the model is frozen and all the results are reported on the test split.

\subsection{Evaluation Metric}
We use pixel-wise intersection over union (IoU) of the water class to validate our model performance. It is defined as follows:

$$ IoU = \frac{\sum_{i=1}^{N} {TP}_i}{\sum_{i=1}^{N} ({TP}_i + {FP}_i + {FN}_i )}  $$

In the above formula, $i$ is the iterator over all the images and $N$ represents the total number of images. For the $i^{th}$ image, ${TP}_i$, ${FP}_i$ and ${FN}_i$ denotes the number of true positives, false positives and false negatives of water class respectively.

\subsection{Results}
\textbf{Comparison with our baselines}

Table \ref{tab:1} shows the result of the supervised baseline model trained on the Sen1Floods11 hand labelled data. As expected, the model using Sentinel-2 as input image is much higher than the models using Sentinel-1 image because Sentinel-2 image is a richer modality. However, Sentinel-2 image is not suitable for inference because of cloud cover issues mentioned in Section \ref{subsection:input}. Hence, even though we can't use the model using stacked Sentinel-1 and Sentinel-2 image during inference, we can opportunistically utilize this as a richer set of features to train the teacher model for cross modal distillation as discussed in Section \ref{subsec:cross}.

\begin{table}[]
\centering
\begin{tabular}{lll}
\toprule
\textbf{Input image}             & \textbf{Bands}                                                           & \textbf{IoU}  \\
\midrule
Sentinel-1              & VV, VH                                                          & $67.63 \pm 0.45 $ \\
\noalign{\vskip 2mm}
Sentinel-2              & B2, B3, B4, B8                                                  & $79.02 \pm 1.05$ \\
\noalign{\vskip 2mm}
Sentinel-1 + Sentinel 2 & VV, VH & $79.25 \pm 1.07$\\
 & B2, B3, B4, B8 & \\
\bottomrule
\end{tabular}
\vspace{1.5mm}
\caption{Test split results of our model trained on Sen1Floods11 hand labelled data at 16m resolution. The numbers show the aggregated mean and standard deviation of IoU from 5 runs.}
\label{tab:1}
\end{table}

\begin{table}[]
\centering
\setlength\belowcaptionskip{-5pt}
\begin{tabular}{ll}
\toprule
\textbf{Method}                                                                                & \textbf{IoU}  \\
\hline
Hand labelled supervised                                                                &  $67.63 \pm 0.45$ \\
Weak labelled supervised: Sen1Floods11 weak       & $67.76 \pm 2.41$ \\
Weak labelled supervised: Sen1Floods11 + Floods208 weak & $68.94 \pm 1.11$ \\
Weak labelled supervised: Floods208 weak   & $67.53 \pm 0.19$ \\
Improved weak labelled supervised: Sen1Floods11 + Floods208 weak & $70.64 \pm 0.91$ \\
Cross modal distillation          &                                                    \boldmath{$71.86\pm0.91$}\\
\bottomrule
\end{tabular}
\vspace{1.5mm}
\caption{Result of our Sentinel-1 supervised baseline models, improved weak label supervised model and our cross modal distillation framework on Sen1Floods11 handlabel test split at 16m resolution. The numbers show the aggregated mean and standard deviation of IoU from 5 runs.}\label{tab:2}
\end{table}

\begin{figure*}[h!]
 \centering
\includegraphics[width=12cm]{quality4.png}
\caption{Model inference visualisation on Sen1Floods11 hand labelled test split on selected images. In the output predictions dry pixels are shown in green, water pixels in blue and invalid pixels in black. The Sentinel-2 image is not passed as input to the model and only shown for visualisation purpose. The ground truth is hand labelled on the Sentinel-2 image and can contain clouds which are masked in white color. It can be seen that cross modal distillation produces sharper and more accurate results. Weak labelled supervised baseline on the other hand sometimes misses big parts of river due to mistakes learnt from the training data.}
\label{fig:s9}
\end{figure*}

Table \ref{tab:2} shows a quantitative comparison of the supervised baseline method with model trained after improving the weak labels and the cross distillation model. In Table \ref{tab:2} first two rows, it can be seen that a supervised model trained on Sentinel-2 weak label can match the performance of a supervised model trained on small set of hand labelled data. This empirically verifies the claim that a large weak labelled dataset can act as a quick substitute for a small amount of costly hand label annotations. Including Floods208 weak labelled data to Sen1Floods11 weak labelled data, further led to an increase in the model performance by $1.18\%$ from Sen1Floods11 weak label supervised baseline. This shows that there were still more gains to be had by increasing the weak label dataset size. Even though Floods208 weak labelled dataset by itself is also a very large dataset, the model trained on only Floods208 does not perform good. This is because Floods208 data is localised to a few specific locations while Sentinel-2 weak labelled split has data from 6 continents across the world. This emphasizes the importance of sampling training data strategically, such that the locations are more global and includes various topographies. We can also see that improving weak labels using water occurrence map as described in Section \ref{subsec:weak_label_improv} increases the model IoU by $1.7\%$ compared to its weak supervised counterpart and by $2.88\%$ compared to the Sen1Floods11 weak label baseline. This emphasises the importance of quality of training labels in producing more accurate models. Our cross distillation model further produces improves the quality of training label using a teacher model and performs better than all the other models. It exceeds Sen1Floods11 weak label baseline by $4.1\%$ IoU and improved weak label supervised model by $1.22\%$ IoU. Figure \ref{fig:s9} shows a qualitative comparison of the cross distillation model with the baseline model.

\textbf{Comparison with other methods}

\begin{table}[H]
\centering
\setlength\belowcaptionskip{-5pt}
\begin{tabular}{ll}
\toprule
\textbf{Method}                                                                                & \textbf{IoU}  \\
\hline
Sen1Floods11 Otsu thresholding \cite{bonafilia2020sen1floods11} & $54.58$ \\
Sen1Floods11 Sentinel-2 Weak label model \cite{bonafilia2020sen1floods11} & $66.21$ \\
BASNet \cite{bai2021enhancement}  & $53.90$ \\
AN-34 \cite{helleis2022sentinel} & $49.70$ \\
S-1FS \cite{helleis2022sentinel} & $54.90$ \\
Cross modal distillation (Ours)          &                                                    \boldmath{$72.74$}\\
\bottomrule
\end{tabular}
\vspace{1.5mm}
\caption{Performance comparison of our cross modal distillation model with other methods on all water hand labels from Sen1Floods11 test set at 10m resolution. }\label{tab:c1}
\end{table}

Benchmark comparisons of IoU on Sen1Floods11 test split are provided in Table \ref{tab:c1}. Note that even though our method is trained at input images with resolution of 16m, here we do an evaluation at 10m Sentinel-1 images and hand labels for a fair comparison with other methods. To get an inference output at a resolution of 10m, we first downsample the original image of shape $512\times512$ to a resolution of 16m i.e. $320\times320$, feed the downsampled image to the model and then  upsample the probabilities to their original resolution.

From Table \ref{tab:c1} we can see that our method outperform Sen1Floods11 ~\cite{bonafilia2020sen1floods11} weak label baseline by a absolute margin of 6.53\% IoU.

\vspace{-1em}

\section{Sensitivity Analysis}
\subsection{Effect of decoder stride}

In the Deeplabv3+~\cite{chen2018encoder} architecture, an input image is first passed to an encoder to generate semantically meaningful features at different scales.  The final encoder output feature has an output stride of 16, i.e. the input image is down-sampled by a factor of 16. This feature is then passed through ASPP (Atrous Spatial Pyramid Pooling) layers and the decoder to produce the output logits. The decoder processes the encoder features at a list of multiple strides - a hyper-parameter that can be tuned to refine the output segmentation masks. At each stride, the low level features from the encoder having the same spatial dimensions, are concatenated with the decoder features to process them further. The smallest stride in the list refers to the final ratio of the original input image size with the final logits size. The final logits are then bi-linearly upsampled to the required input size. Table \ref{tab:a1} shows the effect of the decoder stride on the validation split results. It can be seen that as we concatenate finer resolutions features from the encoder with the decoder features, the results improve. However, the rate of improvement declines. Considering practical considerations related to the number of model parameters, we decided to go with decoder stride (4, 2).

\begin{table}[H]
\centering
\begin{tabular}{ll}
\toprule
\textbf{Method}        & \textbf{IoU} \\
\midrule
Decoder stride 4       & $66.63 \pm 1.04$          \\
Decoder stride 4, 2    & $67.76 \pm 1.41$            \\
Decoder stride 4, 2, 1 & $67.79 \pm 1.67$      \\
\bottomrule
\end{tabular}
\vspace{1.5mm}
\caption{Validation split results for decoder stride comparison on Sen1Floods11 hand labeled split. The numbers show the aggregated mean and standard deviation of IoU from 5 runs.}
\label{tab:a1}
\end{table}

\subsection{Effect of loss function}
We also show the comparison of different loss function in Table \ref{tab:a2} on the validation split. As can be seen from the table that vanilla cross entropy loss performs poorly. On the other hand, cross entropy with edge weighting improves vanilla cross entropy loss by $9.68\%$ IoU. Also Tversky focal loss outperforms edge weighted Cross Entropy loss by a margin of $1.91\%$ IoU. However, we observed that the water class probability outputs with Tversky focal loss were not calibrated. A neural network is said to be calibrated if its output confidence is equal to the probability of it being correct. Compared to Tversky loss that classifies both correct and incorrect water pixels with high confidence, Cross Entropy loss produces a more calibrated output. Because a calibrated output helps in getting a better estimate of the uncertainty of the model prediction, we decided to go with edge weighted Cross Entropy loss.

\begin{table}[H]
\centering
\begin{tabular}{ll}
\toprule
\textbf{Method}                & \textbf{IoU} \\
\midrule
Cross Entropy                  & $58.08 \pm 2.85$             \\
Cross Entropy + Edge weighting & $67.76 \pm 1.41$            \\
Tversky focal loss             & $69.67 \pm 0.84$            \\
\bottomrule
\end{tabular}
\vspace{1.5mm}
\caption{Validation split results for loss comparison on Sen1Floods11 hand labeled split. The numbers show the aggregated mean and standard deviation of IoU from 5 runs.}
\label{tab:a2}
\end{table}

\section{Conclusion}
We proposed a simple cross modal distillation framework to effectively leverage large amounts of unlabelled and paired satellite data and a limited amount of high quality hand labelled data. We distill knowledge from a teacher trained on the hand labelled images using the more informative modality as input. This helped us generate more accurate labels for the student network as compared to weak labels created by a simple thresholding technique. The student network trained this way outperforms both the supervised hand label and weak label baselines. A promising avenue for future research would be to include temporal imagery to improve performance.

\section{Acknowledgements}
We would like to thank Yotam Gigi and John Platt for helping us review the paper and providing invaluable suggestions throughout the whole process.

\bibliographystyle{plain}

redpajama_set_name: RedPajamaArXiv


text:
That's Not Real Socialism!
By Alexander William Salter
Wikimedia Commons / Alexandria Ocasio-Cortez @ SXSW
Also published in National Review Tue. November 9, 2021 « Show Fewer
Is America having a socialist moment? Democrats are more likely than ever to embrace the label. Republicans strongly condemn it. Both sides agree that socialism is a live issue.
They're wrong. If we assume that socialism is only about the efficient management of the economy—no small assumption, to be sure—it is as dead as a doornail. Two economists, Ludwig von Mises and F. A. Hayek, put that theory to rest by demonstrating that it's impossible for socialism to out-produce capitalism.
Let's be clear: Socialism is not actually on the agenda in the U.S. As it's traditionally defined, socialism means public ownership of the means of production—i.e., government control of the economy's commanding heights. Socialism does not mean the welfare state, public services, public–private partnerships, or even the occasional state-owned enterprise. Western Europe is not socialist. The Nordic democracies are not socialist. And despite their collectivist proclivities, the likes of Senator Elizabeth Warren and Representative Alexandria Ocasio-Cortez are not—in any intellectually rigorous sense—socialists, regardless of what they may call themselves on occasion.
It's quite fashionable to play with socialist ideas. It's much less fashionable to be a socialist.
Almost a hundred years ago, economists fiercely debated the merits of competing economic systems. The issue then turned on whether socialism could beat capitalism at its own game. Socialists argued that the capitalist system was hopelessly "anarchic" because it lacked top-down coordination. Enlightened planners could fine-tune production to create more wealth for everyone. Using models from state-of-the-art economic theory, socialists could engineer economic efficiency from on high.
This argument appealed to many intellectuals and not a few politicians. But it was completely wrong. Mises and Hayek showed the world why. The greatest representatives of the Austrian school, both were accomplished economists who ranked with the profession's elite. Hayek won the Nobel prize in 1974; while Mises never did, arch-Keynesian Paul Samuelson considered him worthy of the honor. Both made crushing arguments against socialism, demonstrating why it was not only difficult but impossible. There's no way for socialism to produce more wealth than capitalism.
Mises's argument was simple and elegant. In a socialist economy, the factors of production cannot be private property. Without ownership there's no exchange, and hence no markets. Without markets, there are no market prices—those indispensable indicators of resource value across lines of production. And without market prices, nobody can compute profits or losses. Producers, then, will not have any reliable information about whether they're satisfying customer demand. So much for rational economic calculation! Mises proved that socialism is like throwing darts at a moving board while blindfolded.
Hayek built on Mises's arguments by further explaining the informational role of prices. His classic article, "The Use of Knowledge in Society," is one of the most-cited papers in the history of economics. Contrary to the assumptions of would-be social planners, the knowledge required to coordinate an economy doesn't exist in a single place. It's scattered throughout the minds and intentions of producers and consumers themselves. How, then, can buyers and sellers get what they want? Hayek's answer: Market prices contain the necessary information and act as information surrogates. Supply and demand can be brought into harmony through price adjustments. Thanks to Hayek, we know the market-price system is the most powerful communications network ever devised.
This is the genius of capitalism: Free economies work because planning is decentralized. Households and businesses, not a national committee, make the decisions. By abolishing property, prices, and profits, socialism destroys the very foundations of economic harmony. No amount of good intentions or detailed production analysis by planners can cope with this fatal defect. That's why socialist countries—such as Cuba or North Korea—are poor. Socialists lucky enough to live in liberal democracies want to disqualify such examples as "not real socialism." On the contrary: They are what socialism inevitably becomes.
Thankfully, there are very few advocates of socialism today and none of them have the ability to pass their agenda in the United States. We can—and should—argue about the costs and benefits of the Build Back Better Act and the Green New Deal, but even the most ambitious left-wing plans on offer from Democratic legislators won't turn the U.S. into Venezuela. Not even close. They can still do a great deal of damage, of course. Tax hikes, corporate welfare, and other forms of cronyism bring economic pain—all of which we should resist on principle. Abusing the s-word, however, will not help us do that.
None of this means that the Mises–Hayek critique is irrelevant to today's political debates. The Austrian economists' insights about property rights and information should make us hesitant to tinker with the economy. Alas, it's hard to see much evidence of that hesitancy amid large swaths of our political class—left and right. Nevertheless, the fundamental point stands: Regulated market economies are a fundamentally different thing from centrally controlled economies. We blunt the force of Mises's and Hayek's masterstroke when we call everything we don't like "socialism." Doing so, in fact, diminishes our ability to hold actual socialist regimes accountable for the poverty and misery of their citizens. Furthermore, moving the goalposts could backfire: If progressive Democrats pass their agenda and the economy doesn't immediately tank, many will mistakenly conclude that socialism works.
We absolutely should use Mises's and Hayek's insights to explain why so many of the economic initiatives of today's Left will fail, even if they fall a long way short of full-blooded central planning. But let's not fall into the trap of helping D.C. busybodies revive a dead economic philosophy.
Alexander William Salter is a Research Fellow at the Independent Institute and Associate Professor of Economics in the Rawls College of Business at Texas Tech University.
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Topic: Designing a NAND gate with "my gate"
Re: Designing a NAND gate with "my gate"
2 gates. Gate 1 has inputs 0 and A. Gate 2 has inputs B and 1.
1 gate. Gate 3 has inputs output of gate 1 and output of gate 2.
In the description of the gates above with boolean function y= A+B' , the first input is A pin and second input is B pin.
Topic: How does DFT logic affect PD ?


I was just traveling back from Ireland on Friday night. We arrived beginning of the afternoon on Saturday. Weather was not great with wind reaching 28 knots. We decided to stay in the marina, get some work done and of course have a nice dinner !
We found some great crab and shrimps on the road and decided to buy some.
Sunday morning was clear and wonderful, but unfortunately wind came back around 11 AM and showers for the afternoon.
Our boat at the marina second boat (next the motor boat) from the left.


Reserving a hotel room at Days Inn & Suites by Wyndham Niagara Falls/Buffalo is easy to do using our online booking system. Get the best rates available and comparison-shop right on our website. Get started by entering your travel dates and the number of guests in your party. You'll instantly see our room availablity. If you prefer, give us a call or email us. We'd be happy to answer any of your questions. We look forward to hearing from you!


text:
Bengals topple Bills to head to AFC Championship: How Cincinnati's defense stifled Josh Allen
By Carol Acevedo / January 22, 2023 January 22, 2023 / Sports / 5 minutes of reading
By Paul Dehner Jr., Jay Morrison, Joe Buscaglia and Tim Graham
The Bengals are heading back to their second straight AFC Championship Game as they knocked off the Bills 27-10 Sunday in Buffalo. Here's what you need to know:
The Bengals will face the Chiefs in the AFC Championship game in Kansas City next Sunday at 6:30 p.m. ET. Kansas City, the AFC's top seed, knocked off the Jacksonville Jaguars in the other AFC divisional round game Saturday.
Joe Burrow started hot and remained steady through the snowy conditions going 23-of-36 passing for 242 yards with two touchdown passes (Ja'Marr Chase, Hayden Hurst) resulting in a 101.9 passer rating. Joe Mixon also produced with 105 rushing yards on 20 carries and a TD.
The Bengals defense held up strong against the Bills offense. Josh Allen completed 25 of his 42 passes for 264 passing yards with no TD throws and one interception for a 68.0 passer rating. He added a first-half TD to go along with his 26 rushing yards.
Bills safety Damar Hamlin attended Sunday's AFC divisional round with his family. It's the first time he's made it back to a game since collapsing on the field with cardiac arrest during the Bills' Week 17 game at Cincinnati.
The Athletic's instant analysis:
Pick your Bengals' poison
The Bengals just have so many ways to win. They are basically telling opponents to pick their destroyer. Come to Buffalo, in a relentless snowfall and take on an elite Bills team? Sure. Do it featuring a running game that had gone dormant for a month but the Bills dared to beat them? Sure. Do it without even needing an Allen turnover, but just moving the ball surgically and holding the potent Bills attack to just 10 points, seven less than the previous season low? Absolutely. The Bengals are better than last year's Super Bowl team. Sunday confirmed it. Now they just have to go to Kansas City and re-create the 2021 team's crowning achievement. — Dehner Jr.
Complete Bengals effort
The most competitive aspect of this game was which unit was more impressive, the Bengals offense or defense. Burrow and the offense drove for a touchdown on the opening possession and never looked back, scoring a pair of red zone touchdowns against the NFL's No. 2 red zone defense while racking up more than 400 yards of offense. They made it look easy at times, and even when it wasn't, they made it work, twice converting on third and 10 to extend a pair of touchdown drives. Maybe the most impressive aspect was how well an offensive line that was missing starters performed. Burrow saw little pressure, and a run game that had struggled over the last month topped 100 yards and averaged nearly 6.0 per carry. — Morrison
Cincinnati's defense a force again
Lou Anarumo and the Bengals defense turned out another gem. The coordinator who made a name for himself last postseason with game plans that turned Ryan Tannehill over three times and befuddled Patrick Mahomes, had Allen forced to play hero ball on repeat. The defensive line kept winning up front to force Allen on the run and Mike Hilton and the corners made play after play on balls forced into tight spaces. Anarumo hasn't drawn a head coach interview yet this cycle, but you almost wonder after watching this masterpiece if an owner will have a message on his voicemail when he gets back to the locker room. — Dehner Jr.
Bills outmatched from start
It seemed right from the opening kickoff, the Bills were playing from behind. Every time the Bengals touched the ball, they were a threat to secure a first down and push it down the field for a score. Every time the Bills held the ball, it looked disjointed and they never seemed in a flow offensively. The Bills appeared a bit lethargic on the sideline after the Bengals raced to a 14-0 lead. Whether the Bills were out of gas after a tumultuous season, or they just came into the game flat, they were nowhere near the level they needed to advance to the AFC championship game. Instead, they fall short in the divisional round for a second straight season. — Buscaglia
Bills defense no match for Burrow, Bengals
The Bills defense had no answers for Burrow and the Bengals offense early in the game. They couldn't register consistent pressure on him, they tackled poorly in the open field, and they had no answer to the Bengals' run game right up the middle of their defensive line. The Bills had to be able to do one of those things to force enough stops to win, but with none of them working, it was the beginning of the end right at the beginning of the game. — Buscaglia
Buffalo's campaign ended with a whimper
Buffalo's day was summed up in a span of three minutes early in the fourth quarter. Top cornerback Tre'Davious White was penalized for pass interference in the end zone, where he also collided with top safety Jordan Poyer. Both were sent out of the game with head injuries. The other starting safety, Dean Marlowe, already was out with a groin injury. Cincinnati kicked a field goal to take a three-score lead.
On the next series, Allen was sent to the blue tent while a strip-sack fumble was reviewed. That call was overturned, and Allen went back into the game, but there was no mistake: The Bengals were simply ragdolling the preseason Super Bowl betting favorites.
Buffalo's offense was unable to generate much despite zero giveaways. The defense couldn't disrupt Burrow's precision pass attack and missed a load of tackles in open space. Buffalo sacked Burrow just once and hit him three times (compared to Cincinnati getting Allen once and hitting him eight times) behind a patchwork offensive line missing three starters.
And now Bills Mafia won't get to see a revenge game next Sunday against an injured Patrick Mahomes at a neutral site, two juicy factors in their favor. – Graham
Highlight of the game
TD + Griddy + stepback. Good start for @Real10jayy__.
📺: #CINvsBUF on CBS
📱: Stream on NFL+ https://t.co/RqOcaBu1Zc pic.twitter.com/EswqF8EFdM
— NFL (@NFL) January 22, 2023
Stat of the game
This is the Bengals' fifth playoff win with Burrow as quarterback. The Bengals had five postseason wins in their entire franchise history before drafting Burrow.
(Photo: Kareem Elgazzar / USA Today)


text:
Honoring Our Beloved Law Enforcement Dogs
Let's show some love for AFSCME canines, who help make public safety happen.
NJ Minimum Wage Will Rise to $15/Hour By 2024
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Union Leaders Discuss the Future of American Labor
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Sally Meckling, OCSEA | February 08, 2019
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