Library Catalog Ask a Librarian Request a Meeting Room


A holiday for everyone

by on December 9th, 2016
A holiday for everyone Cover Image

I grew up with a Christmas experience that I think will be familiar to many in one way or another. I was raised Catholic, so for the first 18 years of my life I did attend mass; this was usually on Christmas Eve, and it was very exciting to me as a child because the church lights would be turned off as the priest walked down the aisle, swinging a thurible filled with smoky incense. It seemed very exotic, not the regular day at church. We would hear the story of the birth of Christ and the three wise men, sing songs, and depending on which mass we were at, there would be a children’s pageant. After, we would go home and have a meal together, and my sister and I would head to bed while my parents stayed up a little longer. In the morning, we would be led from our rooms to the kitchen, eyes covered so that we couldn’t peek at the presents under the tree. Only after breakfast were we allowed to go open the presents; one person was designated to pass out the gifts, and they were opened one at a time. In this way, a good hour or two was spent opening presents and watching others do the same, eventually covering our living room floor with colored paper. Read the rest of this entry »

Recovering the Classics exhibit ends

by on December 6th, 2016

Our exhibit of redesigned book covers has come to a close, and we hope you enjoyed it!

If you contributed one of the beautiful and thoughtful original designs, you may now retrieve your piece from the Help Desk.  Thank you for participating!

The centerpiece of the exhibit was this year’s Art Purchase Prize winner.  This beautiful redesign of The Oregon Trail by Francis Parkman was made by local artist Doran Pearson and is almost ready for 8-week checkouts.  See if it’s available or place a hold.

And finally, if you were inspired to make your own design too late, you can always submit a book cover to the online Recovering the Classics project.recovering1

Enter: Batwoman

by on December 5th, 2016
Enter: Batwoman Cover Image

Batwoman is one of the most high-profile LGBTQ super-heroes in comic books today.  She has become an interesting and integral part of the Batman Family.  In February 2017, she will be joining forces with Batman to co-lead a new squad of crime fighters to protect Gotham City in Detective Comics: v.1: Rise of the Batmen (Rebirth).  This is an excellent time to catch-up on her background and origin story in Batwoman: Elegy by the superstar creative team of Greg (Wonder Woman, Gotham Central) Rucka and J.H. (Promethea, Sandman Overture) Williams III.  Part super-hero epic, part military thriller, with elements of the supernatural and horror thrown into the mix, this book, with stunningly designed artwork by Williams, will have you clamoring for more, which fortunately there is.  Although Rucka does not continue writing Batwoman after this initial volume, Williams does a bang-up job of co-plotting as well contributing artwork starting with Batwoman: Hydrology.

Mock Caldecott Award 2016

by on December 5th, 2016

This year we are trying something new at ICPL, a Mock Caldecott award.  Every year, the American Library Association awards the Randolph Caldecott Medal to a distinguished American picture book. For full eligibility requirements and criteria please visit the AlA’s Caldecott website. Also, stop by the Children’s Room to see a wonderful and informative display regarding the history of the award that Mari Redington has put together in the small display case.

Keeping eligibility requirements in mind we have put together a list of 15 possible contenders for the 2017 award. We ask that you read all of these titles before voting, or as many as you can get your hands on. When voting please pick and rank your top five titles: one winner (1) and four honor books (2-5). Paper ballots are available and are being collected at the Children’s Room Desk.  If you are unable to cast a paper ballot and are familiar with the titles, then please feel free to comment with your top five on or before December 31st.

We will be announcing the winning ICPL Caldecott titles at the beginning of 2017, shortly before the ALA midwinter meeting where they will be announcing the Medal and Honor winners.  How fun would it be if we have picked a winner or an honor book?!  Read the rest of this entry »

Come to Way Cool Chemistry on Dec. 10

by on December 2nd, 2016

Students interested in chemistry will have the opportunity to participate in hands-on demonstrations and experiments from 2 to 3 p.m. on Saturday, December 10, in Meeting Room A at the Iowa City Public Library.

Way Cool Chemistry is designed to make chemistry accessible and fun for fifth- through eighth-grade students. Pre-registration is not required.

For more information, contact the Library at 319-356-5200.

Iowa City Public Library closed on Dec. 9

by on December 1st, 2016

The Iowa City Public Library will be closed Friday, December 9, for Staff Inservice Day.

“The annual Inservice Day is an opportunity for all Library staff to attend training designed to improve services for patrons,” Community and Access Services Coordinator Kara Logsden said.

The day begins with staff recognition during which seven Library employees will be recognized for service to ICPL. This year, the Library will recognize individuals who have worked at the Library for five, 10, 25 and 45 years.

Read the rest of this entry »

The earth without “art” is just “eh”

by on December 1st, 2016
The earth without “art” is just “eh” Cover Image

Every once in a while, I catalog an art book that is so beautiful it makes my jaw drop. Such was the case with Benjamin Grant’s Overview. Grant took satellite images of the earth and humans’ impact on it, and turned them into art. He has a website with such images, too. He captures the geometric beauty in these aerial portraits of landscapes. It’s the kind of book you don’t see everyday, perfect for art and earth lovers.

 

 

More Reviews
Postelection Therapy: View Swing States From Space from The New York Times
Earth from the Air from The Economist
You’ll Never See Earth from Space, but this Book is Close from Wired

Words on the Move

by on November 30th, 2016

One of my favorite radio programs/podcasts is On Point with John Ashbrook.  A few weeks ago, Ashbrook had John McWhorter on his show to talk about his latest book, Words on the move: why English won’t – and can’t – sit still (like, literally).  McWhorter is a linguist and an English professor and he’s a delight to listen to.  He has his own podcast too.  The gist of McWhorter’s book is that English, and all languages, change over time and that, all things considered, it’s better that way.  Language is best viewed like a story, he argues, and we want a story to go new places.  Dictionaries are merely snapshots in time of those stories. words-on-the-move

As you may guess from the title, McWhorter makes a case for the frequent use and varied meanings of like and for using literally to mean figuratively.  He’s pretty convincing with these, I suppose, but I’ll be curious to see if those two words are still so prominent in ten or twenty years. Read the rest of this entry »

Children’s Room December Events Announced

by on November 30th, 2016

Snowman socks, reindeer games and gingerbread fun are among the programs and events the Iowa City Public Library’s Children Room has planned during December. Our calendar is stuffed with things to do, see and make all month long!

Wednesday, December 7, 10:30 a.m. in Meeting Room A: Preschool Storytime – Winter Sing-Along

Bring your best caroling voice, and get ready to interact with instruments, stories and winter fun! Whether there’s snow on the ground yet or not, stick around for an indoor snowball fight to end the program!

Read the rest of this entry »

The Reverend vs Spam

by on November 29th, 2016

Thanks to the Reverend Thomas Bayes your inbox is not full of spam.

What does spam have to do with a Presbyterian minister who died in 1761? He was also a statistician and formulated a theorem which bears his name, Bayes’ Theorem. Basically it allows us to adjust the probability of an event given new information that might be related to the event. We can change the probability of an email being spam after getting more information by looking at the contents of the email.

Without Bayes’ Theorem we could look at our inbox and see that on an average day we get a certain amount of spam. The probability that any given email is spam would be the number of spam emails divided by the total number of emails. For example if I received 100 emails today and 70 of them were spam then the chance that any random email would be spam is 70/100 = 70%. But without any more information we still would not be able to predict that any given email was more likely spam than the others. The probability is the same for all of them so not helpful.

But what if we have some new information? What if we know that spam emails often have the word FREE in them? Bayesian Spam Filtering will look through the text in each email, if it finds the word FREE then using Bayes’ Theorem the probability of that being a spam email increases. Spam filters are all a little bit different and look at a variety of things to help them make predictions. Most will have a large list of words, with different weighting, that it looks for. Other things might be if the email has a lot of html tags, the image to text ratio is high, or the subject is in ALL CAPS. The filter is also constantly learning and adjusting the weights by looking at what users have marked as spam. In the end it gives each email a probability and if it is above the threshold set in that filter, then it is automatically marked as spam, put in your spam folder and you do not have to deal with it.

Thanks Reverend!

If you are interested in two slightly more detailed examples of Bayes’ Theorem keep reading.

WARNING: MATH BELOW
vvvvvvvvvvvvvvvvvvvvvvvvv

The basic equation is:

           P(B|A) * P(A)
P(A|B) = ----------------
                P(B)

P(A) and P(B) are the probabilities of A and B by themselves.
P(B|A) is the probability of B if we know A.
P(A|B) is the probability of A if we know B, which is what we are trying to determine.

An intuitive example is if you pull a card from a deck of cards what is the probability that it is a King? We know there are 4 Kings in a deck of 52 cards, so the probability that we drew a King, P(King), is 4/52 or 1/13. Now what if we had some new information about the card? What if we knew that the card we drew was a face card? Now we can use Bayes’ Theorem.

                 P(Face|King) * P(King)
P(King|Face) = -------------------------
                        P(Face)

We already know that P(King) = 1/13. There are 12 face cards in a deck of 52 cards, which means that P(Face) = 12/52 or 3/13. The last part is P(Face|King). We know that if we drew a King then it is definitely a Face card, so P(Face|King) = 1. Plugging these parts into Bayes’ Theorem gives us:

                 1 * 1/13
P(King|Face) = ------------- = 1/13 * 13/3 = 1/3
                   3/13

Having the extra information about the card changes the probability that it is a King.

A counterintuitive example is testing for a disease. If we know that 1% of people have a disease. The test for the disease is 90% accurate. If you have the test done and it comes back positive what is your chance of having the disease? The intuitive, but wrong, answer is that you have an 90% chance of having the disease. Let’s use Bayes’ Theorem again.

                           P(Positive Test|Disease) * P(Disease)
P(Disease|Positive Test) = --------------------------------------
                                     P(Positive Test)

The tricky part on this one is the P(Positive Test). Of the 1% of people who have the disease it will correctly give a positive result 90% of the time. But, of the 99% who do not have the disease it will incorrectly give a positive result 10% of the time. The resulting formula is:

                                  .9 * .01
P(Disease|PositiveTest) = ------------------------- = 8.33%
                           (.9 * .01) + (.1 * .99)

That means that instead of a 90% chance of having the disease, you really only have an 8.33% chance of having it. In this case knowing the accuracy of the test makes a very big difference in how the test results should be interpreted.





login