Statistics Show California Wildfire Acreage Down This Year – TIME

Statistics Show California Wildfire Acreage Down This Year - TIME nevin manimala

(LOS ANGELES) — California is not burning. At least not as much as it has in recent years.

Acreage burned through Sunday is down 90% compared to the average over the past five years and down 95% from last year, according to statistics from the Department of Forestry and Fire Protection.

The stats are good news for a state that has seen terrifyingly destructive and deadly blazes the past two years, but the worst of those fires occurred in the fall.

The precipitous drop could be due to the amount of precipitation the state received during a winter of near-record snowfall and cooler-than-average temperatures — so far.

Scott McLean, a spokesman for CalFire, said the state hasn’t dried out as quickly this year and the temperatures haven’t been as consistently hot. Hot spells have been followed by cooler weather and winds haven’t been strong.

“It’s a roller coaster with temperatures this year,” McLean said. “There have been very little winds so far. We’re crossing all fingers and appendages.”

The most current U.S. Drought Monitor map released last week shows only a tiny portion of California listed as abnormally dry. A year ago, almost the entire state was listed in a range from abnormally dry to extreme drought.

Even after another very wet year in 2017 when Gov. Jerry Brown declared the end to a years-long drought, hot weather quickly sapped vegetation of moisture and nearly 4,000 fires had already burned more than 350 square miles (906 square kilometers) at this time of year. In October 2017, fast-moving, wind-driven blazes in Northern California killed 44 people and destroyed thousands of homes.

Last year began with less rainfall and a smaller snowpack and the state dried out even faster with more dire the consequences. It was the worst fire year in state history in both acreage and deaths with the Camp Fire in November wiping out the town of Paradise, destroying nearly 15,000 homes and killing 86 people. At the same time, a Southern California wildfire burned across the Santa Monica Mountains and destroyed more than 1,500 structures.

CalFire has fought fires on 38 square miles (98 square kilometers) this year, down from an average of 416 square miles (1,077 square kilometers) from 2014-18.

Through the same date last year, a total of nearly 4,000 fires had burned more than 970 square miles (2,512 square kilometers). The number of fires this year, about 3,400, is only down about 15% from last year, meaning the fires are much smaller.

Typically, 95% of the fires CalFire fights are smaller than 10 acres and “boy are we living up to that,” McLean said.

The state’s figures don’t compare data on fires on all federal lands, which account for about 45 percent of the state’s acreage.

Fires on U.S. Forest Service land this year, however, have also declined. To date, only 41 square miles have burned in national forests, compared to 350 square miles at this time last year, according to fire officials.

Contact us at editors@time.com.

Why neuroscience needs computers and statistics – Stanford University News

Byline Nathan Collins

Scott Linderman always kind of knew he wanted to work at the intersection of computer science, statistics and neuroscience. He wrote about the interface between computation and biology for his college application essays back in high school. After college, he worked as an engineer at Microsoft, but maintained his interest in the brain, reading neuroscience textbooks on the bus to and from work. A class he took on machine learning and the brain at the University of Washington eventually motivated him to take the plunge and apply to graduate school.  Not long after that he started his PhD at Harvard University.

Today, Linderman is an assistant professor of statistics, which on the surface might not seem like it has much to do with neuroscience. In fact, he said, statistics and computer science have become essential components of brain science in the last decade. A member of the Wu Tsai Neurosciences Institute who arrived on campus just two months ago, Linderman’s research is focused on developing statistical models and computer algorithms that try to make sense of a flood of data on the activity of neurons in animal brains. Here, he explains why neuroscience needs statistics and computer science and what he hopes to accomplish at that intersection.

When you started graduate school a little under a decade ago, neuroscience was undergoing a sort of data revolution. What was that about, and what did computer science and statistics have to do with it?

One thing is that recording techniques were really blossoming into what they’ve become today, which is a powerful set of tools for interrogating neural circuits at scales that we really haven’t had access to in the history of neuroscience. We already had techniques to study populations of neurons, but there’s been an exponential growth in the numbers of neurons that can be recorded simultaneously, and not only recorded but also perturbed using optogenetics.

These new recording techniques let us study the brain with unprecedented precision, but they also demanded new computational techniques and statistical techniques for analyzing the types of data that were being collected.  Fortunately, the field of machine learning was taking off at the same time, offering new possibilities for modeling large, complex datasets.

Why did that data demand new techniques? Was it just that there was a lot more data?

The first challenge is that the sheer scale of the data is just orders of magnitude larger than what we’ve been dealing with in the past, and that presents computational challenges. A single experiment can generate hundreds of gigabytes of data per hour. You need new computational infrastructure and new techniques just for data wrangling. Small differences in an algorithm can make a big difference in your ability to rapidly iterate on new ideas versus having to spin something off and come back a week later to see what happens.

But scale is just scratching the surface. It’s also that as we record more and more neurons, we realize that the complexity of the data is greater. When you look at the computations that are being performed by neural circuits, they’re manifested in highly nonlinear dynamical systems, and so as we collect richer and richer datasets, we’re also trying to push the flexibility of the probabilistic and statistical models that we’re applying to those data in order to get a clearer picture of what’s going on in these neural circuits, how they’re operating and computing.

The third challenge is that it’s not just that we’ve recorded more neurons for a longer period of time from a single animal. We’re collecting very heterogeneous datasets that are stitched together from many individual subjects from different trials and different experimental conditions. In order to answer the scientific questions, we have to be thinking about how to put these things together in a principled way in order to make the most of the information we have.

That’s interesting – there’s this flood of data, but at the same time it’s got this complex structure and you have these complex hypotheses, many of them based in machine learning and artificial intelligence, that you’re trying to test. It’s an awesome challenge.

Yes, and that’s why I think it’s fun to be working in this space right now. There are incredible opportunities but still very clear challenges that need to be addressed in order to make progress.

My work has been thinking about how to take current models to the next level, endowing them with greater flexibility to capture more complex dynamics, while still retaining some interpretability. Our models need to be able to make predictions with high accuracy – this is critical for many applications, like building brain-machine interfaces – but to advance neuroscience, we also want to understand the principles that guide those predictions.  As our models become more sophisticated, it becomes harder to understand how they work.  So I’m trying to thread a needle, balancing flexibility and interpretability.

We’ve been talking about this in sort of abstract terms, but as a practical matter, you’re studying relatively simple animals including zebrafish and mice. One animal you’re interested in, a tiny worm, C. elegans, has just 302 neurons, compared to something like 100 billion in the human brain. Can we learn anything about our own brains from studying something so simple?

C. elegans is one of the few animals in which we can really go from recordings of the neural circuit in action to detailed measurements of behavior and sensory inputs, so we can see the worm’s neural computational system unfolding in real time. That’s only recently become possible, and so I’ve found it to be a really exciting model to be thinking about as we try to tie theories of neural computation to actual measurements of neural activity.

To your question about how studying model organisms ultimately translates to more complex nervous systems like ours, I think there is good reason to believe that certain principles of neural computation may be maintained from one species to the next. And in any case, the techniques that we need to develop in order to analyze this data and start to ask questions are going to persist as we move up the stack to more complex organisms. I think the types of lessons we’re learning as we look at simple organisms will translate to more complex organisms as well.

Now that you’re here at Stanford, what are your goals for the immediate future?

I’m really excited about the potential for new collaborations here. Not only are we conceptually at the intersection of many disciplines, we’re physically located within about a five minute walk of world class departments that are all contributing to this scientific endeavor. I’m looking for people who are working on problems in neuroscience and looking for new computational collaborators to help analyze and understand data, but also I think this is a great opportunity to get some word out to people on the computational and statistical side. Maybe you’re curious about neuroscience but haven’t done anything there. That’s not a problem from my perspective. I think those are really ideal candidates to lure into this type of interdisciplinary work. There’s a lot of really interesting challenges going on right now in neuroscience that could benefit from new thinking and new ideas.

Combined statistics for Syracuse’s 4 games in Italy – syracuse.com

Combined statistics for Syracuse's 4 games in Italy - syracuse.com nevin manimala

The most interesting student loan debt statistics that may surprise you – Ladders

The most interesting student loan debt statistics that may surprise you - Ladders nevin manimala
The most interesting student loan debt statistics that may surprise you - Ladders nevin manimala

There is no doubt, you are likely quite aware of some student loan debt statistics that are out there. It’s a hot topic issue for people, families, and of course a top talking point for the majority of politicians.

But I’m not here to talk about politics with student debt, there is plenty of that out there on the Internet for you to find. Instead, I wanted to explore some of the student loan statistics that are circling the world.

Many of these data points might not be surprising and others might be a bit shocking, to say the least.

However, there are hundreds, if not thousands of stats currently out there. Instead of sharing them all,  I handpicked the ones that really stood out to me.

Some High-Level Student Loan Statistics

Before we jump in, I put this post together for a number of reasons.

  • First being, I’m a data nerd and really like seeing the numbers behind things — whether good or bad.
  • Secondly, I think understanding the data is important to educate ourselves about what is going on in higher education.
  • And lastly, some of this info may help you make better-informed decisions about college, paying your debt, refinancing, etc.

That said, I wanted to start with just a taste of some of the higher-level student loan stats.

Of course, the number of borrowers is no surprise, but seeing the actual data still made me go “Wow.” Add those to the total remaining student loan debt — which continues to rise — and you can see why the media and many will label this a crisis.

If you are interested in refinancing your student loans, are curious about ReFi, or if it is a good fit for you, then you can get some options in two minutes for free with Credible. No obligation to refinance just gives a list of the best rates and options. Get started here.

Student Loan Debt Statistics That Are Interesting

As I mentioned early, I dove into some student debt statistics online to find ones that were intriguing, even surprising. The challenge was, there are so many data points that it was somewhat difficult to choose.

However, here are some of the student loan debt statistics I think are interesting to know.

The current share of student loans borrowed is as follows:

  • Federal Subsidized Loans: 20%
  • Federal Unsubsidized Loans: 46%
  • Parent PLUS Loans: 12%
  • Grad PLUS Loans: 10%
  • Perkins Loans: 1%
  • Nonfederal Loans: 11%

According to The Trends in Student Aid Report from CollegeBoard:

  • As of March 2018, 52% of the outstanding federal education loan debt was held by the 14% of borrowers owing $60,000 or more; 56% of borrowers with outstanding debt owed less than $20,000
  • Federal education tax credits and deductions reached an estimated 12.0 million students in 2016-17, 5.0 million more than the 7.0 million Pell Grant recipients in 2017-18.
  • After a decade of rapid growth in annual borrowing, total federal loans to undergraduate students declined by 23% between 2012-13 and 2017-18 after adjusting for inflation, and federal loans to graduate students rose by 2%.

Students attending a private college pay nearly three times as much as those attending an in-state public university. It follows then that bachelor’s, master’s, and doctorate program graduates from private universities owe much more than their public college counterparts.

Data from the New York Federal Reserve tells us that borrowers ages 39 and under have the highest total student loan balance.

As of 2017, nearly 3.2 million people age 60+ are still paying off debt—three times more than were a decade ago. For this age group, the total loan balance is 85.4 billion dollars.

Sallie Mae found that in 2018, around 14% of college costs were covered by student borrowing while parent loans covered 10%.

Student Loan Statistics About Repayments

For the borrowers who can’t make payments, they can opt to postpone them through deferment or forbearance. However, interest typically accrues during these periods, but borrowers with subsidized loans don’t owe the interest that accrues during deferment.

These statistics came from the Federal Student Aid, Q1 2019 Report:

  • Current federal loan borrowers in repayment: 18.6 million.
  • The number of federal loan borrowers in deferment: 3.4 million.
  • Federal loan borrowers with loans in forbearance: 2.7 million.
  • And the number of federal loan borrowers with loans in default: 5.2 million.

Similarly, borrowers can also postpone private student loan payments via deferment or forbearance, but interest always accrues regardless of whether the borrower is making payments.

The data below came from MeasureOne Private Student Loan Report:

  • The percentage of outstanding private loan balance in deferment: 18.01%.
  • The percentage of outstanding private loan balance in forbearance: 2.39%.
  • And the percentage of private loans in repayment that are 90+ days past due: 1.75%.

If you can’t afford to repay your federal student loan, then many may choose the path of income-driven repayment plans. There are a few stipulations to these options, but also some interesting student loan debt statistics behind it.

The data below, came from Federal Student Aid, Q1 2019:

  • Federal loan borrowers on an income-driven repayment plan: 7.37 million.
  • Federal loan borrowers on Income-Based Repayment: 2.82 million.
  • Federal loan borrowers on Revised Pay As You Earn: 2.56 million.
  • Federal loan borrowers on Pay As You Earn: 1.31 million.
  • Federal loan borrowers on Income-Contingent Repayment: 680,000.

Many borrowers are behind on payments of student loans as well.

Check out a few numbers below that came from the Federal Reserve:

  • 37% of borrowers who are no longer enrolled in school and have less than an associate’s degree are behind on payments.
  • 21% of borrowers with associate’s degrees are behind.
  • 10% of borrowers with bachelor’s degrees are behind.
  • 6% of borrowers with graduate degrees are delinquent.

Final Thoughts

There you have it, some of the most interesting and potentially, surprising student loan debt statistics that are out there.

If you are a student loan borrower, the above statistics may help you make better decisions when it comes to your education and finances.

For example, you have a few options at your disposal like student loan refinance, loan consolidation, how you repay your student loans, etc.

There is no doubt though looking at these stats, that there is a major problem that only appears to be getting worse.

I know I don’t have the solutions, but for future generations of people looking to attend college, I hope we do see some change with the rising costs.

This article first appeared on Invested Wallet. 

VITAL STATISTICS: 081819 – News – La Junta Tribune Democrat

VITAL STATISTICS: 081819 - News - La Junta Tribune Democrat nevin manimala
VITAL STATISTICS: 081819 - News - La Junta Tribune Democrat nevin manimala

DEATHS

PUEBLO

Aug. 16

Chang: Jen Pin Han Chang, 88. Montgomery & Steward.
Hixson: Betty E. Hixson, 87. Montgomery & Steward.

Aug. 15

Bennett: JoAnne Bennett, 53. Montgomery & Steward.

PUEBLO WEST

McHenry: Avonelle McHenry, 98, of Pueblo West, Aug. 14. Montgomery & Steward.

LONE TREE

Healy: Thomas Michael “Mike” Healy, formerly of Pueblo, Aug. 3. Apollo Funeral Service, Littleton.

RYE

Hakes: Bruce Alan Hakes, 70, of Rye, Aug. 11. Montgomery & Steward.

WESTCLIFFE

Mount: Jerry Mount, 71, of Westcliffe, Aug. 15. Montgomery & Steward.

VITAL STATISTICS: 081819 – News – Fowler Tribune

VITAL STATISTICS: 081819 - News - Fowler Tribune nevin manimala
VITAL STATISTICS: 081819 - News - Fowler Tribune nevin manimala

DEATHS

PUEBLO

Aug. 16

Chang: Jen Pin Han Chang, 88. Montgomery & Steward.
Hixson: Betty E. Hixson, 87. Montgomery & Steward.

Aug. 15

Bennett: JoAnne Bennett, 53. Montgomery & Steward.

PUEBLO WEST

McHenry: Avonelle McHenry, 98, of Pueblo West, Aug. 14. Montgomery & Steward.

LONE TREE

Healy: Thomas Michael “Mike” Healy, formerly of Pueblo, Aug. 3. Apollo Funeral Service, Littleton.

RYE

Hakes: Bruce Alan Hakes, 70, of Rye, Aug. 11. Montgomery & Steward.

WESTCLIFFE

Mount: Jerry Mount, 71, of Westcliffe, Aug. 15. Montgomery & Steward.

Stats + Stories: Back to School Statistics – WYSO

Stats + Stories: Back to School Statistics - WYSO nevin manimala
Stats + Stories: Back to School Statistics - WYSO nevin manimala

WYSO is partnering with Stats and Stories, a podcast produced at Miami University.

School districts across the United States are working to understand how to best meet the educational needs of their students as well as the instructional needs of their teachers. Increasingly districts are turning to data to help them do that. The data of education and educational policy is the focus of the latest episode of Stats & Stories. Rosemary Pennington is joined in studio by regular panelists John Bailer, Chair of Miami’s Statistics Department, and Richard Campbell former and founding Chair of Media, Journalism and Film. Their two guests are both from education research non-profit Education Analytics. Nicole Webster is a Research Analyst with the organization and Libby Pier is the Research Manager. 

Stats and Stories is a partnership between Miami University’s Departments of Statistics and Media, Journalism and Film and the American Statistical Association. You can follow us on Twitter or iTunes. If you’d like to share your thoughts on our program, send your e-mail to statsandstories@miamioh.edu and be sure to listen for future editions of Stats and Stories where we discuss the statistics behind the stories and the stories behind the statistics.    

Amazon Web Services just shared some mind-boggling statistics on how it dealt with Prime Day, Amazon’s biggest shopping event ever – Business Insider

Amazon Web Services just shared some mind-boggling statistics on how it dealt with Prime Day, Amazon's biggest shopping event ever - Business Insider nevin manimala
Amazon Web Services just shared some mind-boggling statistics on how it dealt with Prime Day, Amazon's biggest shopping event ever - Business Insider nevin manimala

Every year, the teams at Amazon Web Services spend months preparing for their sister company’s biggest event of the year: Prime Day. It’s a major test for the company’s tech teams, especially Amazon Web Services, the cloud-infrastructure service that provides much of the tech that underpins the shopping site. (Amazon is one of AWS’s biggest customers.)

This year had such a big Prime Day that it lasted for two days, and although a handful of customers reported some technical glitches, it was mostly smooth sailing. That was a nice change from the 2018 Prime Day, when so many people overwhelmed the site that it crashed. That episode caused Amazon’s competitors to troll Amazon pretty mercilessly in 2019, with eBay announcing a “crash sale” and the online betting site Bovada letting gamblers wager on the odds of a crash.

Amazon evangelist Jeff Barr.YouTube/AmazonAmazon said it sold more than 175 million items in 2019, compared with 100 million items last year. That’s more sales than Black Friday and Cyber Monday made last year combined.

To support that level of frenzied buying, AWS’s Jeff Barr rattled off some stats on what went on behind the scenes. Barr is AWS’s prolific evangelist blogger who has become so famous in the AWS world that there are cartoon stickers of him available for developers who like to decorate their laptops with such things.

Here’s a few stats he shared:

The Amazon Dynamo database is used by Alexa and all 442 Amazon warehouse fulfillment centers.

  • It fielded 7.11 trillion calls to the Dynamo API.
  • At one point, it was handling 45.4 million requests per second.

(An API is a service that links the database to other applications.)

Amazon’s other database, Aurora, is also used by the warehouse fulfillment centers. It’s stats for Prime Day are also mind boggling:

  • 1,900 database instances (aka the number of databases that were running)
  • 148 billion transactions processed
  • 609 terabytes of data stored, and
  • 306 terabytes of data transferred.

Prime Day also used AWS computing services, which amounted to

  • the equivalent of 372,000 servers at the start of the day
  • and scaled up to 426,000 server equivalents at the peak.

As for storage, the event used a high-performance service called Amazon Elastic Block Store.

The AWS team added an additional 63 petabytes of storage for Amazon ahead of Prime Day. A petabyte is 1 million gigabytes. All told, this storage system fielded:

  • 2.1 trillion requests per day
  • and transferred 185 petabytes of data per day.

Green Bay Packers: 3 Vital statistics for 2019 season – NFL Spin Zone

Green Bay Packers: 3 Vital statistics for 2019 season - NFL Spin Zone nevin manimala
Green Bay Packers: 3 Vital statistics for 2019 season - NFL Spin Zone nevin manimala

It’s been a rough few seasons for a franchise used to being in the playoffs. So what do the numbers say about the Green Bay Packers in 2019?

It’s a franchise that is accustomed to winning championships. At the very least, the team that resides in “Titletown” is used to posting winning seasons and reaching the playoffs. But that hasn’t been the case the past two years for the Green Bay Packers.

Regardless of the reasons, a club finished 7-9 in 2017 and followed that up with a 6-9-1 showing this past season.  Keep in mind that this was an organization that from 2009-16 owned a combined 87-40-1 record and reached the season in each of those eight campaigns.

The new head coach in Green Bay is Matt LaFleur, who a year ago was the offensive coordinator of the Tennessee Titans. He takes over for Mike McCarthy, who was with the Packers for more than a decade, led them to nine playoff appearances and a victory in Super Bowl XLV. But he was dismissed 12 games into last year’s disappointing showing.

So it’s onward to 2019. Under second-year general manager Brian Gutekunst, the organization uncharacteristically spent its share of money in free agency this offseason. The Packers were still very busy during the draft, adding a pair of first-round picks and eight selections overall.

It’s a team with a lot of newcomers while familiar faces such as outside linebacker Clay Matthews, wide receiver Randall Cobb and defensive tackle Mike Daniels are now with other organizations.

So can the Packers bounce back and return to the postseason for the first time since 2016? What do the facts and figures tell us about the Packers over the next four-plus months?

statistics; +198 new citations

statistics; +198 new citations Report, nevin manimala
statistics; +198 new citations Report, nevin manimala

Wong YL, Hysi P, Cheung G, Tedja M, Hoang QV, Tompson SWJ, Whisenhunt KN, Verhoeven V, Zhao W, Hess M, Wong CW, Kifley A, Hosoda Y, Haarman AEG, Hopf S, Laspas P, Sensaki S, Sim X, Miyake M, Tsujikawa A, Lamoureux E, Ohno-Matsui K, Nickels S, Mitchell P, Wong TY, Wang JJ, Hammond CJ, Barathi VA, Cheng CY, Yamashiro K, Young TL, Klaver CCW, Saw SM; Consortium of Refractive Error, Myopia (CREAM).

PLoS One. 2019 Aug 15;14(8):e0220143. doi: 10.1371/journal.pone.0220143. eCollection 2019.

Connect with Nevin Manimala on LinkedIn
Nevin Manimala SAS Certificate